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Ecological Footprint of Energy Development in Eastern Venezuela's Heavy Oil Belt

Permanent Link: http://ufdc.ufl.edu/UFE0022008/00001

Material Information

Title: Ecological Footprint of Energy Development in Eastern Venezuela's Heavy Oil Belt
Physical Description: 1 online resource (156 p.)
Language: english
Creator: Baynard, Chris
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2008

Subjects

Subjects / Keywords: ecological, heavy, land, oil, venezuela
Geography -- Dissertations, Academic -- UF
Genre: Geography thesis, Ph.D.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: Nearly all land-use and land-cover change (LUCC) studies concern conversions of land related to two renewable resource activities, agriculture and forestry. LUCC related to nonrenewable resource development is nonetheless another major agent of environmental change that has been studied very little. This work examines specific land-use and land-cover changes resulting from the production patterns of four petroleum operations between 1990 and 2005 in eastern Venezuela's heavy oil belt, an area containing some of the largest worldwide deposits of heavy crude oil which only recently have been developed. The four concessions consisted of the following consortia of public-private partnerships: Sincor (PDVSA, Total, and Statoil); Petrozuata (PDVSA and ConocoPhillips); Ameriven (PDVSA, ConocoPhillips and Chevron); and Cerro Negro (PDVSA, ExxonMobil, and British Petroleum, or BP). Since mid-2007 the Venezuelan government has increased its role in these operations and plans to expand production over the next two decades by establishing 27 new concessions mostly with other state-owned oil companies. These activities will affect 18,000 km? of dry tropical savannas and forest, a natural region that is already threatened. This work incorporates remote sensing and GIS techniques to calculate indicators of environmental alteration. The objective is to determine which operations produced the most and least landscape disturbances based on six important landscape measures: amount of vegetation-cover loss (based on a vegetation index); petroscape density; edge-effect zones; core areas; number of rivers crossed; and well density. It also measures nonpetroscape features related to agriculture and transportation. This work also uses statistical correlation analysis to determine the relationship between observed LUCC and the type of public-private partnership. Findings show that Petrozuata had the highest overall rank, meaning it had the highest level of landscape disturbances. It was followed by Sincor, Cerro Negro and Ameriven. Statistical results, however, do not show sufficient correlations between the type of company mix and petroscape-related alterations. These findings contribute to emerging research using geospatial technologies to monitor LUCC in heavy oil production zones and can be used to assess environmental performance of petroleum operations, map evolving landscape changes in current operations and plan for the establishment of new concessions.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Chris Baynard.
Thesis: Thesis (Ph.D.)--University of Florida, 2008.
Local: Adviser: Binford, Michael W.
Electronic Access: RESTRICTED TO UF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE UNTIL 2010-08-31

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2008
System ID: UFE0022008:00001

Permanent Link: http://ufdc.ufl.edu/UFE0022008/00001

Material Information

Title: Ecological Footprint of Energy Development in Eastern Venezuela's Heavy Oil Belt
Physical Description: 1 online resource (156 p.)
Language: english
Creator: Baynard, Chris
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2008

Subjects

Subjects / Keywords: ecological, heavy, land, oil, venezuela
Geography -- Dissertations, Academic -- UF
Genre: Geography thesis, Ph.D.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: Nearly all land-use and land-cover change (LUCC) studies concern conversions of land related to two renewable resource activities, agriculture and forestry. LUCC related to nonrenewable resource development is nonetheless another major agent of environmental change that has been studied very little. This work examines specific land-use and land-cover changes resulting from the production patterns of four petroleum operations between 1990 and 2005 in eastern Venezuela's heavy oil belt, an area containing some of the largest worldwide deposits of heavy crude oil which only recently have been developed. The four concessions consisted of the following consortia of public-private partnerships: Sincor (PDVSA, Total, and Statoil); Petrozuata (PDVSA and ConocoPhillips); Ameriven (PDVSA, ConocoPhillips and Chevron); and Cerro Negro (PDVSA, ExxonMobil, and British Petroleum, or BP). Since mid-2007 the Venezuelan government has increased its role in these operations and plans to expand production over the next two decades by establishing 27 new concessions mostly with other state-owned oil companies. These activities will affect 18,000 km? of dry tropical savannas and forest, a natural region that is already threatened. This work incorporates remote sensing and GIS techniques to calculate indicators of environmental alteration. The objective is to determine which operations produced the most and least landscape disturbances based on six important landscape measures: amount of vegetation-cover loss (based on a vegetation index); petroscape density; edge-effect zones; core areas; number of rivers crossed; and well density. It also measures nonpetroscape features related to agriculture and transportation. This work also uses statistical correlation analysis to determine the relationship between observed LUCC and the type of public-private partnership. Findings show that Petrozuata had the highest overall rank, meaning it had the highest level of landscape disturbances. It was followed by Sincor, Cerro Negro and Ameriven. Statistical results, however, do not show sufficient correlations between the type of company mix and petroscape-related alterations. These findings contribute to emerging research using geospatial technologies to monitor LUCC in heavy oil production zones and can be used to assess environmental performance of petroleum operations, map evolving landscape changes in current operations and plan for the establishment of new concessions.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Chris Baynard.
Thesis: Thesis (Ph.D.)--University of Florida, 2008.
Local: Adviser: Binford, Michael W.
Electronic Access: RESTRICTED TO UF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE UNTIL 2010-08-31

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2008
System ID: UFE0022008:00001


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ECOLOGICAL FOOTPRINT OF ENERGY DEVELOPMENT IN EASTERN VENEZUELA'S
HEAVY OIL BELT





















By

CHRIS W. BAYNARD


A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA

2008


































2008 Chris W. Baynard


































To my wife Ana for her infinite patience, support and encouragement during this long process; to
my children, Nico and Isabela, who will be glad I am no longer a student; and to my parents
Sylvia and Whaley for their help and encouragement









ACKNOWLEDGMENTS

I thank my wife, Ana; my children; and my parents for their support and patience. Jorge

Febles, Gary Harmon, Allen Tilley, Mark Workman and the Department of World Languages at

the University of North Florida gave me the flexibility to pursue my studies at the University of

Florida while working there. I thank Dr. Michael W. Binford at the University of Florida's Dept

of Geography for help in overseeing this project. I also thank Drs. Jane Southworth, Tim Fik,

Julie Silva, Eric Keys, Grenville Barnes and Terry McCoy at the University of Florida for help in

data acquisition, analysis, ideas and support. Dr. David Lambert and Robert Richardson at the

University of North Florida offered help and suggestions with the GIS and remote sensing

components of this project. Dan Richard at the University of North Florida and Dr George

Casella at the University of Florida helped with statistical analysis. Matt Marsik, Ph.D.

Candidate at the University of Florida's Dept of Geography also helped with remote sensing data

analysis, while Andres Guhl proved to be a very helpful and congenial colleague.

I also thank Carlos Guerra and R6mulo Medina, at Operadora Cerro Negro in Venezuela

for their help in better understanding the dynamics of heavy oil production and for logistical

support. Maria Madalena Godoy Mello at Brazil's National Institute for Space Research (INPE)

provided CBERS 2 satellite images. Representatives from ExxonMobil Venezuela, PDVSA,

Chevron Venezuela, Total, Shell Venezuela, BP Venezuela, ConocoPhillips USA, and Marathon

met and/or spoke with me about heavy oil operations in eastern Venezuela, provided gray

literature, reports and maps, and insights into corporate social responsibility practices in the

petroleum industry. ExxonMobil Venezuela also provided logistical support during fieldwork.

Thanks also to Alicia Moreau, Deud Dumith and Jose Acosta at the Instituto Geografico de

Venezuela Sim6n Bolivar for their support and map data. Victor and Rosella Perez and Dr. Elisa

Perez provided housing and logistical support. Finally, thanks to Tom Knode, Halliburton









Energy Services, and Mark Shemaria, Tidelands Oil Production Company, who served as SPE

E&P Environmental Safety Conference 2007 organizers and encouraged me to present my

research to the petroleum industry.









TABLE OF CONTENTS

page

A C K N O W L E D G M E N T S ..............................................................................................................4

LIST OF TABLES ......... ..... .... ......... ....................................9

LIST OF FIGURES .................................. .. .... ..... ................... 10

L IST O F A B B R E V IA T IO N S ......... ................. ....................................................................... 12

A B S T R A C T ......... ....................... ............................................................ 13

CHAPTER

1 LAND-COVER CHANGE AND NATURAL RESOURCE USE .....................................15

In tro d u ctio n ............... ... ........ ....... ............. ................. .................. ............... 1 5
Natural Resource Use, Land-use Land-Cover Change and Political Ecology ......................16
Renewable and Nonrenwable Resource Extraction.................. ...... .................. 18
Heavy Oil Production in Eastern Venezuela .................................................... ....................19
Environmental Performance Variability in the Heavy Oil Belt ....................................20

2 LAND-USE AND LAND-COVER CHANGE RESULTING FROM RENEWABLE
AND NONRENEWABLE RESOURCE EXTRACTION: A REVIEW ...............................22

In tro du ctio n ................... ...................2...................2..........
Background ............................................... .. ...................................... 24
LUCC and Political Ecology (Theoretical Framework) ...............................................24
Land-Cover Modification and Conversion.................................. ........................ 27
Specific Issues: Renew able N natural Resources ................................................. ..............30
A g ricu ltu re ...................................... ......................................................3 0
L jogging ................... ............................................................ ................33
T y p e of log g in g ................................................................3 3
F ir e ..................................................................................................................... 3 4
O th er factors ....................................................................................................... 3 6
Specific Issues: Nonrenewable Natural Resources................. .............................................37
G old M inning .............................................................................37
L an d degradation ......... .................................................................... ........ .. ....... .. 37
R eg en eration ...................................... ............................................... 39
M ercury pollution .......... ..... .......................................................... ........... ...... 40
Solutions ....................................................................................................... ..... 4 1
O il E exploration and Production ......................................................................... .. ...... 42
W wildlife: carib ou ...............................................................42
Seismic lines............................................. 44
R oad s .............. ... ................................................................44
C o n clu sio n ................... ...................4...................6..........









3 DO NATIONAL OIL COMPANIES PRODUCE GREATER ENVIRONMENTAL
ALTERATIONS THAN MULTINATIONAL OIL COMPANIES? THE CASE OF
VENEZUELA'S HEAVY OIL BELT .................................. .......................... 49

Remote Sensing, Geographic Information Systems (GIS) and Petroleum.............................52
M materials and M methods ...................................... .. .......... ....... ...... 54
Site D description ...................................................................... 54
Data Description (Concessions and Control Groups) ............................................. 55
T im e F ram e ......................... .........................................5 6
Image Processing and GIS Procedures................................ .................58
Petroenergy Maps .... ..__...... .. ....... ......... ....................... 59
Control Areas....................................... ....................60
Normalized Difference Vegetation Index (NDVI)........................................................60
Change-D election: 1990 to 2000 ........................................................ ............... 60
GIS Techniques: the 2005 Petroscape............................... ...............61
Petroscape density ........ ...................... ................................. 62
Edge-effect zones .................................... ..... .......... ...... ........ .. 63
Core-areas .......................... .............................64
A g ricu ltu re ............................................................................................................... 6 4
R rainfall P patterns ................................................................... 64
Results and Discussion ..................................... ................. ........ .... 65
V vegetation Change .............................................. .. .. ........... ..... ...... 65
P etroscape ................. ................................... ...........................66
N onpetroscape ..................................... ................................. ........... 68
Petro and N onpetroscape ........................................................... .. ............... 69
C o n c lu sio n ................... .......................................................... ................ 7 1

4 SPATIAL ANALYSIS AND STATISTICAL CORRELATIONS AMONG
PETROSCAPE FEATURES IN THE HEAVY OIL BELT ...............................................95

Introdu action ....................................................... ................... ................. 9 5
Production in the H eavy Oil Belt ............................................................................. 97
Land-U se Land-Cover Change ............................................. .............................. 98
E expected Findings ................. ........ ............................ .. .......... .. .............99
M e th o d s ........................ ..... .. ................................................... ....................................1 0 3
LUCC Related to the Petro and N onpetroscape...........................................................104
The Coefficient of D eterm nation ........................ ..... ............... ........................ 105
N onparam etric Statistical A analysis ........................................ .......................... 106
R results and D discussion ................................................. .......... .. ........ .... 108
D eterm ining the R2 Coefficient ........................................................... .............. 108
N onparam etric Statistical A analysis ........................................ .......... ............... 108
European and U S results ............................................... ............................. 109
L o cal resu lts ...............................................................10 9
C o n c lu sio n ............................................................. ................................................ 1 1 1









5 LAND-COVER CHANGE AND NONRENEWABLE NATURAL RESOURCES ..........124

S u m m ary ...........................................................................12 4
F future W ork ......................................................127

L IST O F R E F E R E N C E S ......................................... .. .. ....................... ...................................13 1

B IO G R A PH ICA L SK ETCH ........................................................................... ..................... .... 156















































8









LIST OF TABLES


Table page

3-1 Venezuelan heavy oil belt strategic associations, company ownership by percentage,
syncrude (synthetic oil) production and upgraded quality 2005..................... ........ 93

3-2 Change-detection measures showing amounts of positive and negative change per
site in km2 and as a percentage of the site area .......... ................. ............ ..... .......... 93

3-3 2005 petroscape measures for the six study sites................................... ..................94

3-4 2005 nonpetroscape measures for the six study sites............................................. 94

3-5 Overall rankings for land-cover change and petroscape features in the 6 sites ...............94

4-1 Venezuelan heavy oil belt strategic associations (concessions), company
participation by percentage and upgraded syncrude quality............... ..................115

4-2 The R2 coefficients for seven petroscape-related land-cover change measures and the
three consortia of companies operating in the HOB ....................................................... 116

4-3 The R2 coefficients of determination between three types of company participation
and 16 LUCC measures in the HOB........................... .................... 117

4-4 Kendall's tau-b correlation coefficients showing the relationship between Local,
European and US dominated concessions and seven important LUCC measures
related to petroscape expansion ............................ ................................ .....................118

4-5 Kendall's tau-b correlation coefficients and R2 coefficients showing the relationship
between concessions that were PDVSA-dominated, European-dominated, and US-
dominated and important landscape-change measures...................................................119

4-6 Petro and nonpetroscape data values for the four HOB concessions.............................. 122









LIST OF FIGURES


Figure pe

3-1 Venezuela, the heavy oil belt and concessions, and surrounding countries 2005. ............73

3-2 Venezuela's heavy oil belt, its four concessions: Sincor, Petrozuata, Ameriven, and
Cerro Negro, and the upgrading/refinery plant called Jose located on the coast of
Anzoategui state, 2005.............. .... ................................. .. 74

3-3 Vegetation-cover change across all six sites (three concessions and three controls)
betw een 1990 and 2000.. ........................ ...... ................................... .. .....75

3-4 Increases in Normalized Difference Vegetation Index (NDVI) across all six sites
betw een 1990 and 2000. ......................... ...... ................................... .. .....76

3-5 NDVI losses and gains for all six sites between 1990 and 2000....................................77

3-6 Gains and losses of the Normalized Difference Vegetation Index (NDVI) for the
th re e c o n c e ssio n s ...............................................................................................................7 8

3-7 Rainfall amounts (in mm) for Ciudad Bolivar for the October, November and
December dry months preceding the 1990 and 2000 Landsat images used in this
stu d y ................... ................... ......................................................... .. 8 1

3-8 Decrease in NDVI between 1990 and 2000 overlain by 2005 petroscape features..........82

3-9 Edge-effect zones, or areas where significant ecological effects extend...........................83

3-10 Core-areas show size and distribution of the patches of land that remain after
dissolving the edge-effect zones in figure 3-7.............. ..............................................84

3-11 Linear nonpetroscape 2005................................................................ 85

3-12 E dge-effect nonpetroscape zones............................................................ .....................86

3-13 Core-areas that remain after nonpetroscape features are dissolved from each site ..........87

3-14 Petro and nonpetroscapes across all 6 sites................................... ......................... 88

3-15 Agricultural patterns in the three concessions and the three control groups in 1990........89

3-16 Agricultural patterns in the three concessions and the three control groups in 2000........90

3-17 Amount of agricultural land (in km2) found in the three concessions and three control
groups in 1990 and 2000.......... .................. ...................... ............91

3-18 Future heavy oil development in Venezuela's Heavy Oil Belt................. ............. ...92









4-1 Venezuela's heavy oil belt and the four current operations ...................................113

4-2 Oil company participation in the HOB in 2005 and under the new changes
im plem ented in m id-2007. .......... .......... ........... ...... ....................... ............... 114

4-3 Potential E&P area within the heavy oil belt by 2030 ............................................. 115









LIST OF ABBREVIATIONS

BP British Petroleum

CVG Corporaci6n Venezolana de Guayana

E&P Exploration and production: two key activities of the petroleum industry

FVI Fuel value index, a measure of the physical properties of wood as a fuel
source

GIS Geographic information systems

GLCF Global Land Cover Facility, University of Maryland satellite image
repository

GPS Global positioning system

HOB Heavy oil belt, the zone of heavy oil located in central and eastern
Venezuela.

INPE Instituto Nacional de Pesquisas Espaciais, Brazil's National Institute for
Space Research

LUCC Land-use and land-cover change

M&E Management and Excellence, a sustainability ranking firm

MISR Multi-angle Imaging SpectroRadiometer

MNOC Multinational oil company

NDVI Normalized difference vegetation index

NOC National oil company

PDVSA Petr6leos de Venezuela, the state-owned oil company

PSI Pacific sustainability index









Abstract of Dissertation Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Doctor of Philosophy

ECOLOGICAL FOOTPRINT OF ENERGY DEVELOPMENT IN EASTERN VENEZUELA'S
HEAVY OIL BELT

By

Chris W. Baynard

August 2008

Chair: Michael W. Binford
Major: Geography

Nearly all land-use and land-cover change (LUCC) studies concern conversions of land

related to two renewable resource activities, agriculture and forestry. LUCC related to

nonrenewable resource development is nonetheless another major agent of environmental change

that has been studied very little. This work examines specific land-use and land-cover changes

resulting from the production patterns of four petroleum operations between 1990 and 2005 in

eastern Venezuela's heavy oil belt, an area containing some of the largest worldwide deposits of

heavy crude oil which only recently have been developed.

The four concessions consisted of the following consortia of public-private partnerships:

Sincor (PDVSA, Total, and Statoil); Petrozuata (PDVSA and ConocoPhillips); Ameriven

(PDVSA, ConocoPhillips and Chevron); and Cerro Negro (PDVSA, ExxonMobil, and British

Petroleum, or BP). Since mid-2007 the Venezuelan government has increased its role in these

operations and plans to expand production over the next two decades by establishing 27 new

concessions mostly with other state-owned oil companies. These activities will affect 18,000 km2

of dry tropical savannas and forest, a natural region that is already threatened.

This work incorporates remote sensing and GIS techniques to calculate indicators of

environmental alteration. The objective is to determine which operations produced the most and









least landscape disturbances based on six important landscape measures: amount of vegetation-

cover loss (based on a vegetation index); petroscape density; edge-effect zones; core areas;

number of rivers crossed; and well density. It also measures nonpetroscape features related to

agriculture and transportation. This work also uses statistical correlation analysis to determine

the relationship between observed LUCC and the type of public-private partnership.

Findings show that Petrozuata had the highest overall rank, meaning it had the highest

level of landscape disturbances. It was followed by Sincor, Cerro Negro and Ameriven.

Statistical results, however, do not show sufficient correlations between the type of company mix

and petroscape-related alterations. These findings contribute to emerging research using

geospatial technologies to monitor LUCC in heavy oil production zones and can be used to

assess environmental performance of petroleum operations, map evolving landscape changes in

current operations and plan for the establishment of new concessions.









CHAPTER 1
LAND-COVER CHANGE AND NATURAL RESOURCE USE

Introduction

This Alterations to land cover affect central components of earth system functions (Fujita

and Fox, 2005; Lambin et al., 2003; Read and Lam, 2002, Southworth, 2004; Soares-Filho et al.,

2004) and these changes are proceeding at a pace, magnitude and scale unprecedented in human

history (Jensen 2005). Understanding the factors that contribute to changes in land-cover and

land-use (LUCC) practices are of prime importance since these changes influence climate,

ecosystem goods and services, gross carbon fluxes and the vulnerability of people and places at

various spatial and temporal scales (Lambin et al., 2003; Fujita and Fox, 2005; Southworth,

2004; Asner et al., 2005; Rindfuss et al., 2007; Keys and McConnell, 2005).

Land-change science is a central component of geography since (in part) it examines

spatial patterns of human-environment interactions on the landscape. This complex system of

interactions (Veldkamp and Lambin, 2001) involves political, economic, cultural, social,

technological, institutional and environmental variables operating under various spatial and

social scales and combines natural and social science methods (Folke et al., 2007). As most

LUCC studies demonstrate, understanding social-ecological system dynamics involves teams of

researchers comprised of specialists in given disciplines (see: Lambin et al., 2001; Rindfuss et

al., 2007; Wassenaar et al., 2007; Gibbons et al., 2008; Soares-Filho et al., 2006; Caldas et al.,

2007, Pielke et al., 2007; Folke et al., 2007).

The complexity of understanding social-ecological system integration (Folke et al., 2007),

creates two main problems. One, findings from a particular study tend to be case-specific; not

applicable to a wide range of situations and settings (Rindfuss et al., 2007; Keys and McConnell,

2005; Veldkamp and Lambin, 2001). Two, it is difficult to reconcile natural and social science









systems and methods, particularly across spatial scales (Veldkamp and Lambin, 2001)-what

Folke et al. (2007) refer to as the problem of "fit".

Thus land-use researchers "often confine themselves to either a single process (e.g.

deforestation), or a single discipline (e.g. economic models)" (Veldkamp and Lambin, 2001, pp.

3). Since this project is the product of one researcher, I too confine myself to a single process:

land-cover change resulting from nonrenewable natural resource use in a dry-tropical forest zone

in eastern Venezuela. At the same time my field, geography, which itself is cross-disciplinary,

justifies that I draw here from land-use land cover-change science, political ecology approaches

and sustainability research.

Natural Resource Use, Land-use Land-Cover Change and Political Ecology

My interest lies not only in understanding what types of landscape changes result from

natural resource use, since they also lead to social alterations and subsequent land management

responses. I am also intrigued by the concept of sustainability-that is the interplay between

ecological, economic and socio-cultural dimensions taking place at various spatial and temporal

scales as well as organizational and institutional levels (Folke et al., 2007; Ellsworth, 2002). This

interplay "affects the chances that environmental and social conditions will indefinitely support

human security, well-being, and health" (McMichael et al., 2003, 1919).

Obviously some practices and institutions are more sustainable than others; that is some

natural resource use patterns (shaped by the nature of the resource itself, topography and the

rules governing them) create larger and more permanent changes on the landscape that in turn

cause important and long-term alterations such as habitat fragmentation; land degradation; land,

air and water pollution; soil erosion, increased sedimentation and biodiversity loss (Armenteras,

et al., 2006; Ferraz et al., 2006; Schneider and Dyer, 2006; McMichael et al., 2003; Dietz et al.,

2003; Cumming and Barnes, 2007). These shifts can lead to regional and global climate changes,









thus affecting earth system functions and the organisms that depend on them (Pielke et al., 2007;

Keys and McConnell, 2005).

What socioeconomic and socioecological factors most affect this capacity to create

sustainable natural resource use, or to at least reduce the negative environmental (and therefore)

social effects resulting from such practices? Secondly, what are some of the incentives for land

managers, including organizations, to behave in a more sustainable manner (Ostrom et al.,

1999)?

Political ecology theory proposes that human-environment interactions-as related to land

and natural resource use-consist of economic, political and environmental processes that

together produce particular landscapes (Robbins, 2004). In this context, humans react to

economic opportunities they perceive the natural system offers them in order to meet local

consumption needs or market demands. In essence it focuses on the political economy (the

relationship between politics and economics) of human-environment interactions. These

interactions, however, create competition, resistance and conflict among land managers to

access, use and exclude others from valuable land and natural resources-involving institutional

structures such as land and resource tenure, as well as social actors possessing various degrees of

power (Schmink and Wood, 1992, Folke et al., 2007; Wannasai and Shrestha, 2008; Rindfuss et

al., 2007; Simmons, 2002; Simmons et al., 2007; Ankersen and Barnes, 2004; Kennedy, 2003).

This competition for land and resources poses problems that require political solutions in terms

of policy and its implementation (Bryant and Bailey, 1997), underlining the importance that

political/governance structures have in this process. As Kennedy (2003, pp. 1861) notes on

sustainability: "The big question in the end is not whether science can help. Plainly it could.









Rather, it is whether scientific evidence can successfully overcome social, economic and political

resistance."

Finally, human-environment interactions create landscape changes that may be temporary

or permanent; resulting in alterations or modifications (Veldkamp and Lambin, 2001) which can

then affect subsequent social adaptive behavior as well as ecosystem responses (Folke et al.,

2007).

Renewable and Nonrenwable Resource Extraction

Chapter 2 is a literature review of the environmental effects resulting from renewable and

nonrenewable natural resource extraction. This overview concerns LUCCs and secondary

outcomes that result from agriculture and logging (renewable natural resources) and gold mining

and petroleum exploration and production (E&P) (nonrenewables). A good deal of this literature

concentrates on tropical regions, particularly Amazonia (Soares-Filho et al., 2006; Flamenco-

Sandoval et al., 2007; Caldas et al., 2007; Nepstad et al., 1999) and tries to determine the most

important drivers that lead to these changes. These, however, tend to be scale specific

(Veldkamp and Lambin, 2001; Rindfuss et al., 2007; Keys and McConnell, 2005).

For example: at the local scale, social variables and accessibility to resources are the main

drivers of change; at the landscape scale, topography, climate and agricultural potential appear to

matter most; while at the national/regional level, climate, macroeconomic factors (such as

economic development, government policies and indebtedness, population pressures, market

accessibility) and demographic variables, such as household size and labor availability are most

important (Scrieciu, 2007; Veldkamp and Lambin, 2001; Caldas et al., 2007; Rindfuss et al.,

2007). Since the bulk of the LUCC literature addresses deforestation related to agricultural

expansion and logging, I found that studies concerning nonrenewable natural resources are

lagging despite the important and long-term landscape changes that result from their extraction









(Almeida-Filho, 2002; Peterson and Heemskerk, 2001). The work related to gold mining,

particularly small-scale mining (done by individuals and small groups) is a growing area of

research, nevertheless. On the other hand, investigations related to oil E&P are less abundant,

especially for Venezuela, a major oil producer and exporter to the US.

Heavy Oil Production in Eastern Venezuela

Venezuela makes an interesting case because it has very large resources of heavy oil; that

is degraded oil that requires more refining to produce a usable and valuable product

(Phizackerley and Scott, 1978; Jiayu and Jianyi, 1999; Meyer and Attanasi, 2003; Galarraga et

al., 2007). The technology is now viable, especially given the high price of oil worldwide, for the

Venezuelan government to expand its operations from the current 4 concessions to 27 in the next

two decades, potentially affecting 18,000 km2 of territory across three states. This aggressive

development of heavy oil will have important implications on the regional dry tropical forest

ecosystem, which is already threatened (Fajardo et al., 2005).

Chapter 3 therefore addresses the lacunae in the literature by examining some general

LUCCs resulting from the four current operations and three control sites. I measure four general

landscape changes using GIS and remote sensing (satellite image analysis) techniques. They are:

1) land-cover loss, represented by positive and negative changes in the NDVI vegetation index 2)

petroscape (petroleum-infrastructure) density-the density of petroleum infrastructure features in

km/km2; 3) edge-effect zone, or the area over which significant ecological effects extend

outward from a road or petroleum infrastructure (Forman and Deblinger, 2000; Schneider et al.,

2003; Morton et al., 2004; Thomson et al., 2005); and 4) core areas, or the amount of intact land

mosaics that remain after the edge-effect zone has been taken into account (Morton et al., 2004;

Thomson et al., 2005; Griffith et al., 2002b). In this chapter I present an efficient low-cost way

for land managers and government officials to measure map and monitor LUCCs associated with









petroleum exploration and production using GIS methods and (often) readily available satellite

images.

Environmental Performance Variability in the Heavy Oil Belt

Petrozuata's petroscape values leads to the question: do state-owned oil companies pay

less attention to the environmental effects created by oil operations and therefore create larger

and longer-lasting landscape alterations? Alternately: do multinational oil companies (MNOCs)

place greater importance on their environmental performance, and in particular European

companies? (Marica et al., 2008; Moser, 2001; Gouldson, 2006). If good environmental

performance leads to good economic results (Morse 2008), then why does variability exist

among four oil operations located very near each other, operating in the same landscape and

implementing the same business model? While land-use history is an important component

(Peterson and Heemskerk, 2001; Lambin et al., 2003; Flamenco-Sandoval, 2007; Cumming and

Barnes, 2007; Abizaid and Coomes, 2004) perhaps the mix of private-public partnerships that

exist among each of the four oil concessions also influences the outcome.

Chapter 4 examines the statistical relationship between oil-related LUCC and company

ownership. It divides the concessions into those dominated by 1) local-company (state); 2)

European; and 3) US interests. It expands the number and type of environmental-change

measures among all four HOB concessions (excluding control groups) and hypothesizes that

local-company dominated concessions will exhibit the most environmental alterations, followed

by US-dominated concessions, and that European-strong concessions will have the least changes.

Results show statistically significant associations between local-participation and petroscape

density and well density. No statistically significant correlations exist between US and European

participation and important petroscape measures. Finally, chapter 5 summarizes the results from

the literature review, the analysis of petroscape expansion using satellite images and GIS









techniques, and the statistical analysis of petroscape changes and the type of ownership among

the HOB concessions.









CHAPTER 2
LAND-USE AND LAND-COVER CHANGE RESULTING FROM RENEWABLE AND
NONRENEWABLE RESOURCE EXTRACTION: A REVIEW

Introduction

There is now essentially universal agreement across the disciplines involved in the
emerging land change science that to understand the causes of land cover and use change,
it is essential to incorporate human behavior in our theories, models, data, and analyses
(Rindfuss et al., 2007, pp. 739).

A central way humans interact with the environment is through the extraction of natural

resources in order to meet the needs for internal consumption and external demand. As humans

compete with each other to access, control and use these resources while excluding or resisting

others, their actions are shaped by political and economic structures as well as by perceived

ecosystem opportunities and constraints operating at various spatial and temporal scales

(Schmink and Wood, 1992; Zimmerer and Bassett, 2003; Robbins, 2004; Bryant, 2001; Mambo

and Archer, 2007; Kennedy, 2003).

Natural resources, divided into renewable and nonrenewable, are a key component of

human-driven land-use land-cove change (LUCC)1. The distinction between the two types is that

renewables (or flow resources) are capable of being replaced by natural ecological processes or

sound management practices, while nonrenewables (or stock resources) are finite, exhaustible

(Harriss, 2006; Neumayer, 2000; Cutter and Renwick, 2004). The extraction of both types of

resources can result in land-cover modification and conversion and involves competition among

various actors wielding different levels of power (Schmink and Wood, 1992). At the same time,

these actors possess "ideas about ecology and political economy [that] actively shape human

perceptions and uses of nature (Bryant, 2001, pp. 162; emphasis in original).



1 Cutter and Renwick (i" 14, 4, 5) additionally differentiate between two other types of resources, perpetual, like the
sun, and potential, which does not have value today but may in the future, such as solid trash and wastewater









The study of the interaction between environment, politics and society (Wolford, 2005)

constitutes a concise definition of political ecology. This subfield of geography (and other social

sciences) injects political considerations into traditional human-environment interaction and

global environmental-change science "by demanding that researchers look both at what changes

take place and why they occur" (Keys, 2005, pp. 229).

This paper responds to this demand by reviewing the LUCC and related literature and

synthesizing the general environmental changes resulting from two examples each of renewable

and nonrenewable natural resource use and some of the drivers behind these human-environment

interactions. Agriculture (land and soil) and logging (tree-cutting), as dominant drivers of

deforestation, are important examples of renewable natural resources extraction. Meanwhile

gold-mining and oil exploration and production (E&P) are two activities that can produce lasting

changes on the landscape even after operations have ceased. They represent examples of

nonrenewable natural resources use. The goal of this paper is to better understand the complex

dynamics behind these interactions by focusing on the political, economic and environmental

factors that help produce particular landscapes and the persistent changes that result once these

extractive activities cease. I link political ecology ideas to LUCC research and thus advance the

debate that land-change science, a complex study of the interaction of many variables, can first

be approached by focusing on the relationships between political, economic and environmental

factors.

This review can serve investigators, government officials, conservationists, industry and

others interested in better understanding the relationship between renewable and nonrenewable

resource extraction and the associated environmental changes. I limit the scope of this review by

concentrating on northern South America (Brazil, Venezuela, Guyana, Suriname and French









Guiana) when possible, given that a good deal of work exists on Amazonia and I am more

familiar with this region, particularly Venezuela.

Background

A review of the LUCC and similar literature shows a heavy focus on deforestation in

tropical regions, particularly in Amazonia's humid forests (Soares-Filho et al., 2006; Asner et al.,

2005, 2004; Sebbenn et al., 2007; Flamenco-Sandoval et al., 2007; Vieira et al., 2003; Almeida-

Filho and Shimabukuro, 2004; Caldas et al., 2007; Espirito-Santo et al., 2005; Nepstad et al.,

1999). Meanwhile, dry tropical forests and savannas receive less attention even though they are

the dominant forest type in the tropics, have more productive soils, support much of the world's

agriculture and human habitation (Southworth, 2004; Jepson, 2005; Fajardo et al., 2005; Daniels

et al., 2008; Trejo and Dirzo, 2000; Romero-Duque et al., 2007) and rank among the most

endangered terrestrial ecosystems (Lawrence et al., 2007; Trejo and Dirzo, 2000; Gordon et al.,

2004).

Nonrenewable natural resource extraction receives even less attention, since many of the

investigators concerned with deforestation study the alterations created by agriculture and

logging, rather than by gold mining and oil extraction despite the long-term changes these latter

activities create on forest cover (Peterson and Heemskerk, 2001; Almeida-Filho and

Shimabukuro, 2002; 2000). When the disturbances created by both types of resource extraction

are combined, they create cumulative effects that fragment the landscape, alter ecosystems,

reduce wildlife populations and create social risks for nearby inhabitants (Schneider et al., 2003)

LUCC and Political Ecology (Theoretical Framework)

Early LUCC research tried to model land-use change by measuring and predicting rates

(quantities) of change among biophysical attributes (Veldkamp and Lambin, 2001) while

ignoring social drivers. This work focused on learning what changes were taking place rather









than asking why they were occurring (Keys, 2005). Later efforts integrated socioeconomic

variables and the role of policies (Veldkamp and Lambin, 2001) resulting in case studies that

moved the inquiry beyond description toward an understanding of LUCC (Rindfuss et al., 2007).

But the resulting case studies lacked generalizability and sufficient detail to evaluate data and

methods (Veldkamp and Lambin, 2001; Rindfuss et al., 2007; Keys and McConnell, 2005),

leading Rindfuss et al. (2007, pp. 739) to recently state: "Indeed, the land change field itself does

not have agreement on what should be reported."

This is reflected in the many processes examined that are believed to lead to LUCC. These

include complex interactions of economic, ecological, political, social, cultural, technological

and infrastructure factors operating at various temporal and spatial scales (Veldkamp and

Lambin, 2001; Hietel et al., 2007; Keys and McConnell, 2005; Turner et al., 2007). Not

surprisingly incorporating all these variables "is complicated by profound cultural differences

between social science and natural science disciplines" (Rindfuss et al., 2007, 739). Furthermore,

"not all causes of land-use change and all levels of organization are equally important. For any

given human-environment system, a limited number of causes are essential to predict the general

trend in land use" (Lambin et al., 2003, 222). The need to provide parsimonious explanations is

one reason that political ecology lends generalizability to studies concerning LUCC; after all

competition, conflict and landscape changes are central outcomes of human-environment

interaction concerning land and resources, particularly when they are scarce (Bryant and Bailey,

1997; Schmink and Wood, 1992; Rubenstein, 2004).

Keys and McConnell (2005) analyzed LUCC trends related to agricultural practices

worldwide by reviewing 91 case studies on this subject and found that the most common drivers

were economic (market), demographic, policy (political) and institutions (property regimes).









This does not imply that natural environmental variations, such as climate changes/extremes,

droughts, floods and insect outbreaks do not affect this process (Binford et al., 1997; Hodell et al

1995; Gasse et al., 1990), rather the basic premise is that people's response to economic

opportunities, as meditated by political and market institutions-and environmental

characteristics (such as climate, topography and soils) drives LUCC at various scales (Lambin et

al., 2001, 2003; Zimmerer and Bassett, 2003; Keys and McConnell, 2005; Rindfuss et al., 2007).

Furthermore, in some cases such as floods, these "natural" disasters have social causes (filling

drains with refuse, inadequate maintenance, using drainage areas for housing and agriculture)

and they affect social groups unequally (Pelling, 2003).

As scale varies so do important drivers. Veldkamp and Lambin (2001) note that: 1) at the

local level social factors and accessibility are important; 2) at the landscape level topography and

agroclimatic variables matter most; while 3) at the national/regional level the important ones are

climatic, macroeconomic and demographic. Thus regarding a large region like Amazonia,

Armenteras et al. (2006, pp. 354) conclude that "deforestation is primarily determined by human

population, accessibility, land use and land tenure issues."

Meanwhile, Robbins (2004, 2003a) promotes moving away from the binary notion that

environmental change results in destruction of nature, or that it's socially created2, and instead

accept that nature is produced by human and non-human actors. This aspect of production

demonstrates that landscapes are never static; they are being altered, degraded, converted or

regenerated by human and natural processes continuously. At the same time political power, the

distribution of resources, and perceptions of what types of resources are scarce and valuable, are

not static either (Ellsworth, 2002).


2 The social creation of nature argument "is defined, delimited, and even physically reconstituted by different
societies, often in order to serve specific, and usually dominant, social interests" (Castree 2001, 3).









Political ecology and land-change science pursue the same goal: understanding human-

environment interaction by studying and modeling how and why people interact with land and

resources and the outcomes of those interactions for both the social and natural world. One

difference, however, is that political ecology tends to focus on (contested versions of) human-

environment interactions related to particular resources such as gold or rubber (Schmink and

Wood, 1992; Salisbury and Schmink, 2007), while land change science treats "the environment

in terms of its array of ecosystem (environmental) goods and services" (Turner et al., 2007, pp.

20666). Thus integrating research that joins social, natural, with GIS and remote sensing science

(Turner et al., 2001) results in a land-system science whereby: 1) the pixel becomes the finest

grain of spatial specificity (Tuner et al., 2007); 2) political and economic forces regulate human

behavior and interactions with the environment; and 3) where the aim is to "better inform

environmental policy" (Redman et al., 2004, 161, 162). This fusion is an outgrowth of works

such as People andPixels (Liverman et al., 1998), which attempted to span the gap between

remote sensing and social science research. Interestingly, even in traditional land-change science,

the political is ever present. As McCusker and Weiner (2003, pp. 197, 201) point out, "the use of

geospatial technologies to study landscape change has led to differing, and sometimes

contradictory, representations of nature." This is because "the satellite image is... a social agent

of environmental change, serving as an ally in disputes over the nature of the landscape"

(Robbins, 2003b, 197).

Land-Cover Modification and Conversion

Land use is never static (Lambin et al., 2003); it "is a dynamic canvas on which human and
natural systems interact" (Parker et al., 2003, pp. 314).

Land-use decisions comprise the single most important issue in environmental

management since they define the kinds of resources we use and the extent of environmental









alterations (Cutter and Renwick, 2004). First, land-cover is defined "by the attributes of the

earth's land and immediate subsurface, including biota, soil, topography, surface and

groundwater, and human structures" (Lambin et al., 2003, 213). It "refers to the type of material

present on the landscape (e.g., water, sand, crops, forest, wetland, human-made materials such as

asphalt)" (Jensen, 2005, pp. 340). Land use refers to the "purposes for which humans exploit

land cover" (Lambin et al., 2003, pp. 216), or to "what people do on the land surface (e.g.,

agriculture, commerce, settlement)" (Jensen 2005, pp. 340).

Some alterations create expansive changes and endure a longer period than others,

resulting in land-cover conversion rather than modification. Conversion involves the shifting of

one land-cover type to an entirely different one, such as occurs in large-scale agriculture, logging

by clear-cutting of trees, urbanization and deforestation (Southworth, 2004; Lambin et al., 2003;

Daniels et al., 2008). Conversion is also associated with nonrenewable resource use, such as

mining and oil and gas extraction since these actions tend to create degraded areas as well as

large-scale permanent changes on the landscape that are often followed by the establishment of

urban infrastructure (Schneider et al., 2003; Almeida-Filho and Shimabukuro, 2002, 2000).

Roads, bridges, pipelines, tank farms, electric poles and power lines further affect the

surrounding setting transforming the landscape into an urban or periurban form. Another term for

conversion is land transformation, which "refers to radical changes in land use and cover, usually

over the long term" (Turner et al., 2007, pp. 20666).

Modification, on the other hand, refers to changes in the type of cover without changing its

overall classification (Lambin et al., 2003). Here, the land-cover remains in the same category

and is exemplified by selective tree cutting, thinning of forests, degraded grasslands, or changes

in crop patterns and input use (Southworth, 2004; Southworth et al., 2004; Daniels et al., 2008;









Veldkamp and Lambin, 2001). However, it can also refer to vegetation regeneration (land-cover

gain) such as successional forest growth and recovery on abandoned lands following natural and

social disturbances such as seismic exploration and certain gold-mining activities (Lee and

Boutin, 2006; Peterson and Heemskerk, 2001; de Figuer6a et al., 2006; Romero-Duque et al.,

2007; Hartter et al., 2008; MacFarlane, 2003), as well as the establishment of tree plantations,

also known as afforestation. Thus, not all land-cover changes involve degradation or conversion.

Yet even afforestation involves logging and converting tropical forest at times in order to

establish tree plantations. In southeastern Venezuela, for example, over 600,000 hectares of dry

tropical forest have been converted to Caribbean pine (Pinus caribaea var. hondurensis) in the

last 40 years (Barnola and Cedefio, 2000). Across the globe, parts of Indonesia's rainforests are

being converted for oil palm production, a pattern expected to double in the next two-to-three

decades (Sandker et al., 2007). Furthermore, managed forests and plantations "tend to support a

lower biodiversity than unmanaged forests" (Timoney and Lee 2001, pp. 400).

Two other terms used in the LUCC literature related to land-use are intensification and

extensification. Both activities involve shifts in land-use patterns. Whereas intensification refers

to increasing the value of product per land unit, extensification refers to the opposite (McCusker

and Wiener, 2003). Intensification would involve planting more corn plants per hectare and

adding fertilizer in order to produce more, while extensification would involve opening up more

land to agriculture to increase production. Agricultural intensification also involves altering

plants and animals so that humans "supplant natural processes of nutrient and biological

regeneration" (Keys and McConnell, 2005, 321).

Intensification leads to other problems as well: 1) hybridization of local seed varieties, the

spread of monoculture and possible eradication on non-economically valuable plants; 2)









hydrological changes resulting from capturing and diverting rivers and streams; 3) introducing

high levels of agrochemicals (including pesticides and herbicides); 4) loss of nutrients such as

carbon; and 5) erosion caused by heightened (intense) soil use and increased mechanization

(Keys and McConnell, 2005; Donald et al., 2006). Land managers commonly use intensification

strategies when agricultural policies provide incentives, note Donald et al. (2006), and these

practices tend to reduce biodiversity. Through this perspective policy instruments are causing

this problem, and removing economic incentives (altering these policies) would help halt this

loss (Donald et al., 2006).

Another important outcome, aside from the alterations caused by both renewable and

nonrenewable resource extraction, is the establishment of settlements, towns and cities spurred

by the expansion of goods and service industries catering to the influx of workers attracted to a

natural resource boom (Schmink and Wood, 1992, Consiglio et al., 2006). These settlements

further urbanize the surrounding landscape and permanently alter the land cover (Lambin et al

2001) even after the boom ends. Lambin (1999, pp. 191-192) concludes that "Land-cover

modifications are generally more prevalent than land-cover conversions," meaning that land-use

practices tend to keep the land cover in the same classification, rather than completely change it.

