<%BANNER%>

Effect of Fire Size and Severity on Subsequent Fires Using Differenced Normalized Burn Ratios in Pine Dominated Flatwood...

Permanent Link: http://ufdc.ufl.edu/UFE0042271/00001

Material Information

Title: Effect of Fire Size and Severity on Subsequent Fires Using Differenced Normalized Burn Ratios in Pine Dominated Flatwood Forests in Florida
Physical Description: 1 online resource (136 p.)
Language: english
Creator: Malone, Sparkle
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2010

Subjects

Subjects / Keywords: fire, normalized, osceola, pine, precribed, wildfire
Forest Resources and Conservation -- Dissertations, Academic -- UF
Genre: Forest Resources and Conservation thesis, M.S.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: EFFECT OF FIRE SIZE AND SEVERITY ON SUBSEQUENT FIRES USING DIFFERENCED NORMALIZED BURN RATIOS IN PINE DOMINATED FLATWOOD FORESTS IN FLORIDA Florida forests naturally experienced frequent low intensity fires, yet fire exclusion polices have altered the forest structure. The Osceola National Forest in north Florida has experienced high wildfire occurrence for a number of years. Vegetation communities within the Osceola are fire dependent and require regular burning for ecosystem health. Although prescribed fire has been used to reduce wildfire risk and maintain ecosystem integrity across much of the forest, managers are still working to reintroduce fire to long-unburned units. The objective of this study is to use differenced Normalized Burn Ratio (dNBR) to evaluate the relationships between previous fire severity, size, and historical frequency to inform prioritization and timing of future fire use. Based on remotely-sensed Landsat imagery, dNBR analysis captures spectral features over a time interval, and indicates the degree of change that is due to fire. This analysis has shown that fires in areas burned 5 or more years prior exhibited a higher probability of experiencing moderate-high severity fire and have a higher probability of increasing in severity level in subsequent fires. Areas that have not experienced fire in 10 years are indistinguishable from areas that have never burned. Using dNBR as a method of analyzing past fire severity is a useful tool for managers to determine the lasting effects of prior fire severity. The analysis has further provided an effective method of determining fire frequencies necessary to maintain the optimum level of wildfire protection.
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 Sparkle Malone.
Thesis: Thesis (M.S.)--University of Florida, 2010.
Local: Adviser: Staudhammer, Christina Lynn.
Local: Co-adviser: Kobziar, Leda Nikola.

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2010
System ID: UFE0042271:00001

Permanent Link: http://ufdc.ufl.edu/UFE0042271/00001

Material Information

Title: Effect of Fire Size and Severity on Subsequent Fires Using Differenced Normalized Burn Ratios in Pine Dominated Flatwood Forests in Florida
Physical Description: 1 online resource (136 p.)
Language: english
Creator: Malone, Sparkle
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2010

Subjects

Subjects / Keywords: fire, normalized, osceola, pine, precribed, wildfire
Forest Resources and Conservation -- Dissertations, Academic -- UF
Genre: Forest Resources and Conservation thesis, M.S.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: EFFECT OF FIRE SIZE AND SEVERITY ON SUBSEQUENT FIRES USING DIFFERENCED NORMALIZED BURN RATIOS IN PINE DOMINATED FLATWOOD FORESTS IN FLORIDA Florida forests naturally experienced frequent low intensity fires, yet fire exclusion polices have altered the forest structure. The Osceola National Forest in north Florida has experienced high wildfire occurrence for a number of years. Vegetation communities within the Osceola are fire dependent and require regular burning for ecosystem health. Although prescribed fire has been used to reduce wildfire risk and maintain ecosystem integrity across much of the forest, managers are still working to reintroduce fire to long-unburned units. The objective of this study is to use differenced Normalized Burn Ratio (dNBR) to evaluate the relationships between previous fire severity, size, and historical frequency to inform prioritization and timing of future fire use. Based on remotely-sensed Landsat imagery, dNBR analysis captures spectral features over a time interval, and indicates the degree of change that is due to fire. This analysis has shown that fires in areas burned 5 or more years prior exhibited a higher probability of experiencing moderate-high severity fire and have a higher probability of increasing in severity level in subsequent fires. Areas that have not experienced fire in 10 years are indistinguishable from areas that have never burned. Using dNBR as a method of analyzing past fire severity is a useful tool for managers to determine the lasting effects of prior fire severity. The analysis has further provided an effective method of determining fire frequencies necessary to maintain the optimum level of wildfire protection.
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 Sparkle Malone.
Thesis: Thesis (M.S.)--University of Florida, 2010.
Local: Adviser: Staudhammer, Christina Lynn.
Local: Co-adviser: Kobziar, Leda Nikola.

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2010
System ID: UFE0042271:00001


This item has the following downloads:


Full Text





EFFECT OF FIRE SIZE AND SEVERITY ON SUBSEQUENT FIRES USING
DIFFERENCE NORMALIZED BURN RATIOS IN PINE DOMINATED FLATWOOD
FORESTS IN FLORIDA


















By

SPARKLE LEIGH MALONE


A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF
FLORIDA IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE DEGREE
OF MASTER OF SCIENCE

UNIVERSITY OF FLORIDA

2010


























2010 Sparkle Leigh Malone



























To all those who supported me through this process









ACKNOWLEDGMENTS


Without the help and support of many people this project would not have been

possible. I am most grateful to my committee members (Dr. Amr Adb-Elrahman, Dr.

Leda Kobziar, and Dr. Christina Staudhammer) for their endless guidance and their

commitment to this project. I would also like to express my gratitude for the support

offered by faculty (Dr. Taylor Stein and Dr. George Blakeslee), students in the school of

forestry, and friends who not only provided encouragement but sacrificed their own time

for the sake of this project.

Data analysis assistance was graciously provided by Dr. Mary Christman, Dr.

Christina Staudhammer, and Nilesh Timilsina. Many thanks to the Quantitative Biology

Lab (Nilesh Timilsina, Todd Bush, Helen Claudio, and Dr. Louise Loudermilk) for their

relentless encouragement throughout this process. I would also like to thank the

Kobziar Fire Science Lab for their assistance.

Funding was provided by Conserved Forest Ecosystems: Outreach and Research

(CFEOR). Special thanks are necessary for Jason Drake at the U.S. Forest Service

Supervisors Office in Tallahassee, Florida for providing both data for this project and

inspiration. Finally, I would like to thank my family for their continued support in my

academic endeavors.









TABLE OF CONTENTS

page

A C KNOW LEDG M ENTS .......... ..................... ....... .. ......................................... 4

LIST O F TA B LE S .......... ..... ..... .................. ............................................. ...... .. 7

LIS T O F F IG U R E S .................................................................. 8

LIST OF ABBREVIATIONS......................................... ............... 11

A BST RA CT ............... ... ..... ......................................................... ...... 12

CHAPTER

1 INTRODUCTION TO FIRE IN THE SOUTHEASTERN UNITED STATES.......... 14

Introduction ........................................................... ............... 14
S uppression in P ine Flatw oods ....................................................... ........ 15
Fire as a Forest Management tool.......................................... 16
Fire Severity ................... ....................................... 19
Measuring Fire Severity with DNBRs .............. ............................ 20
S tu d y S ite ................................................... .............................................. 2 3
C o n c lu s io n .................23.............................................

2 EFFECTS OF FIRE FREQUENCY, SIZE, AND TIME BETWEEN FIRE
EVENTS IN NORTH FLORIDA FLATWOODS ............................... 26

In tro d u c tio n ................. ......................................... 2 6
Measuring Fire Severity ..... ................................... ......... ........ 27
S tu d y S ite .................31............................................
M e th o d s .................32.............................................
D a ta .................................................................................. 3 2
M o d e l D eve lo p m e nt .................................................................. 3 3
R e s u lts ................3 7.............................................
Data....................... ...................... 37
Probability Modeling ................... .... .. ................. 38
Probability of experiencing moderate to high severity during a fire............ 38
Probability of increasing in severity in subsequent fires ............................. 39
Probability of burning during a fire ........................ .............................. 40
Probability of decreasing in severity in subsequent fires......................... 41
F ire s iz e a n a ly s is ................................................... ...................... 4 2
D discussion ......................... ......... ... ..... .. ....... ........................................42
Probability of Experiencing Moderate to High Severity During a Fire............ 42
Probability of Increasing in Severity ........................... ...................... 45
Probability of Burning ........................................ .............. ......... 46
Probability of Decreasing in Severity................................................... 47









C o n c lu s io n ............. ......... .. .. .......... ... .... .................... ................ 4 8

3 PREDICTING FIRE SEVERITY IN PINE FLATWOODS USING DIFFERENCE
NORMALIZED BURN RATIOS TO RECORD FIRE EVENTS.............................. 80

Introduction .............. ................................... ........ .. ............ 80
M easuring Fire S everity ........................ ....... ...... ... .. ........... ............... 80
Study Site ............................................ 83
M methods ................. ....................................... .............. ................... 84
Im age A analysis ................................................................ ........................... 84
D a ta .................................................................................................... ....... 8 4
M odel D evelopm ent ...................... .......................... .... ............................ 85
Spatial Model............................................. ............... 89
R e s u lts ............... ......... ... ................................................................. ......... .... 8 9
Probability of High Severity Prescribed Fire ...................... ................ 89
Probability of Moderate to High Severity Wildfire ........................ ....... 90
Spatial Models............................................ ............... 91
Discussion ................ ... ...... .......................... ............... 92
Probability of High Severity Prescribed Fire ............. ............. ............... 92
Probability of Moderate to High Severity Wildfire ......................... ....... 93
Spatial Models............................................ ............... 94
C o n c lu s io n ....................... .................. .. ..............5

4 CO NC LUSIO N ............................................. .......... .... 118

APPENDIX: SEVERITY DATASETS........................... ................ ............... 120

LIST O F REFERENCES ......... ....................... ............... ............... 131

BIOGRAPHICAL SKETCH .......... ... ........ ........ ........ 136





















6









LIST OF TABLES


Table page

2-1 Severity class descriptions for the time analysis and fire size datasets.............. 50

2-2 Palmer Drought Severity Index values and descriptions ................................ 50

2-3 Time interval classification for time analysis dataset. ........................................ 50

2-4 Covariate classifications for fire size m odel ..................................................... 51

2-5 Parameter estimates and their respective standard errors and p-values for
the model predicting the probability of high severity fire. .............. ............... .. 52

2-6 Parameter estimates and their respective standard errors and p-values for
the model predicting the probability of increased severity in the second fire...... 53

2-7 Parameter estimates and their respective standard errors and p-values for
the model predicting the probability of burning. .......... .............. ............... 54

2-8 Parameter estimates and their respective standard errors and p-values for
the model predicting probability of decreased severity in the second fire......... 55

2-9 Parameter estimates and their respective standard errors and p-values for
model predicting the probability of high severity fire by fire size class ............ 56

3-1 Number of pixels in each severity class by year ............................................... 98

3-2 Covariates for the model measuring the probability of high severity
prescribed fire and moderate to high severity wildfire................... .......... 98

3-3 Parameter estimates and their respective standard errors and p-values for
the model predicting the probability of high severity prescribed fire. .................. 98

3-4 Parameter Estimates and their respective standard errors and p-values for
model predicting the probability of Moderate to High severity wildfire. ............. 99









LIST OF FIGURES


Figure page

1-1 Osceola National Forest in North Florida................ .......................................... 25

2-1 USFS forest type classifications ................................................ ................. 57

2-2 NRCS soil drainage class classification..................... .................. 58

2-1 Portion of pixels burned in each severity level in fire 1 and fire 2 .................. ... 59

2-2 Distribution of pixels among severity classes with 1-2 years between fire
events separated by type of fire and the probability of moving from one
severity class to the next. ............................................................. ...... .... 60

2-3 Distribution of pixels among severity classes with 3-4 years between fire
events separated by type of fire and the probability of moving from one
severity class to the next. ............................................................. ...... .... 61

2-4 Distribution of pixels among severity classes with 5-6 years between fire
events separated by type of fire and the probability of moving from one
severity class to the next. ............................................................. ...... .... 62

2-5 Distribution of pixels among severity classes with 7-8 years between fire
events separated by type of fire and the probability of moving from one
severity class to the next. ............................................................. ...... .... 63

2-6 Distribution of pixels among severity classes with 9-10 years between fire
events separated by type of fire and the probability of moving from one
severity class to the next. ....................................... ................ 64

2-7 Percentage of pixels increasing and decreasing in severity level by time and
type of fire ........................ .......... ...... ............................... 65

2-3 Fire size compared with Palmer drought severity index between 1996 and
2010. This suggests large fire events are associated with prolonged
droughts. ............ ............................................. 66

2-4 Percentage of pixels burned at each severity class by fire size class. Larger
fires have a higher portion of their cells in the high severity class. ....... ........ 67

2-8 Probability of experiencing high severity in fire 2 by time interval and fire
type............................................... .......... 68

2-9 Probability of experiencing high severity in fire 2 by severity level of fire 1 and
time interval for prescribed fires................................................ 69









2-10 Probability of experiencing high severity in fire 2 by severity level of fire 1 and
tim e interval for w ildfires. ............................................................ .. .......... 70

2-11 Probability of increasing fire severity by time interval and fire type................. 71

2-12 Probability of increasing fire severity by severity level of the last fire and time
between fires for wildfires ............................................................ ........ 72

2-13 Probability of increasing fire severity by severity level of the last fire and time
betw een fires for prescribed fires.................................................. .... ................. 73

2-14 Probability of burning by time interval, fire type, and fire severity level............ 74

2-15 Probability of burning by fire severity level and time interval for wildfires. ......... 75

2-16 Probability of burning by fire severity level and time interval for prescribed
fire s .................. .................................. ....... ..... ...... 7 6

2-17 Probability of decreasing in severity level by time interval and severity level of
fire 1 ....................................................... .. ............. ........... 77

2-18 Probability of decreasing in severity by severity level of fire 1 and time
inte rva l fo r w ildfires............................................ ........................ ........... 78

2-19 Probability of decreasing in severity by severity level of fire 1 and time
interval for prescribed fires. .......................................................... ..... ..... 79

3-1 Time since last fire for the Osceola National Forest (1998-2008)..................... 100

3-2 Fire frequency from 1998-2008 for the Osceola National Forest...................... 101

3-3 Severity level of the last fire event (1998-2007) ........................ ............ 102

3-4 Florida Geographic Database Library Map of forest types for the Osceola
National Forest. ... ...... ......... ......... ... ............. ............... 103

3-5 Map of the community types, hydric and mesic, for the Osceola National
Forest. ......... ........ ......... .............................. ........................... 104

3-6 Relationship between the probability of high severity prescribed fire,
frequency of fire, and time since last fire .................................... ................ 105

3-7 Relationship between the probability of high severity prescribed fire, the
severity level of the last fire event, and time since last fire. ............................ 106

3-8 Relationship between the probability of high severity prescribed fire,
frequency of fire, and time since last fire .................................... ................ 107









3-9 Relationship between the probability of moderate to high severity wildfire,
frequency of fire, and time since last fire. .......... ...... .................. 108

3-10 Relationship between the probability of moderate to high severity wildfire, fire
frequency, and tim e since last fire. ................. ....... ............ ..... ............. 109

3-11 Probability of high severity prescribed fire versus observed severity levels for
2008 prescribed fires ....... ................. ................................. 110

3-12 The probability of high severity prescribed fire in 2008................................... 111

3-13 The probability of high severity prescribed fire in 2008 by community type...... 112

3-14 Severity levels of 2008 prescribed fires on the Osceola National forest........... 113

3-15 The probability of high severity prescribed fire in 2008 by forest type ............ 114

3-16 The probability of moderate to high severity fire for 2008.............................. 115

3-17 The probability of moderate to high severity wildfire for 2008 by community
type............................................. ........... 116

3-18 The probability of moderate to high severity wildfire in 2008 by forest type...... 117

A-1 Severity levels of fire events for the 1998 fire season. ............. ............... 120

A-2 Severity levels of fire events for the 1999 fire season. ............. ............... 121

A-3 Severity levels of fire events for the 2000 fire season. ............. ............... 122

A-4 Severity levels of fire events for the 2001 fire season. ............. ............... 123

A-5 Severity levels of fire events for the 2002 fire season. ............. ............... 124

A-6 Severity levels of fire events for the 2003 fire season. ............. ............... 125

A-7 Severity levels of fire events for the 2004 fire season. ............. ............... 126

A-8 Severity levels of fire events for the 2005 fire season. ............. ............... 127

A-9 Severity levels of fire events for the 2006 fire season. ............. ............... 128

A-10 Severity levels of fire events for the 2007 fire season ............... ................. 129

A-11 Severity levels of fire events for the 2008 fire season ............... ................. 130









LIST OF ABBREVIATIONS


AIC Akaike's information criterion

BIC Bayesian information criterion

dNBR difference Normalized Burn Ratio

MTBS Monitoring trends in burn severity

NBR Normalized burn ratio

NRCS Natural Resource Conservation Service

NRMSC Northern Rocky Mountain Science Center

PDSI Palmer drought severity index

TSLF Time since last fire

USFS United States Forest Service

USGS United States Geological Survey

WUI Wildland urban interface









Abstract of Thesis Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Master of Science

EFFECT OF FIRE SIZE AND SEVERITY ON SUBSEQUENT FIRES USING
DIFFERENCE NORMALIZED BURN RATIOS IN PINE DOMINATED FLATWOOD
FORESTS IN FLORIDA

By

Sparkle Malone

August 2010

Chair: Christina Staudhammer
Cochair: Leda Kobziar

Florida forests naturally experienced frequent low intensity fires, yet fire exclusion

polices have altered the forest structure. The Osceola National Forest in north Florida

has experienced high wildfire occurrence for a number of years. Vegetation

communities within the Osceola are fire dependent and require regular burning for

ecosystem health. Although prescribed fire has been used to reduce wildfire risk and

maintain ecosystem integrity across much of the forest, managers are still working to

reintroduce fire to long-unburned units. The objective of this study is to use difference

Normalized Burn Ratio (dNBR) to evaluate the relationships between previous fire

severity, size, and historical frequency to inform prioritization and timing of future fire

use. Based on remotely-sensed Landsat imagery, dNBR analysis captures spectral

features over a time interval, and indicates the degree of change that is due to fire. This

analysis has shown that fires in areas burned 5 or more years prior exhibited a higher

probability of experiencing moderate-high severity fire and have a higher probability of

increasing in severity level in subsequent fires. Areas that have not experienced fire in

10 years are indistinguishable from areas that have never burned. Using dNBR as a









method of analyzing past fire severity is a useful tool for managers to determine the

lasting effects of prior fire severity. The analysis has further provided an effective

method of determining fire frequencies necessary to maintain the optimum level of

wildfire protection.









CHAPTER 1
INTRODUCTION TO FIRE IN THE SOUTHEASTERN UNITED STATES

Introduction

Fuel is any combustible material that is used to maintain fire. Without regular

fire, fuel loads in forested ecosystems grow to dangerous levels increasing the risk of

catastrophic wildfire. In systems where fire is a natural component, fuel management is

important for ecosystem health. Wildfire risk is not only affected by fuel, the increase in

population in the wildland urban interface (WUI) is also of great importance.

Anthropogenic influences are a major source of wildfire ignitions. Land managers are

currently working to reduce fuel accumulation in efforts to reduce the risk of catastrophic

wildfires but sensitive areas within WUI create additional problems. Land managers are

challenged with protecting surrounding land in a way that contributes to their

management goals.

The focus of this project is on a forest wide burn severity analysis in a north

central Florida forest using difference Normalized Burn Ratios (dNBR) for fires that

occurred between 1998 and 2008. This analysis is important for the evaluation of past

fire history and the effects it can have on subsequent fires. This study provides

valuable information regarding appropriate fire regimes to keep fuel loads low enough to

mitigate the effects of wildfires. This method of fire assessment using remote sensing

techniques can easily be modified to evaluate past fire effects for any land manager to

impart site specific statistics to their land management practices. The main objectives of

this study are:









1. Determine how past fire size and severity level effect subsequent fire
behavior?

2. Identify the relationship between fire size and the proportion of area burned at
high severity?

Suppression in Pine Flatwoods

Pine flatwoods are successional communities with southern mixed hardwoods,

mixed hardwoods, or bay heads as the climax community (Monk 1968). Without regular

disturbance, this fire maintained community shifts to one of the 3 climax communities.

Soil moisture and fertility determine which climax community is attained (Monk 1968).

Historically, fires were ignited by Indian hunting parties to corral game, by naval store

operators to reduce wildfire risk, by cattle owners to encourage grass growth, and by

lightning (Heyward 1939). Pine flatwoods burned at a frequency of every 1-15 years

(Maliakal et al. 2000). In the 1920s fire suppression began in the region (Frost 1993).

Long-term fire exclusion altered stand structure permitting hardwood species to occupy

pine flatwood forest at high densities (Gilliam et al. 1999; Heyward 1939). The lack of

disturbance created conditions outside the evolutionary history of species adapted to

this disturbance regime giving species adapted to less frequent disturbance the

advantage (Maliakal et al. 2000).

Pyrogenic species survive fire by either sprouting to regenerate or are able to

withstand repeated burning by maintaining features that allow the plant to survive fires

(Abrahamson 1984). Pine species have evolved to have thick bark and high crowns

(Waldrop et al. 1992) while other species re-sprout or seed (Abrahamson et al. 1996).

The majority of non-coniferous woody species re-sprout from underground reserves

rather that re-seeding (Abrahamson et al. 1996). Changes in vegetation following









extended periods of suppression leads to more intense, patchier, and less frequent

fires which may require more extreme conditions to burn (Maliakal et al. 2000).

Fire as a Forest Management tool

One of the most effective tools for fuel management in the southeastern United

States is prescribed burning (Davis et al. 1963). The purpose of using prescribed fire as

a management tool is to reduce fuel accumulations to levels that minimize damage from

wildfire and wildfire occurrence (Davis et al. 1963), improve wildlife habitat, reintroduce

fire to pyrogenic communities and, conserve biodiversity (Outcalt et al. 2004). Fire

management in Florida is largely dictated by urban encroachment, forest fragmentation,

and the challenges associated with smoke management (Wolcott et al. 2007). As long

as fuel loads are kept below 5 years, using fire to reduce the occurrence of catastrophic

wildfires is a profitable investment (Davis et al. 1963). Past research has shown that

wildfires could be kept small and damage limited with regular use of prescribed fire.

Regular prescribed burning keeps fuel accumulations on the forest floor and in the

understory within tolerable levels (Outcalt et al. 2004).

The amount of time that has passed after fire can greatly affect wildfire behavior

and effects. Davis et al. (1963) found the wildfire occurrence rate for areas on the

Osceola that contained fuel loads 3 years and older were higher than lower fuel loads.

Large fires were also found to be restricted by roughs 5 years and greater (Davis et al.

1963). As fires moved into younger roughs, intensity level was reduced to a degree

where suppression was possible (Davis et al. 1963). Outcalt et al. (2004) also found a

significant relationship between time since last fire and fire intensity. As time increased,

fire intensity also increased (Outcalt et al. 2004). Fuel accumulations of 3 years or less









support fewer fires, lower fire intensities, and lower annual burned acreage (Davis et al.

1963).

Prescribed burns are implemented under optimal circumstances where

conditions are suitable for vegetation consumption but not at levels to cause fire to

become unmanageable. Favorable conditions are characterized by cool weather,

relatively constant winds, dry litter, and wet soil (Davis et al. 1963). During prescribed

burns wet areas burn lightly if at all. Understory fuel is partially consumed with little

consumption of the duff layer (Outcalt et al. 2004). Therefore, wet areas (cypress

ponds) generally carry very heavy fuel volumes. During extended drought periods,

these areas (cypress ponds) dry up making them capable of very large very intense

wildfires (Davis et al. 1963).

Mortality is a major issue in prescribed fire management. Prescribed fire is used

to reduce the effects of catastrophic wildfire where a higher amount of mortality is likely

to result. Outcalt et al. (2004) found prescribed fire to be efficient in reducing mortality

levels and timber loss. Tree mortality was 64% in previously unburned areas and 17%

in areas burned within the last 3 years (Outcalt et al. 2004). Outcalt et al. (2004) also

found that relative moisture levels of an area influenced tree mortality. Mortality was

significantly higher on wetter sites, likely due to high fuel loads. It was also shown that

during extreme drought conditions, mortality was significantly higher on sites where fires

had been absent for 5 or more years.

The most favorable timing of prescribed fire depends on management objectives

and site characteristics. Flatwoods are generally burned either during winter (dormant









season) or summer burns (growing season). Vegetation and fuel consumption differs

significantly between the two.

Winter, in north central Florida, is typically a dry season with most precipitation

coming from periodic cold fronts. Ambient temperatures are lower reducing the total

amount of heat transferred to surrounding vegetation during fire, resulting in less

damage to plant tissues. Prescribed fires following fronts are manageable and allow the

upper layers of litter to carry fire while lower layers are unavailable. This time of year,

grasses and other fine fuels are avialible to burn while deciduous hardwoods have their

food reserves below ground and are prepared to sprout back following fire. Dormant

season burning affects the size, cover, and vigor of hardwoods but is not effective at

reducing abundance.

Early spring is typically a season marked by thunderstorm development and

lightning ignitions. Hydric communities are most likely available to burn during this time

yet the prolonged time between precipitation events, make this season less desirable

for most management objectives. Spring fires are useful for stimulating seed, raising

insect populations, and increasing the quality of browse to boost food availability for

wildlife.

Although summer is the hottest season, it is also the wettest. The increase in

temperature causes fires to be more intense and more likely to cause damage to plants.

This is also the season of thunderstorms. Unstable atmospheres associated with such

events bring lighting, unpredictable wind speeds and direction that can complicate

prescribed burning. Burns during this season must be carefully monitored. Summer

fires reduce hardwood vigor allowing grasses and forbs to increase in abundance.









Fire Severity

Fire severity is a measure of ecological and physical change attributed to fire

(Agee 1993; Hardy 2005). It is influenced by both abiotic and biotic factors. Abiotic

determinants include weather, moisture, time of day, sunlight incidence, and slope

(Oliveras et al. 2009). Vegetation attributes such as species, tree size, succession

stage, and pathogens are among the many factors influencing fire severity (Cocke et al.

2005). The variability in landscape and weather conditions during a fire are the cause

of heterogeneous burn patterns (Cocke et al. 2005). Major differences in severity are

also associated with the location of the fire perimeter (Oliveras et al. 2009). Head fires

burn with greater flame lengths and intensity than backing fires. Head fires move in the

same direction as the wind while backing fires move against the wind. Consequently,

we would expect to see greater severity in areas burned by heading fires than in areas

burned by backing fires.

Low severity burns are characterized by lightly burned areas where only fine

fuels are consumed with minor scorching of trees in the understory (Wagtendonk et al.

2004). Areas of moderate severity retain some fuels on the forest floor and have crown

scorching in mid-large trees with mortality of small trees (Wagtendonk et al. 2004).

High severity zones are generally composed of complete combustion of all litter, duff

and small logs, mortality of small-med trees, and consumption of large tree crowns

(Wagtendonk et al. 2004). Unburned and low burn areas serve as seed sources for

more severely burned sections (Cocke et al. 2005).

Severity is important to monitor as its effects on exotic species establishment,

soil responses, and regeneration can be significant. Large fires may remove existing

plant biomass, providing ideal habitat for exotic species (Kuezi et al. 2008). Responses









in soil condition following fire can range from affirmative nutrient availability to loss of

nutrients, soil micro-organisms, and changes in physical structure of the soil (Busse et

al. 2005). The degree of canopy degeneration due to cambium and crown scorch can

severely impact the ability to re-sprout or seed. Combined with the biophysical

condition, plant recovery following a severe fire can prove nearly impossible for remnant

vegetation (White et al.1996).

The same fire behavior can result in very different severity effects in over and

understory vegetation, as well as in soil conditions (Wagtendonk et al. 2004). Burn

severity effects aren't always evident directly following fire. Therefore, a fire severity

analysis will help managers anticipate the short and long term effects of severity level,

and how to better predict areas of potential high severity. The burn severity analysis will

further improve our understanding of why and where fires burn severely.

Measuring Fire Severity with DNBRs

Fire severity can be effectively measured through remote sensing techniques. A

difference Normalized Burn Ratio (dNBR) captures the spectral response, over a time

interval, and indicates the degree of change that is due to fire (Wagtendonk et al. 2004;

Miller et al. 2006). The mapping methodology was initially developed and tested by the

USGS Northern Rocky Mountain Science Center (NRMSC). Multi-temporal image

differencing was employed to enhance contrast and detection of changes from pre- and

post-fire images using Landsat Thematic Mapper (TM) bands 4 and 7 (Wagtendonk et

al. 2004). Normalized Burn Ratios (NBR) were designed to enhance the bands'

response to fire by normalizing their difference to compensate for variations in the

overall brightness of the scene (Wagtendonk et al. 2004). The use of shortwave

infrared bands was found to have the highest accuracy (Cocke et al. 2005). Employed









as a radiometric index, dNBRs are directly related to burn severity (Wagtendonk et al.

2004) and as long as the fire is within the resolution range of the satellite sensor, 30m, it

is detectable (White et al. 1996).

Sensitivity to vegetation and soil moisture, changes in canopy cover, biomass

removal, and soil chemical composition allow dNBRs to define different levels of burn

severity. Fire effects on soil, litter, and vegetation impact the spectral response of the

post-fire image (White et al. 1996 and Cocke et al. 2005). The degree of change

between the two images determines the extent to which fire has affected the area of

interest (White et al.1996). An increase in dNBR corresponds to an increase in severity

level. Unburned areas have values near zero, signifying little to no change between the

pre- and post-fire image (Wagtendonk et al. 2004). High severity areas have higher

DNBRs due to greater vegetation die off (Kuezi et al. 2008). In order to model the fire

severity accurately it is important to pair the pre- and post-fire images by phenology and

moisture levels (Wagtendonk et al. 2004). Timing of acquisition can impact dNBRs if

there is a significant difference in vegetation and moisture levels due to phenology, not

fire (Wagtendonk et al. 2004).

Important to consider when using dNBRs is the chance that values are being

influenced by events other than fire. Turner et al. (1994) used dNBRs in Yellowstone

National Park and discovered bias in particular severity classes due to pine beetle

infestation (White et al. 1996). Jakubauskas et al. (1990) found that burn severity is

detected differently among conifers, deciduous trees, and shrubs due to re-vegetation

patterns. In addition, drought stress and vegetation re-growth makes it difficult to

discern low severity and unburned areas (Cocke et al. 2005). The highest accuracy is









achieved in detecting high severity burns (Cocke et al. 2005). More severely burned

areas have a much greater difference in vegetation cover changing the radiation budget

in the post fire image by a greater degree (White et al. 1996).

DNBR is used within the United States to appraise fire severity following major

fires (Wagtendonk et al. 2004; Godwin 2008). Image differencing is one of the most

accurate methods of detecting the level of change caused by fire (Cocke et al. 2005). It

can accurately detect burn severity in a way that is repeatable. Beyond any other band

combinations, NBRs emphasize the effects of fire. Other methods that use bands in the

visible part of the spectrum introduce atmospheric interference from dust and smoke

(Cocke et al. 2005). And, indices derived from near infrared and mid-infrared

reflectance are not sensitive enough to remotely sense water stress (Wagtendonk et al.

2004).

Studies using dNBRs have been in efforts to calibrate severity levels (Cocke et

al. 2005; Hoy et al. 2008), compare severity levels of a previous fires to a subsequent

fires (Collins et al. 2009; Allen et al. 2008;), interpret the effects of fuel management on

severity (Safford et al. 2009), and to monitor changes in vegetation over time (White

1996; Kuenzi et al. 2008) and topographical variations (Holden 2009; Oliveras et al.

