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Application of remotely sensed data to a geographic information system for microclimate change analysis

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Title:
Application of remotely sensed data to a geographic information system for microclimate change analysis
Creator:
Jordan, Jonathan David, 1962-
Publication Date:
Language:
English
Physical Description:
xxvii, 515 leaves : ill. ; 29 cm.

Subjects

Subjects / Keywords:
Agriculture ( jstor )
Biological rhythms ( jstor )
Forests ( jstor )
Highlands ( jstor )
Land cover ( jstor )
Orchards ( jstor )
Soils ( jstor )
Suburbs ( jstor )
Surface temperature ( jstor )
Swamps ( jstor )
Agricultural Engineering thesis, Ph. D
Dissertations, Academic -- Agricultural Engineering -- UF
Miami metropolitan area ( local )
Genre:
bibliography ( marcgt )
non-fiction ( marcgt )

Notes

Thesis:
Thesis (Ph. D.)--University of Florida, 1994.
Bibliography:
Includes bibliographical references (leaves 492-514).
General Note:
Typescript.
General Note:
Vita.
Statement of Responsibility:
Jonathan David Jordan.

Record Information

Source Institution:
University of Florida
Holding Location:
University of Florida
Rights Management:
Copyright [name of dissertation author]. Permission granted to the University of Florida to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder.
Resource Identifier:
002042877 ( ALEPH )
33315000 ( OCLC )
AKN0755 ( NOTIS )

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APPLICATION OF REMOTELY SENSED DATA TO A GEOGRAPHIC
INFORMATION SYSTEM FOR MICROCLIMATE CHANGE ANALYSIS


















By


JONATHAN DAVID JORDAN


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

UNIVERSITY OF FLORIDA


1994



























To my parents,

Don and Dorothy Jordan














ACKNOWLEDGMENTS


This research was performed with the help of the staff

and facilities of the Remote Sensing Application Laboratory

(RSAL) located at the Agricultural Engineering Department of

the University of Florida. The author is grateful for the

assistance provided by the RSAL director, Dr. Sun F. Shih;

RSAL manager, Orlando Lanni; departmental computer technician

Curtis Weldon; and RSAL assistants Chih-Hung Tan, Yu Rong Tan,

and Bruce E. Myhre.

Acknowledgments are also due to additional persons and

agencies for key assistance in various portions of this study.

Technical information concerning the Advanced Very High

Resolution Radiometer (AVHRR) satellite imagery used in this

research was provided by Mary Hughes, Emily Harrod, Dr. Andrew

Horvitz, Dr. Katherine Kidwell, Dr. Carolyn Ng, Richard

DeRycke, and others of the National Oceanic and Atmospheric

Administration (NOAA). Both technical information and data

tapes concerning the Heat Capacity Mapping Mission (HCMM)

imagery used in this research were provided by Dr. William L.

Barnes, Barbara Pope, Locke M. Stuart, and others of the

National Space Science Data Center (NSSDC).

Essential water-body surface temperature data were

provided by Dr. Leslie Wedderburn, Brian Turkotte, Ernest

Gallego, and others of the South Florida Water Management

iii









District (SFWMD); William L. Osburn, Gail Gallagher, and

others of the St. Johns River Water Management District

(SJRWMD); David Hornsby and others of the Suwannee River Water

Management District (SRWMD); Kenneth Romie, Mark Rials, and

others of the Southwest Florida Water Management District

(SWFWMD); Thomas Cardenel, R. Malloy, and others of the

Environmental Protection Commission of Hillsborough County

(EPCHC); Donald D. Moores of the Pinellas County Department of

Environmental Management (PCDEM); and Dr. David Gowan of the

Florida Department of Environmental Protection (DEP). Both

water-body surface temperature data and statewide land-cover

maps were supplied by John Steyes and others of the Florida

Game and Freshwater Fish Commission (FGFFC). Land-cover

information and maps of the lower Lake Wales Ridge area were

provided by the Archbold Biological Station.

Aerial photographs used in this research were made

available by Dr. Helen J. Armstrong of the University of

Florida Map Library. Statewide aquaculture information and

maps were provided by Dr. Edward P. Lincoln and Dr. C. Direlle

Baird of the University of Florida Agricultural Engineering

Department. Phosphate mine and mine reclamation information

was made available by Dr. Lawrance N. Shaw of the University

of Florida Agricultural Engineering Department. Crop

information and assistance with site visits to St. Johns River

agricultural areas were provided by Dr. Dale R. Hensel of the

Hastings Agricultural Research and Education Center (AREC).

Crop information, maps, and assistance with site visits to the









Everglades Agricultural Area (EAA) were provided by Dr. George

H. Snyder of the Everglades Research and Education Center

(EREC). Assistance with site visits to south Florida citrus

orchards, pastures, and Lake Okeechobee water-temperature

stations was given by Michael Piper, David Soballe, and others

of the SFWMD; assistance with site visits to the Lake Apopka

water-temperature station and marsh restoration project was

given by J. Palenkas and others of the SJRWMD. Land-cover

information and assistance with wetland site visits in north

and panhandle Florida were provided by Jay L. Johnson of the

NWFWMD. An infrared radiometer was made available by Dr.

Donald J. Pitts of the Immokalee AREC. Use of soil sampling

and analysis equipment was provided by Dr. Donald L. Myhre and

Joseph Nguyen of the University of Florida Soil and Water

Science Department. Thanks are also given to professors Sun

F. Shih, Jerome J. Gaffney, Dorota Z. Haman, Edward P.

Lincoln, Byron E. Ruth, and George H. Snyder of my supervisory

committee for their help and support.















TABLE OF CONTENTS


page

ACKNOWLEDGMENTS......................................... iii

LIST OF TABLES........................................ xiii

LIST OF FIGURES ................................................... xxiv

ABSTRACT ............................................... xxvi

INTRODUCTION............................................ 1

Purpose and Objectives................................. 1

Importance of Surface Temperature in Climatology....... 2

Importance of Surface Temperature in Hydrology......... 4

Importance of Surface Temperature in Agriculture and
Forestry............................................. 5

Factors Affecting Surface Temperature Patterns........ 6

Temperature Impacts of Changes in Land-Cover........ 9

Temporal Inter-Relation of Forcing Factors........... 13

Spatial Inter-Relation of Forcing Factors............ 19

Potential for Future Changes in Soil Type........... 21

REVIEW OF LITERATURE..................................... 22

Difficulties of Surface Temperature Measurement....... 22

Atmospheric Correction Techniques................... 24

Emissivity Correction Techniques.................... 26

Previous Studies..................................... 30

MATERIALS AND METHODS.................................... 39

Study Area......................... ................... 39

Panhandle Zone.......... ............................ 39









North Zone .................................... ..... 41

South Zone..................................... ....... 41

Geographic Information System........................ 42

Raster Datasets........... ........... ............. 42

Vector Datasets.................................... 42

Geographic Referencing............................... 43

GIS Analyses in Raster Environment................. 44

AVHRR Image Processing................................ 45

AVHRR Data Types.................................. 46

Calibration to At-Satellite Radiant Temperature...... 49

Geographic Correction and Registration............... 51

Conversion from Radiant to Kinetic Temperature...... 61

Accuracy Assessment of Kinetic Temperature Images... 68

Water and Cloud Masking............................ 70

Final Forms of Images in the GIS Database........... 74

HCMM Historical-Image Processing...................... 74

Geographic Correction of HCMM Images................ 77

Calibration of HCMM At-Satellite Radiant
Temperature ....................................... 78

Ground-Based DSTV/Soil-Moisture Work.................. 79

Mineral Soil Investigation.......................... 79

Organic Soil Investigation.......................... 83

Vegetated Soil Investigation........................ 85

Soil Type Data........................................ 86

Mineral Soils. ...................................... 87

Organic Soils.................................... 89

Artificial Soil Type Change........................ 90


vii









Land-Cover Data...................................... 91

Natural Land-Cover............................... 91

Agricultural Land-Cover............................. 115

Urban/Industrial Land-Cover......................... 123

Special Land-Cover Conditions....................... 129

RESULTS AND DISCUSSION................................. 136

Analyses Across Macroclimate Zones.................... 136

Analyses Within Macroclimate Zones.................... 147

Spring Afternoon Natural Land-Cover Thermal
Patterns............................................ 147

Spring Afternoon Agricultural Land-Cover Thermal
Patterns........................................... 154

Spring Afternoon Urban/Industrial Land-Cover Thermal
Patterns........................................... 163

Spring Afternoon Change Analyses--Natural to
Agricultural Land-Cover........................... 172

Spring Afternoon Change Analyses--Natural to Urban/
Industrial Land-Cover............................. 178

Spring Afternoon Change Analyses--Agricultural to
Urban/Industrial Land-Cover....................... 185

Spring Afternoon Comparison of Agricultural to
Natural Heat Islands............................. 193

Spring Afternoon Comparison of Urban/Industrial to
Natural Heat Islands............................... 196

Spring Afternoon Change Analyses--Special Factors... 199

Spring Nighttime Natural Land-Cover Thermal
Patterns........................................... 203

Spring Nighttime Agricultural Land-Cover Thermal
Patterns.......................................... 208

Spring Nighttime Urban/Industrial Land-Cover Thermal
Patterns.......... ...................... ...... 218

Spring Nighttime Change Analyses--Natural to
Agricultural Land-Cover........................... 227


viii









Spring Nighttime Change Analyses--Natural to Urban/
Industrial Land-Cover............................. 232

Spring Nighttime Change Analyses--Agricultural to
Urban/Industrial Land-Cover....................... 238

Spring Nighttime Comparison of Agricultural to
Natural Cold Islands.............................. 242

Spring Nighttime Comparison of Urban/Industrial to
Natural Cold Islands.............................. 245

Spring Nighttime Change Analyses--Special Factors... 246

Spring Diurnal Natural Land-Cover Thermal Patterns.. 252

Spring Diurnal Agricultural Land-Cover Thermal
Patterns........................................... 259

Spring Diurnal Urban/Industrial Land-Cover Thermal
Patterns............................................ 267

Spring Diurnal Change Analyses--Natural to
Agricultural Land-Cover........................... 276

Spring Diurnal Change Analyses--Natural to Urban/
Industrial Land-Cover............................. 281

Spring Diurnal Change Analyses--Agricultural to
Urban/Industrial Land-Cover....................... 286

Spring Diurnal Comparison of Agricultural to
Natural Extreme Islands........................... 294

Spring Diurnal Comparison of Urban/Industrial to
Natural Extreme Islands........................... 296

Spring Diurnal Change Analyses--Special Factors..... 299

Winter Afternoon Natural Land-Cover Thermal
Patterns.......................................... 303

Winter Afternoon Agricultural Land-Cover Thermal
Patterns.......................................... 310

Winter Afternoon Urban/Industrial Land-Cover Thermal
Patterns........................................... 317

Winter Afternoon Change Analyses--Natural to
Agricultural Land-Cover........................... 326

Winter Afternoon Change Analyses--Natural to Urban/
Industrial Land-Cover.............................. 330









Winter Afternoon Change Analyses--Agricultural to
Urban/Industrial Land-Cover....................... 330

Winter Afternoon Comparison of Agricultural to
Natural Heat Islands.............................. 335

Winter Afternoon Comparison of Urban/Industrial to
Natural Heat Islands.............................. 340

Winter Afternoon Change Analyses--Special Factors... 344

Winter Nighttime Natural Land-Cover Thermal
Patterns.................... ........... ........ 348

Winter Nighttime Agricultural Land-Cover Thermal
Patterns........................................ 353

Winter Nighttime Urban/Industrial Land-Cover Thermal
Patterns........................................... 359

Winter Nighttime Change Analyses--Natural to
Agricultural Land-Cover........................... 366

Winter Nighttime Change Analyses--Natural to Urban/
Industrial Land-Cover............................. 370

Winter Nighttime Change Analyses--Agricultural to
Urban/Industrial Land-Cover....................... 376

Winter Nighttime Comparison of Agricultural to
Natural Cold Islands.............................. 382

Winter Nighttime Comparison of Urban/Industrial to
Natural Cold Islands.............................. 384

Winter Nighttime Change Analyses--Special Factors... 387

Winter Diurnal Natural Land-Cover Thermal Patterns.. 390

Winter Diurnal Agricultural Land-Cover Thermal
Patterns........................................... 395

Winter Diurnal Urban/Industrial Land-Cover Thermal
Patterns ............ .............................. 405

Winter Diurnal Change Analyses--Natural to
Agricultural Land-Cover........................... 414

Winter Diurnal Change Analyses--Natural to Urban/
Industrial Land-Cover............................. 414

Winter Diurnal Change Analyses--Agricultural to
Urban/Industrial Land-Cover....................... 419









Winter Diurnal Comparison of Agricultural to
Natural Extreme Islands........................... 423

Winter Diurnal Comparison of Urban/Industrial to
Natural Extreme Islands .......................... 429

Winter Diurnal Change Analyses--Special Factors..... 432

Analyses of Micro-Scale Maritime Effects.............. 436

Hammock Comparisons... .............................. 436

Marsh Comparisons.................................. 437

Maritime Micro-Scale Thermal Moderation............. 438

Analyses of Seasonal Effects on Deciduous Vegetation.. 438

Historical HCMM-Based Analyses........................ 439

HCMM Analyses Across Macroclimate Zones............. 439

HCMM Historical Special Condition Change Analyses... 443

Results of Ground-Based DSTV/Soil-Moisture............ 450

Mineral Soil Results................................ 451

Organic Soil Results................................ 453

Vegetated Soil Results.............................. 453

SUMMARY AND CONCLUSIONS................................ 455

Principal Findings................................... 456

Importance of Soil Type and Land-Cover.............. 456

Differences Among Natural Land-Cover Types.......... 457

Differences Among Agricultural Land-Cover Types..... 457

Differences Among Urban/Industrial Land-Cover
Types ............................................. 458