Specific Issues: Renewable Natural Resources

Agriculture

Globally the conversion of forests consumes around 13 million hectares per year (FAO,

2006) and the key driver of this change is the expansion of agriculture (FAO, 2006; Lambin et

al., 2003; Daniels et al., 2008). This holds true in Amazonia where growing cash crops (such as

soy, cotton) and raising cattle are the predominant large-scale agricultural activities leading to

deforestation (Schmink and Wood, 1992; Espirito-Santo et al., 2005; Soares-Filho et al., 2004;

Branddo and Souza, 2006; Ferraz et al., 2006; Flamenco-Sandoval, 2007). Deforestation is the









removal of vegetative cover beyond a minimal threshold-set at 10% by the United Nations

Food and Agriculture Organization (FAO, 2006), while agriculture is defined as "the cultivation

of domesticated crops and the raising of domesticated animals... to produce food, feed, drink and

fiber" (Jordan-Bychkov and Domosh 2003, pp. 441, 261). This now includes biofuels as

substitutes for fossil fuels (Eickhout et al., 2007; Demirbas and Demirbas, 2007). As Lambin et

al., note (2003, pp. 208), agricultural activity "has expanded into forests, savannas, and steppes

in all parts of the world" resulting in broad areas of land-conversion.

The agricultural pattern in some of Brazil's forests and savannas is to first convert land to

cattle pastures and then to fields for growing soybeans and other crops as the agricultural frontier

advances (Ferraz et al., 2006; Nepstad et al., 2006). Rindfuss et al. (2007) identify the

commonalities in frontier land-use change as:

* New people with their own social organization and technologies enter an occupied but
relatively natural landscape.

* Natural population increase and in-migration create population pressure.

* Settlement patterns at the village and household level alter the landscape.

* As these settlements become linked to national transportation systems they grow.

* Agriculture intensifies and expands in order to sustain a growing population. Cash crops
are introduced and these local markets are linked to those at the regional, national and
global level. This also brings additional LUCCs.

* Next, spatial patterns change, vegetation decreases, and the habitat is fragmented.

* Finally, environmental constraints and social factors such as state policy and institutional
arrangements such as land tenure lead to changes in land use and settlement patterns.

By contrast since shifting agricultural plots tend to serve the household level they: 1) affect

small areas; 2) do not result in complete vegetation removal; and 3) tend to be abandoned after

three to five years unless population pressures are too great (Bassett and Zueli, 2004; Folster,

1995; Salazar, 1995; Peterson and Heemskerk, 2001; Lawrence et al., 2007). Several factors can









influence cultivation and fallow periods. Environmental variables include soil types and

condition, topography and rainfall patterns; while social factors include government policies and

livelihood strategies (Hartter et al., 2008; Donald et al., 2006; Keys and McConnell, 2005). As

abandoned fields regenerate to secondary forest they replenish nutrients and organic matter, as

well as remove and fix carbon from the atmosphere (Daniels et al., 2008; Kupfer et al., 2004;

Fearnside et al., 2007), highlighting an important point: reforestation is also an outcome of

LUCC (Dalle et al., 2006; Espirito-Santo et al., 2005; Lambin et al., 2003; Vieria et al., 2003;

Keys and McConnell, 2005). However, the "demands of an increasing human population are

causing the fallow period to be shortened" (Vieira et al., 2003, pp. 471), reducing the regrowth of

secondary forests which serve as important ecosystem reservoirs and which are important to

forest-based economies (Hartter et al., 2008).

While extended-use patterns are becoming more prevalent in tropical deforestation

frontiers as a response to population increase (Lawrence et al., 2007) colonizers also contribute

to deforestation by clearing plots for their homes and agricultural fields and as a way to increase

land value. Schmink and Wood (1992) found that some colonization schemes involved migrants

occupying a plot, clearing out the trees and then selling it as one of the few ways to earn money

in this region. At times these plots are sold to farmers with larger holdings that in turn

consolidate land for agroindustrial purposes (Schmink and Wood, 1992). For the most part,

however, shifting cultivation results in land-cover modification, while commercial crop and

cattle raising results in land conversion.

Concerning dry tropical forests, other factors such as overgrazing by cattle and goats can

lead to intense alterations, resulting in shrubland and grassland conversion which limits the

regeneration capacity of these forests (Trejo and Dirzo, 2000). The traditional solution has been









to reduce animal stock size, but this simplistic view, notes Turner (2003, pp. 163) overlooks "the

linkages between political economy, management, and ecology... [because] livestock populations

are also economic entities-they are managed by their owners as producers of milk or traction,

as commodities, and as stores of wealth." This highlights the message by Rindfuss et al. (2007)

that LUCC studies must consider the human perspective.

Logging

Logging is a second important driver of LUCC, but less significant than agricultural

expansion (crops and cattle) in terms of area affected and effects on faunal diversity (Dunn,

2004, pp. 215). This is perhaps due to the regeneration of many abandoned logged sites, such as

in the Choc6 rainforests of western Colombia, where only 20 to 30% of logged forests are

converted to agricultural use (Sierra et al., 2003). Sierra et al. (2003) note that this proportion is

lower than what has been reported in other regions such as Amazonia and attribute this lower

conversion rate to land tenure policies that allow local communities to legally exclude migrant

farmers. This highlights the importance of land tenure security in LUCC (Wannasai and

Shrestha, 2008), or as Anderies et al. (2004, pp. 4, 6) put it, "the rules devised to constrain

actions of agents" form the link between resource users and public infrastructure providers.

Thus, failure to "take into account the diversity of land tenure systems within" an area affects the

success of land management projects (Mambo and Archer, 2007, pp. 383).

Type of logging

The type of logging and location also matter. Selective logging (cutting down a few

marketable species) or reduced-impact logging (RIL) practices (extracting valuable trees while

trying to maintain forest integrity and meeting legal restrictions) (Asner et al., 2004; Zarin et al.,

2007) result in the thinning of forests. These actions lead to land-cover modification rather than

conversion since they cause fewer disturbances to the landscape and to faunal diversity (Dunn,









2004). Even forests that have been logged by conventional methods and experience severely

altered structure and species composition may still possess a "relatively undisturbed seed bank

and sprouting stumps... [that] contribute to fast regrowth" (Mesquita 2000, pp. 132). Yet this

holds true as long as the forest cover is not removed by bulldozers or heavy machinery. Boletta

et al. (2006, pp. 112, 113) noticed that when logging patterns in the xerophytic forests of the

Argentine Chaco changed from selective deforestation during the 1970s and 1980s to the use of

bulldozers in the mid-1990s, these new actions resulted in "the total loss of forest, including

seed-bearing trees." In this scenario the pattern became one of: 1) unselective deforestation, 2)

clearing land with heavy machinery, 3) burning the vegetation that remained, 4) plowing the soil,

and 5) planting different crops (Boletta et al., 2006). Romero-Duque et al. (2007) also found that

dry tropical forest land cleared by bulldozers for home building in Mexico and later abandoned

also exhibited greatly diminished regeneration rates.

On the other hand, even when selective (not indiscriminate) logging takes place, the

resulting land-cover changes can be more pronounced than first thought. Asner et al. (2005) and

Nepstad et al. (1999) have observed that when the amount of selective logging occurring in

Amazonia is quantified via remote sensing techniques the true figure of deforestation doubles-

perhaps shifting the LUCC classification from modification to conversion.

Fire

The underestimation of true logging figures (by more than 50%) also applies to fires that

burn large areas of forest every year in Amazonia but that are not included in standard

deforestation measures (Nepstad et al., 1999; Soares-Filho et al., 2006). Nepstad et al. (2001)

believe that fires pose the greatest threat to Amazon forests. However, logging and fires are

related; logged forests are more vulnerable to fires than nonlogged forests (Asner et al., 2004).

Nepstad et al. (1999, pp. 505) explain: "Logging also increases forest flammability by reducing









forest leaf canopy coverage by 14-50%, allowing sunlight to penetrate the forest floor, where it

dries out the organic debris created by the logging." Fires also reduce forest diversity (Gordon et

al., pp. 2004) by compromising living trees intentionally left on logged sites for conservation of

future wood potential, note Gibbons et al. (2008).

By damaging the stems, fires spur invertebrate and fungal decay which can lead to tree

collapse (Gibbons et al., 2008). Additionally, fires "ignited on agricultural lands can penetrate

logged forests, killing 10-80% of the living biomass and greatly increasing the vulnerability of

these forests to future burning" (Nepstad et al., 1999, pp. 505). Forest fires may grow larger than

normal during unusually dry seasons or droughts brought on by El Nifio Southern Oscillation

(ENSO) (Nepstad et al., 1999, Whitlock et al., 2006; Siegert and Hoffmann, 2000; Tareq et al.,

2005). Finally, biomass burning during the dry season worldwide contributes to global

atmospheric pollution (Holzinger et al., 2001; Almeida-Filho and Shimabukuro, 2004). A major

obstacle to changing this behavior, note Nepstad et al. (2001, pp. 396) is that anthropogenic fires

are "deeply imbedded in the culture and economic logic of four million rural Amazonians trying

to survive or grow wealthy on the agricultural frontier." It's an inexpensive way for farmers to

clear a field, fertilize it and claim ownership (Nepstad et al., 2001).

Fires are also a part of the natural disturbance processes affecting forests, and total fire

suppression can lead to the encroachment of shrubs and rangeland loss (Timoney and Lee, 2001).

When fires do grip these old-growth forests they can cause large-scale damage. Therefore fire

suppression poses a greater threat than logging in some areas such as the dry forests of the

western US (Egan, 2007). In Alberta's boreal forests, fires play a crucial role in maintaining the

complex mosaic that make up the forest (Dyer et al., 2001). Alternately, agricultural landscapes

affected by or maintained by fires, such as cattle pastures, can shift to secondary succession









forests if abandoned (Gordon et al., 2004) and can be covered by regenerated vegetation in 1 to 5

years (Nepstad et al., 1999).

Other factors

Another important limiting factor is the cutting cycle. For logging practices to be

sustainable, reduced-impact approaches must be accompanied by long cutting cycles of 60 years

or more, though 30 years or less is the norm (Kammesheidt et al., 2001; Zarin et al., 2007). The

loss of phosphorus during cycles of shifting cultivation in tropical forests can lead to slower

regeneration rates and therefore introducing phosphorus fertilizer to plots under production could

slow deforestation by reducing the need to cut new plots (Lawrence et al., 2007). Interestingly,

the majority of ground damage related to a wide range of logging practices is the creation of skid

trails, or paths where logs are dragged across the forest floor until they reach loading trucks

(Asner et al., 2004). These create even more lasting alterations than logging roads (Asner et al.,

2004).

Finally, fuelwood spurs a great deal of biomass removal around the world in order to

create energy for cooking, heating and boiling water (Masera et al., 2006). Since biomass

represents 50% to 90% of primary energy consumption in developing countries (Berrueta et al.,

2008), its removal also comprises an important driver of deforestation (Ramos et al., 2008). In

Swaziland and other areas, observes Wheldon (1990, pp. 83), "fuelwood demand exceeds

sustainable supply from accessible land."

Interestingly, Ramos et al. (2008) found that physical properties of biomass as a fuel

source, such as the fuel value index (FVI) affected local preference for cutting and burning

specific types of fuelwood in northeastern Brazil. The authors' recommendations pointed to

establishing political and social efforts toward "stimulating the cultivation of these trees in

fuelwood plantations and in the home-gardens within the community" (Ramos et al., 2008, 7).









Another important recommendation is adopting more efficient wood-burning stoves since they

not only reduce fuelwood consumption but also decrease negative health effects resulting from

prolonged inhalation of smoke from the cooking fires-which tends to affect women and

children the most (Wheldon, 1990; Berrueta et al., 2008; Ekici et al., 2005).

In summary, the results of large-scale deforestation include habitat fragmentation, loss of

biodiversity, shrinking of forest remnants, soil erosion, increased sedimentation, liquidation of

old growth forests and the species that depend on them; introduction of exotic biota, wetland

drainage, pollution, loss of carbon sinks and subsequent increased C02 emissions (Ferraz et al.,

2006; Leslie et al., 2001; Miranda et al., 1998; Southworth, 2004; Timoney and Lee 2001;

Armenteras, et al., 2006). As Zarin et al. (2007, 922) put it: "Standing forests, regardless of their

long-term management potential, benefit society through ecosystem services not furnished by

agriculture or cattle ranching."

Specific Issues: Nonrenewable Natural Resources

Gold Mining

Compared to renewable natural resources, mineral mining receives less attention in the

LUCC and related literature despite the serious environmental and social alterations associated

with theses activities (Parrotta et al., 2001; Peterson and Heemskerk, 2001, Almeida-Filho and

Shimabukuro, 2002; Leger 1991). Gold mining, as opposed to agriculture and logging, creates

two effects that produce long-lasting environmental and social problems: mercury pollution and

land degradation (Hilson, 2002; Almeida-Filho and Shimabukuro, 2002).

Land degradation

Gold is extracted two main ways. The first one involves large-scale commercial mining of

land-based hard-rock deposits that require advanced technology and resources (often from

foreign companies) and result in open-pit mines that exemplify large-scale land-use changes in









terms of cleared land, roads for transporting the material, tailing ponds (which hold waste

material and water containing high levels of mercury or cyanide), and growth of settlements that

house the miners and the accompanying service industry (Veiga and Hinton, 2002; Schmink and

Wood, 1992; Hilson and Monhemius, 2006; Lacerda and Marins, 1997; Castro and Sanchez,

2003).

Despite the visual impression caused by large open pit mines, the second and more

common method is performed by small-scale artisanal miners (10-15 million worldwide) who

both excavate surface soil deposits and perform fluvial mining by searching for flakes, pebbles

and nuggets along riparian streams in tropical Asia, Africa and South America (Veiga et al.,

2006; Lacerda, 1997; Hilson, 2002; Heemskerk, 2001; Malm, 1998; Peterson and Heemskerk,

2001; Schmink and Wood, 1992; Vieira, 2006). Small-scale miners often employ extraction

methods that entirely remove vegetation and degrade land, and incorporate mercury (and

cyanide) to amalgamate gold. Combined these two actions create thousands of polluted sites

across 55 countries (Veiga and Hinton, 2002; Veiga et al., 2006). In Ghana, "small-scale gold

mining activities cause a disproportionately large percentage of environmental degradation"

(Hilson, 2002, pp. 70). The driving force: "gold is most commonly mined on a small scale

because of its propensity to generate wealth quickly" (Hilson, 2002, pp. 58).

Peterson and Heemskerk (2001) describe the process: 1) about five miners clear an 80 x 80

m forest plot. They cut, remove and burn most of the trees while those that are difficult to

remove are left standing. 2) They then use a high-pressure diesel-powered water hose to remove

the topsoil layer and then the gold bearing sand and clay layer. 3) They pump the slurry

containing gold into a sluice box where they introduce mercury or cyanide to adhere to the gold.









4) They keep the gold clumps and release the tailings (slurry waste) into the adjacent forest or

into an abandoned mining pit.

Alternately, miners operating on watercourses 1) dredge river bottoms for 20 to 30 hour

phases; 2) sieve the sediments; 3) place the selected material in barrels; 4) amalgamate the

material with mercury or cyanide by hand or with mechanical stirrers until all gold is found; 5)

release the mercury-rich residues back into the river (Lacerda, 1998; Hilson, 2002; Kligerman et

al., 2001). During this process a lot of mercury also vaporizes into the atmosphere and oil from

the dredge equipment and motors enters the water, negatively affecting plankton and aquatic

fauna (Lacerda, 1998; Kligerman et al., 2001; Lacerda and Marins, 1997).

Regeneration

Not surprisingly, pressure washing river banks with hydraulic pumps, digging large open

pits, and dredging riverbeds stimulates soil erosion and stream sedimentation (affecting

hydrological patterns, reducing fish diversity and their community structure, and threatening

hydroelectric production); leads to land degradation, habitat destruction and the loss of wildlife;

causes microclimatic changes from the removal of vegetation; and creates potential new

problems from the remaining contaminated tailing ponds (Mol and Ouboter, 2004; Heemskerk,

2001; Almeida-Filho and Shimabukuro, 2002, 2000; Peterson and Heemskerk, 2001; Lacerda,

1998; Hilson, 2002; Kumah, 2006; Kligerman et al., 2001). In places like Ghana, miners remove

the vegetation, dig for gold and move on, leaving a scarred landscape composed of pits and

trenches that is not suitable for other uses (Hilson, 2002). In many cases, notes Kitula (2006, pp.

409) "both agricultural and grazing lands have been destroyed," which affects local livelihoods.

The complete removal of most vegetation and roots also removes the seed bank, thus, like

areas that are converted to agriculture via the use of bulldozers (Boletta et al., 2006; Romero-

Duque et al., 2007), mining practices impede regeneration on abandoned plots (Peterson and









Heemskerk, 2001). When regeneration does occur, the rates vary over space and time (Almeida-

Filho and Shimabukuro, 2002). The edges of abandoned degraded areas regenerate faster than

the central zone where damage is more intense; indicating that recovery of the entire degraded

area takes much longer than anticipated, up to 20 years or more (Almeida-Filho and

Shimabukuro, 2002).

Rodrigues et al. (2004) observe that conserving forest fragments along these degraded

areas is crucial for allowing natural regeneration of shrub and tree species which act as a seed

source and animal refuge. Yet two other factors can also speed the regeneration process: gold

exhaustion and law enforcement since they encourage small-scale miners to abandon the area

and companies to close the large mines (Almeida-Filho and Shimabukuro, 2002; Castro and

Sanchez, 2003). The first is an outcome of geology or of limited tools and technology. The

second one points to the importance that resource tenure or good environmental governance has.

Concerning LUCC, heavily utilized areas result in land-cover conversion, while the lighter

impacted areas reflect modification. Finally, restoration projects by mining companies involve

afforestation "to rapidly establish tree cover on reclaimed mine sites and therefore facilitate

natural forest succession," but often fast-growing exotic species are planted (Parrotta and

Knowles, 2001, 220), rather than native trees, altering ecosystem composition.

Mercury pollution

Though its use is illegal in many countries mercury (and cyanide-mainly used in

industrial applications) is widely implemented to help recover gold from the rocks, soils or river

sediments because it is effective, cheap and easy to use (Hilson, 2002; Mol and Ouboter, 2004;

Lacerda, 1997; Hilson et al., 2007; Donato et al., 2007; Sandoval et al., 2006, 416; Hilson and

Monhemius, 2006; Veiga et al., 2006). However, after it is utilized the mercury is often

discharged into local streams where it enters the food chain. There it is metabolized from an









inorganic form to methyl-mercury and biomagnified. By the time humans eat large (carnivorous)

fish it has become highly toxic (Hilson, 2002; Sandoval et al., 2006; Mela et al., 2007; Pinheiro

et al., 2008; Bose-O'Reilly et al., 2008; da Costa et al., 2008). Mercury also pollutes drinking

water and poisons workers and residents who handle the metal or inhale the fumes during the

burning process that separates the mercury from the gold (Lima et al., 2008; Sandoval et al.,

2006; Almeida -Filho and Shimabukuro, 2000, 2002; Reed, 2002; Castro and Sanchez, 2003;

Drake et al., 2001). These vapors are re-released when the gold is burned again in gold shops or

smelters, affecting areas within 1 km of the emission source (Veiga and Hinton, 2002; Bastos et

al., 2004)-not just the mining areas. Also noteworthy: if terrestrial animal populations are

completely driven away in mining areas (Heemskerk, 2001), then miners and local populations

are more prone to increase their fish intake, thus exacerbating their levels of toxic methyl-

mercury.

Solutions

Some ways to address the environmental problems associated with mining include: 1)

using biological treatments to take up metal from wastewaters (Moore et al., 2008; 2), including

almond shells (Estevinho et al., 2008); training small-scale miners on how to efficiently use

mercury (Castro and Sanchez, 2003; Hilson, 2002); 3) formalize (legalize) small-scale miners in

countries such as Ghana in order to regulate production so that greater transparency and control

will limit environmental degradation and improve local livelihoods (Hilson et al., 2007); 4)

introduce alternatives to mercury and cyanide as leach reagents for gold mining (Hilson and

Monhemius, 2006; Vieira, 2006); 5) use retorts to help capture a lot of the lost mercury (up to

95%) (Drake et al., 2001; Veiga and Hinton, 2002; Hilson, 2002); 6) establish decontamination

(recovery) plans between the government and mining companies within a specific time period

(Castro and Sanchez, 2003); 7) reduce gold consumption (demand) with awareness campaigns;









and finally 8) spur European Union countries-as the largest producers of mercury-to regulate,

tax and control its export (Veiga et al., 2006).

Oil Exploration and Production.

Though less expansive than the LUCC literature related to gold mining, research on

landscape changes and oil exploration and production appears to be the purview of petroleum

geologists, biologists and others interested in environmental management. The first group has

been using geographic information systems (GIS) and remote sensing techniques in their work

for over 30 years, primarily for geologic exploration (Jackson et al., 2002). Today geospatial

applications in the petroleum industry continue to focus on exploration but also on detecting and

modeling spills; implementing least-cost analysis for pipeline constructions; reducing operational

costs by managing spatial information; environmental baselining3; impact assessment and

analyzing settlement patterns (Gaddy, 2003; Jackson et al 2002; Janks and Prelat, 1994, 1995;

Osejo et al., 2004; Musinsky et al., 1998; Pochettino, 2001; Satapathy et al., 2008; Almeida-

Filho et al., 2002; Almeida-Filho, 2002; Zhang et al., 2007; Jaggernauth, 2003).

Wildlife: caribou

The second group of researchers studying oil (E&P) includes biologists concerned with the

landscape and habitat changes resulting from these activities. Many of these scientists are

primarily interested in avoidance and stress responses by wildlife to oil roads, wellpads and

activities-particularly caribou in Canada and Alaska (Nelleman and Cameron, 1996; Noel et al.,

2006; Schneider et al., 2007; Wolfe et al., 2000; Bradshaw et al., 1998; Harron, 2007; Dyer et al.,

2001). They have found, for example, that petroleum development in oilfields near Prudhoe Bay,


3 Baselining is the process of documenting landscape conditions in a given area to create a profile prior to
exploration and production phases (Janks and Prelat 1994; Jackson et al., 2002). Over time, as activities increase in
concession areas, the environmental and social alterations can be monitored through the analysis of air photos,
satellite images and field site visits.









Alaska, displaced maternal female caribou by 4 km (Nelleman and Cameron, 1996). Schneider et

al. (2003) found that caribou in Alberta, Canada avoided seismic lines by 100 m, while for oil

roads the distance was 500 m. Harron (2003) disputes findings for seismic lines, pointing out that

caribou avoidance distances are only 40 m on each side.

Bradshaw et al. (1998) point out that disturbances to caribou caused by petroleum

exploration in Alberta can cause maternal females to expend more energy during winter months,

leading to undernutrition which delays parturition. Therefore a newborn calf has less time to

grow before the onset of the next winter, making it more prone to die from predation or

undernutrition (Bradshaw et al., 1998; Wolfe et al., 2000). Dyer et al. (2001) also studied caribou

avoidance behaviors to oil and gas activities by fitting 36 caribou with GPS (global positioning

system) collars. They recorded avoidance distances of 1,000 m for wells and 250 m for seismic

lines and roads, noticing that if caribou are displaced to less suitable habitats, their numbers may

go down, affecting demographic patterns (Dyer et al., 2001). Wolfe et al. (2000) found that

calving caribou avoided oil field infrastructures by 5-6 km, indicating that the effects of the

infrastructure footprint were much larger than thought. Furthermore, activities that displaced

reindeer and caribou affected subsistence and cultural resources for Arctic dwelling indigenous

groups (Wolfe et al., 2000). Since drilling permits allow for petroscape expansion, energy policy

affects local livelihood options.

Interestingly, as compared to agriculture, logging and gold mining, oil (and gas)

"developments are typically configured as point and linear disturbances scattered throughout

broader areas" that often encompasses 5-10% of the concession site (Wyoming GFD, 2004, pp.

5). Therefore, as opposed to clear-cutting forestry practices, the alterations created by oil

exploration and production may be harder to detect. Seismic lines are one exception.









Seismic lines


An important outcome of oil exploration is the creation of seismic lines (roads cut during

seismic phases of oil exploration). In addition to disturbing wildlife, seismic lines can create

permanent changes on the landscape that remain long after exploration activity has ceased. Lee

and Boutin (2006) examined the persistence of seismic lines on the Alberta boreal landscape

over 35 years and made some interesting findings:

* Wide seismic lines (5 to 8 m) cleared with bulldozers persist on the landscape for more
than 35 years. Recovery to woody vegetation occurred in 8% of the cases and based on
median recovery rates it would take 112 years for a near complete recovery. Lands cleared
with bulldozers flatten and compact the terrain, altering hydrological patterns, reducing
sites for seedling establishment, and converting the landscape to a wetter sedge-dominated
community.

* The presence of seismic lines provided transportation corridors that in turn facilitate other
industrial development, such as logging. These routes were subsequently used by the
forestry industry to monitor regeneration rates and silvicultural practices. They also created
routes ripe for 4-wheel-drive vehicles that hampered regeneration via tire track damage,
water channelization, soil compaction and soil erosion on and next to the seismic lines.

* Changes to oil and gas exploration licenses (resource tenure/energy policy) combined with
increased market prices spurred the creation of seismic lines in the 1970s. Today's demand
and tightening supplies is also prompting exploration and drilling. In fact a "crash
programme production" is underway in Canada's oil sands, partly to offset a peaking
world oil production (Soderbergh et al., 2007, pp. 1931).

* The transfer of exploratory drilling licenses among oil companies is common; however
seismic data remains proprietary information. This requires the new license holder to
reopen the existing seismic lines and/or create new ones.

Roads

One commonality among all types of resources examined is the creation of roads that

result when land managers want to access trees, new plots of land, gold-rich areas or oil

reservoirs often in previously inaccessible areas. Roads, after all, are the largest human artifact

on earth (Forman et al., 2003) and a central catalyst for LUCC. In Amazonia 85% of









deforestation "is concentrated at up to 50 km from the main roads" (Brandao and Souza, 2006,

pp. 177).

Besides deforestation, road openings lead to the establishment of new agricultural

frontiers, particularly for soybeans and cattle ranching; increase fire advancement on forests;

prompt legal and illegal timber harvesting; and result in land speculation and colonization

(Brandao and Souza, 2006; Ferraz et al., 2006; Lele et al., 2000; Peres and Terborgh, 1995;

Soares-Filho et al., 2004; Schmink and Wood, 1992). In some cases "as soon as there was even

the prospect of a new road, migrant farmers would move into the area, taking de facto possession

of small plot" (Schmink and Wood, 1992, pp. 79).

Most of these roads can be defined as local since their primary function is land access

(Forman et al., 2003). But roads also serve economic, political and social purposes at broader

scales. They stimulate the growth of commerce and increase market accessibility; they

consolidate territorial control; and tie land together for society (Brandao and Souza, 2006;

Ciccantel, 1999; Forman et al., 2003). The study of landscape changes resulting from the

creation, use and interaction of roads (and vehicles) and the surrounding environment is known

as road ecology (Forman et al., 2003), a growing subfield of landscape ecology, which

emphasisess the effect of spatial patterns of ecosystems on ecological processes" (Griffith et al.,

2002b, 1).

Concerning petroleum and roads, Musinsky et al. (1998) analyzed satellite images and

found that oil roads in national park in Guatemala were directly related to the establishment of

new settlements in the surrounding tropical rainforest. These actions were tied to political and

economic forces such as 1) inadequate park management; 2) poorly guarded roads and other

infrastructure that allowed easy access; 3) contradictory government policies and jurisdictions;









and 4) inefficient agricultural practices and rapid population growth (Musinsky et al., 1998).

While government policy solutions and enforcement should address some of these problems,

Musinsky et al. (1998) spurs energy companies to attend to oil roads by: 1) planning routes to

avoid habitat fragmentation and disruptions to hydrological cycles; 2) use existing roads and

waterways fully; 3) destroy non-necessary roads, bridges and entry points; 4) bury pipelines and

restore vegetation; and 5) monitor remaining roads and access points through field monitoring

and remote sensing techniques.

The same holds true for seismic roads; Lee and Boutin (2006) propose: 1) establishing

narrow seismic lines, less than 5 m; 2) using helicopters and hand cut trails when possible-as

opposed to using bulldozers; 3) reclaiming seismic lines so that they are not converted to

permanent paths and roads; and 4) implement best practices by sharing roads with other

extractive industries, such as forestry and logging, rather than creating new ones.

Conclusion

A survey of the LUCC and related literature shows a strong preference for work related to

natural resource use, particularly agriculture and logging as they relate to deforestation in

tropical regions. Nonrenewables receive much less attention, though studies related to gold-

mining are become more prevalent, particularly given the problems associated with mercury and

cyanide use. On the other hand, land-cover change research related to petroleum E&P is even

less abundant, even though these activities create large-scale and permanent changes on the

landscape and affect they way people respond in larger areas around the zones of direct

extraction.

Two zones containing some of the largest petroleum reserves in the world and undergoing

new exploration and production include the heavy oil sands in Canada and Venezuela's heavy oil

belt. Alberta's oil sand development has attracted a growing number of researchers, many of









whom examine how petroleum infrastructure affects wildlife, particularly caribou (Bradshaw et

al., 1998; Wolfe et al., 2000; Dyer et al., 2001). Others such as Schneider et al. (2003) address

forest disturbances created by oil E&P and provide a model of how to address this issue.

By contrast almost no work that I could find addresses this topic in Venezuela's heavy oil

belt, despite the estimated 270 billion barrels of heavy crude located in central and eastern

Venezuela. The research concerning the heavy oil belt instead addresses drilling strategies

(Gipson et al., 2002), reservoir modeling (Tankersley and Waite, 2002), and heavy crude

chemical properties (Galarraga et al., 2007), but not land-cover changes despite the claim by

energy companies that they are pursuing sustainable business practices that minimize social and

environmental alterations (Kumah, 2006; Garvin et al., 2008; Mudd, 2007a, 2007b; Sandoval et

al., 2006).

Right now the Venezuelan government plans to expand the heavy oil belt operations from

four concessions to 31 in the next two decades potentially affecting 18,000 km2 of dry tropical

forest and savanna. Quantification and certification efforts of oil in place have started in some of

these new blocks. Therefore urgent research is needed to better understand what types of

petroscapes (petroleum-related infrastructure landscapes) the four functioning concessions have

produced and which ones exhibit the least amounts of petroscape disturbances. This information

should be used during baseline studies (Kalacksa et al., 2008) for the upcoming concessions, if

possible before seismic activity begins, and then implemented during the design and production

phases. Introducing best business practices and lessons learned will reduce environmental and

therefore social changes (Gipson et al., 2002; Moser 2001). The application of geographic

information systems (GIS) and remote sensing techniques are very useful and efficient ways to

monitor LUCCs at the landscape level (Janks and Prelat, 1995, 1994; Musinsky et al., 1998;









Almeida-Filho, 2002; Almeida-Filho et al., 2002; Almeida-Filho and Shimabukuro, 2004, 2002,

2000; Gaddy, 2003). This is what now required in Venezuela's heavy oil belt.









CHAPTER 3
DO NATIONAL OIL COMPANIES PRODUCE GREATER ENVIRONMENTAL
ALTERATIONS THAN MULTINATIONAL OIL COMPANIES? THE CASE OF
VENEZUELA'S HEAVY OIL BELT

While energy-producing hydrocarbon resources dominate the global economy, their

mining and extraction create important land-use and land-cover changes (LUCC) that affect

climate, ecosystem goods and services and people's vulnerability at different spatial and

temporal scales (Prakash and Gupta, 1999; Almeida-Filho and Shimabukuro, 2002, 2000;

Diamond, 2005; Schmidt and Glaesser, 1998; Lambin et al., 2003; Fujita and Fox, 2005;

Southworth, 2004). Concerning petroleum exploration and production (E&P), a global question

exists as to whether state-owed firms, also known as national oil companies (NOCs), can or do

perform as well as multinational oil corporations (MNOCs) in terms of balancing economic

efficiency, environmental and social outcomes.

This paper addresses this topic by examining the environmental outcomes in terms of

important LUCC measures related to petroleum E&P in eastern Venezuela's heavy oil belt

(figure 3-1). This 54,000 km2 zone contains over 1 trillion barrels of tar-like heavy crude oil, of

which 270 billion are considered recoverable using current technology (Gipson et al., 2002;

Talwani, 2002; Trebolle et al., 1993; Tankersley and Waite, 2002; Balke and Rosauer, 2002). In

this paper the local question is: does the local NOC perform as well as its MNOC partners in

terms of balancing environmental alterations associated with E&P at the landscape level? I

answer the question by examining the expansion of petroleum-related infrastructure features, or

the petroscape, in three of Venezuela's four heavy oil belt operations between 1990 and 2005

using Landsat TM and ETM+ and CBERS satellite images and GIS techniques.

The concessions (see figure 3-2) and the amounts of company ownership involved are

shown in table 3-1. In terms of company participation, the breakdown is:









* Sincor: Local (NOC) 38%; European 62%
* Petrozuata: Local 49.9%; US 50.1%
* Ameriven: Local 30%; US 70%
* Cerro Negro: Local 41.67%; US 41.67%; European 16.66%

Therefore, the local NOC, from here on referred to as Local, dominates Petrozuata;

European companies dominate Sincor; US companies dominate Ameriven and both Local and

US companies are tied in Cerro Negro (see table 3-1). Nevertheless, Cerro Negro is not included

in this analysis due to excessive cloud cover in the imagery data set corresponding to this

concession. Drawing from political ecology and organizational theory, as well as research from

oil and gas E&P alterations to landscape and wildlife, shows that energy policy and corporate

attitudes toward environmental planning and environmental regulations can affect LUCC in

terms of land degradation, landscape fragmentation, loss of biodiversity, river siltation, increased

pollution and greenhouse gas emissions (Bryant and Bailey, 1997; Nelleman and Cameron,

1996; Schneider et al., 2003; Morton et al., 2002; Morton et al., 2004; Lee and Boutin, 2006;

Lawrence, 2007; Schneider et al., 2003).

Through lessons learned, multinational oil companies (MNOCs) have increased their

efforts to minimize and monitor environmental and social impacts tied to E&P (Moser, 2001).

Several reasons exist for improving performance in health, safety, and environment (HSE) as

well as corporate social responsibility (CSR), or sustainable development initiatives. They

include preventing: costly environmental disasters, political protests, negative press coverage,

work stoppages, sabotage, project terminations, forced closings, and human rights and worker

abuses (Diamond, 2005; Lawrence, 2007; Anderson, 1994; Gladwin, 1977; Orlitzky et al., 2003).

Furthermore, implementing best business practices can increase employee and customer loyalty,

attract the attention and money of financial analysts, investors and lenders (Morhardt, 2002), can

offset actual or impending government regulation (Dashwood, 2007), improve a company's









reputation, better address human rights (Kakabadse, 2007), improve community relations, and

help stabilize long-term costs (Reinhardt, 2000). As Shapiro (2000, pp. 63) notes: "Far from

being a soft issue grounded in emotion or ethics, sustainable development involves cold, rational

business logic." Part of this logic involves identifying opportunities through sustainability

integration, rather than traditionally responding to regulations, risk reduction and cost controls

(Wilcoxon and Cramer, 2002). It also promotes voluntary adoption and reporting of sustainable

business practices defined by such groups as the International Petroleum Industry Environmental

Conservation Association (IPIECA). Furthermore, European companies tend to be ahead of US

companies in terms of implementing sustainability practices into their business plan (Marica et

al., 2008; Moser, 2001).

On the other hand, NOCs often tend to lack the experience and culture of minimizing

environmental changes particularly since (until recently) they do not compete internationally for

contracts and pursue their business as usual approach domestically (Diamond, 2005). When they

do participate in the global economy they must implement the most advanced technology and

methods in terms of their inputs to successfully compete (Porter and van der Linde, 2000), which

involves costly and sustained investments. A second challenge is that most developing countries

face major challenges and lack resources for planning and for managing natural systems and

environmental quality (Gladwin, 1977). Third, many leaders in the developing world, whose

countries depend on the production and export of primary products such as beef, bananas,

timber, cocoa, copper or petroleum, and who don't have alternative sources of national income

"perpetuate practices that contribute to environmental degradation" (Bryant and Bailey, 1997,

pp.59). Finally, when the production of a natural resource comprises a country's primary

industry and staple export product and is strongly controlled by the federal executive office, as is









petroleum in Venezuela, then other government bodies charged with the oversight of these

activities, such as the ministry of the environment, may not have the capacity or will to enforce

compliance. Based on the findings from this literature, I propose three hypotheses.

* Hypothesis 1: The Local-company dominated concession, Petrozuata, will exhibit the
greatest amount of LUCC.

* Hypothesis 2: The US-company dominated concession, Ameriven, will display a smaller
amount of LUCC.

* Hypothesis 3: The European-dominated concession, Sincor, will show the least amount of
LUCC.

I test these hypotheses by measuring and ranking four important landscape ecology metrics

related to oil E&P: vegetation-cover change, petroscape density, edge-effect zones, and core-

areas using remote sensing and GIS methods (Griffith et al., 2002b; Morton et al., 2002;

Thomson et al., 2004; Morton et al., 2004) to determine how each performed.

Remote Sensing, Geographic Information Systems (GIS) and Petroleum

Remote sensing (satellite image analysis) and geographic information systems techniques

have proven useful and cost-effective prospecting tools for oil and gas deposits for over 30 years

(Almeida-Filho, 2002; Jackson et al., 2002). These methods have been applied to detecting

hydrocarbon seepages in northeastern Brazil (Almeida-Filho et al., 2002; Almeida-Filho et al.,

1999) and the Netherlands (Noomen et al., 2006); to identify oil-bearing sands in inner

Mongolia, China (Zhang et al., 2007); and to optimize costs of field infrastructure facilities in

western Argentina (Pochettino and Kovacs, 2001). A few studies address the role of oil E&P on

landscape-change. These include Janks and Prelat (1994; 1995) who use remote sensing and

promote these techniques to: 1) assess the health of vegetation in and around oil fields; 2)

identify local environmental effects of oil drilling and waste management; 3) optimize the

placement of infrastructure features such as exploratory wells, roadways, camps and pipelines;









and 4) monitor the progress of remediation efforts on abandoned well sites. Meanwhile

Musinksy et al. (1998) applied these technologies to determine the relationship between the

construction of oil roads and subsequent deforestation in Guatemala's Laguna del Tigre National

Park. They found "a direct relationship between the construction of access roads for oil

exploration and production, and the settlement into undisturbed tropical rainforest ecosystems"

(Musinsky et al., 1998, pp. 1). Jackson et al. (2002) and Gaddy (2003) also note how remote

sensing technology has important business applications for the petroleum industry. These include

monitoring environmental change, managing existing field operations; evaluating land property

before leasing or abandonment; and least-cost analysis for pipeline construction (Jackson et al.,

2002).

Concerning spills and pollution, Kwarteng (1998, 1999) and Ud din et al. (2008) used

remote sensing to monitor contaminated oil lakes and the recovery of surrounding vegetation in

Kuwait; while Chust and Sagarminaga (2007) used remote sensing to discriminate oil slicks in

Venezuela's Lake Maracaibo. Morton et al. (2002) used GIS analysis to estimate the economic

viability of undertaking oil and gas E&P activities in protected roadless areas of 6 western US

states. Thomson et al. (2005) conducted a spatial analysis of the gasscape (gas related

infrastructure features) in western Wyoming to determine the potential effects on 4 wildlife

species. Morton et al. (2004) implemented remote sensing and GIS analysis to estimate E&P-

driven changes to land-use and land-cover such as habitat fragmentation and loss of terrestrial

wildlife and aquatic species in the US Rocky Mountains. The Wilderness Society (2006) also

used remote sensing and GIS methods to measure habitat fragmentation and the effects on

wildlife in Wyoming. They, along with Morton et al. (2002) and Morton et al. (2004) conclude

that the expansion of E&P activities will cause permanent landscape changes that will reduce









wildlife diversity and numbers; will negatively affect local economies that depend on tourism

and hunting; and will lead to further habitat fragmentation through the expansion of new roads

and more people to wilderness areas. More importantly, they estimate that the recovery amounts

of oil and gas will be quite small, in some cases supplying US consumption needs 24 days (for

oil) and 9 to 11 weeks for gas (Morton et al., 2002).

This paper builds on the LUCC literature related to energy development by focusing on

Venezuela's heavy oil belt, an underrepresented topic about an area that is about to expand its

concessions by more than 600% in the next 22 years in order to help double production. New

operations will mostly involve other NOCs from countries such as Brazil, Iran, Cuba, Chile,

China, India, Uruguay, Russia, Argentina and Ecuador, rather than MNOCs (PDVSA, 2008c,

2007a, 2007c, 2007e, 2007g, 2006a, 2006b; Petroguia, 2006-2007). These new partnerships will

create their own E&P landscapes with greater or lesser alterations than currently exist. Better

understanding how the four original operations managed their petroscapes can prove useful for

planning subsequent new ones.

Materials and Methods

Site Description

The heavy oil belt (HOB) is a 54,000km2 stratigraphic trap that contains over one trillion

barrels of heavy and extra heavy crude oil of which 270 billion are considered recoverable using

current technologies (Gipson et al., 2002; Talwani, 2002; Trebolle et al., 1993; Tankersley and

Waite, 2002; Balke and Rosauer, 2002; Boza and Romero, 2001; Briceno et al., 2002). Located

north of the Orinoco River in central and eastern Venezuela, the HOB is subdivided into four

separate E&P zones spanning the states of Guarico, Anzoategui, Monagas and a small portion of

Delta Amacuro. From west to east the E&P zones are Machete, Zuata, Hamaca and Cerro Negro

(Balke and Rosauer, 2002; Kopper et al., 2001). Recently, they have been renamed to Boyaca,









Junin, Ayacucho and Carabobo, respectively (PDVSA, 2005a, 2005b), though much of the

literature uses the old names when referring to these zones (see figure 3-2).

The four current operations are located in the southern llanos, or plains, of Anzoategui

state located above the Orinoco River. This area is marked by sandy soils with low natural

fertility, grasslands, low and dispersed drought tolerant vegetation, gallery forests along the

Orinoco River tributaries, and a sparse population comprised of small scattered settlements

(Hernandez et al., 1999; Dumith, 2004). Average annual temperature is 26C with mean diurnal

and seasonal fluctuations of 9.50 C and 3 C respectively (Chac6n-Moreno 2004). A four-to-six

month dry season affects the phenological variation of this seasonal savanna ecosystem where

the environmental conditions are related to the amount of water availability (deficit or excess) in

the soil during the year (Chac6n-Moreno 2004).

The important economic activities in these llanos include small-scale fishing along the

many Orinoco-tributaries, small and large-scale farming (soy, cotton, sorghum, corn, rice), cattle

ranching (since the 1548-White, 1956), petroleum production (since the 1930s), Caribbean pine

plantations (since the 1960s), and employment in the oil fields and pine plantations (Dumith,

2004; IADB, 1997; Parra, 2007; Mauricio et al., 2005; Hernandez et al., 1999; Talwani, 2001).

All these activities help produce a heterogeneous landscape. The terrain is mainly flat, with a

slope of less than 2% (Castel et al., 2002). Elevations across the concessions range from near

zero to 120 meters above sea level (m.a.s.l) along the banks of Orinoco River tributaries, to 121

to 250 m.a.s.l. in the northern part of the HOB.

Data Description (Concessions and Control Groups)

The data used in this chapter include LUCC measures for 3 concessions and for 3 control group

areas quantified from satellite images of the study area and through GIS techniques. Researchers

such as (Morton et al., 2002; Morton et al., 2004; Schneider et al., 2003; Thomson et al., 2005;









and Griffith et al., 2002b) have found that the following landscape-measures provide important

indicators of ecological disturbance related to road and energy development:

* Vegetation-cover change-refers to how vegetation land-cover has changed between time
periods. Here I examine the conversion of vegetation using the Normalized Difference
Vegetation Index (NDVI), an algorithm that measures the amount and condition of
vegetation in a satellite image (Jensen, 1996), has been widely used in vegetation-change
studies (Mambo and Archer, 2006; Tarnavsky et al., 2008) and is particularly related to the
expansion of the petroscape (Musinsky et al., 1998). This index also detects gains in
vegetation cover. In order to detect changes between 1990 and 2000 I performed image
differencing of NDVI images of the study area, a common technique in change-detection
studies (Mambo and Archer, 2006) that involved subtracting the 1990 from the 2000
images.

* Road density-is a rough but useful measure that takes the total number of petroscape
linear features measured in kilometers (km) and divided by the area in km2-in this case
the size of the concession. It is used to assess the potential impact of roads and
infrastructures on local environments (Forman et al., 2003). Because it's a density
measure, the length of linear features is somewhat normalized by dividing this figure by
the concession size.