2009). Currently in the United States there is a multi agency project, Monitoring Trends

in Burn Severity (MTBS), using dNBRs to map burn severity and perimeters of large

fires. This project uses data from 1984 2010 to identify national trends in burn

severity in efforts to determine the effectiveness of the National Fire Plan and Healthy

Forest Restoration Act.









Study Site

The Osceola National Forest is located in the northeastern portion of the state of

Florida (Latitude: 30.34371, Longitude: -82.47322) about 40 miles west of the city of

Jacksonville (Figure 1-1). The forest consists of pine flatwoods and areas of cypress

and bay swamps. Pine flatwoods have an overstory of pines on low, flat, sandy, acidic

soils with an understory of herbaceous plants and grasses. This community is fire

dependent and requires regular burning for pine germination and maintenance of plant

and animal communities. The lack of fire for prolonged periods will increase broad leaf

woody vegetation and reduce herbaceous plant cover and eventually reduce pine

germination. The main communities found within flatwoods on the Osceola are

Longleaf (Pinus palustris) wiregrass (Aristida beyrichiana), and slash pine (Pinus elliotti)

-gallberry (/llex glabra) -palmetto. In the low lying wet areas scattered throughout the

forest are cypress (Taxodium spp) ponds.

Fire management of the forest consist of a prescribe burn fire frequency of 2-5

years for most managed compartments with areas that have never been an active part

of their prescribed fire program. Fire frequencies are determined based on current

forest type and the desired future condition of the forest. The largest struggle fire

managers' face on this forest is burning large acreages every year given few days that

are within specified prescribed fire weather conditions. Forest managers must also deal

with smoke management issues associated with being near a major urban area, an

interstate highway, and an airport.

Conclusion

Fire management in the southeast plays a crucial role in maintaining ecosystem

health and protecting private and public land. Evaluating fire severity for 11 years of









fire data for the Osceola National Forest has the potential to provide very important

information regarding fire frequencies necessary to reduce wildfire risk and the effects

of previous fires on subsequent fires. The analysis aims to identify the effects of fire

frequency, the time since last fire, and the severity level of past fires on fire behavior

using inexpensive remote sensing techniques. This information can then be used to

identify areas that should be a high priority for prescribed burning and areas that may

require immediate attention if threatened by wildfire.









Osceola National Forest

I I I Meters


--m
V K

ii


Jacksonville, FL


Lake City Municipal Air Port


Figure 1-1. Osceola National Forest in North Florida.


N









CHAPTER 2
EFFECTS OF FIRE FREQUENCY, SIZE, AND TIME BETWEEN FIRE EVENTS IN
NORTH FLORIDA FLATWOODS

Introduction

Prescribed fire is an important management tool in the south eastern United

States. In pyrogenic communities that require regular burning for ecosystem health,

forest managers are working to implement prescribed fire in place of natural wildfire

cycles. Florida forest naturally experienced frequent low intensity fires yet high

population, forest fragmentation, and dwindling budgets make prescribed fire

management increasingly difficult. Sensitive areas (around major highways and roads,

airports, and communities) reduce the amount of prescribed burning that can be done

safely. Land managers are faced with decisions on how to implement prescribed fire in

a manner that meets their management objectives and reduces the risk of catastrophic

wildfire.

At present, land manager objectives include: reduced fuel accumulation to levels

that minimize damage from wildfire (Davis et al. 1963), improved wildlife habitat, and the

conservation of biodiversity (Outcalt et al. 2004). Timing of burning, fire frequency

necessary to meet objectives, and the effects of fire are of major concern to land

managers. Managers could greatly benefit from a quantifiable method of evaluating fire

effects that is site specific. This study aims to develop a spatially explicit fire history for

the Osceola National Forest that can be used to determine past fire effects and future

implications.

Forested communities are in a constant state of change. They are continuously

recovering from some sort of disturbance. The state of the community is a function of

the frequency of disturbance, the time between disturbance events, and the severity of









the disturbance. The main source of disturbance associated with the species

composition and abundance of pine flatwoods forests is fire.

In pyrogenic communities the frequency, intensity, and the amount of time

between disturbances dictate community composition and further impacts the

vegetative response to fire. Pyrogenic species promote and are able to support the

spread of fire through the community. Without fire, pyrogenic communities become

invaded with fire sensitive species reducing the communities' flammability. Fire

sensitive species affect the way fire spreads through this community. These species

don't facilitate fire as well as pyrogenic communities and may promote dangerous fire

behavior if fuel loads are high.

Measuring Fire Severity

Fire severity is a measure of ecological and physical change attributed to fire

(Agee 1993; Hardy 2005). Severity is influenced by weather, moisture, time of day,

sunlight incidence, slope (Oliveras et al. 2009), species, tree size, succession stage,

and pathogens (Cocke et al. 2005). Landscape variability and differences in micro-

climate contribute to heterogeneous burn patterns and hence patchy severity (Cocke et

al. 2005). Major variations in severity are also associated with the location of the fire

perimeter (Oliveras et al. 2009). Head fires burn with greater flame lengths and

intensity than backing fires. As a result, we would expect to see greater severity in

areas burned by a head fires than in areas burned by backing fires.

Severity levels are characterized by the amount of fuel consumed, fire effects on

residual vegetation, mortality, and changes in moisture levels. Low severity burns are

characterized by lightly burned areas where only fine fuels are consumed with minor

scorching of trees in the understory (Wagtendonk et al. 2004). Areas of moderate









severity retain some fuels on the forest floor and have crown scorching in mid-large

trees with mortality of small trees (Wagtendonk et al. 2004). High severity zones have a

high degree of combustion of litter, duff and small logs, mortality of small-med trees,

and consumption of large tree crown foliage (Wagtendonk et al. 2004).

The same fire behavior can result in very different severity effects in over- and

understory vegetation (Wagtendonk et al. 2004). Large, high severity fires have the

potential to remove existing plant biomass, providing ideal habitat for exotic species

(Kuezi et al. 2008). Responses in soil condition can range from affirmative nutrient

availability to loss of nutrients, soil micro-organisms, and changes in physical structure

of the soil (Busse et al. 2005). The degree of canopy degeneration due to cambium and

crown scorch can severely impact the ability to re-sprout or seed. Plant recovery

following a severe fire can prove nearly impossible for remnant vegetation (White et

al.1996). Therefore, severity is important to monitor as its effects on exotic species

establishment, soil responses, and regeneration can be significant.

To identify the effects of fire, remote sensing techniques can be utilized to model

changes that are due to fire. Techniques have been developed to measure the amount

of change to a system caused by fire. Normalized Burn Ratios (NBR) were designed to

enhance the response of Landsat Thematic Mapper (TM) bands 4 and 7 to fire

(Wagtendonk et al. 2004)(1). Multi-temporal image differencing is then employed to

enhance contrast and detection of changes from pre- and post-fire images

(Wagtendonk et al. 2004).




NBR =-
(B4 + B7)










dNBR = NBR -j -NBRoj


2
Differenced Normalized Burn Ratios determine the level of severity a 30 meter by

30 meter unit of landscape experienced due to a fire event by measuring the amount of

change between a pre and post fire image (2). Employed as a radiometric index,

dNBRs are directly related to burn severity (Cocke et al. 2005; Hoy et al. 2008; Godwin

2008; Allen et al. 2008, Wagtendonk et al. 2004). Fires within the resolution range of

the satellite sensor, 30 meter, can be detected (White et al.1996).

Previous studies have used dNBRs to identify and monitor the effects of fire.

Studies have used dNBRs to calibrate severity levels to specific forest types (Cocke et

al. 2005; Hoy et al. 2008; Godwin 2008; Allen et al. 2008), compare severity levels

between fire events (Collins et al. 2009; Allen et al. 2008;), interpret the effects of fuel

management techniques on severity levels (Safford et al. 2009; Finney et al. 2005;

Safford et al. 2009; Wimberly et al. 2009), and to monitor changes in vegetation over

time (White 1996; Kuenzi et al. 2008) and topographical variations (Holden 2009;

Oliveras et al. 2009; Duffy et al. 2007). There have also been efforts to relate remotely

sensed severity to biophysical attributes and processes. Boer et al. (2008) used dNBRs

to define severity as a change in leaf area index (LAI) in a pre and post fire image.

Currently, there is a multi agency project, Monitoring Trends in Burn Severity (MTBS),

using dNBRs to map burn severity and the perimeters of large wildfires for the entire

United States. MTBS is using data from 1984-2010 to identify national trends in burn

severity to determine the effectiveness of the National Fire Plan and Healthy Forest

Restoration Act. Duffy et al. (2007) analyzed the relationship between the area burned









by wildfire and remotely sensed severity level. This study used NBRs for 24 wildfires in

Alaska. The study found that the average burn severity increased with the natural

logarithm of the area of the wildfire. Larger fires were more likely to contain areas that

were more severely burned than smaller fires. Epting et al. (2005) evaluated the

usefulness of 13 remotely sensed indices of burn severity to find that NBR and dNBR

were the most accurate (Escuin et al. 2009), exhibiting high accuracy when compared

with field based severity indices in forested areas. To our knowledge, no other study

has used dNBRs to model how fire severity from previous fire affects subsequent fire

over time. The Osceola National Forest in North Florida presents a unique opportunity

to conduct such an analysis. Landsat imagery enables an investigation into the

effectiveness of the Osceola's prescribed burning program for reducing wildfire severity,

and lends insight into the complex interplay between fire severity, fuels recovery rates,

time between fires, and subsequent fire severity.

Detecting burn severity for fires on the Osceola National Forest is in efforts to

anticipate the short and long term effects of severity level and the effects of time

intervals between fire events, and to predict areas of potential high severity. The burn

severity analysis will further improve our understanding of why and where fires burn

severely. The following questions fuel this investigation:

* 1. How does past fire size and severity level affect subsequent fire behavior?

* 2. Is there a relationship between the size of fires and the proportion of area
burned at high severity?

We hypothesize that fires with a high severity level will have a negative effect on

the severity level of fires occurring within three years. High severity fires are expected to

have a lower severity level in subsequent fires as long as the second fire is within three









years. Vegetation recovery is not expected to reach pre-fire conditions within this time

frame. We also hypothesize that larger fires will have a higher probability of

experiencing high severity.

Study Site

On the Osceola National Forest, thousands of acres are burned every year to

reduce fuel levels and manipulate succession stages. The Osceola is Located in north

central Florida (Latitude: 30.34371, Longitude: -82.47322) 40 miles outside the city of

Jacksonville. The Osceola consist of pine flatwoods with an overstory of pines on low,

flat, sandy, acidic soils; pine flatwoods have an understory of herbaceous plants,

grasses, palmetto, and woody species. This community is fire dependent and requires

regular burning for ecosystem health. The main communities found within flatwoods on

the Osceola are longleaf pine (Pinus palustris) -wiregrass (Aristida beyrichiana) and

slash pine (Pinus elliotti) -gallberry (/llex glabra) -saw palmetto (Serenoa repens). Fire

management on the Osceola and much of Florida is largely dictated by urban

encroachment, forest fragmentation, and the challenges associated with smoke

management (Wolcott et al. 2007; Duncan et al. 2004). These anthropogenic

influences have reduced fire sizes and recurrence, increasing fuel connectivity and load

(Duncan et al. 2004).

Prescribed burns are implemented under conditions that are suitable for

vegetation consumption, yet not at levels to cause fire to become unmanageable.

Favorable conditions are characterized by cool weather, consistent winds, dry litter, and

wet soil (Davis et al. 1963). Prescribed fires are performed under conditions that

promote low severity fire though variability in the landscape and weather conditions can

cause higher severity levels. Hydric areas burn lightly if at all during prescribed burns.









Understory fuel is partially consumed with little consumption of the duff layer (Outcalt et

al. 2004). Therefore, wet areas generally carry very heavy fuel volumes and during

extended drought periods, these areas dry up making them capable of very large, very

intense wildfires (Davis et al. 1963; Maliakal et al. 2000). The season of prescribed fire

is determined by management objectives and site characteristics. Flatwoods are

generally burned either during winter (dormant season) or early summer (growing

season).

Methods

Data

DNBRs were developed for each fire event greater than 1ac on the Osceola

National Forest. Severity levels were defined based on general severity classes

provided by the United States Geological Survey (USGS). Severity classes were

reclassified and merged into 4 main levels; unburned cells, low severity cells, moderate

severity, and high severity (Table 2-1). To test the hypotheses, two datasets were

developed, a time and fire size dataset. The time analysis dataset consisted of

consecutive fire events (prescribed and wildfire), that were then separated into time

intervals to indicate the time between fire events. To control for the number of times a

pixel burned between fire events, pixels had to be unburned previous to the first fire and

remain unburned until the second fire. For each pixel the following information was

included in the data set: severity level of fire event 1, severity level of fire event 2,

community type hydricc or mesic), forest type (pine, hardwood, and pine/hardwood), and

Palmer drought severity index (PDSI) for the year before each event and the year of

each event.









PDSI, developed in the 1965 by Wayne Palmer, is the most effective way of

determining long-term drought (NOAA 2009). This method compares weather

conditions to determine if they are abnormally dry or abnormally wet compared to

historical weather data. The palmer index is based on the supply-and-demand concept

of the water balance equation, taking into account more than just the precipitation deficit

at specific locations. The index uses temperature, rain fall information, and the local

available water content of the soil to determine dryness that is standardized to local

climate. Standardization allows the index to be compared against different locations

and time periods. PDSI uses 0 as normal and negative numbers (-1 to -6) to indicate

drought (Table 2-2). Moderate drought is a -2, severe -3, and extreme drought is -4. To

reflect excess rain the index uses positive numbers. A major advantage of this index is

that it is standardized to local climate and can be applied to any part of the United

States.

The fire size dataset was comprised of the 115 wildfires that occurred and were

recorded on the Osceola National Forest from 1998-2008. Fires had to be at least 1

acre to be included in the dataset. For each fire the portion of cells burned in each

severity class, the size in acres, season of fire, Forest Service forest type classification

(Figure 2-1), soil drainage class (Figure 2-2), and PDSI values for the year of and

before the fire event were recorded.

Model Development

Logistic regression techniques were utilized to model the probability of

experiencing a high severity fire (model 1), the probability of increasing in severity level

(model 2), the probability of burning (model 3), and the probability of decreasing in

severity (model 4) for the time dataset. Logistic regression can be used to measure









binary responses by describing the relationship between one or more independent

variables and the binary response.

(1 success
=0 failure

3

Responses are coded as [0, 1] to [-o, C] and y, is a realization of a random variable

Y, that can take on the values of 0 and 1 with probabilities w, and 1- ir (3). The

distribution of Yj is a Bernoulli distribution with the mean (4) and variance (5)

depending on the underlying probability ?r,.

E(Y, ) =

4
var(Y, )= ,r (1 -- ri)

5

To make the probability a linear function of a vector of observed covariates

x the probability is transformed to remove the range restrictions (6).

logi (Tr,) = -og. X-P
6

Logits map probabilities from [0, 1] to [-co, co]. Negative logits represent probabilities

below 1/ and positive logits represent probabilities above 1/. Solving for the probability

of success requires exponentiating the logit and calculating the odds of success (7).

exp(,i f)
1 + lexp(x' )
7


Maximum likelihood methods were used for parameter estimation. With this

approach, parameters are estimated iteratively until parameters that maximize the log of









the likelihood are obtained. Goodness of fit statistics, Akaike's information criterion

(AIC) and Bayesian information criterion (BIC), were used to compare competing

models. AIC is a statistic that is used to rank different models based on how close fitted

values are to true values (8) (Littell et al. 2006).

AIC = 2k 2In(L)
8



Where: k is the number of parameters in the statistical model and L is the maximized

value of the likelihood function for the estimated model (8). Like AIC, BIC was used to

rank models with a different numbers of parameters to avoid increasing the likelihood by

over-fitting the model (Littell et al. 2006).

BIC = -2 In(L) + k L(n)
9

Where: n is the sample size. Unexplained variation in the dependent variable and the

number of covariates increases the BIC and AIC values (9). For both AIC and BIC, the

lowest score indicates the best model.

The ratio of the Pearson chi-square to its degrees of freedom is used to

determine if the model displays lack of fit. Values closer to 1 indicate that the model fits

the data well (Littell et al. 2006). To address the assumption of independence among

observations, a generalized linear mixed model was used using the SAS procedure

PROC GLIMMIX. Correlation among responses is incorporated into the model by

adding random components to the linear predictor. To account for the correlation

among responses, random residuals were modeled.

Raster data is spatially correlated due to the adjacency of pixels. Although it

would have been more effective to model the spatial correlation directly, without the aid









of a super computer this option is infeasible. The GLIMMIX procedure can also make

use of several predictor variables that may be either numerical or categorical (Littell et

al. 2006).

In this analysis we evaluated the probability of experiencing (1) moderate to high

severity, (2) increased severity level, (3) burning, and (4) decreased severity between

the first fire and the second fire at different time intervals. Variables used in the 4

models include: the severity level of the first fire event (unburned, low severity,

moderate severity, and high severity), the time interval between fires (1-2, 3-4, 5-6, 7-8

and 9-10 years) (Table 2-3), the type of fire in the second fire event (wild or prescribed),

and the PDSI for the year before and the year of each fire. The logit of the probability

was modeled as

logait (T7jk) = a7 + aTX + l + rk + 1XXjki + r2Xjkj + -Z73Xjp + l4Xjki + Sjk
10

where: r is the intercept, ai (for i =1, 2, 3, 4) is the net effect of the ith severity level for

the first fire, 3, (for j = 1, 2, 3, 4, 5) is the net effect of the jth time intervals between fire

events, yk (for k= 1,2 ) is the effect of the type of fire, -i is the effect of PDSI for the year

prior to fire 1, 2 is the PDSI for the year of fire 1, 3 is the PDSI for the year before fire

2, T4 is the PDSI for the year of fire 2 and uk is the random error (10). Final model

covariates were indicated by parameters that were significant based on the Wald chi-

square statistic and the model with the lowest AIC and BIC value. Interactions between

all parameters were also considered. Non- significant parameters were removed from

the full model one at a time. To test for differences among categorical levels, least

square means were produced and differences were tested.









Logistic regression was also used to examine the probability of burning at high

severity for each fire size class for the fire size data set. Variables used in this model

include: season of fire (winter, spring, summer, and fall), soil drainage class (1-9),

Forest type (pine, hardwood, pine/hardwood, and hardwood/pine), and PDSI for the

year before and the year of each fire (Table 2-4). Model selection was determined by

goodness of fit statistics AIC and BIC. A backward selection method was used to

determine the final model; first all parameters were included within the model, and then

parameters were removed one by one based on the Wald chi square statistic.


Results

Data

The time data set is composed of 484,715 pixels. The majority of these pixels

burned as prescribed fires in the second fire (341,143). Over all years for fire 2, there

were higher percentages of cells experiencing low severity (40%), and high severity

(~10%) (Figure 2-1). The proportion of cells burned in each severity class is shown by

time (Figure 2-2, Figure 2-3, Figure 2-4, Figure 2-5, Figure 2-6). In fire 1 there was also

a higher percentage of cells experiencing low severity (51%), while ~4% experienced

high severity. The largest difference between the fires is the portion of cells in the low

severity category, a 10% increase between fire 1 and fire 2, and the difference in cells in

the high severity category,-5.6%. The major difference between the distributions of

cells among severity levels is that unburned cells in fire 1 moved to a higher severity

level.

Burned pixels were not evenly distributed over time. To reduce the amount of

variation between years, categories were created (Table 2-3). Fires with 5-6 years









between events had the highest percentage of cells that burned at high severity in the

second fire, with 53% for wild fires and 24.9% for prescribed fires burning at high

severity (Figure 2-4). Time interval 3-4 years and 7-8 years had the highest portion of

cells remaining unburned in the second fire event; ~70.8% and 49.4% remained

unburned 3-4 years and 7-8 years after wildfire, respectively 51.4% and 81.6%

remained unburned 3-4 years and 7-8 years after prescribed fires, respectively (Figure

2-3, Figure 2-5). In the second fire, wildfires had a much larger portion of the cells in

the unburned and high severity category, 44% and 17%, respectively, versus prescribed

fires. Overall, there was very little change in the proportion of pixels burned in each

severity class between fire 1 and fire 2 ignoring time between events. Until time

between fires reaches 5-6 years, prescribed fires decease in severity in the second fire

more than they increase in severity. After 5-6 years more cells increased in severity

than cells decreased in severity. Wildfires had a higher portion of the pixels decrease in

severity over all time intervals except time interval 5-6 years where 77% of the cells

increased in severity between the first and second fire.


Probability Modeling

Probability of experiencing moderate to high severity during a fire

Severity level (a1) at the first fire, time intervals between the first and second fire (,),

type of fire (yk), and the interaction between severity level and time interval(f)e,, were

significant predictors of the probability of experiencing high severity fire (11).

l0'git (FJ = t+ +, +, +ry, + ( j11 +
11









The effects of PDSI were not significant parameters. The overall model was significant

and the parameters were significant based on the Wald chi-square statistic (Table 2-5).

Moderate and high severity levels were merged for this analysis to avoid convergence

issues associated with low counts in the high severity category. The ratio of the

Pearson chi-square statistic to its degrees of freedom is approximately 1 indicating good

fit of the model to the data.

The probability of experiencing a moderate to high severity fire was higher for

wildfires than prescribed fires. Overall, the probability of burning at a moderate to high

severity class was low for all severity classes in fire 1 for prescribed fires (Figure 2-9).

The probability of moderate to high severity was high for wildfires when the time interval

was 5-6 years between fires (Figure 2-10). Areas with moderate and high severity in

the first fire had the highest probability of high severity fire for both wild and prescribed

fires (Figure 2-10, Figure 2-9). At 1-2 years between fire events, the probability of

moderate to high severity fire was the lowest (Figure 2-9). The highest probability by

time interval was at 5-6 years between fires, followed by 7-8, then 9-10 years. For

wildfires, 3-4 and 7-10 years between events yielded very low (<1%) probability of

moderate to high severity (Figure 2-10). Time interval 5-6 years had very high (>70%)

probabilities of moderate to high severity for wildfire (Figure 2-10).

Probability of increasing in severity in subsequent fires

Model 2 estimates the probability of severity level increasing from the first fire to the

second fire.

log it (myTEul)J = q7 a+ + + X + + + Y + + j+ + +
12









The model includes the effects of severity level at fire 1 (,), time interval between fire

events ( ,), fire type (yk), and PDSI value for the year prior to and the year of each fire

(-1,72,Trg, and i4) for the kth measurement in the ith severity level and the jth time

interval (12). The overall model was significant and the 8 parameters were significant

based on their Wald chi-square statistics (Table 2-6). The ratio of the Pearson chi-

square statistic to its degrees of freedom was close to 1(0.99), indicating good model fit

to the data.

The probability of increasing in severity was modeled for all events where fire

could increase (where the severity level in fire 1 was less than 4). As expected, the

model shows that the probability of increasing in severity was highest for unburned

cells, then low severity pixels and lowest for medium severity over all time intervals

(Figure 2-11 ,Figure 2-12, Figure 2-13). For all severity levels the probability of

increasing in severity was highest at 5-6 and 9-10 years between fire events (Figure 2-

12, Figure 2-13). The probability of increasing in severity level was higher for wildfires

than for prescribed fires and showed the same decreasing trend with increased severity

both fire types. Time Interval 7-8 years was surprisingly low for both wild and

prescribed fires.

Probability of burning during a fire

Model 3 examines the probability of burning (13).

logit (1),, = a7 + ,, + Jl + yk + Z1rXtjkj + 2Xjk + ZaTXjkj + sjk

13

The severity level of fire 1 (at), time interval (,S), fire type, and PDSI for the year prior to

fire 1 and 2 and the PDSI for the year of fire 1 (-1,-2, and Tarespectively) were all









included within the final model(13). The model was significant and all parameters were

significant based on their Wald chi-square statistics (Table 2-7). The ratio of the

Pearson chi-square statistic to its degrees of freedom was close to 1(1.02) indicating

good fit of the model to the data.

The probability of burning was approximately the same for each severity class

(Figure 2-15, Figure 2-16). Areas that had been burned by prescribed fires had a higher

probability of burning than areas that had been burned by wildfires for all time intervals

and severity levels (Figure 2-15). Over time, the probability of burning peaked (~80-

90% depending on severity level) at 5-6 years and, was the lowest for 1-2 and 7-8 years

between fires.

Probability of decreasing in severity in subsequent fires

Model 4 predicts the probability of fire severity decreasing in the second fire (14).

logit (m-kk) = 11 + a, + f + Yk T i + + j+ 3XI + (rZ+Fy)e + Efik

14

The severity level of the first fire (a,), time interval between fire and fire 2 (,), fire

type (y,), PDSI value for the year prior to and the year of both fire 1 and fire 2 (zi,2,rT,

and r4), and the interaction between severity level and fire type were kept in the final

model (14). The model was significant and the parameters were significant based on

their Wald chi-square statistic (Table 2-8). The ratio of the Pearson chi-square statistic

to its degrees of freedom was close to 1(1.03) indicating good model fit.

The probability of decreasing fire severity was modeled for all severity classes

except unburned. Over all time intervals and severity levels, the probability of

decreasing was lower for wildfires than for prescribed fires except at the low severity









level (Figure 2-17). At the low severity level, the probability of fire severity level

decreasing for wildfires was the lowest and the probability increased with increased

severity level. Both wild and prescribed fires show a reduced probability of decreasing

fire severity level when fires were 5-6 years apart. The probability of decreasing fire

severity level increased as the severity level increased for both fire types (Figure 2-18,

Figure 2-19).

Fire size analysis

A useful model could not be found for the probability of burning at high severity

using the fire size dataset. Fire size class was not a significant indicator of the

probability of experiencing a high severity fire. The data indicated that larger fires had a

higher portion of their pixels in the high severity size class so it was expected that larger

fires would have a higher probability of experiencing high severity fire. The best model

of the probability of high severity fire based on goodness of fit statistics included only

fire size class yet the model yielded no significant relationship between fire size and the

probability of experiencing high severity. The model parameters were not significant

based on their Wald chi-square statistics (Table 2-9). The ratio of the Pearson chi-

square statistic to its degrees of freedom was equal to 1 indicating good model fit.

Discussion

Probability of Experiencing Moderate to High Severity During a Fire

The probability of experiencing high severity fire has important implications for fire

effects and the degree to which wildfires are being mitigated. Based on the severity

level of the first fire event and time between events, this also has the capacity to identify

target intervals between fires. The probability of experiencing moderate to high severity

in the second fire was highest for time interval 5-6 years for all severity levels of the first









fire and both fire types (Figure 2-10). This indicates that by this point, vegetation has

reached pre-fire conditions regardless of the severity level it burned at in the first fire.

Davis et al. (1963) collected ground data from 380 fires in Florida and Georgia from

1955 to 1958 to evaluate prescribe fire effectiveness in reducing fire size and intensity.

This analysis found that fuel loads must be less than 5 years to adequately reduce the

occurrence of catastrophic wildfire on the Osceola National Forest. Vegetation is able

to recover quickly due to fast growing and re-sprouting species further fueled by an

increase in nutrient availability as a result of fire. Lemon (1949) found that the

maximum amount of litter is approached at 5 years and, by 8 years vegetation returned

to pre-burn status. This study used permanent plots on the Alapaha Experimental

Range (Georgia) to monitor changes in vegetation following prescribed fire. At 1-2

years between fires, wildfires have a higher probability of moderate to high severity fire

compared to longer time intervals where the probability is nearly 0. Factors beyond the

length of time between fire events may be the cause for the relationship between short

time intervals and the probability of moderate to high severity for wildfires. Weather

conditions and errors associated with the amount of biomass present in the pre-fire

image may be affecting this. We would expect the probability of moderate to high

severity fire to increase as the time interval increased yet, the lack of an increase over

time suggests that vegetation that isn't burning as often on the Osceola National Forest

remains unburned (Maliakal et al. 2000). This may be explained by the change in

flammability associated with natural succession in the pine flatwoods forest type. In

long-unburned stands, vegetation composition is shifting away from flammable saw

palmetto gallberryy complex with pine overstory towards less flammable, higher









moisture-content, hardwood dominance. Previously unburned cells likely remained

unburned in subsequent fires due to fuel that was not available to burn and a

combination of weather conditions.

As expected, the probability of high severity fire is higher for wildfires than for

prescribed fires. Prescribed fires are performed under optimal conditions where the

chance of mortality of fire-adapted species such as longleaf and slash pine, saw

palmetto and gallberry, is small. In contrast, most wildfires greater than 1 ac in size

occurred during optimal fire spread conditions, with high winds, lower relative

humidities, and dry fuels. Regardless of the severity class of the first fire, the probability

of moderate to high severity in the second fire was low for prescribed fires (<30%). This

suggests one of two things: either that regardless of the severity of the prescribed burn,

it is mitigating severity in subsequent fires; or the areas that are prescribed burned are

repeatedly prescribed burned, so that the second fire is typically of lower severity.

The moderate and high severity class had the highest probability of moderate to

high severity for both fire types (prescribed and wildfires). Within this dataset, areas

that have a history of burning at a moderate to high severity often continue the trend

regardless of the amount of time since the last fire event or the type of fire. This can be

due to a number of effects such as the type of fuel at the site, delayed mortality inflating

the severity signal over time, or the continued burning resulting in reduced vegetation

vigor, which appears via the dNBR analysis to be higher severity. This may then result

in a bias in the high severity class towards areas with less vegetation and ground fuels.

The reduction in fuel may promote more complete consumption resulting in an increase

in severity.









Variations in the landscape may also be a major cause for unexpected

relationships regarding time intervals between fire events. In hydric areas, if fuel

availability is reduced due to high moisture contents, distortions in the relationship

between time interval between fires and the probability of moderate to high severity may

occur. Even though these areas burned lightly in previous fires and time intervals were

long, the probability of moderate to high severity fire was still low. Variations in the

landscape adds additional variation to fire effects, prescribed fire planning, and fire

suppression efforts. In the future, adding depth to water table, dominant understory

vegetation, and dominant overstory vegetation may help to sort out unexpected

relationships between fire effects and time.

Probability of Increasing in Severity

The model predicting the probability of fires increasing in severity gave similar

results to the previous model (probability of experiencing high severity) for both fire

types. The probability of increasing in severity was higher for wildfire than for prescribed

fire. As expected, the probability of increasing in severity was the highest for unburned

cells and increased as the time interval increased (Figure 2-12) for all time intervals

except 3-4 and 7-8 years where the probability of increasing was close to 0. Most

prescribed fires on the Osceola are maintained at a 3-4year cycle. Therefore, most fires

that occur at this time interval were prescribed fires. Fires occurring with 7-8 years

between events consistently had a lower than expected probability of having higher

severity over all severity levels. Vegetation that has remained unburned for 7-8 years,

in this dataset, may not be available to burn as readily as vegetation with time between

events <6 years due to fuel moisture content and changes in species composition.









Without fire, fire-adapted species are replaced by broadleaf woody species that don't

facilitate the spread of fire as well as fire adapted species.

The time interval 5-6 years was identified once again, this time as being

associated with the highest probability of increasing fire severity, followed very closely

by 9-10 years. This time interval (5-6 years) may be the point at which vegetation has

recovered from previous fire events to a degree where the next fire event has enough

fuel available to burn and at increased severity levels. Lavoie et al. (2010) found that

living biomass recovered within 3 years of a fire event and predicted that fuel loads

would return to pre-fire conditions by 5-8 years in a similar pine flatwoods forest also in

North Florida. This suggest that time between fire events should not exceed 4 years.

Land managers should consider fire return intervals that are between 1-4 years in pine

flatwoods to mitigate moderate to high severity fire and increasing severity levels in

subsequent fires.

Probability of Burning

The probability of burning followed the same trend for each severity class and was

highest for the time interval 5-6 years for both wild and prescribed fires. The probability

of burning was low when fires were 1-2 years apart and increased with time interval.