Potential for Soil Moisture Monitoring.............. 458

Recommendations for Future Research................... 459

Ground-Based Data Collection Improvement............ 459

Satellite System Improvement........................ 460










Direction of Future Research........................ 462

GLOSSARY............................................... .. 464

APPENDIX A IMAGE DOCUMENTATION ......................... 468

APPENDIX B WATER-BODY TEMPERATURE MEASUREMENT STATIONS. 475

APPENDIX C LAND-COVER POLYGON DETAILS.................. 476

REFERENCES...................... ....................... 492

BIOGRAPHICAL SKETCH...... ............................... 515


xii














LIST OF TABLES


page

1 Spring afternoon surface temperature
across-zone differences among natural
land-cover types ........................... 137

2 Spring nighttime surface temperature
across-zone differences among natural
land-cover types .......................... 139

3 Spring diurnal surface temperature variation
across-zone differences among natural
land-cover types ........................... 141

4 Winter afternoon surface temperature
across-zone differences among natural
land-cover types ........................... 143

5 Winter nighttime surface temperature
across-zone differences among natural
land-cover types ........................... 144

6 Winter diurnal surface temperature variation
across-zone differences among natural
land-cover types .......................... 145

7 Spring afternoon surface temperature
differences among natural land-cover
types in panhandle zone..................... 148

8 Spring afternoon surface temperature
differences among natural land-cover
types in north zone......................... 150

9 Spring afternoon surface temperature
differences among natural land-cover
types in south zone......................... 152

10 Spring afternoon surface temperature
differences among agricultural land-cover
types in panhandle zone..................... 155

11 Spring afternoon surface temperature
differences among agricultural land-cover
types in north zone......................... 157


xiii









12 Spring afternoon surface temperature
differences among agricultural land-cover
types in south zone........................ 160

13 Spring afternoon surface temperature
differences among urban/industrial land-
cover types in panhandle zone............... 164

14 Spring afternoon surface temperature
differences among urban/industrial land-
cover types in north zone................... 166

15 Spring afternoon surface temperature
differences among urban/industrial land-
cover types in south zone................... 170

16 Spring afternoon surface temperature change
from natural to agricultural in panhandle
zone ........................................ 173

17 Spring afternoon surface temperature change
from natural to agricultural in north
zone......................................... 175

18 Spring afternoon surface temperature change
from natural to agricultural in south
zone......................................... 177

19 Spring afternoon surface temperature change
from natural to urban/industrial in
panhandle zone.............................. 179

20 Spring afternoon surface temperature change
from natural to urban/industrial in
north zone.................................. 181

21 Spring afternoon surface temperature change
from natural to urban/industrial in
south zone .................................. 184

22 Spring afternoon surface temperature change
from agricultural to urban/industrial in
panhandle zone.............................. 186

23 Spring afternoon surface temperature change
from agricultural to urban/industrial in
north zone.................................. 188

24 Spring afternoon surface temperature change
from agricultural to urban/industrial in
south zone .................................. 191


xiv









25 Spring afternoon surface temperature of
agricultural land-cover types vs hottest
natural land-cover......................... 194

26 Spring afternoon surface temperature of
urban/industrial land-cover types vs
hottest natural land-cover.................. 197

27 Spring afternoon surface temperature change
for special conditions..................... 200

28 Spring nighttime surface temperature
differences among natural land-cover
types in panhandle zone...................... 204

29 Spring nighttime surface temperature
differences among natural land-cover
types in north zone.......................... 206

30 Spring nighttime surface temperature
differences among natural land-cover
types in south zone......................... 209

31 Spring nighttime surface temperature
differences among agricultural land-cover
types in panhandle zone..................... 211

32 Spring nighttime surface temperature
differences among agricultural land-cover
types in north zone......................... 213

33 Spring nighttime surface temperature
differences among agricultural land-cover
types in south zone......................... 215

34 Spring nighttime surface temperature
differences among urban/industrial land-
cover types in panhandle zone............... 219

35 Spring nighttime surface temperature
differences among urban/industrial land-
cover types in north zone................... 221

36 Spring nighttime surface temperature
differences among urban/industrial land-
cover types in south zone................... 225

37 Spring nighttime surface temperature change
from natural to agricultural in panhandle
zone........................................ 228









38 Spring nighttime surface temperature change
from natural to agricultural in north
zone...................... ................. 229

39 Spring nighttime surface temperature change
from natural to agricultural in south
zone..... ......... .................. ...... 231

40 Spring nighttime surface temperature change
from natural to urban/industrial in
panhandle zone .............................. 233

41 Spring nighttime surface temperature change
from natural to urban/industrial in
north zone ........................... ...... 234

42 Spring nighttime surface temperature change
from natural to urban/industrial in
south zone.................................. 237

43 Spring nighttime surface temperature change
from agricultural to urban/industrial in
panhandle zone.............................. 239

44 Spring nighttime surface temperature change
from agricultural to urban/industrial in
north zone......... ........................ 240

45 Spring nighttime surface temperature change
from agricultural to urban/industrial in
south zone. ................................... 243

46 Spring nighttime surface temperature of
agricultural land-cover types vs coldest
natural land-cover.......................... 244

47 Spring nighttime surface temperature of
urban/industrial land-cover types vs
coldest natural land-cover................. 247

48 Spring nighttime surface temperature change
for special conditions...................... 249

49 Spring diurnal surface temperature variation
differences among natural land-cover
types in panhandle zone..................... 253

50 Spring diurnal surface temperature variation
differences among natural land-cover
types in north zone........................ 255


xvi









51 Spring diurnal surface temperature variation
differences among natural land-cover
types in south zone......................... 257

52 Spring diurnal surface temperature variation
differences among agricultural land-cover
types in panhandle zone..................... 260

53 Spring diurnal surface temperature variation
differences among agricultural land-cover
types in north zone......................... 262

54 Spring diurnal surface temperature variation
differences among agricultural land-cover
types in south zone......................... 264

55 Spring diurnal surface temperature variation
differences among urban/industrial land-
cover types in panhandle zone............... 268

56 Spring diurnal surface temperature variation
differences among urban/industrial land-
cover types in north zone................... 270

57 Spring diurnal surface temperature variation
differences among urban/industrial land-
cover types in south zone.................... 274

58 Spring diurnal surface temperature variation
change from natural to agricultural in
panhandle zone.............................. 277

59 Spring diurnal surface temperature variation
change from natural to agricultural in north
zone......................................... 279

60 Spring diurnal surface temperature variation
change from natural to agricultural in south
zone ....................................... 280

61 Spring diurnal surface temperature variation
change from natural to urban/industrial in
panhandle zone.............................. 282

62 Spring diurnal surface temperature variation
change from natural to urban/industrial in
north zone................................. 284

63 Spring diurnal surface temperature variation
change from natural to urban/industrial in
south zone................ .... ........ ...... 287


xvii









64 Spring diurnal surface temperature variation
change from agricultural to urban/industrial
in panhandle zone........................... 288

65 Spring diurnal surface temperature variation
change from agricultural to urban/industrial
in north zone................................ 290

66 Spring diurnal surface temperature variation
change from agricultural to urban/industrial
in south zone............................... 293

67 Spring diurnal surface temperature variation
of agricultural land-cover types vs highest-
DSTV natural land-cover..................... 295

68 Spring diurnal surface temperature variation
of urban/industrial land-cover types vs
highest-DSTV natural land-cover.............. 297

69 Spring diurnal surface temperature variation
change for special conditions.............. 300

70 Winter afternoon surface temperature
differences among natural land-cover
types in panhandle zone..................... 304

71 Winter afternoon surface temperature
differences among natural land-cover
types in north zone......................... 306

72 Winter afternoon surface temperature
differences among natural land-cover
types in south zone......................... 308

73 Winter afternoon surface temperature
differences among agricultural land-cover
types in panhandle zone...................... 311

74 Winter afternoon surface temperature
differences among agricultural land-cover
types in north zone......................... 313

75 Winter afternoon surface temperature
differences among agricultural land-cover
types in south zone......................... 315

76 Winter afternoon surface temperature
differences among urban/industrial land-
cover types in panhandle zone............... 319


xviii









77 Winter afternoon surface temperature
differences among urban/industrial land-
cover types in north zone................... 320

78 Winter afternoon surface temperature
differences among urban/industrial land-
cover types in south zone.................. 324

79 Winter afternoon surface temperature change
from natural to agricultural in panhandle
zone............................ ....... .. 327

80 Winter afternoon surface temperature change
from natural to agricultural in north
zone.......................... ............... 328

81 Winter afternoon surface temperature change
from natural to agricultural in south
zone..... ................................... 329

82 Winter afternoon surface temperature change
from natural to urban/industrial in
panhandle zone............................... 331

83 Winter afternoon surface temperature change
from natural to urban/industrial in
north zone .................................. 332

84 Winter afternoon surface temperature change
from natural to urban/industrial in
south zone........................... ...... ..... 334

85 Winter afternoon surface temperature change
from agricultural to urban/industrial in
panhandle zone...... ....................... 336

86 Winter afternoon surface temperature change
from agricultural to urban/industrial in
north zone....... ...... ....... .. ............ 337

87 Winter afternoon surface temperature change
from agricultural to urban/industrial in
south zone........... ..................... ... 339

88 Winter afternoon surface temperature of
agricultural land-cover types vs hottest
natural land-cover.......................... 341

89 Winter afternoon surface temperature of
urban/industrial land-cover types vs
hottest natural land-cover.................. 342


xix









90 Winter afternoon surface temperature change
for special conditions...................... 345

91 Winter nighttime surface temperature
differences among natural land-cover
types in panhandle zone..................... 349

92 Winter nighttime surface temperature
differences among natural land-cover
types in north zone.......................... 351

93 Winter nighttime surface temperature
differences among natural land-cover
types in south zone......................... 354

94 Winter nighttime surface temperature
differences among agricultural land-cover
types in panhandle zone..................... 357

95 Winter nighttime surface temperature
differences among agricultural land-cover
types in north zone......................... 358

96 Winter nighttime surface temperature
differences among agricultural land-cover
types in south zone......................... 360

97 Winter nighttime surface temperature
differences among urban/industrial land-
cover types in panhandle zone............... 363

98 Winter nighttime surface temperature
differences among urban/industrial land-
cover types in north zone.................... 364

99 Winter nighttime surface temperature
differences among urban/industrial land-
cover types in south zone.................... 367

100 Winter nighttime surface temperature change
from natural to agricultural in panhandle
zone......................................... 369

101 Winter nighttime surface temperature change
from natural to agricultural in north
zone.................. ............. ..... 371

102 Winter nighttime surface temperature change
from natural to agricultural in south
zone........................................ 372









103 Winter nighttime surface temperature change
from natural to urban/industrial in
panhandle zone.............................. 373

104 Winter nighttime surface temperature change
from natural to urban/industrial in
north zone.................................. 374

105 Winter nighttime surface temperature change
from natural to urban/industrial in
south zone .................................. 377

106 Winter nighttime surface temperature change
from agricultural to urban/industrial in
panhandle zone.............................. 378

107 Winter nighttime surface temperature change
from agricultural to urban/industrial in
north zone .................................. 379

108 Winter nighttime surface temperature change
from agricultural to urban/industrial in
south zone.................................. 381

109 Winter nighttime surface temperature of
agricultural land-cover types vs coldest
natural land-cover.......................... 383

110 Winter nighttime surface temperature of
urban/industrial land-cover types vs
coldest natural land-cover.................. 385

111 Winter nighttime surface temperature change
for special conditions...................... 388

112 Winter diurnal surface temperature variation
differences among natural land-cover
types in panhandle zone..................... 391

113 Winter diurnal surface temperature variation
differences among natural land-cover
types in north zone......................... 393

114 Winter diurnal surface temperature variation
differences among natural land-cover
types in south zone......................... 396

115 Winter diurnal surface temperature variation
differences among agricultural land-cover
types in panhandle zone..................... 399


xxi









116 Winter diurnal surface temperature variation
differences among agricultural land-cover
types in north zone......................... 400

117 Winter diurnal surface temperature variation
differences among agricultural land-cover
types in south zone......................... 403

118 Winter diurnal surface temperature variation
differences among urban/industrial land-
cover types in panhandle zone............... 406

119 Winter diurnal surface temperature variation
differences among urban/industrial land-
cover types in north zone.................... 408

120 Winter diurnal surface temperature variation
differences among urban/industrial land-
cover types in south zone.................... 412

121 Winter diurnal surface temperature variation
change from natural to agricultural in
panhandle zone............................... 415

122 Winter diurnal surface temperature variation
change from natural to agricultural in north
zone......................................... 416

123 Winter diurnal surface temperature variation
change from natural to agricultural in south
zone ........................................ 417

124 Winter diurnal surface temperature variation
change from natural to urban/industrial in
panhandle zone............................... 418

125 Winter diurnal surface temperature variation
change from natural to urban/industrial in
north zone.................................. 420

126 Winter diurnal surface temperature variation
change from natural to urban/industrial in
south zone.................... .. ........ 422

127 Winter diurnal surface temperature variation
change from agricultural to urban/industrial
in panhandle zone........................... 424

128 Winter diurnal surface temperature variation
change from agricultural to urban/industrial
in north zone ............................... 425


xxii









129 Winter diurnal surface temperature variation
change from agricultural to urban/industrial
in south zone.............................. 427