* Edge-effect zones-related to petroscape density, these zones include the area of
significant ecological effects extending out from petroscape features (Forman and
Deblinger, 2000). They are created by using a 600 meter (m) buffer around the linear
petroscape features. I selected this distance based on work by Forman and Deblinger
(2003) regarding roads, Schneider et al. (2003) and Dyer et al. (2001) in reference to
energy roads in Alberta, Canada, and Morton et al. (2004), Thomson et al. (2005) and the
Wilderness Society (2006) regarding LUCC and energy development in the western US.

* Core areas-which are the intact habitat areas that remain after the concession has been
fragmented by petroleum production activities.

Time Frame

The objective was to measure the petroscape-related changes in land-cover between 1990,

2000 and 2005 and determine if the Local-dominated concession exhibited greater amounts of

change than American-company and European-company dominated sites. The year 1990

corresponds to the period before E&P began in the HOB concessions (Talwani, 2002), officially

known as strategic associations, and following a business model that involves production,

upgrading and commercializing heavy oil (Fipke and Celli, 2008.) The steps involve include:









* Extract crude from the ground using cold production. This means not adding gas or steam
since reservoir temperature is warm enough for crude to be pumped to the surface.

* Once at the surface the crude becomes tar-like so a diluent, or a high-grade oil product, is
added to improve mobility and make it more transportable.

* It is piped to a central processing facility (in the concession) where gas, water and salt are
removed. The produced water is injected underground.

* The crude is then piped 200 km to the coastal processing plant called Jose, located west of
the town of Barcelona (see figure 3-2).

* At Jose it is "upgraded" which involves the removal of nickel, sulfur, vanadium, liquid
petroleum gas, petroleum coke and heavy gas oil to produce a valuable higher-grade oil,
called syncrude (synthetic crude). During this process the diluent is recuperated and sent
via pipeline back to field operations where it is reused.

* Some of the syncrude, is sent via ships to refineries in the US Gulf coast and to Germany.
The highest grade syncrude, however, is sold on the open market.

(Oil company representative, 2005a; Guerra, 2005; Talwani, 2002; Robles, 2001; Kopper et al.,

2001; Pina-Acuna and Ferreira, 2004; Briceno et al 2003; Neff and Hagemann, 2007; Gipson et

al., 2002).

The baseline year, 1990, predates the authorization by the Venezuelan National Congress

to establish the concessions (Talwani, 2002). However, oil E&P has been part of the economic

activities in the eastern Venezuela basin since the early 1900s and in the area known as the HOB

since 1935 (Talwani, 2002; Rodriguez et al., 1997; Boza and Romero, 2001; Gonzalez and

Reina, 1994; Corfield, 1948). Between 1978 and 1983 the state-company, PDVSA, carried out

an intensive exploration and evaluation program in the HOB and in 1990 began horizontal

drilling projects in areas near the present concessions of Petrozuata, Ameriven (known as Bare),

Cerro Negro and other areas (Boza and Romero, 2001; Gonzalez and Reina, 1994; Rodriguez et

al., 1997). Exploration is still ongoing (Galarraga et al., 2007) and therefore E&P patterns are

likely to be discernible in satellite images of the study area apart from the four HOB concessions.









The next date, 2000, corresponds to early-to-mid production phases when operations were

underway, while 2005 includes full production, when the upgrader/refinery located on the coast

was operating. In addition to examining changes in the four concessions, I also examined

changes in three control areas, which were randomly selected polygons of a uniform size to

match the average size of all four concessions, 575 km2. This was done to test whether the

infrastructure expansion changes occurring in the concessions were related to petroleum E&P

actions or other activities such as agriculture (crops and cattle).

Image Processing and GIS Procedures

I utilized the following images obtained from the Global Land Cover Facility (GLCF) at

the University of Maryland and through Brazilian National Institute for Space Research (INPE):

* Landsat TM path 2, row 54, acquired 19 April, 1990
* Landsat ETM+ path 2, row 54, acquired 30 April 2000
* CBERS CCD2 path 179, row 90, acquired 12 May 2005
* CBERS CCD2 path 180, row 90, acquired 20 February 2005
* CBERS CCD2 path 180, row 91, acquired 25 January 2005

The first two Landsat scenes (path 2 row 54) encompassed the three western concessions, Sincor,

Petrozuata and Ameriven and the dates correspond to the end of the dry season. These same

concessions lay across two CBERS images, path 180 row 90 and path 180 row 91, whose dates

correspond to the middle of the dry season. I mosaicked and feathered these two scenes to

produce an image encompassing the three concessions Sincor, Petrozuata and Ameriven.

The CBERS imagery was captured two-to-three months apart from the anniversary

Landsat imagery, making it difficult to compare changes during different times of the year4

(Jensen, 2005). More importantly, due to excessive noise inherent in these particular scenes5,


4 Daniels et al., 2008 indicate that image calibration corrects for differences due to non-anniversary date images.
5 Performing a Fourier transformation followed by an inverse Fourier transformation (Jensen 2005) still did not
resolve some of the problems such as striping.









such as missing lines of data and some corrupted bands, I did not use them in the change-

detection process but did for the subsequent measures: petroscape density, edge-effect zone and

core-areas.

All images were processed with ERDAS Imagine 9.1. I used a Landsat ETM+ 2000 WRS2

path 2 row 54 scene rectified to the Universal Transverse Mercator (UTM) 20 N projection,

WGS-84 datum, as the base image for image-to-image registration of the Landsat TM 1990

image for the same path and row 6. Subsequently, I used the rectified 2000 Landsat TM 15m

panchromatic band to rectify the CBERS 2 2005 imagery because the improved spatial

resolution aided in detecting petroscape features. As with the 1990 TM scenes, a nearest

neighbor resampling algorithm was used with a root mean square error of less than 15 m for each

point (Southworth et al., 2004). The 20-m resolution on the CBERS 2 scenes was kept in the

rectified output. An overlay function then confirmed that all the scenes overlapped exactly across

the three image dates (Southworth et al., 2004). Waypoints and routes collected in the field, as

well as road networks, oil wells and production facilities were also examined to confirm proper

overlap.

Petroenergy Maps

After scanning paper petroenergy maps at high resolution (Petroguia, 2004-2005, 2006-

2007), 1) I rectified them to the first base image. Next I digitized the outlines for the Orinoco

heavy oil belt and each concession block to create vector polygons (in a GIS7). Then I overlaid

them over the Landsat 1990 and 2000 scenes and on the CBERS 2005 mosaicked scene to

provide the location of the concessions boundaries. Finally I used the concession polygons to



6 This was accomplished using Leica Geosystems ERDAS Imagine 9.1.

7 ESRI ArcMap 9.1 was used for GIS work and analysis in this project.









create 9 subset images-3 each for 1990, 2000 and 2005, representing the before-during-and-

after establishment of the heavy oil operations.

Control Areas

To further test whether the infrastructure expansion changes occurring in the concessions

were related to petroleum E&P actions as opposed to other activities such as agriculture, I

randomly generated polygons (using Hawth's Analysis tools extension for ESRI ArcGIS 9.2).

Each one was equivalent to an average size association block, or 575km2. Out of a possible 15,

three were selected, referred to here as control 02, 04 and 07 respectively. These three were

chosen because they had minimal cloud cover, fit within the smaller footprint created by the

CBERS mosaicked image (113km wide) overlain onto the Landsat ETM+ (185km wide) scene,

and did not overlap current HOB concessions.

Normalized Difference Vegetation Index (NDVI)

After masking out the clouds on the subset images I calculated the NDVI vegetation index

(mentioned above under data description) for the Landsat scenes:

Band 4 (NIR) Band 3 (R)
Band 4 (NIR) + Band 3 (R)

I chose NDVI because it analyzes continuous data, rather than fixed classifications; is better at

detecting land-cover modifications; and has been used in studies involving petroscape-related

LUCC because of the contrast against healthy biomass (Southworth, 2004; Southworth et al.,

2004; Musinsky et al., 1998; Kwarteng, 1998, 1999).

Change-Detection: 1990 to 2000

Here I examined the changes in vegetation-cover between 1990 and 2000 through image

differencing (subtracting the imagery of one date from another) of the NDVI images for 3

concessions and 3 control polygons. This is a commonly used technique in change-detection









(Mambo and Archer, 2007) where "the subtraction results in positive and negative values in

areas of radiance change and zero values in areas of no change" (Jensen, 2005, pp. 471). This

process of selecting positive, negative and zero change values is termed thresholding and "is

rather dependent on the analyst" (Mambo and Archer, 2007, pp. 382). After examining three

threshold levels, I selected 25% since it best brought out petroscape features in the images. These

change-detection maps in figures 3-3, 3-4 and 3-5 show negative and positive changes in the

NDVI for the concessions and control polygons. Negative changes were much greater than

positive changes for all six images and involved petroscape and nonpetroscape features such as

agricultural land and roads. Meanwhile gains in the vegetation index primarily followed the river

courses, where riparian or riverine forests grow (San Jose et al., 2001). Figure 3-6 graphs the

negative and positive change in NDVI for the three concessions and the three control groups.

Such broad changes across all six images suggest that not only petroscape and agricultural

activities were driving land-cover change, but that climatological factors were also involved.

Figure 3-7.shows how rainfall amounts in 1989 and 1999, which preceded the capture of

the 1990 and 2000 Landsat scenes used in this study, varied from average values. The 1989 dry

season was abnormally dry, while the 1999 dry season was unusually wet. These differences in

precipitation likely affected the spectral profiles of the dry tropical savanna landscape, not to

mention river levels.

GIS Techniques: the 2005 Petroscape

The 2005 CBERS subset images were used to map infrastructure features, consisting of the

linear petro and nonpetroscape attributes in km to test if the land-cover changes found between

1990 and 2000 were related to petroscape expansion. First, I digitized the discernible road









network and petroscapes in the six sites (using on-screen digitizing)8. Using ground control

points of well sites gathered in the field and provided by Carrera (August 2004, 2005) along with

the 20 m resolution from the CBERS imagery it was possible to distinguish the petroscape,

which included wells and wellpads, roads and central production facilities. Nonpetroscapes were

equally discernible. In instances where cloud cover or noise obstructed features I referred to the

Landsat 2000 scene, where in some cases petroscape features where already established.

Agriculture was certainly part of the landscape as early as 1979, based on Landsat scenes of the

study area.

Once all features were digitized I overlaid this vector data onto the 1990-2000 change-

detection maps to help determine their accuracy. By analyzing the digitized full petroleum

production pattern in the concession sites as it related to the change-detection maps it was

possible to determine if observed negative change followed (and would continue to follow) the

expansion of the petroscape established by 2005. For both Sincor and Petrozuata, the change-

detection maps did fit the pattern of petroscape expansion found in 2005. Thus, this threshold

was a good predictor of ultimate changes-see figure 3-8.

Petroscape density

This measure is calculated by dividing the total length of linear features in km, by the

concession size in km2 and is commonly used in landscape change studies to determine the effect

of roads and other infrastructure features on the landscape (Forman et al., 2003; Morton et al.,

2002; Thomson et al., 2004; Morton et al., 2004; Schneider et al., 2003). Habitat fragmentation is

a particularly important outcome of road and infrastructure building. It's defined "as a process

where a large habitat area is transformed into small patches, isolated among each other by a



8 Robert Richardson, GIS Specialist at the University of North Florida, helped with this task.









habitat matrix different to the original one" (Ferraz et al., 2006, pp. 460). Since greater

petroscape density results in increased habitat fragmentation this suggests that best practices

regarding infrastructure building and the associated ecological effects are not implemented

(Forman et al., 2003; Morton et al., 2004; Forman and Deblinger, 2000; Morton et al., 2004).

Edge-effect zones

These zones include areas extending out from infrastructure features that experience

significant ecological effects (Forman and Deblinger, 2003). I calculated these by creating a 600

m buffer around the linear petro and nonpetroscape. This buffer distance is based on work from

three groups. First, Forman and Deblinger (2000), who calculated average value of 600 m for

what they term road-effect zones affecting moose, white-tailed deer, forest and grassland birds,

and amphibians on a suburban highway in eastern Massachusetts. Second, investigators

interested in avoidance and stress responses by caribou to oil roads, wellpads and petroleum

activities in Alaska and Canada found avoidance zones ranging from 250 m to 4 km, with 600 m

as an average value (Nelleman and Cameron, 1996; Noel et al., 2006; Schneider et al., 2007;

Wolfe et al., 2000; Bradshaw et al., 1998; Harron, 2007; Dyer et al., 2001).

Third, Thomson et al. (2005, pp. 17), noted that in open landscapes in western Wyoming

mule deer avoidance zones may be close to 500 m and that these animals "showed no evidence

of acclimating to energy-related infrastructure." Other large wildlife in the same area, such as

pronghorn exhibited a weak avoidance of areas within 965 m, while elk avoidance zones to roads

and active oil and gas wells was 1.9 km in the summer and almost 1 km in winter (Thomson et

al., 2005). In the case of sage-grouse (birds), hens that nested an average of 1.1 km from roads

were most successful at raising chicks, while those averaging 270 km from these features tended

to have broods that did not survive the first three weeks after hatching (Thomson et al., 2005).









Taking these figures into account and noting that deer and tapir and parrots live in the

Venezuelan ilanos (Perez, 2000; Gorzula, 1978; Marin et al., 2007; L6pez-Femandez and

Winemiller, 2000; Veneklaas et al., 2005; Rosales et al., 1999; Padilla and Dowler, 1994) I

selected the 600 m buffer. Figure 3-9 shows the extent of the edge-effect zones for both the

concessions and the control areas.

Core-areas

Core-areas define land beyond a given distance or effect zone from transportation areas

(Thomson et al., 2005). Here, they refer to intact habitat areas that remain after the concession

has been fragmented by petroleum production activities (in km2). These patches are important for

maintaining wildlife diversity as well as ecosystem goods and services (Forman and Deblinger,

2000; Spellberger 1998; Jones et al., 2000; Vos and Chardon, 1998). Core-areas are essentially

the land patches that remain after the edge-effect petro and nonpetroscapes are dissolved from

the concession using GIS techniques-see figures 3-10 and 3-13.

Agriculture

In order to better gauge the role of agriculture in these llanos I also digitized the

agricultural areas within the concessions and control groups. I used spectral analysis and field-

gathered waypoints to find and classify these plots, as well as the 15 m panchromatic bands and

composite Landsat images for 1990 and 2000 to delineate these areas-see figures 3-15 through

3-17. Given season dynamics at times it was difficult to distinguish dry savanna vegetation from

cleared agricultural plots.

Rainfall Patterns

Finally, I examined rainfall records for the town of Ciudad Bolivar, the nearest

metropolitan area with relevant rainfall records and the same climatological regime as the study

area. Data showed significant rainfall variation between the months of October through









December 1989 and 19999. These correspond to part of the dry season preceding the acquisition

of the 1990 and 2000 Landsat images.

Results and Discussion

Vegetation Change

Image differencing of NDVI images (2000-1990) of three HOB concessions and three

control groups revealed a pattern whereby vegetation biomass decreased much more than it

increased across all six sites. Table 3-2 shows the amount of land in km2 per site that exhibited

NDVI losses and gains, as well as the percentage of the site that was affected. Based on the

changes in NDVI, Ameriven showed the greatest loss of vegetation cover-with almost 50% of

its land affected, followed by the three controls, then Sincor and finally Petrozuata. Figures 3-3,

3-4, and 3-5 display the patterns of land-cover change based on the NDVI. Meanwhile, Sincor

had the greatest gains in vegetation-cover, followed by Sincor. Control 04 and 06 came next,

respectively. Ameriven and Control 07 showed the least gains. Excluding Sincor and Petrozuata,

agricultural activity appears to be a predominant feature in the remaining sites.

Since the Landsat images were acquired in April, at the end of the dry season, the NDVI

highlights the healthy biomass, which is strongly affected by variations in temperature and

rainfall (Funk and Brown 2006). This is relegated to the riparian forests and agricultural areas

not yet harvested. Furthermore, as the end of the dry season approaches, a lot of agricultural and

cattle land has been burned in anticipation of the rainy season-a long-time land management

tool (Chac6n-Moreno, 2004; White, 1956). All these factors help explain why gains in healthy

biomass are sparse, particularly in Ameriven and the control sites.





9 Rainfall records for the dry months of January and February 1990 were not available.









The detected gains in NDVI are likely due to the difference in rainfall between 1990 and

2000. Based on rainfall records of Ciudad Bolivar, October through Decemberl989 were

markedly drier than average, while the same time period in 1999 was wetter than average (see

figure 3-7). These months with complete data, correspond to the dry season prior to the capture

of the Landsat 1990 and 2000 scenes. 1999 was the wettest year in 68 years (1921-2002, for

years with complete 12 month records), while March 2000 was the wettest March in 68 years.

Such high levels of rainfall during the normally driest months of the year apparently had an

impact on the area rivers, leading to unseasonable flooding and vegetation growth along the

riparian forests in 2000. Thus with a month's lag time, the observed April patterns of vegetation

growth along the rivers is probably due to excess rainfall.

Petroscape

When the 2005 petroscape is overlain on the change-detection maps, it covers the bulk of

Petrozuata and Sincor, but also extends into parts of Ameriven and control 07. This measure is

an important indicator of habitat fragmentation. In Sincor and Petrozuata the petroscape

primarily extends over the areas of negative NDVI. This suggests that the 25% threshold selected

(because it highlighted petroscape features) captured areas that indeed exhibited petroscape

expansion in 2005.

Controls 02 and 04 do not appear to have a petroscape. While Petrozuata has the longest

linear km petroscape and petroscape density, followed by Sincor, control 07 also has substantial

petroscape. This site is not an HOB concession and yet it has more linear features and density

than Ameriven. This confirms that oil E&P has been part of the land history in the HOB for

decades (Boza and Romero, 2001; Gonzalez, 1994; Rodriguez et al., 1997; Galarraga et al.,

2007), though it is distinct from the strategic association business model to which Sincor,

Petrozuata and Ameriven belong.









It is curious that Ameriven shows the least amount of petroscape even though its

development was approved in mid-1999, initial drilling commenced in 2000 and production

started in November 2001, only three years after Petrozuata (Gipson et al., 2002; Chevron,

2001). It reached commercial production in October 2004 with the completion of the upgrading

plant, and full capacity in the first quarter of 2005 (Chevron, 2006, 2005, 2004; oil company

representative, 2005e). Therefore the full petroscape would have been established by the time the

April 2005 CBERS image was acquired. This helps support the claim by Gipson et al. (2002)

that this concession has implemented best business practices including the reduction of field

processing facilities and pipelines, and through longer horizontal wells, fewer wells, wellpads

and related infrastructure.

Table 3-3 shows important petroscape values, including linear length, density, edge-effect zones

and core-areas. While Sincor has a similar petroscape length to Petrozuata, the petroscape

density values are quite different. This is due to the concession size, where Petrozuata is the

smallest. This means that for every km2 there exists 1.13 km of linear petroscape features in

Petrozuata. Sincor is next, followed by control 07 and Ameriven (with none in controls 02 and

04). Morton et al. (2004) observe that in open landscapes with little surrounding vegetation (such

as many parts of the Venezuelan llanos) road avoidance by wildlife occurs at road density rates

of 0.8 km per km2 or higher. Petrozuata is the only site with values above this threshold,

suggesting that this concession may have fewer wildlife densities than surrounding areas.

The edge-effect zones appear in figure 3-9 and represent a 600 m buffer extending from

the linear petroscape. This time the land occupied is calculated in km2 and Sincor shows the

highest values followed by Petrozuata, Control 07 and Ameriven last. While this measure

provides a distance from which significant ecological effects extend (Forman and Deblinger,









2000), when size of the edge-effect per site is calculated then nearly 75% of Petrozuata's

concession is affected. This compares with 62% for control 07, 53% of Sincor and almost 20%

of Ameriven.

Finally, the size and distribution of core-areas is an important measure because it shows

how many land patches remain intact after the petroscape edge-effect has been dissolved. Figure

3-10 displays this pattern. This time Ameriven had the largest core-area, occupying 82% of this

concession. Next came control 07, followed by Sincor and Petrozuata-with only 26% of its land

consisting of nonpetroscape patches. It is important to note that these core-areas do not represent

intact, roadless mosaics providing unaltered habitat for local wildlife. Clearly agricultural

activity and a network of roads exist in all the sites, as the next results indicate. This core-area

measure assumes that if only E&P activities are modifying or converting the land, then the

particular patterns selected by the consortia of each concession show that Ameriven has

potentially altered the least amount of habitat. This may be due to the implementation of best

environmental business practices.

Nonpetroscape

As table 3-4 and figures 3-11 through 3-13 illustrate (see following pages), five of the six

sites displayed nonpetroscape features-primarily non-oil roads and farm roads. Ameriven, with

486 km of linear nonpetroscape had the highest values and density (0.73), primarily related to the

pine plantations and agricultural lands on its site. The actual petroscape comprised a much

smaller amount of the overall infrastructure.

Control 07 followed and had a significant nonpetroscape density of 0.69. This was

followed by control 02, control 04 and Petrozuata. Sincor did not appear to have nonpetroscape

features. The edge-effect zones showed control 07 having the broadest expanse, followed very

closely by Ameriven. Control 04 came next; followed by control 02 and Sincor with the smallest









amount (again Sincor had zero petroscape). Based on these patterns control 07 has the most

habitat fragmentation since the infrastructure features extend throughout the site. Ameriven, with

similar values had larger areas not affected by infrastructure.

Finally, the core-areas map, figure 3-13, shows how control 07 has the fewest intact areas

at 220.6 km2. Petrozuata followed, then Ameriven, control 04 and control 02. Control 02 is

located closest to the Orinoco River, an area that is sparsely populated (Dumith, 2004), which

helps explain how it has the largest core-areas of all sites (excluding Sincor). However, when the

overall size of core-areas is normalized per concession size, then Petrozuata has the largest

percentage of core-areas, 85%, followed by control 02, control 04, Ameriven and control 07.

Petro and Nonpetroscape

Figure 3-14 shows the overlay of the petro and nonpetroscape on each site. Controls 02

and 04 appear to have the least amount of landscape disturbances, while the rest have been

clearly altered by human activity. Out of these four, the alterations in Sincor seem exclusively

tied to petroleum E&P. Petrozuata's LUCC is centered on petroleum activities agriculture is also

evident. Based on an examination of the 1990 and 2000 images, it predated oil activities in this

concession. Out of the HOB concessions, Ameriven had the smallest petroscape, but a significant

nonpetroscape related to agriculture, in particular Caribbean pine plantations. Part of the reason

for a decreased petroscape could be the implementation of lessons learned and best practices in

this, the newest concession (Gipson et al., 2002). On the other hand, as the newest operation

perhaps it has not fully explored and drilled the site allotted to it, although Chevron (2006) says

"In 2005 the project facility reached total design capacity of processing and upgrading." Perhaps

as production declines, new wells will be drilled to maintain capacity, which would require

further changes in land-cover. A further explanation for the reduced petroscape pattern could be

the timber damage fees that oil companies have to pay to forestry companies for removing wood









as part of their operations (Schneider et al., 2003; Carrera 2005). This could be offset by the high

price of oil, though world prices did not consistently reach above $30 until April 2004 (EIA,

2008).

Petrozuata and Sincor show similar linear petroscape expansions. However Petrozuata's

smaller size means that the density of petroscape features is the greatest at 1.13 km/km2. This

was the first concession established and could in part explain the greater petroscape expansion.

Had Cerro Negro been included these results would perhaps differ. Rankings

As table 3-5 highlights, Petrozuata and control 07 tied and exhibited the most alterations

related to petroleum E&P between 1990 and 2005. Sincor followed, then Ameriven. Controls 02

and 04 showed no discernible petroscape. A higher number means more disturbances. Since

positive changes in NDVI and core-areas petroscape refer to reduced alterations, their rank

values were inverted so as not to confound results. After all, a larger core-area number meant

that more land remained undisturbed.

Based on these rankings, one of three hypotheses is supported. As expected, Petrozuata,

with the highest level of local-company participation had most petroscape-related disturbances

with a rank of 25. On the other hand, Sincor, with European-company dominance did not show

the least landscape alterations, ranking 22. Instead, Ameriven, with US-company dominance

showed the least, though it was expected to rank second, below Petrozuata. It ranked 21. Control

04 ranked 18 and control 02, 15. Nevertheless these two showed no petroscape, but since they

were included in the rankings, their zero values were assigned values of 1.5. Consequently

though they had no core-areas, when the values were inverted, instead of receiving ranks of 1.5,

they got 5.510


10 Not inverting these rankings for Controls 02 and 04 did not change rankings.









Conclusion

When the important petroscape values are ranked (NDVI change, linear and petroscape

density, edge-effect zones and core-areas) Petrozuata exhibits the greatest changes, supporting

the hypothesis that it would. Sincor, where European companies dominate did not show the

smallest alterations as expected. Instead, Ameriven, where US companies have the greatest

presence showed the least alterations. Thus Sincor and Ameriven switched rankings. Perhaps

most surprising was finding that one of the control sites, 07, exhibited a substantial petroscape,

tying with Petrozuata. While this block is not one of the strategic associations to which the oil

concessions examined belong, it confirms that E&P is part of the land-use history in the HOB.

The area where control 07 is located is known as the Bare field, where more than 150 wells had

been drilled by 2002 (Gipson et al., 2002).

Though only three out of four concessions were examined in this paper, including three

control groups helped increase the sample and determine which types of economic activities are

most prevalent in this part of the eastern llanos. The methods used here provide an innovative

approach to studying landscape change related to nonrenewable resource extraction. Future work

could build on these findings by implementing these methods to establish baseline conditions

prior to subsequent E&P. This would help implement best practices during the planning phase in

order to reduce associated environmental changes. Subsequent efforts would continue to use

remote sensing and GIS techniques as a cost-effective way to monitor the expansion of

operations throughout the project's life cycle and then during recuperation phases.

Finally, while these results are based on a very small sample, they do indicate that US-

company participation may be influential in the implementation of best business practices since

the Ameriven concession, which had a 70% US-company participation exhibited the least

petroscape alterations. Consequently, two US MNOCs recently stopped operating in Venezuela,









ExxonMobil and ConocoPhillips, while the Local company increased its participation to a

majority status in all four concessions. ConocoPhillips was a partner in Ameriven, while

ExxonMobil was a partner in Cerro Negro.

If E&P continues to expand as planned by the Venezuelan government and the Local

company, then the HOB stands to experience alterations to 18,000 km2, or 33% of the HOB land

area, see figure 3-18. The types of business partners selected may likely affect the extent of

petroscape related alterations.






































Figure 3-1. Venezuela, the heavy oil belt and concessions, and surrounding countries 2005
(Petroguia, 2006-2007; ESRI Geographic Network Services 2008). The yellow
outline delimits the heavy oil belt, which is divided into four E&P zones, and spreads
across the dry tropical savanna landscape. The red smaller polygons show the
location of the four concessions.




































Figure 3-2. Venezuela's heavy oil belt, its four concessions: Sincor, Petrozuata, Ameriven, and
Cerro Negro, and the upgrading/refinery plant called Jose located on the coast of
Anzoitegui state, 2005. Though the HOB spans Guarico, Anzoitegui and Monagas
states, the four concessions are all located in Anzoitegui (ESRI 2006; Petroguia,
2006-2007). From west to east the four heavy oil belt exploration and production
zones, marked by the red divisions are: Machete, Zuata, Hamaca and Cerro Negro.
However, they have now been renamed to Boyaca, Junin, Ayacucho and Carabobo
(PDVSA, 2005a, 2005b; Kopper et al., 2001; Balke and Rosauer, 2002). Note: the
HOB also reaches into the eastern state of Delta Amacuro, but its reach is so slight it
is not included on this map or mentioned in the text.









N0
W+E 07
S





E04

Petrozuata
Sincor Ameriven





02
I II lometers
0 5 10 20 30 40
Figure 3-3. Vegetation-cover change across all six sites (three concessions and three controls)
between 1990 and 2000. The areas in red indicate a decrease in NDVI by 25% or
more. The dark background represents areas of some increase, unchanged, or some
decrease. In Sincor and Petrozuata, the petroscape becomes discernible as lines
(roads) and polygons, production facilities are established.











W E 07
S








Petrozuata
i o 04 Ameriven
Sincor





02
I Ilometers
0 5 10 20 30 40

Figure 3-4. Increases in Normalized Difference Vegetation Index (NDVI) across all six sites
between 1990 and 2000. The green areas represent gains in NDVI by 25% or more.
The dark background represents areas of some increase, unchanged, or some
decrease. Note that green areas are mostly riparian forests following river courses in
the study area. These smaller changes in vegetation cover contrast with the large
changes in figure 3-3.










W E 07
S







04
Petrozuata
Si r Ameriven






02
0 5 10 20 30 40

Figure 3-5. NDVI losses and gains for all six sites between 1990 and 2000. Red indicates areas
of NDVI increase, and green gains. The agricultural landscape becomes evident in
control 07 and in Ameriven, where rectangular polygons represent pine plantations in
the area.













Sincor NDVI Change Detection, 1990-2000

460,000
440,000
420,000
400,000
380,000
360,000
340,000
320,000
m 300,000
S280,000 244,099 Increased I
0 260,000
o 240,000
- 220,000
E
S200,000
Z 180,000
160,000
140,000
120,000
100,000
80,000
60,000........
40,000
20,000
0 -


Decreased


NDVI Gains and Losses


Increased


Petrozuata
380,000 35
360,000
340,000
320,000
300,000
280,000
260,000
240,000
220,000
200,000-
180,000
160,000
140,000
120,000
100,000
80,000
60,000-
40,000
20,000
0


NDVI Change Detection, 1990-2000
0,063 Decreased |


187,210 Increased


Decreased Increased
NDVI Gains and Losses


Figure 3-6. Gains and losses of the Normalized Difference Vegetation Index (NDVI) for the
three concessions: A) Sincor, B) Petrozuata, and C) Ameriven, as well as the three
control groups: D) Control 02, E) Control 04, and F) Control 07.













Ameriven NDVI Change Detection 1990-2000
1,700,000 1 1,594,834 Decreased
1,600,000
1,500,000
1,400,000
1,300,000
1,200,000
1,100,000
1,000,000
o 900,000
800,000
= 700,000
600,000

500,000
400,000
300,000
200,000
100,000- 11,427 Increased
0
Decreased Increased
NDVI Gains and Losses
C



Control 02 NDVI Change Detection, 1990-2000
1,164,302 Decreased
1,200,000
1,100,000

900,000
800,000
8 700,000

8 600,000
= 500,000
z
400,000

300,000
200,000......... 92,232 Increased
100,000
0
Decreased Increased
NDVI Gains and Losses
D


Figure 3-6. Continued.













Control
1,100,000

1,000,000

900,000

800,000

700,000

600,000

500,000

400,000

300,000

200,000

100,000

0


04 NDVI Change Detection, 1990-2000
| 1,031,890 Decreased


110,087 Increased


Decreased Increased
NDVI Gains and Losses




Control 07 NDVI Change Detection, 1990-2000
1I 0a0 nnn-,


1 1,461,576 Decreased |


1,833 Increased

Decreased Increased
NDVI Gains and Losses


Figure 3-6. Continued.


1i500,000
1,500,000
1,400,000
1,300,000
1,200,000
1,100,000
1,000,000
900,000
800,000
700,000
600,000
500,000
400,000
300,000
200,000
100,000
0


----











120.00


100.00


80.00


60.00


40.00 -


20.00


0.00
October November December
S1989 13.50 39.30 17.30
SAverage 91.80 59.60 41.50
1999 99.00 72.00 94.00
Month


Figure 3-7. Rainfall amounts (in mm) for Ciudad Bolivar for the October, November and
December dry months preceding the 1990 and 2000 Landsat images used in this
study. Note that all three months were drier in 1989 than average values and 1999
measures, helping explain the phenological differences in both images.


I










































0 1 2 4 6 ers
0 10 20 40 60 80


Figure 3-8. Decrease in NDVI between 1990 and 2000 overlain by 2005 petroscape features. The
red lines show the extent of the petroscape in four sites. Controls 02 and 04 displayed
no discernible petroscape. Petrozuata has the longest petroscape in linear km and
density, followed by Sincor, control 07 and finally Ameriven.







































0 1 2 4 meters
0 10 20 40 60 80


Figure 3-9. Edge-effect zones, or areas where significant ecological effects extend (Forman and
Deblinger, 2000), appear in red. Based on the petroscape linear features, these 600 m
buffered zones indicate how much land of each site is potentially affected by
infrastructure features which in turn have negative consequences for wildlife and
ecosystems (Morton et al., 2004; Thomson et al., 2005; Forman and Deblinger, 2000).
Controls 02 and 04 displayed no petroscape edge-effect zone. The white background
areas show the negative changes in NDVI between 1990 and 2000. The areas in black
represent little positive or negative change as well as areas of no change.












































o meters
0 10 20 40 60 80


Figure 3-10. Core-areas, marked in green, show size and distribution of the patches of land that
remain after dissolving the edge-effect zones in figure 3-7. Controls 02 and 04
displayed no petroscape core-areas.







































S 1 2 4 6 meters
0 10 20 40 60 80


Figure 3-11. Linear nonpetroscape 2005. The yellow lines represent roads related to agriculture
and transportation not associated with petroleum activities. While Sincor appeared
not to have a nonpetroscape, Petrozuata displayed the smallest one out of the
remaining sites. This indicates that these two concessions are petroleum E&P zones.
Controls 02 and 04 had some transportation and agricultural roads across their sites,
but Ameriven and control 07 had the most. The checkerboard pattern near the center
of Ameriven shows the 1-km blocks typical of the Caribbean pine plantations in this
part of Venezuela.








































S 1 2 lo meters
0 10 20 40 60 80


Figure 3-12. Edge-effect nonpetroscape zones. Yellow lines indicate the extent of the edge-effect
zones. With a 600 m buffer extending out from the linear nonpetroscape both
Ameriven and control 07 infrastructure disturbances stretch across a significant
amount of their plot. They are followed by control 04 and control 02. Petrozuata has
the least and Sincor was not included because of no discernible nonpetroscape.











W E 07










04
PETROZUATA AMERIVEN
AMERIVEN
SINCOR






02
kilometers
0 10 20 40 60 80

Figure 3-13. Core-areas that remain after nonpetroscape features are dissolved from each site.
Overall controls 02 and 04 had the largest areas, followed by Ameriven, Petrozuata.
Control 07 had the least.









































S 1 2 lo meters
0 10 20 40 60 80


Figure 3-14. Petro and nonpetroscapes across all 6 sites. Red indicates petro, yellow reflects
nonpetro. The NDVI background has been cleared to make the two types of features
more discernible. Control 07 exhibits high levels of both. Ameriven shows more
nonpetro than petroscape features while Petrozuata shows a petroscape mostly.











w +E 07
S I










04
PETROZUATA

SINCOR AMERIVEN









0 125 25 50 75 100

Figure 3-15. Agricultural patterns in the three concessions and the three control groups in 1990.
Both Ameriven and Control 07 had more than half their area dedicated to agriculture.











W E ^ .07











04
PETROZUATA
SINCOR AMERIVEN








L-02
Kilometers
0 12.5 25 50 75 100

Figure 3-16. Agricultural patterns in the three concessions and the three control groups in 2000.
Activity appears to have increased everywhere except in Ameriven-see figure 3.17.











450

400

350

-a 300
-- 250
0
0 200
E
150
100

50 -
50
Sincor Petrozuata Ameriven Control 02 Control 04 Control 07

AGR 1990 Amt Land Use Km2 1.52 21.27 402.78 35.47 28.39 252.28
AGR 2000 Amt Land Use Km3 2.32 40.58 373.93 42.63 40.31 412.8
Location


Figure 3-17. Amount of agricultural land (in km2) found in the three concessions and three
control groups in 1990 and 2000. Note how Ameriven and Control 07 have large
amounts of their territory dedicated to agriculture. In Ameriven's case, Caribbean
pine plantations extend through the concession.



































Figure 3-18. Future heavy oil development in Venezuela's Heavy Oil Belt.









Table 3-1. Venezuelan heavy oil belt strategic associations, company ownership by percentage,
syncrude (synthetic oil) production and upgraded quality 2005 (Petroguia, 2005-2006;
Talwani, 2002; Mommer, 2004).
Sincor Petrozuata Ameriven Cerro Negro


Company PDVSA PDVSA 49.9% PDVSA 30% PDVSA
Ownership 38% ConocoPhillips Chevron 30% 41.67%
Total 50.1% ConocoPhillips ExxonMobil
(France) 40% 41.67%
47% BP(United
Statoil Kingdom)
(Norway) 16.66%
15%
Syncrude 180,000 b/d 104,000 b/d 190,000 b/d 105,000 b/d
Production From 8.50 to From 90 to 19- From 7-10 to From 6-10
and 320 API 250 API 260 API to 160 API
Upgraded
Quality
Congressional 1993 1993 1997 1997
Authorization

Early 2001 1999 2002 1999
Production

Full Mar 2002 Jan 2001 Oct 2004 Aug 2001
Production
(upgrader
Start-up)


Table 3-2.


Change-detection measures showing amounts of positive and negative change per site
n i km2 and as a percentage of the site area


Negative Positive
Concession/ NDVI NDVI % Veg % Veg
Site km2 km2 Loss Gain
Sincor 77.08 41.36 14.90 8.20
Petrozuata 39.07 21.00 12.99 6.95
Ameriven 309.90 2.19 46.57 0.33
Control 02 235.70 17.33 40.81 3.23
Control 04 208.95 23.12 36.15 3.86
Control 07 296.03 0.35 51.35 0.06









Table 3-3. 2005 petroscape measures for the six study sites.
% of
Edge Site Core
Petroscap Petroscap Effect Affecte Area % of Site
Concessio e Linear e Density Petroscap d by Petroscap Affected
Concession n Area 2005 2005 e 2005 Edge- e 2005 by Core
/ Site (km2) (km) (k/km2) (km2) Effect (km2) Areas
Sincor 517.00 302.33 0.58 271.57 53. 00 245.80 48.00
Petrozuata 300.00 338.70 1.13 222.49 74.00 78.29 26.00
Ameriven 665.00 119.63 0.18 117.07 18.00 548.37 82.00
Control 02 575.55 0.00 0.00 0.00 0.00 0.00 0.00
Control 04 578.00 0.00 0.00 0.00 0.00 0.00 0.00
Control 07 576.50 153.86 0.27 164.80 62.00 411.70 72.00


Table 3-4. 2005 nonpetroscape measures for the six study sites.
% of % of
Non Site Site
Non petroscape Edge Affected Core Area Affected
petroscape Density Effect Non by Non by
Concession/ Concession Linear 2005 petroscape Edge- petroscape Core-
Site Area km2 2005 km k/km2 2005 km2 Effect 2005 km2 area
Sincor 517.38 0.00 0.00 0.00 0.00 0.00 0.00
Petrozuata 300.77 47.39 0.16 45.61 15.16 255.17 84.84
Ameriven 665.44 486.02 0.73 348.32 52.34 317.11 47.65
Control 02 577.55 168.26 0.29 148.62 25.73 428.93 74.27
Control 04 578.00 128.78 0.22 152.36 26.36 425.73 73.66
Control 07 576.50 397.48 0.69 355.91 61.74 220.60 38.27


Table 3-5.


Overall rankings for land-cover change and petroscape features in the 6 sites. A
higher number means greater overall landscape disturbances. Note: positive NDVI


and core-area )etroscape values were inverted to maintain rank consistency.
Edge
Negative Positive Petroscape Petroscape Effect Core Area
Concession/ NDVI NDVI Linear Density Petroscape Petroscape Overall
Site km2 km2 km2 2005 2005 km2 2005 km2 Rank
Sincor 2 1 5 5 6 3 22
Petrozuata 1 3 6 6 5 4 25
Ameriven 6 5 3 3 3 1 21
Control 02 4 4 1.5 1.5 1.5 5.5 18
Control 04 3 2 1.5 1.5 1.5 5.5 15
Control 07 5 6 4 4 4 2 25









CHAPTER 4
SPATIAL ANALYSIS AND STATISTICAL CORRELATIONS AMONG PETROSCAPE
FEATURES IN THE HEAVY OIL BELT

Introduction

Worldwide, heavy and extra-heavy oil comprise about 40% to 50% of total petroleum

resources (Ascencio-Cendejas and Reyes-Venegas, 2006; Khelil, 2006). Compared to traditional

petroleum, this tar-like crude requires different financial and technological inputs to convert it to

usable petroleum products and until the late 1990s was not an attractive investment (particularly

in Venezuela). Today, increased demand and higher prices mean that exploration and production

(E&P) activities for heavy oil are not only viable but will expand in the next decade especially in

Canada, Mexico, Brazil and Venezuela11 (Ascencio-Cendejas and Reyes-Venegas, 2006). In

Canada, Alberta's oil sands deposits total 95% of that country's proven reserves (EIA, 2007a).

By 2020 Mexico's heavy oil production is expected to increase to 50% of overall national

production (Ascencio-Cendej as and Reyes-Venegas, 2006).

In Venezuela current heavy oil production is centered in the eastern part of the country

known as the heavy oil belt (HOB), located on the plains, or llanos, north of the Orinoco River

(see figure 4-1). Current production is around 635,000 barrels per day12 (b/d) or about 21% of

Venezuela' total production-which ranges from 2.8 to 3.6 million barrels per day (mb/d)

depending on the source (OPEC, 2007, EIA, 2007a; BP, 2007).

In the late 1990s Venezuela began four concessions-also known as strategic associations

in the HOB, comprised of public-private partnerships between seven companies. One local

company, state-owned Petroleos de Venezuela SociedadAn6nima, or PDVSA, had an ownership


11 In addition to these countries, other major deposits are found in the US, Trinidad, Madagascar, Albania, Rumania,
and Russia and China (Phizackerley and Scott 1978; Jiayu and Jianyi 1999; Zhang et al., 2007).
12 An oil "barrel" is 42 US gallons, or 158.984 liters.









stake in each of the four concessions. The other partners were multinational oil companies

(MNOCs) from Europe and the US, each with different levels of participation and investment,

see table 4-1.

The companies operated until mid-2007, when Venezuela's President Hugo Chavez

changed the contract terms 25 days after the National Congress passed a law allowing him to rule

by decree in certain matters (Gaceta Oficial, 2007). The new law (number 5,200) required the

HOB operations to reorganize. The local company, PDVSA, increased its role by becoming a

majority partner and decision maker. This reduced the role of the MNOCs and provided them

four months to agree to the new terms or cease operations (see figure 4-2). Two companies,

ExxonMobil and ConocoPhillips withdrew not only from the HOB, but from Venezuela

(ConocoPhillips, 200813). This study, concerns the time period 1990-2005, before these changes

took effect.

The global research question is: what are the environmental implications of any natural

resource development done by local institutions supported by governments, multi-national

companies subject to international scrutiny, or a consortia of locals and multinationals overseen

by governments? More specifically in this chapter the local research question is: does the

government make it easier for the local oil company to avoid environmental regulations as

evidenced in the amount of important land-cover changes measured in each concession?

I address the relationship between the observed land-use land-cover changes (LUCC)

occurring in the HOB, particularly between 2000 and 2005, and the three types of partnerships

defined by the role of participation among the concessions. While geological events explain the

presence of petroleum-which is often located in sedimentary basins such as Venezuela's

13 ExxonMobil moved its case to international arbitration, resulting in PDVSA having $12 billion in assets frozen
in the US. This ruling was lifted.









Orinoco basin, the two actors extracting oil are the state and multinational oil corporations

(MNOCs) because they have the resources, equipment, expertise and legal framework to provide

global energy supplies.

Production in the Heavy Oil Belt

As table 4-1 shows, the four concessions were comprised of seven companies. Their

extraction and production model was to:

* Extract heavy crude from HOB wells at reservoir temperature-this means that no heat or
steam injected into the wells was necessary to pump the crude to the surface (due to its
tropical location). This practice is now changing with the intention to increase recovery
rates, since higher reservoir temperatures reduce viscosity. However, these practices also
involve the use of solvents inside the reservoir which increases the potential for air and
water pollution.

* Add a diluent at the wellhead. Once it reaches the surface this heavy crude is tar-like and
requires "thinning" which is accomplished by adding high-grade petroleum which allows
the extracted crude to flow freely through pipelines.

* Once diluted the heavy crude is sent to the central processing facility in the concession
area where it is degassed, dewatered and desalted.