Short time intervals between fires affect the way fire spreads due to the lack of

continuous combustible material to maintain fire spread. Once again 7-8 years between

fires had a lower probability of burning than expected indicating vegetation that had

been burning at this interval has reduced availability. The probability of burning was

higher for prescribed fires than for wildfires. Prescribed fires are performed under

conditions and in areas that facilitate understory vegetation and litter consumption









whereas wildfires often result in incomplete patchy burning of the under and over story

species due to rapid changes in climatic conditions and vegetation availability.

Probability of Decreasing in Severity

As severity levels increased, the probability of subsequent fires decreasing in

severity level increased. At all severity levels the probability of decreasing in severity

was lowest for fires occurring 5-6 years apart followed by 9-10 years apart. By 5-6 years

between fires we would expect fuel levels to recover to a point where wildfire risk is high

and past fires no longer have an effect on subsequent fires. This model supports the

hypothesis that fires with moderate to high severity levels have a negative effect on

severity level of fires occurring within 3 years. Land managers should consider 1-4 year

fire frequencies for pine flatwoods to reduce the risk of moderate to high severity

prescribed and wildfires. This evidence strongly suggests that beyond a five year

interval, severity will be higher than what the majority of management objectives seek.

Areas previously burned by low severity fire had a high probability of remaining

unburned in the next fire event if they were burned in a wildfire. This relationship

indicates that during a wildfire, land that previously burned at a low severity level may

have had vegetation that was unavailable to burn during the subsequent fire. Because

the land previously burned at a low severity level, there should be enough vegetation

there to carry higher severity fires should conditions be suitable. For moderate and high

severity levels first fires, the probability of decreasing severity was higher for prescribed

fires. So, areas that previously burned at moderate and high severity levels had a

higher probability of decreasing in severity level if they were prescribed burned.









Conclusion

Fire history for the Osceola National Forest was effectively modeled to determine

past trends in fire effects and future implications of fire management decisions. The

models also provide valuable information regarding the influence of severity level and

time between events for both prescribed and wildfires. The data shows that vegetation

on this forest recovers quickly following fire and that fuel loads reach levels where they

are available to burn within 1 year and are at pre-fire conditions by 5 years. The data

also identifies areas that are within fire perimeters and are consistently remaining

unburned. Likely hydric communities, land that has gone unburned for 7-8 years

showed signs that the fuel just wasn't available to carry high severity fire from 1998-

2008. Hydric communities may require extreme drought condition to reduce moisture

levels.

All four models identified the time interval 5-6 years as a point where the effects of

previous fires had little to no effect on subsequent fires. At this point, the probability of

high severity fire, increasing severity level in subsequent fire, and the likelihood of

burning at all is highest. This is also a point where the probability of decreasing severity

in subsequent fires was lowest. These findings indicate that time between fires should

be kept below 5 years. Results from this work are supported by other studies

suggesting that the use of remote sensing techniques sufficiently represent

relationships between time since last fire and the severity level of past fire events on

subsequent fire behavior.

The relationship involving time between fire events and fire severity are influenced

by variations in the landscape. Fire effects are influenced by the type of vegetation and

the availability of that vegetation. Land managers must consider vegetation recovery









and availability differences by both forest and community types to determine the risk of

the high severity fire. Although hydric communities are often unavailable to burn, fuel

loads in these communities are high and must be managed. Land managers may

consider other alternatives to mitigate high fuel loads in hydric communities.

Although previous studies have found a relationship between fire size and high

severity (Duffy et al 2007) a useful model could not be found for the probability of high

severity fire using the fire size dataset. The data indicated that larger fires had a higher

portion of area in the high severity size class yet this relationship was not significant.

Out of 115 wildfires included within this dataset, few fires were large. Most fires were

less than 50 ac in size (93 fires). Although large fires had a higher portion of their cells

in high the high severity class, the vast majority of the area was burned by moderate

and low severity fire. A larger dataset may be required to capture the relationship

between fire size and high severity.

Errors introduced by severity level classification may also influence the models.

General severity level classifications were used and further generalized from seven

levels to four. In the future, severity levels should be calibrated to pine flatwood forest

of the southeastern U.S for the best results. Also, delineation of fire perimeters is not

exact and may introduce error into the unburned and low severity levels.









Table 2-1. Severity class descriptions for the time analysis and fire size datasets.
Severity Class Description Reclassified Severity Classes
1 Unburned within a fire 1
perimeter (DNBR -100 99) Unburned within a fire perimeter


Enhanced Regrowth/Low
Severity
(DNBR -500 --101, 100- 269)
Low-Med Severity
(DNBR 270 439)

Med-High Severity
(DNBR 440 659)
High Severity
(DNBR 660- 1300)


(DNBR 100-99)
2
Low Severity
(DNBR -500 --101, 100- 269)
3
Med Severity
(DNBR 270 439)
4
High Severity
(DNBR 440- 1300)


Table 2-2. Palmer Drought Severity Index values and descriptions
Palmer Drought Severity Index
4.0 or more exceptionally wet
3.0 to 3.99 very wet
2.0 to 2.99 moderately wet
1.0 to 1.99 slightly wet
0.5 to 0.99 incipient wet spell
0.49 to -0.49 near normal
-0.5 to -0.99 incipient dry spell
-1.0 to -1.99 mild drought
-2.0 to -2.99 moderate drought
-3.0 to -3.99 severe drought
-4.0 or less extreme drought


Table 2-3. Time interval
Time Interval (years)


classification for time analysis dataset.
Code Observations


1-2 1 115,273

3-4 2 136,254

5-6 3 131,409

7-8 4 79,886

9-10 5 21,893









Table 2-4. Covariate classifications for fire size model.
Variable Class Code
Fire Size Class 1-15ac 1
16-50ac 2
50- 150ac 3
150-500ac 4
>500ac 5
Season Spring 1
Summer 2
Fall 3
Winter 4
Forest Type Pine 1
Hardwood 2
Pine Hardwood 3
Hardwood Pine 4
Soil Drainage Somewhat poorly drained 1

Somewhat- poorly drained 2
Somewhat-very poorly drained 3
Poorly Drained 4
Poorly- very poorly drained 5
very poorly drained 6
standing water- poorly drained 7









Table 2-5. Parameter estimates and their respective standard errors and p-values for the model predicting the probability
of high severity fire.
Parameter Categories Estimate Std. Error P-value
Intercept -3.8923 0.06453 <0.0001
Severity of fire 1 Unburned -0.1251 0.01703 <0.0001
Low -0.4520 0.01716 <0.0001
Med-High 0
Time between fires 1-2 years -1.7801 0.1018 <0.0001
3-4 years -0.5907 0.07174 <0.0001
5-6 years 3.0129 0.06509 <0.0001
7-8 years 1.4293 0.06713 <0.0001
9-10 years 0
Type of fire Wildfires -3.0456 0.5039 <0.0001
Prescribed Fires 0
Time between fires* 1-2 years- Wildfire 6.6121 0.5102 0.0034
Type of Fire 1-2 years- Prescribed 0
3-4 years- Wildfire 2.2647 0.5190 <0.0001
3-4 years- Prescribed 0
5-6 years- Wildfire 4.3310 0.5040 <0.0001
5-6 years- Prescribed 0
7-8 years- Wildfire 0.2111 0.5137 0.6812
7-8 years- Prescribed 0
9-10 years- Wildfire 0
9-10 years- 0
Prescribed
Residual 0.9981









Table 2-6. Parameter estimates and their respective standard errors and p-values for the model predicting the probability
of increased severity in the second fire.
Parameter Categories Estimate Std. Error P-value
Intercept -1.3176 0.02894 <0.0001
Severity of fire 1 Unburned 3.2190 0.01976 <0.0001
Low 0.9239 0.01917 <0.0001
Med 0
Time between fires 1-2 years -1.9621 0.02665 <0.0001
3-4 years -1.1353 0.01973 <0.0001
5-6 years -0.1612 0.02780 <0.0001
7-8 years -1.8658 0.02556 <0.0001
9-10 years 0
Type of Fire Wildfires 0.1889 0.009466 <0.0001
Prescribed Fires 0
PDSI (year before Fire 1) -0.04698 0.007130 <0.0001
PDSI (year of Fire 1) 0.09065 0.005490 <0.0001
PDSI (year before Fire 2) 0.4761 0.005154 <0.0001
Residual 0.9879









Table 2-7. Parameter estimates and their respective standard errors and p-values for the model predicting the probability
of burning.
Parameter Categories Estimate Std. Error P-value
Intercept 2.2829 0.02519 <0.0001
Severity of fire 1 Unburned -0.8139 0.01715 <0.0001
Low -0.6254 0.01654 <0.0001
Med -0.5244 0.01985 <0.0001
High 0
Time between fires 1-2 years -0.8888 0.01790 <0.0001
3-4 years -0.8372 0.01525 <0.0001
5-6 years 0.6326 0.02318 <0.0001
7-8 years -1.0399 0.01969 <0.0001
9-10 years 0
Type of Fire Wildfires -0.4576 0.007613 <0.0001
Prescribed fires 0
PDSI (year before Fire 1) -0.2168 0.005665 <0.0001
PDSI (year of Fire 1) 0.2542 0.004379 <0.0001
PDSI (year before Fire 2) 0.2874 0.003676 <0.0001
Residual 1.0249









Table 2-8. Parameter estimates and their respective standard errors and p-values for the model
decreased severity in the second fire.


predicting probability of


Parameter Categories Estimate Std. Error P-value


Intercept
Severity of fire 1


Time between fires





Type of Fire

PDSI (year before Fire 1)
PDSI (year of Fire 1)
PDSI (year before Fire 2)
PDSI (year of Fire 2)
Severity of fire 1* Type of Fire






Residual


Low
Medium
High
1-2 years
3-4 years
5-6 years
7-8 years
9-10 years
Wildfires
Prescribed Fires




Low-Wildfire
Low- Prescribed
Medium- Wildfire
Medium- Prescribed
High Wildfire
High Prescribed


2.0265
-3.6857
-1.5663
0
0.6019
0.7416
-0.7121
0.6949
0
-1.9317
0
0.6048
-0.4611
-0.2332
0.01577
2.2143
0
1.2070
0
0
0
1.0264


0.03806
0.02957
0.03233

0.02928
0.02167
0.03419
0.02871

0.04718

0.009813
0.007046
0.006323
0.003421
0.04818

0.05505


<0.0001
<0.0001
<0.0001

<0.0001
<0.0001
<0.0001
<0.0001

<0.0001

<0.0001
<0.0001
<0.0001
<0.0001
<0.0001

<0.0001

<0.0001









Table 2-9. Parameter estimates and their respective standard errors and p-values for
model predicting the probability of high severity fire by fire size class.
Parameter Categories Estimate Std. Error P-value
Intercept -1.9994 0.09005 <0.0001
Fire size class 1 -1.2949 2.8899 0.6550
2 -0.2208 1.2268 0.8575
3 -0.2040 1.2681 0.8725
4 -0.8258 0.4992 0.1009
5 0












Legend
Forest Type
Class
Hardwood
SHardwood Poe
I rne HaroPiod


0 2,Olr00040 8.000 1 2,000 16 ,0 e
0 2,0004,000 SO00 T2,000 16,000


Figure 2-1. USFS forest type classifications.


N
N


USFS Forest Types


+-ftq
I


' V










NRCS Soil Drainage Class


A
N


Figure 2-2. NRCS soil drainage class classification.


Legend

IDRAINAGECL
MODERATELY WELL DRAINED
POORLYDRAINED
SOMEWHAT POORLY DRAINED
SSt-nding Water
VERY POORLY DRAINED
I WELLDRAINED



LS-- b










*.






L

rIEH ^T --- I I I MOalers
0 6 O410 9,600 14.400 19,200
~ -,
k











60 -


50 -


40 -


53Q-
-a







10
:3 30 -


D 20
I.

10


* Fire 1 m Fire 2


LI I I I
Unburned Low Moderate High
Severity Level

Figure 2-1. Portion of pixels burned in each severity level in fire 1 and fire 2.











1-2 YEARS BETWEEN FIRES (Wildf Prescribed) N=115273
FIRE 2
FIRE1 1 2 3 4 Percentage
1 13.27 10.59 0.46 0.14 24.46
2 26.62 27.69 4.73 2.80 61.85
3 3.32 3.89 1.63 0.08 8.92
4 2.22 1.56 0.10 0.89 4.77
Percentage 45.44 43.73 6.91 3.91


1-2 YEARS BETWEEN FIRES (Wild) N=56351
FIRE 2
FIRE1 1 2 3 4 Percentage
1 8.35 3.17 0.68 0.23 12.43
2 33.35 33.77 8.95 5.57 81.63
3 0.18 0.19 3.04 0.10 3.50
4 0.19 0.35 0.08 1.82 2.44
Percentage 42.06 37.47 12.74 7.72


1-2 YEARS BETWEEN FIRES (Prescribed) N=58922
FIRE 2
FIRE1 1 2 3 4 Percentage
1 17.97 17.69 0.25 0.05 35.97
2 20.19 21.88 0.70 0.15 42.92
3 6.33 7.43 0.27 0.07 14.10
4 4.17 2.72 0.11 0.01 7.01
Percentage 48.67 49.72 1.34 0.27


EFIRE1 FIRE2


Unburned


Low Moderate
Severity Level


High


M


Unburned Low Moderate High
Severity Level


Im


I -


Unburned Low Moderate
Severity Level


High


Figure 2-2. Distribution of pixels among severity classes with 1-2 years between fire events separated by type of fire and
the probability of moving from one severity class to the next.










3-4 YEARS BETWEEN FIRES (Wild/Prescribed) N=136254
FIRE 2
FIRE1 1 2 3 4 Percentage
1 18.75 13.76 1.30 0.21 34.02
2 26.71 20.01 2.33 0.29 49.34
3 5.30 2.35 0.88 0.12 8.64
4 3.09 2.30 2.40 0.20 8.00
Percentage 53.85 38.42 6.91 0.81



3-4 YEARS BETWEEN FIRES (WildPrescribed) N=17086
FIRE 2
FIRE1 1 2 3 4 Percentage
1 9.02 2.08 0.79 0.33 12.23
2 39.42 9.61 6.43 0.07 55.53
3 13.58 4.31 1.81 0.00 19.71
4 8.85 3.13 0.56 0.00 12.54
Percentage 70.88 19.13 9.59 0.40


3-4 YEARS BETWEEN FIRES (Wild/Prescribed) N=119168
FIRE 2
FIRE 1 1 2 3 4 Percentage
1 20.15 15.43 1.37 0.19 37.15
2 24.89 21.50 1.75 0.32 48.45
3 4.11 2.07 0.75 0.13 7.06
4 2.27 2.18 2.66 0.23 7.34
Percentage 51.41 41.19 6.53 0.87


m FIRE 1
* FIRE


I


Unburned Low Moderate
Severity Level










Unburned Low Moderate
Severity Level


aI


Unburned Low


Moderate


High


Severity Level

Figure 2-3. Distribution of pixels among severity classes with 3-4 years between fire events separated by type of fire and
the probability of moving from one severity class to the next.


High











High











5-6 YEARS BETWEEN FIRES (WildlPrescribed) N=131409
FIRE 2
FIRE 1 1 2 3 4 Percentage
1 4.69 17.10 5.70 16.02 43.50
2 6.39 16.75 6.15 13.12 42.40
3 0.41 3.15 1.83 2.66 8.06
4 0.24 2.84 1.76 1.20 6.04
Percentage 11.73 39.83 15.44 33.00


5-6 YEARS BETWEEN FIRES (Wild) N=37712
FIRE 2
FIRE1 1 2 3 4 Percentage
1 2.23 10.25 6.77 25.91 45.16
2 4.03 14.02 7.81 24.05 49.91
3 0.04 0.90 0.63 2.51 4.08
4 0.00 0.15 0.10 0.60 0.85
Percentage 6.30 25.32 15.30 53.07


5-6 YEARS BETWEEN FIRES (Prescribed) N=93697
FIRE 2
FIRE1 1 2 3 4 Percentage
1 5.68 19.85 5.27 12.03 42.83
2 7.34 17.84 5.48 8.72 39.38
3 0.56 4.05 2.31 2.73 9.66
4 0.33 3.92 2.43 1.45 8.13
Percentage 13.91 45.67 15.49 24.93


40
o
E 30
m 20

10
S 1


FIRE 1


0 1 I I
Unburned Low Moderate High
Severity Level


60
E -
50
S40
f
5 30
m
. 20
. 10
-
0



50

40 -
S40
S
E 30

m 20

1.


Unburned Low Moderate
Severity Level


High


Unburned Low Moderate
Severity Level


Figure 2-4. Distribution of pixels among severity classes with 5-6 years between fire events separated by type
the probability of moving from one severity class to the next.


High


of fire and










7-8 YEARS BETWEEN FIRES (Wild/Prescribed) N=79886
FIRE 2
FIRE 1 1 2 3 4 Percentage
1 22.23 12.74 4.02 2.06 41.04
2 23.59 22.53 4.49 1.84 52.45
3 2.90 1.83 0.49 0.15 5.37
4 0.68 0.23 0.16 0.08 1.14
Percentage 49.40 37.33 9.15 4.13


7-8 YEARS BETWEEN FIRES (Wild) N=26522
FIRE 2
FIRE 1 1 2 3 4 Percentage
1 39.98 3.85 0.87 0.18 44.88
2 32.06 10.66 1.19 0.21 44.12
3 7.62 1.33 0.04 0.00 8.99
4 1.95 0.06 0.00 0.00 2.01
Percentage 81.60 15.90 2.11 0.39


7-8 YEARS BETWEEN FIRES (Prescribed) N=53364
FIRE 2
FIRE 1 1 2 3 4 Percentage
1 13.41 17.15 5.58 3.00 39.14
2 19.38 28.44 6.13 2.64 56.59
3 0.55 2.08 0.71 0.23 3.57
4 0.05 0.31 0.23 0.11 0.70
Percentage 33.39 47.98 12.65 5.98


60
50
a 40
= 30
m
.p 20
a
S10
0

90
80
70
a 60
S50
m 40
. 30
S20
E 10 -
0


60 -
50 -
4 0

S40 -
-
5 30
22-
0 20
a
x
S10-
0-


Unburned Low
Seve


FIRE 1 FIRE








Moderate High
rity Level


--


Unburned Low Moderate
Severity Level


Unburned
Unburned


-I


Low Moderate
Severity Level


High


High


Figure 2-5. Distribution of pixels among severity classes with 7-8 years between fire events separated by type of fire and
the probability of moving from one severity class to the next.











9-10 YEARS BETWEEN FIRES (Wild/Prescribed) N=21893
FIRE 2
FIRE1 1 2 3 4 Percentage
1 15.32 17.24 5.64 0.70 38.89
2 25.70 25.57 6.95 0.39 58.60
3 1.01 0.69 0.28 0.03 2.01
4 0.25 0.23 0.00 0.01 0.49
Percentage 42.28 43.72 12.88 1.13


9-10 YEARS BETWEEN FIRES (Wild) N=5901
FIRE 2
FIRE 1 1 2 3 4 Percentage
1 12.37 6.81 2.93 0.03 22.15
2 45.38 22.50 7.68 0.03 75.60
3 2.12 0.08 0.03 0.00 2.24
4 0.02 0.00 0.00 0.00 0.02
Percentage 59.89 29.40 10.64 0.07


9-10 YEARS BETWEEN FIRES (Prescribed) N=15992
FIRE 2
FIRE 1 1 2 3 4 Percentage
1 16.40 21.09 6.64 0.94 45.07
2 18.44 26.69 6.68 0.52 52.33
3 0.60 0.91 0.38 0.04 1.93
4 0.34 0.31 0.01 0.01 0.67
Percentage 35.78 49.00 13.70 1.52


70
-
*60
50 -
c 40
m 30
S20
-10
10


80
770
,s60
C 50
3 40
30
Z 20
-
E 10
0



60
S50
S40-
C
3 u-
= 30
m
20
S10
0-


I


FIRE1 FIRE2






I-E


Unburned Low Moderate
Severity Level


Unburned


Unburned


Low Moderate
Severity Level


High


High


Low Moderate High


Severity Level


Figure 2-6. Distribution of pixels among severity classes with 9-10 years between fire events separated by type of fire and
the probability of moving from one severity class to the next.












Wildly Prescribed Fires
TimeInterval INCREASE(%) DECREASE(%)
1-2 18.81 37.72
3-4 18.01 42.16
5-6 60.74 14.79
7-8 25.29 29.38
9-10 30.95 27.88



Wildfires
TimeInterval INCREASE(%) DECREASE{%)
1-2 18.69 34.33
3-4 9.70 69.86
5-6 77.30 5.23
7-8 6.30 43.02
9-10 17.49 47.60



Prescribed Fires
TimeInterval INCREASE(%) DECREASE{%)
1-2 18.92 40.95
3-4 19.20 38.18
5-6 54.08 18.64
7-8 34.73 22.60
9-10 35.91 20.60


1-2 3-4 5-6 7-8
Time Interval (Years)










1-2 3-4 5-6 7-8
Time Interval (Years)


1-2 3-4 5-6 7-8
Time Interval (Years)


Figure 2-7. Percentage of pixels increasing and decreasing in severity level by time and type of fire.


*INCREASE
*DECREASE


9-10


9-10


9-10










1996 199B 2000 2 200 2004 2006 2008


- PDSI


li


6

4




0


-2



-4
-6


f
F


Cypress Creek
(2,635ac)


0 a


2010
4 90000


~,


Florida Bugaboo
(82,165ac)

















'-- Impassible
(29,052ac)


Figure 2-3. Fire size compared with Palmer drought severity index between 1996 and 2010. This suggests large fire
events are associated with prolonged droughts.


Oak
(26.668)


CN
~".,, .,,,


- -J


- 80000

- 70000

- 60000

- 50000

- 40000

- 20000

- 20000
a9O0O

-0


L


a 0








" High Severity
* Moderate Severity
SLow Severity


SO
40
30
20


1SO-Soo


Fire Sie Class (ac)


>500


Figure 2-4. Percentage of pixels burned at each severity class by fire size class. Larger fires have a higher portion of their
cells in the high severity class.




















I Wild Fires

* Prescribed Fires


I- -


60
K-


_' 50


4 40

--
S3




l
20
0
a-
n., -


Time Interval (Years)


Figure 2-8. Probability of experiencing high severity in fire 2 by time interval and fire
type.


9-10


1-2


3-4


5-6













Prescribed_1-2 years
a0 Prescribed_3-4years
SPrescribed 5-6 years
S40 m Prescribed_7-8 years
.Ca Prescribed 9-10 year;
3O

10
220

.0
2 10



Unburned Low Moderate-High
Severity level of the last fire

Figure 2-9. Probability of experiencing high severity in fire 2 by severity level of fire 1
and time interval for prescribed fires.









90 Wildfires_1-2 years
a Wildfires_3-4years
80 -
Wildfires_5-6 years
70 I Wildfires_7-8 years
Sa Wildfires_9-10 years
S60-

50 -

40 -
30 -

i20 -

.. 10 -

0
Unburned Low Moderate-High
Severity level of the last fire

Figure 2-10. Probability of experiencing high severity in fire 2 by severity level of fire 1
and time interval for wildfires.











Unburned


1-2 3-4 5-6 7-8
Time Interval (Years)


9-10


Low
Severity


3-4 5-6 7-8
Time Interval (Years)
Moderate
Severity


9-10


5-6 7-B 9-10


Time Interval (Years)

Figure 2-11. Probability of increasing fire severity by time interval and fire type.


















71


SWildfires
* Prescribed


1-2











1-2









100

90 m Wildfires_1-2 years
S r80 l of t last fire
> 80 Wildfires_3-4 years
W70
70 0 Wildfires 5-6 years

S60 Wildfires_7-8 years




o 30 -







Severity level of the last fire

Figure 2-12. Probability of increasing fire severity by severity level of the last fire and
time between fires for wildfires.
time between fires for wildfires.










* Precribed_1-2 years
* Precribed_3-4 years
* Prescribed_5-6 years
* Prescribed_7-8 years
* Prescribed_9-10 years


Unburned Low
Severity level of the last fire


Med


Figure 2-13. Probability of increasing fire severity by severity level of the last fire and
time between fires for prescribed fires.


100

90 -

80 -

70

60 -

50 -

40

30 -

20 -

10 -
0 -













100 -
mWildfires Unburned E 90 -
S80 -
so-
m Prescribed
c 70 -
a-

4- 50
0
~ 40 -
30 -
20 -
lo -
2 10-
I I I I I
1-2 3-4 5-6 7-8 9-10
Time Interval (Years)


Moderate Severity


CO

a-
0




C-
- -


I I I I
1-2 3-4 5-6 7-8 9-10
Time Interval (Years)


100
90
80-
80
70
60 -
50

40 -
30
20
30
20
10
0


Low Severity


1-2 3-4 5-6 7-8 9-10
Time Interval (Years)


High Severity


1-2 3-4 5-6 7-8 9-10
Time Interval (Years)


Figure 2-14. Probability of burning by time interval, fire type, and fire severity level.


100 -
90 -
80 -
70 -
60 -
50 -
40 -
30 -
20 -
10 -
0-

















Wildfires 1-2 years
* Wildfires_3-4 years
* Wildfires_5-6 years
* Wildfires-7-8years
* Wildfires_9-10 years


Unburned Low Med High
Severity level of the last fire

Figure 2-15. Probability of burning by fire severity level and time interval for wildfires.


100

90

80

70

60

50

40

30

20

10

0


I


I













Prescribed_1-2 years
Prescribed_3-4 years
Prescribed_5-6 years
Prescribed_7-8years
Prescribed_9-10 years


-F-


Unburned Low Med High
Severity level of the last fire


Figure 2-16. Probability of burning by fire severity level and time interval for prescribed
fires.


100 -

90 -

80

70 -

60 -

50 -

40 -

30 -

20 -

10 -


I











Low Severity


SWildfires Prescribed


S100

, 70 -
Sso -
7>f0-

S50


2 10 -
=" 040




100
S 90 -
,E 80 -
a, o
so
' 70
60

's 0 -
S30
= 20
*- 10 -
1 0
I-

100




S so-


a 20 -

. 0
50


1-2 3-4 5-6
High Severity ime Interval (Years)
High Severity


1-2 3-4 5-6
Time Interval (Years)


7-8 9-10


7-8 9-10


7-8 9-10


Figure 2-17. Probability of decreasing in severity level by time
of fire 1.


interval and severity level


1-2 3-4 5-6
Time Interval Years)
Moderate Severity









Wildfires_1-2 years
* Wildfires_3-4 years
* Wildfires_5-6 years
* Wildfires 7-8 years
* Wildfires_9-10 years


Low


100

90

80

70

60

50

40

30

20

10

0


High


Figure 2-18. Probability of decreasing in severity by severity level of fire 1 and time
interval for wildfires.


Med
Severity level of the last fire









100

90

80

70

60

50

40

30

20

-
10

0


Low


Prescribed_1-2 years
m Prescribed3-4 years
* Prescribed_5-6 years
* Precribed_7-8 years
* Prescribed_9-10 years


r


Med
Severity level of the last fire


Figure 2-19. Probability of decreasing in severity by severity level of fire 1 and time
interval for prescribed fires.


r


High









CHAPTER 3
PREDICTING FIRE SEVERITY IN PINE FLATWOODS USING DIFFERENCE
NORMALIZED BURN RATIOS TO RECORD FIRE EVENTS

Introduction

Fire severity can be measured using remote sensing techniques to monitor

changes in fire regimes over time and to map fire history. Fire severity is a measure of

ecological and physical change attributed to fire (Agee 1993; Hardy 2005) and is

influenced by both biotic and abiotic factors. Severity is altered by weather, moisture,

time of day, sunlight incidence (Oliveras et al. 2009), species, tree size, succession

stage, and pathogens (Cocke et al. 2005). Severity is important to monitor as it can

have a significant effect on exotic species establishment, soil responses, regeneration,

and ecosystem health.

Measuring Fire Severity

Normalized burn ratios (NBR) use short wave inferred bands, from Landsat

Thematic Mapper (TM) bands 4 and 7 (Wagtendonk et al. 2004), to detect the severity

level of a burned area (1). At this spectrum, differences in reflectance due to fire

induced changes in soil moisture, canopy cover, biomass, and soil chemical

composition is captured and compared to pre-fire conditions to determine the level of

change or severity that occurred as a result of the fire event.

NBR = 4- B7
NBR= --
(04 + B7)



Difference normalized burn ratios (dNBR) capture the degree of change that can be

attributed to fire by using a pre- and post- fire image (2).

dNBR = NBRVJ_ NBR f.











The mapping methodology was originally developed and tested by the USGS

Northern Rocky Mountain Science Center (NRMSC). Employed as a radiometric index,

dNBRs are directly related to burn severity (Wagtendonk et al. 2004) and, as long as

the fire is within the resolution range of the satellite sensor, 30m, it is detectable (White

et al.1996). Combined with existing information about fire locations and perimeters, fire

histories can be mapped to monitor trends in severity over time, frequency of fire, and

time since last fire on a pixel level. This detailed dataset can then be used to make

inferences about future fires.

Using remote sensing data to determine specific and effective return intervals

can have serious implications for land managers. Currently land managers are using

indiscriminate frequencies that range anywhere from 1-10 years between fire events for

pine flatwoods management. Depending on site characteristics, frequencies may

require modification for more or less productive sites. With a detailed fire history, land

managers can identify areas that require immediate attention to both mitigate the risk of

wildfire and prevent successional change.

Previous studies have used dNBRs to calibrate severity levels to specific forest

types (Cocke et al. 2005; Hoy et al. 2008; Godwin 2008), compare severity levels

between fire events (Collins et al. 2009; Allen et al. 2008;), interpret the effects of fuel

management techniques on severity levels (Safford et al. 2009), and to monitor

changes in vegetation over time (White 1996; Kuenzi et al. 2008) and topographical

variations (Holden 2009; Oliveras et al. 2009). In the United States there is currently a

multi agency project, Monitoring Trends in Burn Severity (MTBS), which is using dNBRs

to map burn severity and the perimeters of large wildfires in the entire United States.

81









MTBS is using data from 1984-2010 to identify national trends in burn severity to

determine the effectiveness of the National Fire Plan and Healthy Forest Restoration

Act. As of now, no other study has used dNBRs to model the fire history of an entire

forest. This study uses all prescribed and documented wildfires (greater than 1 ac) to

create a complete fire history for the entire Osceola National Forest using dNBRs for

each fire event.

The objective of this analysis is to determine the risk of high severity prescribed

fire and the probability of moderate to high severity wildfires using data from 1998-2008.

The probability high severity prescribed fire is important for monitoring fire effects and

how these effects meet management objectives. Prescribed burns are implemented

under optimal circumstances where conditions are suitable for vegetation consumption

but not at levels to cause fire to become unmanageable and cause high mortality of

overstory species. Optimally, prescribed fires should cause low levels of mortality in

overstory species and understory fuel should be partially consumed with little

consumption of the duff layer (Outcalt et al. 2004). High severity fires are characterized

by complete combustion of most of the litter layer, duff and small logs, with mortality of

small-med trees, and consumption of large tree crowns (Wagtendonk et al. 2004).

During prescribed fires, land managers aim for low-moderate severity fire.

Considering wildfires, fire behavior that causes moderate to high severity levels

may cause extensive challenges in suppression efforts and high mortality rates.

Therefore, a model predicting the probability of moderate to high severity fire would

be appropriate as low severity wildfires would be preferred from a suppression and

salvage stand point. We hypothesize that the number of times a pixel burns will









influence its probability of burning, at high severity for prescribed fires and moderate-

high severity for wildfires, if burned within 5 years and we also expect mesic

communities to have a higher probability of burning at high severity than hydric

communities for prescribed fires and the opposite for wildfires.

Study Site

The Osceola National forest located in north central Florida (Latitude: 30.34371,

Longitude: -82.47322) about 40 miles west of the city of Jacksonville (Figure 1-1). The

majority of the forest is pine flatwoods with scattered areas of cypress and bay swamps.