130 Winter diurnal surface temperature variation
of agricultural land-cover types vs highest-
DSTV natural land-cover ..................... 428

131 Winter diurnal surface temperature variation
of urban/industrial land-cover types vs
highest-DSTV natural land-cover.............. 430

132 Winter diurnal surface temperature variation
change for special conditions............... 433

133 HCMM Winter approximate afternoon surface
temperature across-zone differences among
natural land-cover types ................... 440

134 HCMM Winter approximate nighttime surface
temperature across-zone differences among
natural land-cover types ................... 441

135 HCMM Winter approximate diurnal surface
temperature variation across-zone
differences among natural land-cover types 442

136 HCMM Winter approximate afternoon surface
temperature change for special conditions... 444

137 HCMM Winter approximate nighttime surface
temperature change for special conditions... 447

138 HCMM Winter approximate diurnal surface
temperature variation change for special
conditions.................................. 449

139 DSTV/soil-moisture relation for soil types.... 452

140 Image accuracy evaluation details............ 469


xxiii














LIST OF FIGURES


Figure page

1 Study area with climate zones and water-body
temperature stations (see Appendix B for
details) .................................... .40

2 AVHRR image without geographic correction
(polygon outlines true position of Florida). 52

3 AVHRR image with first-stage (ELP-based)
geographic correction (polygon outlines true
position of Florida)........................ 54

4 Example of explosive extrapolation of third-
order global polynomial surface model
outside of control points.................... 56

5 AVHRR image with second-stage (GCP-based)
geographic correction (polygon outlines true
position of Florida)........................ 59

6 At-satellite radiant temperature image........ 62

7 Emissivity image.............................. 63

8 Surface temperature image..................... 64

9 Water/cloud mask (NDVI) image.................. 71

10 Daytime (spring) masked surface temperature
image........................................ 73

11 Nighttime (spring) masked surface temperature
image........................................ 75

12 DSTV (spring) image............................ 76

13 HCMM daytime (winter) at-satellite radiant
temperature image......................... 80

14 HCMM nighttime (winter) at-satellite radiant
temperature image........................ 81

15 HCMM approximate-DSTV (winter) image.......... 82


xxiv









16 Natural land-cover polygons (see Appendix C
for details)............................. 93

17 Agricultural land-cover polygons (see
Appendix C for details).................. 116

18 Urban/industrial land-cover polygons (see
Appendix C for details).................. 124


xxV














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

APPLICATION OF REMOTELY SENSED DATA TO A GEOGRAPHIC
INFORMATION SYSTEM FOR MICROCLIMATE CHANGE ANALYSIS

By

Jonathan David Jordan

December 1994

Chairman: Sun F. Shih
Major Department: Agricultural Engineering

This study demonstrates the monitoring of surface

temperature pattern impacts of meso-scale changes in land-

cover and hydrology through the use of a geographic

information system (GIS) incorporating remotely sensed data.

Advanced Very High Resolution Radiometer (AVHRR) thermal-

infrared images, land-cover maps, and soil-type maps were

assembled as GIS database layers for a 1-km spatial resolution

study of Florida. Seasonal and diurnal surface temperature

patterns of many combinations of land-cover and soil type were

analyzed quantitatively. Effects of changes in land-cover and

hydrology due to both artificial (agriculture, urbanization,

wetland disturbance, and exotic-plant introduction) and

natural factors (drought, freeze, and hurricane) were studied.

The thermal-infrared images were calibrated to at-

satellite radiant temperature and geographically corrected for

importation into the GIS, then corrected for atmospheric


xxvi









effects and surface emissivity to produce surface kinetic

temperature. Seven soil types (from maps) and 32 land-cover

types (from maps, aerial photographs, and site visits) were

imported into the GIS as digitized polygons.

Results of analyses performed using the GIS indicated

that both land-cover and soil type, as well as soil moisture

and season, were significant factors influencing surface

temperature patterns in Florida. Surface temperature effects

(daytime "heat island", nighttime "cold island", and diurnal

variation "extreme island") of several agricultural and urban

soil type/season combinations matched or exceeded those found

among natural land-cover types. The surface temperature

effects of certain agricultural soil type/season combinations

matched or exceeded those of urban counterparts. Drought,

freeze damage, hurricane damage, wetland disturbance, and

exotic-plant introduction all produced significant changes in

surface temperature.


xxvii














INTRODUCTION


Surface temperature is a parameter relevant to many

fields. It is in demand for studies and models of

climatology, hydrology, agriculture, and forestry. There are

difficulties to be overcome in obtaining and applying surface

temperature data, which might be solved with new techniques

involving remote sensing and geographic information systems

(GIS). These matters are discussed below, with emphasis on

their importance to the state of Florida.


Purpose and Objectives


The purpose of this research was to use satellite imagery

together with a geographic information system to

quantitatively investigate the kilometer-scale relationship

between changes in land cover and those in surface

temperature. The specific objectives were 1) to obtain

remotely sensed thermal-infrared data at 1-km spatial

resolution for the state of Florida, using the National

Oceanic and Atmospheric Administration (NOAA) Television

Infrared Observation Satellite (TIROS) Advanced Very High

Resolution Radiometer (AVHRR) thermal-infrared imagery; 2) to

calibrate this thermal-infrared data to at-satellite radiant

temperature and to geographically correct the resulting

temperature imagery for inclusion in a GIS; 3) to calculate







2

surface kinetic temperature from at-satellite radiant

temperature through atmospheric correction and emissivity

correction; 4) to digitize land-cover and soil-type data

(obtained from maps, aerial photography, and site visits) into

polygons for inclusion in a GIS; 5) to build a GIS database

containing as coverage layers the surface temperature images,

soil type, land-cover (natural, agricultural, urban, and

industrial types), and special condition (drought, hydrologic

disturbance, freeze-damage, storm-damage); 6) to utilize the

GIS database in performing a quantitative analysis of seasonal

and diurnal relationships between land-cover, soil type, and

surface temperature; and 7) to utilize the GIS database in

performing a quantitative analysis of seasonal and diurnal

relationships between changes in land-cover type and changes

in surface temperature.

There were two additional objectives to supplement the

AVHRR work. These were 1) to perform a historical (1979)

surface temperature study of Florida based on the at-satellite

radiant temperature data of the National Aeronautics and Space

Administration (NASA) Heat Capacity Mapping Mission (HCMM)

satellite, and 2) to perform a ground-based evaluation of the

DSTV/soil moisture relationship for both mineral and organic

soil types.


Importance of Surface Temperature in Climatology


Surface temperature is one of critical parameters of

climate at macro-scale, meso-scale (Davis and Giles, 1990;







3

Giorgi and Mearns, 1991; Barron, 1992, Lewis and Wang, 1992;

McCabe and Wolock, 1992), and micro-scale (Auer, 1978; Lewis,

1984; Rourke, 1985; French and Krajewski, 1994). Macro-scale

climate (100 km to global spatial resolution, multi-year

temporal resolution) includes the basic macroclimate, or

"average weather", with its global weather systems,

atmospheric/oceanic/topographic influences, and long-term

perturbations from factors such as the El Nifo/Southern

Oscillation (ENSO), volcanic dust plumes, and greenhouse gases

(McClain et al., 1985; Harries, 1990; Mather and Sdasyuk,

1991). Macro-scale climate is not directly concerned with

near-surface (< 2 m above surface) processes (Akin, 1991).

Meso-scale climate (1 to 100 km spatial resolution, 1 day

to 1 year temporal resolution) includes local weather and is

directly concerned with near-surface processes (Henderson-

Sellers and Robinson, 1986; Harries, 1990; Hostetler and

Giorgi, 1993; Johannessen et al., 1993). Micro-scale climate

(1 km or finer spatial resolution, 1 day or finer temporal

resolution) is directly concerned with highly localized

effects, which are nested within (and do not influence) the

meso-scale climate (Henderson-Sellers and Robinson, 1986;

Harries, 1990; Akin, 1991).

Meso-scale is the smallest scale at which surface factors

have potential to force weather patterns; land-cover and/or

surface moisture organized at this scale produces an organized

atmospheric response (Shuttleworth, 1991). This response

includes air convection and turbulence (Henderson-Sellers and







4

Robinson, 1986), and even shifts in rainfall patterns due to

urban "heat-islands", irrigated-desert "oases", drained-marsh

"heat-plateaus", etc. (Auer, 1978; Dickinson, 1988; Abtew and

Khanal, 1994). In combination with soil moisture and acidity,

surface temperature controls soil-biogenic greenhouse-gas

emissions (Schimel et al., 1988; Yienger and Levy, 1994). Due

to these effects on weather patterns and greenhouse gas

emission, surface temperature is a meso-scale parameter which

must be linked to the current macro-scale global-circulation

models for further study of the global-change/greenhouse-

effect (Bolin, 1988; Risser et al., 1988; MacCracken et al.,

1990; Mather and Sdasyuk, 1991).


Importance of Surface Temperature in Hydrology


Surface temperature is an important parameter of surface

hydrology and its components such as evapo-transpiration (ET)

and soil moisture (Soer, 1980; Haan et al., 1982; Heimberg et

al., 1982; Price, 1984; Reiniger and Seguin, 1986; Ottle et

al., 1989; Sucksdorff and Ottle, 1990; Novak, 1991; Rodriguez-

Iturbe et al., 1991a, 1991b; Brutsaert and Parlange, 1992;

Chang et al., 1992; Nikolaidis et al., 1993; Zelt and Dugan,

1994). It is also an important factor in water conservation

topics such as lake and reservoir evaporation (Miller and

Millis, 1989; Hondzo and Stefan, 1991; Steinhorn, 1991; Mahrer

and Assouline, 1993), streamflow (Cayan, 1993), and effects of

oil spills on ocean evaporation (Mather and Sdasyuk, 1991).







5

A typical application of surface temperature in hydrology

is in the computation of the Bowen ratio (Linsley et al.,

1982):


P = 0.00066 p (T, Ta) / (eo ea) [1]


where P is the ratio of sensible heat transport to latent heat

transport, R is the atmospheric pressure (mbar), T, is the

surface temperature, T, is the air temperature (C), eo is the

air saturation vapor pressure (mbar) at T,, and ea is the air

vapor pressure (mbar). The Bowen ratio is used in the study

of reservoir evaporation and vegetation evapotranspiration

(Linsley et al., 1982).


Importance of Surface Temperature in Agriculture and
Forestry


Surface temperature is important to agriculture and

forestry as a factor of water-stress and growing-region for

crops and trees (Henderson-Sellers and Robinson, 1986; Seguin,

1989). Meso-scale surface temperature is of interest to

studies involving agricultural topics such as regional crop

condition and yield prediction (Idso et al., 1979, 1981;

Reginato, 1983; Taconet et al., 1986b; Hope and Jackson,

1989). It is a major factor involved in soil conservation

issues--such as soil subsidence (Lucas, 1982) and soil

degradation (Kilmer, 1982), which are critical to long-term

agricultural planning. Surface temperature is also of







6

interest in the assessment of forest-fire risk (Waters, 1976;

Chuvieco and Martin, 1994).

Together with soil moisture, meso-scale patterns and

changes of surface temperature are a major factor in outbreaks

of pests, parasites, and diseases in agricultural crops,

forest trees, and livestock (Uvarov, 1931; Geiger, 1950; Akin,

1991). Examples of such impacts include locust swarm-

behavior, Dutch elm disease, chestnut blight, tobacco blue

mold, Japanese beetle, Colorado potato beetle, potato blight,

cotton leaf worm, seedling-scald, and liver fluke (Uvarov,

1931; Rourke, 1985; Akin, 1991).


Factors Affecting Surface Temperature Patterns


Land surface temperature patterns at meso-scale are

forced by several factors which can change spatially and

temporally (Hillel, 1980; Risser et al., 1988; Lewis and Wang,

1992). These include macro-scale climate, solar irradiation,

geothermal heat sources, maritime effects, orogenic effects,

vegetation transpiration, root-zone soil moisture, soil type,

and land cover type.

Macro-scale climate has been discussed previously; its

impact on meso-scale surface temperature takes the form of

annual cyclic changes in average values of precipitation and

air temperature, which are well-documented for most places.

The macro-scale climate effect on surface temperature can be

estimated from comparison of data from similar natural land-

cover type pairs across macro-scale climate zones (temperate-







7

zone pine forest and subtropical-zone pine forest, temperate-

zone marsh and subtropical marsh, etc.). Advection effects

(strong winds, precipitation, etc.) from transient weather

phenomena such as storms, winter frontal systems, and

especially desert-winds harmattann, etc.) can have a

substantial impact on meso-scale temperature (and relative-

humidity), but these effects are sporadic and transient

outside of continental interiors (Hillel, 1980; Haan et al.,

1982), and are not addressed in this study. Solar irradiation

influences surface temperature through daily cyclic changes,

and is a primarily a function of latitude, date, and hour.

Geothermal heat sources are common at micro-scale--such as hot

springs, subterranean steam-lines (Axelsson, 1988), and

artesian wells (Jordan and Shih, 1988), but rare at meso-scale

(volcano and geyser areas).

Maritime effects operate at micro-scale (air advection

immediately adjacent to the coast) and at meso-scale

(increased humidity further inland) (Henderson-Sellers and

Robinson, 1986; Dickinson, 1988). The positions of meso-scale

water bodies and wetlands are well documented. Florida, due

to its proximity (at meso-scale) to the sea on every side, its

near sea-level elevation, and its lack of topographic

obstructions (mountains), is free from sources of major

variation in the meso-scale maritime effect. The micro-scale

maritime effect on surface temperature can be estimated from

comparison of data from coastal/inland pairs of similar







8

natural land-cover type, such as saltmarsh and freshwater

marsh, coastal hammock and inland hammock, etc.