* From there it is sent via a 200 km pipeline to the coastal upgrading/refinery plant called
Jose, where it is converted to syncrude, which is short for synthetic crude oil. This process
removes the high levels of vanadium, nickel and sulfur and produces a valuable petroleum
product.

* Finally, depending on the quality of the upgraded syncrude (see table 4-1), it is sent to
offshore refineries (in Germany or the US), or is sold as is. Sincor produces the highest-
quality syncrude, followed by Ameriven, Petrozuata and finally Cerro Negro.

(oil company representative, 2005a, 2005d; Guerra, 2005; PDVSA, 2008a, 2007e; 2005a;

Petroguia, 2006-2007; Pawlewicz ,1995; Tankersley and Waite, 2002, 4; Galarraga et al., 2007).

An important issue concerning LUCC in Venezuela concerns government plans to increase

overall production to 5.85 mb/d by 2012. Much of this growth is expected to come from the

HOB as 27 more operations are developed by 2030 (oil company representative, 2005a; PDVSA,

2007h) This time, however, the local state-company will have a majority role in each operation









and mainly rely on national oil company (NOC) partnerships from other countries (PDVSA,

2007a; 2006; 2005; Petroguia, 2006-2007). This combination of public-public partnerships will

produce particular E&P landscapes with possibly greater or lesser amounts of landscape

alterations than under the old partnerships.

Land-Use Land-Cover Change

Lambin (2001) observes that LUCC is a function of individual and social responses to

opportunities and constraints for new land uses created by markets and policies (Lambin, 2001).

In this context, positive incentives are the opportunities available while negative ones are the

constraints-operating in the framework of energy markets and energy policies. In the HOB the

positive incentives are to control, extract, produce, refine and sell very large amounts of

profitable heavy crude using proven technology in a mostly uniform seasonal savanna landscape

(Chac6n-Moreno, 2007).

Some disincentives include barriers to entry because of the high capital costs, advanced

technology and expertise needed. These issues are less challenging for MNOCs than for local

companies. A main challenge for MNOCs, however, is working in a country where energy laws

are evolving, where previous contracts are changed, where capital and technological investments

may not be fully realized, and where the state now requires MNOCs to work under its leadership

and rules. In Venezuela this framework is shaped by a state-centered political economy funded

by petro-dollars and marked by strong state control and centralized planning; price controls;

fixed currency rates; increased social spending on the poor (the government's political

supporters); increased state control of key industries such as telecommunications, electricity,

banking and steel; anti-privatization measures; increased state and reduced private property

rights; and complete control over the local oil company, PDVSA.









Such command-and-control governance strategies tend to discourage innovation in

behavior and technology, are less effective and economically inefficient (Dietz et al., 2003).

Furthermore, when local oil companies don't operate internationally or compete for contracts in

other countries, they may not have an incentive to reduce social and environmental alterations

tied to E&P activities (Diamond, 2005). But does the level of local company participation

actually affect environmental performance when it comes to heavy oil exploration and

production in eastern Venezuela? What about the level of European or US company

participation? Referring to energy production in Alberta, Canada, Timoney and Lee (2001, pp.

388) observe that "economic activity leads to environmental degradation unless ecosystem-based

management is integrated into economic decision making" In this context, a higher level of local

company participation in Venezuela would correlate with greater levels of environmental

alteration.

Expected Findings

This study tries to answer the question: do governments make it easier for local companies

to avoid environmental regulation while MNOCs meet or exceed regulations as evidenced in the

amount of important land-cover changes measured in each concession? In terms of company

dominance the HOB concessions exhibit the following patterns: Ameriven 70% US; Sincor 62%

European; Petrozuata 49.9% Local; Cerro Negro tied-41.67% Local, 41.67% US.

Using statistical correlation analysis I test three hypotheses related to the three levels of

company participation and the LUCC measures examined in the previous chapter and expanded

in this chapter.

The hypotheses are:

* Local company-dominated concessions will exhibit the greatest changes in land-cover

* US company-dominated concessions will show lower levels of LUCC









* European company-dominated concession will exhibit the lowest levels of LUCC

These hypotheses are based on a review of petroleum geology, LUCC, organizational and

natural resource management literature particularly in relation to oil and gas E&P, field

interviews with oil company representatives and results from the previous chapter (Thomson et

al., 2005; Morton et al., 2004, 2002; Wilderness Society, 2006; Musinsky et al., 1998; Smith et

al., 2003; Moser, 2003; oil company representative, 2005a, b, c, d, e, f, g, h).

The first hypothesis is derived in part from the regional context, where the local oil

company is strongly controlled by the federal executive office; where political and economic

power is concentrated (McCoy and Fensom, 2006, 17; CIA, 2008). Such entrenching of political-

economic institutions often lacks checks and balances (Timoney and Lee, 2001). In Venezuela

petroleum has been the traditional export earner since the 1920s (when it overtook coffee) and

currently encompasses about 90% of export earnings, 50% of federal budget revenues and about

30% of gross domestic product (GDP) (CIA, 2008; EIA, 2007b; Trujillo, 2004; Torres, 2004;

Williams, 2006).

McCoy and Fensom (2006, pp. 8) note that in Venezuela "adherence to the rule of law is

low and the prevalence of corruption is high." Also concerning Venezuela's business

environment, Lopez-Claros (2006) states the following:

What is especially noteworthy about Venezuela is that there has been a notable
deterioration in the quality of the business environment, with serious concerns about
property rights, the independence of the judiciary, waste in government spending and
overall levels of corruption. Venezuela is not using oil revenues to enhance the economy's
competitiveness such as through a major improvement in the educational system or in the
country's dilapidated infrastructure.

While at the regional level, Chac6n-Moreno (2007, pp. 3-4) observes: "Government

policies are focused on increasing the use and exploitation of the Llanos areas in order to elevate









the quality of life of the Venezuelan people, but most of the time, these policies lack

environmental controls."

Since petroleum is such a central component of the Venezuelan economy and crucial to

large domestic social programs and international energy agreements promoted by the current

administration (PDVSA, 2008b), activities that reduce production and therefore profits, such as

meeting environmental regulations, may not be fully enforced on local companies since

ecological concerns may not be part of the decision making process (Timoney and Lee, 2001).

For example, the Ministry of the Environment and Natural Resources (MARN) requires a

complete environmental impact assessment (EIA) report and a register of activities susceptible to

degrade the environment (RASDA) report for the establishment of new industrial facilities,

including oil operations (Sebastiani et al., 2001). However, Sebastiani et al. (2001, pp. 140)

reported that MARN "has adopted a conciliatory and not enforcing attitude once a facility is in

operation." This facility was the upgrader for the Ameriven HOB concession (Sebastiani et al.,

2001).

Another issue concerns oversight of one government agency such as the Ministry of

Energy and Petroleum by another like MARN. This sets up a conflict of interest whereby one

government (environmental) agency is responsible for monitoring another powerful state-

controlled company whose earnings comprise key government and export revenues, help fund

sizeable social programs14 benefiting the President's political base, finance international oil-

driven agreements benefiting its allies such as Ecuador, Bolivia, Argentina, Nicaragua and Cuba,

and pay for infrastructure projects.



14 These social programs include 17 Missions that foster education, health services, nutrition and microenterprises
(MENPET 2007).









Wages earned by government employees may also be a contributing factor because "most

government officials are poorly paid, which encourages acceptance of bribes, and this is

particularly likely when they are responsible for managing natural resources with high financial

value" observe Smith et al. (2003, 68). Finally, in cases of negligence the judicial branch may

not have the capacity (will and/or desire) to bring legal action against a state-owned energy

company.

The second hypothesis, that US company-dominated concessions will show lower levels of

LUCC, is based on their technological know-how and experience in addressing environmental

changes related to E&P worldwide, as well as international scrutiny by governments, non-

government organizations (NGOs), sustainability rating agencies and investors such as the Dow

Jones sustainability world index (Dow Jones, 2007a; 2007b; Bruni, 2007), World Economic

Forum (WEF, 2005); the Global 100 (Global 100, 2008); Management and Excellence (M&E,

2008, 2007, 2006, 2005); the Pacific Sustainability Index (PSI, 2007) from Claremont McKenna

College; and the Environmental Sustainability Index (ESI, 2005) from Yale University and

Columbia University15. A traditional view in organizational theory proposes that a company's

obligations extend only to its shareholders (Dashwood, 2007). This perspective has dominated in

the US but is beginning to change, and eventually environmental reporting may be as

commonplace as financial reporting is today (Morhardt, 2002).

The third hypothesis, that European company-dominated concession will exhibit the lowest

levels of LUCC, is also supported by their technological know-how as well as a longer history

and culture of sustainable business practices in Western Europe (Lawrence, 2007). In this region,

sustainability is a concept tied to a sense of balance between the market and the social sphere


15 In future work I will examine the statistical relationship of these indices with the observed LULCC in the HOB.









(Lawrence, 2007), which contrasts with the objective of maximizing profits every quarter. The

European Union, for example, has adopted stricter environmental standards and their policies are

based on the precautionary principle whereby insufficient or inconclusive scientific evidence is

considered when reaching policy decisions such as bans on questionably hazardous products

(Schapiro, 2007). Also, some of the sustainability indices mentioned above, very often rank

European companies higher in environmental and social performance than US companies, while

Venezuelan companies and government rank at the bottom (M&E, 2008, 2007, 2006, 2005;

Global 100, 2007, 2008; ESI, 2005).

Given the declining production from conventional wells; the difficulty in finding and

securing new oil sources; the importance of maintaining a positive company image for

shareholders, the general public and state governments; and the challenge of establishing new

business ventures in countries with increased state control over the energy industry; it is in the

interest of both US and European MNOCs to engage in sustainable practices that reduce

environmental and social changes related to their E&P operations. This concept of sustainability

refers to a company's capacity to reduce environmental and therefore social disturbances

resulting from business operations while still managing a profitable enterprise and maintaining a

healthy ecosystem for future generations (Lovins et al., 2000). This same concept holds true for

local companies that want to compete internationally.

Methods

I first used GIS and remote sensing techniques outlined in the previous chapter to expand

the land-cover change findings to 16 measures (noted below). Then I used bivariate correlation

analysis to determine if a statistically significant relationships exists, as well as the strength of

the associations between the amounts of 1) local (PDVSA); 2) European; or 3) US participation

in the four HOB operations and the magnitude of resulting land-cover changes









LUCC Related to the Petro and Nonpetroscape


All these landscape-level changes for the HOB concessions were measured using remote

sensing and GIS techniques (Baynard 2007). They were:

* NDVI negative change-the index amount of vegetation in km2 that showed a negative
change based on the Normalized Difference Vegetation Index (NDVI) between 1990 and
2000. This index is a model that helps detect vegetation vigor and is often used as an
indicator ofbiomass (Jensen, 2005; Fung and Siu, 2000; Mangiarotti et al., 2008).

* NDVI positive change-the index amount of vegetation cover that showed increases
between 1990 and 2000 measured in km2 and using the NDVI.

* Petroscape density-the linear amount of petroscape features in km divided by the
concession size in km2.

* Nonpetroscape density-the linear amount of agricultural and other nonpetroscape features
in km/km2.

* Allscape density-the combined density of petro and nonpetroscape features in km/km2.

* Concession area-the size of the E&P zone in km2.

* Edge-effect zone petroscape-the area extending 600m from petroscape features in which
significant ecological effects are expected to extend in km2. The 600 m buffer was selected
after reviewing work by Forman and Deblinger (2003) regarding roads, Schneider et al.
(2003) and Dyer et al. (2001) in reference to energy roads in Canada, and Morton et al.
(2004), Thomson et al. (2005) and the Wilderness Society (2006) regarding LUCC and
energy development in the western US.

* Edge-effect zone nonpetroscape-the area extending 600m from nonpetroscape features in
which significant ecological effects are expected to extend in km2 (Forman and Deblinger,
2003; Schneider et al 2003; Morton et al., 2004; Thomson et al., 2005; Wilderness Society,
2006).

* Core areas petroscape-the size in km2 of intact areas that remain after the effects of
petroscape features (edge-effect zones) are accounted for in each concession (Thomson et
al., 2005; Morton et al., 2004; Wilderness Society, 2006; Morton et al., 2002)

* Core areas nonpetroscape-the size in km2 of intact areas that remain after the effects of
nonpetroscape features (edge-effect zones) are accounted for in each concession.









* Number of wells-the total number of wells and wellpads identified in the 2005 satellite
images16 of the concession areas.

* Density of wells-the number of wells and wellpads per concession area identified in the
2005 satellite images.

* Wells edge-effect zone-the area extending 600m from the wells and wellpads/km2
identified in the 2005 satellite images of each concession. The same buffer distance was
selected as with edge-effect zone petroscape.

* Wells core-areas-the size in km2 of intact areas that remain after the edge-effects of the
2005 wells and wellpads are accounted for in each concession.

* Number of rivers crossed-the sum of river crossings created by petro and nonpetroscape
features in each concession. Since many infrastructure features reduce connectivity
between rivers and their floodplains (Stein et al., 2002), a lower number implies fewer
ecosystem disruptions.

* Riverscape density-the linear amount of rivers/km2. These rivers were measured in each
concession using Landsat 2000 images of the study region17

The Coefficient of Determination

An examination of scatterplots provided an R2 coefficient of determination showing how

well a regression line approximated real data points for the different types of company

participation. With a small sample size (n=4), coefficients below 0.70 are not considered

significant-and even those above 0.70 require further scrutiny, see table 4-3.

NDVI change (negative and positive) was measured between 1990 and 2000 for three of

the concessions; Cerro Negro was excluded. Most of the measures discussed here concern 2000

and 2005 (or both) because of data availability and the fact that HOB operations did not begin

until the late 1990s.





16 These subset images were created using Leica Geosystems Erdas Imagine 9.1 software from the China-Brazil
Earth Resources Satellite (CBERS) CCD sensor with a 20 m spatial resolution.

1 The 2000 image date was selected since it coincided with high seasonal flooding (following an abnormally heavy
rainy season-see chapter 3) and thus showed a full extent of area rivers.









Nonparametric Statistical Analysis

For the next step I selected a nonparametric test because the sample size was small (n=4)

and the data did not meet the assumptions of parametric inferential statistics (Ferguson et al.,

1999; Lehmkuhl, 1996). As with research regarding environmental and sustainability reporting,

note Morhardt et al. (2002), different types and intensities of environmental alterations by

different companies are best measured as categorical rather than continuous variables, and

therefore are inherently appropriate for nonparametric statistical analysis.

Kendall's tau-b rank correlation coefficient measures a precise degree of association

between two sets of ranks (Burt and Barber, 1996), rather than their actual values. This addresses

differences in magnitude in the data which was necessary since the variables in my dataset were

measured in different units (e.g. area in km2 of vegetation cover loss, percentage of company

participation and number of wellpads).

Like Pearson's correlation coefficient Kendall's tau-b has the following properties: 1) a

value of +1 means that the agreement between two ranks is perfect; 2) a value of-1 means the

disagreement between two ranks is perfect; 3) other arrangements have values that fall between

+1 and -1; and 4) a value of 0 means that the rankings are independent (Rumsey, 2007).

However, unlike Pearson's, Kendall's (and other nonparametric measures like Spearman's rank

correlation) work with the median rather than the mean, making this statistic more flexible since

it's not affected by outliers (Rumsey, 2007).

Kendall's tau-b is calculated by comparing each pair of ranked observations. Rankings that

are in agreement, or ordinally correct, are considered concordant, while those that are not in

agreement are discordant (Burt and Barber, 1996). This measure compares all pairs of

observations, records the number of concordant and discordant pairs and uses the function:









* = NC-ND
n(n-1)/2

(Burt and Barber, 1996).

Furthermore, Kendall's tau-b (as opposed to tau-a) considers ties. Pett (1997, 268)

explains: "the range of possible values for this coefficient is smaller because the number of

concordant and discordant pairs will always be smaller than the total number of pairs

(concordant + discordant + ties)."

Since a correlation coefficient of 0 means the paired variables are independent, this is used

to set the null hypothesis. Here, Ho: T= 0, whereby the amount of local company participation

paired with each of the 16 ecological variables is independent (i.e., there is no relationship). The

alternate hypothesis is: HA: T 0, whereby the paired variables are dependent. The same

hypotheses hold for European-company participation and for US-company participation. Table

4-5 shows the matrix for Kendall correlation coefficients and significance levels for the three

types of company consortia and the 16 measures analyzed above for the R2 correlation of

determination, as well as the R2 scores.

The decision rule to determine significance with a sample of 4 is to compute a sampling

distribution containing a finite number of possible combinations between paired variables ranked

1 through 4 (Abdi, 2007). Excluding ties, the number is 24, which means that an exact

correspondence in rank would occur only 1 in 24 times, or .042, which is less than the

conventional .05 cutoff for significance (Abdi, 2007; Richard, 2008; Rumsey, 2007). In order to

use a test statistic of significance of Kendall's tau, such as the Z score, the sample size n must >

10 (Burt and Barber, 1996; Abdi, 2007; Lehmkuhl, 1996).









Results and Discussion


Determining the R2 Coefficient

Table 4-2 shows the R2 coefficients of determination for seven important petroscape-

related LUCC measures, while table 4-3 displays all LUCC values. Local participation showed a

strong downward association (R2 >0.9) with core-area petroscape for 2000 and 2005; a upward

relationship with petroscape density (R2 >0.73); and an downward relationship with negative

NDVI (R2 >0.76). This means that for Petrozuata, core-areas were smaller and petroscape

density was higher which mean larger alterations. However, negative-changes in vegetation-

cover were smaller indicating that local-company ownership may not be related to negative

changes in land cover, but rather US or European ownership, or other explanations.

European participation showed only one statistically significant correlation with positive

NDVI (R2=0.770). But how does a concession exhibit vegetation-cover gain over time if E&P

activities are increasing? In the case of Sincor it had the largest number for total length of rivers

for the four concessions, at 561.40 linear km. Thus when the rivers flooded during an abnormally

wet dry season, captured in the 30 April, 2000 Landsat ETM+ image (described in chapter 3), the

numerous riparian forests expanded, likely revealing the reported positive changes (gains) in

NDVI. US-company presence was correlated with larger core areas presumably because of lower

petroscape density in Ameriven.

Nonparametric Statistical Analysis

Table 4-4 shows the Kendall's tau-b coefficients for seven important petroscape-related

LUCC measures, while table 4-5 displays all LUCC values.









European and US results

Results for Kendall's tau show that no variables exhibited significant correlation with

European participation, thus I fail to reject the null hypothesis that European company

participation is independent of the LUCC variables examined.

Six landscape measures showed 1.0 correlation for US participation, but only three had

statistically significant levels. In this case they were all related to the nonpetroscape, such as the

roads created by crop agriculture and pine plantations. Thus, I fail to reject the null hypothesis

that US-participation is independent of any of the important landscape-change measures

necessary to monitor direct E&P activities during the life-cycle of a given concession. Therefore,

attention must turn to local-company participation.

Local results

Local participation is strongly positively correlated with petroscape density, well density,

number of wells and river density (1.0, p < 0.01). The first two suggest that best practices are not

implemented in terms of minimizing seismic and future roads or not closing off unnecessary

roads resulting in further alterations to wildlife and ecosystems (Lee and Boutin, 2006;

Wilderness Society, 2006; Schneider et al., 2003). It can also indicate that drilling practices focus

on quantity of wells, rather than employing long lateral wells (> 1,500 ) that can reach oil sands

from far away thus increasing production while reducing well count (Gipson et al., 2002). Best

practices, however, apply to more than wells. As Kharaka and Dorsey (2005, pp. 61) note, the

cumulative ground-surface disturbances from oil E&P can be high, since these activities involve:

"site clearance, construction of roads, tank batteries, brine pits and pipelines, and other

modifications necessary for the drilling of exploration and production wells and construction of

production facilities."









The correlation between well density and Petrozuata may be related to being the first

concession to begin operations. It is reasonable that it has the most wells established. On the

other hand, given the coarse spatial resolution of 30 km and 20 km for the 2000 Landsat ETM+

and 2005 CBERS CC2 satellite images respectively, and the similar spectral profiles between dry

savanna vegetation and vegetation disturbed by E&P, it's very possible that the number of wells

identified in the satellite images is inaccurate for all four concessions. This in turn would

complicate comparisons and therefore it is recommendable to use imagery with higher spatial

resolution (Tang et al., 2007).

While not a key LUCC measure in this paper, the relationship with river density was also

strong. Petrozuata had the highest river density (number of linear river kilometers per

concession) at 1.14, followed closely by Sincor at 1.09 (though Sincor had twice the number of

river crossings). Because the sites selected for Petrozuata and Sincor are crossed by many rivers

and tributaries, the potential exists to create numerous hydrological disturbances. The Local

chose Petrozuata to begin HOB operations because it believed this site held the best potential for

drilling success (oil company representative 2005b), though newer technology such as horizontal

and directional drilling can reduce landscape disturbances (Gipson et al., 2002). Table 4-6 shows

important petro and nonpetroscape measures used in this paper.

Local participation was very weakly correlated with edge-effect petroscape (0.333,

p=0.602), despite the strong relationship to petroscape. As with core-areas this lack of strong

correlation contradicts the relationship that petroscape density has on edge-effect zones and

subsequent core areas. The lack of negative correlation to nonpetroscape density values is also

surprising. Though Petrozuata had a low nonpetroscape, Cerro Negro, which has the second









highest local-company participation had the highest nonpetroscape levels of all concessions (2.11

for 2000 and 2.22 for 2005). Presumably this would influence results, though it didn't.

Another unexpected finding was the lack of correlation to number of rivers crossed since

Petrozuata, at 25, had the second highest number (while Sincor had 52, Ameriven 1, and Cerro

Negro 0 in 2005).

Though Local participation was correlated with petroscape density and well density,

correlations to other important measures such as negative NDVI, core-area petroscape, edge-

effect petroscape and number of rivers crossed were weak or close to zero. Therefore I fail to

reject the null hypothesis that Local-company participation is independent of the important

changes in land-cover associated with petroscape expansion.

Conclusion

After examining the correlations between the three different consortia of oil companies

(Local, European and US) operating in the HOB and important LUCC measures for 2000 and

2005 I fail to reject the three hypotheses of statistical independence for all of them.

Only one, the Local company, with its largest interests in Petrozuata, exhibited statistically

significant associations with petroscape density and well density. These two measures represent

the amount of oil roads, wells and facilities digitized from satellite images at two time periods

(2000 and 20005). However, these two correlations in themselves were not enough to reject the

null hypothesis of statistical independence since additional significant correlations would have

been required given the small sample size. Higher resolution imagery would increase accuracy in

petroscape feature detection such as wells and wellpads, allowing for more accurate comparisons

across concessions.

Concerning Petrozuata, whether the relationship between Local participation and the

petroscape is attributed to a lack of government oversight requires more research and data. The









Local company is now a majority partner in the current HOB concessions and will legally remain

so during the establishment of 27 new concessions.

Finally, using the lowest and highest linear and density petroscape values can provide a

scenario of what future changes may look like as the HOB operations expand. Using 2005

petroscape values for Petrozuata and Ameriven reveals the following figures. If the 27 new

planned concessions are established over the next 12 years, each with an average concession size

of 671 km2-- which is larger than Ameriven at 665 km2 (PDVSA, 2008c), then the potential

petroscape density per concession could range from 0.18 to 0.50, a 36% difference. Regionally,

this translates into linear petroscape features of 3,230 km vs. 9,145 km, a sizeable difference,

which combined with regional development plans for the area above the Orinoco could

transform this entire region within a few years.




































Figure 4-1. Venezuela's heavy oil belt and the four current operations. From west to east they
are: Sincor, Petrozuata, Ameriven and Cerro Negro. The Jose upgrading complex,
located on the coast, processes the HOB crude to produce syncrude, or synthetic
crude.












90.00


80.00

70.00
-
8 60.00

50.00
0
0 40.00

30.00

2 20.00
(LQ" 10.00

0.00
Statoil BP Chevron Exxon Total Conoco PDVSA
02005 3.75 4.17 7.50 10.42 11.75 22.53 39.89
M2007 2.43 4.17 7.50 0.00 7.58 0.00 78.34

Company


Figure 4-2. Oil company participation in the HOB in 2005 and under the new changes
implemented in mid-2007.





































Figure 4-3. Potential E&P area within the heavy oil belt by 2030. New concessions will occupy
about 18,000 km2, or 33% of the HOB land area (Petroguia, 2006-2007).

Table 4-1. Venezuelan heavy oil belt strategic associations (concessions), company participation
by percentage and upgraded syncrude quality.
Concession Name Company Ownership Syncrude Production
and Upgraded Quality
Sincor PDVSA 38% 180,000 b/d
Total (France) 47% From 8.50 to 32 API
Statoil (Norway) 15%
Petrozuata PDVSA 49.9% 104,000 b/d
ConocoPhillips 50.1% From 9 to 19-250 API

Ameriven PDVSA 30% 190,000 b/d
Chevron 30% From 7-10 to 260 API
ConocoPhillips 40%
Cerro Negro PDVSA 41.67% 105,000 b/d
ExxonMobil 41.67% From 6-100 to 160
BP(United Kingdom) 16.66% API












Table 4-2. The R2 coefficients for seven petroscape-related land-cover change measures and the
three consortia of companies operating in the HOB. Some of the repeated categories
represent values for the years 2000 and 2005.
PDVSA European
Participation Participation US Participation
Negative NDVI (1990-2000) km2
0.765 0.147 0.388
Positive NDVI (1990-2000) km2
0.143 0.77 0.952
Petroscape Density 2000 0.758 0 0.069
Petroscape Density 2005 0.73 0.003 0.034
Core Area Petroscape 2000
0.905 0.017 0.156
Core Area Petroscape 2005
0.923 0.021 0.171
Edge Effect Petroscape 2000
0.428 0.35 0.595
Edge Effect Petroscape 2005
0.122 0.377 0.502
Well Density 2005 0.895 0.084 0.001
Number of Rivers Crossed 2005 0.054 0.613 0.712









Table 4-3. The R2 coefficients of determination between three types of company participation
and 16 LUCC measures in the HOB.
PDVSA European
Participation Participation US Participation
Negative NDVI (1990-2000)
km2 0.765 0.147 0.388
Positive NDVI (1990-2000)
km2 0.143 0.77 0.952
Petroscape Density 2000 0.758 0 0.069
Petroscape Density 2005 0.73 0.003 0.034
Nonpetroscape Density 2000
0.02 0.096 0.121
Nonpetroscape Density 2005
0.011 0.094 0.111
Allscape Density 2000 0.09 0.14 0.083
Allscape Density 2005 0.144 0.159 0.084
Concession Area Km2 0.83 0.016 0.017
Core Area Petroscape 2000
0.905 0.017 0.156
Core Area Petroscape 2005
0.923 0.021 0.171
Core Area Nonpetroscape
2000 0.011 0.662 0.703
Core Area Nonpetroscape
2005 0.018 0.657 0.711
Edge Effect Petroscape 2000
0.428 0.35 0.595
Edge Effect Petroscape 2005
0.122 0.377 0.502
Edge Effect Nonpetroscape
2000 0.37 0.309 0.523
Edge Effect Nonpetroscape
2005 0.352 0.328 0.541
Number of Wells 2005 0.978 0.005 0.043
Well Density 2005 0.895 0.084 0.001
Wells Edge-effect 2005 Km2
0.496 0.004 0.066
Wells Core Area 2005 Km2
0.91 0.013 0.024









Table 4-3. Continued.
Number of Rivers 2000 0.23 0.317 0.482
Number of Rivers 2005 0.054 0.613 0.712
River Density 0.251 0.142 0.265


Table 4-4. Kendall's tau-b correlation coefficients showing the relationship between Local,
European and US dominated concessions and seven important LUCC measures
related to petroscape expansion. Sig refers to significance level while ** means
correlations are significant at the 0.01 level (2-tailed).

PDVSA European US
Participation Participation Participation
Negative NDVI Correlation
-1.000 0.000 0.333
(1990-2000) km2 Coefficient
N =3 Sig. (2-tailed) 0.117 1.000 0.602
Positive NDVI Correlation
0.333 0.816 -1.000
(1990-2000) km2 Coefficient
N=3 Sig. (2-tailed) 0.602 0.221 0.117
Petroscape Density Correlation 1.000(**) 0.000 -0.333
2000 and 2005 Coefficient
Sig. (2-tailed) 1.000 0.602
Core Area Petroscape Correlation
-1.000 0.000 0.333
Km2 2000 and 2005 Coefficient
Sig. (2-tailed) 0.117 1.000 0.602
Edge Effect Petroscape Correlation 0 1 1
0.333 0.816 -1.000
Km2 2000 and 2005 Coefficient
Sig. (2-tailed) 0.602 0.221 0.117
Well Density 2005 Correlation
1.000(**) 0.000 -0.333
Coefficient
Sig. (2-tailed) 1.000 0.602
Number of Rivers Correlation
Crossed 2000 and Coefficient 0.333 0.816 -1.000
2005
Sig. (2-tailed) 0.602 0.221 0.117









Table 4-5. Kendall's tau-b correlation coefficients and R2 coefficients showing the relationship
between concessions that were PDVSA-dominated, European-dominated, and US-
dominated and important landscape-change measures. Sig refers to significance level
while ** means correlations are significant at the 0.01 level (2-tailed).

PDVSA European US
Participation Participation Participation
PDVSA Participation Correlation
Coefficient
Sig. (2-tailed) .1.000 0.602
R2 0.015 0.026
European Participation Correlation
0.000 1.000 -0.816
Coefficient
Sig. (2-tailed) 1.000 0.221
R2 0.015 0.923
US Participation Correlation -
-0.333 -0.816 1.000
Coefficient
Sig. (2-tailed) 0.602 0.221
R2 0.026 0.923
Negative NDVI Correlation
-1.000 0.000 0.333
(1990-2000) km2 Coefficient
N =3 Sig. (2-tailed) 0.117 1.000 0.602
R2 .765 .147 .388
Positive NDVI Correlation
0.333 0.816 -1.000
(1990-2000) km2 Coefficient
N=3 Sig. (2-tailed) 0.602 0.221 0.117
R2 0.143 0.770 0.952
Petroscape Density Correlation .000(**) 0.000 -0.333
2000 and 2005 Coefficient
Sig. (2-tailed) .1.000 0.602
2000 R2 0.758 0.000 0.069
2005 R2 0.730 0.003 0.034
Nonpetroscape Density Correlation
-0.333 -0.816 1.000(**)
2000 and 2005 Coefficient
Sig. (2-tailed) 0.602 0.221
2000 R2 0.02 0.096 0.121
2005 R2 0.011 0.094 0.111
Allscape Density 2000 Correlation 0.333 -
0.333 -0.816 0.333
and 2005 Coefficient
Sig. (2-tailed) 0.602 0.221 0.602
2000 R2 0.09 0.140 0.083
2005 R2 0.144 0.159 0.084









Table 4-5. Continued.
Concession Area km2 Correlation -
-1.000 0.000 0.333
Coefficient
Sig. (2-tailed) 0.117 1.000 0.602
R2 0.830 0.016 0.017
Core Area Petroscape Correlation
-1.000 0.000 0.333
Km2 2000 and 2005 Coefficient
Sig. (2-tailed) 0.117 1.000 0.602
2000 R2 0.905 0.017 0.156
2005 R2 0.923 0.021 0.171
Core Area Correlation
Core Area correlation -0.333 -0.816 1.000(**)
Nonpetroscape Km2 Coefficient
2000 and 2005 Sig. (2-tailed) 0.602 0.221
2000 R2 0.011 0.662 0.703
2005 R2 0.018 0.657 0.711
Edge Effect Petroscape Correlation
0.333 0.816 -1.000
Km2 2000 and 2005 Coefficient
Sig. (2-tailed) 0.602 0.221 0.117
2000 R2 0.428 0.350 0.595
2005 R2 0.122 0.377 0.502
Edge Effect Correlation 0.333 -0.816
-0.333 -0.816 1.000(**)
Nonpetroscape Km2 Coefficient
2000 and 2005 Sig. (2-tailed) 0.602 0.221
2000 R2 0.370 0.309 0.523
2005 R2 0.352 0.328 0.541
Number of Wells 2005 Correlation
Coefficient 1.000(**) 0.000 -0.333
Coefficient
Sig. (2-tailed) .1.000 0.602
R2 0.978 0.005 0.043
Well Density 2005 Correlation
Coefficient 1.000(**) 0.000 -0.333
Coefficient
Sig. (2-tailed) .1.000 0.602
R2 0.895 0.084 0.001
Wells Edge-effect 2005 Correlation
KmZ Coefficient 1.000(**) 0.000 -0.333
Km2 Coefficient
Sig. (2-tailed) .1.000 0.602
R2 0.496 0.004 0.066
Wells Core Area 2005 Correlation
-1.000 0.000 0.333
Km2 Coefficient
Sig. (2-tailed) 0.117 1.000 0.602
R2 0.910 0.013 0.024
Number of Rivers Correlation
0.333 0.816 -1.000
Crossed 2000 and 2005 Coefficient
_Sig. (2-tailed) 0.602 0.221 0.117









Table 4-5. Continued.
2000 R2 0.23 0.317 0.482
2005 R2 0.054 0.613 0.712
River Density Correlation000(** 0000 -0
1.000(**) 0.000 -0.333
Coefficient
Sig. (2-tailed) 1.000 0.602
R2 0.251 0.142 0.265









Table 4-6. Petro and nonpetroscape data values for the four HOB concessions.
Concession Name Sincor Petrozuata Ameriven Cerro Negro

Concession Area (area km2) 517.38 300.77 665.44 303.15
PDVSA Participation (%) 38.00 49.90 30.00 41.67

European Participation (%) 62.00 0.00 0.00 16.66
US Participation (%) 0.00 50.10 70.00 41.67

Negative NDVI 1990-2000 (km2) 77.55 39.00 312.55 0.00

Positive NDVI 1990-2000 (km2) 41.36 21.00 2.19 0.00

Petroscape Linear 2000 (km) 243.07 276.79 19.85 61.51

Nonpetroscape Linear 2000 (km) 0.00 39.19 523.69 578.19

Petroscape Density 2000 (km/km2) 0.47 0.92 0.03 0.20

Nonpetroscape Density 2000 km/km2) 0.00 0.13 0.79 1.91

Allscape Density 2000 (area km2) 0.47 1.05 0.82 2.11

Edge-effect Petroscape 2000 (km2) 217.87 193.60 26.04 96.58


Edge-effect Nonpetroscape 2000 (km2) 0.00 37.41 351.33 274.00

Core-area Petroscape 2000 (km2) 299.50 107.17 639.40 206.57

Core-area Nonpetroscape 2000 (km2) 0.00 263.36 314.11 29.15

Rivers Crossed 2000 (number) 22.00 18.00 0.00 0.00

Petroscape Linear 2005 (km) 302.33 338.70 119.63 90.56

Nonpetroscape Linear 2005 (km) 0.00 47.39 485.71 580.97

Petroscape Density 2005 (km/km2) 0.58 1.13 0.18 0.30

Nonpetroscape Density 2005 (km/km2) 0.00 0.16 0.73 1.91

Allscape Density 2005 (km/km2) 0.59 1.28 0.91 2.22

Edge-effect Petroscape 2005 (km2) 271.57 222.49 117.07 96.27


Edge-effect Nonpetroscape 2005 (km2) 0.00 45.61 348.32 274.00









Table 4-6. Continued.

Edge-effect Allscape 2005 (km2) 271.60 268.00 468.40 370.27

Core-area Petroscape 2005 (km2) 245.80 78.29 548.37 206.88

Core-area Nonpetroscape 2005 (km2) 0.00 255.17 317.11 29.15

Rivers Crossed 2005 (number) 52.00 25.00 1.00 0.00

Wells and Wellpads 2005 (number) 142.00 195.00 98.00 168.00

Well Density 2005 (wells/concession) 0.27 0.65 0.15 0.55

Well Edge-effect 2005 (km2) 111.15 134.25 86.05 81.46

Wells Core-areas 2005 (km2) 406.22 166.52 579.38 221.68
Rivers Length (km) 561.40 342.74 185.01 26.88

Riverscape Density (rivers/concession) 1.09 1.14 0.28 0.09









CHAPTER 5
LAND-COVER CHANGE AND NONRENEWABLE NATURAL RESOURCES

Summary

Land-use land-cover change involves the complex interaction of social and ecological

variables across various spatial and temporal scales. This work has explored human-environment

interactions in the context of natural resource extraction, since the use of these resources for local

needs or to meet external market demands creates different and important types of landscape

alterations. These perturbations can create short-lived or nonpermanent changes, known as

modifications, or they can lead to new land-cover classes known as conversion (Veldkamp and

Lambin, 2001; Lambin et al., 2003; Southworth, 2004; Daniels et al., 2008). Both of these results

imply a loss of land cover. However, not all natural resource use leads to land-cover loss. Forest

plots, logging sites and grazing pastures may revert back to secondary forest once these activities

cease (Kupfer et al., 2004, 510; Fearnside et al., 2007, 678). This transition may be fostered by

land tenure; policy changes and implementation; environmental feedbacks and climate change;

different market demands and varying household makeup. The planting of forests also leads to

land-cover gain, though tree plantations do not provide the same ecosystem services as do

natural forests (Parrotta and Knowles, 2001, 220).

Nonrenewable natural resource extraction, on the other hand, tends to create permanent

land-cover changes because of the nature of mining and drilling, the establishment of permanent

infrastructure needed for operations, as well as the waste sites required for the large volumes of

saline water produced (Kharaka and Dorsey, 2005, 61). Furthermore, once viable extractivist

activities take place, they tend to attract new settlers to the areas of direct interaction. These

settlers create additional LUCCs through the establishment of homes and agricultural plots; new

roads that create access to area resources, thus expanding LUCC; and the growth of service









industries that can eventually convert a small settlement into a town or city (Schmink and Wood,

1992, Consiglio et al., 2006).

Whereas agricultural activity in the tropics converts a great deal of forested land every year

for the establishment of soybean farms and cattle ranches or small-scale agricultural plots (FAO,

2006, Lambin et al., 2003; Wassenaar et al., 2007), logging and fires also contribute to

significant land conversion (Nepstad et al., 2001; Siegert and Hoffmann, 2000; Hammond et al.,

2007). Gold mining, on the other hand, tends to be pursued at the small-scale level (individuals,

or groups of five) (Peterson and Heemskerk, 2001) and thus creates smaller pockets change

though cumulatively they can create widespread degradation (Hilson, 2002). While large-scale

gold mining involves digging large open pits and establishing large tailing ponds that hold

contaminated waste material, small-scale mining can involve spray-washing land and riverine

deposits with motorized equipment that removes the vegetation, roots, soil and seeds, thus

degrading the landscape considerably and delaying regeneration for up to 20 years or more

(Almeida-Filho and Shimabukuro, 2002; Peterson and Heemskerk, 2001). On top of this,

mercury (and cyanide-in the large operations) is used to help recover gold from the extracted

soil and rocks. Thereafter it is released into the water, where it can convert to toxic

methylmercury. It can also contaminate miners and (gold) business owners through skin

contamination and inhalation of fumes when the mercury is burned away from the gold both in

the field and in gold processing shops (Veiga and Hinton, 2002, 19; Bastos et al., 2004; Mol et

al., 2001). The simple technology needed and the potential for monetary earnings, as compared

to other options in many developing countries, leads to thousands implementing these practices

in a single site (Hilson, 2002). Worldwide around six million miners operate in more than 40









countries (Veiga et al., 2006) and it's a central driver of deforestation in Surimane (Heemskerk,

2001).

Oil exploration and production can be the most lucrative of all these activities, and given

the necessary knowledge, technology and capital, this activity is the purview of state

governments and multinational oil companies (MNOCs). Different management practices during

the exploration and production phase can create greater or lesser LUCCs which can result in

modifications or conversions lasting 35 years or more. State-owned oil companies, who often

don't compete abroad, may not have the culture and history of including environmental

performance as part of their operations, while for MNOCs this is increasingly an important

aspect of day-to-day business (Diamond, 2005; Moser 2001; Consiglio et al., 2006).

Negative press coverage, pressure from employees and customers, complaints from area

residents affected by operations; the threat of legal action; the desire to be perceived as attractive

to stock market investors and customers; and the competition for dwindling opportunities can

help explain the importance of sustainability to MNOCs (Lawrence, 2007; Orlitzky et al., 2003;

Morhardt, 2002). Even so, the "cumulative impacts from these operations are high, because a

total of about 3.5 million oil and gas wells have been drilled to date in the United States, but

currently, only about 900,000 are in production (Kharaka and Dorsey, 2005, pp. 61-62).

Worldwide we would expect to see similar ratios.

In Venezuela, plans to greatly expand exploration and production of the vast deposits in

the heavy oil belt will potentially affect 18,000 km2 of dry tropical savanna/forest. It is important

the government and the oil companies involved create baseline studies of the concession areas

prior to exploration. The innovative methods presented in this study integrate the use of GIS and

remote sensing techniques to measure landscape disturbances related to petroleum exploration









and production. This approach is particularly useful for monitoring and measuring current

landscape patterns and subsequent changes (Janks and Prelat, 1994; Griffith et al., 2002a),

though in personal conversations with industry representatives it appears that this methodology is

not widely used for these purposes. It is also important that environmental impact assessment

(EIA) studies be implemented throughout the life of the project, not just as a precondition to

begin drilling (Sebastiani et al., 2001). Finally, the implementation of best business practices,

such as minimizing the size of seismic lines and the equipment used to create them; closing and

recuperating routes that are no longer needed; implement long horizontal drilling; partner with

area industries to share roads and infrastructure; reduce the number of river crossings; and be

transparent in environmental reporting are important ways for oil companies to reduce

environmental alterations and become more sustainable (Musinsky et al., 1998; Schneider et al.,

2003; E&P Forum/UNEP, 1997; Morhardt, 2002; Gipson et al., 2002; Stein et al., 2002;

Musinsky et al., 1998).

One commonality among all four types of resources is accessibility. In order to access

these resources routes and trails must be built. Thus, roads are the catalyst that opens up new

landscapes for local and migrant land managers (Perz et al., 2007; Musinsky et al., 1998; Forman

et al., 2002; E&P Forum/UNEP, 1997; Consiglio et al., 2003; Soares-Filho et al., 2004). Once

the roads are created to access a particular resource (trees, gold, oil), then settlers move in and

establish homes, agricultural plots and cattle ranches; previously inaccessible forests are now

open to loggers and miners; and LUCC speeds up.

Future Work

As Morton et al. (2004, pp. 8) observe, "there are few studies that examine the exact size

and extent of the ecological footprint of energy development. Spatial analysis can help fill this

information gap." Concerning petroleum E&P in the heavy oil belt, using additional satellite









images captured at different times of the year, such as the wet season, can help distinguish

landscape disturbances from dry tropical savanna. Field surveys of oil roads will permit seismic

and other roads to be more easily identified, which in turn can direct reclamation efforts and the

closing of nonessential or redundant roads (Wilderness Society, 2006; Musinsky et al., 1998; Lee

and Boutin, 2006). This will likely reduce habitat fragmentation, as well as the building of new

roads which increases access to land and resources and can lead to the building of settlements.

Other factors to consider include identifying rivers or tributaries that do not need to be crossed

and therefore avoid interruptions to flow patterns and their connectivity to floodplains (Stein et

al., 2002, 1); distinguishing between main-channel and floodplain habitats, and the "presence of

migratory barriers (e.g., waterfalls) that create predator-free environments" (Wantzen et al.,

2006, pp. 63).

Additional future work should consider other remote sensing techniques to identify (urban)

infrastructure features such as fuzzy-spectral mixture analysis (Tang et al., 2007), the normalized

difference built-up index (NDBI) used to monitor growth and distribution of urban built-up

areas, as well as built-up area calculation which involves subtracting the NDVI from the NDBI

(Jensen 2005). Almeida-Fihlo and Shimabukuro (2000) used radar images (JERS-1 SAR) to

detect areas disturbed by gold mining in northern Brazil. This method might prove useful for

studying petroscapes, particularly since many are located in tropical countries where dense cloud

cover on satellite imagery is often a problem.

Wildlife studies, for which several exist in Canada's heavy oil sands, could help determine

more precise avoidance zones toward petroscape features by of local fauna such as the caiman,

tapir, Capuchin monkeys, capybara, bats, deer, manatee, reptiles such as the green anaconda,

parrots, amphibians and many fish (Perez, 2000; Gorzula, 1978; Marin et al., 2007; L6pez-









Fernandez and Winemiller, 2000; Veneklaas et al., 2005; Rosales et al., 1999; Padilla and

Dowler, 1994) This is turn could be considered when establishing new infrastructures,

particularly in areas containing endangered species.