With an overstory of pines on low, flat, sandy, acidic soils; pine flatwoods have an

understory of herbaceous plants, grasses, palmetto, and woody species. Flatwoods

communities are fire dependent and require regular burning for regeneration of fire-

adapted species and ecosystem health. On the Osceola National Forest, communities

include Longleaf pine (Pinus palustris) -wiregrass (Aristida beyrichiana), and slash pine

(Pinus elliotti) -gallberry (/llex glabra) -saw palmetto (Serenoa repens). Cypress ponds

(Taxodium spp) are found scattered throughout the forest in low lying wet areas. In this

fire maintained community the lack of fire for prolonged periods will increase broad leaf

woody vegetation and reduce herbaceous plant cover and eventually reduce pine

germination. Fire suppression would cause significant changes in species composition

that would then lead to changes in ecological processes within this system.

Fire management on the Osceola National forest is quite active. The majority of

the forest is prescribed burned at a frequency of every 2-5 years. There are also

sensitive areas within the forest that are not currently and actively managed by fire. Fire

regimes are determined on a compartment level based on the current forest type and

the desired future condition of the compartment. On this forest, fire managers are faced

83









with burning large acreages annually with few days that are within prescribed fire

weather conditions. Sensitive areas near the forest like Lake City Municipal Airport, I-

10, and the City of Jacksonville, provide addition constraints for fire managers.

Methods

Image Analysis

Landsat 7 ETM imagery was provided by the United States Geological Survey

(USGS). The USGS provided geometrically and radiometerically corrected NBRs.

Geometric corrections involved removing distortions from imagery caused by the sensor

geometry. The geocorrection process consisted of two steps: (1) rectification, and (2)

resampling. Geo-rectification was performed in order to relate pixels to their exact

ground location and resampling determined the pixel values. Radiometric corrections

involved the removal of atmospheric noise to accurately represent ground conditions. In

this process the pixel values were modified to account for noise produced by

atmospheric interference, sun-sensor geometry, and the sensor itself.

Following geometric and radiometric corrections, pixel values were in digital

numbers. Digital numbers are a measure of at-satellite radiance. Finally, digital

numbers are converted to at-satellite reflectance. NBRs were derived from a ratio of

bands 4 and 7 (1) that has been corrected to at-satellite reflectance and range from ~-

1000 to 1000. Pre-processed NBRs were provided by the USGS Global Visualization

Viewer (http://glovis.usgs.gov).

Data

A fire history dataset was created using the dNBRs for each fire event (prescribed

and wildfires). DNBRs were created for each event using images closest to the date of

the fire event. General severity levels provided by the United States Geological Survey

84









(USGS) were reclassified to 4 severity levels; unburned, low severity, moderate

severity, and high severity (Table 2-1). To account for variation due to phenology and

surface moisture conditions in the pre- and post- fire images, the mean value of

unchanged pixels were subtracted from the dNBR (Collins et al. 2009). DNBRs were

then clipped using fire perimeter shape files provided by the U.S. Forest Service. Next,

fires were merged to create an image that represented fire events for each year

(Appendix A, Table 3-1). The layers created for each year were finally used to calculate

model covariates. (1) Time since last fire is the number of years since last fire (Figure 3-

1). (2) Frequency is the number of times a pixel has burned within the dataset (Figure

3-2). (3) Latest severity level is the severity level of the last fire event (Figure 3-3).

These three layers were then compiled to create a fire history for each individual pixel.

Calculations were made using ArcGIS software.

Forest type and community type were obtained from the Florida Geographic Data

Library (http://www.fgdl.org/metadataexplorer/explorer.jsp). The forest type layer was

developed by the University of Florida Geoplan Center (Figure 3-4). Vegetative

communities were distinguished based on Davis (1967). Swamps, marshes and other

areas classified by the National Hydraulic Dataset as having standing water were

classified as hydric and the rest of the forest was classified as mesic based on soil and

forest types (Figure 3-5).

Model Development

Logistic regression was utilized to determine the probability of burning at a high

severity for prescribed fires and moderate-high severity for wild fires, on a pixel level, in

2008. Logistic regression is used to measure binary responses by describing the









relationship between one or more independent variables and the binary response (Littel

et al. 2006). Responses are coded as 0 or 1:

(1 success
=0 failure

3

Where y, is a realization of a random variable Yj that can take on the values of 0 and 1

with probabilities 7r and 1- ir (3). The distribution of Y, is a Bernoulli distribution with

the mean (4) and variance (5) depending on the underlying probability i.

E(Y, )7= ,

4



5

To make the probability, wi a linear function of a vector of observed covariates (x,) 7r,

the probability is transformed to remove range restrictions (6).

logit ) = log- X
6

Logits map probabilities from range [0, 1] to [-0, C]. Negative logits represent

probabilities below 1/ and positive logits represent probabilities above 1/. Solving for

the probability of success requires exponentiating the logit and calculating the odds of

success (7).

expTi1 i)
1 + lexp (X )
7

Maximum likelihood methods were used for parameter estimation. With this

approach, parameters were estimated iteratively until parameters that maximized the









log of the likelihood were obtained. Goodness of fit statistics, Akaike's information

criterion (AIC) and Bayesian information criterion (BIC), were used to compare

competing models. AIC is a statistic used for model selection that ranks different

models based on how close fitted values are to true values (8) (Littell et al. 2006).

AIC = 2k 2In(L)
8

Where: k is the number of parameters in the statistical model and L is the maximized

value of the likelihood function for the estimated model (8). Like AIC, BIC was used to

rank models with a different numbers of parameters to avoid increasing the likelihood by

over fitting the model (Littell et al. 2006).

BIC = -2 In(L)+ k Ln(n)
9

Where: n is the sample size (9). Unexplained variation in the dependent variable and

the number of covariates increases the AIC and BIC values. For both AIC and BIC, the

lowest score indicates the best model.

The ratio of the Pearson chi-square to its degrees of freedom is used to

determine if the model displays lack of fit. Values closer to 1 indicate that the model fits

the data well (Littell et al. 2006). To address the assumption of independence among

observations, a generalized linear mixed model was used using the SAS procedure

PROC GLIMMIX (Littell et al. 2006). Correlation among responses is incorporated into

the model by adding random components to the linear predictor. To account for the

correlation among responses, random residuals were modeled. Raster data is spatially

correlated due the adjacency of pixels. Although it would have been more effective to

model the spatial correlation directly, without the aid of a super computer this option is









infeasible. The GLIMMIX procedure can also make use of several predictor variables

that may be either numerical or categorical.

In this analysis we evaluated the probability of experiencing high severity and

moderate to high severity based on the history of fire for prescribed and wild fires.

Covariates included frequency of fire, time since last fire, severity level of the latest fire

(categorical), forest type (categorical) and, community type (categorical) (Table 3-2).

Frequency of fire is the number of times a fire occurred within the data frame. Time

since last fire is the number of years that passed since the last fire event. The latest

severity level is the severity level of the last fire event. Forest types are classified as

pine flatwoods, longleaf pine / xeric oaks, fresh water marshes and swamp forest; and

community types are classified as hydric or mesic.

A backward selection method was used to determine the appropriate covariates

for the final model. The Wald chi-square statistic was used to identify significant

covariates. Final model selection was also determined based on significant parameters

and the model with the lowest AIC and BIC value. Interactions between all parameters

were also considered. Non-significant parameters were removed from the full model

one at a time. To test for differences among categorical levels least square means

were produced and differences were tested.

Data used to create the logistic model included the years 1998-2006 for prescribed

fires and 1998-2007 for wildfires. Fire history was developed for pixels using data up to

2005. This data was used to predict the probability of prescribed fires burning at a high

severity level in year 2006. Fire history from 1998-2006 were used to model the

probability of wildfires burning at a moderate-high severity in 2007. These models were









then used to predict the probability of experiencing a high severity prescribed fire and

the probability of experiencing a moderate-high severity wildfire in 2008.

Spatial Model

The models, probability of high severity prescribed fire and moderate to high

severity wildfire, were recreated spatially using parameter estimates from logistic

regression and ArcGIS spatial analyst extension. Layers were created for each

parameter and calculations were made using the spatial analyst/ raster calculator. The

spatial model was used to show the probability of high severity prescribed fire and the

probability of moderate-high severity wildfire in 2008.

Results

Probability of High Severity Prescribed Fire

Severity level of the last fire yi, frequency of fire Xiij, time since last fire X2ij, and the

interaction between frequency and time since last fire were significant parameters in the

model for prescribed fires (10).

logit(zr,) = r+ y, X +aX, +a 2X2, +a3X1 X2 + j

10

The model was significant and the parameters were significant based on their

Wald chi-square statistics (Table 3-3). The ratio of the Pearson chi-square statistic to

its degrees of freedom was approximately 1 indicating good model fit.

Time since last fire showed a positive relationship with the probability of high

severity; as the time interval increased the probability of high severity fire also increased

(Figure 3-6, Figure 3-7, Figure 3-8). The effect of the severity level of the last fire varied

by severity level; unburned, moderate, and high severity levels in the last fire increased

the probability of high severity in the subsequent fire and low severity level in the last

89









fire reduced the probability of high severity in the subsequent fire. Unburned areas had

a very high probability (>80%) of experiencing high severity fires regardless of the

amount of time that had passed since the last fire event (Figure 3-8). Areas that had

experienced low and high severity in the last fire had a low probability of experiencing

high severity in subsequent fire, followed by areas that experienced a moderate severity

level which approached a 50% probability at 7-9 years since the last fire. Frequency of

fire also had a positive relationship with the probability of high severity fire. As

frequency of fire increased, the probability of experiencing high severity subsequent fire

also increased (Figure 3-6).

Probability of Moderate to High Severity Wildfire

The model for the probability of experiencing a moderate to high severity wildfire

incorporated frequency of fire Xlij, time since last fire X2ij, and the interaction between

the two (11).

logit(z, ) = r + a,X + a2X2, + a3XIX2, + +
11

The model was significant and the parameters were significant based on their Wald chi-

square statistics (Table 3-4). The ratio of the Pearson chi-square statistic to its degrees

of freedom was approximately 1 indicating good model fit. As the time since last fire

increased, the probability of moderate-high severity fire also increased (Figure 3-9).

The increased probability over time since last fire varied by the number of fires that

occurred since 1998. Areas that had never experienced fire had a much higher

probability than previously burned areas. As the frequency of fire increased, the

probability decreased (Figure 3-10).









Spatial Models

The spatial models identified areas that had increased probability of burning at a

high severity based on fire history for prescribed fire (Figure 3-11, Figure 3-12), and

identified the probability of moderate to high severity for wildfires. Areas with

probabilities greater than 95% are highlighted as areas we would expect to burn

severely if a fire event were to occur.

The High severity model (for prescribed fires) identified areas that burned often,

as targets for high severity. Probabilities range from 35-99% for the entire forest. A

small portion of the forest had a probability greater than 95% (6.2%), while most of the

forest had a probability of high severity greater than 50% (84%). Although forest type

was not a significant predictor of high severity fire, the model indicated that mesic

communities had a small portion of the area with a probability greater than 95% (6.3%)

(Figure 3-13). Hydric communities had a higher portion of area (45%) with a probability

of high severity greater than 95%. A small percentage of the prescribed fires in 2008

actually burned at a high severity level (2.4%) (Figure 3-14) and these areas were often

found to have a probability of high severity that was at least 95%.

In the 2008 fire season there were very few wildfires greater than 1 ac that

occurred on the Osceola National forest. The model Identified areas that had not

burned or had burned only once from 1998-2007 as having a higher probability of

burning at a moderate-high severity level. Most of the forest had a probability less than

15% (Figure 3-16). The probability of burning at a moderate to high severity level was

quite low for the entire dataset (<20%). Although forest and community types were not

significant predictors of moderate to high severity, the model indicated that hydric

communities had a probability less than 1% for most areas (Figure 3-17). Mesic

91









communities had a higher probability (below 5%) for the majority of the area. Of the

four forest types, fresh water marshes had the highest probability of moderate-high

severity (>15%) (Figure 3-18).

Discussion

Probability of High Severity Prescribed Fire

The model predicting the probability of high severity for prescribed fires yielded

important information regarding the relationship between time between fire events, the

severity level of previous fires, and the frequency of fire. As time since last fire

increased, the probability of experiencing a high severity fire also increased. Previous

studies conducted on the Osceola National Forest, found that as time between fire

events increased, fire intensity also increased causing greater tree mortality following

fires (Outcalt et al. 2004). Vegetation recovery and fuel loads increase with time since

the last disturbance event. So, as the time since the last fire increases there is also an

increase in the amount of fuel and an increase in vertical structure of fuel. As fuel and

vertical structure increases, so should the probability of burning at a high severity level

due to the increase in combustible material. Increases in vertical structure also provide

ladder fuels that increase the chance of ground fires moving into tree crowns.

Areas previously burned by low severity fire had a lower probability of high severity

prescribed fire just as areas with high and moderate severity levels had a higher

probability of high severity fire. This indicates that fuel availability may be influencing

the amount of change caused by fire more than previous fires. Low severity fires may

be the result of fuel availability and not fuel accumulation. Areas with high and

moderate severity levels that have high probabilities of experiencing high severity at

short return intervals suggest that vegetation on these sites quickly recovered from fire

92









events and were able to burn severely again. Alternatively this may reflect a bias in the

high severity class. If these areas burned severely then there is likely less vegetation to

burn during subsequent events. If this vegetation is consumed during a fire it would

take less fuel consumption to cause a large amount of change between pre- and post-

fire images. This effect increases subsequent fire severity level and increases severity

level with increased fire frequencies. This phenomenon would explain the unexpected

relationship between frequency and the probability of high severity as well as the high

probability associated with prescribed fires versus wild fires.

Hydric communities had a higher probability of high severity prescribed fire then

mesic communities. This may be explained by the conditions chosen to perform

prescribed fires under. In stands that have been burned multiple times in the past, land

managers may choose weather conditions that are more risky to execute prescribed

fires. And, even though hydric areas are usually unavailable during prescribed fires,

when they are avialible they may burn at a high severity level.

Probability of Moderate to High Severity Wildfire

Predicting the probability of moderate to high severity wildfires yielded

information unlike the prescribed fire model. Time since last fire had the same

increasing relationship, yet fire frequency had a decreasing relationship in this model.

The relationship between frequency and the probability of moderate to high severity

wildfire is what we would expect; as the number of times an area burned increased, we

would expect there to be a reduced chance of experiencing higher severity levels

because fuel loads were reduced. Increased frequency also reduces the vigor in

vegetation recovery so that with each fire, vegetation re-growth declines.









Forest and community types were not significant indicators of high and moderate

to high severity fire. During prescribed fires we might expect similar fire effects (low

severity) in the different forest and community types as areas are burned under optimal

conditions. Yet, during wildfires we expect hydric communities to burn more severely if

the vegetation is avialible to burn due to high fuel loads especially during prolonged

drought periods (Outcalt et al. 2004). Within this dataset, few hydric communities

burned severely indicating that fuel was not avialible to burn during wildfires. We would

also expect that forest types would influence severity levels. The lack of significance

may be due to Osceola National Forest being composed mostly of pine flatwood and

mesic forest.

Spatial Models

The spatial models were effective in identifying, spatially, where you would expect

to observe high severity fire in the event a prescribed fire occurred and moderate to

high severity in the event a wildfire occurred. The prescribed fire model identified areas

that have a history of burning often as being at an increased risk of high severity fire.

Areas that have not burned in 10 years also had an elevated risk of high severity fire.

Most of the area burned in 2008 was burned by prescribed fire at moderate (45%) and

low (18%) severity levels. Sections of prescribed fires that actually burned at high

severity had probabilities of high severity greater than 50% and most of the area had

probability greater than 95%. This suggests that the model adequately identified areas

that were at a high risk of high severity fire based on its ability to recover from previous

fire, the effects of the last fire event, and the amount of time between events.

The wildfire model had low probabilities of moderate-high severity for the entire

forest for 2008. Areas that had not been burned were identified as having increased

94









risk of moderate to high severity fire. A single wildfire occurred on the Osceola in 2008

(less than 5 acres) and this wildfire had no areas of moderate or high severity.

Conclusion

Remote sensing techniques were successfully used to model fire history for the

Osceola National Forest to determine the risk of experiencing high and moderate

severity fire in the event of a fire. The models identified areas that require attention in

order to reduce the risk of high and moderate to high severity fire. The prescribed fire

model identified areas that burn often as having an elevated risk of high severity. This

relationship between fire frequencies and high severity implied that either the vegetation

with high frequencies was at the highest risk due largely to fast recovery time for

prescribed fires or that there was a bias in the high severity class for areas with less

vegetation. Forests that can burn on short time intervals need to as a response to the

short time period required for fuel loads and live vegetation to return to pre-fire

conditions. Yet, continued burning would also reduce the amount of vegetation available

for subsequent fires and this reduction could be causing a bias in the high severity

class.

Conditions suitable for prescribed fire are determined by climatic factors and fuel

loads, and are increasingly influenced by burned acreage quotas set by regional or

federal management. Forest managers are under pressure to burn as many acres as

possible each year. They may be willing to burn areas with high fire frequencies under

more risky weather conditions due to reduced fuel loads and short time since last fire.

Fire effects in these areas may then end up being more severe than in areas that are

burned under less extreme fire weather conditions.









Smoke sensitive areas are at an elevated risk for high severity and moderate to

high severity. These areas are dangerous to burn due to the risk of disrupting

transportation, reducing air quality, or damaging property. Conditions suitable to burn

sensitive areas occur rarely often increasing the amount of time between fire events.

Parts of the Osceola just north of the airport and that surround Interstate 10 have a

probability of high severity that ranges from 50-75% for prescribed fires. During

wildfires, the risk is elevated compared to probabilities for the rest of the forest (10-

19%). Both models identify these areas as being at an elevated risk requiring

significant suppression efforts in the event of fire.

The relationship between frequency and the probability of high severity may also

be due to error introduced by differences in biomass. Additional research to address

the amount of biomass in relation to dNBR values is necessary to determine if areas

with lower biomass have a higher probability of high severity due to the smaller amount

of vegetation necessary to cause a significant change in pre- and post- fire images. It

may also be useful to look at the effects of delayed mortality in areas with short fire

return intervals to identify if this would cause further bias in the high severity class.

Overall, the probability of moderate to high severity (for wildfires) is less than what

we would expect. The low probability may be caused by how wildfires are mapped.

Wildfire perimeters are mapped using Landsat imagery based on ocular estimates of

where fires occurred. The perimeters are not exact so wildfires tend to have a high

amount of unburned and low severity pixels. Also, most wildfires within this dataset are

less than 50 acres. Wildfire size is determined by both suppression efforts and fuel

availability. Therefore, smaller wildfires indicate that wildfires were not often exhibiting









fire behavior that would likely cause high severity fire effects. There were few wildfires

(Oak Fire 1998, Impassible 2004, and the Bugaboo 2007) that were large in size and

that required great suppression efforts assisted by weather conditions for suppression.

Larger fires had higher portions of moderate and high severity than smaller fires (not

significantly larger). So, for the entire dataset, very few areas burned at high severity

during wildfires (excluding the impassible fire of 2004) and there was not a very large

increase in the area burned by moderate severity for larger fires. Biases introduced by

perimeter estimates and the greater amount of smaller wildfires are the likely cause for

the low probability of experiencing moderate to high severity.









Table 3-1. Number of pixels in each severity class by year.
Year Severity
High Moderate Low Unburned
1998 9720 18442 214519 106581
1999 5946 18791 111048 149600
2000 6404 1785 4961 7996
2001 56 180 372 505
2002 1 50 42680 71144
2003 1 27 179 72
2004 119570 58799 127579 31298
2005 3668 13045 63549 43737
2006 22283 23283 25804 9565
2007 120 7182 26136 69340
2008 2757 40332 17183 2757

Table 3-2. Covariates for the model measuring the probability of high severity
prescribed fire and moderate to high severity wildfire.
Forest Type Community Frequency Time since Last Severity
Type Last Fire Level

Pine flatwoods Hydric The Number Number of Severity level
of times a years since of the last fire
Longleaf/ xeric Mesic pixel burned at last fire event event
oaks a severity where pixel
level greater burns at a
Fresh water than 1 severity level
marshes greater than 1

Swamp forest


Table 3-3. Parameter estimates and their respective standard errors and p-values for
the model predicting the probability of high severity prescribed fire.
Parameter Estimate Std. Error P-value
Intercept -3.0979 0.3210 <0.0001
Frequency of Fire 1.9709 0.1899 <0.0001
Time Since Last Fire 0.09361 0.02556 0.003
Severity Level of the last Unburned 1.5268 0.1458 <0.0001
Fire Low Severity -0.5854 0.07704 <0.0001
Medium Severity 0.3493 0.08290 <0.0001
High Severity 0
Residual 1.009










Table 3-4. Parameter Estimates and their respective standard errors and p-values for
model predicting the probability of Moderate to High severity wildfire.
Parameter Estimate Std. Error P-value
Intercept -3.2121 0.1391 <0.0001
Frequency of Fire -1.5005 0.1302 <0.0001
Time Since Last Fire -0.1470 0.01383 <0.001
Frequency Time Since Last Fire 0.2194 0.01404 <0.001
Residual 0.9975









A Time Since Last Fire
N (1998-2008)
rL0 2I20 40 90 15I Meters
0 2.2504.500 9.000 13.500 18,000


Legend

1
2
3

E ]5


Mg


1 11
12 or more


Figure 3-1. Time since last fire for the Osceola National Forest (1998-2008)


100









A Fire Freque
N (1998-20(
IL lI I Mveters
0 2.250 4.500 9.000 13.500 18.000


ncy
)8)


Legend
I lo

LM2


-M4
Ms


r1+ k

.*- ,. '


.'I.r."


-J -I
jsip~

.4

'it.


Figure 3-2. Fire frequency from 1998-2008 for the Osceola National Forest.


101


%9-T


. r,
, i- -.










A Severity level of the Last Fire
N (1998-2007)
I0L5 I rI IMelers
0 2,250 4.500 9,000 13,500 18,000


*1


Legend
Severity Levl
1 Unrmned
S]Low Severty
SI~derate Severity
High Sveriy


." -'- .- -.. .. .

.
- C,. -
.. .d


Figure 3-3. Severity level of the last fire event (1998-2007).
102


, L
'a -.


5~71 f











A
N


Forest Types


02_,7_1 I ,0 I I Meters
0 2.375 4.750 9.500 14.250 19.000


-g Legend
ForestTypeN
ForestTpe
SLON GLEAF PINE/XEROPHYTIC OAJS
FRESH VATER MARSHES
hINE FLATWODOS
-mm RAWh FRESFTS


Figure 3-4. Florida Geographic Database Library Map of forest types for the Osceola
National Forest.


103









A Community Type
N
1l,14 I I I1Metwrs
0 2,250 4.500 9,000 13,500 18,000


Figure 3-5. Map of the community types, hydric and mesic, for the Osceola National
Forest.


104



















- 1 year sincefire
-------3 years since Fire
- years sincefire
---7 years sincefire




Unburned


0 1 2 3 4
Frequency of Fire


5 6


Moderate Severity


100 -


90 -

80 -


70 -

60 -


50

40


100


o90 -

80 -
to

70
Is





40 --


100 -

S90

S80-
U
70 -

60 -

S50 -

40


1 2 3 4
Frequency of fire


High Severity


0 1 2 3 4 5 6 0 1 2 3 4 5 6
Frequency of Fire Frequency of Fire


Figure 3-6. Relationship between the probability of high severity prescribed fire, frequency of fire, and time since last fire.


105


Low Severity


100 -

90 -

80 -

70

60 -

50

40


5 6


I I I I I I


















1 year since last fire
* 3 years sice last fire
* 5 years since last fire
* 7 years since last fire
* 9 years since last fire















I


Unburned


Low


Moderate


Severity level


Figure 3-7. Relationship between the probability of high severity prescribed fire, the
severity level of the last fire event, and time since last fire.


106


100 -

90 -

S80

I 70-

o 60 -

me-
50 -

?40 -

5 30 -

2 20 -
0.
10


r


I


High















100
........................................ .. ..-.... .. .. ....

90 .. ...............-- ...............
S...........-- """"......-- Frequency_1
80 ..- ....... Frequency_2
S------- Frequency_3
70
n ---Frequency_4

C- 60 -Frequency_5

S50 -
C
c 40 -
0
30

S20-
.0
C 10 -
0-----------------------------------------

0 2 4 6 8 10
Time since last fire (Years)


Figure 3-8. Relationship between the probability of high severity prescribed fire, frequency of fire, and time since last fire.


107













25 -


20 -



15 -








5 -


Frequency_O
-...... Frequency_1
---Frequency_3












. ................... ..


-4


Time since last fire (Years)


Figure 3-9. Relationship between the probability of moderate to high severity wildfire, frequency of fire, and time since last
fire.


108










18
016

> 14 -

12 \

S10 -1 year since fire
S......... .3 years since fire
8 5 years since fire
Sc -- 7 years since fire
6 6
o .\
4





0 1 2 3 4 5 6
Frequency of fire


Figure 3-10. Relationship between the probability of moderate to high severity wildfire, fire frequency, and time since last
fire.


109











A
N


Probability of High Severity


L J L J JI I IMeters
5.500 11.000 22.000 33,000 44.000


Legend
Rx_Model

F 1 35 50
50.1 75
75.1 90
90.1 -95
S95.1 -99


4 .


Legend
1 Unburned
[ |Low Severity
Moderate Severty
S High Severity


Figure 3-11. Probability of high severity prescribed fire versus observed severity levels
for 2008 prescribed fires.


110


" "5~
;










A Probability of High Severity
N
IIL J I [ I Meters
3,550 7,100 14,200 21,300 28.400


kC


- ..,

. .. .. ,


Legend
Rx Model
W4ALUE>
m 35 50
50O 1 -75
75.1 -90
90 1 95
95 1 -99


Figure 3-12. The probability of high severity prescribed fire in 2008.


111


.4
-fI









A Probability of High Severity
N
L LJI I IMeters
3.550 7.100 14,200 21,300 28.400


.. -. Legend
S I Hydrnc_oreE
,l 1." I Mesiclores
Rx Model
VALUE>
.F il" l 35-50
S] 50 1 75
75 1 -90
S90 1 95
S95.1 -99

Figure 3-13. The probability of high severity prescribed fire in 2008 by community type.


112










2008 Precribed Fires


I I ,87I IMeters
1 2,875 5,750 11.500 17.250 23,000


D .


Legend
I |Unbuned
W Low Severty
- Moderate Severity
- High SEVrty


I


C


' -9 -


A.

. .,


Figure 3-14. Severity levels of 2008 prescribed fires on the Osceola National forest.


113


A
N











A
N


Probability of High Severity


LL FI I I Meters
I 3,600 7.200 14,400 21.600 28.800


Legend L
ForestType
EI-I LONGLEAF FPNE >WROPHYTIC OAKS
J FRESH WATER MARSHES
PINE FLATWOODS
SISWAMP I HARCWOODS
Rx_Model f

35 50

75 1 9
i 01- 95
95., 99


Figure 3-15. The probability of high severity prescribed fire in 2008 by forest type.


114








A


Probability of Moderate to High Severity


L 11 J0I I IMeters
5.500 11.000 22.000 33,000 441.000


Figure 3-16. The probability of moderate to high severity fire for 2008.


115









A Probability of Moderate to High Severity
N
TL WLJ I I IMeters
3,550 7,100 14,200 21,300 28.400


'Legend
I Mesic-fcrest
EJ H-ydlrictuat
W- model

"o-1
1.1 -5
S5.1 -10
L__ 10.1-19
Figure 3-17. The probability of moderate to high severity wildfire for 2008 by community
type.


116









A


Probability of Moderate to High Severity


LJ1-L II I IMeters
3,600 7.200 14.400 21.600 28.800


Legend
ForestType
J LONGLEAF PINE XEROPHYTIC OAKS
I FRESH WATER MARSHES 4
I PFE FLATWOODS
SSWAMP / HARDWOODS
Wrmodel


- 1.1-5
5.1 10
10.1- 19 t


Figure 3-18. The probability of moderate to high severity wildfire in 2008 by forest type.


117









CHAPTER 4
CONCLUSION

Remote sensing techniques used to model fires on the Osceola National forest

has provided valuable information regarding fire severity, the effect of time between

burns and the risks incurred by management decisions. Fuels on the Osceola National

Forest have fast recover times as evident by the relationship between frequency and

the probability of high severity. Forest land that burns more frequent also burns at a

higher severity. This indicates that fuels are able to regenerate at a rate to support

higher severity fires in short time periods.

The analysis has provided valuable information regarding the influence of

severity level and time between events. Models identified the time interval 5-6 years as

a point where the effects of previous fires had little to no effect on subsequent fires. At

this point, the probability of high severity fire, increasing severity level in subsequent

fire, and burning in successive fire is highest. This is also a point where the probability

of decreasing severity in subsequent fires was lowest. It has also become evident that

effects of previous fires have little to no influence on subsequent fires past 5-6 years.

Therefore fire frequencies larger than this will not adequately mitigate wildfire risk in

pine flatwoods.

Variations in the landscape influence the relationship between time between fire

events and fire severity. Fire effects are influenced by the type of vegetation and the

availability of that vegetation. Land managers must consider vegetation recovery and

availability differences by both forest and community types to determine the risk of the

high severity fire. Hydric areas have exhibited a lack of fire activity during wildfires,

implying that these areas have not been available to burn often. This raises the risk of


118









experiencing high severity fire during prolonged drought periods. When these high fuel

loads become available to burn they will likely burn quite severely (Maliakal et al. 2000).

To incorporate this into the model it may be useful to add a weighted overlay to identify

hydric communities and further weight them by time since last fire during prolonged

drought conditions. Land managers should also consider other options to treat heavy

fuel loads in these areas; including mechanical treatments.

To increase model performance it may be effective to include more

meteorological attributes into the models. This would allow the models to take into

account weather effects that may further identify areas that are at highest risk of

experiencing high severity. Spatial autocorrelation of fire severity and other model

covariates should also be incorporated into the models to account for variations in

space.


119







APPENDIX: SEVERITY DATASETS


A 1998 Severity
N
0 3 0 6 \I I IMaters
0 3,000ooo 6,000 12o000 18,o000oo 24,00ooo


Figure A-1. Severity levels of fire events for the 1998 fire season.


120









A 1999 Severity
N
S.JO\O 1,000 I IMeters0
0 3,0006,000 12,000 18,000 24,000


Figure A-2. Severity levels of fire events for the 1999 fire season.


121








N
N


2000 Severity


SIloLJ ooo I oI IMO ers
0 3,000 000 12,000 18,000 24,000


Figure A-3. Severity levels of fire events for the 2000 fire season.


122








A 2001 Severity
N
S-IILJ---- I I- I-- Meters
0 3,000 000 12,000 18,000 24,000


Figure A-4. Severity levels of fire events for the 2001 fire season.


123








A 2002 Severity
N
C 3, OOOCOI I IMeters
0 3,000 6000 12000 18,000 24,000


Figure A-5. Severity levels of fire events for the 2002 fire season.


124








A
N


2003 Severity


F TLO-O -- I I IMeters
0 3300 6000 12,OOO 1000 24,000


Figure A-6. Severity levels of fire events for the 2003 fire season.


125








A 2004 Severity
N
S3TJ 6 000 I0 I I Meters
0 3,000 6,000 12,000 18,000 24,000


Figure A-7. Severity levels of fire events for the 2004 fire season.


126








A 2005 Severity
N
C 4TOO0 6,OI I I0Metars
0 3,006,0 0012,000 18,000 24000


Figure A-8. Severity levels of fire events for the 2005 fire season.


127








A 2006 Severity
N
C IOI 10J0 0I I IMeters
0 3,0006,0 12000 oo 18,000 24,000


Figure A-9. Severity levels of fire events for the 2006 fire season.