Orogenic effects on meso-scale surface temperature can be

very pronounced in mountainous regions (Atkinson, 1985;

Henderson-Sellers, 1986; Barros and Lettenmaier, 1994). They

are nonexistent in level-terrain regions such as Florida.

Vegetation transpiration, root-zone soil moisture, soil

type, and land cover type are forcing factors of meso-scale

surface temperature which are strongly inter-related.

Vegetation transpiration is a daily cyclic phenomenon. Root-

zone soil moisture can change over hours or days; it is

artificially controlled in urban and agricultural areas, and

is a function of the soil type and macroclimate in natural

areas. Soil type is generally constant over time, and is

documented to various degrees in most of the world; it is a

particularly important surface temperature factor for cleared

areas.

Land-cover type is subject to both annual cyclic changes

(seasonal tree leaf cover, agricultural crop seasons) and

sudden changes (agricultural/urban development, natural

disasters). Land-cover type is well-documented at meso-scale

for most of the world, although available maps are often

overly simplistic in land-cover distinction--particularly for

agricultural land-cover. Meso-scale land-cover information

for application to meso-scale surface temperature work should

contain distinctions comparable to Level-III designations in

the United States Geological Survey (USGS) land use







9

classification system (Anderson et al., 1976)--for example,

"pasture" or "cropland" instead of simply the Level-II

"cropland and pasture" or Level-I "agriculture" designations.


Temperature Impacts of Changes in Land-Cover


Land-cover is a forcing factor of surface temperature

pattern which can experience meso-scale changes that are non-

cyclic and discontinuously-distributed both spatially and

temporally. These changes can be due either to natural causes

(volcanoes, storms, floods, droughts, wildfires, pests, etc.)

or artificial causes (related to agricultural and urban/

industrial development). International attention has mounted

in recent years concerning the worldwide extent of

deforestation (Mather and Sdasyuk, 1991), compared to the very

few regions currently experiencing a significant degree of

reforestation (Ireland, Senegal, England, and Algeria as of

1984).

Urban effects. Previous research has established the

concept of the urban heat island, which is characterized by

increased surface and air temperature (by 5 to 10 C) and

decreased relative-humidity in an urbanized area relative to

its surroundings (Eagleman, 1974; Lewis, 1984; Atkinson, 1985;

Balling and Brazel, 1988; Henry et al., 1989; Akin, 1991).

The more high buildings, more smog, and fewer trees, the more

pronounced the effect (Henderson-Sellers and Robinson, 1986).

The heat island is primarily a daytime effect within the

urbanized area, and has been shown to have a cellular







10

topology, rather than a smooth dome shape, due to the peak and

canyon geometry of the urban skyline (Atkinson, 1985). There

is a similar, but lesser, nighttime heat-island effect

(Eagleman, 1974; Henry et al., 1989). The importance of urban

temperatures to human well-being has been noted (Lewis, 1984;

Henderson-Sellers and Robinson, 1986; Meerow and Black, 1988).

There is a need for more detailed investigation of the

heat island effect. The heat island of urban areas has

usually been compared simply to non-urbanized areas nearby;

the influence of soil type has often been ignored. Thus, it

may be that a well-drained soil area selected for urban use

has always had an associated heat island relative to

surrounding areas of different soil type, even under its

natural cover. In addition, there is the possibility that the

inclusion of water-bodies, as is common in certain Florida

suburbs (finger canals) and mines (tailings ponds), may

counteract the urban heat island effect. The presence of

windbreak trees, as in golf-course suburb communities,

restricts the lateral flow of near-surface air, leading to

higher daytime temperatures in the open area between trees

than would be the case for a completely open field (Geiger,

1950; Crowe, 1971; Meerow and Black, 1988a; McCarty et al.,

1990). In closed-canopy parkland, the removal of undergrowth

and low tree branches increases the lateral flow of near-

surface air, producing a moderating effect on temperature

compared to open urban areas, but a decrease in humidity

compared to a natural forest (Lewis, 1984).







11

Agricultural effects. Agricultural effects on surface

temperature patterns have received less attention than urban

effects, which is undeserved, considering the vastly greater

areal extent of agricultural land-cover compared to urban

land-cover. For irrigated areas in deserts, a measurable

daytime oasis effect characterized by decreased surface and

air temperature and increased relative-humidity compared to

the surroundings has been noted (Hillel, 1980; Haan et al.,

1982; Henderson-Sellers and Robinson, 1986). For agricultural

land-cover in forest/wetland areas, a measurable daytime heat-

plateau effect characterized by increased surface and air

temperature and decreased relative-humidity compared to the

natural surroundings has been observed (Ghuman and Lal, 1987),

together with a corresponding nighttime cold-plateau of

decreased surface and air temperature compared to the natural

surroundings (Chen, 1979). The presence of belt-planted

trees, as in agricultural field windbreaks, restricts the

lateral flow of near-surface air, leading to higher daytime

temperatures in the open area between trees than would be the

case for a completely open field (Geiger, 1950; Crowe, 1971;

Meerow and Black, 1988a; McCarty et al., 1990).

Florida land-cover change. In Florida, natural causes of

meso-scale land-cover change have consisted of storms

(hurricanes), droughts, wildfires, and freezes. Artificial

factors of land-cover change have consisted of agricultural

development, urban development, industrial development, water-







12

control projects, naturalization projects, and invasion by

exotic vegetation.

Agricultural development in previous times (pre-Columbian

to early 1900s for panhandle and north; primarily from 1910 to

1950 for south) displaced much of the natural land-cover in

Florida (Fernald and Patton, 1984); in recent times it has

consisted primarily of changes in the type of agriculture--

especially between row-crops, pasture, and citrus orchard--on

the same land. Urban/industrial development in previous times

(primarily in the form of urban centers and strip mines)

displaced much of the natural and agricultural land-cover; in

recent times this development is continuing--particularly in

the form of suburbs.

Water-control projects are located in the Everglades

Agricultural Area (EAA), Kissimmee River basin, and Upper

Suwannee River basin (north-central Florida as well as south-

central Georgia). Naturalization projects include the

restoration of marshes (Payne's Prairie, Lake Apopka,

Kissimmee River basin, Lake Jessup, and the activity of

beavers naturally re-colonizing parts of the panhandle),

saltmarshes (Indian River Lagoon, Tampa Bay), and scrub (state

parks, local parks, and Archbold Biological Station). In

addition, efforts have begun in recent decades to reclaim

mined land for naturalization and even agriculture (Blakey,

1973).

Invasion by exotic vegetation has occurred at meso-scale

in south Florida (EPPC, 1990). This vegetation consists of







13

evergreen trees which have a very fast growth rate, and

transpire enough to decrease the root-zone soil moisture--

which is precisely why some of them were introduced in earlier

decades--to dry out wetlands (EPPC, 1990). The spread of

exotic forest has been beyond effective human control since

the 1950s (Barrett, 1956; EPPC, 1990), and has now reached

proportions of serious ecological and hydrological concern to

south Florida.


Temporal Inter-Relation of Forcing Factors


The net surface energy flux, soil type, and root-zone

soil moisture are forcing factors of the surface temperature

pattern which are inter-related temporally (Geiger, 1950;

Crowe, 1971; Kahle, 1977; Hillel, 1980; Bolin, 1988; Pollak,

1992). A bare-soil, flat-terrain, conductive heat-transfer

(non-advective) surface energy balance can be modeled

temporally by the harmonic equation (Mulders, 1987):


T(z,t) = Ta, + [F0/(PWl/2)]e-z/dsin(wt-z/d-r/4) [2]


where T(z,t) is the soil temperature (K) at depth (m) and

time 1 (t = 0 s at 0000 h) expressed as local solar time

(LST), Tv,, is the average (treated as constant) soil

temperature (K) at a depth of 2 to 3 m, Fo is the amplitude (W

m"2) of the net surface energy flux F, P is the soil thermal

inertia (J m-2 K-' s-1/2), d is the damping depth (m), and w is

the Earth angular rotation frequency (7.27x10-5 s-l). The net







14

surface energy flux, modeled in the form F = Fosin(wt), is

primarily a function of solar irradiation and near-surface air

temperature (Budyko, 1974; Henderson-Sellers and Robinson,

1986; Lewis and Wang, 1992). Soil thermal inertia is

expressed by the formula (Price, 1982):


P = (Apc)1/2 [3]


where A is the soil thermal conductivity (W m-1 K-1), p is the

soil density (kg m-3), and c is the soil heat capacity (J kg-1

K-1). The components X, p, and c are directly related to the

soil moisture content for a given soil type (Lillesand and

Kiefer, 1979; Carlson et al., 1981; Price, 1984; Curran,

1985). Therefore, the higher the soil moisture content, the

warmer the soil temperature during the night and the cooler

the soil temperature during the day, as has been noted in

numerous studies (Shih et al., 1986; Taconet et al., 1986b;

Sugita and Brutsaert, 1992). The above equation can be solved

for surface temperature, T,, yielding the equation (Mulders,

1987):


Ts(t) = T.,, + [Fo/(P 1/2)]sin(Ot-7/4) [4]


where the quantities are described as before. The w/4 term

translates (by 2r = 24 h) to a 3-hour time lag between maximum

F, (1200 h LST) and maximum surface temperature (1500 h LST).

Non-conductive components of soil heat transfer (dew

evaporation) can cause the surface temperature to vary







15

somewhat from that indicated by equation 4 (Hinkel and

Outcalt, 1993), but only for a short period in the early

morning (Schmugge, 1978; Price, 1982).

Diurnal surface temperature variation. Taking the

diurnal surface temperature variation (DSTV) of equation 4

leads to the equation (Mulders, 1987):


DSTV = T. T.i, = 2 F/ (PW1/2) [5]


where T,, is the maximum surface temperature (K) at t = 1500h

LST, T.in is the minimum surface temperature (K) at t = 0300h

LST, and P, w, and Fo are defined as before. Thus, DSTV is

inversely related to the root-zone soil moisture content, and

is a strong indicator of relative root-zone soil moisture

conditions for different locations of the same soil type on

the same day (Engman and Gurney, 1991). The fact that DSTV is

an indicator of daily-average root-zone soil moisture makes it

particularly useful to hydrologic modeling studies (Parlange

et al., 1992). The presence of a clay hardpan or bedrock

within the root-zone depth of shallow soils will lead to

deviations from the predicted DSTV of equation 5 (Hillel,

1980). This influence of foreign bodies within the root-zone

soil depth on DSTV has in fact been used at micro-scale to

locate buried objects/features such as abandoned mine tunnels

and bombs (Cloud, 1992).

Estimated seasonal DSTV. Seasonal values of bare-soil

DSTV can be estimated based on soil parameters. A typical







16

Florida value of Fo in summer is 0.003941 (cal cm-2 s-1) and in

winter is 0.001433 (cal cm-2 s-1) (ASHRAE, 1981). For a

mineral soil (sand or clay with typical 40% porosity), the

value of pc is 0.3 (cal cm-3 K-1) under dry condition and 0.7

under saturated condition, and the value of A is 0.0007 (cal

cm-1 s-1 K-1) under dry condition and 0.0052 under saturated

condition (Hillel, 1980). For an organic soil (peat with

typical 80% porosity), the value of pc is 0.35 (cal cm-3 K-1)

under dry condition and 1.15 under saturated condition, and

the value of X is 0.00014 (cal cm-' s-' K-I) under dry condition

and 0.0012 under saturated condition (Hillel, 1980). Plugging

these values into equations 3 and 5 produces estimated summer

DSTV (K) ranging from 15 (saturated) to 64 (dry) for mineral

soil, and from 25 (saturated) to 132 (dry) for organic soil;

it produces estimated winter DSTV (K) ranging from 6

(saturated) to 23 (dry) for mineral soil, and from 9

(saturated) to 48 (dry) for organic soil. Agricultural land-

cover values of DSTV can be expected to lie somewhere between

those of the saturated condition and those of the totally dry

condition.

Relevant depth of surface temperature/soil moisture

relation. The depth of soil to which the soil moisture

content is relevant to the surface temperature is a function

of the damping depth, d, which is given by the equation

(Hillel, 1980):


d = [2A/pcw)]1/2







17

where A, p, w, and c are defined as before. The values of X,

p, and c are functions of both soil moisture and soil type.

For a mineral soil (sand or clay with typical 40% porosity)

under a totally-dry condition, the value of d is about 8 cm;

for an organic soil (peat with typical 80% porosity) under a

dry condition, the value of d is about 3 cm (Hillel, 1980).

The attenuation factor of equation 2 is e-z/d, so that an

attenuation (100 e-z/d)% of 95% (relevant depth limit for

estimated soil-moisture) is reached at a depth of 3d,

corresponding to 24 cm for a mineral soil and 9 cm for an

organic soil. Under saturated soil condition, the value of d

increases to about 14 cm for mineral soil and about 5 cm for

organic soil (Hillel, 1980), increasing the respective

relevant depth limits to 42 cm and 15 cm. The moisture

content of such root-zone depths is of importance to surface

hydrologic modeling (Rourke, 1985; Risser et al., 1988; Milly,

1994; Zelt and Dugan, 1994) and climatological modeling

(Gillies and Carlson, 1994; Salvucci and Entekhabi, 1994;

Smith et al., 1994).

Vegetation influence on DSTV. Vegetated land cover

affects both the maximum and minimum surface-temperature

components of DSTV (Geiger, 1950; Luval et al., 1990).