Therefore, adapting spatial technologies to the environmental impact assessment (EIA)

plan is a useful and cost-effective way of monitoring E&P-driven changes throughout the life-

cycle of oil operations, as well as aiding in policy formulation and decision making (Sebastiani et

al., 2001; Satapathy et al., 2008; Griffith et al., 2002a). While geospatial technologies may be

used in finding oil, drilling and production scenarios (Yatabe and Fabbri, 1986; Janks and Prelat,

1994, 1995; Almeida-Filho, 2002; Almeida-Filho et al., 2002; Zhang et al., 2007) these

technologies do not appear to be widely used to monitor "Above Ground Review" of oil-related

landscape changes (Consiglio et al., 2006, pp. 2). Some exceptions include Chust and

Sagarminaga (2007) who used data gathered from the Multi-angle Imaging SpectroRadiometer

(MISR) to detect oil spills in Venezuela's Lake Maracaibo; Kwarteng (1998, 1999) who detected

contaminated vegetation surrounding oil lakes in Kuwait using data from Landsat TM, Indian

Remote Sensing Satellite (IRS) 1-D, and Spot satellite; and Ud-Din et al. (2008) who used

Landsat thermal data, (band 6) to map hydrocarbon polluted sites in Kuwait. These efforts show

a small but growing effort to use geospatial technologies to monitor petroleum related spills.

However, they do not focus on the establishment and extension of petroscape among regional

sites as a way of reducing landscape alterations.

On the other hand, work by Musinsky et al. (1998) correlated deforestation rates in a

Guatemalan national park with the development of an oil road. Thomson et al. (2005), Morton et

al. (2002); Morton et al. (2004); and the Wilderness Society (2006) embrace the use of GIS and

remote sensing techniques to monitor how E&P operations affect LUCC and use this information









to propose best business practices. Meanwhile, Schneider and Dyer (2006) do the same in

Canada's Alberta oil sands. My project marks a first step to perform similar work in Venezuela,

especially in the important heavy oil belt region where alterations to regional ecosystems

stemming from heavy oil E&P will have important human-environment implications at the local

and regional scale, which in turn contributes to global environmental change.









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BIOGRAPHICAL SKETCH

Chris W. Baynard was born in Maryland in 1966. Within a month his family and he moved

to Spain where they lived in Madrid for 9 years. Thereafter they moved back to the US and

settled in Charleston, SC. There, Chris attended the College of Charleston, earning a B.S. in

psychology and minoring in Spanish and communications. Afterward, he earned his M.A. in

Spanish from Louisiana State University (LSU) in Baton Rouge, and a minor concentration in

geography and anthropology. At LSU he met his soon-to-be wife Ana, from Venezuela, and they

were married in Baton Rouge and in Venezuela. After completing his M.A. Chris entered

academics as a Spanish instructor first at LSU, then at the University of North Florida, in

Jacksonville, FL. Convinced he wanted to continue with an academic career, Chris began his

doctoral program in geography at the University of Florida (UF), studying part-time while

working full-time and living in Jacksonville. At UF his research interests were land-use and

land-cover change related to nonrenewable natural resources, environmental business practices,

Latin America, and sustainable development in frontier regions.

Following completion of his PhD Chris will be joining the Department of Economics and

Geography at the University of North Florida as an assistant professor. Chris has been married to

Ana Baynard for 11 years and they have two children, Nico and Isabela.





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1 ECOLOGICAL FOOTPRINT OF ENERGY DE VELOPMENT IN EASTERN VENEZUELAS HEAVY OIL BELT By CHRIS W. BAYNARD A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLOR IDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2008

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2 2008 Chris W. Baynard

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3 To my wife Ana for her infinite patience, suppor t and encouragement during this long process; to my children, Nico and Isabela, who will be glad I am no longer a student; and to my parents Sylvia and Whaley for their help and encouragement

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4 ACKNOWLEDGMENTS I thank m y wife, Ana; my children; and my parents for their support and patience. Jorge Febles, Gary Harmon, Allen Tilley, Mark Workma n and the Department of World Languages at the University of North Florida gave me the flexib ility to pursue my studies at the University of Florida while working there. I th ank Dr. Michael W. Binford at th e University of Floridas Dept of Geography for help in overseeing this project I also thank Drs. Ja ne Southworth, Tim Fik, Julie Silva, Eric Keys, Grenville Barnes and Terry McCoy at the University of Florida for help in data acquisition, analysis, ideas and support. Dr. David Lambert and Robert Richardson at the University of North Florida offered help and suggestions with the GIS and remote sensing components of this project. Dan Richard at the University of North Florida and Dr George Casella at the University of Florida helped with statistical analysis. Matt Marsik, Ph.D. Candidate at the University of Floridas Dept of Geography also helped with remote sensing data analysis, while Andrs Guhl proved to be a very helpful and congenial colleague. I also thank Carlos Guerra and Rmulo Medi na, at Operadora Cerro Negro in Venezuela for their help in better understanding the dynami cs of heavy oil product ion and for logistical support. Mara Madalena Godoy Mello at Brazil s National Institute for Space Research (INPE) provided CBERS 2 satellite images. Represen tatives from ExxonMobil Venezuela, PDVSA, Chevron Venezuela, Total, Shell Venezuela, BP Venezuela, ConocoPhillips USA, and Marathon met and/or spoke with me about heavy oil ope rations in eastern Venezuela, provided gray literature, reports and maps, and insights into co rporate social responsib ility practices in the petroleum industry. ExxonMobil Venezuela also provided logistical s upport during fieldwork. Thanks also to Alicia Moreau, Deud Dumith and Jos Acosta at the Instituto Geogrfico de Venezuela Simn Bolvar for their support and map data. Vctor and Rosella Prez and Dr. Elisa Prez provided housing and logistical support. Finally, thanks to Tom Knode, Halliburton

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5 Energy Services, and Mark Shemaria, Tidelands Oil Production Company, who served as SPE E&P Environmental Safety Conference 2007 organi zers and encouraged me to present my research to the petroleum industry.

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6 TABLE OF CONTENTS page ACKNOWLEDGMENTS...............................................................................................................4 LIST OF TABLES................................................................................................................. ..........9 LIST OF FIGURES.......................................................................................................................10 LIST OF ABBREVIATIONS........................................................................................................ 12 ABSTRACT...................................................................................................................................13 CHAP TER 1 LAND-COVER CHANGE AND NATURAL RESOURCE USE........................................ 15 Introduction................................................................................................................... ..........15 Natural Resource Use, Land-use Land-Cover Change and Political Ecology ....................... 16 Renewable and Nonrenwabl e Resource Extraction ................................................................ 18 Heavy Oil Production in Eastern Venezuela.......................................................................... 19 Environmental Performance Variab ility in the Heavy Oil Belt .............................................. 20 2 LAND-USE AND LAND-COVER CHANGE RESULTING FROM RENEWABLE AND NONRENE WABLE RESOURCE EXTRACTION: A REVIEW............................... 22 Introduction................................................................................................................... ..........22 Background......................................................................................................................24 LUCC and Political Ecology (T heoretical F ramework)..................................................24 Land-Cover Modification and Conversion...................................................................... 27 Specific Issues: Renewable Natural Resources...................................................................... 30 Agriculture.......................................................................................................................30 Logging............................................................................................................................33 Type of logging........................................................................................................33 Fire...........................................................................................................................34 Other factors.............................................................................................................36 Specific Issues: Nonrenewab le Natural Resources .................................................................37 Gold Mining....................................................................................................................37 Land degradation......................................................................................................37 Regeneration.............................................................................................................39 Mercury pollution..................................................................................................... 40 Solutions...................................................................................................................41 Oil Exploration and Production....................................................................................... 42 Wildlife: caribou......................................................................................................42 Seismic lines.............................................................................................................44 Roads...............................................................................................................................44 Conclusion..............................................................................................................................46

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7 3 DO NATIONAL OIL COMPANIES PR ODUCE GREATER ENVIRONMENTAL ALTERATI ONS THAN MULTINATIONAL OI L COMPANIES? THE CASE OF VENEZUELAS HEAVY OIL BELT................................................................................... 49 Remote Sensing, Geographic Information System s (GIS) and Petroleum............................. 52 Materials and Methods...........................................................................................................54 Site Description...............................................................................................................54 Data Description (Concessi ons and Control Groups) ..................................................... 55 Time Frame.....................................................................................................................56 Image Processing and GIS Procedures............................................................................ 58 Petroenergy Maps............................................................................................................ 59 Control Areas...................................................................................................................60 Nor malized Difference Vegetation Index ( NDVI)..........................................................60 Change-Detection: 1990 to 2000.....................................................................................60 GIS Techniques: the 2005 Petroscape.............................................................................61 Petroscape density.................................................................................................... 62 Edge-effect zones.....................................................................................................63 Core-areas.................................................................................................................64 Agriculture...............................................................................................................64 Rainfall Patterns..............................................................................................................64 Results and Discussion......................................................................................................... ..65 Vegetation Change.......................................................................................................... 65 Petroscape........................................................................................................................66 Nonpetroscape.................................................................................................................68 Petro and Nonpetroscape.................................................................................................69 Conclusion..............................................................................................................................71 4 SPATIAL ANALYSIS AND STATIS TICAL C ORRELATIONS AMONG PETROSCAPE FEATURES IN THE HEAVY OIL BELT.................................................. 95 Introduction................................................................................................................... ..........95 Production in the Heavy Oil Belt.................................................................................... 97 Land-Use Land-Cover Change........................................................................................ 98 Expected Findings...........................................................................................................99 Methods................................................................................................................................103 LUCC Related to the Petro and Nonpetroscape............................................................ 104 The Coefficient of Determination................................................................................. 105 Nonparametric Statistical Analysis............................................................................... 106 Results and Discussion......................................................................................................... 108 Determining the R Coefficient..................................................................................... 108 Nonparametric Statistical Analysis............................................................................... 108 European and US results........................................................................................ 109 Local results...........................................................................................................109 Conclusion............................................................................................................................111

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8 5 LAND-COVER CHANGE AND NONREN E WABLE NATURAL RESOURCES.......... 124 Summary...............................................................................................................................124 Future Work..........................................................................................................................127 LIST OF REFERENCES.............................................................................................................131 BIOGRAPHICAL SKETCH.......................................................................................................156

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9 LIST OF TABLES Table page 3-1 Venezuelan heavy oil belt strategic associations, com pa ny ownership by percentage, syncrude (synthetic oil) pr oduction and upgraded quality 2005........................................ 93 3-2 Change-detection measures showing am ounts of positiv e and negative change per site in km and as a percentage of the site area.................................................................. 93 3-3 2005 petroscape measures for the six study sites............................................................... 94 3-4 2005 nonpetroscape measures for the six study sites......................................................... 94 3-5 Overall rankings for land -cover change and petroscape features in the 6 sites. ................94 4-1 Venezuelan heavy oil belt strategi c associations (concessions), com pany participation by percentage a nd upgraded syncrude quality............................................115 4-2 The R coefficients for seven petroscape-related land-c over change m easures and the three consortia of companies operating in the HOB........................................................ 116 4-3 The R coefficients of de termination between three t ypes of company participation and 16 LUCC measures in the HOB................................................................................ 117 4-4 Kendalls tau-b correlation coefficients showing the relationship between Local, European and US dom inated concessions and seven important LUCC measures related to petroscape expansion.......................................................................................118 4-5 Kendalls tau-b correlation coefficients a nd R coefficients showing the relationship between concessions that were PDVSAdom inated, European-dominated, and USdominated and important landscape-change measures.................................................... 119 4-6 Petro and nonpetroscape data valu es for the four HOB concessions............................... 122

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10 LIST OF FIGURES Figure page 3-1 Venezuela, the heavy oil belt and concessions, and surrounding countries 2005. ............73 3-2 Venezuelas heavy oil belt, its four c oncessions: Sincor, Petrozuata, Am eriven, and Cerro Negro, and the upgrading/refinery plant called Jose located on the coast of Anzotegui state, 2005....................................................................................................... 74 3-3 Vegetation-cover change across all six si tes (three concessions and three controls) between 1990 and 2000.. ...................................................................................................75 3-4 Increases in Normalized Difference Ve getation Index (NDVI) across all six sites between 1990 and 2000. ....................................................................................................76 3-5 NDVI losses and gains for all six sites between 1990 and 2000....................................... 77 3-6 Gains and losses of the Normalized Difference Vegetation Index (NDVI) for the three concessions .............................................................................................................. .78 3-7 Rainfall amounts (in mm) for Ciudad Bolvar for the October, November and Decem ber dry months preceding the 1990 and 2000 Landsat images used in this study...................................................................................................................................81 3-8 Decrease in NDVI between 1990 and 2000 overlain by 2005 petroscape features.. ......... 82 3-9 Edge-effect zones, or areas where significant ecological effects extend. ..........................83 3-10 Core-areas show size and distribution of the patches of land that rem ain after dissolving the edge-effect zones in figure 3-7...................................................................84 3-11 Linear nonpetroscape 2005................................................................................................85 3-12 Edge-effect nonpetroscape zones....................................................................................... 86 3-13 Core-areas that remain after nonpetroscape features are dissolved from each site........... 87 3-14 Petro and nonpetroscape s across all 6 sites........................................................................ 88 3-15 Agricultural patterns in the three concessions and the three control groups in 1990.. ...... 89 3-16 Agricultural patterns in the three concessions and the three control groups in 2000.. ...... 90 3-17 Amount of agricultural land (in km) found in the three concessions and three control groups in 1990 and 2000. ...................................................................................................91 3-18 Future heavy oil development in Venezuelas H eavy Oil Belt.......................................... 92

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11 4-1 Venezuelas heavy oil belt a nd the four current operations. ............................................ 113 4-2 Oil company participation in the HOB in 2005 and under the new changes im plemented in mid-2007................................................................................................ 114 4-3 Potential E&P area within the heavy oil belt by 2030.. ................................................... 115

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12 LIST OF ABBREVIATIONS BP British Petroleum CVG Corporacin Venezolana de Guayana E&P Exploration and production: two key activities of the petroleum industry FVI Fuel value index, a measure of the p hysical properties of wood as a fuel source GIS Geographic information systems GLCF Global Land Cover Facility, Univ ersity of Maryland satellite image repository GPS Global positioning system HOB Heavy oil belt, the zone of hea vy oil located in ce ntral and eastern Venezuela. INPE Instituto Nacional de Pesquisas Espaciais, Brazils National Institute for Space Research LUCC Land-use and land-cover change M&E Management and Excellence, a sustainability ranking firm MISR Multi-angle Imaging SpectroRadiometer MNOC Multinational oil company NDVI Normalized difference vegetation index NOC National oil company PDVSA Petrleos de Venezuela, the state-owned oil company PSI Pacific sustainability index

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13 Abstract of Dissertation Pres ented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy ECOLOGICAL FOOTPRINT OF ENERGY DE VELOPMENT IN EASTERN VENEZUELAS HEAVY OIL BELT By Chris W. Baynard August 2008 Chair: Michael W. Binford Major: Geography Nearly all land-use and land-cover change (L UCC) studies concern conversions of land related to two renewable res ource activities, agriculture and forestry. LUCC related to nonrenewable resource development is nonetheless another major agent of environmental change that has been studied very little. This work examines specific land-us e and land-cover changes resulting from the production patterns of four petroleum ope rations between 1990 and 2005 in eastern Venezuelas heavy oil belt, an area contai ning some of the largest worldwide deposits of heavy crude oil which only recently have been developed. The four concessions consiste d of the following consortia of public-private partnerships: Sincor (PDVSA, Total, and Statoil); Petroz uata (PDVSA and ConocoPhillips); Ameriven (PDVSA, ConocoPhillips and Chevron); and Ce rro Negro (PDVSA, ExxonMobil, and British Petroleum, or BP). Since mid-2007 the Venezuelan government has increased its role in these operations and plans to expand production over the next two decades by establishing 27 new concessions mostly with other state-owned oil companies. These activit ies will affect 18,000 km of dry tropical savannas and forest, a natu ral region that is already threatened. This work incorporates remote sensing a nd GIS techniques to cal culate indicators of environmental alteration. The objective is to de termine which operations produced the most and

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14 least landscape disturbances ba sed on six important landscape measures: amount of vegetationcover loss (based on a vegetation index); petros cape density; edge-eff ect zones; core areas; number of rivers crossed; and well density. It also measures nonpetroscape features related to agriculture and transportation. This work also us es statistical correlation analysis to determine the relationship between observed LUCC and th e type of public-private partnership. Findings show that Petrozuata had the highest overall rank, meaning it had the highest level of landscape disturbances. It was fo llowed by Sincor, Cerro Negro and Ameriven. Statistical results, however do not show sufficient correlations between the type of company mix and petroscape-related alterati ons. These findings contribute to emerging research using geospatial technologies to monitor LUCC in h eavy oil production zones and can be used to assess environmental performance of petroleu m operations, map evolving landscape changes in current operations and plan for the establishment of new concessions.

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15 CHAPTER 1 LAND-COVER CHANGE AND NATURAL RESOURCE USE Introduction This Alterations to land cover affect central com ponents of ea rth system functions (Fujita and Fox, 2005; Lambin et al., 2003; Read and La m, 2002, Southworth, 2004; Soares-Filho et al., 2004) and these changes are proceeding at a pace magnitude and scale unprecedented in human history (Jensen 2005). Understandin g the factors that contribute to changes in land-cover and land-use (LUCC) practices are of prime importa nce since these changes influence climate, ecosystem goods and services, gross carbon fluxes a nd the vulnerability of people and places at various spatial and temporal scales (Lambin et al., 2003; Fujita and Fox, 2005; Southworth, 2004; Asner et al., 2005; Rindfuss et al., 2007; Keys and McConnell, 2005). Land-change science is a central component of geography since (in part) it examines spatial patterns of human-environment interactio ns on the landscape. This complex system of interactions (Veldkamp and Lambin, 2001) i nvolves political, economic, cultural, social, technological, institutional and environmental variables operating under various spatial and social scales and combines natural and social science methods (Folke et al., 2007). As most LUCC studies demonstrate, understanding socialecological system dynamics involves teams of researchers comprised of specialis ts in given disciplines (see: Lambin et al., 2001; Rindfuss et al., 2007; Wassenaar et al ., 2007; Gibbons et al., 2008; Soares -Filho et al., 2006; Caldas et al., 2007, Pielke et al., 2007; Folke et al., 2007). The complexity of understanding social-ecological system inte grations (Folke et al., 2007), creates two main problems. One, findings from a particular study tend to be case-specific; not applicable to a wide range of situations and settings (Rindfuss et al., 2007; Keys and McConnell, 2005; Veldkamp and Lambin, 2001). Two, it is difficu lt to reconcile natura l and social science

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16 systems and methods, particularly across spa tial scales (Veldkamp and Lambin, 2001)what Folke et al. (2007) refer to as the problem of fit. Thus land-use researchers often confine th emselves to either a single process (e.g. deforestation), or a single di scipline (e.g. economic models) (Veldkamp and Lambin, 2001, pp. 3). Since this project is the product of one researcher, I too confine myself to a single process: land-cover change resulting from nonrenewable natural resource use in a dry-tropical forest zone in eastern Venezuela. At the same time my field, geography, wh ich itself is cr oss-disciplinary, justifies that I draw here from land-use land cover-change science, political ecology approaches and sustainability research. Natural Resource Use, Land-use Land-Cover Change and Political Ecology My interest lies not only in understanding what types of landscape changes result from natural reso urce use, since they also lead to social alterations and subsequent land management responses. I am also intrigued by the concept of sustainabilitythat is the interplay between ecological, economic and socio-cultural dimensions taking place at various spatial and temporal scales as well as organizational and institutional levels (Folke et al., 2007; Ellsworth, 2002). This interplay affects the chances that environmenta l and social conditions will indefinitely support human security, well-being, and health (McMichael et al., 2003, 1919). Obviously some practices and in stitutions are more sustainable than others; that is some natural resource use patterns (shaped by the natu re of the resource itself, topography and the rules governing them) create larger and more permanent changes on the landscape that in turn cause important and long-term alterations such as habitat fragmentation; land degradation; land, air and water pollution; soil eros ion, increased sedimentation and biodiversity loss (Armenteras, et al., 2006; Ferraz et al., 2006; Schneider and Dyer, 2006; McMich ael et al., 2003; Dietz et al., 2003; Cumming and Barnes, 2007). These shifts can lead to regional and global climate changes,

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17 thus affecting earth system func tions and the organisms that de pend on them (Pielke et al., 2007; Keys and McConnell, 2005). What socioeconomic and socioecological factor s most affect this capacity to create sustainable natural resource use, or to at least reduce the negativ e environmental (and therefore) social effects resulting from such practices? S econdly, what are some of the incentives for land managers, including organizations, to behave in a more sustainable manner (Ostrom et al., 1999)? Political ecology theory proposes that human-environment inte ractionsas related to land and natural resource useconsist of economic, political and environmental processes that together produce particular landscapes (Robbins 2004). In this context, humans react to economic opportunities they perceive the natural system offers them in order to meet local consumption needs or market demands. In esse nce it focuses on the political economy (the relationship between politics and economics) of human-envi ronment interactions. These interactions, however, create competition, resi stance and conflict among land managers to access, use and exclude others from valuable land and natural resourcesinvolving institutional structures such as land and res ource tenure, as well as social actors possessing various degrees of power (Schmink and Wood, 1992, Folke et al., 2007; Wannasai and Shrestha, 2008; Rindfuss et al., 2007; Simmons, 2002; Simmons et al., 2007 ; Ankersen and Barnes, 2004; Kennedy, 2003). This competition for land and resources poses probl ems that require political solutions in terms of policy and its implementation (Bryant and Bailey, 1997), underlining the importance that political/governance structures have in this process. As Kennedy (2003, pp. 1861) notes on sustainability: The big question in the end is not whether science can help. Plainly it could.

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18 Rather, it is whether scientific evidence can successfully overcom e social, economic and political resistance. Finally, human-environment intera ctions create landscape changes that may be temporary or permanent; resulting in alterations or modi fications (Veldkamp and Lambin, 2001) which can then affect subsequent social adaptive behavior as well as ecos ystem responses (Folke et al., 2007). Renewable and Nonrenwable Resource Extraction Chapter 2 is a literature review of the envi ronm ental effects resulting from renewable and nonrenewable natural re source extraction. This overview concerns LUCCs and secondary outcomes that result from agriculture and logging (renewable natural res ources) and gold mining and petroleum exploration and pr oduction (E&P) (nonrenewables). A good deal of this literature concentrates on tropical regions particularly Amazonia (Soares-Filho et al., 2006; FlamencoSandoval et al., 2007; Caldas et al ., 2007; Nepstad et al., 1999) a nd tries to determine the most important drivers that lead to these changes. These, however, tend to be scale specific (Veldkamp and Lambin, 2001; Rindfuss et al., 2007; Keys and McConnell, 2005). For example: at the local scale, social variables and accessibility to resources are the main drivers of change; at the landscap e scale, topography, climate and agri cultural potential appear to matter most; while at the national/regional leve l, climate, macroeconomic factors (such as economic development, government policies and indebtedness, population pressures, market accessibility) and demographic variables, such as household size and labor availability are most important (Scrieciu, 2007; Veldkamp and Lambi n, 2001; Caldas et al., 2007; Rindfuss et al., 2007). Since the bulk of the LUCC literature addre sses deforestation related to agricultural expansion and logging, I found th at studies concerning nonrenew able natural resources are lagging despite the important and long-term lands cape changes that result from their extraction

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19 (Almeida-Filho, 2002; Peterson and Heemsker k, 2001). The work related to gold mining, particularly small-scale mini ng (done by individuals and sma ll groups) is a growing area of research, nevertheless. On the other hand, investigations relate d to oil E&P are less abundant, especially for Venezuela, a major o il producer and expor ter to the US. Heavy Oil Production in Eastern Venezuela Venezuela m akes an interesting case because it has very large resources of heavy oil; that is degraded oil that requires more refini ng to produce a usable and valuable product (Phizackerley and Scott, 1978; Jiayu and Jianyi, 1999; Meyer and Attanasi, 2003; Galarraga et al., 2007). The technology is now viab le, especially given the high price of oil worldwide, for the Venezuelan government to expand it s operations from the current 4 concessions to 27 in the next two decades, potentially affecti ng 18,000 km of territory across th ree states. This aggressive development of heavy oil will have important imp lications on the regional dry tropical forest ecosystem, which is already thre atened (Fajardo et al., 2005). Chapter 3 therefore addresses the lacunae in the literature by ex amining some general LUCCs resulting from the four current operations a nd three control sites. I measure four general landscape changes using GIS and remote sensing (sat ellite image analysis) techniques. They are: 1) land-cover loss, represented by positive and negative changes in the NDVI vegetation index 2) petroscape (petroleum-infrastructure) densitythe density of petroleum infrastructure features in km/km; 3) edge-effect zone, or the area ove r which significant ecological effects extend outward from a road or petroleum infrastructure (Forman and Deblinger, 2000; Schneider et al., 2003; Morton et al., 2004; Thomson et al., 2005); a nd 4) core areas, or th e amount of intact land mosaics that remain after the edge-effect zone has been taken into acc ount (Morton et al., 2004; Thomson et al., 2005; Griffith et al., 2002b). In this chapter I present an efficient low-cost way for land managers and government officials to measure map and monitor LUCCs associated with

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20 petroleum exploration and producti on using GIS methods and (often) readily available satellite images. Environmental Performance Variability in the Heavy Oil Belt Petrozuatas petroscape values leads to th e question: do state-owned oil com panies pay less attention to the environmental effects created by oil operations and therefore create larger and longer-lasting landscape alterations? Altern ately: do multinational oil companies (MNOCs) place greater importance on their environmental performance, and in particular European companies? (Marica et al., 2008; Moser, 2001; Gouldson, 2006). If good environmental performance leads to good economic results (M orse 2008), then why does variability exist among four oil operations located very near each other, operating in the same landscape and implementing the same business model? While land-use history is an important component (Peterson and Heemskerk, 2001; Lambin et al., 2003; Flamenco-Sandoval, 2007; Cumming and Barnes, 2007; Abizaid and Coomes, 2004) perhaps the mix of private-public partnerships that exist among each of the four oil concessions also influences the outcome. Chapter 4 examines the statistical relati onship between oil-related LUCC and company ownership. It divides the concessions into thos e dominated by 1) local-company (state); 2) European; and 3) US interests. It expands th e number and type of environmental-change measures among all four HOB concessions (e xcluding control groups) and hypothesizes that local-company dominated concessions will exhibit the most environmental alterations, followed by US-dominated concessions, and that European-s trong concessions will ha ve the least changes. Results show statistically significant associations between local-participation and petroscape density and well density. No statistically significa nt correlations exist between US and European participation and important petroscape measures. Finally, chapter 5 summarizes the results from the literature review, the analysis of petroscape expansion using satellite images and GIS

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21 techniques, and the statistical an alysis of petroscape changes and the type of ownership among the HOB concessions.

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22 CHAPTER 2 LAND-USE AND LAND-COVER CHANGE RESULTING FROM RENEWABLE AND NONRENE WABLE RESOURCE EXTRACTION: A REVIEW Introduction There is no w essentially universal agreement across the disciplines involved in the emerging land change science that to understand the causes of land cover and use change, it is essential to incorporate human behavior in our theories models, data, and analyses (Rindfuss et al., 2007, pp. 739). A central way humans interact with the envi ronment is through the extraction of natural resources in order to meet the needs for intern al consumption and external demand. As humans compete with each other to access, control and use these resources while excluding or resisting others, their actions are shaped by political a nd economic structures as well as by perceived ecosystem opportunities and constr aints operating at various sp atial and temporal scales (Schmink and Wood, 1992; Zimmerer and Basse tt, 2003; Robbins, 2004; Bryant, 2001; Mambo and Archer, 2007; Kennedy, 2003). Natural resources, divided into renewable and nonrenewable, are a key component of human-driven land-use la nd-cove change (LUCC)1. The distinction between the two types is that renewables (or flow resources) ar e capable of being replaced by natural ecological processes or sound management practices, while nonrenewables (or stock resources) are finite, exhaustible (Harriss, 2006; Neumayer, 2000; Cutter and Re nwick, 2004). The extraction of both types of resources can result in land-cover modificat ion and conversion and involves competition among various actors wielding different levels of power (Schmink and Wood, 1992). At the same time, these actors possess ideas about ecology and political economy [that] actively shape human perceptions and uses of nature (Bry ant, 2001, pp. 162; emphasis in original). 1 Cutter and Renwick (2004, 4, 5) add itionally differentiate between two other types of resources, perpetual, like the sun, and potential, which does not have value today but may in the future, such as solid trash and wastewater

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23 The study of the interaction between environment, politic s and society (Wolford, 2005) constitutes a concise definition of political ecolog y. This subfield of geography (and other social sciences) injects political considerations in to traditional human-environment interaction and global environmental-change science by demandi ng that researchers look both at what changes take place and why they occur (Keys, 2005, pp. 229). This paper responds to this demand by revi ewing the LUCC and re lated literature and synthesizing the general environm ental changes resulting from tw o examples each of renewable and nonrenewable natural resource use and some of the drivers behind these human-environment interactions. Agriculture (land and soil) and logging (tree-cutting), as dominant drivers of deforestation, are important examples of rene wable natural resources extraction. Meanwhile gold-mining and oil exploration and production (E&P ) are two activities that can produce lasting changes on the landscape even after operations have ceased. They represent examples of nonrenewable natural resour ces use. The goal of this paper is to better understand the complex dynamics behind these interactions by focusi ng on the political, economic and environmental factors that help produce particul ar landscapes and the persistent changes that result once these extractive activities cease. I link political ecology ideas to LUCC research and thus advance the debate that land-change science, a complex study of the interacti on of many variables, can first be approached by focusing on the relationships between political, economic and environmental factors. This review can serve investig ators, government officials, conservationists, industry and others interested in better understanding the relati onship between renewable and nonrenewable resource extraction and the associated environmen tal changes. I limit the scope of this review by concentrating on northern South America (Brazil, Venezuela, Guyana, Suriname and French

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24 Guiana) when possible, given that a good deal of work exists on Amazonia and I am more familiar with this region, particularly Venezuela. Background A review of the LUCC and sim ilar literature shows a heavy focus on deforestation in tropical regions, particularly in Amazonias humid forests (Soares-Filho et al., 2006; Asner et al., 2005, 2004; Sebbenn et al., 2007; Flamenco-Sandoval et al., 2007; Vieira et al., 2003; AlmeidaFilho and Shimabukuro, 2004; Caldas et al., 2007; Es prito-Santo et al., 2005; Nepstad et al., 1999). Meanwhile, dry tropical forest s and savannas receive less at tention even though they are the dominant forest type in the tropics, have mo re productive soils, support much of the worlds agriculture and human habitation (Southworth, 2004; Jepson, 2005; Fajardo et al., 2005; Daniels et al., 2008; Trejo and Dirzo, 2000; Romero-D uque et al., 2007) and rank among the most endangered terrestrial ecosystems (Lawrence et al., 2007; Trejo and Dirzo, 2000; Gordon et al., 2004). Nonrenewable natural resource extraction receives even less attention, since many of the investigators concerned with de forestation study the alterations created by agriculture and logging, rather than by gold mining and oil extrac tion despite the long-ter m changes these latter activities create on forest cover (Peters on and Heemskerk, 2001; Almeida-Filho and Shimabukuro, 2002; 2000). When the disturbances cr eated by both types of resource extraction are combined, they create cumulative effects that fragment the landscape, alter ecosystems, reduce wildlife populations and create social risk s for nearby inhabitants (Schneider et al., 2003) LUCC and Political Ecology (Theoretical Framew ork) Early LUCC research tried to model land-us e change by measuring and predicting rates (quantities) of change among biophysical attr ibutes (Veldkamp and Lambin, 2001) while ignoring social drivers. This work focused on learning what ch anges were taking place rather

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25 than asking why they were occurring (Keys, 2005). Later efforts integrated socioeconomic variables and the role of policie s (Veldkamp and Lambin, 2001) re sulting in case studies that moved the inquiry beyond description toward an understanding of LUCC (R indfuss et al., 2007). But the resulting case studies lacked generalizab ility and sufficient detail to evaluate data and methods (Veldkamp and Lambin, 2001; Rindfuss et al., 2007; Keys and McConnell, 2005), leading Rindfuss et al. (2007, pp. 739) to recently st ate: Indeed, the land change field itself does not have agreement on what should be reported. This is reflected in the many processes examin ed that are believed to lead to LUCC. These include complex interactions of economic, ecological, political, social, cultural, technological and infrastructure factors opera ting at various temporal and spatial scales (Veldkamp and Lambin, 2001; Hietel et al., 2007; Keys and McConnell, 2005; Turner et al., 2007). Not surprisingly incorporating all these variables is complicated by profound cultural differences between social science and natural science disciplines (Rindfuss et al., 2007, 739). Furthermore, not all causes of land-use change and all levels of organizatio n are equally important. For any given human-environment system, a limited number of causes are essential to predict the general trend in land use (Lambin et al., 2003, 222). The need to provide parsimonious explanations is one reason that political ecology lends generalizability to stud ies concerning LUCC; after all competition, conflict and landscape changes are central outcomes of human-environment interaction concerning land and re sources, particularly when they are scarce (Bryant and Bailey, 1997; Schmink and Wood, 1992; Rubenstein, 2004). Keys and McConnell (2005) analyzed LUCC tre nds related to agricultural practices worldwide by reviewing 91 case st udies on this subject and found that the most common drivers were economic (market), demographic, policy (political) and institutions (property regimes).

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26 This does not imply that natural environmental variations, such as climate changes/extremes, droughts, floods and insect outbreak s do not affect this process (Bin ford et al., 1997; Hodell et al 1995; Gasse et al., 1990), rather the basic prem ise is that peoples response to economic opportunities, as meditated by political and market institutionsand environmental characteristics (such as climate, topography and soils) drives LUCC at vari ous scales (Lambin et al., 2001, 2003; Zimmerer and Bassett, 2003; Keys and McConnell, 2005; Rindfuss et al., 2007). Furthermore, in some cases such as floods, thes e natural disasters have social causes (filling drains with refuse, inadequate maintenance, us ing drainage areas for housing and agriculture) and they affect social groups unequally (Pelling, 2003). As scale varies so do important drivers. Vel dkamp and Lambin (2001) note that: 1) at the local level social factors and acces sibility are important; 2) at th e landscape level topography and agroclimatic variables matter most; while 3) at the national/regional level the important ones are climatic, macroeconomic and demographic. T hus regarding a large region like Amazonia, Armenteras et al. (2006, pp. 354) co nclude that defores tation is primarily determined by human population, accessibility, land use and land tenure issues. Meanwhile, Robbins (2004, 2003a) promotes moving away from the binary notion that environmental change results in destruction of nature, or that it s socially created2, and instead accept that nature is produced by human and non-human actors. This aspect of production demonstrates that landscapes are never static; they are being altered, degraded, converted or regenerated by human and natural processes cont inuously. At the same time political power, the distribution of resources, and per ceptions of what types of resources are scarce and valuable, are not static either (Ellsworth, 2002). 2 The social creation of nature argument is defined, delimited, and even physically reconstituted by different societies, often in order to serve specific, and usually dominant, soci al interests (Castree 2001, 3).

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27 Political ecology and land-change science pursue the same goal: understanding humanenvironment interaction by studying and modeling how and why people interact with land and resources and the outcomes of t hose interactions for both the so cial and natural world. One difference, however, is that political ecology te nds to focus on (contested versions of) humanenvironment interactions related to particular resources such as gol d or rubber (Schmink and Wood, 1992; Salisbury and Schmink, 2007), while land change science treats the environment in terms of its array of ecosystem (environm ental) goods and services (Turner et al., 2007, pp. 20666). Thus integrating research that joins social natural, with GIS and remote sensing science (Turner et al., 2001) results in a land-system science whereby: 1) the pixel becomes the finest grain of spatial specificity (Tune r et al., 2007); 2) political and economic forces regulate human behavior and interactions with the environment; and 3) where the aim is to better inform environmental policy (Redman et al., 2004, 161, 162) This fusion is an outgrowth of works such as People and Pixels (Liverman et al., 1998), which attempted to span the gap between remote sensing and social science research. Intere stingly, even in traditi onal land-change science, the political is ever present. As McCusker a nd Weiner (2003, pp. 197, 201) poi nt out, the use of geospatial technologies to study landscape change has led to differing, and sometimes contradictory, representations of nature. This is because the satellite image is a social agent of environmental change, serving as an ally in disputes over the na ture of the landscape (Robbins, 2003b, 197). Land-Cover Modification and Conversion Land use is never static (Lam bin et al., 2003); it is a dynamic canvas on which human and natural systems interact (Parker et al., 2003, pp. 314). Land-use decisions comprise the single most important issue in environmental management since they define the kinds of res ources we use and the extent of environmental

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28 alterations (Cutter and Renwick, 2004). First, land-cover is defined by the attributes of the earths land and immediate subsurface, incl uding biota, soil, topography, surface and groundwater, and human structures (Lambin et al., 2003, 213). It refers to the type of material present on the landscape (e.g., water, sand, crops, forest, wetland, human-made materials such as asphalt) (Jensen, 2005, pp. 340). Land use refers to the purposes for which humans exploit land cover (Lambin et al., 2003, pp. 216), or to what people do on the land surface (e.g., agriculture, commerce, settle ment) (Jensen 2005, pp. 340). Some alterations create expansive change s and endure a longer period than others, resulting in land-cover conversi on rather than modification. Conve rsion involves th e shifting of one land-cover type to an entirely different one, such as occurs in large-scale agriculture, logging by clear-cutting of trees, urbani zation and deforestation (Southw orth, 2004; Lambin et al., 2003; Daniels et al., 2008). Conversion is also associated with nonrenewable resource use, such as mining and oil and gas extraction since these acti ons tend to create degraded areas as well as large-scale permanent changes on the landscape th at are often followed by the establishment of urban infrastructure (Schneid er et al., 2003; Almeida-Fi lho and Shimabukuro, 2002, 2000). Roads, bridges, pipelines, tank farms, electr ic poles and power lines further affect the surrounding setting transforming the landscape into an urban or peri urban form. Another term for conversion is land transformation, which refers to radical changes in land use and cover, usually over the long term (Turner et al., 2007, pp. 20666). Modification, on the other hand, refers to change s in the type of cover without changing its overall classification (Lambin et al., 2003). Here, the land-cover remains in the same category and is exemplified by selective tr ee cutting, thinning of forests, de graded grasslands, or changes in crop patterns and input use (Southworth, 2004; Southworth et al., 2004; Daniels et al., 2008;

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29 Veldkamp and Lambin, 2001). However, it can also refer to vegetation re generation (land-cover gain) such as successional forest growth and recovery on abandoned lands following natural and social disturbances such as seismic explora tion and certain gold-mining activities (Lee and Boutin, 2006; Peterson and Heemskerk, 2001; de Figuera et al., 2006; Romero-Duque et al., 2007; Hartter et al., 2008; MacFar lane, 2003), as well as the esta blishment of tree plantations, also known as afforestation. Thus, not all land-c over changes involve degr adation or conversion. Yet even afforestation involves logging and converting tropical forest at times in order to establish tree plantations. In southeastern Ven ezuela, for example, over 600,000 hectares of dry tropical forest have been c onverted to Caribbean pine ( Pinus caribaea var. hondurensis ) in the last 40 years (Barnola and Cedeo, 2000). Across the globe, parts of Indonesias rainforests are being converted for oil palm production, a pattern expected to double in th e next two-to-three decades (Sandker et al., 2007). Furthermore, mana ged forests and plantations tend to support a lower biodiversity than unmanaged forests (Timoney and Lee 2001, pp. 400). Two other terms used in the LUCC literature re lated to land-use are intensification and extensification. Both activ ities involve shifts in land-use patterns. Whereas intensification refers to increasing the value of product per land unit, extensification refers to the opposite (McCusker and Wiener, 2003). Intensificati on would involve planting more corn plants per hectare and adding fertilizer in order to produce more, while extensification would involve opening up more land to agriculture to increase production. Agricu ltural intensification also involves altering plants and animals so that humans supplant natural processes of nutrient and biological regeneration (Keys and McConnell, 2005, 321). Intensification leads to other problems as well: 1) hybridization of loca l seed varieties, the spread of monoculture and po ssible eradication on non-economically valuable plants; 2)

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30 hydrological changes resulting from capturing and diverting rivers and streams; 3) introducing high levels of agrochemicals (including pesticides and herbicides); 4) loss of nutrients such as carbon; and 5) erosion caused by heightened (intense) soil us e and increased mechanization (Keys and McConnell, 2005; Donald et al., 2006) Land managers commonly use intensification strategies when agricultural policies provide incentives, note Donald et al. (2006), and these practices tend to reduce biodive rsity. Through this perspectiv e policy instruments are causing this problem, and removing economic incentives (a ltering these policies) would help halt this loss (Donald et al., 2006). Another important outcome, aside from the alterations caused by both renewable and nonrenewable resource extraction, is the establis hment of settlements, towns and cities spurred by the expansion of goods and serv ice industries catering to the in flux of workers attracted to a natural resource boom (Schmink and Wood, 1992, C onsiglio et al., 2006). These settlements further urbanize the surrounding landscape and perm anently alter the land cover (Lambin et al 2001) even after the boom ends. Lambin ( 1999, pp. 191-192) concludes that Land-cover modifications are generally more prevalent than land-cover conversions, meaning that land-use practices tend to keep the land cover in the same classification, rather than completely change it. Specific Issues: Renewable Natural Resources Agriculture Globally the conversion of forests consum es around 13 million hectares per year (FAO, 2006) and the key driver of this change is the expansion of ag riculture (FAO, 2006; Lambin et al., 2003; Daniels et al., 2008). This holds true in Amazonia wher e growing cash crops (such as soy, cotton) and raising cattle are the predominant larg e-scale agricultural activities leading to deforestation (Schmink and Wood, 1992; Esprito-Santo et al., 2005; Soares-Filho et al., 2004; Brando and Souza, 2006; Ferraz et al., 2006; Fl amenco-Sandoval, 2007). Deforestation is the

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31 removal of vegetative cover beyond a minimal thresholdset at 10% by the United Nations Food and Agriculture Organization (FAO, 2006), while agriculture is defined as the cultivation of domesticated crops and the raising of domesticated animals to produce food, feed, drink and fiber (Jordan-Bychkov and Domosh 2003, pp. 441, 261). This now includes biofuels as substitutes for fossil fuels (Eic khout et al., 2007; Demirb as and Demirbas, 2007). As Lambin et al., note (2003, pp. 208), agricultural activity h as expanded into forests, savannas, and steppes in all parts of the world resulting in broad areas of land-conversion. The agricultural pattern in some of Brazils forests and savannas is to first convert land to cattle pastures and then to fields for growing soybeans and other cr ops as the agricultural frontier advances (Ferraz et al., 2006; Nepstad et al., 2006). Rindfuss et al. (2007) identify the commonalities in frontier land-use change as: New people with their own social organization and technologies enter an occupied but relatively natural landscape. Natural population increase and in-m igration create population pressure. Settlement patterns at the village a nd household level alter the landscape. As these settlements become linked to national transportation systems they grow. Agriculture intensifies and expands in orde r to sustain a growi ng population. Cash crops are introduced and these local markets are linked to those at the regional, national and global level. This also brings additional LUCCs. Next, spatial patterns change, vegetation decr eases, and the habitat is fragmented. Finally, environmental constraints and social factors such as state policy and institutional arrangements such as land tenure lead to ch anges in land use and settlement patterns. By contrast since shifting agricu ltural plots tend to serve the h ousehold level they: 1) affect small areas; 2) do not result in complete vegetati on removal; and 3) tend to be abandoned after three to five years unless populat ion pressures are too great (Ba ssett and Zuli, 2004; Flster, 1995; Salazar, 1995; Peterson and Heemskerk, 2001; Lawrence et al., 2007). Several factors can