128








A 2007 Severity
N
Sl000I6IJ00I I IMe1Zrs
0 3,000 6,000 12,000 18,I000 24,0o00


Figure A-10. Severity levels of fire events for the 2007 fire season.


129








A 2008 Severity
N
[ LJ- 1---- I 0 I -IMeters
0 3,0006,000 12,000 18,000 24,000


Figure A-11. Severity levels of fire events for the 2008 fire season.


130









LIST OF REFERENCES


Abrahamson, Warren G. 1984. Species response to fire on the Florida Lake Wales
Ridge. American Journal of Botany. 71: 35-43.

Abrahamson, Warren G. & Abrahamson, Christy R. 1996. Effects of fire on long
unburned Florida uplands. Journal of Vegetation Science.7: 565-574.

Agee, J.K. 1993. Fire Ecology of Pacific Northwest Forest. Island Press,
Washington,D.C.

Allen, Jennifer. & Sorbel Brian. 2008. Assessing the differnced Normalized Burn Ratio's
ability to map burn severity in boreal forest and tundra ecosystems of Alaska's
national parks. International Journal of Wildland Fire. 17: 463-475.

Boer, M M. Macfarlane, C. Norris, J. Sadler, R J. Wallace, J. & Grierson, P F. 2008.
Mapping burned areas and burn severity patterns in SW Australian eucalypt
forest using remotely-sensed changes in leaf area index, Remote Sensing of
Environment. 112(12): 4358-4369.

Brose, Patrick. & Wade, Dale. 2002. Potential fire behavior in pine flatwood forest
following three different fuel reduction treatments. Forest Ecology and
Management. 163: 71-84.

Busse, Matt. Hubber, Ken. Fiddler,Gary. Shestak, Carol. & Powers, Robert. 2005.
Lethal soil temperatures during burning of masticated forest residues.
International Journal of Wildland Fire. 14: 267-276.

Cocke, Allison. Fule, Peter. & Crouse, Joseph. 2005. Comparison of Burn severity
assessment using difference Normalized Burn Ratio and ground data.
International Journal of Wildland Fire. 14: 189-198.

Collins, Brandon. Miller, Jay. Thode, Andrea. Kelly, Maggi. Wagtendonk,Jan. &
Stephens, Scott. 2009. Interactions Among Wildland Fires in a Long-Established
Sierra Nevada Natural Fire Area. Ecosystems. 12: 114-128.

Crawford, Julie A. Wahren, C-H. A, Kyle, S. & Moir, W. H. 2001. Responses of exotic
plant species to fires in Pinus ponderosa forests in northern Arizona. Journal of
Vegetation Science. 12: 261-268.

Davis, John H. 1967. General map of natural vegetation of Florida. Agricultural
Experiment Stations, Institute of Food and Agricultural Sciences, University of
Florida.

Davis, Lawrence S. & Cooper, Robert W. 1963. How prescribed burning affects wildfire
occurrence. Journal of Forestry. 61(12): 915-917.


131









Dombeck, Michael P. Williams, Jack E. & Wood, Christopher A. 2004.Wildfire policy
and public lands: integrating scientific understanding with social concerns across
landscapes. Conservation Biology. 18: 4.

Duffy, P A. Epting, J. Graham, J M. Rupp, T S. & McGuire, A D. 2007. Analysis of
Alaskan burn severity patterns using remotely sensed data. International Journal
of Wildland Fire. 16: 277-284.

Duguy, Beatriz. Antonio Alloza, Jose. Roder, Achim. Vallejo, Ramon. & Pastor,
Francisco. 2007. Modeling the effects of landscape fuel treatments on fire growth
and behavior in a Mediterranean landscape (eastern Spain). International Journal
of Wildland Fire. 16(5): 619-632.

Duncan, Brean W. & Schmalzer, Paul A. 2004. Anthropogentic influences on potential
fire spread in a pyrogenic ecosystem of Florida, USA. Landscape Ecology. 19:
153-165.

Earth Resources Observation and Science (EROS) 2009. http://glovis.usgs.gov/
distribution/downloadnotices.shtml. Accessed January 2010.

Epting, J. Verbyla, D. & Sorbel, B. 2005. Evaluation of remotely sensed indices for
assessing burn severity in interior Alaska using Landsat TM and ETM+. Remote
Sensing of Environment. 96(3-4): 328-339.

Escuin, S. Navarro, R. & Fernandez, P. 2009. Fire severity assessment by using NBR
(Normalized Burn Ratio) and NDVI (Normalized Difference Vegetation Index)
derived from Landsat TM/ETM Images. International Journal of Remote Sensing.
29(4): 1053-1073.

Finney, M.A. McHugh, C.W. & Grenfell, I.C. 2005. Stand- and landscape-level effects of
prescribed burning on two Arizona wildfires. Canadian Journal of Forest
Research. 35(7): 1714-1722.

FireModels.org (Fire Behavior and Fire Danger Software). 2009. FARSITE.
http://firemodels.fire.org/content/view/112/143/. Accessed January 2010.

Florida Exotic Pest Plant Council. 2009. 2009 Invasive plant list. http://www.fleppc.org/
list/list. htm. Accessed January 2010.

Gilliam, Frank S. & Platt, William J. 1999. Effects of longterm fire exclusion on tree
species composition and stand structure in an oldgrowth Pinus palustris
(Longleaf pine ) forest. Plant Ecology. 140: 15-26.

Glitzenstein, Jeff. Streng, Donna. Achtemeier, Gary. Naeher, Luke. & Wade, Dale.
2006. Fuels and fire behavior in chipped and unchipped plots: implications for
land management near wildland urban interface. Forest Ecology and
Management. 236:18-29.


132









Godwin, David R. 2008. Burn severity in a central Florida sand pine scrub wilderness
area. University of Florida Press.

Hardy, C.C. 2005. Wildland fire hazard and risk: problems, definitions, and context.
Forest Ecology and Management. 211: 73-82.

Heyward, Frank. 1939. The relation of fire to stand composition of longleaf pine forests.
Ecology. 20(2): 287-304.

Holden, Zachary. Morgan, Penelope. & Evans, Jeffery. 2009. A predictive model of burn
severity based on 20-year satellite- infrared burn severity data in a large
southwestern U.S. wilderness area. Forest Ecology and Management. 258:
2399-2406.

Hoy, Elizabeth. French, Nancy. Turetsky, Merritt. Trigg, Simon. & Kasischke, Eric. 2008.
Evaluating the potential of landsat TM/ETM+ imagery for assessing fire severity
in Alaska black spruce forest. International Journal of Wildland Fire. 17: 500-514.

Jakubauskas, M.E. Lulla, K.P. & Mausel, P.W. 1990. Assessment of vegetation change
in fire altered forest landscape. Photogrammetric Engineering and Remote
Sensing. 56(3): 371-377.

Kane, Jeffery. Varner, Morgan. & Knapp, Eric. 2009. Novel fuel bed characteristic with
mechanical mastication treatments in northern California and south-western
Oregon, USA. International Journal of Wildland Fire. 18: 686-697.

Kobziar, Leda N. McBride, Joe R. & Stephens, Scott L. 2009. The efficacy of fire and
fuels reduction treatments in Sierra Nevada pine plantation. International Journal
of Wildland Fire. 18: 791-801.

Kreye, Jesse. Fuels break study sampling. 2009. Kobziar Fire Science Lab.
Conversation: December 2009.

Kuenzi, Amanda. Fule, Peter. & Sieg, Carolyn. 2008. Effects of fire severity and pre-fire
stand treatment on plant community recovery after a large wildfire. Forest
Ecology and Management. 255: 855-865.

Lavoie, M. Starr, G. Mack, M.C. Martin, T.A. & Gholz, H.L. 2010. Effects of a prescribed
fire on understory vegetation carbon pools, and soil nutrients in longleaf pine-
slash pine forest in Florida. Natural Areas Journal. 30: 82-94.

Lemon, Paul C. 1949. Successional responses of herbs in the longleaf- slash pine
forest after fire. Ecology. 30: 135-145.

Littell, Ramon C. Milliken, George A. Stroup, Walter W. Wolfinger, Russell D. &
Schabenberger, Oliver. 2006. SAS for Mixed Models. 2nd Edition. SAS
Publishing, Caryn, NC.


133









Maliakal, Satya K. & Menges, Eric S. 2000. Community composition and regeneration of
Lake Wales Ridge wiregrass flatwoods in relation to time since last fire. Journal
of Torrey Botanical Society. 127(2): 125-138.

Miller, Jay.D. & Thode, Andrea E. 2007. Quantifying burn severity in a heterogeneous
landscape with relative version of the delta Normalized Burn Ratio (dNBR).
Remote Sensing of Environment. 109: 66-80.

Monk, Carl D. 1968. Succesional and environmental relationships of the forest
vegetation of north central Florida. American Midland Naturalist. 79(2): 441-457

Monroe, Martha. Long, Alan. & Maynowski, Susan. 2003. Wildland Fire in the
southeast: negotiating guidelines for defensible space. Journal of Forestry. 101:
14-19.

National Fire and Aviation Management. 2009. Fire Weather Data.
http://fam.nwcg.gov/fam-web/weatherfirecd/. Accessed January .2010

National Invasive Species Information Center (USDA). 2009. National Invasive Species
Management Plan. 2006. http://www.invasivespeciesinfo.gov/. Accessed
January 2010.

National Oceanic and Atmospheric Administration (NOAA). 2009 Palmer Drought
Severity Index. http://www.drought.noaa.gov/palmer.html. Accessed June 2010.

Oliveras, Imma. Gracia, Marc. More, Gerard. & Retana, Javier. 2009. Factors
influencing the patterns of fire severity in large wildland fire under extreme
meteorological conditions in the Mediterranean basin. International Journal of
Wildland Fire. 18: 755-764.

OTA. 1993. Harmful non-indigenous species in the United States. Office of Technology
and Assessment, United States Congress, Washington DC.

Outcalt, Kenneth W. & Wade, Dale D. 2004. Fuels management reduces tree mortality
from wildfires in southeastern United States. Southern Journal of Applied
Forestry. 28(1): 28-34.

Pimentel, David. Zuniga, Rodolfo. & Morrison, Doug. 2005. Update on the
environmental and economic cost associated with alien-invasive species in the
United States. Ecological Economics. 52: 273-288.

Reiner, Alicia L. Vaillant, Nicole M. Fite-Kaufman, JoAnn. & Dailey, Scott N. 2009.
Mastication and prescribed fire impacts on fuel in a 25-year old ponderosa pine
plantation, southern Sierra Nevada. Forest Ecology and Management. 258:
2365-2372.


134









Safford, Hugh. Schmidt, David. & Carson, Chris. 2009. Effects of fuel treatments on fire
severity in an area of wildland-urban interface, Angora Fire, Lake Tahoe Basin,
California. Forest Ecology and Management. 258: 773-787.

Schmidt, Davis A. Taylor, Alan H. & Skinner, Carl N. 2008. The influence of fuels
treatment and landscape arrangement on simulated fire behavior, Southern
Cascade range, California. Forest Ecology and Management. 255: 3170-3184.

Stephens, Scott. & Moghadds, Jason. 2005. Experimental fuel treatment impacts on
forest structure, potential fire behavior, and predicting tree mortality in California
mixed conifer forest. Forest Ecology and Management. 215: 21-36.

Stratton, Richard. 2005. Assessing the effectiveness of landscape fuel treatments on
fire growth and behavior. Journal of Forestry. 93; 1041-1052.

Tuner, Monica G. Hargrove, William W. Gardner,Robert H. & Romme, William H. 1994.
Effects of fire on landscape heterogeneity in Yellowstone National Park,
Wyoming. Journal of Vegetation Science. 5: 731-742.

United States Geologic Service (USGS). 2009. Digital Elevation Model.
http://seamless.usgs.gov/website/seamless/ viewer.htm. Accessed January
2010.

United States Geologic Service (USGS). 2009. Difference Normalized Burn Ratio.
http://burnseverity.cr.usgs.gov/pdfs/LAv4_BR_ CheatSheet.pdf. Accessed
January 2010.

Wagtendonk, Jan. Root, Ralph. & Key, Carl. 2004. Comparison of AVIRS and Landsat
ETM+ detection capabilities for burn severity. Remote Sensing of Environment.
92: 397-408.

Waldrop, Thomas A. White, David L. & Jones, Steven M. 1992. Fire regimes for pine-
grassland communities in the southeastern United States. Forest Ecology and
Management. 47: 195-210.

White, Joseph D. Ryan, Kevein C. Key, Carl C. & Runnig, Stephen W. 1996. Remote
sensing of forest fire severity and vegetation recovery. International journal of
Wildland Fire. 6(3): 125-136.

Wimberly, Michael C. Cochrane, Mark A. Baer, Adam D. & Pabst, Kari. 2009. Assessing
fuel treatment effectiveness using satellite imagery and spatial statistics.
Ecological Applications. 19(6): 1377-1384.

Wolcott, Leslie. O'Brien, Joseph J. & Mordecai, Kathryn. 2007. A Survey of Land
Managers on Wildland Hazardous Fuels Issues in Florida: a technical note.
Southern Journal of Applied Forestry. 31(30): 148-150.


135









BIOGRAPHICAL SKETCH

Sparkle Leigh Malone was born in the spring of 1985, in Chicago, Illinois to Rita

and Rodney Malone. She grew up in Miami, Florida, after her family moved from

Chicago to Miami when she was an infant. Sparkle graduated from Dr. Michael Krop

Senior High School in 2003. Her college career began in 2005 at Florida Agricultural

and Mechanical University in Tallahassee, FL. Her major area of study was agronomy.

In 2007 she transferred into the University of Florida's School of Forest Resources and

Conservation by way of the 1890s scholars program. Here, Sparkle majored in forestry

with a specialization in informatics. She obtained a Bachelor of Science from the

university in the spring of 2009 and a master's degree in the summer of 2010.


136





PAGE 1

1 EFFECT OF FIRE SIZE AND SEVERITY ON SUBSEQUENT FIRES USING DIFFERENCE D NORMALIZED BURN RATIOS IN PINE DOMINATED FLATWOOD FORESTS IN FLORIDA By SPARKLE LEIGH MALONE A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSI TY OF FLORIDA IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE UNIVERSITY OF FLORIDA 2010

PAGE 2

2 2010 Sparkle Leigh Malone

PAGE 3

3 To all those who supported me through this process

PAGE 4

4 ACKNOWLEDGMENTS Without the help and support of many people this project would not have been possible. I am most grateful to my committee members ( Dr. Amr Adb Elrahman, Dr. Leda Kobziar, and Dr. Christina Staudhammer) for their endless guidance and their commitment to this project. I would also li ke to express my gratitude for the support offered by faculty (Dr. Taylor Stein and Dr. George Blakeslee), students in the school of forestry and friends who not only provided encouragement but sacrificed their own tim e for the sake of this project. Data analysis assistance was graciously provided by Dr. Mary Christman, Dr. Christina Staudhammer, and Nilesh Timilsina. Many thanks to the Quantitative Biology Lab (Nilesh Timilsina, Todd Bush, Helen Claudio, and Dr. Louise Loudermilk) for their relentless encouragement throughout this process. I would also like to thank the Kobziar Fire Science Lab for their assistance. Funding was provided by Conserved Forest Ecosystems: Outreach and Research (CFEOR). Speci al thanks are necessary for Jason Drake at the U S Forest S ervice Supervisors Office in Tallahassee, Florida for providing both data for this project and inspiration. Finally I would like to thank my family for their continued support in my academic endeavors.

PAGE 5

5 TABLE OF CONTENTS page ACKNOWLEDGMENTS .................................................................................................. 4 LIST OF TABLES ............................................................................................................ 7 LIST OF FIGURES .......................................................................................................... 8 LIST OF ABBREVIATIONS ........................................................................................... 11 ABSTRACT ................................................................................................................... 12 C H A P T E R 1 INTRODUCTION TO FIRE IN THE SOUTHEASTERN UNITED STATES ............. 14 Introduction ............................................................................................................. 14 Suppression in Pine Flatwoods ........................................................................ 15 Fire as a Forest Management tool .................................................................... 16 Fire Severity ..................................................................................................... 19 Measuring Fire Severity with DNBRs ............................................................... 20 Study Site ............................................................................................................... 23 Conclusion .............................................................................................................. 23 2 EFFECTS OF FIRE FREQUENCY, SIZE, AND TIME BETWEEN FIRE EVENTS IN NORTH FLORIDA FLATWOODS ....................................................... 26 Introduction ............................................................................................................. 26 Measuring Fire Severity .................................................................................... 27 Study Site ......................................................................................................... 31 Methods .................................................................................................................. 32 Data .................................................................................................................. 32 Model Development ......................................................................................... 33 Results .................................................................................................................... 37 Data .................................................................................................................. 37 Probability Modeling ......................................................................................... 38 Probability of experiencing moderate to high severity during a fire ............ 38 Probability of increasing in severity in subsequent fires ............................. 39 Probability of burning during a fire ............................................................. 40 Probability of decreasing in severity in subsequent fires ............................ 41 Fire size analysis ....................................................................................... 42 Discussion .............................................................................................................. 42 Probability of Experiencing Moderate to High Severity During a Fire ............... 42 Probability of Increasing in Severity ................................................................. 45 Probability of Burning ....................................................................................... 46 Probability of Decreasing in Severity ................................................................ 47

PAGE 6

6 Conclusion .............................................................................................................. 48 3 PREDICTING FIRE SEVERITY IN PINE FLATWOODS USING DIFFERENCED NORMALIZED BURN RATIOS TO RECORD FIRE EVENTS ................................ 80 Introduction ............................................................................................................. 80 Measuring Fire Severity .................................................................................... 80 Study Site ......................................................................................................... 83 Methods .................................................................................................................. 84 Image Analysis ................................................................................................. 84 Data .................................................................................................................. 84 Model Development ......................................................................................... 85 Spatial Model .................................................................................................... 89 Results .................................................................................................................... 89 Probability of High Severity Prescribed Fire ..................................................... 89 Probability of Moderate to High Severity Wildfire ............................................. 90 Spa tial Models .................................................................................................. 91 Discussion .............................................................................................................. 92 Probability of High Severity Prescribed Fire ..................................................... 92 Probability of Moderate to High Severity Wildfire ............................................. 93 Spa tial Models .................................................................................................. 94 Conclusion .............................................................................................................. 95 4 CONCLUSION ...................................................................................................... 118 APPENDIX: SEVERITY DATASETS ........................................................................... 120 LIST OF REFERENCES ............................................................................................. 131 BIOGRAPHICAL SKETCH .......................................................................................... 136

PAGE 7

7 LIST OF TABLES Table page 2 1 Severity class descriptions for the time analysis and fire size datasets. ............. 50 2 2 Palmer Drought Severity Index values and descriptions .................................... 50 2 3 Time interval classification for time analysis dataset. ......................................... 50 2 4 Covariate classifications for fire size model. ....................................................... 51 2 5 Parameter estimates and their respective standard errors and p values for the model predicting the probability of high severity fire. .................................... 52 2 6 Parameter estimates and their respective standard errors and pvalues for the model predicting the probability of increased severity in the second fire. ..... 5 3 2 7 Parameter estimates and their respective standard errors and pvalues for the model predicting the probability of burning. .................................................. 54 2 8 Parameter estimates and their respective standard errors and pvalues for the model predicting probability of decreased severity in the second fire. .......... 55 2 9 Parameter estimates and their respective standard errors and pvalues for model predicting the probability of high severity fire by fire size class. ............... 56 3 1 Number of pixels in each severity class by year. ................................................ 98 3 2 Covariates for the model measuring the probability of high severity prescribed fire and moderate to high severity wildfire. ........................................ 98 3 3 Parameter estimates and their respective standard errors and pvalues for the model predicting the probability of high severity prescribed fire. .................. 98 3 4 P arameter Estimates and their respective standard errors and pvalues for model predicting the probability of Moderate to High severity wildfire. ............... 99

PAGE 8

8 LIST OF FIGURES Figure page 1 1 Osceola National Forest in North Florida. ........................................................... 25 2 1 USFS forest type classifications. ........................................................................ 57 2 2 NRCS soil drainage class classification. ............................................................. 58 2 1 Portion of pixels burned in each severity level in fire 1 and fire 2. ...................... 59 2 2 Distribu tion of pixels among severity classes with 12 years between fire events separated by type of fire and the probability of moving from one severity class to the next. ................................................................................... 60 2 3 Distribution of pix els among severity classes with 34 years between fire events separated by type of fire and the probability of moving from one severity class to the next. ................................................................................... 61 2 4 Distribution of pixels among s everity classes with 56 years between fire events separated by type of fire and the probability of moving from one severity class to the next. ................................................................................... 62 2 5 Distribution of pixels among severity classes with 7 8 years between fire events separated by type of fire and the probability of moving from one severity class to the next. ................................................................................... 63 2 6 Distribution of pixels among severity classes with 910 years between fire events separated by type of fire and the probability of moving from one severity class to the next. ................................................................................... 64 2 7 Percentage of pixels increasing and decreasing in severity le vel by time and type of fire. .......................................................................................................... 65 2 3 Fire size compared with Palmer drought severity index between 1996 and 2010. This suggests large fire events are associated with prolonged droughts. ............................................................................................................ 66 2 4 Percentage of pixels burned at each severity class by fire size class. Larger fires have a higher portion of their cells in the high severity class. ..................... 67 2 8 Probability of experiencing high severity in fire 2 by time interval and fire type. .................................................................................................................... 68 2 9 Probability of experiencing high severity in fire 2 by severity level of fire 1 and time interval for prescribed fires. ......................................................................... 69

PAGE 9

9 2 10 Probability of experiencing high severity in fire 2 by severity level of fire 1 and time interval for wildfires. .................................................................................... 70 2 11 Probability of increasing fire severity by time interval and fire type. .................... 71 2 12 Probability of increasing fire severity by severity level of the last fire and time between fires for wildfires. .................................................................................. 72 2 13 Probability of increasing fire severity by severity level of the last fire and time between fires for prescribed fires. ....................................................................... 73 2 14 Probability of burning by time interval, fire type, and fire severity level. .............. 74 2 15 Probability of burning by fire severity level and time interval for wildfires. .......... 75 2 16 Probability of burning by fire severity level and time interval for prescribed fires. .................................................................................................................... 76 2 17 Probability of decreasing in severity level by time interval and severity level of fire 1. .................................................................................................................. 77 2 18 Probability of decreasing in severity by severity level of fire 1 and time interval for wildfires. ............................................................................................ 78 2 19 Probability of decreasing in severity by severity level of fire 1 and time interval for prescribed fires. ................................................................................ 79 3 1 Time since last fire for the Osceola National Forest (19982008) ..................... 100 3 2 Fire frequency from 19982008 for the Osceola National Forest. ..................... 101 3 3 Severity level of the last fire event (19982007). ............................................... 102 3 4 Florida Geographic Database Library Map of forest types for the Osceola National Forest. ................................................................................................ 103 3 5 Map of the community types, hydric and mesic, for the Osceola National Forest. .............................................................................................................. 104 3 6 Relationship between the probability of high severity pr escribed fire, frequency of fire, and time since last fire. ......................................................... 105 3 7 Relationship between the probability of high severity prescribed fire, the severity level of the last fire event, and time s ince last fire. .............................. 106 3 8 Relationship between the probability of high severity prescribed fire, frequency of fire, and time since last fire. ......................................................... 107

PAGE 10

10 3 9 Relationship between the probability of moderate to high severity wildfire, frequency of fire, and time since last fire. ......................................................... 108 3 10 Relationship between the probability of mo derate to high severity wildfire, fire frequency, and time since last fire. ................................................................... 109 3 11 Probability of high severity prescribed fire versus observed severity levels for 2008 prescribed fires. ....................................................................................... 110 3 12 The probability of high severity prescribed fire in 2008. .................................... 111 3 13 The probability of high severity prescribed fire in 2008 by community type. ..... 112 3 14 Severity levels of 2008 prescribed fires on the Osceola National forest. .......... 113 3 15 The probability of high severity prescribed fire in 2008 by forest type. ............. 114 3 16 The probability of moderate to high severity fire for 2008. ................................ 115 3 17 The probability of moderate to high severity wildfire for 2008 by community type. .................................................................................................................. 116 3 18 The probability of moderate to high severity wildfire in 2008 by forest type. ..... 117 A 1 Severity levels of fire events for the 1998 fire season. ..................................... 120 A 2 Severity levels of fire events for the 1999 fire season. ..................................... 121 A 3 Severity levels of fire events for the 2000 fire season. ..................................... 122 A 4 Severity levels of fire events for the 2001 fire season. ..................................... 123 A 5 Severity levels of fire events for the 2002 fire season. ..................................... 124 A 6 Severity levels of fire events for the 2003 fire season. ..................................... 125 A 7 Severity levels of fire events for the 2004 fire season. ..................................... 126 A 8 Severity levels of fire events for the 2005 fire season. ..................................... 127 A 9 Severity levels of fire events for the 2006 fire season. ..................................... 128 A 10 Severity levels of fire events for the 2007 fire season. ..................................... 129 A 11 Severity levels of fire events for the 2008 fire season. ..................................... 130

PAGE 11

11 LIST OF ABBREVIATION S AIC Akaikes information criterion BIC Bayesian information criterion d NBR d ifferenced N ormalized B urn R atio MTBS Monitoring trends in burn severity NBR Normalized burn ratio NRCS Natural Resource Conservation Service NRMSC Northern Rocky Mountain Science Center PDSI Palmer drought severity index TSLF Time since last fire U SFS United States Forest Service USGS United States Geological Survey WUI Wildland urban interface

PAGE 12

12 Abstract of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Science EFFECT OF FIRE SIZE AND SEVERITY ON SUBSEQUENT FIRES USING DIFFERENCE D NORMALIZED BURN RATIOS IN PINE DOMINATED FLATWOOD FORESTS IN FLORIDA By Sparkle Malone August 2010 C hair: Christina Staudhammer Cochair: Leda Kobziar Florida f orests naturally experienced frequent low intensity fires, yet fire exclusion polices have altered the forest structure. The Osceola National Forest in north Florida has experienced high wildfire occurrence for a number of years. Vegetation communities wit hin the Osceola are fire dependent and require regular burning for ecosystem health. Although prescribed fire has been used to reduce wildfire risk and maintain ecosystem integrity across much of the forest, managers are still working to reintroduce fire to long unburned units. The obj ective of this study is to use differenced Normalized Burn Ratio (dNBR) to evaluate the relationships between previous fire severity, size, and historical frequency to inform prioritization and timing of future fire use. Based on remote ly sensed Landsat imagery, dNBR analysis captures spectral features over a time interval, and indicates the degree of change that is due to fire. This analysis has shown that fires in areas burned 5 or more years prior exhibited a higher probability of experien cing moderatehigh severity fire and have a higher probability of increasing in severity level in subsequent fires Areas th at have not experienced fire in 10 years are indistinguishable from areas that have never burned. Using d NBR as a

PAGE 13

13 method of analyzing past fire severity is a useful tool for managers to determine the lasting effects of prior fire severity. The analysis has further provided an effective method of determining fire frequencies necessary to maintain the optimum l evel o f wildfire protection.

PAGE 14

14 CHAPTER 1 INTRODUCTION TO FIRE IN THE SOUTHEASTERN UNITED STATES Introduction Fuel is any combustible material that is used to maintain fire. Without regular fire fuel loads in forested ecosystems grow to dangerous levels increas ing the risk of catastrophic wildfire. I n systems where fire is a natural component, fuel management is important for ecosystem health. Wildfire risk is not only affected by fuel the increase in population in the wildland urban interface (WUI) is also o f great importance. Anthropogenic influences are a major source of wildfire ignitions. Land managers are currently working to reduce fuel accumulation in efforts to reduce the risk of catastrophic wildfires but sensitive areas within WUI create additional problems. Land managers are challenged with protecting surrounding land in a way that contributes to their management goals. The focus of this project is on a forest wide burn severity analysis in a north central Florida forest usin g d ifferenced Normali zed Burn Ratios (dNBR) for fires that occu rred between 1998 and 2008. This analysis is important for the evaluation of past fire history and the effects it can have on subsequent fires. This study provides valuable information regarding appropriate fire regimes to keep fuel loads low enough to mitigate the effects of wildfires. This method of fire assessment using remote sensing techniques can easily be modified to evaluate past fire effects for any land manager to impart site specific statistics to thei r land management practices The main objectives of this study are:

PAGE 15

15 1 Determine how pas t fire size and severity level e ffect subsequent fire behavior? 2 Identify the relationship between fire size and the proportion o f area burned at high severity? Suppressi on in Pine Flatwoods Pine flatwoods are successional communities with southern mixed hardwoods, mixed hardwoods, or bay heads as the climax community (Monk 1968). Without regular disturbance, this fire maintained community shifts to one of the 3 climax com munities. Soil moisture and fertility determine which climax community is attained (Monk 1968). Historically, fires were ignited by Indian hunting parties to corral game, by naval store operators to reduce wildfire risk, by cattle owners to encourage grass growth, and by lightning (Heyward 1939). Pine flatwoods burned at a frequency of every 115 years (Maliakal et al. 2000). In the 1920 s fire suppression began in the region (Frost 1993). Long term fire exclusion altered stand structure permitting hardwood species to occupy pine flatwood forest at high densities (Gilliam et al. 1999 ; Heyward 1939) The lack of disturbance created conditions outside the evolutionary history of species adapted to this disturbance regime giving species adapted to less frequent disturbance the advantage (Maliakal et al. 2000). Pyrogenic species survive fire by either sprouting to regenerate or are able to withstand repeated burning by maintaining features that allow the plant to survive fires (Abrahamson 1984) Pine spec ies have evolved to have thick bark and high crowns ( Waldrop et al.1992) while other species resprout or seed ( Abrahamson et al. 1996). The majority of nonconiferous woody species resprout from underground reserves rather that reseeding ( Abrahamson et al. 1996 ). Changes in vegetation following

PAGE 16

16 extended periods of suppression leads to more intense, patchier, and less frequent fires which may require more extreme conditions to burn (Maliakal et al. 2000) Fi re as a Forest Management tool One of the m ost effective tools for fuel management in the southeastern United States is prescribed burning (Davis et al. 1963). The purpose of using prescribed fire as a management tool is to reduce fuel accumulations to levels that minimize damage from wildfire and wildfire occurrence (Davis et al. 1963), improve wildlife habitat reintroduce fire to pyrogenic communities and, conserve biodiversity (Outcalt et al. 2004). Fire management in Florida is largely dictated by urban encroachment, forest fragmentation, and the challenges associated with smoke management (Wol cott et al. 2007). As long as fuel loads are kept below 5 years, using fire to reduce the occurrence of catastrophic wildfires is a profitable investment (Davis et al. 1963). Past research has shown tha t wildfires could be kept small and damage limited with regular use of prescribed fire. Regular prescribed burning keeps fuel accumulations on the forest floor and in the understory within tolerable levels (Outcalt et al 2004). The amount of time that ha s passed after fire can greatly affect wildfire behavior and effects Davis et al. (1963) found the wildfire occurrence rate for areas on the Osceola that contained fuel loads 3 years and older were higher than lower fuel loads Large fires were also found to be restricted by roughs 5 years and greater (Davis et al. 1963). As fires moved into younger roughs, intensity level was reduced to a degree where suppression was possible (Davis et al. 1963). Outcalt et al. (2004) also found a significant relationship between time since last fire and fire intensity. As time increased, fire intensity also increased (Outcalt et al. 2004). Fuel accumulations of 3 years or less

PAGE 17

17 support fewer fires, lower fire intensities, and lower annual burned acreage (Davis et al. 1963). Prescribed burns are implemented under optimal circumstances where conditions are suitable for vegetation consumption but not at levels to cause fire to become unmanageable. Favorable conditions are characterized by cool weather, relatively constant winds, dry litter, and wet soil (Davis et al. 1963). During prescribed burns wet areas burn lightly if at all. Understory fuel is partially consumed with little consumption of the duf f layer (Outcalt et al. 2004). Therefore, wet areas (cypress ponds) generally carry very heavy fuel volumes. During extended drought periods, these areas (cypress ponds) dry up making them capable of very large very intense w ildfires (Davis et al. 1963). Mortality is a major issue in prescribed fire management. Prescrib ed fire is used to reduce the effects of catastrophic wildfire where a higher amount of mortality is likely to result. Outcalt et al (2004) found prescribed fire to be efficient in reducing mortality levels and timber loss. Tree mortality was 64% in previously unburned areas and 17% in areas burned within the last 3 years (Outcalt et al. 2004). Outcalt et al (2004) also found that relative moisture levels of an area influenced tree mortality. Mortality was significantly higher on wetter sites, likely due to high fuel loads. It was also shown that during extreme drought conditions, mortality was significantly higher on sites where fires had been absent for 5 or more years. The most favorable timing of prescribed fire depends on management objectives and site characteristics. Flatwoods are generally burned either during winter (dormant

PAGE 18

18 season) or summer burns (growing season). Vegetation and fuel consumption differs significantly between the two. Winter, in north central Florida, is typically a dry season with most precipitation coming from periodic cold fronts. Ambient temperatures are lower reducing the total amount of heat transferred to surrounding vegetation during fire, resulting in less damage to plant tissues. Prescribed fires following fronts are manageable and allow the upper layers of litter to carry fire while lower layers are unavailable. This time of year, grasses and other fine fuels are avialible to burn while deciduous hardwoods have their food reserves below ground and are prepared t o sprout back following fire. Dormant season burning affects the size, cover, and vigor of hardwoods but is not ef fective at reducing abundance. Early spring is typically a season marked by thunderstorm development and lightning ignitions. Hydric communi ties are most likely available to burn during this time yet the prolonged time between precipitation events, make this season less desirable for most management objectives. Spring fires are useful for stimulating seed, raising insect populations, and incr easing the quality of browse to boost foo d availability for wildlife. Although summer is the hottest season, it is also the wettest. The increase in temperature causes fires to be more intense and more likely to cause damage to plants. This is also the s eason of thunderstorms. Unstable atmospheres associated with such events bring lighting, unpredictable wind speeds and direction that can complicate prescribed burning. Burns during this season must be carefully monitored. Summer fires reduce hardwood v igor allowing grasses and f orbs to increase in abundance.