Minimum surface temperature is raised by nighttime reflection

of soil-emitted energy back to the surface. The radiation

contribution from vegetation foliage at night is negligible--

foliage quickly reaches equilibrium with air temperature

(Hillel, 1980; Chen et al., 1982; Reiniger and Seguin, 1986;







18

Seguin, 1989; van de Griend and van Boxel, 1989). The

nighttime vegetation effect will decrease during winter for

deciduous vegetation species.

Maximum surface temperature is lowered by the daytime

evapotranspiration (ET) from vegetation. ET rate is a

function of vegetation type (exact species or cultivar),

ambient water-vapor pressure deficit, air temperature,

vegetation species, and vegetation water stress (Idso et al.,

1981a, 1981b; Wetzel et al., 1984; Reiniger and Seguin, 1986;

Taconet et al., 1986a, 1986b; van de Griend and van Boxel,

1989; Doyle, 1992). It should be noted that the transpiration

component of ET is present for most vegetation only from late

morning to afternoon (Bolin, 1988). If the source of

vegetation water stress is limited to root-zone soil moisture

(rather than salinity or damage from pests, diseases, wind,

hail, etc.), and the other ET factors are measured, the soil

moisture condition can be calculated. The relevant depth in

this case is dependent on the vertical distribution of the

root system (according to plant species and maturity), not on

the d-value of equation 6 (Rubin and Or, 1993). This relation

is the basis for the Crop Water Stress Index (CWSI), which is

widely used for scheduling the irrigation of agricultural

fields (Howell et al., 1983; Reginato, 1983; SOEMC, 1987; EI,

1991). Again, the daytime vegetation effect will decrease

during winter for deciduous vegetation species.

The diurnal effect on the net heat flux of the vegetated

surface, G (W m-2) can be represented (for a non-advective







19

situation) in form (Haan, 1982):


G = Fo sin(wt) LE [7]


where LE is the latent heat flux (W m-2) caused by the

vegetation. The term LE can be approximated using the Bowen

ratio, 3, producing the equation (Haan, 1982):


Fo sin(wt)
LE= [8]
1 +


where P typically ranges from 0.1 to 0.3 for humid-climate

conditions. Assuming P = 0.2, and substituting the LE from

equation 8 into equation 7, it follows that the DSTV for the

vegetated surface, DSTVv is


DSTVv = (0.3334) Fo/(PWl/2) [9]


which is a reduced-amplitude version of equation 5. The

assumption of full canopy closure is made here; the estimation

of exact effects of partial canopy (as in many forms of

agricultural land-cover) are a complex matter for study at

very fine spatial and temporal scales (Taconet et al., 1986a,

1986b; Massman, 1992).


Spatial Inter-Relation of Forcing Factors


The soil type, root-zone soil moisture, and land-cover

type are forcing factors of the surface temperature pattern

which are inter-related spatially (Akin, 1991). Different







20

types of agriculture require the maintenance of different

levels of soil moisture (Ziegler and Wolfe, 1961; Snyder,

1978; Henley, 1983; McCarty and Cisar, 1990)--standing water

for rice, taro, and fish-farm; high water-table for sod-farm;

medium water-table for winter-vegetables, sugarcane, potato,

strawberry, blueberry, blackberry, and pasture; and relatively

low water-table for leatherleaf fern, citrus, and most other

fruit trees.

Likewise, different soil types require different

agricultural management practices--irrigation of deep sands

and loams; drainage of organic soils and marls; and both

irrigation and drainage of spodosols and rockland soils

(Jones, 1948; Stewart et al., 1963; Hochmuth and Hanlon,

1989). Soil type and seasonal soil-moisture levels dictate

the natural land-cover type (scrub, forest, swamp, marsh,

etc.) and limit the possibilities of agricultural land-cover

types (citrus primarily to mineral soil, sugarcane primarily

to organic soil, blueberry to acid soil, atemoya to sub-

alkaline soil, etc.) (Critchfield, 1960; Schimel, 1988; Akin,

1991).

Urban development, however, is less impeded by soil type.

This is particularly evident in Florida, where coastal sands,

flatwoods sands, marls, and even mucks have been urbanized, as

well as the more conventional deep sands, upland loamy sands,

and sandy rockland.







21

Potential for Future Changes in Soil Type


General soil type, which is a constant forcing factor of

meso-scale surface temperature for mineral-soil areas, can

change for organic-soil areas, or even for mineral-soil areas

where mass-wasting occurs (Risser et al., 1988). Organic

soils drained for conventional agricultural use tend to

subside and eventually disappear (Snyder, 1978; Kilmer, 1982;

Lucas, 1982; Abtew and Khanal, 1994). This effect was well

understood even at the time the EAA water-control system was

being planned and implemented (Jones, 1948). The current rate

of organic soil subsidence in the EAA is about 1 inch per year

(Snyder, 1978); the natural temporal scale of soil type change

is typically of the order of 10,000 years (Dickinson, 1988;

Lucas, 1982). The potential mesoscale surface-temperature

impact of such a soil-type change in Florida is greatest in

the EAA, where organic soil will likely be replaced in the

near future by sandy/marly rockland if the agricultural land-

cover does not change to aquatic crops or restored marsh.














REVIEW OF LITERATURE


The techniques involved in the remote sensing of land

surface temperature are described. Previous studies, their

methods, and their results are discussed.


Difficulties of Surface Temperature Measurement


Unlike air temperature, surface temperature cannot be

interpolated between point measurements on land surfaces (due

to differences in several spatially-variable factors); to

obtain it synoptically at meso-scale requires some form of

satellite-based radiometry. Thermal-infrared and passive-

microwave radiometry are the two types applicable to the

typical Earth-surface range of temperatures (Lillesand and

Kiefer, 1979; Owe and Chang, 1988; Engman and Gurney, 1991).

Both of these are available from various satellite platforms.

Passive-microwave sensor data have the advantage of cloud

penetration, but are much more limited in spatial resolution

and temperature accuracy than are thermal-infrared sensor data

at this temperature range, and are sensitive to extraneous

factors such as surface microwave-roughness and radio-

communication interference (Lillesand and Kiefer, 1979; Owe

and Chang, 1988; Harries, 1990; Engman and Gurney, 1991; Owe

et al., 1992). Passive microwave data are typically used at

meso-scale (from satellite platform) for ice/snow water-

22







23

content mapping (Mather and Sdasyuk, 1991) or atmospheric

sounding (Miller et al., 1994), or at micro-scale (requiring

an aircraft platform) for surface temperature or soil moisture

mapping (Ijjas and Rao, 1992; Appleby et al., 1993; Paloscia

et al., 1993). Due to the above considerations, thermal-

infrared radiometry was selected as the source of surface

temperature data in this study.

The extraction of land surface temperature data from

satellite thermal-infrared radiometer measurements involves

three different processes--sensor calibration, atmospheric

correction, and emissivity correction. Errors incurred in any

of these processes will degrade the accuracy of the surface

temperature data (Marlatt, 1967; Llewellen-Jones et al., 1984;

Schott, 1989; Ben-Dor et al., 1994). Each satellite-based

thermal-infrared sensor has its own level of radiometric

precision and its own standardized calibration technique,

which the user must consult (Wolfe and Zizzis, 1978; Tebo,

1994a). A typical order of magnitude of radiometric precision

for thermal-infrared radiometers is 0.1 C (Myhre et al., 1988;

NOAA, 1991).

Ignoring the atmospheric correction can result in surface

temperature underestimates of up to 20 C (Llewellyn-Jones et

al., 1984; Price, 1984; Sobrino et al., 1991); this source of

error is primarily of concern in comparison of surface

temperatures from more than one time or date (in addition,

substantial spatial variation in the atmospheric effect can be

expected for images covering continental distances). Ignoring







24

emissivity correction over land surfaces can result in

underestimates of up to 10 C (Barton and Takashima, 1986);

these errors will vary spatially within an individual image--

resulting in an inability to accurately compare the surface

temperature of one location to the next. Therefore, if

atmospheric or emissivity corrections are not used, the

remotely-sensed surface temperatures may be in error

(conservative) by at least two orders of magnitude compared to

the sensor radiometric precision.


Atmospheric Correction Techniques


Atmospheric correction is needed for remotely sensed

thermal data, due to the combined effects of absorption,

scattering, and in-path radiance by the atmosphere upon

thermal-infrared imagery, even in "atmospheric window" bands

(Lillesand and Kiefer, 1979; Ben-Dor et al., 1994). The net

effect on satellite thermal-infrared radiometry is an

attenuation. Three general types of atmospheric correction

techniques have been developed.

Atmospheric modeling. Detailed modeling of the

atmosphere has been used successfully for atmospheric

correction of thermal-infrared data (Henry et al., 1989;

Wukelic et al., 1989; Luval et al., 1990; Gillies and Carlson,

1994; Sadot et al., 1994). It requires atmospheric data,

which in most instances has been provided by radiosondes,

although laser-based techniques are currently being developed

for this purpose (Tebo, 1994b). Atmospheric sensor







25

instruments exist on most current satellite platforms, but

these have very crude spatial resolution and are used for

global atmospheric research rather than meso-scale atmospheric

modeling (Zhang, 1993; Aumann and Pagano, 1994; Ellingson et

al., 1994). The collection of atmospheric data by radiosondes

is relatively expensive, and cannot be substituted with non-

local radiosonde data, non-simultaneous radiosonde data, or

estimated atmospheric values (Kerr et al., 1992).

Split-window techniques. Empirical "split-window"

techniques, based on differential atmospheric absorption

effects in different thermal-infrared bands, have long been

used successfully in atmospheric correction of thermal-

infrared data (Price, 1984; Llewellyn-Jones et al., 1984;

McClain et al., 1985; Cornillon et al., 1987; Cooper and

Asrar, 1989; Vidal, 1991). They require a sensor that

possesses multiple thermal-infrared bands, which is

fortunately available from various satellite platforms. They

also require ground-based surface-temperature measurements to

allow the initial calculation of their coefficients, but these

have already been established and reported for the individual

techniques (McClain et al., 1985; Di and Rundquist, 1994).

There are inherent difficulties in applying these techniques

to land surface studies (Becker, 1987; Sobrino et al., 1991),

the most important of which are the assumptions that the

surface emissivity is homogeneous within the instantaneous

field of view (IFOV) of the sensor, and that it is equal to 1.

Therefore, split-window techniques are primarily restricted to







26

sea-surface studies, where these assumptions are usually

valid.

Water-body reference technique. The water-body reference

technique requires one or more large water bodies, with near-

surface water temperature measurements taken simultaneously

with the thermal-infrared image (Lillesand and Kiefer, 1979).

These measurements must be taken for each image date and time,

which can pose a logistical problem for studies involving a

temporal series of images. Fortunately, such water-body data

are collected and made readily available in Florida by various

environmental agencies.

This technique includes the assumption that within the

sensor IFOV the water-body surface temperature and emissivity

(not necessarily equal to 1) are homogeneous. This assumption

is generally valid. It also assumes that the water body is

not under conditions such as very hot and dry (desert-climate)

ambient air or strong winds, which would produce a difference

between skin and near-surface bulk temperature of the water.

This assumption is valid for non-desert water bodies under low

wind conditions (Cornillon et al., 1987).


Emissivitv Correction Techniques


Radiant (blackbody) temperature values can be converted

to kinetic (surface) temperature values if the emissivity (in

the sensor bandwidth) is known with sufficient accuracy. The

physical foundation for kinetic temperature calculation by







27

radiometry is described by the Planck function (Saito et al.,

1992):


e C A-5
E(A, T) = [10]
exp [C2/(A T)] 1


where E is the measured energy (W m-2 m-m1), X is wavelength

(Im), Ci is a constant (3.74x108 W m-2 Am4), C2 is the Boltzmann

constant (14,388 Am K), T is the kinetic temperature (K), and

e is the emissivity. A commonly encountered formula for

kinetic temperature calculation is the Stefan-Boltzmann

equation (Lillesand and Kiefer, 1979):


Tkin = b1/4 Trad [11

where Tki, is the kinetic temperature (K), Trad is the radiant

temperature (K), and eb is the broadband emissivity (0 to 1).

However, this formula was designed for application to

laboratory situations of broadband (all wavelengths)

radiometry, rather than the "atmospheric window" band

radiometry performed by satellite sensors (midwave thermal-

infrared window at 3-5 im, longwave thermal-infrared window at

8-14 Am). To account for this, some researchers (Davies et

al., 1971; Price, 1983) have used a version of the Stefan-

Boltzmann equation modified for use with longwave thermal-

infrared sensor bands (Price, 1983):


Tkin = 6w1/4.5 Trad


[12]







28

where Tkl, and Trad are defined as before, and el, is the

longwave emissivity. This modified Stefan-Boltzmann formula

still contains simplifying approximations which limit its

accuracy in application to remotely sensed thermal data. The

integration of equation 10 over the bandwidth of given sensor

provides a better formula for quantitative longwave thermal-

infrared radiometry (Singh, 1985; Driggers et al., 1992; Saito

et al., 1992). It is given by the equation (Singh, 1985):


k CWNi
Tkin = [13]
In [1 E1, + E,1 exp(k CWN, / Tradi)


where Tkin is defined as before, k is the Boltzmann constant

(1.43883 cm K), Trad,i is the radiant temperature (K) in band i,

CWNi is the central wave number of band i (cm-1), and e is the

longwave emissivity. The central wave number is generally

documented for each band of a given sensor.

Emissivity presents the difficulty of being a property

that is calculable, rather than directly measurable (Fuchs and

Tanner, 1968; Friedman, 1969; Hejazi et al., 1992). Two

different methodologies have been employed to obtain

emissivity for processing remotely sensed thermal image data.