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32 influence cultivation and fallow periods. Environmental variables include soil types and condition, topography and rainfall pa tterns; while social factors in clude government policies and livelihood strategies (Hartter et al., 2008; Donald et al., 2006 ; Keys and McConnell, 2005). As abandoned fields regenerate to s econdary forest they replenish nut rients and organic matter, as well as remove and fix carbon from the atmosphe re (Daniels et al., 2008; Kupfer et al., 2004; Fearnside et al., 2007), highlighti ng an important point: reforestat ion is also an outcome of LUCC (Dalle et al., 2006; Esprito -Santo et al., 2005; Lambin et al., 2003; Vieria et al., 2003; Keys and McConnell, 2005). However, the dem ands of an increasing human population are causing the fallow period to be shortened (Vie ira et al., 2003, pp. 471), reducing the regrowth of secondary forests which serve as important ecosy stem reservoirs and which are important to forest-based economies (H artter et al., 2008). While extended-use patterns are becoming more prevalent in tr opical deforestation frontiers as a response to populat ion increase (Lawrence et al., 2007) colonizers also contribute to deforestation by clearing plots for their homes and agricultural fields and as a way to increase land value. Schmink and Wood (1992) found that some colonization schemes involved migrants occupying a plot, clearing out the tr ees and then selling it as one of the few ways to earn money in this region. At times these plots are sold to farmers with larger holdings that in turn consolidate land for agroindus trial purposes (Schmink and Wood, 1992). For the most part, however, shifting cultivation results in land-cover modification, while commercial crop and cattle raising results in land conversion. Concerning dry tropical forest s, other factors such as ove rgrazing by cattle and goats can lead to intense alterations, resulting in shrubland and grassland conversion which limits the regeneration capacity of these forests (Trejo and Dirzo, 2000). The traditi onal solution has been

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33 to reduce animal stock size, but this simplistic view, notes Turner (2003, pp. 163) overlooks the linkages between political economy, management and ecology[because] livestock populations are also economic entitiesthey are managed by th eir owners as producers of milk or traction, as commodities, and as stores of wealth. This highlights the message by Rindfuss et al. (2007) that LUCC studies must consider the human perspective. Logging Logging is a second im portant driver of LU CC, but less significan t than agricultural expansion (crops and cattle) in terms of area affected and effects on faunal diversity (Dunn, 2004, pp. 215). This is perhaps due to the regenera tion of many abandoned logged sites, such as in the Choc rainforests of western Colombia, where only 20 to 30% of logged forests are converted to agricultural use (Sierra et al., 2003). Sierra et al. (2003) note that this proportion is lower than what has been reported in other regi ons such as Amazonia and attribute this lower conversion rate to land tenure pol icies that allow local communities to legally exclude migrant farmers. This highlights the importance of land tenure security in LUCC (Wannasai and Shrestha, 2008), or as Anderies et al. (2004, pp. 4, 6) put it, t he rules devised to constrain actions of agents form the link between resource users and public infrastructure providers. Thus, failure to take into account the diversity of land tenure syst ems within an area affects the success of land management project s (Mambo and Archer, 2007, pp. 383). Type of logging The type of logging and locat ion also m atter. Selectiv e logging (cutting down a few marketable species) or reduced-impact logging (R IL) practices (extracting valuable trees while trying to maintain forest integrity and meeting le gal restrictions) (Asner et al., 2004; Zarin et al., 2007) result in the thinning of forests. These actions lead to land-cover modification rather than conversion since they cause fewe r disturbances to the landscap e and to faunal diversity (Dunn,

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34 2004). Even forests that have been logged by c onventional methods and experience severely altered structure and species composition may st ill possess a relatively undisturbed seed bank and sprouting stumps [that] contribute to fa st regrowth (Mesquita 2000, pp. 132). Yet this holds true as long as the forest cover is not removed by bulldozers or heavy machinery. Boletta et al. (2006, pp. 112, 113) noticed that when logging patterns in th e xerophytic forests of the Argentine Chaco changed from selective defore station during the 1970s an d 1980s to the use of bulldozers in the mid-1990s, these new actions resu lted in the total loss of forest, including seed-bearing trees. In this scenario the patt ern became one of: 1) unselective deforestation, 2) clearing land with heavy machinery, 3) burning th e vegetation that remained, 4) plowing the soil, and 5) planting different crops (Boletta et al., 2006). Romero-Duque et al. (2007) also found that dry tropical forest land cleared by bulldozers for home building in Mexico and later abandoned also exhibited greatly diminished regeneration rates. On the other hand, even when selective ( not indiscriminate) logging takes place, the resulting land-cover changes can be more pronoun ced than first thought. Asner et al. (2005) and Nepstad et al. (1999) have obser ved that when the amount of se lective logging occurring in Amazonia is quantified via remote sensing techni ques the true figure of deforestation doubles perhaps shifting the LUCC classificati on from modification to conversion. Fire The underestim ation of true logging figures (by more than 50%) also applies to fires that burn large areas of forest every year in Am azonia but that are not included in standard deforestation measures (Nepstad et al., 1999; Soares -Filho et al., 2006). Ne pstad et al. (2001) believe that fires pose the grea test threat to Amazon forests. However, logging and fires are related; logged forests are more vulnerable to fires than nonlogge d forests (Asner et al., 2004). Nepstad et al. (1999, pp. 505) expl ain: Logging also increases forest flammability by reducing

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35 forest leaf canopy coverage by 14-50%, allowing sun light to penetrate the fo rest floor, where it dries out the organic debris created by the loggin g. Fires also reduce forest diversity (Gordon et al., pp. 2004) by compromising living trees intentionally left on l ogged sites for conservation of future wood potential, note Gibbons et al. (2008). By damaging the stems, fires spur invertebra te and fungal decay which can lead to tree collapse (Gibbons et al., 2008). A dditionally, fires ignited on agri cultural lands can penetrate logged forests, killing 10-80% of the living biomass and greatly increasing the vulnerability of these forests to future burning (Nepstad et al., 1999, pp. 505). Forest fires may grow larger than normal during unusually dry seasons or droughts brought on by El Nio Southern Oscillation (ENSO) (Nepstad et al., 1999, Whitlock et al., 2006; Siegert and Hoff mann, 2000; Tareq et al., 2005). Finally, biomass burning during the dry season worldwide contributes to global atmospheric pollution (Holzinger et al., 2001; Almeida-Filho and Shimabukuro, 2004). A major obstacle to changing this behavior, note Nepstad et al. (2001, pp. 396) is th at anthropogenic fires are deeply imbedded in the culture and economic logic of four million rural Amazonians trying to survive or grow wealthy on th e agricultural frontier. Its an inexpensive way for farmers to clear a field, fertili ze it and claim ownership (Nepstad et al., 2001). Fires are also a part of the natural disturbanc e processes affecting forests, and total fire suppression can lead to the encroachment of shrubs and rangeland loss (Timoney and Lee, 2001). When fires do grip these old-growth forests they can cause large-scale damage. Therefore fire suppression poses a greater threat than logging in some areas such as the dry forests of the western US (Egan, 2007). In Albertas boreal forests, fires play a crucial ro le in maintaining the complex mosaic that make up the forest (Dyer et al., 2001). Alternately, agricultural landscapes affected by or maintained by fires, such as ca ttle pastures, can shift to secondary succession

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36 forests if abandoned (Gordon et al., 2004) and can be covered by regenerated vegetation in 1 to 5 years (Nepstad et al., 1999). Other factors Another im portant limiting factor is the cutt ing cycle. For logging practices to be sustainable, reduced-impact approaches must be accompanied by long cutting cycles of 60 years or more, though 30 years or less is the norm (Ka mmesheidt et al., 2001; Zarin et al., 2007). The loss of phosphorus during cycles of shifting cultiva tion in tropical forest s can lead to slower regeneration rates and therefore introducing phos phorus fertilizer to plot s under production could slow deforestation by reducing the need to cut new plots (Lawrence et al., 2007). Interestingly, the majority of ground damage related to a wide ra nge of logging practices is the creation of skid trails, or paths where logs are dragged across th e forest floor until they reach loading trucks (Asner et al., 2004). These create even more las ting alterations than logg ing roads (Asner et al., 2004). Finally, fuelwood spurs a great deal of bi omass removal around the world in order to create energy for cooking, heating and boiling water (Masera et al., 2006). Since biomass represents 50% to 90% of primary energy consum ption in developing countries (Berrueta et al., 2008), its removal also comprises an important driver of deforest ation (Ramos et al., 2008). In Swaziland and other areas, observes Wh eldon (1990, pp. 83), fuelwood demand exceeds sustainable supply from accessible land. Interestingly, Ramos et al. (2008) found that physical properties of biomass as a fuel source, such as the fuel value index (FVI) affected local preference for cutting and burning specific types of fuelwood in northeastern Brazil. The authors recommendations pointed to establishing political and social efforts toward stimulating the cultivation of these trees in fuelwood plantations and in the home-gardens w ithin the community (Ramos et al., 2008, 7).

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37 Another important recommendation is adopting mo re efficient wood-burning stoves since they not only reduce fuelwood consumption but also d ecrease negative health effects resulting from prolonged inhalation of smoke from the cooki ng fireswhich tends to affect women and children the most (Wheldon, 1990; Berruet a et al., 2008; Ekici et al., 2005). In summary, the results of large-scale deforestation include habitat fragmentation, loss of biodiversity, shrinking of forest remnants, soil erosion, increased sedi mentation, liquidation of old growth forests and the species that depend on them; introduction of exotic biota, wetland drainage, pollution, loss of carbon sinks and subse quent increased CO2 emissions (Ferraz et al., 2006; Leslie et al., 2001; Mira nda et al., 1998; Southworth, 2004; Timoney and Lee 2001; Armenteras, et al., 2006). As Za rin et al. (2007, 922) put it: Sta nding forests, regardless of their long-term management potential, benefit societ y through ecosystem services not furnished by agriculture or cattle ranching. Specific Issues: Nonrenewable Natural Resources Gold Mining Com pared to renewable natura l resources, mineral mining re ceives less attention in the LUCC and related literature despite the serious e nvironmental and social alterations associated with theses activities (Parrott a et al., 2001; Peterson and Heemskerk, 2001, Almeida-Filho and Shimabukuro, 2002; Leger 1991). Gold mining, as oppos ed to agriculture and logging, creates two effects that produce long-last ing environmental and social problems: mercury pollution and land degradation (Hilson, 2002; Alme ida-Filho and Shimabukuro, 2002). Land degradation Gold is extracted two main ways. The first one involves large-scale com mercial mining of land-based hard-rock deposits that require ad vanced technology and resources (often from foreign companies) and result in open-pit mines that exemplify large-scale land-use changes in

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38 terms of cleared land, roads for transporting the material, tailing ponds (which hold waste material and water containing high levels of mercury or cyanide), and growth of settlements that house the miners and the accompanying service industry (Veiga and Hinton, 2002; Schmink and Wood, 1992; Hilson and Monhemius, 2006; Lacerda and Marins, 1997; Castro and Snchez, 2003). Despite the visual impression caused by larg e open pit mines, the second and more common method is performed by sm all-scale artisanal miners (10-15 million worldwide) who both excavate surface soil deposits and perform fl uvial mining by searching for flakes, pebbles and nuggets along riparian stream s in tropical Asia, Africa and South America (Veiga et al., 2006; Lacerda, 1997; Hilson, 2002; Heemskerk, 2001; Malm, 1998; Peterson and Heemskerk, 2001; Schmink and Wood, 1992; Vi eira, 2006). Small-scale miners often employ extraction methods that entirely remove vegetation a nd degrade land, and incorporate mercury (and cyanide) to amalgamate gold. Combined these tw o actions create thousands of polluted sites across 55 countries (Veiga and Hinton, 2002; Veiga et al., 2006). In Ghana, small-scale gold mining activities cause a dispropor tionately large percentage of environmental degradation (Hilson, 2002, pp. 70). The driving force: gold is most commonly mined on a small scale because of its propensity to generate wealth quickly (Hilson, 2002, pp. 58). Peterson and Heemskerk (2001) describe the proce ss: 1) about five miners clear an 80 x 80 m forest plot. They cut, remove and burn most of the trees while those that are difficult to remove are left standing. 2) They then use a high-pressure diesel-powered water hose to remove the topsoil layer and then the gold bearing sand and clay layer. 3) They pump the slurry containing gold into a sluice box where they introdu ce mercury or cyanide to adhere to the gold.

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39 4) They keep the gold clumps and release the taili ngs (slurry waste) into the adjacent forest or into an abandoned mining pit. Alternately, miners operating on watercourses 1) dredge river bottoms for 20 to 30 hour phases; 2) sieve the sediments; 3) place the selected material in barrels; 4) amalgamate the material with mercury or cyanide by hand or with mechanical stir rers until all gold is found; 5) release the mercury-rich residue s back into the river (Lacerda, 1998; Hilson, 2002; Kligerman et al., 2001). During this process a lot of mercury also vaporizes into the atmosphere and oil from the dredge equipment and motors enters the wa ter, negatively affecti ng plankton and aquatic fauna (Lacerda, 1998; Kligerman et al., 2001; Lacerda and Marins, 1997). Regeneration Not surprisingly, pressure washing river banks w ith hydraulic pumps, digging large open pits, and dredging riverbeds stimulates soil erosion and stream sedimentation (affecting hydrological patterns, reducing fish diversity and their community structure, and threatening hydroelectric production); leads to land degradation, habitat destru ction and the lo ss of wildlife; causes microclimatic changes from the remova l of vegetation; and creates potential new problems from the remaining contaminated ta iling ponds (Mol and Ouboter, 2004; Heemskerk, 2001; Almeida-Filho and Shimabukuro, 2002, 2000; Peterson and Heemskerk, 2001; Lacerda, 1998; Hilson, 2002; Kumah, 2006; Kligerman et al., 2001 ). In places like Ghana, miners remove the vegetation, dig for gold and move on, leaving a scarred landscape composed of pits and trenches that is not suitable for other uses (Hilson, 2002). In many cases, notes Kitula (2006, pp. 409) both agricultural and grazing lands have b een destroyed, which affects local livelihoods. The complete removal of most vegetation and roots also removes the seed bank, thus, like areas that are converted to agri culture via the use of bulldozer s (Boletta et al., 2006; RomeroDuque et al., 2007), mining practices impede regeneration on abandoned plots (Peterson and

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40 Heemskerk, 2001). When regeneration does occur, the rates vary over space and time (AlmeidaFilho and Shimabukuro, 2002). The edges of abandoned degraded areas regenerate faster than the central zone where damage is more intense; indicating that recovery of the entire degraded area takes much longer than anticipated, up to 20 years or more (Almeida-Filho and Shimabukuro, 2002). Rodrigues et al. (2004) observe that conser ving forest fragments along these degraded areas is crucial for allowing natural regeneration of shrub and tree species which act as a seed source and animal refuge. Yet tw o other factors can also speed the regeneration process: gold exhaustion and law enforcement since they encourage small-scale miners to abandon the area and companies to close the large mines (A lmeida-Filho and Shimabukuro, 2002; Castro and Snchez, 2003). The first is an outcome of geology or of limited tools and technology. The second one points to the importan ce that resource tenure or good environmental governance has. Concerning LUCC, heavily utilized areas result in land-cover convers ion, while the lighter impacted areas reflect modification. Finally, re storation projects by mining companies involve afforestation to rapidly establis h tree cover on reclaimed mine sites and therefore facilitate natural forest succession, but often fast-growi ng exotic species are planted (Parrotta and Knowles, 2001, 220), rather than native trees, altering ecosystem composition. Mercury pollution Though its use is illegal in m any countries mercury (and cyanidemainly used in industrial applications) is widely implemented to help recover gold from the rocks, soils or river sediments because it is effective, cheap and easy to use (Hilson, 2002; Mol and Ouboter, 2004; Lacerda, 1997; Hilson et al., 2007; Donato et al., 2007; Sandoval et al., 2006, 416; Hilson and Monhemius, 2006; Veiga et al., 2006). However, after it is utilized the mercury is often discharged into local streams where it enters the food chain. Th ere it is metabolized from an

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41 inorganic form to methyl-mercury and biomagnifi ed. By the time humans eat large (carnivorous) fish it has become highly toxic (Hilson, 2002; Sandoval et al., 2006; Mela et al., 2007; Pinheiro et al., 2008; Bose-OReilly et al ., 2008; da Costa et al., 2008). Mercury also pollutes drinking water and poisons workers and residents who ha ndle the metal or inhale the fumes during the burning process that separates the mercury from the gold (Lima et al., 2008; Sandoval et al., 2006; Almeida Filho and Shimabukuro, 2000, 2002; Reed, 2002; Castro and Snchez, 2003; Drake et al., 2001). These vapors ar e re-released when the gold is burned again in gold shops or smelters, affecting areas within 1 km of the emission source (V eiga and Hinton, 2002; Bastos et al., 2004)not just the mining ar eas. Also noteworthy: if terre strial animal populations are completely driven away in mining areas (Heem skerk, 2001), then miners and local populations are more prone to increase their fish intake, thus exacerbating their le vels of toxic methylmercury. Solutions Som e ways to address the environmental pr oblems associated with mining include: 1) using biological treatments to take up metal from wastewaters (Moore et al., 2008; 2), including almond shells (Estevinho et al., 2008); training small-scale miners on how to efficiently use mercury (Castro and Snchez, 2003; Hilson, 2002); 3) formalize (legalize) small-scale miners in countries such as Ghana in order to regulate prod uction so that greater tr ansparency and control will limit environmental degradation and improve local livelihoods (Hilson et al., 2007); 4) introduce alternatives to mercury and cyanide as leach reagents for gold mining (Hilson and Monhemius, 2006; Vieira, 2006); 5) use retorts to help capture a lot of th e lost mercury (up to 95%) (Drake et al., 2001; Veiga and Hinton, 2002; Hilson, 2002); 6) establish decontamination (recovery) plans between the gove rnment and mining companies within a specific time period (Castro and Snchez, 2003); 7) reduce gold consumption (dema nd) with awareness campaigns;

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42 and finally 8) spur European Union countriesa s the largest producers of mercuryto regulate, tax and control its export (Veiga et al., 2006). Oil Exploration and Production. Though less expansive th an the LUCC literatu re related to gold mining, research on landscape changes and oil exploration and produc tion appears to be the purview of petroleum geologists, biologists and others interested in environmental management. The first group has been using geographic information systems (GIS) and remote sensing techniques in their work for over 30 years, primarily for geologic explor ation (Jackson et al., 200 2). Today geospatial applications in the petroleum industry continue to focus on exploration but also on detecting and modeling spills; implementing least-cost analysis for pipeline constructions; reducing operational costs by managing spatial information; environmental baselining3; impact assessment and analyzing settlement patterns (Gaddy, 2003; Jackson et al 200 2; Janks and Prelat, 1994, 1995; Osejo et al., 2004; Musinsky et al., 1998; Pochettino, 2001; Sa tapathy et al., 2008; AlmeidaFilho et al., 2002; AlmeidaFilho, 2002; Zhang et al., 2007; Jaggernauth, 2003). Wildlife: caribou The second group of researchers studying oil (E &P) includes bi ologists concerned with the landscape and habitat changes resulting from these activities. Many of these scientists are primarily interested in avoida nce and stress responses by wild life to oil roads, wellpads and activitiesparticularly caribou in Canada and Al aska (Nelleman and Cameron, 1996; Noel et al., 2006; Schneider et al., 2007; Wolf e et al., 2000; Bradshaw et al ., 1998; Harron, 2007; Dyer et al., 2001). They have found, for example, that petrol eum development in oilfields near Prudhoe Bay, 3 Baselining is the process of documenting landscape c onditions in a given area to create a profile prior to exploration and production phases (Janks and Prelat 1994; J ackson et al., 2002). Over time, as activities increase in concession areas, the environmental and social alterations can be monitored through the analysis of air photos, satellite images and field site visits.

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43 Alaska, displaced maternal female caribou by 4 km (Nelleman and Cameron, 1996). Schneider et al. (2003) found that caribou in Alberta, Canada avoided seismic lines by 100 m, while for oil roads the distance was 500 m. Harron (2003) disput es findings for seismic lines, pointing out that caribou avoidance distances are only 40 m on each side. Bradshaw et al. (1998) point out that di sturbances to caribou caused by petroleum exploration in Alberta can cause maternal female s to expend more energy during winter months, leading to undernutrition whic h delays parturition. Therefore a newborn calf has less time to grow before the onset of the next winter, making it more prone to die from predation or undernutrition (Bradshaw et al., 199 8; Wolfe et al., 2000). Dyer et al. (2001) also studied caribou avoidance behaviors to oil and gas activities by fitting 36 caribou with GPS (global positioning system) collars. They recorded avoidance di stances of 1,000 m for wells and 250 m for seismic lines and roads, noticing that if caribou are disp laced to less suitable habitats, their numbers may go down, affecting demographic patterns (Dyer et al., 2001). Wolfe et al. (2000) found that calving caribou avoided oil field in frastructures by 5-6 km, indicat ing that the effects of the infrastructure footprint were much larger than thought. Furthermore, activities that displaced reindeer and caribou affected subsistence and cu ltural resources for Arctic dwelling indigenous groups (Wolfe et al., 2000). Since drilling permits allow for petroscape expansion, energy policy affects local livelihood options. Interestingly, as compared to agriculture, logging and gold mining, oil (and gas) developments are typically configured as poi nt and linear disturbanc es scattered throughout broader areas that often encompasses 5-10% of the concession site (Wyoming GFD, 2004, pp. 5). Therefore, as opposed to clear-cutting fore stry practices, the alte rations created by oil exploration and production may be harder to detect. Seis mic lines are one exception.

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44 Seismic lines An i mportant outcome of oil exploration is the creation of seismic lines (roads cut during seismic phases of oil explorati on). In addition to di sturbing wildlife, seismic lines can create permanent changes on the landscape that remain long after exploration activity has ceased. Lee and Boutin (2006) examined the persistence of seismic lines on the Alberta boreal landscape over 35 years and made some interesting findings: Wide seismic lines (5 to 8 m) cleared with bulldozers persist on the landscape for more than 35 years. Recovery to woody vegetation occurred in 8% of the cases and based on median recovery rates it would take 112 years for a near complete recovery. Lands cleared with bulldozers flatten and compact the terrain, altering hydrologica l patterns, reducing sites for seedling establishment, and converting the landscape to a wetter sedge-dominated community. The presence of seismic lines provided transporta tion corridors that in turn facilitate other industrial development, such as logging. Th ese routes were subsequently used by the forestry industry to monitor regeneration rates and silvicultural practices. They also created routes ripe for 4-wheel-drive vehicles that hampered regene ration via tire track damage, water channelization, soil compaction and soil erosion on and next to the seismic lines. Changes to oil and gas exploration licenses (r esource tenure/energy policy) combined with increased market prices spurred the creation of seismic lines in the 1970s. Todays demand and tightening supplies is also prompting exploration and drilling. In fact a crash programme production is underway in Canadas oil sands, partly to offset a peaking world oil production (Sderb ergh et al., 2007, pp. 1931). The transfer of explorator y drilling licenses among oil companies is common; however seismic data remains proprietary information. This requires the new license holder to reopen the existing seismic lin es and/or create new ones. Roads One comm onality among all types of resources examined is the creation of roads that result when land managers want to access tr ees, new plots of land, gold-rich areas or oil reservoirs often in previously inaccessible areas. Roads, after all, are the largest human artifact on earth (Forman et al., 2003) and a central catalyst for LUCC. In Amazonia 85% of

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45 deforestation is concentrated at up to 50 km from the main roads (Brando and Souza, 2006, pp. 177). Besides deforestation, road openings lead to the establishment of new agricultural frontiers, particularly for soybeans and cattle ra nching; increase fire advancement on forests; prompt legal and illegal timber harvesting; and result in land speculation and colonization (Brando and Souza, 2006; Ferraz et al., 2006; Lele et al., 2000; Peres and Terborgh, 1995; Soares-Filho et al., 2004; Schmi nk and Wood, 1992). In some cases as soon as there was even the prospect of a new road, migrant farmers woul d move into the area, taking de facto possession of small plot (Schmink and Wood, 1992, pp. 79). Most of these roads can be defined as loca l since their primary function is land access (Forman et al., 2003). But roads also serve econo mic, political and social purposes at broader scales. They stimulate the growth of commerce and increase market accessibility; they consolidate territorial control; and tie land together for so ciety (Brando and Souza, 2006; Ciccantel, 1999; Forman et al., 2003). The stud y of landscape changes resulting from the creation, use and interaction of roads (and ve hicles) and the surrounding environment is known as road ecology (Forman et al., 2003), a gr owing subfield of landscape ecology, which emphasises the effect of spatial patterns of ecosystems on ecologica l processes (Griffith et al., 2002b, 1). Concerning petroleum and roads, Musinsky et al. (1998) analyzed satellite images and found that oil roads in national park in Guatemala were direc tly related to the establishment of new settlements in the surrounding tropical rainforest. These actio ns were tied to political and economic forces such as 1) inadequate park management; 2) poorly guarded roads and other infrastructure that allowed eas y access; 3) contradictory govern ment policies and jurisdictions;

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46 and 4) inefficient agricultural practices and rapid population growth (M usinsky et al., 1998). While government policy solutions and enforcem ent should address some of these problems, Musinsky et al. (1998) spurs energy companies to attend to oil roads by: 1) planning routes to avoid habitat fragmentation and disruptions to hydrological cycles; 2) use existing roads and waterways fully; 3) destroy non-necessary roads, bridges and entry points; 4) bury pipelines and restore vegetation; and 5) monitor remaining roads and access points through field monitoring and remote sensing techniques. The same holds true for seismic roads; L ee and Boutin (2006) propose: 1) establishing narrow seismic lines, less than 5 m; 2) using he licopters and hand cut trails when possibleas opposed to using bulldozers; 3) reclaiming seismi c lines so that they are not converted to permanent paths and roads; and 4) implement best practices by sharing roads with other extractive industries, such as forestry a nd logging, rather than creating new ones. Conclusion A survey of the LUCC and related literature shows a strong preference f or work related to natural resource use, particularly agriculture and logging as they relate to deforestation in tropical regions. Nonrenewables receive much less attention, though stud ies related to goldmining are become more prevalent, particularly given the problems associated with mercury and cyanide use. On the other hand, land-cover change research related to petroleum E&P is even less abundant, even though these activities create large-scale and permanent changes on the landscape and affect they way people respond in larger areas around the zones of direct extraction. Two zones containing some of the largest pe troleum reserves in the world and undergoing new exploration and production in clude the heavy oil sands in Ca nada and Venezuelas heavy oil belt. Albertas oil sand development has attracte d a growing number of researchers, many of

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47 whom examine how petroleum infrastructure aff ects wildlife, particular ly caribou (Bradshaw et al., 1998; Wolfe et al., 2000; Dyer et al., 2001). Others such as Schneider et al. (2003) address forest disturbances created by oil E&P and prov ide a model of how to address this issue. By contrast almost no work that I could find addresses this topic in Venezuelas heavy oil belt, despite the estimated 270 b illion barrels of heavy crude lo cated in central and eastern Venezuela. The research concerning the heavy oil belt instead addresses drilling strategies (Gipson et al., 2002), reservoir modeling (Tan kersley and Waite, 2002), and heavy crude chemical properties (Galarraga et al., 2007), but not land-cover change s despite the claim by energy companies that they are pursuing sustaina ble business practices that minimize social and environmental alterations (Kumah, 2006; Ga rvin et al., 2008; Mudd, 2007a, 2007b; Sandoval et al., 2006). Right now the Venezuelan government plans to expand the heavy oil belt operations from four concessions to 31 in the next two decades potentially affecting 18,000 km of dry tropical forest and savanna. Quantification and certification e fforts of oil in place have started in some of these new blocks. Therefore urgent research is needed to better understand what types of petroscapes (petroleum-related infrastructure la ndscapes) the four functioning concessions have produced and which ones exhibit the least amounts of petroscape disturba nces. This information should be used during baseline studies (Kalacksa et al., 2008) for the upcoming concessions, if possible before seismic activity begins, and th en implemented during the design and production phases. Introducing best business practices and lessons learned will reduce environmental and therefore social changes (Gip son et al., 2002; Moser 2001). Th e application of geographic information systems (GIS) and remote sensing tec hniques are very useful and efficient ways to monitor LUCCs at the landscape level (Janks and Prelat, 1995, 1994; Musinsky et al., 1998;

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48 Almeida-Filho, 2002; Almeida-Filho et al., 2002; Almeida-Filho and Shimabukuro, 2004, 2002, 2000; Gaddy, 2003). This is what now requi red in Venezuelas heavy oil belt.

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49 CHAPTER 3 DO NATIONAL OIL COMPANIES PR ODUCE GREATER ENVIRONMENTAL ALTERATI ONS THAN MULTINATIONAL OI L COMPANIES? THE CASE OF VENEZUELAS HEAVY OIL BELT While energy-producing hydrocarbon resour ces dominate the global economy, their mining and extraction create important land-use and land-cover changes (LUCC) that affect climate, ecosystem goods and services and peopl es vulnerability at different spatial and temporal scales (Prakash and Gupta, 1999; Almeida-Filho an d Shimabukuro, 2002, 2000; Diamond, 2005; Schmidt and Glaesser, 1998; La mbin et al., 2003; Fujita and Fox, 2005; Southworth, 2004). Concerning petroleum exploration and production (E&P), a global question exists as to whether state-owed firms, also known as national oil companies (NOCs), can or do perform as well as multinational oil corporations (MNOCs) in terms of balancing economic efficiency, environmental and social outcomes. This paper addresses this t opic by examining the environmental outcomes in terms of important LUCC measures related to petroleum E&P in eastern Venezuelas heavy oil belt (figure 3-1). This 54,000 km zone contains over 1 tr illion barrels of tar-like heavy crude oil, of which 270 billion are considered recoverable using current technology (Gipson et al., 2002; Talwani, 2002; Trebolle et al., 1993; Tankersle y and Waite, 2002; Balke and Rosauer, 2002). In this paper the local question is: does the local NOC perform as well as its MNOC partners in terms of balancing environmental alterations as sociated with E&P at the landscape level? I answer the question by examining the expansion of petroleum-related infras tructure features, or the petroscape, in three of Venezuelas four heavy oil be lt operations between 1990 and 2005 using Landsat TM and ETM+ and CBERS satellite images and GIS techniques. The concessions (see figure 3-2) and the amounts of company ownership involved are shown in table 3-1. In terms of co mpany participation, the breakdown is:

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50 Sincor: Local (NOC) 38%; European 62% Petrozuata: Local 49.9%; US 50.1% Ameriven: Local 30%; US 70% Cerro Negro: Local 41.67%; US 41.67%; European 16.66% Therefore, the local NOC, from here on refe rred to as Local, dominates Petrozuata; European companies dominate Sincor; US companies dominate Ameriven and both Local and US companies are tied in Cerro Negro (see table 3-1). Nevertheless, Cerro Negro is not included in this analysis due to exce ssive cloud cover in the imager y data set corresponding to this concession. Drawing from political ecology and organizational theor y, as well as research from oil and gas E&P alterations to landscape and wildlife, shows th at energy policy and corporate attitudes toward environmenta l planning and environmental re gulations can affect LUCC in terms of land degradation, landscap e fragmentation, loss of biodivers ity, river siltation, increased pollution and greenhouse gas emissions (Bry ant and Bailey, 1997; Nelleman and Cameron, 1996; Schneider et al., 2003; Morton et al., 2002; Morton et al., 2004; Lee and Boutin, 2006; Lawrence, 2007; Schneider et al., 2003). Through lessons learned, multinational oil companies (MNOCs) have increased their efforts to minimize and monitor environmental and social impacts tie d to E&P (Moser, 2001). Several reasons exist for improving performance in health, safety, and environment (HSE) as well as corporate social responsibility (CSR), or sustainable development initiatives. They include preventing: costly environmental disaster s, political protests, negative press coverage, work stoppages, sabotage, project terminations, forced closings, and human rights and worker abuses (Diamond, 2005; Lawrence, 2007; Anders on, 1994; Gladwin, 1977; Or litzky et al., 2003). Furthermore, implementing best business practices can increase employee and customer loyalty, attract the attention and money of financial analysts, investors and lenders (Morhardt, 2002), can offset actual or impending government regulation (Dashwood, 2007), improve a companys

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51 reputation, better address human rights (Kakabadse, 2007), improve community relations, and help stabilize long-term cost s (Reinhardt, 2000). As Shapiro (2000, pp. 63) notes: Far from being a soft issue grounded in emotion or ethics sustainable developmen t involves cold, rational business logic. Part of this logic involves identifying opportunities th rough sustainability integration, rather than traditionally responding to regulations, risk reduc tion and cost controls (Wilcoxon and Cramer, 2002). It also promotes voluntary adoption and repor ting of sustainable business practices defined by such groups as the International Petroleum Industry Environmental Conservation Association (IPIECA). Furthermore, European companies tend to be ahead of US companies in terms of implementing sustainability practices into their business plan (Marica et al., 2008; Moser, 2001). On the other hand, NOCs often tend to lack the experience and culture of minimizing environmental changes particularly since (until recently) they do not compete internationally for contracts and pursue their business as usual ap proach domestically (Diamond, 2005). When they do participate in the global economy they must implement the most advanced technology and methods in terms of their inputs to successfully compete (Porter and van der Linde, 2000), which involves costly and sustained investments. A second challenge is that most developing countries face major challenges and lack resources for planning and for managing natural systems and environmental quality (Gladwin, 1977). Third, many leaders in the developing world, whose countries depend on the production and export of primary products such as beef, bananas, timber, cocoa, copper or petroleum, and who don t have alternative sources of national income perpetuate practices that cont ribute to environmental degrad ation (Bryant and Bailey, 1997, pp.59). Finally, when the production of a natura l resource comprises a countrys primary industry and staple export product an d is strongly controlled by the fe deral executive office, as is

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52 petroleum in Venezuela, then other government bodies charged with the oversight of these activities, such as the ministry of the environment, may not have the capacity or will to enforce compliance. Based on the findings from this literature, I propos e three hypotheses. Hypothesis 1: The Local-company dominated conce ssion, Petrozuata, will exhibit the greatest amount of LUCC. Hypothesis 2: The US-company dominated concession, Ameriven, will di splay a smaller amount of LUCC. Hypothesis 3: The European-dominated concession, Sinc or, will show the least amount of LUCC. I test these hypotheses by measuring and ranki ng four important la ndscape ecology metrics related to oil E&P: vegetation-cover change, pe troscape density, edge-effect zones, and coreareas using remote sensing and GIS methods (Griffith et al., 2002b; Morton et al., 2002; Thomson et al., 2004; Morton et al., 2004) to determine how each performed. Remote Sensing, Geographic Informat ion Systems (GIS) and Petroleum Rem ote sensing (satellite image analysis) and geographic information systems techniques have proven useful and cost-effec tive prospecting tools for oil and gas deposits for over 30 years (Almeida-Filho, 2002; Jackson et al., 2002). Thes e methods have been applied to detecting hydrocarbon seepages in northeas tern Brazil (Almeida -Filho et al., 2002; Almeida-Filho et al., 1999) and the Netherlands (Noomen et al., 2006); to identify oil-bear ing sands in inner Mongolia, China (Zhang et al., 2007); and to optimi ze costs of field infras tructure facilities in western Argentina (Pochettino and Kovacs, 2001). A few studies address the role of oil E&P on landscape-change. These include Janks and Prel at (1994; 1995) who use remote sensing and promote these techniques to: 1) assess the health of vegetation in and around oil fields; 2) identify local environmental effects of oil drilling and waste management; 3) optimize the placement of infrastructure featur es such as exploratory wells, roadways, camps and pipelines;

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53 and 4) monitor the progress of remediation efforts on abandoned well sites. Meanwhile Musinksy et al. (1998) applied these technologies to determin e the relationship between the construction of oil roads and subsequent defore station in Guatemalas Laguna del Tigre National Park. They found a direct relationship betw een the construction of access roads for oil exploration and production, and the settlement in to undisturbed tropical ra inforest ecosystems (Musinsky et al., 1998, pp. 1). Jackson et al. (2002) and Gaddy (2003) also note how remote sensing technology has important business applica tions for the petroleum industry. These include monitoring environmental change, managing exis ting field operations; evaluating land property before leasing or abandonment; and least-cost an alysis for pipeline cons truction (Jackson et al., 2002). Concerning spills and pollution, Kwarteng (1998, 1999) and Ud din et al. (2008) used remote sensing to monitor contaminated oil la kes and the recovery of surrounding vegetation in Kuwait; while Chust and Sagarminaga (2007) used remote sensing to discriminate oil slicks in Venezuelas Lake Maracaibo. Mort on et al. (2002) used GIS an alysis to estimate the economic viability of undertaking oil and gas E&P activities in protected roadless areas of 6 western US states. Thomson et al. (2005) conducted a spa tial analysis of the gasscape (gas related infrastructure features) in western Wyoming to determine the potential effects on 4 wildlife species. Morton et al. (2004) implemented remote sensing and GIS analysis to estimate E&Pdriven changes to land-use and land-cover such as habitat fragm entation and loss of terrestrial wildlife and aquatic species in the US Rocky Mountains. The W ilderness Society (2006) also used remote sensing and GIS methods to meas ure habitat fragmentation and the effects on wildlife in Wyoming. They, along w ith Morton et al. (2002) and Mo rton et al. (2004) conclude that the expansion of E&P activities will cause permanent landscape changes that will reduce

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54 wildlife diversity and numbers; will negatively affect local ec onomies that depend on tourism and hunting; and will lead to further habitat fr agmentation through the expansion of new roads and more people to wilderness areas. More importa ntly, they estimate that the recovery amounts of oil and gas will be quite small, in some cases supplying US consumption needs 24 days (for oil) and 9 to 11 weeks for gas (Morton et al., 2002). This paper builds on the LUCC literature related to energy development by focusing on Venezuelas heavy oil belt, an underrepresented t opic about an area that is about to expand its concessions by more than 600% in the next 22 years in order to help double production. New operations will mostly involve other NOCs from countries such as Brazil, Iran, Cuba, Chile, China, India, Uruguay, Russia, Argentina and Ecuador, rather than MNOCs (PDVSA, 2008c, 2007a, 2007c, 2007e, 2007g, 2006a, 2006b; Petrogua, 2006-2007). These new partnerships will create their own E&P landscapes wi th greater or lesser alterations than currently exist. Better understanding how the four origin al operations managed their petr oscapes can prove useful for planning subsequent new ones. Materials and Methods Site Description The heavy o il belt (HOB) is a 54,000km stratigraphic trap that contains over one trillion barrels of heavy and extra heavy crude oil of which 270 billion are considered recoverable using current technologies (Gipson et al., 2002; Talwani, 2002; Trebo lle et al., 1993; Tankersley and Waite, 2002; Balke and Rosauer, 2002; Boza and Romero, 2001; Briceno et al., 2002). Located north of the Orinoco River in ce ntral and eastern Venezuela, th e HOB is subdivided into four separate E&P zones spanning the states of Gurico, Anzotegui, Monagas and a small portion of Delta Amacuro. From west to east the E&P zone s are Machete, Zuata, Hamaca and Cerro Negro (Balke and Rosauer, 2002; Kopper et al., 2001). Recently, they have been renamed to Boyac,

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55 Junn, Ayacucho and Carabobo, respectively (PDVSA, 2005a, 2005b), though much of the literature uses the old names when refe rring to these zones (see figure 3-2). The four current operations are located in the southern llanos or plains, of Anzotegui state located above the Orinoco River. This ar ea is marked by sandy soils with low natural fertility, grasslands, low and di spersed drought tolerant vegeta tion, gallery forests along the Orinoco River tributaries, and a sparse populat ion comprised of small scattered settlements (Hernndez et al., 1999; Dumith, 2004). Average annual temperature is 26C with mean diurnal and seasonal fluctuations of 9.5 C and 3 C respectively (Cha cn-Moreno 2004). A four-to-six month dry season affects the phenological variat ion of this seasonal savanna ecosystem where the environmental conditions are related to the amount of water availability (deficit or excess) in the soil during the year (Chacn-Moreno 2004). The important economic activities in these llanos include small-scale fishing along the many Orinoco-tributaries, small and large-scale farming (soy, cotton, sorghum, corn, rice), cattle ranching (since the 1548White, 1956), petroleum production (since the 1930s), Caribbean pine plantations (since the 1960s), a nd employment in the oil fields and pine plantations (Dumith, 2004; IADB, 1997; Parra, 2007; Mauricio et al., 2005; Hernndez et al., 1999; Talwani, 2001). All these activities help produce a heterogeneous landscape. The terrain is mainly flat, with a slope of less than 2% (Castel et al., 2002). El evations across the concessions range from near zero to 120 meters above sea level (m.a.s.l) along the banks of Orinoco Ri ver tributaries, to 121 to 250 m.a.s.l. in the northern part of the HOB. Data Description (Concessions and Control Groups) The data used in this chapter include LUCC m easures for 3 concessions and for 3 control group areas quantified from satellite images of the study area and th rough GIS techniques. Researchers such as (Morton et al., 2002; Morton et al., 2004; Schneider et al., 2003 ; Thomson et al., 2005;

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56 and Griffith et al., 2002b) have found that the following landscape-measures provide important indicators of ecological disturbance re lated to road and energy development: Vegetation-cover changerefers to how vegeta tion land-cover has changed between time periods. Here I examine the conversion of vegetation using the Normalized Difference Vegetation Index (NDVI), an algorithm th at measures the amount and condition of vegetation in a satellite image (Jensen, 1996), has been widely used in vegetation-change studies (Mambo and Archer, 2006; Tarnavsky et al., 2008) and is particularly related to the expansion of the petroscape (Musinsky et al ., 1998). This index also detects gains in vegetation cover. In order to detect ch anges between 1990 and 2000 I performed image differencing of NDVI images of the study ar ea, a common technique in change-detection studies (Mambo and Archer, 2006) that involved subtra cting the 1990 from the 2000 images. Road densityis a rough but useful measure that takes the total number of petroscape linear features measured in kilometers (km) and divided by the area in kmin this case the size of the concession. It is used to assess the potential impact of roads and infrastructures on local environments (F orman et al., 2003). Because its a density measure, the length of linear features is somewhat normalized by di viding this figure by the concession size. Edge-effect zonesrelated to petroscape density, these zones include the area of significant ecological effects extending out from petroscape features (Forman and Deblinger, 2000). They are created by us ing a 600 meter (m) buffer around the linear petroscape features. I selected this dist ance based on work by Forman and Deblinger (2003) regarding roads, Schneid er et al. (2003) and Dyer et al. (2001) in reference to energy roads in Alberta, Canada, and Morton et al. (2004), Thomson et al. (2005) and the Wilderness Society (2006) regarding LUCC a nd energy development in the western US. Core areaswhich are the intact habitat areas that remain after the concession has been fragmented by petroleum production activities. Time Frame The objective was to m easure the petroscape -related changes in la nd-cover between 1990, 2000 and 2005 and determine if the Local-dominat ed concession exhibited greater amounts of change than American-company and Europ ean-company dominated sites. The year 1990 corresponds to the period before E&P began in the HOB concessions (Talwani, 2002), officially known as strategic associations, and followi ng a business model that involves production, upgrading and commercializing heavy oil (Fipke and Celli, 2008.) The steps involve include:

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57 Extract crude from the ground using cold pr oduction. This means not adding gas or steam since reservoir temperature is warm enough for crude to be pumped to the surface. Once at the surface the crude becomes tar-like so a diluent, or a high-grade oil product, is added to improve mobility and make it more transportable. It is piped to a central processing facility (i n the concession) where gas, water and salt are removed. The produced water is injected underground. The crude is then piped 200 km to the coastal processing plant called Jose, located west of the town of Barcelona (see figure 3-2). At Jose it is upgraded which involves the removal of nickel, sulfur, vanadium, liquid petroleum gas, petroleum coke and heavy gas oil to produce a valuab le higher-grade oil, called syncrude (synthetic crude). During this process the diluent is recuperated and sent via pipeline back to field ope rations where it is reused. Some of the syncrude, is sent via ships to re fineries in the US Gulf coast and to Germany. The highest grade syncrude, howev er, is sold on the open market. (Oil company representative, 2005a ; Guerra, 2005; Talwani, 2002; Robles, 2001; Kopper et al., 2001; Pina-Acuna and Ferreira, 2004; Briceno et al 2003; Neff and Ha gemann, 2007; Gipson et al., 2002). The baseline year, 1990, predates the author ization by the Venezuel an National Congress to establish the concessions (Talwani, 2002). Howe ver, oil E&P has been part of the economic activities in the eastern Venezu ela basin since the early 1900s a nd in the area known as the HOB since 1935 (Talwani, 2002; Rodriguez et al., 1997; Boza and Romero, 2001; Gonzalez and Reina, 1994; Corfield, 1948). Between 1978 and 1983 the state-company, PDVSA, carried out an intensive exploration and evaluation progr am in the HOB and in 1990 began horizontal drilling projects in areas near th e present concessions of Petrozuata, Ameriven (known as Bare), Cerro Negro and other areas (Boza and Romer o, 2001; Gonzalez and Reina, 1994; Rodriguez et al., 1997). Exploration is still ongoing (Galarraga et al., 2007) and therefore E&P patterns are likely to be discernible in satellite images of th e study area apart from the four HOB concessions.