PAGE 19

19 Fire S everity Fire severity is a measure of ecological and physical change attributed to fire (Agee 1993; Hardy 2005). It is influenced by both abiotic and biotic factors. Abiotic determinants inc lude weather, moisture, time of day, sunlight incidence, and slope (Oliveras et al. 2009). Vegetation attributes such as species, tree size, succession stage, and pathogens are among the many factors influencing fire severity (Cocke et al. 2005). The var iability in landscape and weather conditions during a fire are the cause of heterogeneous burn patterns (Cocke et al. 2005). Major differences in severity are also associated with the location of the fire perimeter (Oliveras et al. 2009). Head fires burn with greater flame lengths and intensity than backing fires. Head fires move in the same direction as the wind while backing fires move against the wind. Consequently, we would expect to see greater severity in areas burned by heading fires than in areas burned by backing fires. Low severity burns are characterized by lightly burned areas where only fine fuels are consumed with minor scorching of trees in the understory (Wagtendonk et al. 2004). Areas of moderate severity retain some fuels on the forest floor and have crown scorching in midlarge trees with mortality of small trees (Wagtendonk et al. 2004). High severity zones are generally composed of complete combustion of all litter, duff and small logs, mortality of small med trees, and consumption o f large tree crowns (Wagtendonk et al. 2004). Unburned and low burn areas serve as seed sources for more severely burned sections (Cocke et al. 2005). Severity is important to monitor as its effects on exotic species establishment, soil responses, and reg eneration can be significant. Large fires may remove existing plant biomass, providing ideal habitat for exot ic species (Kuezi et al 2008). Responses

PAGE 20

20 in soil condition following fire can range from affirmative nutrient availability to loss of nutrients, soil micro organisms, and changes in physical structure of the soil (Busse et al. 2005). The degree of canopy degeneration due to cambium and crown scorch can severely impact the ability to resprout or seed. Combined with the biophysical condition, plant recovery following a severe fire can prove nearly impossible for remnant vegetation (W hite et al.1996). The same fire behavior can result in very different severity effects in over and understory vegetation, as well as in soil conditions (Wagtendonk et al. 2004). Burn severity effects arent always evident directly following fire. Therefore, a fire severity analysis will help managers anticipate the short and long term effects of severity level, and how to better predict areas of potential high severity The burn severity analysis will further improve our understanding of why and where fires burn severely. Measuring Fire S everity with DNBRs Fire severity can be effectively measured through remote sensing techniques. A d ifferenced Normalized Burn Ratio (d NBR) captures the spectral response, over a time interval, and indicates the degree of change that is due to fire (Wagtendonk et al. 2004; Miller et al. 2006). The mapping methodology was initially developed and tested by the USGS Northern Rocky Mountain Science Center (NRMSC). Multi temporal image differencing was employed to enhance contrast and detection of changes from preand post fire images using Landsat Thematic Mapper (TM) bands 4 and 7 (Wagtendonk et al 2004). Normalized Burn Ratios (NBR) we re designed to enhance the bands response to fire by normalizing their difference to compensate for variations in the overall brightness of the scene (Wagtendonk et al. 2004). The use of shortwave infrared bands was found to have the highest accuracy (Cocke et al. 2005). Em ployed

PAGE 21

21 as a radiometric index, dNBRs are directly related to burn severity (Wagtendonk et al. 2004) and as long as the fire is within the resolution range of the satellite sensor, 30m, it is de tectable (White et al. 1996). Sensitivity to vegetation and soil moisture, changes in canopy cover, biomass removal, and soil chemical composition allow dNBRs to define different levels of burn severity. Fire effects on soil, litter, and vegetation impact the spectral response of the post fire im age (White et al. 1996 and Cocke et al. 2005). The degree of change between the two images determines the extent to which fire has affected the area of interest (White et al.1996). An increase in dNBR corresponds to an increase in severity level. Unburned areas have values near zero, signifying little to no change between the pre and post fire image (Wagtendonk et al. 2004). High severity areas have higher DNBRs due to greater vegetation die off (Kuezi et al 2008). In order to model the fire severity accurately it is important to pair the preand post fire images by phenology and moisture levels (Wagtendonk et al. 2004). Ti ming of acquisition can impact dNBRs if there is a significant difference in vegetation and moisture levels due to phenology, not fire (Wagtendonk et al. 2004). Im portant to consider when using dNBRs is the chance that values are being influenced by events other than f ire. Turner et al. (1994) used dNBRs in Yellowstone National Park and discovered bias in particular severity class es due to pine beetle infestation (White et al. 1996). Jakubauskas et al. (1990) found that burn severity is detected differently among conifers, deciduous trees, and shrubs due to revegetation patterns. In addition, drought stress and vegetation regro wth makes it difficult to discern low severity and unburned areas (Cocke et al. 2005). The highest accuracy is

PAGE 22

22 achieved in detecting high severity burns (Cocke et al. 2005). More severely burned areas have a much greater difference in vegetation cover changing the radiation budget in the post fire image by a greater degree (White et al. 1996). DNBR is used within the United States to appraise fire severity following major fires (Wagtendonk et al. 2004; Godwin 2008). Image differencing is one of the most accurate methods of detecting the level of change caused by fire (Cocke et al. 2005). It can accurately detect burn severity in a way that is repeatable. Beyond any other band combinations, NBRs emphasize the effects of fire. Other methods that use bands in the visible part of the spectrum introduce atmospheric interference from dust and smoke (Cocke et al. 2005). And, indices derived from near infrared and midinfrared reflectance are not sensitive enough to remotely sense water stress (Wagtendonk et al. 2004). Studies using dNBRs have been in efforts to calibrate severity levels (Cocke et al. 2005; Hoy et al. 2008), compare severity levels of a previous fires to a subsequent fires (Collins et al. 2009; Allen et al 2008;), interpret the effects of fu el management on severity (Safford et al. 2009), and to monitor changes in vegetation over time (White 1996; Kuenzi et al. 2008) and topographical variations (Holden 2009; Oliveras et al 2009). Currently in the United States there is a multi agency project, Monitoring Trends in Burn Severity (MTBS), using dNBRs to map burn severity and perimeters of large fires. This project uses data from 1984 2010 to identify national trends in burn severity in efforts to determine the effectiveness of the National F ire Plan and Healthy Forest Restoration Act.

PAGE 23

23 Study Site The Osceola National Forest is located in the northeastern porti on of the state of Florida (Latitude: 30.34371, Longitude: 82.47322) about 40 miles west of the city of Jacksonville ( Figure 11 ). The forest consists of pine flatwoods and areas of cypress and bay swamps. Pine flatwoods have an overstory of pines on low, flat, sandy, acidic soils with an understory of herbaceous plants and grasses. This community is fire d ependent and requires regular burning for pine germination and maintenance of plant and animal communities The lack of fire for prolonged periods will increase broad leaf woody vegetation and reduce herbaceous plant cover and eventually reduce pine germi nation. The main communities found within flatwoods on the Osceola are Longleaf ( Pin us palustris ) wiregrass (Aristida beyrichiana) and slash pine ( Pinus e lliotti) gallberry ( Illex glabra) palmetto. In the low lying wet areas scattered throughout the f orest are cypress ( Taxodium spp ) ponds. Fire management of the forest consist of a prescribe burn fire frequency of 25 years for most managed compartments with areas that have never been an active part of their prescribed fire program. Fire frequencies are determined based on current forest type and the desired future condition of the forest. The largest struggle fire managers face on this forest is burning large acreages every year given few days that are within specified prescribed fire weather conditions. Forest managers must also deal with smoke management issues associated with being near a major urban area, an interstate highway, and an airport. Conclusion Fire management in the southeast plays a crucial role in maintaining ecosystem health and protecting private and public land. Evaluating fire severity for 11 years of

PAGE 24

24 fire data for the Osceola National Forest has the potential to provide very important information regarding fire frequencies necessary to reduce wildfire risk and the effects of previous fires on subsequent fires. The analysis aims to identify the effects of fire frequency, the time since last fire, and the severity level of past fires on fire behavior using inexpensive remote sensing techniques. This information can then be us ed to identify areas that should be a high priority for prescribed burning and areas that may require immediate attention if threatened by wildfire.

PAGE 25

25 Figure 11 Osceola National Forest in North Florida.

PAGE 26

26 CHAPTER 2 EFFECTS OF FIRE FREQUENCY SIZE AND TIME BETWEEN FIR E EVENTS IN NORTH FLORIDA FLATWO ODS Introduction Prescribed fire is an important management tool in the south eastern United States. In pyrogenic communities that require regular burning for ecosystem health, fo rest managers are working to implement prescribed fire in place of natural wildfire cycles. Florida forest naturally experienced frequent low intensity fires yet high population, forest fragmentation, and dwindling budgets make prescribed fire management increasingly difficult. Sensitive areas (around major highways and roads, airports, and communities) reduce the amount of prescribed burning that can be done safely. Land managers are faced with decisions on how to implement prescribed fire in a manner t hat meets their management objectives and reduces the risk of catastrophic wildfire. At present land manager objectives include: reduce d fu el accumulation to levels that minimize damage from wildfire (Davis et al. 1963), improved wildlife habitat and the conservation of biodiversity (Outcalt et al. 2004). Timing of burning, fire frequency necessary to meet objectives, and the effects of fire are of major concern to land managers Managers could greatly benefit from a quantifiable method of evaluating fire effects that is site specific. This s tudy aims to develop a spatially explicit fire history for the Osceola National Forest that can be used to determine past fire effects and future implications. Forested communities are in a constant state of ch ange. They are continuously recovering from some sort of disturbance. The state of the community is a function of the frequency of disturbance, the time between disturbance events, and the severity of

PAGE 27

27 the disturbance. The main source of disturbance associated with the species composition and abundance of pine flatwoods forests is fire. In pyrogenic communities the frequency, intensity, and the amount of time between disturbances dictate community composition and further impacts the vegetative response t o fire. Pyrogenic species promote and are able to support the spread of fire through the community. Without fire, pyrogenic communities become invaded with fire sensitive species reducing the communities flammability. Fire sensitive species a ffect the way fire spreads through this community. These species dont facilitate fire as well as pyrogenic communities and may promote dangerous fire behavior if fuel loads are high. Measuring Fire Severity Fire severity is a measure of ecological and physical change attributed to fire (Agee 1993; Hardy 2005). Severity is influenced by weather, moisture, time of day, sunlight incidence, slope (Oliveras et al. 2009), species, tree size, succession stage, and pathogens (Cocke et al. 2005). Landscape variability and differences in microclimate contribute to heterogeneous burn patterns and hence patchy severity (Cocke et al. 2005). Major variations in severity are also associated with the location of the fire perimeter (Oliveras et al. 2009). Head fires burn wit h greater flame lengths and intensity than backing fires. As a result, we would expect to see greater severity in areas burned by a head fires than in areas burned by backing fires. Severity levels are characterized by the amount of fuel consumed, fire ef fects on residual vegetation, mortality and changes in moisture levels. Low severity burns are characterized by lightly burned areas where only fine fuels are consumed with minor scorching of trees in the understory (Wagtendonk et al. 2004). Areas of moderate

PAGE 28

28 severity retain some fuels on the forest floor and have crown scorching in midlarge trees with mortality of small trees (Wagtendonk et al. 2004). High severity zones have a high degree of combustion of litter, duff and small logs, mortality of sma llmed trees, and consumption of large tree crown foliage (Wagtendonk et al. 2004). The same fire behavior can result in very different severity effects in over and understory vegetation (Wagtendonk et al. 2004). Large, high severity fires have the potential to remove existing plant biomass, providing ideal habitat for exotic species (Kuezi et al 2008). Responses in soil condition can range from affirmative nutrient availability to loss of nutrients, soil micro organisms, and changes in physical structur e of the soil (Busse et al. 2005). The degree of canopy degeneration due to cambium and crown scorch can severely impact the ability to resprout or seed. P lant recovery following a severe fire can prove nearly impossible for remnant vegetation (White et al.1996). Therefore, severity is important to monitor as its effects on exotic species establishment, soil responses, and regeneration can be significant. To identify the effects of fire, remote sensing techniques can be utilized to model changes that are due to fire. Techniques have been developed to measure the amount of change to a system caused by fire. Normalized Burn Ratios (NBR) were designed to enhance the response of Landsat Thematic Mapper (TM) bands 4 and 7 to fire (Wagtendonk et al. 2004)( 1 ). Mult i temporal image differencing i s then employed to enhance contrast and detection of changes from preand post fire images (Wagtendonk et al. 2004).

PAGE 29

29 1 2 Difference d Normalized Burn Ratios determine the level of severity a 30 meter by 30 meter unit of landscape experienced due to a fire event by measuring the amount of change between a pre and post fire image ( 2 ). Em pl oyed as a radiometric index, d NBRs are directly related to burn severity ( Cocke et al. 2005; Hoy et al. 2008; Godwin 2008; Allen et al. 2008, Wagtendonk et al. 2004 ). Fires within the resolution range of the satellite sensor, 30 meter, can be detected (Wh ite et al.1996). Previous studies have used dNBRs to identify and monitor the effects of fire. Studies have used dNBRs to calibrate severity levels to specific forest types (Cocke et al. 2005; Hoy et al. 2008; Godwin 2008; Allen et al. 2008), compare severity levels between fire events (Collins et al. 2009; Allen et al 2008;), interpret the effects of fuel management techniques on severity levels (Safford et al. 2009; Finney et al. 2005; Safford et al. 2009; Wimberly et al. 2009), and to monitor changes i n vegetation over time (White 1996; Kuenzi et al. 2008) and topographical variations (Holden 2009; Oliveras et al 2009; Duffy et al. 2007). There have also been efforts to relate remotely sensed severity to biophysical attributes and processes. Boer et al. (2008) used dNBRs to define severity as a change in leaf area index (LAI) in a pre and post fire image. Currently, there is a multi agency project, Monitoring Trends in Burn Severity (MTBS), using dNBRs to map burn severity and the perimeters of large wildfires for the entire United States MTBS is using data from 1984 2010 to identify national trends in burn severity to determine the effectiveness of the National Fire Plan and Healthy Forest Restoration Act. Duffy et al. (2007) analyzed the relationship between the area burned

PAGE 30

30 by wildfire and remotely sensed severity level. This study used NBRs for 24 wildfires in Alaska. The study found that the average burn severity increased with the natural logarithm of the area of the wildfire. Larger fires w ere more likely to contain areas that were more severely burned than smaller fires. Epting et al. (2005) evaluated the usefulness of 13 remotely sensed indices of burn severity to find that NBR and dNBR were the most accurate (Escuin et al. 2009), exhibit ing high accuracy when compared with field based sever ity indices in forested areas. To our knowledge, no other study has used dNBRs to model how fire severity from previous fire affects subsequent fire over time. The Osceola National Forest in North Florida presents a unique opportunity to conduct such an analysis. Landsat imagery enables an investigation into the effectiveness of the Osceolas prescribed burning program for reducing wildfire severity, and lends insight into the complex interplay between fire severity, fuels recovery rates, time between fires, and subsequent fire severity Detecting burn severity for fires on the Osceola National Forest is in efforts to anticipate the short and long term effects of severity level and the effects of tim e intervals between fire events, and to predict areas of potential high severity. The burn severity analysis will further improve our understanding of why and where fires burn severely. The following questions fuel this investigation: 1. How does pas t fi re size and severity level a ffect subsequent fire behavior? 2. Is there a relationship between the size of fires and the proportion of area burned at high severity? We hypothesize that fires with a high severity level will have a negative effect on the sev erity level of fire s occurring within three years. High severity fires are expected to have a lower severity level in subsequent fires as long as the second fire is within three

PAGE 31

31 years. Vegetation recovery is not expected to reach prefire conditions withi n this time frame. We also hypothesize that larger fires will have a higher probability of experiencing high severity. Study Site On the Osceola National Forest, thousands of acres are burned every year to reduce fuel levels and manipulate succession st ages. The Osceola is Located in north central Florida (Latitude: 30.34371, Longitude: 82.47322) 40 miles outside the city of Jacksonville. The Osceola consist of pine flatwoods with an overstory of pines on low, flat, sandy, acidic soils; p ine flatwoods have an understory of herbaceous plants, grasses, palmetto, and woody species. This community is fire dependent and requires regular burning for ecosystem health. The main communities found withi n flatwoods on the Osceola are l ongleaf pine ( Pin us palustr is ) wiregrass ( Aristida beyrichiana) and slash pine ( Pinus e lliotti) gallberry ( Illex glabra) saw palmetto ( Serenoa repens ) Fire management on the Osceola and much of Florida is largely dictated by urban encroachment, forest fragmentation, and the challenges associated with smoke management (Wolcott et al. 2007; Duncan et al. 2004). These anthropogenic influences have reduced fire sizes and recurrence, increasing fuel connectivity and load ( Duncan et al. 2004). Prescribed burns are implemented under c onditions that are suitable for vegetation consumption, yet not at levels to cause fire to become unmanageable. Favorable conditions are characterized by cool weather cons istent winds, dry litter, and wet soil (Davis et al. 1963). Prescribed fires are performed under conditions that promote low severity fire though variability in the landscape and weather conditions can cause higher severity levels. Hydric areas burn lightly if at all during prescribed burns

PAGE 32

32 Understory fuel is partially consumed with little consumption of the duff layer (Outcalt et al. 2004). Therefore, wet areas generally carry very heavy fuel volumes and d uring extended drought periods, these areas dry up making them capable of very large, very intense wildfires (Davis et al. 1963; Maliakal et al. 2000 ). The season of prescribed fire is determined by management objectives and site characteristics. Flatwoods are generally burned either during winter (dormant season) or early summer (growing season). Methods Data DNBRs were developed for each fire event greater than 1ac on the Osceola National Forest. Severity levels were defined based on general severity classes provided by the United States Geological Survey (USGS). Severity classes were reclassified and merged into 4 main levels; unburned cells, low severity cells, moderate severity, and high severity ( Table 21 ). To test the hypotheses, two datasets were developed, a time and fire size dataset. The time analysis dataset consisted of consecutive fire events (prescribed and wildfire), that were then separated into time intervals to indic ate the time between fire events. To control for the number of times a pixel burned between fire events pixels had to be unburned previous to the first fire and remain unb urned until the second fire. For each pixel the following information was included in the data set: severity level of fire event 1, severity level of fire event 2, community type (hydric or mesic), forest type (pine, hardwood, and pine/hardwood), and Palm er drought severity index (PDSI) for the year before each event and the year of each event.

PAGE 33

33 PDSI, developed in the 1965 by Wayne Palmer, is the most effective way of determining long term drought (NOAA 2009). This method compares weather conditions to det ermine if they are abnormally dry or abnormally wet compared to historical weather data. The palmer index is based on the supply anddemand concept of the water balance equation, taking into account more than just the precipitation deficit at specific locations. The index uses temperature, rain f all information, and the local available water c ontent of the soil to determine dryness that is standardized to local climate. Standardization allows the index to be compared against different locations and time periods. PDSI uses 0 as n ormal and negative numbers ( 1 to 6) to indicate drought ( Table 22 ) Moderate drought is a 2, severe 3, and extreme drought is 4. To reflect excess rain the index uses positive numbers. A major advantage of this index is that it is standardized to local climate and can be applied to any part of the United States. The fire size dataset was comprised of the 115 wildfires that occurred and were recorded on the Osceola National Forest from 19982008. Fires had to be at least 1 acre to be included in the dataset. For each fire the portion of cells burned in each severity class, the size in acres, season of fire, Forest Service forest type classification ( Figure 2 1 ), soil drainage class ( Figure 22 ), and PDSI values for the year of and before the fire event were recorded. Model Development Logistic regression techniques were utilized to model the probability of experiencing a high severity fire (model 1), the probability of increasing in severity level (model 2), the probability of burning (model 3), and the probability of decreasing in severity (model 4) for the time dataset Logistic regression can be used to measure

PAGE 34

34 binary responses by describing the relationship between one or more independent var iables and the binary response. 3 Responses are coded as [0, 1] to [ ] and is a realization of a random variable that can take on the values of 0 and 1 with probabilities and 1( 3 ). The distribution of is a Bernoulli distribution with the mean ( 4 ) an d variance ( 5 ) depending on the underlying probability 4 5 To make the probability a linear function of a vector of observed covariates the probability is transformed to remove the range restrictions ( 6 ). 6 Logit s map probabilities from [0, 1 ] to [ ] Negative logits represent probabilities below and positive logits represent probabilities above Solving for the probability of success requires exponentiating the logit and calculating the odds of success ( 7 ). 7 Maximum likelihood methods were used for parameter estimation. With this approach, parameters are estimated iteratively until parameters that maximize the log of

PAGE 35

35 the likelihood are obtained. Goodness of fit statistics, Akaikes information criterion (AIC) and Bayesian information criterion ( BIC ) were used to compare competing models. AIC is a statistic that is used to rank different models based on how close fitted values are to true values ( 8 ) (Littell et al. 2006) 8 Where: k is the number of parameters in the statistical model and L is the maximized value of the likelihood function for the estimated model ( 8 ). Like AIC, BIC was used to rank models with a different numbers of parameters to avoid increasing the likelihood by over fitting the model (Littell et al. 2006). 9 Where: n is the sample size. Unexplained variation in the dependent variable and the number of covariates increases the BIC and AIC values ( 9 ). For both AIC and BIC, the lowest score i ndicates the best model. T he ratio of the Pearson chi square to its degrees of freedom is used t o determine if the model displays lack of fit. Values closer to 1 indicate that the model fits the data well (Littell et al. 2006). To address the assumption of independence among observations, a generalized linear mixed model was used using the SAS procedure PROC GLIMMIX. Correlation among responses is incorporated into the model by adding random components to the linear predictor. To account for the correlation among responses, r andom residuals were modeled. Raster data is spatially correlated due to the adjacency of pixels Although it would have been more effective to model the spatial correlation directly, without the aid

PAGE 36

3 6 of a super computer this option is infeasible. Th e GLIMMIX procedure can also make use of several predictor variables that may be either numerical or categorical (Littell et al. 2006). In this analysis we evaluated the probability of experiencing (1) moderate to high severity, (2) increased severity level, (3) burning, and (4) decreased severity between the first fire and the sec ond fire at different time intervals Variables used in the 4 models include: the severity level of the first fire event ( unburned, low severity moderate severity, and high severity ), th e time interval between fires (12 3 4 5 6, 7 8 and 910 years ) ( Table 23 ) the type of fire in the second fire event (wild or prescribed) and the PDSI for the year before and the year of each fire. The logit of the probability was modeled as 10 where: is the intercept, (for =1, 2, 3, 4) is the net effect of the i th severity level for the first fire, (for j = 1, 2, 3, 4, 5 ) is the net effect of the j th tim e intervals between fire events, (for k = 1,2 ) is the effect of the type of fire, is the effect of PDSI for the year prior to fire 1, is the PDSI for the year of fire 1, is the PDSI for the year before fire 2 is the PDSI for the year of fire 2 and ijk is the random error ( 10 ). Final model covariates were indicated by parameters that were significant based on the Wald chi square statistic and the model with the lowest AIC and BIC value. Interactions between all parameters were also considered. Nonsignificant parameters were removed from the full model one at a time. To test for differences among categorical levels least square means were produced and differences were tested.

PAGE 37

37 Logistic regression was also used to examine the probability of burning at high severity for each fire size class for the fire size data set. Variables used in this model include: season of fire ( winter spring summer, and fall ), soil drainage class (19), Forest type ( pine, hardwood, pine/hardwood, and hardwood/pine), and PDSI for the year before and the year of each fire ( Table 2 4 ). Model selection was determined by goodness of fit statistics AIC and BIC. A backward select ion method was used to determ ine the final model; first all parameters were included within the model and then parameters were removed one by one based on the Wald chi square statistic. Results Data The time data set is composed of 484,715 pixels. The majority of thes e pixels burned as prescribed fires in the second fire (341,143). Over all years for fire 2 there were higher percentages of cells experiencing low severity ( 40% ) and high severity (~10%) ( Figure 21 ). The proportion of cell s b urned in each severity class is shown by time ( Figure 22 Figure 23 Figure 24 Figure 25 Figure 26 ). In fire 1 there w as also a higher percentage of cells experiencing low severity (51%), while ~4% experienced high severity. The largest difference bet ween the fires is the portion of cells in the low severity category, a 10% increase between fire 1 and fire 2, and the dif ference in cells in the high severity category, 5.6%. The major difference between the distributions of cells among severity levels is that unburned cells in fire 1 moved to a higher severity level. Burned pixels were not evenly distributed over time. T o reduce the amount of variation between years, categories were created ( Table 23 ) Fires with 5 6 years

PAGE 38

38 between events had the highest percentage of cells that burned at high severity in the second fire, with 53% for wild fires and 24.9% for prescribed fires burning at high severity ( Figure 24 ) Time interval 34 years and 78 years had the highest portion of cells remain ing unburned in the second fire event; ~70.8% and 49.4% remained unburned 34 years and 78 years after wildfire, respectively 51.4% and 81.6% remained unburned 34 years and 78 years after prescribed fires, respectively ( Figure 2 3 Fig ure 25 ) In the second fire, w ildfires had a much larger portion of the cells in the unburned and high severity category, 44% and 17% respectively, versus pre scribed fires Overall, there was very little change in the proportion of pixels burned in each severity class between fire 1 and fire 2 ignoring time between events. Until time between fires reaches 56 years, prescribed fires decease in severity in the second fire more than they increase in severity. After 5 6 years more cells increased in severity than cells decrease d in severity. Wildfires had a higher portion of the pixels decrease in severity over all time intervals except time interval 56 years where 77% of the cells increased in severity between the first and second fire. Probability Modeling Probability of e xperiencing moderate to high s everity d uring a f ire Severity level ( ) at the first fire, time intervals between the first and second fire ( ), type of fire ( ), and the interaction between severit y level and time interval were significant predictors of the probability of experiencing high severity fire ( 11) 11

PAGE 39

39 The effects of PDSI wer e not sig nificant parameters. The overall model was significant and the parameters we re significant based on the Wald chi square statistic ( Table 25 ). Moderate and high severity levels were merged for this analysis to avoid convergence issues associated with low counts in the high severity category The ratio of the Pearson chi square statistic to its degrees of freedom is approximately 1 indicating good fit of the model to the data. The probability of experiencing a moderate to high severity fire was higher for wildfires than prescribed fires. Overall the probability of bur ning at a moderate to high severity class was low for all severity classes in fire 1 for prescribed fires ( Figure 29 ) The probability o f moderate to high severity was high for wildfires when the time interval was 5 6 years between fires ( Figure 210). Areas with moderate and high severity in the first fire had the highest probability of high severity fire for bo th wild and prescribed fires (Figure 210 Figure 29 ). At 1 2 years between fire events the probability of moderate to high severity fire was the lowest ( Figure 29 ). The hig hest probability by time interval was at 56 years between fires, followed by 78, th e n 9 10 years. For wildfires, 3 4 and 710 years between events yielded very low (< 1%) probability of moderate to high severity ( Figure 210). T ime interv al 5 6 years ha d very high (>70%) probabilities of moderate to high severity for wildfire ( Figure 210). Probability of i ncreasing in s everity in s ubsequent f ires Model 2 estimates the probability of severity level incre asing from the first fire to the second fire 12

PAGE 40

40 The model includes the effects of severity level at fire 1 ( ), time interval between fire events ( ), fire type ( ), and PDSI value for the year prior to and the year of each fire ( and ) for the k th measurement in the ith severity level and the jth time interval ( 12). The overall model was si gnificant and the 8 parameters were significant based on th eir Wald chi square statistic s ( Table 26 ). The ratio of the Pearson chi square statistic to its degrees of freedom was close to 1(0.99) indicating good model fit to the data. The probability of increasing in severity was modeled for all ev ents where fire could increase ( where the severity level in fire 1 was less than 4). As expected, the model shows that the probabi lity of increasing in severity wa s h ighest for unburned cells, then low severity pixels and lowest for medium sever ity over all time intervals ( Figure 211, Figure 212, Figure 213 ). For all severity levels the probabil ity of increasing in severity was highest at 56 and 9 10 years between fire events ( Figure 212, Figure 213 ). The probability of increasing in severity level was higher for wildfires than for prescribed fires and s howed the same decreasing trend with increased severity both fire types. Time Interval 78 years was surprisingly low for both wild and prescribed fires. Probability of burning d uring a f ire Model 3 examines the probability of burning ( 13) 13 The severity level of fire 1( ), time interval ( ), fire type, and PDSI for the year prior to fire 1 and 2 and the PDSI for the year of fire 1 ( and respectively) were all

PAGE 41

41 included within the final model( 13) The model was significant and all parameters were significant based on their Wald chi square statistics ( Table 27 ). The ratio of the P earson chi square statistic to its degrees of freedom was close to 1(1.02) indicating good fit of the model to the data. The probability of burning wa s approximately the same for each severity class ( Figure 215, Figure 216). Areas that had been burned by prescribed fires had a higher probability of burning than areas that had been burned by wildfires for all time intervals and severity levels ( Figure 215 ). Over time the pr obability of burning peaked (~ 809 0 % depending on severity level) at 56 years and, was the lowest for 12 and 78 years between fires. Probability of d ecreasing in s everity in s ubsequent f ires Model 4 predicts the probability of fire severity decreasing i n the second fire ( 14) 14 The severity level of the first fire ( ), time interval between fire1 and fire 2 ( ), fire type ( ) PDSI value for the year prior to and the year of both fire 1 and fire 2 ( and ) and the interaction betw een s everity level and fire type were kept in the final model ( 14) The model was significant and the parameters were significant based on the ir Wald chi square statistic ( Table 28 ). The ratio of the Pearson chi square statistic to its degre es of free dom wa s close to 1(1.03) indicating good model fit. The probability of decreasing fire severity was modeled for all severity classes except unburned. Over all time intervals and severity levels the probability of decreasing was lower for wildfires than f or prescribed fires except at the low severity

PAGE 42

42 level ( Figure 217). At the low severity level, the probability of fire severity level decreasing for wildfires was the lowest and the probability increased with increased severity l evel. Both wild and prescribed fires show a reduced probability of decreasing fire severity level when fires were 5 6 years apart. The probability of decreasing fire severity level increased as the severity level increased for both fire types ( Figure 218, Figure 219). Fire size analysis A useful model could not be found for the probability of burning at high severity using the fire size dataset. Fire size class was not a significant indicator of the p robability of experiencing a high severity fire The data indicated that larger fires had a higher portion of their pixels in the high severity size class so it was expected that larger fires would have a higher probability of experiencing high severity f ire The best model of the probability of high severity fire based on goodness of fit statistics i ncluded only fire size class yet the model yielded no significant relationship between fire size and the probability of experiencing high severity. The model parameters were not significant based on their Wald chi square statistic s ( Table 29 ). The ratio of the Pearson chi square statistic to its degrees of freed om was equal to 1 indicating good model fit. Discussion Probability of Experiencing Moderate to High Severity During a Fire The probability of experiencing high severity fire has important implications for fire effects and the degree to which wildfires are being mitigated. Based on the severity level of the first fire event and time between events, this also has the capacity to identify target intervals between fires. The probability of experiencing moderate to high severity in the second fire was highest for time interval 56 years for all severity levels of the first

PAGE 43

43 fire and both fire types ( Figure 210). This indicates that by this point, vegetation has reached prefire conditions regardless of the severity level it burned at in the first fire. Davis et al. ( 1963) collected ground data from 380 fires in Florida and Georgia from 1955 to 1958 to evaluate prescribe fire effectiveness in reducing fire size and intensity. This analysis found that fuel loads must be less than 5 years to adequately reduce the occurrence of catastrophic wildfire on the Osceola National Forest. Vegetation is able to recover quickly due to fast growing and resprouting species further fueled by an increase in nutrient availability as a result of fire. Lemon (1949) found that the maximum amount of litter is approached at 5 years and, by 8 years vegetation returned to preburn status This study used permanent plots on the Alapaha Experimental Range (Georgia) to monitor changes in vegetation following prescribed fire. At 1 2 years between fires, wild fires have a higher p robability of moderate to high severity fire compared to longer time intervals where the probability is nearly 0. Factors beyond the length of time between fire events may be the cause for the relationship between short time intervals and the probability of moderate to high severity for wildfires. Weather conditions and errors associated with the amount of biomass present in the prefire image may be affecting this. We would expect the probability of moderate to high severity fire to increase as the tim e interval increased y et, the lack o f an increase over time suggests that vegetation that isnt burning as often on the Osceola National Forest remains unburned (Maliakal et al. 2000). This may be explained by the change in flammability associated with nat ural succession in the pine flatwoods forest type. In long unburned stands, vegetation composition is shifting away from flammable saw palmetto /gallberry complex with pine overstory towards less flammable, higher

PAGE 44

44 moisturecontent, hardwood dominance. Pr eviously u nburned cells likely remained unburned in subsequent fires due to fuel that was not available to burn and a combination of weather conditions As expected, t he probability of high severity fire is higher for wildfires than for prescribed fires. Prescribed fires are performed under optimal conditions where the chance of mortality of fire adapted species such as longleaf and slash pi n e, saw palmetto and gallberry, is small. In contrast, most wildfires greater than 1 ac in size occurred during opti mal fire spread conditions with high winds, lower relative humidities and dry fuels. Regardless of the severity class of the first fire, the probability of moderate to high severity in the second fire was low for prescribed fires (<30%) This suggests one of two things: either that regardless of the severity of the prescribed burn, it is mitigating severity in subsequent fires; or the areas that are prescribed burned are repeatedly prescribed burned, so that the second fire is typically of lower severi ty. The moderate and high severity class had the highest probability of moderate to high severity for both fire types (prescribed and wildfires) Within this dataset areas that have a history of burning at a moderate to high severity often continue the trend regardless of the amount of time since the last fire event or the type of fire. This can be due to a number of effects such as t he type of fuel at the site, delayed mortality inflating the severity signal over time, or the continued burning resulti ng in reduced vegetation vigor which appears via the dNBR analysis to be higher severity This may then result in a bias in the high severity class towards areas with less vegetation and ground fuels The reduction in fuel may promote more complete cons umption resulting in an increase in severity.