Emissivity by assignment. This commonly used technique

is based on longwave emissivity values determined in the field

or laboratory from close-range, longwave thermal-infrared

radiometry of small samples (Buettner and Kern, 1965; Fuchs

and Tanner, 1968; Friedman, 1969; Taylor, 1979; Barton and







29

Takashima, 1986; Rees, 1990; van de Griend et al., 1991;

Vidal, 1991; Salisbury and D'Aria, 1992). An emissivity value

is then assigned to a particular sensor IFOV according to

land-cover information or the normalized-difference vegetation

index (NDVI) (Gervin et al., 1985; Henry et al., 1989; Kerr et

al., 1992; Brown et al., 1993; van de Griend and Owe, 1993).

This method is primarily limited to aircraft or ground

radiometry, since the laboratory-determined emissivity values

are fine-resolution quantities bearing little relation to the

pixel-average emissivity value corresponding to a satellite

sensor IFOV (Curran, 1985; Jupp et al., 1988; Masuda et al.,

1988). An additional problem with this technique in

agricultural areas is that bare sand emissivity is controlled

by the moisture content of a very thin surface layer, and can

change over a short period of time (Fuchs and Tanner, 1968).

Twin-band technique. The physically-based "twin-band"

technique allows the calculation of pixel-average emissivity

from a thermal-infrared image (Artis and Carnahan, 1982):


,ij = exp {k (Tradi-Tradj) / [Trad.i Tad,j (Xi- j)]) [14]


where eij is the emissivity over the wavelengths from band i

to band j, Trad,i and Trad.j are the radiant temperatures (K) in

bands i and j, k is the Boltzmann constant (1.43883x10-2 m K),

and Ai and Xj are the respective central wavelengths (m) of

bands i and j. This method requires a sensor possessing two

thermal-infrared bands that are synoptic, spectrally close,







30

and spectrally narrow. This makes it inapplicable to the

single-band thermal-infrared data acquired by most of the

current satellite sensors (GOES-VISSR, Nimbus-7, Landsat-TM,

Meteor). Fortunately, the twin-band requirement is met by the

NOAA TIROS satellite series AVHRR sensor longwave bands 4 and

5. More sophisticated emissivity correction techniques

involving three thermal-infrared bands have been developed

(Hejazi et al., 1992), but few current satellites produce

imagery containing triplet thermal bands (AVHRR band 3 does

not form a triplet with bands 4 and 5, since it is a midwave

thermal-infrared band). Such multi-band techniques will

likely be the methods of choice for emissivity correction of

satellite-based surface temperatures from the more advanced

sensors aboard satellites of the future international Earth

Observing System (EOS) program.


Previous Studies


Previous studies involving remotely sensed land surface

temperature have been hampered by various factors, including

sensor limitations, lack of adequate processing techniques,

lack of consideration for one or more of the surface

temperature forcing factors, and lack of an adequate

manipulation technique for large quantities of spatial and

temporal data. In particular, there has been a longstanding

difficulty in the mixing of raster (remotely sensed) data with

vector (map) data in climatological studies (Mather and

Sdasyuk, 1991). Consistency of procedural attention to the







31

various components of meso-scale land-surface temperature

research needs to be improved so that individual project

databases can be made mutually compatible.

Chen (1979. 1980). Chen (1979, 1980) used NOAA

Geostationary Operational Environmental Satellite (GOES)

Vertical Infrared Spin-Scan Radiometer (VISSR) thermal-

infrared, winter, nighttime images of peninsular Florida in a

multi-year, meso-scale study of the feasibility of monitoring

agricultural areas for potentially crop-damaging cold

temperatures. Geographic correction was performed by NOAA

using satellite orbital telemetry; the resulting images

required manually-fitted offsets of up to 3 pixels (VISSR

thermal-infrared has 8 km nominal resolution at nadir).

Emissivity by assignment was used for both agricultural and

natural land-cover. Comparison of calibrated at-satellite

temperatures to ground-measured surface temperatures (from

hand-held thermal-infrared radiometer) and near-ground air

temperatures (from thermometry) indicated a range of error of

up to 5 C (for both sets of data), which nonetheless allowed

for determination of statistically significant differences in

temperature between broad categories of land-cover

(agriculture, marsh) and soil (organic). This study indicated

the potential for detailed research into meso-scale,

satellite-based, land-surface temperature impacts of land-

cover type and its relevance to agriculture, as well as the

difficulties of image registration, and especially the







32

importance of full correction for both atmospheric and

emissivity effects.

Cornillon et al. (1987). Cornillon et al. (1987) used

NOAA TIROS AVHRR thermal-infrared images to construct an

archive of quality-assured meso-scale sea-surface (Atlantic

ocean) temperature maps. Geographic correction was performed

based on TIROS satellite orbital telemetry data and ground-

control points; accuracy of pixel registration was to the

nearest 1.5 km (AVHRR has 1.1 km nominal resolution at nadir).

Because entire AVHRR scans (including image extremities or

"limbs") across continental distances were used, the images

were corrected for scan-angle effects. Ordinarily, AVHRR

longwave thermal-infrared images do not require correction for

scan-angle effects (Masuda et al., 1988; Kerr et al., 1992).

The water-body atmospheric correction technique was used, and

verification datasets indicated an accuracy to the nearest

0.51 C for sea-surface temperatures. This study illustrated

the value of accuracy assessment for both image registration

and surface temperature, and the surface temperature accuracy

attainable through atmospheric correction by the water-body

method.

Balling and Brazel (1988). Balling and Brazel (1988)

used NOAA TIROS AVHRR thermal-infrared images in a meso-scale

study of the urban heat-island effect in Phoenix, Arizona.

Images were geographically corrected based on satellite

orbital telemetry data. No atmospheric correction procedure

was reported. Emissivity correction was performed by







33

assignment (several values), and the Stefan-Boltzmann equation

was used to compute surface temperature. A lack of GIS

capability led to analyses based on linear transects of image

features, rather than on areal extractions. No accuracy

evaluation for geographic registration or surface temperature

was reported, but statistically significant differences in

urban center and suburb heat island effects were observed.

This study demonstrated the feasibility of measuring the

surface temperature impact of a purely meso-scale land-cover

type with AVHRR images, and the need for both accuracy

evaluation and a GIS-based analytical technique.

Cooper and Asrar (1989). Cooper and Asrar (1989) used

NOAA TIROS AVHRR thermal-infrared images in a meso-scale study

of land surface temperature in Kansas. Geographic correction

was performed by NOAA using satellite orbital telemetry. No

registration accuracy analysis was reported. Lack of GIS

overlay-analysis capability required the use of triangulation

between image features (lakes) to delineate study areas.

Atmospheric correction was performed by several techniques--

including both atmospheric modeling methods (with radiosonde

data) and split-window techniques. Ground-based radiometer

measurements of temperature were used to evaluate the accuracy

of the satellite-based surface temperature values. These

ground-based measurements themselves had a variance of about

6 (C2), due to the high variability of surface temperature at

their very micro-scale (1 m), even though the land-cover was

uniform (prairie grassland). Emissivity correction was







34

performed by assignment of a single value for the entire land

surface. The atmospheric modeling methods were found to

produce acceptable surface temperature accuracies (to nearest

3 C); all but one of the split-window techniques produced

unacceptable surface temperature accuracies. This study

indicated the difficulties associated with non-GIS

manipulation of remote sensing data, as well as the

fundamental unsuitability of ground-based point measurements

of land-surface temperature as a basis for evaluating

satellite-based meso-scale average land-surface temperature.

Henry et al. (1989). Henry et al. (1989) used NASA HCMM

satellite thermal-infrared images in a meso-scale, GIS-based

study of the urban heat-island effect of Gainesville, Florida.

Detailed land-cover information corresponding to the United

States Geological Survey (USGS) classification system

(Anderson et al., 1976) came from maps and aerial photographs.

Geographic correction was performed based on ground control

points and a first-order polynomial surface model;

registration accuracy was estimated solely by the root-mean-

square (rms) error of the fitted points ( 0.6 pixel).

Atmospheric correction was performed by the atmospheric

modeling method, with radiosonde data. Emissivity correction

was performed by assignment of a single value for all

urban/suburban land-cover. Quality evaluation was very

limited--based on near-surface air temperature measurements

collected non-simultaneously (different year) from the

satellite data; the authors acknowledged that even







35

simultaneous measurements of surface and near-surface air

temperature could vary on the order of 10 C. Despite this

problem, general heat-island impact differences were noted for

several urban and rural land-cover types through GIS-based

analyses. This study showed the power of GIS as an analytical

tool for linking satellite image data and map data, the value

of geographic registration accuracy evaluation, the necessity

of temperature accuracy evaluation, and the unsuitability of

using near-surface air temperature to evaluate surface

temperature accuracy.

Luvall et al. (1990). Luvall et al. (1990) used airborne

thermal-infrared sensor images in a micro-scale study of Costa

Rican rainforest canopy temperature and ET. Atmospheric

correction was performed by the atmospheric modeling method,

with radiosonde data. No emissivity correction was reported,

but the surface-temperature study was limited to full-canopy

vegetation surfaces having emissivity near unity. Lack of

both geographic correction and GIS capability led to eyeball

estimates of image subsets corresponding to polygons on aerial

photographs. Verification data in the form of thermocouple

leaf-temperature measurements at the top of the canopy

indicated an average remotely-sensed surface temperature

accuracy to the nearest 1.1 C. This study indicated the

potential for remote measurement of surface temperature over

forest canopy, the suitability of vegetation surface

temperature measurements for evaluating the accuracy of

remotely-sensed vegetation surface temperature, the need for







36

GIS-based analysis, and the need for geographic registration

and accuracy assessment.

Sucksdorff and Ottle (1990). Sucksdorff and Ottle (1990)

used NOAA TIROS AVHRR thermal-infrared images in a meso-scale

study of ET in Finland. Geographic correction was performed

by registration to a base map. Atmospheric correction was

performed by the atmospheric modeling technique, using

radiosonde data. No accuracy evaluation was reported for the

geographic correction or the temperature data. This study

demonstrated the use of base-map image registration to

construct a raster GIS database, as well as the need for

evaluation of geographic and temperature accuracy.

Leshkevich et al. (1993). Leshkevich et al. (1993) used

NOAA TIROS AVHRR images to construct an archive of quality-

assurred Great Lakes water-surface images. Geographic

correction was performed based on satellite orbital telemetry

data; manual offsets of up to 10 km were required for image

registration to allow construction of a raster GIS. No

further evaluation of geographic accuracy was reported.

Atmospheric correction was performed by a split-window

technique. Lake surface temperatures (day and night) were

determined to be accurate to the nearest 1 C, based on

comparisons with near-surface water-body temperature

verification data. This study illustrated the value of GIS as

an analytical tool, and the suitability of water-body

temperature measurements in accuracy assessment of remotely

sensed water surface temperature values.







37

Gillies and Carlson (1994). Gillies and Carlson (1994)

used NOAA TIROS AVHRR images in a study to estimate meso-scale

surface moisture-availability (ratio of soil moisture content

to that at field capacity) in northeast England. Four

afternoon spring and summer images from 1989 to 1990 were

calibrated to at-satellite radiant temperature. Atmospheric

correction was performed by the atmospheric modeling method,

using radiosonde data. No emissivity correction was reported.

Geographic correction was performed based on ground control

points, and the positional accuracy was determined to be

acceptable (0.8 km maximum rms error). Moisture-availability

estimation was evaluated based on point measured data, and was

found to be accurate to the nearest 5 to 7%. This study

indicated the potential for quantitative analyses of surface

parameters obtained from satellite images, and the increased

geographic accuracy resulting from the use of ground control

points, rather than relying solely on satellite orbital

telemetry for image geographic registration.

Current directions in land surface temperature research.

A methodology for detailed, meso-scale, quantitative study of

land-surface temperature patterns involving a GIS containing

the full set of forcing factors has been called for by several

researchers (Taconet et al., 1986a; Henderson-Sellers and

McGuffie, 1987; Dickinson, 1988; MacCracken et al., 1990;

Lagouarde, 1991; Mather and Sdasyuk, 1991; Sobrino et al.,

1991; Dozier, 1992; Kerr et al., 1992; Brown et al., 1993;

Lindsey et al., 1993; Wheeler, 1993). This methodology would







38

necessarily involve careful attention to the matters of

atmospheric correction, emissivity correction, geographic

correction, and quality analysis of both temperature and pixel

registration (Henderson-Sellers and Robinson, 1986; Harries,

1990; Mather and Sdasyuk, 1991; Peters et al., 1992). These

recommendations were put forth in simplest form by Heimburg et

al. (1982, p. 128):

An estimation procedure can be no more accurate than
allowed by its weakest part. From this perspective, the
most important area for future research is development of
operational methods to determine surface temperature and
net radiation from satellite data. This development
includes solutions to the problems of image registration
and atmospheric absorption corrections. The ability to
accurately overlay visible and [thermal] infrared data
collected at different times from the same area on the
earth's surface is critical to all remote-sensing methods
[for evapotranspiration estimation], as is the ability to
correct temperature and net radiation estimates for
atmospheric effects.

Satellite data suitable for such work now exist (Sader et al.,

1990; NOAA, 1991; Di and Rundquist, 1994; Gillies and Carlson,

1994), as do geographic information systems for performing

sophisticated analyses (ESRI, 1990; Lo and Shipman, 1990; Tan

and Shih, 1990; ERDAS, 1991; Wood, 1991; Rutchey and Vilcheck,

1994; Srinivasan and Engel, 1994; Wong, 1994).














MATERIALS AND METHODS


This research was performed on hardware consisting of a

personal computer (PC), high-resolution monitor, and

digitizing tablet. Software used included the PC versions of

the Earth Resources Laboratory Application Software (ELAS),

Earth Resources Data Acquisition System (ERDAS), and ARC/INFO.