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58 The next date, 2000, corresponds to early-to-m id production phases wh en operations were underway, while 2005 includes full production, when the upgrader/refinery located on the coast was operating. In addition to examining change s in the four concessions, I also examined changes in three control areas, which were ra ndomly selected polygons of a uniform size to match the average size of all four concessions, 575 km. This was done to test whether the infrastructure expansion changes occurring in th e concessions were related to petroleum E&P actions or other activities such as agriculture (crops and cattle). Image Processing and GIS Procedures I utilized the following im ages obtained from the Global Land Cover Facility (GLCF) at the University of Maryland and through Brazilian National Institute for Space Research (INPE): Landsat TM path 2, row 54, acquired 19 April, 1990 Landsat ETM+ path 2, row 54, acquired 30 April 2000 CBERS CCD2 path 179, row 90, acquired 12 May 2005 CBERS CCD2 path 180, row 90, acquired 20 February 2005 CBERS CCD2 path 180, row 91, acquired 25 January 2005 The first two Landsat scenes (path 2 row 54) en compassed the three western concessions, Sincor, Petrozuata and Ameriven and the dates corres pond to the end of the dry season. These same concessions lay across two CBERS images, path 180 row 90 and path 180 row 91, whose dates correspond to the middle of the dry season. I mo saicked and feathered these two scenes to produce an image encompassing the three conces sions Sincor, Petrozuata and Ameriven. The CBERS imagery was captured two-to-thr ee months apart from the anniversary Landsat imagery, making it difficult to compar e changes during different times of the year4 (Jensen, 2005). More importantly, due to excessive noise inherent in these particular scenes5, 4 Daniels et al., 2008 indicate that image calibration correct s for differences due to non-anniversary date images. 5 Performing a Fourier transformation followed by an in verse Fourier transformation (Jensen 2005) still did not resolve some of the problems such as striping.

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59 such as missing lines of data and some corrupt ed bands, I did not use them in the changedetection process but did for the subsequent m easures: petroscape density, edge-effect zone and core-areas. All images were processed with ERDAS Im agine 9.1. I used a Landsat ETM+ 2000 WRS2 path 2 row 54 scene rectified to the Univer sal Transverse Mercator (UTM) 20 N projection, WGS-84 datum, as the base image for imageto-image registration of the Landsat TM 1990 image for the same path and row 6. Subsequently, I used the rectified 2000 Landsat TM 15m panchromatic band to rectify the CBERS 2 2005 imagery because the improved spatial resolution aided in detecting petroscape features. As with the 1990 TM scenes, a nearest neighbor resampling algorithm was used with a root mean square error of less than 15 m for each point (Southworth et al., 2004). The 20-m resolu tion on the CBERS 2 scenes was kept in the rectified output. An overlay functi on then confirmed that all the s cenes overlapped exactly across the three image dates (Southworth et al., 2004). Waypoints and routes collected in the field, as well as road networks, oil wells and production facilities were al so examined to confirm proper overlap. Petroenergy Maps After scanning paper petroenergy m aps at high resolution (P etrogua, 2004-2005, 20062007), 1) I rectified them to the first base image. Next I digiti zed the outlines for the Orinoco heavy oil belt and each concession bloc k to create vector polygons (in a GIS7). Then I overlaid them over the Landsat 1990 and 2000 scenes and on the CBERS 2005 mosaicked scene to provide the location of the c oncessions boundaries. Finally I us ed the concession polygons to 6 This was accomplished using Lei ca Geosystems ERDAS Imagine 9.1. 7 ESRI ArcMap 9.1 was used for GIS work and analysis in this project.

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60 create 9 subset images each for 1990, 2000 and 2005, representing the before-during-andafter establishment of th e heavy oil operations. Control Areas To further test whether the infrastructure ex pansion changes occurring in the concessions were related to petroleum E&P actions as opposed to other activities such as agriculture, I randomly generated polygons (using Hawths Anal ysis tools extension for ESRI ArcGIS 9.2). Each one was equivalent to an average size a ssociation block, or 575km. Out of a possible 15, three were selected, referred to here as cont rol 02, 04 and 07 respectively. These three were chosen because they had minimal cloud cover, f it within the smaller f ootprint created by the CBERS mosaicked image (113km wide) overlain onto the Landsat ETM+ (185km wide) scene, and did not overlap current HOB concessions. Normalized Difference Vegetation Index (NDVI) After m asking out the clouds on the subset im ages I calculated the NDVI vegetation index (mentioned above under data descri ption) for the Landsat scenes: Band 4 (NIR) Band 3 (R) Band 4 (NIR) + Band 3 (R) I chose NDVI because it analyzes continuous data, ra ther than fixed classifi cations; is better at detecting land-cover modifications; and has been used in studies involving petroscape-related LUCC because of the contrast against healthy biomass (Southworth, 2004; Southworth et al., 2004; Musinsky et al., 1998; Kwarteng, 1998, 1999). Change-Detection: 1990 to 2000 Here I exam ined the changes in vegeta tion-cover between 1990 and 2000 through image differencing (subtracting the imagery of one da te from another) of the NDVI images for 3 concessions and 3 control polygons. This is a commonly used techniqu e in change-detection

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61 (Mambo and Archer, 2007) where the subtraction results in positive and negative values in areas of radiance change and zero values in areas of no change (Jensen, 2005, pp. 471). This process of selecting positive, negative and zero change values is termed thresholding and is rather dependent on the analyst (Mambo a nd Archer, 2007, pp. 382). After examining three threshold levels, I selected 25% since it best bro ught out petroscape features in the images. These change-detection maps in figures 3-3, 3-4 and 3-5 show negative and positive changes in the NDVI for the concessions and control polygons. Negative changes were much greater than positive changes for all six images and involved petroscape and nonpetroscape features such as agricultural land and roads. Meanwhile gains in the vegetation index primarily followed the river courses, where riparian or rive rine forests grow (San Jos et al., 2001). Figure 3-6 graphs the negative and positive change in NDVI for the th ree concessions and the three control groups. Such broad changes across all six images sugg est that not only petroscape and agricultural activities were driving land-cover change, but th at climatological factor s were also involved. Figure 3-7.shows how rainfall amounts in 1989 and 1999, which preceded the capture of the 1990 and 2000 Landsat scenes used in this study, varied from aver age values. The 1989 dry season was abnormally dry, while the 1999 dry seas on was unusually wet. These differences in precipitation likely affected the spectral profiles of the dry tropical sa vanna landscape, not to mention river levels. GIS Techniques: the 2005 Petroscape The 2005 CBERS subse t images were used to map infrastructure features, consisting of the linear petro and nonpetroscape attrib utes in km to test if the land-cover changes found between 1990 and 2000 were related to petr oscape expansion. First, I digi tized the discernible road

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62 network and petroscapes in the six sites (using on-screen digitizing)8. Using ground control points of well sites gathered in the field and provided by Carrera (August 2004, 2005) along with the 20 m resolution from the CBERS imagery it was possible to distinguish the petroscape, which included wells and wellpads, roads and cen tral production facilities. Nonpetroscapes were equally discernible. In instances where cloud cover or noise obstruc ted features I referred to the Landsat 2000 scene, where in some cases pe troscape features where already established. Agriculture was certainly part of the landscape as early as 1979, based on Landsat scenes of the study area. Once all features were digitized I overlaid this vector data onto the 1990-2000 changedetection maps to help determine their accur acy. By analyzing the digitized full petroleum production pattern in the concession sites as it related to the change -detection maps it was possible to determine if observed negative change followed (and would continue to follow) the expansion of the petroscape established by 2005. For both Sincor and Petrozuata, the changedetection maps did fit the patt ern of petroscape expansion f ound in 2005. Thus, this threshold was a good predictor of ultimat e changessee figure 3-8. Petroscape density This m easure is calculated by dividing the to tal length of linear features in km, by the concession size in km and is commonly used in la ndscape change studies to determine the effect of roads and other infrastructure features on the landscape (Forman et al., 2003; Morton et al., 2002; Thomson et al., 2004; Morton et al., 2004; Schneider et al., 2003). Habitat fragmentation is a particularly important outcome of road and in frastructure building. Its defined as a process where a large habitat area is transformed into small patches, isolated among each other by a 8 Robert Richardson, GIS Specialist at the Univers ity of North Florida, helped with this task.

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63 habitat matrix different to the original one (Ferraz et al., 2006, pp. 460). Since greater petroscape density results in in creased habitat fragmentation this suggests that best practices regarding infrastructure building and the asso ciated ecological effects are not implemented (Forman et al., 2003; Morton et al., 2004; Form an and Deblinger, 2000; Morton et al., 2004). Edge-effect zones These zones include areas exte nding out from infrastructur e features that experience significant ecological effects (Forman and Debl inger, 2003). I calculated these by creating a 600 m buffer around the linear petro and nonpetroscape. This buffer di stance is based on work from three groups. First, Forman and Deblinger ( 2000), who calculated average value of 600 m for what they term road-effect zones affecting moose, white-tailed deer, forest and grassland birds, and amphibians on a suburban highway in eas tern Massachusetts. Second, investigators interested in avoidan ce and stress responses by caribou to oil roads, wellpads and petroleum activities in Alaska and Canada found avoidan ce zones ranging from 250 m to 4 km, with 600 m as an average value (Nelleman and Cameron, 199 6; Noel et al., 2006; Sc hneider et al., 2007; Wolfe et al., 2000; Bradshaw et al., 1 998; Harron, 2007; Dyer et al., 2001). Third, Thomson et al. (2005, pp. 17), noted that in open landscapes in western Wyoming mule deer avoidance zones may be close to 500 m and that these animals showed no evidence of acclimating to energy-related in frastructure. Other large wildlif e in the same area, such as pronghorn exhibited a weak avoidan ce of areas within 965 m, while elk avoidance zones to roads and active oil and gas wells was 1.9 km in the su mmer and almost 1 km in winter (Thomson et al., 2005). In the case of sage-grouse (birds), hens that nested an averag e of 1.1 km from roads were most successful at raising chicks, while th ose averaging 270 km from these features tended to have broods that did not survive the firs t three weeks after hatc hing (Thomson et al., 2005).

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64 Taking these figures into account and noting th at deer and tapir a nd parrots liv e in the Venezuelan llanos (Prez, 2000; Gorzula, 1978; Marn et al., 2007; Lpez-Fernndez and Winemiller, 2000; Veneklaas et al., 2005; Rosale s et al., 1999; Padilla and Dowler, 1994) I selected the 600 m buffer. Figure 3-9 shows the extent of the edge-effect zones for both the concessions and the control areas. Core-areas Core-areas define land beyond a given distance or effect zone from transportation areas (Thomson et al., 2005). Here, they refer to intact habitat areas that remain after the concession has been fragmented by petroleum production activities (in km). These patc hes are important for maintaining wildlife diversity as well as ecosy stem goods and services (Forman and Deblinger, 2000; Spellberger 1998; Jones et al., 2000; Vos and Chardon, 1998). Core-areas are essentially the land patches that remain after the edge-eff ect petro and nonpetrosca pes are dissolved from the concession using GIS techniquessee figures 3-10 and 3-13. Agriculture In order to better gauge the ro le of agriculture in these llanos I also digitized the agricultural areas within the concessions and control groups. I used spectral analysis and fieldgathered waypoints to find and classify these plot s, as well as the 15 m panchromatic bands and composite Landsat images for 1990 and 2000 to de lineate these areassee figures 3-15 through 3-17. Given season dynamics at times it was difficult to distinguish dry savanna vegetation from cleared agricultural plots. Rainfall Patterns Finally, I ex amined rainfall records for the town of Ciudad Bolvar, the nearest metropolitan area with relevant ra infall records and the same clim atological regime as the study area. Data showed significant rainfall varia tion between the months of October through

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65 December 1989 and 19999. These correspond to part of the dry season preceding the acquisition of the 1990 and 2000 Landsat images. Results and Discussion Vegetation Change Im age differencing of NDVI images (2000-1990) of three HOB concessions and three control groups revealed a patt ern whereby vegetation biomass decreased much more than it increased across all six sites. Table 3-2 shows th e amount of land in km per site that exhibited NDVI losses and gains, as well as the percentage of the site that was affected. Based on the changes in NDVI, Ameriven showed the greatest loss of vegetation coverwith almost 50% of its land affected, followed by the th ree controls, then Sincor and finally Petrozuata. Figures 3-3, 3-4, and 3-5 display the patterns of land-cover change based on the NDVI. Meanwhile, Sincor had the greatest gains in vege tation-cover, followed by Sincor Control 04 and 06 came next, respectively. Ameriven and Contro l 07 showed the least gains. Excluding Sincor and Petrozuata, agricultural activity appears to be a predom inant feature in the remaining sites. Since the Landsat images were acquired in Ap ril, at the end of the dry season, the NDVI highlights the healthy biomass, which is strongl y affected by variations in temperature and rainfall (Funk and Brown 2006). This is relegated to the riparian forests and agricultural areas not yet harvested. Furthermore, as the end of the dry season approaches, a lot of agricultural and cattle land has been burned in anticipation of the rainy seasona longtime land management tool (Chacn-Moreno, 2004; White, 1956). All these factors help explain why gains in healthy biomass are sparse, particularly in Ameriven and the control sites. 9 Rainfall records for the dry months of January and February 1990 were not available.

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66 The detected gains in NDVI are likely due to the difference in rainfall between 1990 and 2000. Based on rainfall records of Ciudad Bolivar, October through December1989 were markedly drier than average, while the same time period in 1999 was we tter than average (see figure 3-7). These months with complete data, correspond to the dry seas on prior to the capture of the Landsat 1990 and 2000 scenes. 1999 was th e wettest year in 68 years (1921-2002, for years with complete 12 month records), while March 2000 was the wettest March in 68 years. Such high levels of rainfall during the normally dr iest months of the year apparently had an impact on the area rivers, lead ing to unseasonable flooding and vegetation growth along the riparian forests in 2000. Thus w ith a months lag time, the obser ved April patterns of vegetation growth along the rivers is pr obably due to excess rainfall. Petroscape When the 2005 petroscape is overlain on the ch ange-detection m aps, it covers the bulk of Petrozuata and Sincor, but also extends into pa rts of Ameriven and control 07. This measure is an important indicator of habitat fragmentati on. In Sincor and Petrozuata the petroscape primarily extends over the areas of negative NDVI. This suggests th at the 25% threshold selected (because it highlighted petroscape features) captured areas that indeed exhibited petroscape expansion in 2005. Controls 02 and 04 do not appear to have a pe troscape. While Petrozuata has the longest linear km petroscape and petroscape density, follo wed by Sincor, control 07 also has substantial petroscape. This site is not an HOB concession and yet it has more linea r features and density than Ameriven. This confirms that oil E&P has been part of the land history in the HOB for decades (Boza and Romero, 2001; Gonzalez, 1994; Rodriguez et al., 1997; Galarraga et al., 2007), though it is distinct from the strategic association business model to which Sincor, Petrozuata and Ameriven belong.

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67 It is curious that Ameriven shows the l east amount of petroscape even though its development was approved in mid-1999, initia l drilling commenced in 2000 and production started in November 2001, only three years afte r Petrozuata (Gipson et al., 2002; Chevron, 2001). It reached commercial pr oduction in October 2004 with the completion of the upgrading plant, and full capacity in the first quarter of 2005 (Chevron, 2006, 2005, 2004; oil company representative, 2005e). Therefore the full petroscape would have b een established by the time the April 2005 CBERS image was acquired. This help s support the claim by Gipson et al. (2002) that this concession has implemented best bus iness practices including the reduction of field processing facilities and pipeli nes, and through longer horizonta l wells, fewer wells, wellpads and related infrastructure. Table 3-3 shows important petroscape values, in cluding linear length, dens ity, edge-effect zones and core-areas. While Sincor has a similar petr oscape length to Petrozuata, the petroscape density values are quite different This is due to the concession size, where Petrozuata is the smallest. This means that for every km there ex ists 1.13 km of linear petroscape features in Petrozuata. Sincor is next, follo wed by control 07 and Ameriven (with none in controls 02 and 04). Morton et al. (2004) observe that in open landscapes with little surrounding vegetation (such as many parts of the Venezuelan llanos ) road avoidance by wildlife occurs at road density rates of 0.8 km per km or higher. Petrozuata is the only site with values above this threshold, suggesting that this concession may have fewer wildlife densities th an surrounding areas. The edge-effect zones appear in figure 3-9 and represent a 600 m buffer extending from the linear petroscape. This time the land occupied is calculated in km and Sincor shows the highest values followed by Petrozuata, Control 07 and Ameriven last. While this measure provides a distance from which significant ecolo gical effects extend (Forman and Deblinger,

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68 2000), when size of the edge-effect per site is calculated then nearly 75% of Petrozuatas concession is affected. This compares with 62% for control 07, 53% of Sincor and almost 20% of Ameriven. Finally, the size and distribution of core-areas is an importa nt measure because it shows how many land patches remain inta ct after the petroscape edge-eff ect has been dissolved. Figure 3-10 displays this pattern. This time Ameriven had the largest core-area, occupying 82% of this concession. Next came control 07, followed by Sinc or and Petrozuatawith only 26% of its land consisting of nonpetroscape patches. It is important to note that these core-areas do not represent intact, roadless mosaics providing unaltered hab itat for local wildlife. Clearly agricultural activity and a network of roads exis t in all the sites, as the next results indicate. This core-area measure assumes that if only E&P activities are modifying or converting the land, then the particular patterns selected by the consortia of each concessi on show that Ameriven has potentially altered the le ast amount of habitat. This may be due to the implementation of best environmental business practices. Nonpetroscape As table 3-4 and figures 3-11 th rough 3-13 illustrate (see follo wing pages), five of the six sites displayed nonpetroscape featuresprim arily nonoil roads and farm roads. Ameriven, with 486 km of linear nonpetroscape had the highest values and density (0.73), pr imarily related to the pine plantations and agricultura l lands on its site. The actual petroscape comprised a much smaller amount of the overall infrastructure. Control 07 followed and had a significan t nonpetroscape density of 0.69. This was followed by control 02, control 04 an d Petrozuata. Sincor did not appear to have nonpetroscape features. The edge-effect zones showed control 07 having the broadest expanse, followed very closely by Ameriven. Control 04 cam e next; followed by control 02 and Sincor with the smallest

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69 amount (again Sincor had zero petroscape). Based on these patterns control 07 has the most habitat fragmentation since the in frastructure features extend th roughout the site. Ameriven, with similar values had larger areas not affected by infrastructure. Finally, the core-areas map, figure 3-13, shows how control 07 has the fewest intact areas at 220.6 km. Petrozuata followed, then Ameriv en, control 04 and c ontrol 02. Control 02 is located closest to the Orinoco River, an area that is sparse ly populated (Dumith, 2004), which helps explain how it has the larges t core-areas of all sites (excluding Sincor). However, when the overall size of core-areas is normalized per concession size, then Petrozuata has the largest percentage of core-areas, 85%, followed by control 02, control 04, Ameriven and control 07. Petro and Nonpetroscape Figure 3-14 shows the o verlay of the petro and nonpetroscape on each site. Controls 02 and 04 appear to have the least amount of lands cape disturbances, while the rest have been clearly altered by human activity. Ou t of these four, the alterations in Sincor seem exclusively tied to petroleum E&P. Petrozuatas LUCC is cente red on petroleum activities agriculture is also evident. Based on an examination of the 1990 and 2000 images, it predated oil activities in this concession. Out of the HOB concessions, Ameriven had the smallest petroscape, but a significant nonpetroscape related to agriculture, in particular Caribbean pine pl antations. Part of the reason for a decreased petroscape could be the implementa tion of lessons learned and best practices in this, the newest concession (Gip son et al., 2002). On the other hand, as the newest operation perhaps it has not fully explored and drilled the site allotted to it, although Chevron (2006) says In 2005 the project facility reach ed total design capacity of processing and upgrading. Perhaps as production declines, new wells will be drill ed to maintain capacity, which would require further changes in land-cover. A further explanation for the reduced petroscape pattern could be the timber damage fees that oil companies have to pay to forestry companies for removing wood

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70 as part of their operations (Sc hneider et al., 2003; Carrera 2005). This could be offset by the high price of oil, though world prices did not consistently reach above $30 until April 2004 (EIA, 2008). Petrozuata and Sincor show similar linear petroscape expansions. However Petrozuatas smaller size means that the density of petroscape features is the greate st at 1.13 km/km. This was the first concession established and could in part explain the greate r petroscape expansion. Had Cerro Negro been included these results would perhaps differ. Rankings As table 3-5 highlights, Petroz uata and control 07 tied and e xhibited the most alterations related to petroleum E&P between 1990 and 2005. Si ncor followed, then Ameriven. Controls 02 and 04 showed no discernible petroscape. A hi gher number means more disturbances. Since positive changes in NDVI and core-areas petros cape refer to reduced alterations, their rank values were inverted so as not to confound resu lts. After all, a larger core-area number meant that more land remained undisturbed. Based on these rankings, one of three hypotheses is supporte d. As expected, Petrozuata, with the highest level of local-company particip ation had most petroscape-related disturbances with a rank of 25. On the other hand, Sincor, with European-company dominance did not show the least landscape alterati ons, ranking 22. Instead, Ameriven, with US-company dominance showed the least, though it was expected to ra nk second, below Petrozuata. It ranked 21. Control 04 ranked 18 and control 02, 15. Nevertheless thes e two showed no petroscape, but since they were included in the rankings, their zero valu es were assigned values of 1.5. Consequently though they had no core-areas, when the values we re inverted, instead of receiving ranks of 1.5, they got 5.510. 10 Not inverting these rankings for Controls 02 and 04 did not change rankings.

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71 Conclusion When the important petroscape values are ranked (NDVI change, lin ear and petroscape density, edge-effect zones and co re-areas) Petrozuata exhibits the greatest changes, supporting the hypothesis that it would. Sincor, where European com panie s dominate did not show the smallest alterations as expected. Instead, Amer iven, where US companies have the greatest presence showed the least alterations. Thus Sincor and Ameriven switched rankings. Perhaps most surprising was finding that one of the contro l sites, 07, exhibited a substantial petroscape, tying with Petrozuata. While this block is not on e of the strategic associations to which the oil concessions examined belong, it confirms that E& P is part of the land-use history in the HOB. The area where control 07 is located is known as the Bare field, where more than 150 wells had been drilled by 2002 (Gipson et al., 2002). Though only three out of four concessions were examined in this paper, including three control groups helped increase the sample and determine wh ich types of economic activities are most prevalent in this part of the eastern lla nos. The methods used he re provide an innovative approach to studying landscape change related to nonrenewable resource extraction. Future work could build on these findings by implementing th ese methods to establish baseline conditions prior to subsequent E&P. This would help implem ent best practices during the planning phase in order to reduce associated environmental change s. Subsequent efforts would continue to use remote sensing and GIS techniques as a cost -effective way to monitor the expansion of operations throughout the projects life cycl e and then during recuperation phases. Finally, while these results are based on a ve ry small sample, they do indicate that UScompany participation may be influential in the implementation of best business practices since the Ameriven concession, which had a 70% US -company participation exhibited the least petroscape alterations. Consequently, two US MNOCs recently stopped operating in Venezuela,

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72 ExxonMobil and ConocoPhillips, while the Local company increased its participation to a majority status in all four concessions. ConocoPhillips was a partner in Ameriven, while ExxonMobil was a partner in Cerro Negro. If E&P continues to expand as planned by the Venezuelan government and the Local company, then the HOB stands to experience alterations to 18,000 km, or 33% of the HOB land area, see figure 3-18. The types of business partne rs selected may likely affect the extent of petroscape related alterations.

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73 Figure 3-1. Venezuela, the heavy oil belt and concessions, and surrounding countries 2005 (Petrogua, 2006-2007; ESRI Geographic Network Services 2008). The yellow outline delimits the heavy oil belt, which is divided into four E&P zones, and spreads across the dry tropical savanna landscap e. The red smaller polygons show the location of the four concessions.

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74 Figure 3-2. Venezuelas heavy oil belt, its four concessions: Sinc or, Petrozuata, Ameriven, and Cerro Negro, and the upgrading/refinery plant called Jose located on the coast of Anzotegui state, 2005. Though the HOB spans Gurico, Anzotegui and Monagas states, the four concessions are all loca ted in Anzotegui (ESRI 2006; Petrogua, 2006-2007). From west to east the four h eavy oil belt exploration and production zones, marked by the red divisions are: Machete, Zuata, Hamaca and Cerro Negro. However, they have now been renamed to Boyac, Junn, Ayacucho and Carabobo (PDVSA, 2005a, 2005b; Kopper et al., 2001; Balke and Rosauer, 2002). Note: the HOB also reaches into the eastern state of De lta Amacuro, but its reach is so slight it is not included on this map or mentioned in the text.

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75 Figure 3-3. Vegetation-cover change across all six sites (three c oncessions and three controls) between 1990 and 2000. The areas in red indicate a decrease in NDVI by 25% or more. The dark background represents areas of some increase, unchanged, or some decrease. In Sincor and Petrozuata, the petroscape becomes discernible as lines (roads) and polygons, production facilities are established.

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76 Figure 3-4. Increases in Normalized Differen ce Vegetation Index (NDVI) across all six sites between 1990 and 2000. The green areas repr esent gains in NDVI by 25% or more. The dark background represents areas of some increase, unchanged, or some decrease. Note that green areas are mostly riparian forests following river courses in the study area. These smaller changes in ve getation cover contra st with the large changes in figure 3-3.

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77 Figure 3-5. NDVI losses and gains for all six sites between 1990 and 2000. Red indicates areas of NDVI increase, and green gains. The agricultural landscape becomes evident in control 07 and in Ameriven, where rectangula r polygons represent pine plantations in the area.

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78 A B Figure 3-6. Gains and losses of the Normalized Difference Vegetation Index (NDVI) for the three concessions: A) Sincor, B) Petrozuata, and C) Ameriven, as well as the three control groups: D) Control 02, E) Control 04, and F) Control 07.

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79 C D Figure 3-6. Continued.

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80 E F Figure 3-6. Continued.

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81 0.00 20.00 40.00 60.00 80.00 100.00 120.00Month 1989 13.5039.3017.30 Average 91.8059.6041.50 1999 99.0072.0094.00 OctoberNovemberDecember Figure 3-7. Rainfall amounts (in mm) for Ciudad Bolvar for the October, November and December dry months preceding the 1990 and 2000 Landsat images used in this study. Note that all three m onths were drier in 1989 than average values and 1999 measures, helping explain the phenol ogical differences in both images.

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82 Figure 3-8. Decrease in NDVI be tween 1990 and 2000 overlain by 2005 petroscape features. The red lines show the extent of the petroscape in four sites. Controls 02 and 04 displayed no discernible petroscape. Petrozuata has th e longest petroscape in linear km and density, followed by Sincor, cont rol 07 and finally Ameriven.

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83 Figure 3-9. Edge-effect zones, or areas where si gnificant ecological effects extend (Forman and Deblinger, 2000), appear in red. Based on the petroscape linear features, these 600 m buffered zones indicate how much land of each site is potentially affected by infrastructure features which in turn have negative consequences for wildlife and ecosystems (Morton et al., 2004; Thomson et al., 2005; Forman and Deblinger, 2000). Controls 02 and 04 displayed no petroscap e edge-effect zone. The white background areas show the negative changes in NDVI between 1990 and 2000. The areas in black represent little positive or negative ch ange as well as areas of no change.

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84 Figure 3-10. Core-areas, marked in green, show si ze and distribution of the patches of land that remain after dissolving the edge-effect zones in figure 3-7. Controls 02 and 04 displayed no petroscape core-areas.

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85 Figure 3-11. Linear nonpetroscape 2005. The yellow lines represent roads related to agriculture and transportation not associated with petr oleum activities. While Sincor appeared not to have a nonpetroscape, Petrozuata displayed the smallest one out of the remaining sites. This indicates that thes e two concessions are petroleum E&P zones. Controls 02 and 04 had some transportation and agricultural roads across their sites, but Ameriven and control 07 had the most. The checkerboard pattern near the center of Ameriven shows the 1-km blocks typical of the Caribbean pine plantations in this part of Venezuela.

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86 Figure 3-12. Edge-effect nonpetroscape zones. Yellow lines indicate the extent of the edge-effect zones. With a 600 m buffer extending out from the linear nonpetroscape both Ameriven and control 07 infrastructure disturbances stretch across a significant amount of their plot. They are followed by control 04 and control 02. Petrozuata has the least and Sincor was not included because of no discernible nonpetroscape.

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87 Figure 3-13. Core-areas that remain after nonpetr oscape features are dissolved from each site. Overall controls 02 and 04 had the largest areas, followed by Ameriven, Petrozuata. Control 07 had the least.

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88 Figure 3-14. Petro and nonpetroscapes across all 6 sites. Red indicates petro, yellow reflects nonpetro. The NDVI background has been cleared to make the two types of features more discernible. Control 07 exhibits hi gh levels of both. Ameriven shows more nonpetro than petroscape features while Petrozuata shows a petroscape mostly.

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89 Figure 3-15. Agricultural patter ns in the three concessions an d the three control groups in 1990. Both Ameriven and Control 07 had more than half their area dedicated to agriculture.

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90 Figure 3-16. Agricultural patter ns in the three concessions an d the three control groups in 2000. Activity appears to have increased ever ywhere except in Amerivensee figure 3.17.

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91 0 50 100 150 200 250 300 350 400 450 LocationKm2 of Land AGR 1990 Amt Land Use Km2 1.5221.27402.7835.4728.39252.28 AGR 2000 Amt Land Use Km3 2.3240.58373.9342.6340.31412.8 SincorPetrozuataAmerivenControl 02Control 04Control 07 Figure 3-17. Amount of agricultu ral land (in km) found in the three concessions and three control groups in 1990 and 2000. Note how Ameriven and Control 07 have large amounts of their territory de dicated to agricultu re. In Amerivens case, Caribbean pine plantations extend through the concession.

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92 Figure 3-18. Future heavy oil development in Venezuelas Heavy Oil Belt.

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93 Table 3-1. Venezuelan heavy oil belt strategic associations, company ownership by percentage, syncrude (synthetic oil) production and upgraded quality 2005 (Petrogua, 2005-2006; Talwani, 2002; Mommer, 2004). Sincor Petrozuata Ameriven Cerro Negro Company Ownership PDVSA 38% Total (France) 47% Statoil (Norway) 15% PDVSA 49.9% ConocoPhillips 50.1% PDVSA 30% Chevron 30% ConocoPhillips 40% PDVSA 41.67% ExxonMobil 41.67% BP(United Kingdom) 16.66% Syncrude Production and Upgraded Quality 180,000 b/d From 8.5 to 32 API 104,000 b/d From 9 to 1925 API 190,000 b/d From 7-10 to 26 API 105,000 b/d From 6-10 to 16 API Congressional Authorization 1993 1993 1997 1997 Early Production 2001 1999 2002 1999 Full Production (upgrader Start-up) Mar 2002 Jan 2001 Oct 2004 Aug 2001 Table 3-2. Change-detection measures showing amounts of positive and negative change per site in km and as a percentage of the site area. Concession/ Site Negative NDVI km Positive NDVI km % Veg Loss % Veg Gain Sincor 77.08 41.36 14.90 8.20 Petrozuata 39.07 21.00 12.99 6.95 Ameriven 309.90 2.19 46.57 0.33 Control 02 235.70 17.33 40.81 3.23 Control 04 208.95 23.12 36.15 3.86 Control 07 296.03 0.35 51.35 0.06

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94 Table 3-3. 2005 petroscape measures for the six study sites. Concession / Site Concessio n Area (km) Petroscap e Linear 2005 (km) Petroscap e Density 2005 (k/km) Edge Effect Petroscap e 2005 (km) % of Site Affecte d by EdgeEffect Core Area Petroscap e 2005 (km) % of Site Affected by Core Areas Sincor 517.00 302.33 0.58 271.57 53. 00 245.80 48.00 Petrozuata 300.00 338.70 1.13 222.49 74.00 78.29 26.00 Ameriven 665.00 119.63 0.18 117.07 18.00 548.37 82.00 Control 02 575.55 0.00 0.00 0.00 0.00 0.00 0.00 Control 04 578.00 0.00 0.00 0.00 0.00 0.00 0.00 Control 07 576.50 153.86 0.27 164.80 62.00 411.70 72.00 Table 3-4. 2005 nonpetroscape measures for the six study sites. Concession/ Site Concession Area km2 Non petroscape Linear 2005 km Non petroscape Density 2005 k/km2 Edge Effect Non petroscape 2005 km % of Site Affected by EdgeEffect Core Area Non petroscape 2005 km2 % of Site Affected by Corearea Sincor 517.38 0.00 0.00 0.00 0.00 0.00 0.00 Petrozuata 300.77 47.39 0.16 45.61 15.16 255.17 84.84 Ameriven 665.44 486.02 0.73 348.32 52.34 317.11 47.65 Control 02 577.55 168.26 0.29 148.62 25.73 428.93 74.27 Control 04 578.00 128.78 0.22 152.36 26.36 425.73 73.66 Control 07 576.50 397.48 0.69 355.91 61.74 220.60 38.27 Table 3-5. Overall rankings for land-cover change and petroscape featur es in the 6 sites. A higher number means greater overall lands cape disturbances. Note: positive NDVI and core-area petroscape values were inverted to maintain rank consistency. Concession/ Site Negative NDVI km Positive NDVI km Petroscape Linear km Petroscape Density 2005 Edge Effect Petroscape 2005 km Core Area Petroscape 2005 km2 Overall Rank Sincor 2 1 5 5 6 3 22 Petrozuata 1 3 6 6 5 4 25 Ameriven 6 5 3 3 3 1 21 Control 02 4 4 1.5 1.5 1.5 5.5 18 Control 04 3 2 1.5 1.5 1.5 5.5 15 Control 07 5 6 4 4 4 2 25

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95 CHAPTER 4 SPATIAL ANALYSIS AND STATISTICAL C ORRELATIONS AMONG PETROSCAPE FEATURES IN THE HEAVY OIL BELT Introduction Worldwide, heavy and extra-heavy oil com pri se about 40% to 50% of total petroleum resources (Ascencio-Cendejas and Reyes-Venega s, 2006; Khelil, 2006). Compared to traditional petroleum, this tar-like crude requires different financial and technological inputs to convert it to usable petroleum products and until the late 1990s was not an attractive in vestment (particularly in Venezuela). Today, increased demand and higher prices mean that exploration and production (E&P) activities for heavy oil are not only viable but will expand in the next decade especially in Canada, Mexico, Brazil and Venezuela11 (Ascencio-Cendejas and Reyes-Venegas, 2006). In Canada, Albertas oil sands deposits total 95% of that countrys proven reserves (EIA, 2007a). By 2020 Mexicos heavy oil production is expect ed to increase to 50% of overall national production (Ascencio-Cendejas and Reyes-Venegas, 2006). In Venezuela current heavy oil production is centered in the eastern part of the country known as the heavy oil belt (HOB ), located on the plains, or llanos north of the Orinoco River (see figure 4-1). Current producti on is around 635,000 barrels per day12 (b/d) or about 21% of Venezuela total productionwhich ranges from 2.8 to 3.6 million barrels per day (mb/d) depending on the source (OPE C, 2007, EIA, 2007a; BP, 2007). In the late 1990s Venezuela began four concessionsalso known as st rategic associations in the HOB, comprised of public-private partne rships between seven companies. One local company, state-owned Petrleos de Venezuela Sociedad Annima, or PDVSA, had an ownership 11 In addition to these countries, other major deposits are found in the US, Trinidad, Ma dagascar, Albania, Rumania, and Russia and China (Phizackerley and Scott 1978; Jiayu and Jianyi 1999; Zhang et al., 2007). 12 An oil barrel is 42 US gallons, or 158.984 liters.

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96 stake in each of the four concessions. The ot her partners were multinational oil companies (MNOCs) from Europe and the US, each with diffe rent levels of partic ipation and investment, see table 4-1. The companies operated until mid-2007, when Venezuelas President Hugo Chvez changed the contract terms 25 days after the Nati onal Congress passed a law allowing him to rule by decree in certain matters (G aceta Oficial, 2007). The new law (number 5,200) required the HOB operations to reorganize. The local comp any, PDVSA, increased its role by becoming a majority partner and decision maker. This reduced the role of the MNOCs and provided them four months to agree to the new terms or ceas e operations (see figure 4-2). Two companies, ExxonMobil and ConocoPhillips withdrew not only from the HOB, but from Venezuela (ConocoPhillips, 200813). This study, concerns the time period 1990-2005, before these changes took effect. The global research question is : what are the environmental implications of any natural resource development done by local institutions supported by governments, multi-national companies subject to international scrutiny, or a consortia of locals and multinationals overseen by governments? More specifically in this chap ter the local research question is: does the government make it easier for the local oil comp any to avoid environmental regulations as evidenced in the amount of important land-cover changes measured in each concession? I address the relationship between the obs erved land-use land-cover changes (LUCC) occurring in the HOB, particularly between 2000 and 2005, and the three types of partnerships defined by the role of participation among the c oncessions. While geological events explain the presence of petroleumwhich is often located in sedimentary basins such as Venezuelas 13 ExxonMobil moved its case to international arbitration, resulting in PDVSA having $12 billion in assests frozen in the US. This ruling was lifted.

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97 Orinoco basin, the two actors extracting oil are the state and multinational oil corporations (MNOCs) because they have the resources, equipm ent, expertise and legal framework to provide global energy supplies. Production in the Heavy Oil Belt As table 4-1 shows, the four concessions were comprised of seven com panies. Their extraction and production model was to: Extract heavy crude from HOB wells at reserv oir temperaturethis means that no heat or steam injected into the wells was necessary to pump the crud e to the surface (due to its tropical location). This practi ce is now changing with the in tention to increase recovery rates, since higher reservoir te mperatures reduce viscosity. Ho wever, these practices also involve the use of solvents in side the reservoir which increa ses the potential for air and water pollution. Add a diluent at the wellhead. Once it reaches the surface this heavy crude is tar-like and requires thinning which is accomplished by adding high-grade petroleum which allows the extracted crude to flow freely through pipelines. Once diluted the heavy crude is sent to the ce ntral processing facili ty in the concession area where it is degassed, dewatered and desalted. From there it is sent via a 200 km pipeline to the coastal upgrading/refinery plant called Jose, where it is converted to syncrude, which is short for synthetic crude oil. This process removes the high levels of vanadium, nickel and sulfur and produces a valuable petroleum product. Finally, depending on the quality of the upgraded syncrude (see table 4-1), it is sent to offshore refineries (in Germany or the US), or is sold as is. Sincor produces the highestquality syncrude, followed by Ameriven, Petrozuata and finally Cerro Negro. (oil company representative, 2005a, 2005d; Guerra, 2005; PDVSA, 2008a, 2007e; 2005a; Petrogua, 2006-2007; Pawlewicz ,1995; Tankersle y and Waite, 2002, 4; Galarraga et al., 2007). An important issue concerning LUCC in Venezu ela concerns government plans to increase overall production to 5.85 mb/d by 2012. Much of th is growth is expected to come from the HOB as 27 more operations are developed by 20 30 (oil company represen tative, 2005a; PDVSA, 2007h) This time, however, the loca l state-company will have a majo rity role in each operation

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98 and mainly rely on national oil company (NOC ) partnerships from other countries (PDVSA, 2007a; 2006; 2005; Petrogua, 2006-2007). This combina tion of public-public partnerships will produce particular E&P landscapes with possi bly greater or lesser amounts of landscape alterations than under the old partnerships. Land-Use Land-Cover Change Lam bin (2001) observes that LUCC is a func tion of individual and social responses to opportunities and constraints for new land uses created by markets and policies (Lambin, 2001). In this context, positive incen tives are the opportunities available while negative ones are the constraintsoperating in the framework of ener gy markets and energy policies. In the HOB the positive incentives are to control, extract, pr oduce, refine and sell very large amounts of profitable heavy crude using proven technology in a mostly uniform seasonal savanna landscape (Chacn-Moreno, 2007). Some disincentives include barriers to entry because of the high capit al costs, advanced technology and expertise needed. These issues ar e less challenging for MNOCs than for local companies. A main challenge for MNOCs, however is working in a country where energy laws are evolving, where previous contracts are chan ged, where capital and technological investments may not be fully realized, and where the state now requires MNOC s to work under its leadership and rules. In Venezuela this framework is sh aped by a state-centered political economy funded by petro-dollars and marked by strong state cont rol and centralized plan ning; price controls; fixed currency rates; increased social spe nding on the poor (the governments political supporters); increased state cont rol of key industries such as telecommunications, electricity, banking and steel; anti-privatization measures; in creased state and reduced private property rights; and complete control over the local oil company, PDVSA.

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99 Such command-and-control governance strategies tend to discourage innovation in behavior and technology, are less effective and economically inefficien t (Dietz et al., 2003). Furthermore, when local oil companies dont operate internationally or compete for contracts in other countries, they may not have an incentive to reduce social and environmental alterations tied to E&P activities (Diamond, 2005). But does the level of local company participation actually affect environmental performance wh en it comes to heavy oil exploration and production in eastern Venezuela? What about the level of European or US company participation? Referring to energy production in Alberta, Canada, Timoney and Lee (2001, pp. 388) observe that economic activity leads to en vironmental degradation unless ecosystem-based management is integrated into economic decision making In this context, a higher level of local company participation in Venezuela would corre late with greater levels of environmental alteration. Expected Findings This study tries to answer th e question: do governm ents make it easier for local companies to avoid environmental regulation while MNOCs meet or exceed regulations as evidenced in the amount of important land-cover changes measured in each concession? In terms of company dominance the HOB concessions exhibit the follo wing patterns: Ameriven 70% US; Sincor 62% European; Petrozuata 49.9% Local; Ce rro Negro tied.67% Local, 41.67% US. Using statistical correlation anal ysis I test three hypotheses re lated to the three levels of company participation and the LUCC measures ex amined in the previous chapter and expanded in this chapter. The hypotheses are: Local company-dominated concessions will exhibit the greatest changes in land-cover US company-dominated concessions will show lower levels of LUCC

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100 European company-dominated concession wi ll exhibit the lowest levels of LUCC These hypotheses are based on a review of petroleum geology, LUCC, organizational and natural resource management literature particularly in relation to oil and gas E&P, field interviews with oil company representatives and results from the previ ous chapter (Thomson et al., 2005; Morton et al., 2004, 2002; Wilderness Soci ety, 2006; Musinsky et al., 1998; Smith et al., 2003; Moser, 2003; oil company representative, 2005a, b, c, d, e, f, g, h). The first hypothesis is derive d in part from the regional context, where the local oil company is strongly controlled by the federal executive office; where political and economic power is concentrated (McCoy and Fensom, 2006, 17; CIA, 2008). Such entrenching of politicaleconomic institutions often lacks checks and ba lances (Timoney and Lee, 2001). In Venezuela petroleum has been the traditi onal export earner si nce the 1920s (when it overtook coffee) and currently encompasses about 90% of export earn ings, 50% of federal budget revenues and about 30% of gross domestic product (GDP) (CIA, 2008; EIA, 2007b; Trujillo, 2004; Torres, 2004; Williams, 2006). McCoy and Fensom (2006, pp. 8) note that in Ve nezuela adherence to the rule of law is low and the prevalence of corruption is hi gh. Also concerning Venezuelas business environment, Lopez-Claros (2006) states the following: What is especially noteworthy about Ven ezuela is that there has been a notable deterioration in the quality of the business environment, with serious concerns about property rights, the independence of the judi ciary, waste in government spending and overall levels of corruption. Venezuela is not using oil revenues to enhance the economys competitiveness such as through a major improvem ent in the educational system or in the countrys dilapidated infrastructure. While at the regional level, Chacn-More no (2007, pp. 3-4) observes: Government policies are focused on increasing th e use and exploitation of the Lla nos areas in order to elevate

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101 the quality of life of the Venezuelan people, but most of the time, these policies lack environmental controls. Since petroleum is such a central component of the Venezuelan economy and crucial to large domestic social programs and international energy agreem ents promoted by the current administration (PDVSA, 2008b), activities that re duce production and theref ore profits, such as meeting environmental regulations, may not be fully enforced on local companies since ecological concerns may not be part of the decision making process (Timoney and Lee, 2001). For example, the Ministry of the Environm ent and Natural Resources (MARN) requires a complete environmental impact assessment (EIA) re port and a register of activities susceptible to degrade the environment (RASDA ) report for the establishment of new industrial facilities, including oil operations (Sebastiani et al., 2001) However, Sebastiani et al. (2001, pp. 140) reported that MARN has adopted a conciliatory and not enforcing attitude once a facility is in operation. This facility was the upgrader for the Ameriven HOB concession (Sebastiani et al., 2001). Another issue concerns oversight of one gove rnment agency such as the Ministry of Energy and Petroleum by another like MARN. This sets up a conf lict of interest whereby one government (environmental) agency is respons ible for monitoring another powerful statecontrolled company whose earnings comprise ke y government and export revenues, help fund sizeable social programs14 benefiting the Presidents political base, finance international oildriven agreements benefiting its allies such as Ecuador, Bolivia, Argentin a, Nicaragua and Cuba, and pay for infrastructure projects. 14 These social programs include 17 Missions that foster education, health services, nutrition and microenterprises (MENPET 2007).