PAGE 45

45 Variations in the landscape may also be a major cause for unexpected relationships regarding time intervals between fire events In hydric areas, if fuel availability is reduced due to high moisture contents distortions in the relationship between time interval between fires and the probability of moderate to high severity may occur. Even though these areas burned lightly in previous fires and time intervals were long, the probability of moderate to high severity fire wa s still low. Variations in the landscape adds additional variation to fire effects, prescribed fire planning, and fire suppression efforts. In the future, adding depth to water table, dominant understory vegetation, and dominant overstory veg etation may help to sort out unexpected relationships between fire effects and time. P robability of Increasing in Severity The model predicting the probability of fires increasing in severity gave similar results to the previous model (probability of exper iencing high severity) for both fire types The probability of increasing in severity was higher for wildfire than for prescribed fire. As expected, the probability of increasing in severity was the highest for unburned cells and increased as the time int erval increased ( Figure 212) for all time intervals except 34 and 78 years where the probability of increasing was close to 0 Most prescribed fires on the Osceola are maintained at a 3 4year cycle. Therefore, most fires that occur at this time interval were prescribed fires. Fires occurring with 7 8 years between events consistently had a low er than expected probability of having higher severity over all severity levels. V egetation that has remained unburned for 7 8 years, in this dataset, may not be available to burn as readily as vegetation with time between events <6 years due to fuel moisture content and changes in species composition.

PAGE 46

46 Without fire, fireadapted species are replaced by broadleaf woody species that dont facilitate the spread of fire as well as fire adapted species. The time interval 5 6 years was identified once again, this time as being associated with the highest pr obability of increasing fire severity, followed very closely by 9 10 years This time i nterval (5 6 years) may be the point at which vegetation has recovered from previous fire events to a degr ee where the next fire event has enough fuel available to burn and at increased severity levels. Lavoie et al. (2010) found that living biomass recov ered within 3 years of a fire event and predicted that fuel loads would return to pre fire conditions by 58 years in a similar pine flatwoods forest also in North Florida. This suggest that time between fire events should not exceed 4 years. Land managers should consider fire return intervals that are between 14 years in pine flatwoods to mitigate moderate to high severity fire and increasing severity levels in subsequent fires. Probability of Burning Th e probability of burning followed the same trend f or each severity class and wa s highest for the time interval 56 years for both wild and prescribed fires The p robability of burning was low when fires were 12 years apart and increased with time interval Sho rt time intervals between fires affect the way fire spreads due to the lack of continuous combustible material to maintain fire spread Once again 78 years between fires had a lower probability of burning than expected indicating vegetation that had been burning at this interval has reduced avail ability. The probability of burning was higher for prescribed fires than for wildfires. Prescribed fires are performed under conditions and in areas that facilitate understory vegetation and litter consumption

PAGE 47

47 whereas w ildfires often result in incomplete patchy burning of the under and over story species due to rapid changes in climatic conditions and vegetation availability. Probability of Decreasing in Severity As severity levels increased, the probability of subsequent fires decreasing in severity leve l increased. At all severity levels the probability of decreasing in severity was lowest for fires occurring 56 years apart followed by 910 years apart. By 56 years between fires we would expect fuel levels to recover to a point where wildfire risk is high and past fires no longer have an effect on subsequent fires. This model supports the hypothesis that f ires with moderate to high s everity levels have a negative effect on severity level of fires occurring within 3 years. Land managers should consider 1 4 year fire frequencies for pine flatwoods to reduce the risk of moderate to high severity prescribed and wildfires. This evidence strongly suggests that beyond a five year interval, severity will be higher than what the majority of management objecti ves seek. Areas previously burned by low severity fire had a high probability of remaining unburned in the next fire event if they were burned in a wildfire. This relationship indicates that during a wildfire, land that previously burned at a low severity level may have had vegetation that was unavailable to burn during the subsequent fire. Because the land previously burned at a low severity level, there should be enough vegetation there to carry higher severity fires should conditions be suitable. For m oderate and high severity levels first fires the probability of decreasing severity was higher for prescribed fires. So, areas that previously burned at moderate and high severity levels had a higher probability of decreasing in severity level if they were prescribed burned.

PAGE 48

48 Conclusion Fire history for the Osceola National Forest was effectively modeled to determine past trends in fire effects and future implications of fire management decisions. The models also provide valuable information regarding t he influence of severity level and time between events for both prescribed and wildfires The data shows that vegetation on this forest recover s quickly following fire and that fuel loads reach levels where they are available to burn within 1 year and are at pre fire conditions by 5 years. The data also identifies areas that are within fire perimeters and are consistently remaining unburned Likely hydric communities land that has gone unburned for 78 years show ed signs that the fuel just wasnt available t o carry high severity fire from 19982008. Hydric communities may require extreme drought condition to reduce moisture levels. All four models identified the time interval 56 years as a point where the effect s of previous fires ha d little to no effe ct on subsequent fires. At this point, the probability of high severity fire, increasing severity level in subsequent fire, and the likelihood of burning at all is highest. This is also a point where the probabi lity of decreasing severity in subsequent f ires was lowest. These findings indicate that time between fir es should be kept below 5 years Results from this work are supported by other studies suggesting that the use of remote sensing techniques sufficiently represent relationships between time si nce last fire and the severity level of past fire events on subsequent fire behavior The relationship involving time between fire events and fire severity are influenced by variations in the landscape. F ire effects are influenced by the type of vegetation and the availability of that vegetation. Land managers must consider vegetation recovery

PAGE 49

49 and availability differences by both forest and community types to determine the risk of the high severity fire. Although hydric communities are often unavailable to burn, fuel loads in these communities are high and must be managed. Land managers may consider other alternatives to mitigate high fuel loads in hydric communities. Although previous studies have found a relationship between fire size and high severity (Duffy et al 2007) a useful model could not be found for the probability of high severity fire using the fire size dataset. The data indicated that larger fires had a higher portion of area in the high severity size class yet this relationship was not si gnificant. Out of 115 wildfires included within this dataset, few fires were large. Most fires were less than 50 ac in size (93 fires) Although large fires had a higher portion of their cells in high the high severity class, the vast majority of the ar ea was burned by moderate and low severity fire. A larger dataset may be required to capture the relationship between fire size and high severity. Errors introduced by severity level classification may also influence the models. General severity level classifications were used and further generalized from seven levels to four. In the future, severity levels should be calibrated to pine flatwood forest of the southeastern U.S for the best results. Also, delineation of fire perimeters is not exact and may introduce error into the unburned and low severity levels.

PAGE 50

50 Table 21 Severity class descriptions for the time analysis and fire size datasets. Severity Class Description Reclassified Severity Classes 1 Unburned within a fire perimeter (DNBR 100 99) 1 Unburned within a fire perimeter (DNBR 100 99) 3 Enhanced Regrowth/ Low Severity (DNBR 500 101, 100 269) 2 Low Severity (DNBR 500 101, 100 269) 4 Low Med Severity (DNBR 270 439) 3 Med Severity (DNBR 270 439 ) 5 Med High Severity (DNBR 440 659) 4 High Severity (DNBR 440 1300) 6 High Severity (DNBR 660 1300) Table 22 Palmer Drought Severity Index values and descriptions Palmer Drought Severity Index 4.0 or more exceptional ly wet 3.0 to 3.99 very wet 2.0 to 2.99 moderately wet 1.0 to 1.99 slightly wet 0.5 to 0.99 incipient wet spell 0.49 to 0.49 near normal 0.5 to 0.99 incipient dry spell 1.0 to 1.99 mild drought 2.0 to 2.99 moderate drought 3.0 to 3.99 sev ere drought 4.0 or less extreme drought Table 2 3 Time interval classification for time analysis dataset. Time Interval (years) Code Observations 1 2 1 115,273 3 4 2 136,254 5 6 3 131,409 7 8 4 79,886 9 10 5 21,893

PAGE 51

51 T able 24 Covariate classification s for fire size model Variable Class Code Fire Size Class 1 15ac 1 16 50ac 2 50 150ac 3 150 500ac 4 >500ac 5 Season Spring 1 Summer 2 Fall 3 Winter 4 Forest Type Pine 1 Hardw ood 2 Pine Hardwood 3 Hardwood Pine 4 Soil Drainage Somewhat poorly drained 1 Somewhat poorly drained 2 Somewhat very poorly drained 3 Poorly Drained 4 Poorly very poorly drained 5 very poorly drained 6 standing water poorly drained 7

PAGE 52

52 Table 25 Parameter estimates and their respective standard errors and pvalues for the model predicting the probability of high severity fire Parameter Categories Estimate Std. Error P value Intercept 3.8923 0.0645 3 <0.0001 Severity of fire 1 Unburned 0.1251 0.01703 <0.0001 Low 0.4520 0.01716 <0.0001 Med High 0 Time between fires 1 2 years 1.7801 0.1018 <0.0001 3 4 years 0.5907 0.07174 <0.0001 5 6 years 3.0129 0.06509 <0. 0001 7 8 years 9 10 years 1.4293 0 0.06713 <0.0001 Type of fire Wildfires 3.0456 0.5039 <0.0001 Prescribed Fires 0 Time between fires* Type of Fire 1 2 years Wildfire 6.6121 0.5102 0.0034 1 2 years Prescribed 0 3 4 years Wildfire 2.2647 0.5190 <0.0001 3 4 years Prescribed 0 5 6 years Wildfire 4.3310 0.5040 <0.0001 5 6 years Prescribed 0 7 8 years Wildfire 0.2111 0.5137 0.6812 7 8 years Prescribed 0 9 10 yea rs Wildfire 0 9 10 years Prescribed 0 Residual 0.9981

PAGE 53

53 Table 26 Parameter estimates and their respective standard errors and pvalues for the model predicting the probability of increased severity in the second fire. Parameter Categories Estimate Std. Error P value Intercept 1.3176 0.02894 <0.0001 Severity of fire 1 Unburned 3.2190 0.01976 <0.0001 Low 0.9239 0.01917 <0.0001 Med 0 Time between fires 1 2 years 1.9621 0. 02665 <0.0001 3 4 years 1.1353 0.01973 <0.0001 5 6 years 0.1612 0.02780 <0.0001 7 8 years 9 10 years 1.8658 0 0.02556 <0.0001 Type of Fire Wildfires 0.1889 0.009466 <0.0001 Prescribed Fires 0 PDSI (year before Fi re 1) 0.04698 0.007130 <0.0001 PDSI (year of Fire 1) 0.09065 0.005490 <0.0001 PDSI (year before Fire 2) 0.4761 0.005154 <0.0001 Residu al 0.9879

PAGE 54

54 Table 27 Parameter estimates and their respective standard errors and pvalues for the model predicting the probability of burning. Parameter Categories Estimate Std. Error P value Intercept 2.2829 0.02519 <0.0001 Severity of fire 1 Unburned 0.8139 0.01715 <0.0001 Low 0.6254 0.01654 <0.0 001 Med 0.5244 0.01985 <0.0001 High 0 Time between fires 1 2 years 0.8888 0.01790 <0.0001 3 4 years 0.8372 0.01525 <0.0001 5 6 years 0.6326 0.02318 <0.0001 7 8 years 9 10 years 1.0399 0 0.01969 <0.0001 Typ e of Fire Wildfires 0.4576 0.007613 <0.0001 Prescribed fires 0 PDSI (year before Fire 1) 0.2168 0.005665 <0.0001 PDSI (year of Fire 1) 0.2542 0.004379 <0.0001 PDSI (year before Fire 2) 0.2874 0.003676 <0.0001 Residual 1.02 49

PAGE 55

55 Table 28 Parameter estimates and their respective standard errors and pvalues for the model predicting probability of decreased severity in the second fire. Parameter Categories Estimate Std. Error P value Inter cept 2.0265 0.03806 <0.0001 Severity of fire 1 Low 3.6857 0.02957 <0.0001 Medium 1.5663 0.03233 <0.0001 High 0 Time between fires 1 2 years 0.6019 0.02928 <0.0001 3 4 years 0.7416 0.02167 <0.0001 5 6 years 0.712 1 0.03419 <0.0001 7 8 years 9 10 years 0.6949 0 0.02871 <0.0001 Type of Fire Wildfires 1.9317 0.04718 <0.0001 Prescribed Fires 0 PDSI (year before Fire 1) 0.6048 0.009813 <0.0001 PDSI (year of Fire 1) 0.4611 0.00704 6 <0.0001 PDSI (year before Fire 2) 0.2332 0.006323 <0.0001 PDSI (year of Fire 2) 0.01577 0.003421 <0.0001 Severity of fire 1* Type of Fire Low Wildfire 2.2143 0.04818 <0.0001 Low Prescribed 0 Medium Wildfire 1.2070 0.0 5505 <0.0001 Medium Prescribed 0 High Wildfire 0 <0.0001 High Prescribed 0 Residual 1.0264

PAGE 56

56 Table 29 Parameter estimates and their respective standard errors and pvalues for model predicting the probability of high severity fire by fire size class Parameter Categories Estimate Std. Error P value Intercept Fire size class 1.9994 0.09005 <0.0001 1 1.2949 2.8899 0.6550 2 0.2208 1.2268 0.8575 3 0.2040 1.2681 0.8725 4 0.8258 0.4992 0.1009 5 0

PAGE 57

57 Figure 2 1 USFS forest type classifications.

PAGE 58

58 Figure 2 2 NRCS soil drainage class classification.

PAGE 59

59 Figure 21 Portion of p ixels b urned in each severity level in fire 1 and fire 2.

PAGE 60

60 Figure 22 Distribution of pixels among severity classes with 12 years between fire events separated by type of fire and the probability of moving from one severity class to the next.

PAGE 61

61 Figure 23 Distribution of pixels among severity classes with 34 years between fire events separated by type of fire and the probability of moving from one severity class to the next.

PAGE 62

62 Figure 24 Distribution of pixels among severity classes with 56 years between fire events separated by type of fire and the probability of moving from one severity class to the next.

PAGE 63

63 Figure 25 Distribution of pixels among severity classes with 7 8 years between fire events separated by type of fire and the probability of moving from one severity class to the next.

PAGE 64

64 Figure 26 Distribution of pixels among severity classes with 9 10 years between fire events separated by type of fire and the probability of moving from one severity class to the next.

PAGE 65

65 Figure 27 Percentage of pixels increasing and decreasing in sev erity level by time and type of fire.

PAGE 66

66 Figure 2 3 Fire size compared with Palmer drought severity index between 1996 and 2010 This suggests large fire events are associated with prolonged droughts.

PAGE 67

67 Figure 2 4 Percentage of pixels burned at each severity class by fire size class. Larger fires have a higher portion of their cells in the high severity class.

PAGE 68

68 Figure 28 Probability of experiencing high severity in fire 2 by time interval and fire type.

PAGE 69

69 Figure 29 Probabili ty of experiencing high severity in fire 2 by severity level of fire 1 and time interval for prescribed fires.

PAGE 70

70 Figure 210. Probability of experiencing high severity in fire 2 by severity level of fire 1 and time interval for wildfires.

PAGE 71

71 Figure 211. Probability of increasing fire severity by time interval and fire type.

PAGE 72

72 Figure 212. Probability of increasing fire severity by sever ity level of the last fire and time between fires for wildfires.

PAGE 73

73 Figure 213. Probability of increasing fire severity by severity level of the last fire and time between fires for prescribed fires

PAGE 74

74 Figure 214. Probability of burning by time interval, fire type, and fire severity leve l

PAGE 75

75 Figure 215. Probability of burning by fire severity level and time interval for wildfires.

PAGE 76

76 Figure 216. Probability of burning by fire severity level and time interval for prescribed fires.

PAGE 77

77 Figure 217. Probability of decreasing in severity level by time interval and severity level of fire 1

PAGE 78

78 Figure 218. Probability of decreasing in severity by severity level of fire 1 and time interval for wildfires.

PAGE 79

79 Figure 219. Probability of decreasing in severity by severity level of fire 1 and time interval for prescribed fires.

PAGE 80

80 CHAPTER 3 PREDICTING FIRE SEVE RITY IN PINE FLATWOO DS USING DIFFERENCE D NORMALIZED BURN RATI OS TO RECORD FIRE EV ENTS Introduction Fire severity can be measured using remote sensing techniques to monitor changes in fire regimes over time and to ma p fire history Fire severity is a measure of ecological and physical change attributed t o fire (Agee 1993; Hardy 2005) and is influenced by both biotic and abiotic factors. Severity is altered by weather, moisture, t ime of day, sunlight incidence (Oliver as et al. 2009), species, tree size, succession stage, and pathogens (Cocke et al. 2005). Severity is important to monitor as it can have a significant effect on exotic species est ablishment, soil responses, regeneration, and ecosystem health. Measuring Fire Severity Normalized burn ratios (NBR) use short wave inferred bands, from Landsat Thematic Mapper (TM) bands 4 and 7 (Wagtendonk et al. 2004), to detect the severity level of a burned area ( 1 ). At this spectrum, differences in reflectance due to fire induced changes in soil moisture, canopy cover, biomass and soil chemical composition is captured and compared to pre fire conditions to determine the level of change or severit y that occurred as a result of the fire event. 1 Diff erence normalized burn ratios (dNBR) capture the degree of change that can be attributed to fire by using a preand post fire image ( 2 ).

PAGE 81

81 2 The mapping methodology was originally developed and tested by the USGS Northern Rocky Mountain Science Center (NRMSC). Em ployed as a radiometric index, d NBRs are directly related to burn severity (Wagtendonk et al. 2004) and as long as the fire is within the resolution rang e of the satellite sensor, 30m, it is detectable (White et al.1996). Combined with existing information about fire locations and perimeters, fire histories can be mapped to monitor trends in severity over time, frequency of fire, and time since last fire on a pixel level. This detailed dataset can then be used to make inferences about future fires. Using remote sensing data to determine specific and effective return intervals can have serious implications for land managers. Currently land managers are us ing indiscriminate frequencies that range anywhere from 110 years between fire events for pine flatwoods management. Depending on site characteristics frequencies may require modification for more or less productive sites. With a detailed fire history, land managers can identify areas that require immediate attention to both mitigate the risk of wildfire a nd prevent successional change. Previous s tudies have used dNBRs to calibrate severity levels to specific forest types (Cocke et al. 2005; Hoy et al. 2008; Godwin 2008), compare severity levels between fire events (Collins et al. 2009; Allen et al 2008;), interpret the effects of fuel management techniques on severity levels (Safford et al. 2009), and to monitor changes in vegetation over time (White 1996; Kuenzi et al. 2008) and topographical variations (Holden 2009; Oliveras et al 2009). In the United States there is currently a multi agency project, Monitoring Trends in Burn Severity (MTBS), which is using d NBRs to map burn severity and the perimet ers of large wildfires in the entire United States.

PAGE 82

82 MTBS is using data from 1984 2010 to identify national trends in burn severity to determine the effectiveness of the National Fire Plan and Healthy Forest Restoration Act. As o f now, no other study has used dNBRs to model the fire history of an entire forest. This study uses all prescribed and documented wildfires (greater than 1 ac) to create a complete fire history for the entire Osceola National Forest usi ng dNBRs for each fire event. The objective o f this analysis is to determine the risk of high severity prescribed fire and t he probability of m oderate to high severity wildfires using data from 19982008. The probability high severity prescribed fire is important for monitoring fire effects and how the se effects meet management objectives. Prescribed burns are implemented under optimal circumstances where conditions are suitable for vegetation consumption but not at levels to cause fire to become unmanageable and cause high mortality of overstory species. Optimally, prescribed fires should cause low levels of mortality in overstory species and understory fuel should be partially consumed with little consumption of the duff layer (Outcalt et al. 2004). High severity fires are characterized by complete combustion of most of the litter layer duff and small logs, with mortality of smallmed trees, and consumption of large tree crowns (Wagtendonk et al. 2004). During prescribed fires, land managers aim for low moderate severity fire. Considering wildfi res, fire behavior that causes moderate to high severity levels may cause extensive challenges in suppression efforts and high mortality rates. Therefore, a model predicting the probability of moderate to high severity fire would be appropriate as low sev erity wildfires would be preferred from a suppression and salvage stand point. We hypothesize that the number of times a pixel burns will

PAGE 83

83 influence its probability of burning, at high severity for prescribed fires and moderatehigh severity for wildfires if burned within 5 years and we also expect m esic communities to have a higher probability of burning at high severity than hydric communities for prescribed fires and the opposite for wildfires. Study Site The Osceola National forest located in north c entral Florida (Latitude: 30. 34371, Longitude: 82.47322) about 40 miles west of the city of Jacksonville ( Figure 11 ). T he majority of the forest is pine flatwoods with scattered areas of cypress and bay swamps. With an overstory of pines on low, flat, sandy, acidic soils; pine flatwoods have an understory of herbaceous plants, grasses, palmetto, and woody species Flatwoods communities are fire dependent and require regular burning for regeneration of fire adapted species and ecosystem health On the Osceola National Forest, communities include Longleaf pine ( Pin us pa lustris ) wiregrass ( Aristida beyrichiana) and slash pine ( Pinus e lliotti) gallberry ( Illex glabra) saw palmetto ( Serenoa repens ) Cypress ponds ( Taxodium spp ) are found scattered throughout the forest in low lying wet areas. In this fire maintained community the lack of fire for prolonged periods will increase broad leaf woody vegetation and reduce herbaceous plant cover and eventually reduce pine germination Fire suppression would cause significant changes in species composition that would then lead to changes in ecological processes within this system. Fire management on the Osceola National forest is quite active. The majority of the forest is prescribed burned at a frequency of every 2 5 years There are also sensitive areas within the forest that are not currently and actively managed by fire. Fire regimes are determined on a compartment level based on the current forest type and the desired future c o ndition of the compartment. On this forest, fire managers are faced

PAGE 84

84 with burning large acreages annually with few days that are within prescribed fire weather conditions. Sensitive areas near the forest like Lake City Municipal Airport, I 10, and the Cit y of Jacksonville, provide addition constraints for fire managers. Methods Image Analysis Landsat 7 ETM imagery was provided by the United States Geological S urvey (USGS). The USGS provide d geometrically and radiometerically corrected NBRs. Geometric cor rections involved removing distortions from imagery caused by the sensor geometry. The geocorrection process consist ed of two steps: (1) rectification, and (2) resampling. Geo r ectification was performed in order to relate pixels to their exact ground location and resampling determined the pixel values. Radiometric corrections involve d the removal of atmospheric noise to accurately represent ground conditions. In this process the pixel values were modified to account for noise produced by atmospheric inter ference, sunsensor geometry, and the sensor itself. Following geometric and radiometric corrections, pixel values were in digital numbers. Digital numbers are a measure of at satellite radiance. Finally, digital numbers are converted to at satellite reflectance. NBRs we re derived from a ratio of bands 4 and 7 (1) that has been corrected to at satellite reflectance and range from ~ 1000 to 1000. Pre processed NBRs we re provided by the USGS Global Visualization Viewer ( http://glovis.usgs.gov ). Data A fire history dataset was created using the d NBRs for each fire event (prescribed and wildfires). D NBRs were created for each event using images closest to the date of the fire event. General severity levels provided by the Unit ed States Geological Survey

PAGE 85

85 (USGS) were reclassified to 4 severity levels; unburned, low severity, moderate severity, and high severity ( Table 21 ) To account for variation due to phenology and surface moisture conditions in the pre and post fire images, the mean value of unchanged pixels were subtracted from the d NBR (Collins et al. 2009). DNBRs were then clipped using fire perimeter shape files provided by the U S Forest Service. Next, fires were merged to create an image that represented fire events for each year (Appendix A Table 31 ). The layers created for each year were finally used to calculate model covariates. (1) Time since last fire is the number of years since last fire ( Figure 31 ). (2) Frequency is the number of times a pixel has burned within the dataset ( Figure 3 2 ). (3) Latest severity level is the severity level of the last fire event ( Figure 33 ). Thes e three layers were then compiled to create a fire history for each individual pixel. Calculations were made using ArcGIS software. Forest type and community type were obtained from the Florida Ge ographic Data Library ( http://www.fgdl.org/metadataexplorer /explorer.jsp) The f orest t ype layer was developed by the University of Florida Geoplan Center ( Figure 34 ). Vegetative communities were distinguished based on Davis (1967). Swamps, m arshes and other areas classified by the National Hydraulic Dataset as having standing water were classified as hydric and the rest of the forest was classified as mesic based on soil and forest types ( Figure 35 ). Model Development Logistic regression was utilized to determine the probability of burning at a high severity for prescribed fires and moderatehigh severity for wild fires on a pixel level, in 2008. Logistic regression is used to measure binary responses by describing the

PAGE 86

86 relationship between one or more independent variables and the binary response (Littel et al. 2006) Responses are coded as 0 or 1: 3 Where is a realization of a random variable that can take on the values of 0 and 1 with probabilities and 1( 3 ). The distribution of is a Bernoulli distribution with the mean ( 4 ) and variance ( 5 ) depending on the underlying probability 4 5 To make the probability, a linear function of a vector of observed covariates ( ) the probability is transformed to remove range restrictions ( 6 ). 6 Logits map probabilities from range [0, 1] to [ probabilities below and positive logits represent probabilities above Solving for the probability of success requires exponenti ating the logit and calculating the odds of success ( 7 ). 7 Maximum likelihood methods were used for parameter estimation. With this approach, parameters were estimated iteratively until parameters that maximize d the

PAGE 87

87 log of the likelihood were obtained. Goodness of fit statistics, Akaikes information criterion (AIC) and Bayesian information criterion ( BIC ) were used to compare competing models. AIC is a statistic used for model selection that ranks different models based on how close fitted values are to true values ( 8 ) (Littell et al. 2006) 8 Where: k is the number of parameters in the statistical model and L is the maximized value of the likelihood function for the estimated model ( 8 ). Like AIC, BIC was used to rank models with a different numbers of parameters to avoid increasing the likelihood by over fitting the model (Littell et al. 2006). 9 Where: n is the sample size ( 9 ) Unexplained variation in the dependent variable and the number of covariates increases the AIC and BIC value s For both AIC and BIC, the lowest score indicates the best model. T he ratio of the Pearson chi square to its degrees of freedom is used t o determine if the model displays lack of fit. Values closer to 1 indicate that the model fits the data well (Littell et al. 2006). To address the assumption of independence among obs ervations, a generalized linear mixed model was used using the SAS procedure PROC GLIMMIX (Littell et al. 2006). Correlation among responses is incorporated into the model by adding random components to the linear predictor. To account for the correlation among responses, random residuals were modeled. Raster data is spatially co rrelated due the adjacen cy of pixels Although it would have been more effective to model the spatial correlation directly, without the aid of a super computer this option is

PAGE 88

88 in feasible. Th e GLIMMIX procedure can also make use of several predictor variables that may be either numerical or categorical. In this analysis we evaluated the probability of experiencing high severity and moderate to high severity based on the history of fire for prescribed and wild fires Covariates include d frequency of fir e, time since last fire, severity level of the latest fire (categorical) forest type (categorical) and community type (categorical) ( Table 32 ). Frequency of fire is the number of times a fire occurred within the data frame. Time since last fire is the number of years that passed since the last fire event. The latest severity level is the severity level of the last fire event. Forest types are classifi ed as pine flatwoods longleaf pine / xeric oaks fresh water marshes and swamp forest ; and community types are classified as hydric or mesic. A backward selection method was used to determine the appropriate covariates for the final model. T he Wald chi square statistic was used to identify significant covariates. Final model selection was also determined based on significant parameters and the model with the lowest AIC and BIC value. Interactions between all parameters were also considered. Nonsignif icant parameters were removed from the full model one at a time. To test for differences among categorical levels l east square means were produced and differences were tested. Data used to create the logistic model included the years 19982006 for prescri bed fires and 19982007 for wildfires Fire history was developed for pixels using data up to 2005. T his data was used to predict the probability of prescribed fires burning at a high severity level in year 2006. Fire history from 19982006 were used to model the probability of wildfires burning at a moderatehigh severity in 2007. These model s we re

PAGE 89

89 then used to predict the probability of experi encing a high severity prescribed fir e and the probability of experiencing a mod eratehigh severity wild fire i n 2008. Spatial Model The mode l s, probability of high severity prescribed fire and moderate to high severity wild fire, were recreated spatially using parameter estimates from logistic regression and ArcGIS spatial analyst extension. Layers were created f or each parameter and calculations were made using the spatial analyst/ raster calculator. The spatial model was used to show the probability of high severity prescribed fire and the probability of moderate high severity wildfire in 2008. Results Probabil ity of High Severity Prescribed Fire Severity level of the last fire i, frequency of fire X1 i j, time since last fire X2 i j, and the interaction between frequency and time since last fire were significant parameters in the model for prescribed f ires (10). ij ij ij ij ij i ijX X X X 2 1 3 2 2 1 1) ( logit 10 The model was significant and the parameters were significant based on th eir Wald chi square statistic s ( Table 33 ). Th e ratio of the Pearson chi square statistic to its degrees of freedom was approximately 1 indicating good model fit. Time since last fire showed a positive relationship with the probability of high severity; as the time interval increased the probability o f high severity fire also increased ( Figure 36 Figure 37 Figure 38 ). The effect of the sev erity level of the last fire varied by severity level; u nburned, moderate, and hig h severity levels in the last fire increased the probability of high severity in the subsequent fire and low severity level in the last

PAGE 90

90 fire reduced the probability of high severity in the subsequent fire. Unburned areas had a very high probability ( >8 0%) of experiencing high severity fires regardles s of the amount of time that had passed since the last fire event ( Figure 38 ). Areas that had experienced low and high severity in the last fire had a low probability of experiencing high severity in subsequent fire, followed by areas that experienced a moderate severity level which approached a 50% probability at 79 years since the last fire. Frequency of fire also had a positive relationship with the probability of high severity f ire As frequency of fire increased the probabilit y of experiencing high severity subsequent fire also increased ( Figure 36 ). Probability of M ode rate to High Severity Wildfire The model for the probabili ty of experiencing a moderate to high severity wildfire incorporated frequency of fire X1ij, time since last fire X2ij, and the interaction between the two ( 11). ij ij ij ij ij ijX X X X 2 1 3 2 2 1 1) ( logit 11 The model was significant and the parameters were significant based on their Wald chi square statistic s ( Table 34 ). The ratio of the Pearson chi square stati stic to its degrees of freedom wa s approximately 1 indicating good model fit. As the time since last fir e increased, the probability of moderatehigh severity fire also increased ( Figure 39 ). The increased probability over time since last fire varied by the number of fires that occur red since 1998. Areas that had never experienced fire had a much higher probability than previously burned areas. As the frequency of fire increased, the probability decreased ( Figure 310).