The ground-based DSTV/soil-moisture work utilized soil augers,

hand-held radiometer, thermistors, and the gravimetric soil-

moisture analysis equipment of the University of Florida Soil

and Water Science Department.


Study Area


The Florida study area is shown in Figure 1. There are

three macroclimate zones--panhandle, north, and south (Fernald

and Patton, 1984; Schmidt, 1992).


Panhandle Zone


The panhandle zone includes the Florida Panhandle, which

for the purpose of this study is defined as the region to the

west of the St. Marks river. Its macroclimate is warm-

temperate, with a relatively wet winter (Fernald and Patton,

1984). Vegetation is limited to warm-temperate species

(Clewell, 1985). The natural forest trees include evergreen

broadleaf types and palms, as well as deciduous broadleaf








40

















Panhwdle







North

4













0 40kmrn
























Figure 1. Study area with climate zones and water-body
temperature stations (see Appendix B for details).







41

types and both evergreen and deciduous conifers. The

agricultural vegetation types include deciduous orchards and

nearly year-round pasture and crops.


North Zone


The north zone, for the purpose of this study, includes

the region to the east of the St. Marks river and to the north

of Lake Okeechobee. It is a transition zone, with a

macroclimate that is warm-temperate to sub-tropical, with a

winter that is relatively drier than that of the panhandle,

but not as dry as that of the south (Fernald and Patton, 1984;

Schmidt, 1992). Vegetation is limited to warm-temperate and

sub-tropical species. The natural forest trees include

evergreen broadleaf types and palms, as well as deciduous

broadleaf types and both evergreen and deciduous conifers.

The agricultural vegetation types include both evergreen and

deciduous orchards, and nearly year-round pasture and crops.


South Zone


The south zone, for the purpose of this study, includes

the region from Lake Okeechobee southwards. Its macroclimate

is sub-tropical, with a distinctly dry winter (Fernald and

Patton, 1984; Schmidt, 1992). Vegetation includes warm-

temperate, sub-tropical, and tropical species (Barrett, 1956;

Elias, 1980; Morton, 1982; FDNR, 1990). The natural forest

trees include evergreen broadleaf types and palms, deciduous

broadleaf types (some tropical), and both evergreen and







42

deciduous conifers. The agricultural vegetation types include

both evergreen and deciduous (some tropical) orchards, year-

round pasture and crops, and multi-season field crops.


Geographic Information System


The GIS database used in this research was assembled from

both raster and vector components, or "layers". The raster

layers included satellite images; the vector layers included

land-cover and soil type data.


Raster Datasets


A raster dataset consists of lines and elements ("rows"

and "columns"). The number of elements per line is a

constant, forming in concept a rectangular grid, each

identically-sized unit (picture element or "pixel") of which

is assigned data in the form of digital numbers (DNs). There

is one DN for each data type (sensor band, etc.) included in

the raster. ELAS and ERDAS were used to store and manipulate

the image raster files. Further information about raster

datasets can be found in ERDAS (1991).


Vector Datasets


A vector dataset consists of nodes and arcs which make up

individual polygons. One or more polygons may be included

within a "coverage" of a particular geographic region. For

example, a pasture-on-muck-soil coverage might consist of

several individual polygons distributed across Florida. Each







43

polygon can be assigned an attribute file, which contains one

or more types of data (such as ownership, water-quality

parameters, etc.). In this research, attribute files were not

constructed for the polygons, since the polygons were intended

for importation into the raster environment. ARC/INFO

(modules "ADS", "CREATE", and "TABLES") was used to record the

digitized map polygons as vector files. Further information

about vector datasets can be found in ESRI (1990).


Geographic Referencing


Both raster and vector datasets must be geographically

referenced in order to be included in a GIS (ESRI, 1990;

Connin, 1994; Wong, 1994). Raster datasets are geographically

referenced by knowledge of pixel size and the position

(measured either from pixel center or a pixel corner) of one

raster-corner pixel (typically the upper-left). Vector

datasets are geographically referenced by knowledge of the

position of each node. A consistent geographic coordinate

system must be used for all of the datasets in the GIS. The

Universal Transverse Mercator (UTM) system was selected for

use throughout this study. It is commonly used on maps having

conformal projection (suitable for navigation) published by

the United States Geological Survey (USGS) and other agencies.

Further information about geographic referencing and map

projections can be found in ERDAS (1990).







44

GIS Analyses in Raster Environment


Analyses in this study were performed in a raster

environment. Polygon files in ARC/INFO vector format were

converted to ERDAS raster format ("DIG" file) equivalents.

This conversion was performed using ARC/INFO modules

"TRANSFORM" and "UUNGEN", and ERDAS module "DXIN".

Statistical data (mean and standard deviation) were then

extracted from the raster data (temperature values)

corresponding to each polygon. This extraction process was

performed using the ERDAS modules "CUTTER", "STITCH" (for

assembling coverages of more than one polygon), and "BSTATS".

Where a given land-cover polygon contained more than one soil

type, it was subdivided (using ERDAS module "DIGSCRN") into

two or more final polygons having a single land-cover and a

single soil type.

Analyses of surface temperature patterns were performed

using the mean, standard deviation, and sample-size data

extracted from the GIS. Separate within-zone analyses were

performed for the three macroclimate zones. T-tests were run

at a = 0.05 and a = 0.01. The test statistic was given by the

separate-variance formula (Ott, 1988):


Xi Xz
T = [15]
(s12/n1 + s22/n2)0.5

where X& and xz are the respective means of samples 1 and 2,

si and s2 are the respective standard deviations of samples 1







45

and 2, and nj and n2 are the respective sample sizes of samples

1 and 2. The difference of two sample means was considered

statistically significant if T < -t /2 or T > t,/2. In the

analyses reported in this study, nj + n2 >> 30, so that the

critical values were t0.025 = 1.960 and to.005 = 2.576 (Walpole

and Myers, 1978).


AVHRR Image Processing


NOAA TIROS-AVHRR images of surface temperature were

obtained for two seasons and two times-of-day. Two day/night

pairs of images (14 December 1989 and 12 December 1992) were

required for complete winter coverage of Florida, due to

partial cloud contamination. A single day/night pair of

images (11 April 1993) was adequate for spring coverage of the

state. Details of individual images are provided in Appendix

A.

Winter (December) images allowed the analysis of

differences in surface temperature patterns due to natural

defoliation (for deciduous vegetation types) and agricultural

management practices (winter-crop season). Deciduous

vegetation in Florida includes both temperate and tropical

species, so that seasonal effects could be studied in all

three climate zones.

Spring (April) images allowed the analysis of differences

in surface temperature patterns due to growth flush (for

natural and many cultivated vegetation types) and agricultural







46

management practices (spring-crop season). It is a season of

particularly high irrigation demand in agricultural and

suburban areas in all three climate zones.

Time-of-day for the images included nighttime (late

night/early morning) and daytime (afternoon). Repeat coverage

of a given spot at nearly the same LST occurs every 9 days

(Kerr et al., 1992) for TIROS satellites, so that a minor

variation in coverage time occurs from day to day within this

period. There are also long-term changes in repeat-coverage

time for TIROS satellites; these are very gradual (years),

compared to changes for other weather-satellites such as the

Russian Meteor series (weeks). Nighttime (c. 0300 h LST),

surface temperature images represented the minimum values in

the diurnal cycle. Daytime (c. 1500 h LST) surface

temperature images represented the maximum values in the

diurnal cycle. The difference between these two values was

the DSTV, which indicated relative values of daily-average

root-zone soil moisture for a given land-cover/soil type

combination.


AVHRR Data Types


The NOAA TIROS-satellite AVHRR images were obtained from

the National Environmental Satellite Data and Information

Service (NESDIS) in the form of local area coverage (LAC)

level lb packed format data on computer-compatible tapes

(CCTs). Each tape was down-loaded, and then one raster file

of image data, one tabular file of Earth Location Points







47

(ELPs), and one tabular file of calibration coefficients were

extracted.

LAC-format AVHRR. AVHRR LAC images have the full spatial

resolution (1.1 km nominal at nadir) of the AVHRR; the

thermal-infrared bands have the full AVHRR radiometric

precision of 0.1 C, stored in 10-bit (0-1024 DN) data

precision (NOAA, 1991). The AVHRR bands are described (NOAA,

1988) as follows: band 1 (red) at 0.58 to 0.68 Am, band 2

(near-infrared) at 0.725 to 11.1 pm, band 3 (midwave thermal-

infrared) at 3.55 to 3.93 Am, band 4 (longwave thermal-

infrared) at 10.3 to 11.3 Am, and band 5 (longwave thermal-

infrared) at 11.5 to 12.5 pm. For typical earth surfaces (not

hot lava flows, fires, etc.), AVHRR bands 1 and 2 measure

reflected energy (daytime only), band 3 measures both

reflected (in daytime) and emitted energy, and bands 4 and 5

measure emitted energy (daytime or nighttime).

Other AVHRR formats. There are other forms of AVHRR

image which do not retain full spatial resolution nor full

radiometric precision, but are made available with greater

frequency than LAC. For each TIROS satellite, a single 10-

minute AVHRR High Resolution Picture Transmission (HRPT) image

per 102-minute orbit can be stored on-board for later

transmission to a NOAA ground reception station (NOAA, 1991),

but there are usually only two orbital coverages of a given

location per day, and not every transmitted HRPT image is

selected for inclusion in the NOAA LAC archive. More frequent

availability is provided by the NOAA-archived global area







48

coverage (GAC) format AVHRR images, which have reduced spatial

resolution (4 km nominal at nadir), but keep the original data

precision (10 bits); a complete orbital path of GAC data can

be stored on-board per orbit for later transmission to a NOAA

station (NOAA, 1991). Users with their own digital-signal

ground station can receive HRPT images directly, and achieve

a coverage frequency of at least twice per day from each

operational satellite, but they also have to calculate their

own ELPs and calibration coefficients from the HRPT telemetry

(Brush, 1985; Emery et al., 1989; Klaes and Georg, 1992).

Users with their own analog-signal ground station can

receive automatic picture transmission (APT) format AVHRR

images, with reduced spatial resolution (4 km nominal at

nadir) and reduced data precision (8 most significant bits

pre-analog), and achieve a coverage frequency of at least

twice per day from each operational satellite every day (NOAA,

1982b). APT images contain their own calibration information

(NOAA, 1982b, 1988; Olivier, 1990), but have no ELPs nor the

telemetry information to calculate them (NOAA, 1988). They

are limited to two NOAA-selected AVHRR bands--typically bands

2 and 4 in daytime, and bands 3 and 4 at nighttime.

Use of TIROS/Meteor APT archive. During the course of

this research, the APT ground station located at the Remote

Sensing Application Laboratory (RSAL) of the University of

Florida Agricultural Engineering Department was utilized to

obtain APT images for purposes of building a browse file for

selecting dates and times for ordering NOAA LAC images. These







49

APT images included multiple daily coverages by the four

current NOAA TIROS satellites (NOAA-9, -10, -11, -12) and the

short-lived but productive NOAA-13, and also the Meteor-APT of

various Russian Meteor-series weather satellites (Meteor 2-21,

3-3, 3-4, and 3-5). The Meteor APT consists of daytime

panchromatic (0.5 to 0.7 jm) images at somewhat finer spatial

resolution (2 km nominal at nadir). It should be noted by

users of weather-satellite data that imagery from the NOAA

TIROS-series satellites (as well as the Russian Meteor-series

satellites) is subject to temporary suspension on rare

occasions due to participation in the international Search and

Rescue Satellite (SARSAT) program in cases of emergencies at

sea (WMO, 1989; NOAA, 1991).


Calibration to At-Satellite Radiant Temperature


The thermal-infrared, 10-bit, image data of AVHRR bands

4 and 5 were calibrated to at-satellite radiant temperature by

the method of NOAA (1991). One pair of calibration

coefficients (scaled slope and intercept of the sensor

internal calibration) was extracted from the level lb LAC CCT

for each scan line of the image. At-satellite radiant

temperature was then calculated for the pixels of each line by

the equation (NOAA, 1991):


C2 CWNi
Tradsati = [16]
In [1 + C, CWNi3 / (Si DN + Ii)]


where Trad,sat,i is the at-satellite radiant temperature (K) in







50

band j, Ci is a constant = 1.1910659x10-5 (mW m-2 ster-1 cm4), C2

is a constant = 1.438833 (cm K), CWNi is the central wave

number (cm-1) for band I in one of three discrete target-

temperature ranges, Si is the scaled calibration slope (mW m-2

ster-1 cm) for band i, and Ii is the scaled calibration

intercept (mW m-2 ster-1 cm) for band i, and DN is the 10-bit

digital number (0 to 1023).

This calibration technique is based on a linear fit of

AVHRR sensor response to target temperature within three

discrete target-temperature ranges (180 to 225 K, 225-275 K,

275-320 K). It greatly reduces the at-satellite radiant

temperature errors (up to 4.3 K at extremities) that would

result from a simple two-point calibration over the entire

target-temperature range (180 to 320 K) of AVHRR data (NOAA,

1988). It should be noted that a although a single target-

temperature range (270-310 K) is generally employed in AVHRR

calibration for sea-surface work (NOAA, 1991), all three

standard target-temperature ranges have to be addressed in

daytime land surface work. In addition, there is an upper

limit of 320 K (47 C) for target temperature; the AVHRR

longwave thermal-infrared band sensors saturate (DN = maximum)

at this limit, and higher at-satellite radiant temperatures

cannot be recorded (NOAA, 1988; Chuvieco and Martin, 1994).

Fortunately, none of the images used in this study contained

at-satellite radiant temperature data reaching this saturation

limit.