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102 Wages earned by government employees may also be a contributing factor because most government officials are poorly paid, which enco urages acceptance of bribes, and this is particularly likely when they are responsible fo r managing natural resources with high financial value observe Smith et al. (2003, 68 ). Finally, in cases of neglig ence the judicial branch may not have the capacity (will and/or desire) to bring legal action against a state-owned energy company. The second hypothesis, that US company-dominat ed concessions will show lower levels of LUCC, is based on their technological know-how and experience in addressing environmental changes related to E&P worldwide, as well as international scru tiny by governments, nongovernment organizations (NGOs), sustainability rating agencies and investors such as the Dow Jones sustainability world index (Dow Jones, 2007a; 2007b; Bruni, 2007), World Economic Forum (WEF, 2005); the Global 100 (Global 100, 2008); Management and Excellence (M&E, 2008, 2007, 2006, 2005); the Pacific Sustainability I ndex (PSI, 2007) from Claremont McKenna College; and the Environmental Sustainability Index (ESI, 2005) from Yale University and Columbia University15. A traditional view in organizational theory proposes that a companys obligations extend only to its sh areholders (Dashwood, 2007). This perspective has dominated in the US but is beginning to change, and even tually environmental reporting may be as commonplace as financial reporting is today (Morhardt, 2002). The third hypothesis, that European companydominated concession will exhibit the lowest levels of LUCC, is also supported by their tec hnological know-how as we ll as a longer history and culture of sustainable busine ss practices in Western Europe (Lawrence, 2007). In this region, sustainability is a concept tied to a sense of ba lance between the market and the social sphere 15 In future work I will examine the statistical relationshi p of these indices with the observed LULCC in the HOB.

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103 (Lawrence, 2007), which contrasts with the objective of maximizing profits every quarter. The European Union, for example, has adopted stricter environmental standards and their policies are based on the precautionary principle whereby insuffi cient or inconclusive scientific evidence is considered when reaching policy decisions such as bans on questionably hazardous products (Schapiro, 2007). Also, some of the sustainability indices mentioned above, very often rank European companies higher in environmental and social performance than US companies, while Venezuelan companies and government rank at the bottom (M&E, 2008, 2007, 2006, 2005; Global 100, 2007, 2008; ESI, 2005). Given the declining production from conven tional wells; the difficulty in finding and securing new oil sources; the importance of maintaining a positive company image for shareholders, the general public and state gover nments; and the challenge of establishing new business ventures in countries with increased state control over th e energy industry; it is in the interest of both US and European MNOCs to engage in sustainable practices that reduce environmental and social changes related to thei r E&P operations. This con cept of sustainability refers to a companys capacity to reduce envi ronmental and therefore social disturbances resulting from business operations while still ma naging a profitable enterp rise and maintaining a healthy ecosystem for future generations (Lovins et al., 2000). This same concept holds true for local companies that want to compete internationally. Methods I f irst used GIS and remote sensing techniques outlined in the previous chapter to expand the land-cover change findings to 16 measures (noted below). Th en I used bivariate correlation analysis to determine if a statistically significan t relationships exists, as well as the strength of the associations between the amounts of 1) local (PDVSA); 2) European; or 3) US participation in the four HOB operations and the magn itude of resulting land-cover changes

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104 LUCC Related to the Petro and Nonpetroscape All these landscape-level changes for the HOB concessions w ere measured using remote sensing and GIS techniques (Baynard 2007). They were: NDVI negative changethe index amount of ve getation in km that showed a negative change based on the Normalized Differen ce Vegetation Index (NDVI) between 1990 and 2000. This index is a model that helps detect vegetation vigor and is often used as an indicator of biomass (Jensen, 2005; F ung and Siu, 2000; Mangiarotti et al., 2008). NDVI positive changethe index amount of vegetation cover that showed increases between 1990 and 2000 measured in km and using the NDVI. Petroscape densitythe linear amount of petroscape features in km divided by the concession size in km. Nonpetroscape densitythe lin ear amount of agricultural and other nonpetroscape features in km/km. Allscape densitythe combined density of petro and nonpetroscape features in km/km. Concession areathe size of the E&P zone in km. Edge-effect zone petroscapethe area extend ing 600m from petroscape features in which significant ecological effects ar e expected to extend in km. The 600 m buffer was selected after reviewing work by Forman and Deblinge r (2003) regarding road s, Schneider et al. (2003) and Dyer et al. (2001) in reference to energy roads in Canada, and Morton et al. (2004), Thomson et al. (2005) and the Wild erness Society (2006) regarding LUCC and energy development in the western US. Edge-effect zone nonpetroscapethe area exte nding 600m from nonpetroscape features in which significant ecological effects are expected to extend in km (Forman and Deblinger, 2003; Schneider et al 2003; Morton et al., 2004; Thomson et al., 2005; Wilderness Society, 2006). Core areas petroscapethe size in km of int act areas that remain after the effects of petroscape features (edge-effect zones) ar e accounted for in each concession (Thomson et al., 2005; Morton et al., 2004; Wilderne ss Society, 2006; Morton et al., 2002) Core areas nonpetroscapethe size in km of in tact areas that remain after the effects of nonpetroscape features (edge-effect zones) are accounted for in each concession.

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105 Number of wellsthe total number of wells and wellpads identified in the 2005 satellite images16 of the concession areas. Density of wellsthe number of wells and wellpads per concession area identified in the 2005 satellite images. Wells edge-effect zonethe area extending 600m from the wells and wellpads/km identified in the 2005 satellite images of each concession. The same buffer distance was selected as with edge-effect zone petroscape. Wells core-areasthe size in km of intact areas that remain after the edge-effects of the 2005 wells and wellpads are accounted for in each concession. Number of rivers crossedthe sum of rive r crossings created by petro and nonpetroscape features in each concession. Since many in frastructure features reduce connectivity between rivers and their floodplains (Stein et al., 2002), a lower number implies fewer ecosystem disruptions. Riverscape densitythe linear amou nt of rivers/km. These rive rs were measured in each concession using Landsat 2000 images of the study region17. The Coefficient of Determination An exa mination of scatterplots provided an R coefficient of determination showing how well a regression line approximated real data points for the different types of company participation. With a small sample size (n=4 ), coefficients below 0.70 are not considered significantand even those above 0.70 re quire further scrutiny, see table 4-3. NDVI change (negative and positive) was measured between 1990 and 2000 for three of the concessions; Cerro Negro was excluded. Most of the measures discussed here concern 2000 and 2005 (or both) because of data availability and the fact that HOB operations did not begin until the late 1990s. 16 These subset images were created using Leica Geosys tems Erdas Imagine 9.1 software from the China-Brazil Earth Resources Satellite (CBERS) CCD sensor with a 20 m spatial resolution. 17 The 2000 image date was selected since it coincided with high seasonal flooding (following an abnormally heavy rainy seasonsee chapter 3) and thus showed a full extent of area rivers.

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106 Nonparametric Statistical Analysis For the nex t step I selected a nonparametric te st because the sample size was small (n=4) and the data did not meet the assumptions of pa rametric inferential statistics (Ferguson et al., 1999; Lehmkuhl, 1996). As with research regardi ng environmental and su stainability reporting, note Morhardt et al. (2002), different types and intensities of environmental alterations by different companies are best measured as catego rical rather than con tinuous variables, and therefore are inherently appropriate for nonparametric statistical analysis. Kendalls tau -b rank correlation coefficient measur es a precise degree of association between two sets of ranks (Burt a nd Barber, 1996), rather than their actual values. This addresses differences in magnitude in the data which was n ecessary since the variables in my dataset were measured in different units (e.g. area in km of vegetation cover loss, percentage of company participation and number of wellpads). Like Pearsons correlation coefficient Kendalls tau -b has the following properties: 1) a value of +1 means that the agr eement between two ranks is perfec t; 2) a value of -1 means the disagreement between two ranks is perfect; 3) ot her arrangements have values that fall between +1 and -1; and 4) a value of 0 means that the rankings are inde pendent (Rumsey, 2007). However, unlike Pearsons, Kendalls (and othe r nonparametric measures like Spearmans rank correlation) work with the median rather than th e mean, making this statistic more flexible since its not affected by outliers (Rumsey, 2007). Kendalls tau -b is calculated by comparing each pair of ranked observations. Rankings that are in agreement, or ordinally correct, are cons idered concordant, while those that are not in agreement are discordant (Burt and Barber, 1996). This measure compares all pairs of observations, records the number of concordant and discordant pairs and uses the function:

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107 = NC N D n(n-1)/2 (Burt and Barber, 1996). Furthermore, Kendalls tau -b (as opposed to tau -a) considers ties. Pett (1997, 268) explains: the range of possible values for this coefficient is smaller because the number of concordant and discorda nt pairs will always be smalle r than the total number of pairs (concordant + discor dant + ties). Since a correlation coefficient of 0 means the pa ired variables are independent, this is used to set the null hypothesis. Here, H O: = 0, whereby the amount of local company participation paired with each of the 16 ecologi cal variables is independent (i.e., there is no relationship). The alternate hypothesis is: HA: 0, whereby the paired variables are dependent. The same hypotheses hold for European-company participa tion and for US-company participation. Table 4-5 shows the matrix for Kendall correlation coefficients and significance levels for the three types of company consortia and the 16 measures analyzed above for the R correlation of determination, as well as the R scores. The decision rule to determine significance w ith a sample of 4 is to compute a sampling distribution containi ng a finite number of possible combinations between paired variables ranked 1 through 4 (Abdi, 2007). Excluding ties, the nu mber is 24, which means that an exact correspondence in rank would occur only 1 in 24 times, or .042, which is less than the conventional .05 cutoff for significance (Abdi, 2007; Richard, 2008; Rumsey, 2007). In order to use a test statistic of significance of Kendalls tau such as the Z score, the sample size n must 10 (Burt and Barber, 1996; Abdi, 2007; Lehmkuhl, 1996).

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108 Results and Discussion Determining the R Coefficient Table 4-2 shows the R coefficients of de term ination for seven important petroscaperelated LUCC measures, while table 4-3 displays all LUCC values. Local participation showed a strong downward association (R 0.9) with core-area petroscap e for 2000 and 2005; a upward relationship with petroscape density (R 0.73); and an downward rela tionship with negative NDVI (R 0.76). This means that for Petrozuata, core-areas were smaller and petroscape density was higher which mean larger alteratio ns. However, negative-changes in vegetationcover were smaller indicating th at local-company ownership ma y not be related to negative changes in land cover, but rather US or Eu ropean ownership, or other explanations. European participation showed only one statistically significant correlation with positive NDVI (R=0.770). But how does a concession exhib it vegetation-cover gain over time if E&P activities are increasing? In the case of Sincor it had the largest number for total length of rivers for the four concessions, at 561.40 linear km. Thus when the rivers flooded during an abnormally wet dry season, captured in the 30 April, 2000 Lands at ETM+ image (described in chapter 3), the numerous riparian forests expanded, likely reve aling the reported positive changes (gains) in NDVI. US-company presence was correlated with larg er core areas presumably because of lower petroscape density in Ameriven. Nonparametric Statistical Analysis Table 4-4 shows the Kendalls tau-b coeffici ents for seven important petroscape-related LUCC measures, while table 4-5 displays all LUCC values.

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109 European and US results Results for Kendalls tau show that no variab les exhibited significan t co rrelation with European participation, thus I fail to reject th e null hypothesis that European company participation is independent of the LUCC variables examined. Six landscape measures showed 1.0 correlati on for US participation, but only three had statistically significant levels. In this case they we re all related to the non petroscape, such as the roads created by crop agriculture and pine planta tions. Thus, I fail to reject the null hypothesis that US-participation is independent of any of the important landscape-change measures necessary to monitor dir ect E&P activities during the life-cyc le of a given concession. Therefore, attention must turn to local-company participation. Local results Local participation is strongly positively corr elated with petroscape density, well density, num ber of wells and river densit y (1.0, p < 0.01). The first two suggest that best practices are not implemented in terms of minimizing seismic a nd future roads or not closing off unnecessary roads resulting in further alterations to wildlife and ecosystems (Lee and Boutin, 2006; Wilderness Society, 2006; Schneider et al., 2003). It can also indicate that drilling practices focus on quantity of wells, rather than employing long lateral wells ( 1,500 ) that can reach oil sands from far away thus increasing production while reducing well count (Gip son et al., 2002). Best practices, however, apply to more than wells As Kharaka and Dorsey (2005, pp. 61) note, the cumulative ground-surface disturbances from oil E&P can be high, since these activities involve: site clearance, construction of roads, tank ba tteries, brine pits a nd pipelines, and other modifications necessary for the drilling of exploration and prod uction wells and construction of production facilities.

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110 The correlation between well density and Petroz uata may be related to being the first concession to begin operations. It is reasonable that it has the most wells established. On the other hand, given the coarse spatial resolution of 30 km and 20 km for the 2000 Landsat ETM+ and 2005 CBERS CC2 satellite images respectively, a nd the similar spectral profiles between dry savanna vegetation and vegetation disturbed by E&P, its very possi ble that the number of wells identified in the satellite imag es is inaccurate for all four c oncessions. This in turn would complicate comparisons and theref ore it is recommendable to use imagery with higher spatial resolution (Tang et al., 2007). While not a key LUCC measure in this paper, the relationship with river density was also strong. Petrozuata had the highe st river density (number of linear river kilometers per concession) at 1.14, followed clos ely by Sincor at 1.09 (though Sincor had twice the number of river crossings). Because the site s selected for Petrozuata and Sincor are crossed by many rivers and tributaries, the potential exists to create numerous hydrol ogical disturbances. The Local chose Petrozuata to begin HOB operations because it believed this site held the best potential for drilling success (oil company representative 2005b), though newer technology such as horizontal and directional drilling can redu ce landscape disturbances (Gips on et al., 2002). Table 4-6 shows important petro and nonpetroscape m easures used in this paper. Local participation was very weakly co rrelated with edge-effect petroscape (0.333, p=0.602), despite the strong relations hip to petroscape. As with co re-areas this lack of strong correlation contradicts the rela tionship that petroscape density has on edge-effect zones and subsequent core areas. The lack of negative correlation to nonpetroscape density values is also surprising. Though Petrozuata had a low nonpetr oscape, Cerro Negro, which has the second

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111 highest local-company participation had the high est nonpetroscape levels of all concessions (2.11 for 2000 and 2.22 for 2005). Presumably this would influence results, though it didnt. Another unexpected finding was the lack of correlation to num ber of rivers crossed since Petrozuata, at 25, had the second highest number (while Sincor had 52, Ameriven 1, and Cerro Negro 0 in 2005). Though Local participation was correlated wi th petroscape density and well density, correlations to other important measures such as negative NDVI core-area petroscape, edgeeffect petroscape and number of rivers crossed were weak or cl ose to zero. Therefore I fail to reject the null hypothesis that Local-company pa rticipation is independent of the important changes in land-cover associated with petroscape expansion. Conclusion After exam ining the correlations between the three different consortia of oil companies (Local, European and US) operating in the HOB and important LUCC measures for 2000 and 2005 I fail to reject the three hypotheses of st atistical independence for all of them. Only one, the Local company, with its largest in terests in Petrozuata, exhibited statistically significant associations with pe troscape density and well densit y. These two measures represent the amount of oil roads, wells and facilities digi tized from satellite images at two time periods (2000 and 20005). However, these two correlations in themselves were not enough to reject the null hypothesis of statistical independence since additional significant correlations would have been required given the small sample size. Highe r resolution imagery would increase accuracy in petroscape feature detection such as wells and wellpads, allowing for more accurate comparisons across concessions. Concerning Petrozuata, whethe r the relationship between Lo cal participation and the petroscape is attributed to a lack of government oversight requires more research and data. The

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112 Local company is now a majority partner in the current HOB concessions and will legally remain so during the establishment of 27 new concessions. Finally, using the lowest and hi ghest linear and density petr oscape values can provide a scenario of what future changes may l ook like as the HOB operations expand. Using 2005 petroscape values for Petrozuata and Ameriven reveals the following figures. If the 27 new planned concessions are establishe d over the next 12 years, each with an average concession size of 671 km-which is larger than Ameriven at 665 km (PDVSA, 2008c), then the potential petroscape density per concession could range from 0.18 to 0.50, a 36% difference. Regionally, this translates into linear pe troscape features of 3,230 km vs. 9,145 km, a sizeable difference, which combined with regional development pl ans for the area above the Orinoco could transform this entire region within a few years.

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113 Figure 4-1. Venezuelas heavy oil belt and the four current operations. From west to east they are: Sincor, Petrozuata, Ameriven and Cerro Negro. The Jose upgrading complex, located on the coast, processes the HOB crude to produce syncrude, or synthetic crude.

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114 0.00 10.00 20.00 30.00 40.00 50.00 60.00 70.00 80.00 90.00CompanyPercentage of ownership 2005 3.754.177.5010.4211.7522.5339.89 2007 2.434.177.500.007.580.0078.34 StatoilBPChevronExxonTotalConocoPDVSA Figure 4-2. Oil company participation in the HOB in 2005 and under the new changes implemented in mid-2007.

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115 Figure 4-3. Potential E&P area within the he avy oil belt by 2030. New concessions will occupy about 18,000 km, or 33% of the HOB land area (Petrogua, 2006-2007). Table 4-1. Venezuelan heavy oil belt strategic as sociations (concessions), company participation by percentage and upgrad ed syncrude quality. Concession Name Company Ow nership Syncrude Production and Upgraded Quality Sincor PDVSA 38% Total (France) 47% Statoil (Norway) 15% 180,000 b/d From 8.5 to 32 API Petrozuata PDVSA 49.9% ConocoPhillips 50.1% 104,000 b/d From 9 to 19-25 API Ameriven PDVSA 30% Chevron 30% ConocoPhillips 40% 190,000 b/d From 7-10 to 26 API Cerro Negro PDVSA 41.67% ExxonMobil 41.67% BP(United Kingdom) 16.66% 105,000 b/d From 6-10 to 16 API

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116 Table 4-2. The R coefficients for seven petros cape-related land-cover ch ange measures and the three consortia of companies operating in th e HOB. Some of the repeated categories represent values for the years 2000 and 2005. PDVSA Participation European Participation US Participation Negative NDVI (1990-2000) km 0.765 0.147 0.388 Positive NDVI (1990-2000) km 0.143 0.77 0.952 Petroscape Density 2000 0.758 0 0.069 Petroscape Density 2005 0.73 0.003 0.034 Core Area Petroscape 2000 0.905 0.017 0.156 Core Area Petroscape 2005 0.923 0.021 0.171 Edge Effect Petroscape 2000 0.428 0.35 0.595 Edge Effect Petroscape 2005 0.122 0.377 0.502 Well Density 2005 0.895 0.084 0.001 Number of Rivers Crossed 2005 0.054 0.613 0.712

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117 Table 4-3. The R coefficients of determinati on between three types of company participation and 16 LUCC measures in the HOB. PDVSA Participation European Participation US Participation Negative NDVI (1990-2000) km 0.765 0.147 0.388 Positive NDVI (1990-2000) km 0.143 0.77 0.952 Petroscape Density 2000 0.758 0 0.069 Petroscape Density 2005 0.73 0.003 0.034 Nonpetroscape Density 2000 0.02 0.096 0.121 Nonpetroscape Density 2005 0.011 0.094 0.111 Allscape Density 2000 0.09 0.14 0.083 Allscape Density 2005 0.144 0.159 0.084 Concession Area Km2 0.83 0.016 0.017 Core Area Petroscape 2000 0.905 0.017 0.156 Core Area Petroscape 2005 0.923 0.021 0.171 Core Area Nonpetroscape 2000 0.011 0.662 0.703 Core Area Nonpetroscape 2005 0.018 0.657 0.711 Edge Effect Petroscape 2000 0.428 0.35 0.595 Edge Effect Petroscape 2005 0.122 0.377 0.502 Edge Effect Nonpetroscape 2000 0.37 0.309 0.523 Edge Effect Nonpetroscape 2005 0.352 0.328 0.541 Number of Wells 2005 0.978 0.005 0.043 Well Density 2005 0.895 0.084 0.001 Wells Edge-effect 2005 Km 0.496 0.004 0.066 Wells Core Area 2005 Km 0.91 0.013 0.024

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118 Table 4-3. Continued. Number of Rivers 2000 0.23 0.317 0.482 Number of Rivers 2005 0.054 0.613 0.712 River Density 0.251 0.142 0.265 Table 4-4. Kendalls tau-b correlation coeffici ents showing the relationship between Local, European and US dominated concessions and seven important LUCC measures related to petroscape expansion. Sig refe rs to significance level while ** means correlations are significant at the 0.01 level (2-tailed). PDVSA Participation European Participation US Participation Correlation Coefficient -1.000 0.000 0.333 Negative NDVI (1990-2000) km N = 3 Sig. (2-tailed) 0.117 1.000 0.602 Correlation Coefficient 0.333 0.816 -1.000 Positive NDVI (1990-2000) km N = 3 Sig. (2-tailed) 0.602 0.221 0.117 Correlation Coefficient 1.000(**) 0.000 -0.333 Petroscape Density 2000 and 2005 Sig. (2-tailed) 1.000 0.602 Correlation Coefficient -1.000 0.000 0.333 Core Area Petroscape Km2 2000 and 2005 Sig. (2-tailed) 0.117 1.000 0.602 Correlation Coefficient 0.333 0.816 -1.000 Edge Effect Petroscape Km2 2000 and 2005 Sig. (2-tailed) 0.602 0.221 0.117 Well Density 2005 Correlation Coefficient 1.000(**) 0.000 -0.333 Sig. (2-tailed) 1.000 0.602 Number of Rivers Crossed 2000 and 2005 Correlation Coefficient 0.333 0.816 -1.000 Sig. (2-tailed) 0.602 0.221 0.117

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119 Table 4-5. Kendalls tau-b correlation coefficients and R coefficients showing the relationship between concessions that were PDVSAdominated, European-dominated, and USdominated and important landscape-change m easures. Sig refers to significance level while ** means correlations are signi ficant at the 0.01 level (2-tailed). PDVSA Participation European Participation US Participation Correlation Coefficient 1.000 0.000 -0.333 PDVSA Participation Sig. (2-tailed) 1.000 0.602 R 0.015 0.026 Correlation Coefficient 0.000 1.000 -0.816 European Participation Sig. (2-tailed) 1.000 0.221 R 0.015 0.923 Correlation Coefficient -0.333 -0.816 1.000 US Participation Sig. (2-tailed) 0.602 0.221 R 0.026 0.923 Correlation Coefficient -1.000 0.000 0.333 Negative NDVI (1990-2000) km N = 3 Sig. (2-tailed) 0.117 1.000 0.602 R .765 .147 .388 Correlation Coefficient 0.333 0.816 -1.000 Positive NDVI (1990-2000) km N = 3 Sig. (2-tailed) 0.602 0.221 0.117 R 0.143 0.770 0.952 Correlation Coefficient 1.000(**) 0.000 -0.333 Petroscape Density 2000 and 2005 Sig. (2-tailed) 1.000 0.602 2000 R 0.758 0.000 0.069 2005 R 0.730 0.003 0.034 Correlation Coefficient -0.333 -0.816 1.000(**) Nonpetroscape Density 2000 and 2005 Sig. (2-tailed) 0.602 0.221 2000 R 0.02 0.096 0.121 2005 R 0.011 0.094 0.111 Correlation Coefficient 0.333 -0.816 0.333 Allscape Density 2000 and 2005 Sig. (2-tailed) 0.602 0.221 0.602 2000 R 0.09 0.140 0.083 2005 R 0.144 0.159 0.084

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120 Table 4-5. Continued. Correlation Coefficient -1.000 0.000 0.333 Concession Area km Sig. (2-tailed) 0.117 1.000 0.602 R 0.830 0.016 0.017 Correlation Coefficient -1.000 0.000 0.333 Core Area Petroscape Km2 2000 and 2005 Sig. (2-tailed) 0.117 1.000 0.602 2000 R 0.905 0.017 0.156 2005 R 0.923 0.021 0.171 Correlation Coefficient -0.333 -0.816 1.000(**) Core Area Nonpetroscape Km2 2000 and 2005 Sig. (2-tailed) 0.602 0.221 2000 R 0.011 0.662 0.703 2005 R 0.018 0.657 0.711 Correlation Coefficient 0.333 0.816 -1.000 Edge Effect Petroscape Km2 2000 and 2005 Sig. (2-tailed) 0.602 0.221 0.117 2000 R 0.428 0.350 0.595 2005 R 0.122 0.377 0.502 Correlation Coefficient -0.333 -0.816 1.000(**) Edge Effect Nonpetroscape Km2 2000 and 2005 Sig. (2-tailed) 0.602 0.221 2000 R 0.370 0.309 0.523 2005 R 0.352 0.328 0.541 Correlation Coefficient 1.000(**) 0.000 -0.333 Number of Wells 2005 Sig. (2-tailed) 1.000 0.602 R 0.978 0.005 0.043 Well Density 2005 Correlation Coefficient 1.000(**) 0.000 -0.333 Sig. (2-tailed) 1.000 0.602 R 0.895 0.084 0.001 Wells Edge-effect 2005 Km Correlation Coefficient 1.000(**) 0.000 -0.333 Sig. (2-tailed) 1.000 0.602 R 0.496 0.004 0.066 Wells Core Area 2005 Km Correlation Coefficient -1.000 0.000 0.333 Sig. (2-tailed) 0.117 1.000 0.602 R 0.910 0.013 0.024 Number of Rivers Crossed 2000 and 2005 Correlation Coefficient 0.333 0.816 -1.000 Sig. (2-tailed) 0.602 0.221 0.117

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121 Table 4-5. Continued. 2000 R 0.23 0.317 0.482 2005 R 0.054 0.613 0.712 River Density Correlation Coefficient 1.000(**) 0.000 -0.333 Sig. (2-tailed) 1.000 0.602 R 0.251 0.142 0.265

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122 Table 4-6. Petro and nonpetroscape data values for the four HOB concessions. Concession Name Sincor Petrozuata Ameriven Cerro Negro Concession Area (area km) 517.38 300.77 665.44 303.15 PDVSA Participation (%) 38.00 49.90 30.00 41.67 European Participation (%) 62.00 0.00 0.00 16.66 US Participation (%) 0.00 50.10 70.00 41.67 Negative NDVI 1990-2000 (km) 77.55 39.00 312.55 0.00 Positive NDVI 1990-2000 (km) 41.36 21.00 2.19 0.00 Petroscape Linear 2000 (km) 243.07 276.79 19.85 61.51 Nonpetroscape Linear 2000 (km) 0.00 39.19 523.69 578.19 Petroscape Density 2000 (km/km) 0.47 0.92 0.03 0.20 Nonpetroscape Density 2000 km/km) 0.00 0.13 0.79 1.91 Allscape Density 2000 (area km) 0.47 1.05 0.82 2.11 Edge-effect Petroscape 2000 (km) 217.87 193.60 26.04 96.58 Edge-effect Nonpetroscape 2000 (km)0.00 37.41 351.33 274.00 Core-area Petroscape 2000 (km) 299.50 107.17 639.40 206.57 Core-area Nonpetroscape 2000 (km) 0.00 263.36 314.11 29.15 Rivers Crossed 2000 (number) 22.00 18.00 0.00 0.00 Petroscape Linear 2005 (km) 302.33 338.70 119.63 90.56 Nonpetroscape Linear 2005 (km) 0.00 47.39 485.71 580.97 Petroscape Density 2005 (km/km) 0.58 1.13 0.18 0.30 Nonpetroscape Density 2005 (km/km)0.00 0.16 0.73 1.91 Allscape Density 2005 (km/km) 0.59 1.28 0.91 2.22 Edge-effect Petroscape 2005 (km) 271.57 222.49 117.07 96.27 Edge-effect Nonpetroscape 2005 (km)0.00 45.61 348.32 274.00

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123 Table 4-6. Continued. Edge-effect Allscape 2005 (km) 271.60 268.00 468.40 370.27 Core-area Petroscape 2005 (km) 245.80 78.29 548.37 206.88 Core-area Nonpetroscape 2005 (km) 0.00 255.17 317.11 29.15 Rivers Crossed 2005 (number) 52.00 25.00 1.00 0.00 Wells and Wellpads 2005 (number) 142.00 195.00 98.00 168.00 Well Density 2005 (wells/concession) 0.27 0.65 0.15 0.55 Well Edge-effect 2005 (km) 111.15 134.25 86.05 81.46 Wells Core-areas 2005 (km) 406.22 166.52 579.38 221.68 Rivers Length (km) 561.40 342.74 185.01 26.88 Riverscape Density (riv ers/concession)1.09 1.14 0.28 0.09

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124 CHAPTER 5 LAND-COVER CHANGE AND NONRENEWABLE NATURAL RESOURCES Summary Land-use land-cover change involves the com p lex interaction of so cial and ecological variables across various spatial a nd temporal scales. This work has explored human-environment interactions in the context of natural resource ex traction, since the use of these resources for local needs or to meet external market demands creates different and important types of landscape alterations. These perturbations can create short-lived or non permanent changes, known as modifications, or they can lead to new land-cover classes known as c onversion (Veldkamp and Lambin, 2001; Lambin et al., 2003; Southworth, 2004; Daniels et al ., 2008). Both of these results imply a loss of land cover. However, not all natu ral resource use leads to land-cover loss. Forest plots, logging sites and grazing pa stures may revert back to sec ondary forest once these activities cease (Kupfer et al., 2004, 510; Fearnside et al ., 2007, 678). This transition may be fostered by land tenure; policy change s and implementation; environmental feedbacks and climate change; different market demands and varying household makeup. The planting of forests also leads to land-cover gain, though tree plantations do not pr ovide the same ecosystem services as do natural forests (Parrotta and Knowles, 2001, 220). Nonrenewable natural resour ce extraction, on the other hand, tends to create permanent land-cover changes because of the nature of mi ning and drilling, the esta blishment of permanent infrastructure needed for operations, as well as the waste sites required for the large volumes of saline water produced (Kharaka and Dorsey, 2005, 61). Furthermore, once viable extractivist activities take place, they tend to attract new set tlers to the areas of direct interaction. These settlers create additional LUCCs through the esta blishment of homes and agricultural plots; new roads that create access to area resources, thus expanding LUCC; and the growth of service

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125 industries that can eventually c onvert a small settlement into a town or city (Schmink and Wood, 1992, Consiglio et al., 2006). Whereas agricultural activity in the tropics c onverts a great deal of forested land every year for the establishment of soybean farms and cattle ranches or small-scale agricultural plots (FAO, 2006, Lambin et al., 2003; Wassenaar et al., 2007 ), logging and fires al so contribute to significant land conversion (Nepstad et al., 2001 ; Siegert and Hoffmann, 2000; Hammond et al., 2007). Gold mining, on the other hand, tends to be pursued at the small-scale level (individuals, or groups of five) (Peterson and Heemskerk, 200 1) and thus creates smaller pockets change though cumulatively they can cr eate widespread degradation (Hilson, 2002). While large-scale gold mining involves digging large open pits a nd establishing large tailing ponds that hold contaminated waste material, small-scale mining can involve spray-wash ing land and riverine deposits with motorized equipment that removes the vegetation, roots, soil and seeds, thus degrading the landscape consider ably and delaying regeneration for up to 20 years or more (Almeida-Filho and Shimabukuro, 2002; Peters on and Heemskerk, 2001). On top of this, mercury (and cyanidein the large operations) is us ed to help recover gold from the extracted soil and rocks. Thereafter it is released in to the water, where it can convert to toxic methylmercury. It can also contaminate mine rs and (gold) business owners through skin contamination and inhalation of fumes when the mercury is burned away from the gold both in the field and in gold processing shops (Veiga and Hinton, 2002, 19; Bastos et al., 2004; Mol et al., 2001). The simple technology needed and the pot ential for monetary earnings, as compared to other options in many deve loping countries, leads to thousa nds implementing these practices in a single site (Hilson, 2002). Worldwide around six million miners operate in more than 40

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126 countries (Veiga et al., 2006) and its a central driver of deforestation in Surimane (Heemskerk, 2001). Oil exploration and production can be the most lucrative of all these activities, and given the necessary knowledge, technology and capital this activity is th e purview of state governments and multinational oil companies (MNOC s). Different management practices during the exploration and production phase can create greater or lesser LUCCs which can result in modifications or conversions lasting 35 years or more. State-owned oil companies, who often dont compete abroad, may not have the culture and history of including environmental performance as part of their operations, while for MNOCs this is increasingly an important aspect of day-to-day business (Diamond, 2005; Moser 2001; Consiglio et al., 2006). Negative press coverage, pressure from employ ees and customers, complaints from area residents affected by operations; the threat of legal action; the desire to be perceive d as attractive to stock market investors and customers; a nd the competition for dwindling opportunities can help explain the importance of sustainability to MNOCs (Lawrence, 200 7; Orlitzky et al., 2003; Morhardt, 2002). Even so, the cumulative imp acts from these operations are high, because a total of about 3.5 million oil and gas wells have b een drilled to date in the United States, but currently, only about 900,000 are in production (Kharaka and Dorsey, 2005, pp. 61-62). Worldwide we would expect to see similar ratios. In Venezuela, plans to greatly expand exploration and production of the vast deposits in the heavy oil belt will potentially affect 18,000 km of dry tropical savanna/forest. It is important the government and the oil companies involved crea te baseline studies of the concession areas prior to exploration. The innovative methods presented in this st udy integrate the use of GIS and remote sensing techniques to measure landscape disturbances related to petroleum exploration

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127 and production. This approach is particularly useful for monitoring and measuring current landscape patterns and subsequent changes (Janks and Prelat, 1994; Griffith et al., 2002a), though in personal conversations wi th industry representatives it appears that this methodology is not widely used for these purposes. It is also important that environmental impact assessment (EIA) studies be implemented th roughout the life of th e project, not just as a precondition to begin drilling (Sebastiani et al., 2001). Finally, the implementati on of best business practices, such as minimizing the size of seismic lines and the equipment used to create them; closing and recuperating routes that are no longer needed; implement long hor izontal drilling; partner with area industries to share roads and infrastructure ; reduce the number of river crossings; and be transparent in environmental reporting are im portant ways for oil companies to reduce environmental alterations and become more sustai nable (Musinsky et al., 1998; Schneider et al., 2003; E&P Forum/UNEP, 1997; Morhardt, 2002; Gipson et al., 2002; St ein et al., 2002; Musinsky et al., 1998). One commonality among all four types of resources is accessibility. In order to access these resources routes and trails must be built. Thus, roads are the catalyst that opens up new landscapes for local and migrant la nd managers (Perz et al., 2007; Musinsky et al., 1998; Forman et al., 2002; E&P Forum/UNEP, 199 7; Consiglio et al., 2003; Soar es-Filho et al., 2004). Once the roads are created to access a particular resour ce (trees, gold, oil), then settlers move in and establish homes, agricultural plots and cattle ra nches; previously inaccessible forests are now open to loggers and miners; and LUCC speeds up. Future Work As Morton et al. (2004, pp. 8) obs erve, there are few studies that exam ine the exact size and extent of the ecological footprint of energy development. Spatial analysis can help fill this information gap. Concerning petroleum E&P in the heavy oil belt, using additional satellite

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128 images captured at different times of the year such as the wet season, can help distinguish landscape disturbances from dry tropical savanna. Field surveys of oil roads will permit seismic and other roads to be more easily identified, which in turn can direct reclamation efforts and the closing of nonessential or redundant roads (W ilderness Society, 2006; Mu sinsky et al., 1998; Lee and Boutin, 2006). This will likely reduce habitat fragmentation, as well as the building of new roads which increases access to la nd and resources and can lead to the building of settlements. Other factors to consider include identifying rivers or tributaries that do not need to be crossed and therefore avoid interruptions to flow patterns and their connec tivity to floodplains (Stein et al., 2002, 1); distinguishing between main-channel a nd floodplain habitats, and the presence of migratory barriers (e.g., waterfalls) that create predator-free environments (Wantzen et al., 2006, pp. 63). Additional future work should consider other re mote sensing techniques to identify (urban) infrastructure features such as fuzzy-spectral mixture analysis (Tang et al., 2007), the normalized difference built-up index (NDBI) used to monitor growth and distribution of urban built-up areas, as well as built-up area calculation whic h involves subtracting the NDVI from the NDBI (Jensen 2005). Almeida-Fihlo and Shimabukuro (2000) used radar images (JERS-1 SAR) to detect areas disturbed by gold mining in northern Brazil. This method might prove useful for studying petroscapes, particularly since many are located in tropical countries where dense cloud cover on satellite imagery is often a problem. Wildlife studies, for which several exist in Ca nadas heavy oil sands, could help determine more precise avoidance zones toward petroscape features by of lo cal fauna such as the caiman, tapir, Capuchin monkeys, capybara, bats, deer, ma natee, reptiles such as the green anaconda, parrots, amphibians and many fish (Prez, 2000 ; Gorzula, 1978; Marn et al., 2007; Lpez-

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129 Fernndez and Winemiller, 2000; Veneklaas et al., 2005; Rosales et al ., 1999; Padilla and Dowler, 1994) This is turn could be consider ed when establishing new infrastructures, particularly in areas cont aining endangered species. Therefore, adapting spatial technologies to the environmental impact assessment (EIA) plan is a useful and cost-effective way of m onitoring E&P-driven changes throughout the lifecycle of oil operations, as well as aiding in polic y formulation and decision making (Sebastiani et al., 2001; Satapathy et al., 2008; Griffith et al., 2002a). While geospatial technologies may be used in finding oil, drilling a nd production scenarios (Yatabe and Fabbri, 1986; Janks and Prelat, 1994, 1995; Almeida-Filho, 2002; Almeida-Filho et al., 2002; Zhang et al., 2007) these technologies do not appear to be widely used to monitor Above Ground Review of oil-related landscape changes (Consiglio et al., 2006, pp. 2). Some exceptions include Chust and Sagarminaga (2007) who used data gathered fr om the Multi-angle Imaging SpectroRadiometer (MISR) to detect oil spills in Venezuelas Lake Maracaibo; Kwarte ng (1998, 1999) who detected contaminated vegetation surrounding oil lakes in Kuwait using data from Landsat TM, Indian Remote Sensing Satellite (IRS) 1-D, and Spot satellite; and Ud-Din et al. (2008) who used Landsat thermal data, (band 6) to map hydrocarb on polluted sites in Kuwait. These efforts show a small but growing effort to use geospatial technologies to monitor pe troleum related spills. However, they do not focus on the establishm ent and extension of petroscape among regional sites as a way of reducin g landscape alterations. On the other hand, work by Musinsky et al. ( 1998) correlated defo restation rates in a Guatemalan national park with the development of an oil road. Thomson et al. (2005), Morton et al. (2002); Morton et al. (2004); and the Wilderne ss Society (2006) embrace the use of GIS and remote sensing techniques to monitor how E&P operations affect LUCC a nd use this information

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130 to propose best business practi ces. Meanwhile, Schneider and Dyer (2006) do the same in Canadas Alberta oil sands. My project marks a fi rst step to perform similar work in Venezuela, especially in the important heavy oil belt region where alte rations to regional ecosystems stemming from heavy oil E&P will have important human-environment implications at the local and regional scale, which in turn cont ributes to global environmental change.

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131 LIST OF REFERENCES Abdi, H., 2007. The Kendall rank correlation coeffici ent. In: S alkind, N. (Ed.), Encyclopedia of Measurement and Statistics. Sage Public ations, Thousand Oaks, CA. Available at < http://www.utdallas.edu/~herve/Abdi -KendallCorrelation2 007-pretty.pdf >. Abizaid, C., Coomes, O. T., 2004. Land use and fo rest fallowing dynamics in seasonally dry tropical forests of the southern Yucatn Pe ninsula, Mexico. Land Use Policy 21, 71-84. Almeida-Filho, R., 2002. Remote detection of hy drocarbon microseepage-induced soil alteration. International Journal of Re mote Sensing 23 (18), 3523-3524. Almeida-Filho, R., Miranda, F. P., Galvo, L. S., Freitas, C. C., 2002. Terrain characteristics of a tonal anomaly remotely detected in an ar ea of hydrocarbon microseepage, Tucano Basin, north-eastern Brazil. International Jour nal of Remote Sensing 23 (18), 3893-3898. Almeida-Filho, R., Shimabukuro, Y., 2004. Monitoring biomass burning in the Brazilian Amazon. International Journal of Remote Sensing 25 (24), 5537-5542. Almeida-Filho, R., Shimabukuro, Y., 2002. Digita l image processing of a Landsat TM time series for mapping and monitoring degraded areas caused by independent gold miners Roraima State, Brazilian Amazon. Remote Se nsing of the Environment 79, (1), 42-50. Almeida-Filho, R., Shimabukuro, Y., 2000. Detecting areas disturbed by gold mining activities through JERS-1 SAR images, Roraima State, Brazilian Amazon. International Journal of Remote Sensing 21 (17), 3357-3362. Anderson, L. E. 1994. The Political Ecology of the Modern Peasant: Calculation and Community. Johns Hopkins University Press, Baltimore. Ankersen, T., Barnes, G., 2004. Inside the polygon: emerging community tenure systems and forest resource extraction. In: Zarin, D. J., Alav alpati, R. J. J., Putz, F. E., Schmink, M. (Eds.), Working Forests in the Neotr opics: Conservation Through Sustainable Management? Columbia University Press, New York, pp. 156-177. Armenteras, D., Rudas, G., Rodriguez, N., Su a, S., Romero, M., 2006. Patterns and causes of deforestation in the Colombian Amaz on. Ecological Indi cators 6, 353-368. Ascencio-Cendejas, F., Reyes-Venegas, O., 2006. Conceptual model design studies for a selfsustained and complete productive utilizati on of high-viscosity/extra heavy-oil onshore reservoir in Mexico. Society of Petroleum Engineers 104019-MS, 1-5.. Asner, G. P., Keller, M., Silva, J. N. M., 2004. Spatial and temporal dynamics of forsest canopy gaps following selective logging in the eastern Amazon. Global Change Biology 10, 765783. Asner, G. P., Knapp, D. E., Broadbent, E. N., O liveira, P. J. C., Keller, M., Silva, J. N., 2005. Selective logging in the Brazilian Amazon. Science 310, 480-482.

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156 BIOGRAPHICAL SKETCH Chris W Baynard was born in Maryland in 1966. Within a month his family and he moved to Spain where they lived in Madrid for 9 year s. Thereafter they moved back to the US and settled in Charleston, SC. There, Chris attende d the College of Charleston, earning a B.S. in psychology and minoring in Spanish and communicat ions. Afterward, he earned his M.A. in Spanish from Louisiana State University (LSU) in Baton Rouge, and a minor concentration in geography and anthropology. At LSU he met his soonto-be wife Ana, from Venezuela, and they were married in Baton Rouge and in Venezuel a. After completing his M.A. Chris entered academics as a Spanish instructor first at LSU, then at the University of North Florida, in Jacksonville, FL. Convinced he wanted to contin ue with an academic career, Chris began his doctoral program in geography at the University of Florida (UF), studying part-time while working full-time and living in Jacksonville. At UF his research intere sts were land-use and land-cover change related to nonrenewable natural resources, environmen tal business practices, Latin America, and sustainable development in frontier regions. Following completion of his PhD Chris will be joining the Department of Economics and Geography at the University of North Florida as an assistant professor. Chris has been married to Ana Baynard for 11 years and they have two children, Nico and Isabela.


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