PAGE 91

91 Spatial Models The spatial model s identified areas that had increased probability of burning at a high severity based on fire history for prescribed fire ( Figure 311 Figure 312) and identified the probability of moderate to high severity for wildfires Areas wi th probabilities great er than 95% a re highlighted as areas we would expect to burn severely if a fire event were to occur. The High severity model (for prescribed fires) identified areas that burned often, as targets for high severity. Probabilities range from 3599% for the entire forest. A small portion of the forest had a probability greater than 95% (6.2%) while most of the forest ha d a probability of high severity greater than 50% (84%). Although forest type was not a significant predictor of high severity fire, the model indicated that m esic communities had a small portion of the area with a probability greater than 95% (6.3% ) ( Figure 313) Hydric communities had a higher portion of area (45%) with a probability of high severity greater than 95%. A small percentage of the prescribed fires in 2008 actually burned at a high severity level (2.4%) ( Figure 314) and these areas were often found to have a probability of high severity that was at least 95%. In the 2008 fir e season there were very few wildfires greater than 1 ac that occurred on the Osceola National forest The model Identified areas that had not burned or h ad burned only once from 19982007 as having a higher probability of burning at a moderatehigh sever ity level. Most of the forest had a p robability less than 1 5 % ( Figure 316) The probability of burning at a m oderate to high severity level was quite low for the entire dataset (<20%). Although forest and community types were not significant predictors of moderate to high severity, the model indicated that hydric communities had a probability less than 1% for mos t areas ( Figure 317) Mesic

PAGE 92

92 communities had a higher probability (below 5%) for th e majori ty of the area. Of the four forest types, fresh water marshes had the highest probability of moderatehigh severity (>15%) ( Figure 318) Discussion Probability of High Severity Prescribed Fire The model predicting the probabili ty of high severity for prescribed fires yielded important information regarding the relationship between time between fire events, the severity level of previous fires, and the frequency of fire. As time since last fire increased, the probability of experiencing a high severity fire also increased. Previous studies conducted on the Osceola National Forest, found that as time between fire events increased, fire intensity also increased causing greater tree mortality following fires (Outcalt et al. 2004). Vegetation recovery and fuel loads increase with time since the last disturbance event. So, as the time since the last fire increases there is also an increase in the amount of fuel and an increase in vertical structure of fuel. As fuel and vertical str ucture increases, so should the probability of burning at a high severity level due to the increase in combustible material. Increases in vertical structure also provide ladder fuels that increase the chance of ground fi res moving into tree crowns. Areas previously burned by low severity fire had a lower probabilit y of high severity prescribed fire just as areas with high and moderate severity level s had a higher probability of high severity fire. This indicates that fuel availability may be influencing t he amount of change caused by f ire more than previous fires. Low severity fires may be the result of fuel availability and not fuel accumulation. Areas with h igh and moderate severity levels that have high probabilities of experiencing high severity at short return intervals suggest that vegetation on these sites quickly recovered from fire

PAGE 93

93 events and w ere able to burn severely again. Alternatively this may reflect a bias in the high severity class. If these areas burned severely then there is likely less vegetation to burn during subsequent events If this vegetation is consumed during a fire it would take less fuel consumption to cause a large amount of change between pre and post fire images. This effect increases subsequent fire severity level and increases severity level with i ncreased fire frequencies. This phenomenon would explain the unexpected relationship between frequency and the probability of high severity as well as the high probability associated with presc ribed fires versus wild fires Hydric communities had a higher probability of high severity prescribed fire then mesic communities. This may be explained by the conditions chosen to perform prescribed fires under. In stands that have been burned multiple times in the past, land mana gers may choose weather conditions that are more risky to execute prescribed fires. And, even though hydric areas are usually unavailable during prescribed fires, when they are avialible they may burn at a high severity level. Probability of Moderate to H igh Severity Wildfire Predicting the probabi lity of moderate to h igh severity wildfires yielded information unlike the prescribed fire model. Time since last fire had the same increasing relationship, yet fire frequency had a decreasing relationship in t his model. The relationship between frequency and the probability of moderate to high severity wildfire is what we would expect; as the number of times an area burned increased, we would expect there to be a reduced chance of experiencing higher severity levels because fuel loads were reduced. Increased frequency also reduces the vigor in vegetation recovery so that with each fire vegetation regrowth declines.

PAGE 94

94 Forest and community types were not significant indicators of high and moderate to high sever ity fire During prescribed fires we might expect similar fire effects (low severity) in the different forest and community type s as areas are burned under optimal conditions. Yet, during wildfires we expect hydric communities to burn more severely if th e vegetation is avialible to burn due to high fuel loads especially during prolonged drought periods (Outcalt et al. 2004). Within this dataset, few hydric communities burned severely indicating that fuel was not avialible to burn during wildfires. We would also expect that forest types would influence severity levels. The lack of significance may be due to Osceola National Forest being composed mostly of pine flatwood and mesic forest Spatial Models The spatial model s were effective in identifying, spatially, where you would expect to observe high severity fire in the event a prescribed fire occurred and moderate to high severity in the event a wildfire occurred The prescribed fire model identified areas that have a history of burning often as being at an increased risk of high severity fire Areas that have not burned in 10 years also had an elevated risk of high severity fire. Most of the area burned in 2008 was burned by prescribed fire at moderate (45%) and low (18%) s everity levels. Sections of prescribed fires that actually burned at high severity had probabilities of high severity greater than 50% and most of the area had probability greater than 95% This suggests that the model adequately identified areas that were at a high risk of high sev erity fire based on its ability to recover from previous fire, the effects of the last fire event, and the amount of time between events. The wildfire model had low probabilities of moderatehigh severity for the entire forest for 2008. A reas that ha d n ot been burned were identified as having increased

PAGE 95

95 risk of moderate to high severity fire A single wildfire occurred on the Osceola in 2008 (less than 5 acres) and this wildfire had no areas of moderate or high severity. Conclusion Remote sensing techniq ues were successfully used to model fire history for the Osceola National Forest to determine the risk of experiencing high and moderate severity fire in the event of a fire. The model s identified areas that require at tention in order to reduce the risk o f high and moderate to high severity fire The prescribed fire model identified areas that burn often as having an elevated risk of high severity. This relationship between fire frequencies and high severity implied that either the vegetation with high f requencies was at the highest risk due largely to fast recovery time for prescribed fires or that there was a bias in the high severity class for areas with less vegetation. Forests that can burn on short time intervals need to as a response to the short time period required for fuel loads and live vegetation to return to prefire conditions. Yet, continued burning would also reduce the amount of vegetation available for subsequent fires and this reduction could be causing a bias in the high severity class. Conditions suitable for prescribed fire are determined by climatic factors and fuel loads and are increasingly influenced by burned acreage quotas set by regional or federal management. Forest managers are under pressure to burn as many acres as possib le each year. They may be willing to burn areas with high fire frequencies under more risky weather conditions due to reduced fuel l oads and short time since last fire. Fire effects in these areas may then end up being more severe than in areas that are burned under less extreme fire weather conditions

PAGE 96

96 Smoke sensitive areas are at an elevated risk for high severity and moderate to high severity. These areas are dangerous to burn due to the risk of disrupting transportation, reducing air quality, or damaging property. Conditions suitable to burn sensitive areas occur rarely often increasing the amount of time between fire events. Parts of the Osceola just north of the airport and that surround Interstate 10 have a probability of high severity that ranges from 50 75% for prescribed fires. During wildfires, the risk is elevated compared to probabilities for the rest of the forest (1019%). Both models identify these areas as being at an elevated risk requiring significant suppression efforts in the event of fire. The relationship between frequency and the probability of high severity may also be due to error introduced by differences in biomass Additional research to address t he amount of biomass in relation to d NBR values is necessary to determine if areas with lower biomass have a higher probabili ty of high severity due to the smaller amount of vegetation necessary to cause a significant change in preand post fire images. It may also be useful to look at the effects of delayed mortality in areas wi th short fire return intervals to identify if this would cause further bias in the high severity class. Overall, the probability of moderate to high severity (for wildfires) is less than what we would expect. The low probability may be caused by how w ildf ires are mapped Wildfire perimeters are mapped using Landsat imagery based on ocular estimates of where fires occurred. The perimeters are not exact so wildfires tend to have a high amount of unburned and low severity pixels. Also, most wildfires withi n this dataset are less than 50 acres. Wildfire size is determined by both suppression efforts and fuel availability. Therefore, smaller wildfires indicate that wildfires were not often exhibiting

PAGE 97

97 fire behavior that would likely cause high severity fire effects. There were few wildfires (Oak Fire 1998, Impassible 2004, and the Bugaboo 2007) that were large in size and that required great suppression efforts assisted by weather conditions for suppression. Larger fires had higher portions of moderate and high severity than smaller fires (not significantly larger). So, for the entire dataset, very few areas burned at high severity during wildfires (excluding the impassible fire of 2004) and there was not a very large increase in the area burned by moderate severity for larger fires. Biases introduced by perimeter estimates and the greater amount of smaller wildfires are the likely cause for the low probability of experiencing moderate to high severity.

PAGE 98

98 Table 31 Number of pixel s in each severity class by year. Year Severity High Moderate Low Unburned 1998 9720 18442 214519 106581 1999 5946 18791 111048 149600 2000 6404 1785 4961 7996 2001 56 180 372 505 2002 1 50 42680 71144 2003 1 27 179 72 2004 119570 58799 127579 312 98 2005 3668 13045 63549 43737 2006 22283 23283 25804 9565 2007 120 7182 26136 69340 2008 2757 40332 17183 2757 Table 32 Covariates for the model measuring the probability of high severity prescribed fire and moderate to high severity wildfire. Forest Type Community Type Frequency Time since Last Fire Last Severity Level Pine flatwoods Hydric The Number of times a pixel burned at a severity level greater than 1 Number of years since last fire event where pixel burns at a severity level greater than 1 Severity level of the last fire event Longleaf/ xeric o aks Fresh water marshes Mesic Swamp forest Table 33 Parameter estimates and their respective standard errors and pvalues for the m odel predicting the probability of high severity prescribed fire. Parameter Estimate Std. Error P value Intercept 3.0979 0.3210 <0.0001 Frequency of Fire 1.9709 0.1899 <0.0001 Time Since Last Fire 0.09361 0.02556 0.003 Severity Level of the last F ire Unburned 1.5268 0.1458 <0.0001 Low Severity 0.5854 0.07704 <0.0001 Medium Severity 0.3493 0.08290 <0.0001 High Severity 0 Residual 1.009

PAGE 99

99 Table 34 Parameter Estimates and their respective standard errors and p values for model predicting the probability of Moderate to High severity wild fire. Parameter Estimate Std. Error P value Intercept 3.2121 0.1391 <0.0001 Frequency of Fire 1.5005 0.1302 <0.0001 Time Since Last Fire 0.1470 0.01383 < 0.001 Frequency Time Since Last Fire 0.2194 0.01404 <0.001 Residual 0.9975

PAGE 100

100 Figure 31 Time since last fire for the Osceola National Forest (19982008)

PAGE 101

101 Figure 32 Fire frequency from 1998 2008 for the Osceola National Forest.

PAGE 102

102 Figure 33 Severity level of the last fire event (19982007).

PAGE 103

103 Figure 34 Florida Geographic Database Library Map of forest types for the Osceola National Forest.

PAGE 104

104 Figure 35 Map of the community types, hydric and mesic, for the Osceola National Forest.

PAGE 105

105 Figure 36 Relationship between the probability of high severity prescribed fire, f requency of fire, and time since last fire.

PAGE 106

106 Figure 37 Relationship between the probability of high severity prescribed fire, the severity level of the last fire event and time since last fire.

PAGE 107

107 Figure 38 Relationship between th e probability of high severity prescribed fire, frequency of fire, and time since last fire.

PAGE 108

108 Figure 39 Relationship between the probability of moderate to high severity wildfire frequency of fire and time since last fire.

PAGE 109

109 Figure 3 10. Relationship between the probability of moderate to high severity wildfire, fire frequency and time since last fire .

PAGE 110

110 Figure 311. Pr obability of h i gh s everity prescribed fire v er sus observed severity levels for 2008 prescribed fires.

PAGE 111

111 Figure 312. The probability of high severity prescribed fire in 2008.

PAGE 112

112 Figure 313. The proba bility of high severity prescribed fire in 2008 by community type.

PAGE 113

113 Figure 314. Severity levels of 2008 prescribed fires on the Osceola National forest.

PAGE 114

114 Figure 315. The probability of high severity prescri bed fire in 2008 by forest type.

PAGE 115

115 Figure 316. The probability of moderate to high severity fire for 2008.

PAGE 116

116 Figure 317. The probability of moderate to high severity wild fire for 2008 by community type.

PAGE 117

117 Figure 318. The probability of moderate to high severity wild fire in 2008 by forest type.

PAGE 118

118 CHAPTER 4 CONCLUSION Remote sensing techniques used to model fires on the Osceola National forest has provided valuable information regarding fire severity, the effect of time between burns and the risks incurred by management decisions. Fuels on the Osceola N ational F orest have fast recover times as evident by the relationship between frequency and the probability of high severity. Forest land that burns more frequent also burns at a higher severity. This indicates that fuels are able to regenerate at a rate to support higher severit y fires in short time periods. The analysis has provided valuable information regardi ng the influence of severity level and time between events. Models identified the time interval 56 years as a point where the effects of previous fires had little to no effect on subsequent fires. At this point, the probability of high severity fire, increasing severity level in subsequent fire, and burning in successive fire is highest. This is also a point where the probability of decreasing severity in subsequent fires was lowest. It has also become evident that effects of previous fires have little to no influence on subsequent fires past 56 years. Therefore fire frequencies larger than this will not adequately mitigate wildfire risk in pine flatwoods Variations in the landscape influence the relationship between time between fire events and fire severity. Fire effects are influenced by the type of vegetation and the availability of that vegetation. Land managers must consider vegetation recovery and availability differences by both forest and community types to determine the risk of the high sev erity fire. Hydric areas have exhibited a lack of fire activity during wildfires implying that these areas have not been available to burn often. This raises the risk of

PAGE 119

119 experiencing high severity fire dur ing prolonged drought periods. When these high fuel loads become available to burn they will likely burn quite severely (Maliakal et al. 2000). To incorporate this into the model it may be useful to add a weighted overlay to identify hydric communities and further weight them by time since last fire d uring prolonged drought conditions. Land managers should also consider other options to t reat heavy fuel loads in these areas; including mechanical treatments. To increase model performance it may be effective to include more meteorological attributes int o the model s. This would allow the model s to take into account weather effects that may further identify areas that are at highest risk of experiencing high severity. Spatial autocorrelation of fire severity and other model covariates should also be incorporated into the models to account for variations in space.

PAGE 120

120 APPENDIX : SEVERITY DATASETS Figure A 1 Severity levels of fire events for the 1998 fire season.

PAGE 121

121 Figure A 2 Severity levels of fire events for the 1999 fire season.

PAGE 122

122 Figure A 3 Severity levels of fire events for the 2000 fire season.

PAGE 123

123 Figure A 4 Severity levels of fire events for the 2001 fire season.

PAGE 124

124 Figure A 5 Severity levels of fire events for the 2002 fire season.

PAGE 125

125 Figure A 6 Severity levels of fire events for the 2003 fire season.

PAGE 126

126 Figure A 7 Severity levels of fire events for the 2004 fire season.

PAGE 127

127 Figure A 8 Severity levels of fire events for the 2005 fire season.

PAGE 128

128 Figure A 9 Severity levels of fire events for the 2006 fire season.

PAGE 129

129 Figure A 10. Severity levels of fire events for the 2007 fire season.

PAGE 130

130 Figure A 11. Severity levels of fire events for the 2008 fire season.

PAGE 131

131 LIST OF REFERENCES Abrahamson, Warren G. 1984. Species response to fire on the Florida Lake Wales Ridge. American Journal of Botany 71: 35 43 Abrahamson, Warren G. & Abrahamson, Christy R. 1996. Effects of fire on long unburned Florida uplands. Journal of Vegetation Science.7: 565574 Agee, J.K. 1993. Fire Ecology of Pacific Northwest Forest Islan d Press Washington,D.C. Allen, Jennifer. & Sorbel Brian. 2008. Assessing the differnced Normalized Burn Ratios ability to map burn severity in boreal forest and tundra ecosystems of Alaskas national parks. International Journal of Wildland Fire. 17: 46 3 475. Boer, M M. Macfarlane, C. Norris, J. Sadler, R J. Wallace, J. & Grierson, P F. 2008. Mapping burned areas and burn severity patterns in SW Australian eucalypt forest using remotely sensed changes in leaf area index, Remote Sensing of Environment 11 2(12): 43584369. Brose, Patrick. & Wade, Dale. 2002. Potential fire behavior in pine flatwood forest following three differ ent fuel reduction treatments. Forest Ecology and Management. 163: 7184. Busse, Matt. Hubber, Ken. Fiddler,Gary. Shestak, Carol. & Powers, Robert. 2005. Lethal soil temperatures during burning of masticated forest residues. International Journal of Wildland Fire. 14: 267 276. Cocke, Allison. Fule, Peter. & Crouse, Joseph. 2005. Comparison of Burn severity assessment using difference Normaliz ed Burn Ratio and ground data. International Journal of Wildland Fire. 14: 189 198. Collins, Brandon. Miller, Jay. Thode, Andrea. Kelly, Maggi. Wagtendonk,Jan. & Stephens, Scott. 2009. Interactions Among Wildland Fires in a Long Established Sierra Nevada Natural Fire Area. Ecosystems. 12: 114 128. Crawford, Julie A. Wahren, C H. A, Kyle, S. & Moir, W. H 2001. Responses of exotic plant species to fires in Pinus ponderosa forests in northern Arizona. Journal of Vegetation Science. 12: 261 268. Davis, John H. 1967. General map of natural vegetation of Florida. Agricultural Experiment Stations, Institute of Food and Agricultural S ciences, University of Florida. Davis, Lawrence S. & Cooper, Robert W. 1963. How prescribed burning affects wildfire occurrence. Journal of Forestry 61(12): 915 917

PAGE 132

132 Dombeck, Michael P. Williams, Jack E. & Wood, Christopher A. 2004.Wildfire policy and public lands: integrating scientific understanding with social concerns across l andscapes. Conservation Biology 18: 4. Duffy, P A Epting, J Graham, J M. Rupp, T S. & McGuire, A D. 2007. Analysis of Alaskan burn severity patterns using remotely sensed data International Journal of Wildland Fire 16: 277 284. Duguy, Beatriz. Antonio Alloza, Jose Roder, Achim. Vallejo, Ramon. & Pastor, Francisco. 2007. Modeling the effects of landscape fuel treatments on fire growth and behavior in a Mediterranean landscape (eastern Spain) I nternational Jour nal of Wildland Fire. 16 (5) : 619632. Duncan, Brean W. & Schmalzer, Paul A. 2004. Anthropogentic influences on potential fire spread in a pyrogenic ecosystem of Florida, USA. Landscape Ecology 19: 153165 Earth Resources Observation and Science (EROS) 2009. http://glovis.usgs.gov/ distribution/download notices.shtml Accessed January 2010. Epting, J. Verbyla, D. & Sorbel, B. 2005. Evaluation of remotely sensed indices for assessing burn severity in interior Alaska using Landsat TM and ETM+. Remote Sensing of Environment 96(3 4): 328 339. Escuin, S. Navarro, R. & Fernandez, P. 2009. Fire severity assessment by using NBR (Normalized Burn Ratio) and NDVI (Normalized Difference Vegetation Index) derived from Landsat TM/ETM Images. International Journal of Remote Sensing 29(4): 10531073. Finney, M.A. McHugh, C.W. & Grenfell, I.C. 2005. Stand and landscapelevel effects of prescribed burning on two Arizona wildfires. Canadian Journal of Forest Research. 35(7): 1714 1722. F ireModels.org (Fire Behavior and Fire Danger Software). 2009. FARSITE. http://firemodels.fire.org/content/view/112/143/ Accessed January 2010. Florida Exotic Pest Plant Council. 2009. 2009 Invasive plant list. http://www.fleppc.org/ list/list.htm Accessed January 2010. Gilliam, Frank S. & Platt, William J. 1999. Effects of longterm fire exclusion on tree species composition and s tand struct ure in an oldgrowth P inus palustris (Longleaf pine ) forest. Plant Ecology 140: 15 26. Glitzenstein, Jeff. Streng, Donna. Achtemeier, Gary. Naeher, Luke. & Wade, Dale. 2006. Fuels and fire behavior i n chipped and unchipped plots: i mplica tions for land management near wildland urban interface. Forest Ecology and Management 236:18 29.

PAGE 133

133 Godwin, David R. 2008. Burn severity in a central Florida sand pine scrub wilderness area. University of Florida Press. Hardy, C.C. 2005. Wildland fire hazard and risk: proble ms, definitions, and context. Forest Ecology and Management 211: 7382. Heyward, Frank. 1939. The relation of f ire to stand composition of longleaf pine forests. Ecology 20(2): 287 304 Holden, Zachary. Morgan, Penelope. & Evans, Jeffery. 2009. A predic tive model of burn severity based on 20year satelliteinfrared burn severity data in a large southwestern U S wilderness area. Forest Ecology and Management 258: 23992406. Hoy, Elizabeth. French, Nancy. Turetsky, Merritt. Trigg, Simon. & Kasischke, Er ic. 2008. Evaluating the potential of landsat TM/ETM+ imagery for assessing fire severity in Alaska black spruce forest. International Journal of Wildland Fire. 17: 500514. Jakubauskas, M.E. Lulla, K.P. & Mausel P.W 1990. Assessment of vegetation change in fire altered forest l andscape. Photogrammetric Engineering and Remote Sensing. 56(3): 371377. Kane, Jeffery. Varner, Morgan. & Knapp, Eric. 2009. Novel fuel bed characteristic with mechanical mastication treatments in northern California and southwe stern Oregon, USA. International Journal of Wildland Fire. 18: 686 697. Kobziar, Leda N. McBride Joe R. & Stephens, Scott L. 2009. The efficacy of fire and fuels reduction treatments in Sierra Nevada pine plantation. International Journal of Wildland Fire. 18: 791 801. Kreye, Jesse. Fuels break study s ampling. 2009. Kobziar Fire Science Lab. Conversation: December 2009. Kuenzi, Amanda. Fule, Peter. & Sieg, Carolyn. 2008. Effects of fire severity and prefire stand treatment on plant community recovery afte r a large wildfire. Forest Ecology and Management 255: 855865. Lavoie, M. Starr, G. Mack, M.C. Martin, T.A. & Gholz, H.L. 2010. Effects of a prescribed fire on understory vegetation carbon pools, and soil nutrients in longleaf pineslash pine forest in Florida. Natural Areas Journal 30: 82 94. Lemon, Paul C. 1949. Successional responses of herbs in the longleaf slash pine forest after fire. Ecology 30: 135145. Littell, Ramon C. Milliken, George A. Stroup, Walter W. Wolfinger, Russell D. & Schabenber ger, Oliver. 2006. SAS for Mixed Models 2nd Edition. SAS Publishing, Caryn, NC.

PAGE 134

134 Maliakal, Satya K. & Men ges, Eric S. 2000. Community composition and regeneration of Lake Wales Ridge wiregrass flatwoods in relation to time since last fire. Journal of Torre y Botanical Society 127(2): 125138 Miller, Jay.D. & Thode, Andrea E. 2007. Quantifying burn severity in a heterogeneous landscape with relative version of the delta Normalized Burn Ratio ( dNBR). Remote Sensing of Environment 109: 66 80. Monk, Carl D. 1 968. Succesional and environmental relationships of the forest vegetation of north central Florida. American Midland Naturalist 79(2): 441 457 Monroe, Martha. Long, Alan. & Maynowski, Sus an. 2003. Wildland Fire in the southeast: negotiating guidelines for defensible space. Journal of Forestry 101: 1419. National Fire and Aviation Management. 2009. Fire Weather Data. http://fam.nwcg.gov/fam web/weatherfirecd/ Accessed January .2010 National Invasiv e Species Information Center (USDA). 2009. National Invasive Species Management Plan 2006. http://www.invasivespeciesinfo.gov/ Accessed January 2010. National Oceanic and Atmospheric Administration (NOAA ). 2009 Palmer Drought Severity Index. http://www.drought.noaa.gov/palmer.html Accessed June 2010. Oliveras, Imma. Gracia, Marc. More, Gerard. & Retana, Javier. 2009. Factors influencing the patt ern s of fire severity in large w ildland fire under extreme meteorological conditions in the Mediterranean basin. International Journal of Wildland Fire 18: 755 764. OTA. 1993. Harmful nonindigenous species in the United States. Office of Technology and Assessment, United States Congress, Washington DC. Outcalt, Kenneth W. & Wade, Dale D. 2004. Fuels management reduces tree mortality from wildfires in southeastern United States. Southern Journal of Applied Forestry 28(1): 2834. Pimentel, David. Zuniga, R odo lfo & Morrison D oug 2005. Update on the environmental and economic cost associated with alieninvasive species in the United States. Ecological Economics 52: 273 288. Reiner, Alicia L. Vaillant, Nicole M. Fite Kaufman, JoAnn. & Dailey, Scott N 2009. M astication and prescribed fire impacts on fuel in a 25year old ponderosa pine plant ation, southern Sierra Nevada. Forest Ecology and Management 258: 23652372.

PAGE 135

135 Safford, Hugh. Schmidt, David. & Carson, Chris. 2009. Effects of fuel treatments on fire sever ity in an area of w ildlandurban interface, Angora Fire, Lake Tahoe Basin, California. Forest Ecology and Management 258: 773 787. Schmidt, Davis A. Taylor, Alan H. & Skinner, Carl N. 2008. The influence of fuels treatment and landscape arrangement on sim ulated fire behavior, Southern Cascade range, California. Forest Ecology and Management 255: 31703184. Stephens, Scott. & Moghadds, Jason. 2005. Experimental fuel treatment impacts on forest structure, potential fire behavior, and predicting tree mortali ty in California mixed conifer forest. Forest Ecology and Management 215: 21 36. Stratton, Richard. 2005. Ass essing the effectiveness of landscape fuel treatments on fire g rowth and b ehavior. Journal of Forestry 93; 10411052. Tuner, M onica G. Hargrove William W Gardner, Robert H. & Romme William H. 1994. Effects of fire on la ndscape heterogeneity in Yellowstone N ational Park, Wyoming. Journal of Vegetation Science. 5: 731 742. United States Geologic Service (USGS) 2009. Digital Elevation Model. http://seamless.usgs.gov/website/seamless/ viewer.htm. Accessed January 2010. United States Geologic Service (USGS) 2009. Difference Normalized Burn Ratio. http://burnseverity.cr.usgs.gov/pdfs/LAv4_BR_ CheatSheet.pdf Accessed January 2010. Wagtendonk, Jan. Root, Ralph. & Key, Carl. 2004. Comparison of AVIRS and Landsat ETM+ detection capabili ties for burn severity. Remote Sensing of Environment 92: 397408. Waldrop, Thomas A. White, David L. & Jones, Steven M. 1992. Fire regimes for pinegrassland communities in t he southeastern United States. Forest Ecology and Management 47: 195 210. White Joseph D Ryan, Kevein C Key, Carl C & Runnig, Stephen W 1996. Remote sensing of forest fire severity and vegetation recovery. International journal of Wildland Fire 6(3): 125136. Wimberly, Michael C. Cochrane, Mark A. Baer, A dam D. & Pabst, K ari 2009. Assessing fuel treatment effectiveness using satellite imagery and spatial statistics. Ecological Applications 19(6): 13771384. Wolcott, Leslie. OBrien, Joseph J. & Mordecai, Kathryn. 2007. A Survey of Land Managers on Wildland Hazardous Fuels I ssues in Florida: a technical note. Sothern Journal of Applied Forestry 31(30): 148150.

PAGE 136

136 BIOGRAPHICAL SKETCH Sparkle Leigh Malone was born in the spring of 1985 in Chicago, Illinois to Rita and Rodney Malone. She grew up in Miami, Florida, after her f amily moved from Chicago to Miami when she was an infant. Sparkle graduated from Dr. Michael Krop Senior High School in 2003. Her college career began in 2005 at Florida Agricultural and Mechanical University in Tallahassee, F L. Her major area of study was a gronomy. In 2007 she transferred into the University of Floridas School of Forest Resources and Conservation by way of the 1890s scholars program. Here Sparkle majored in forestry with a specialization in informatics. She obtained a Bachelor of S cience from the university in the spring of 2009 and a master s degree in the summer of 2010.