51

Scaling of the output radiant temperature values was

performed in order to retain as high a level of radiometric

precision as possible (0.2 C) in the 8-bit (0 to 255 DN) data

storage format to be used in the GIS of this research. The

scaling was given by the equations:


DN, = 5 (Trad,,t,i + 10) [17]

and

DNi = 6 (Tradsa.t,i 15) [18]


where Trad,sat, is the at-satellite radiant temperature (C) in

band i, and DN, is the 8-bit scaled digital number output for

band i. Equation 17 was used for nighttime and winter

afternoon images; equation 18 was used for spring afternoon

images.


Geographic Correction and Registration


A base-map image was constructed to allow registration of

the AVHRR images prior to their importation into the GIS.

Weather-satellite images without any geographic correction are

completely unusable in a GIS (Figure 2). Base-map

construction, and subsequent AVHRR image registration, was

performed using a two-stage geographic correction process, as

is recommended (Thomas et al., 1987; Chen and Lee, 1992;

Peters et al., 1992) for highly warped (containing distortions

requiring a second-order or higher global polynomial surface

model) imagery.
























































Figure 2. AVHRR image without geographic correction (polygon
outlines true position of Florida).







53

The first stage involved the use of the extracted ELP

data from the AVHRR LAC image. These ELPs, which are provided

in the form of latitude/longitude coordinates, are imbedded in

the raw image at every 40th pixel along each scan line. They

form an evenly-distributed network of known geographic

coordinates throughout the entire image, which is the most

desirable situation for application of geographic correction

techniques. The accuracy of these coordinates is dependent

upon the accuracy of the satellite orbital telemetry data used

by NOAA to calculate them (NOAA, 1991). Early in the course

of this study, it was found that geographic correction based

solely on the ELPs produced output images with positional

errors of up to 10 km (Figure 3); this problem has been

reported in several AVHRR-based studies (Cornillon et al.,

1987; Nelson, 1989; Leshkevich et al., 1993). It should be

noted that the positional error shown in Figure 3 is not a

simple offset; there are still second-order distortions

present in the image.

In order to keep output positional errors closer to the

nominal spatial resolution (1.1 km) of the raw AVHRR LAC

images, a second stage of refined geographic correction was

performed based on ground control points (GCPs). The picking

of these GCPs simultaneously from monitor displays of the

AVHRR image (band 2 in daytime, band 4 at night) and from maps

was greatly facilitated by the first stage of correction,

which had removed most of the Earth-curvature and view-angle

distortions. Picking of GCPs directly from the raw image is
























































Figure 3. AVHRR image with first-stage (ELP-based) geographic
correction (polygon outlines true position of Florida).







55

not advisable, since the application of the high-order global

polynomial surface model required for such a distorted image,

combined with the relatively poor distribution of GCP network

obtainable from most images, can easily result in instability

of pixel fits interpolated between the GCPs (Thomas et al.,

1987), and "explosion" of pixel fits extrapolated outside of

the GCP network (Figure 4).

First-stage geographic correction of base-map image.

First-stage geographic correction was performed using the ELP

data extracted from the raw AVHRR LAC image. These ELP data

formed a 240-ELP grid (consisting of 16 grid lines with 15

ELPs each) having a 40 x 40 pixel spacing. The latitude/

longitude values in this ELP grid were converted to Universal

Transverse Mercator (UTM) northing/easting values (UTM zone 17

format) and entered into the ERDAS module "GCP". A second-

order global polynomial surface model was fitted to the ELPs.

The Nearest-Neighbor resampling technique was then applied to

the image; this is the only resampling technique that does not

corrupt (smooth or average) image data values (Lillesand and

Kiefer, 1979; Peters et al., 1992). These two operations were

performed using ERDAS modules "COORDN" and "NRECTIFY." The

polynomial fit (mapping equation) was as follows:

E' = 1000 ao + a1 E + a2 L + 0.001 a3 E2 + 0.001 a4 E L +

0.001 a5 L2
and
L' = 1000 bo + bi E + b2 L + 0.001 b3 E2 + 0.001 b4 E L +

0.001 b5 L2 [19]


























































Figure 4. Example of explosive extrapolation of third-order
global polynomial surface model outside of control
points.







57

where E was the original element number, L was the original

line number, E' was the fitted element number, L' was the

fitted line number, and the values of the coefficients were ao

= 1.371406, a, = -0.1608168E-2, a2 = -0.3716236E-3, a3 =

0.4853050E-6, a4 = 0.1728803E-6, a, = 0.2920270E-7, bo =

-2.333373, b, = -0.1690915E-3, b2 = 0.8806317E-3, b3 =

-0.2704551E-8, b4 = 0.2105952E-7, and b, = 0.3205131E-8, which

are unitless. Resampling was done to an output pixel size of

1 km. Details concerning geographic correction techniques for

satellite images can be found in remote sensing literature

(Lillesand and Kiefer, 1979; Gonzalez and Wintz, 1987; Thomas

et al., 1987; ERDAS, 1990; Novak, 1992; Peters et al., 1992;

Di and Rundquist, 1994).

Second-stage geographic correction of base-map image.

The first-stage output image was then imported into ELAS. A

set of 115 well-distributed GCPs (coastal features and lakes

of size appropriate to the spatial resolution of the image)

was picked simultaneously from a monitor display of the image

and from 1:500,000 scale UTM maps of Florida (DMAAC, 1987).

ELAS module "CPPP" and a digitizing tablet were the tools used

for this process. Panhandle GCP coordinates, located within

UTM zone 16, were converted to their equivalents in UTM zone

17 format. The first-stage output image was then re-imported

into ERDAS along with the GCP set mentioned above. A second-

order global polynomial surface model was fitted to the GCPs.

The Nearest-Neighbor resampling technique was then applied to







58

the raster. The polynomial fit (mapping equation) was as

follows:


E' = 1000 ao + a, E + a2 L + 0.001 a3 E2 + 0.001 a4 E L +

0.001 a5 L2

and

L' = 1000 bo + b, E + b2 L + 0.001 b3 E2 + 0.001 b4 E L +

0.001 b5 L2 [20]


where E was the original element number, L was the original

line number, E' was the fitted element number, L' was the

fitted line number, and the values of the coefficients were

a0 = 0.1952093, a, = 0.9931500E-3, a2 = -0.3316682E-5, a3 =

0.1140539E-7, a4 = 0.3552246E-8, a5 = 0.8012727E-9, b0 =

3.655660, bi = -0.5229729E-4, b2 = -0.1038144E-2, b3 =

0.1049909E-7, b4 = 0.1479114E-7, and b5 = 0.5142951E-8,

which are unitless. Second-stage resampling was done to an

output pixel size of 1 km, to form a base-map image of 1000

elements by 1000 lines covering the entire Florida study area

and small portions of southern Alabama and Georgia (Figure 5).

Location of the first pixel (element 1, line 1) was at

-187,000 m east and 3,584,000 m north (UTM zone 17 format) in

the ERDAS raster GIS reference system.

Accuracy assessment of base-map image. The positional

accuracy of the base-map image was carefully evaluated, since

all future images would be registered to it. ERDAS module

"COORDN" reported that the root-mean-square (rms) error for

























































Figure 5. AVHRR image with second-stage (GCP-based)
geographic correction (polygon outlines true position of
Florida).







60

the second-stage fit in the element direction was 0.648 km,

and in the line direction was 0.597 km; the overall rms error

for the fit was 0.881 km. These figures apply only to the GCP

pixels, not to other resampled pixels; they represent a form

of validation check. The potential for the user to be misled

by these GCP-based rms values can be seen in the

extrapolation-exploded image of Figure 4, which had a GCP-

based rms error of only 1.2 km, even though the image was

clearly rendered useless. In order to verify the positional

accuracy of the entire base-map image, a set of 91 well-

distributed GCPs (different from the 115 used to build the

base-map image) were digitized in the manner described above.

The resulting rms error was found to be 0.495 km in the

element direction, and 0.566 km in the line direction; the

overall rms error was 0.752 km. Considering both the overall

rms error of the fit (0.881 km) and that of the verification

(0.752 km), the base map was demonstrated to be spatially

accurate to within 1 km.

Registration of subsequent images to base-map image.

Subsequent AVHRR LAC images were registered to the base-map

image by a two-stage process with ELP-based first-stage

geographic correction similar to that described above. The

only change was in the manner of picking the GCPs for the

second-stage geographic correction; they were picked from

simultaneous monitor displays of the first-stage corrected

image and the base-map image (rather than maps). The ELAS

module "OCON" was used to simultaneously display these images







61

and pick the GCPs. These GCPs and the AVHRR LAC image being

registered were then imported into ERDAS for the second-stage

geographic correction. Second-order global polynomial surface

models were used whenever possible, but third-order models

were used if second-order models were insufficient to produce

registration rms errors below 1.2 km. Registered AVHRR images

had rms errors ranging from 1.09 to 1.2 km. Registration

details of individual images are given in Table 140 of

Appendix A.


Conversion from Radiant to Kinetic Temperature


In order to produce kinetic surface temperature images

from the at-satellite radiant temperature images, a three-

stage process was implemented. First, atmospheric correction

of an at-satellite radiant temperature image (Figure 6) was

performed by the water-body calibration technique. Second, an

instantaneous pixel-average emissivity image (Figure 7) was

constructed by the twin-band method. Third, the atmospheric-

corrected radiant temperature image and emissivity image were

used to produce a kinetic temperature image (Figure 8) based

on the analytical solution of the Planck equation. The

overall conservative bias due to the atmospheric effect, and

the "hiding away" of high surface temperature in urban and

agricultural areas due to the emissivity effect, are both

evident in the uncorrected image of Figure 6, when it is

compared to the fully corrected image of Figure 8.

























































Figure 6. At-satellite radiant temperature image.

























































Figure 7. Emissivity image.


























































Figure 8. Surface temperature image.









Atmospheric correction. The water-body calibration

technique described by Lillesand and Kiefer (1979) was

performed on the at-satellite radiant temperature images from

bands 4 and 5. This technique is based on the near-uniform

emissivity and relatively stable temperature (over the

satellite overpass time) of water-body pixels. Hourly near-

surface (0.5 m) water temperature data from three permanent

instrument stations located within Lake Okeechobee were

obtained from the South Florida Water Management District

(SFWMD) "DBHYDRO" database (Figure 1). These kinetic

temperatures (thermistor-based values reported to nearest 0.1

C) were converted to AVHRR band 4 and 5 radiant temperature

equivalents by rearranging equation 13 and plugging in the

water temperatures:


k CWNi
Trad.i = [21]
In (c.-1 [exp (k CWN, / Tkin) + E6 1]}


where Trad,i is the radiant temperature (K) calculated in band

i for a station pixel, Tkin is the measured water-surface

kinetic temperature (K), k is the Boltzmann constant (1.438883

cm K), CWNi is the central wave number for band i (cm-1), and

E, is the longwave emissivity of the lake water. A well-

established value of 0.99 was used for e, (Buettner and Kern,

1965; Barton and Takashima, 1986; Saunders, 1986; Masuda et

al., 1988; Wukelic et al., 1989; Salisbury and D'Aria, 1992).

Central wave numbers were obtained from the tabular values







66

published by NOAA (1991) for each TIROS satellite AVHRR

sensor, each band i, and each surface-temperature range. The

highest surface-temperature range was used to find the CWNi

for afternoon LAC images, and the sea-surface range was used

to find the CWN1 for nighttime and early-morning images. The

differences between the at-satellite radiant temperatures and

the atmospherically-corrected radiant temperatures were

averaged (from up to 3 values, according to station data

availability) to obtain atmospheric correction factors which

were applied to the at-satellite radiant temperature data from

bands 4 and 5 of each LAC image.

Emissivity correction. The atmospheric-corrected radiant

temperature images from bands 4 and 5 were used to produce a

longwave thermal-infrared emissivity image for each AVHRR

image by the twin-band technique of Artis and Carnahan (1982).

Instantaneous pixel-average emissivity calculation was

performed by plugging appropriate AVHRR values into equation

14:


ee = exp (k (Tr.d5-Trad,) / [Trad,5 Trad4 (X5-_4)] [22]


where el is the pixel-average longwave thermal-infrared

emissivity, k is the Boltzmann constant (1.43883 cm K), Trad,4

is the atmospheric-corrected band 4 radiant temperature (K),

Trad,5 is the atmospheric-corrected band 5 radiant temperature

(K), and A4 and A5 are the respective central wavelengths

(inverse of central wavelength number, m) of AVHRR bands 4 and







67

5. The last two values come from NOAA (1991) look-up tables

for the TIROS satellite, band, and temperature range, as

described previously. An 8-bit scaling of the calculated

emissivity values preserved the precision to the nearest 0.01:


DN = 100 e, [23]

where the parameters are described as before. The assumption

in this method that surface emissivity values in AVHRR bands

4 and 5 are identical is a simplification (Price, 1984);

slight differences in emissivity values between these bands

(up to 0.01) will result in an uncertainty of 1 C when the

calculated longwave-band emissivity is applied to kinetic

temperature calculation.

Kinetic temperature calculation. The emissivity image

was used with the atmospheric-corrected radiant temperature

image from band 4 to calculate the kinetic temperature image

for each AVHRR image. This computation was performed by

plugging appropriate AVHRR band 4 values and the pixel-average

emissivity values into equation 13:


k CWN4
Tkln = [24]
In [1 E1, + el, exp (k CWN4 / Trad,)]


where Tknf is the pixel-average kinetic temperature (K); Trad.4

is the atmosphere-corrected, pixel-average, band 4 radiant

temperature (K); k is the Boltzmann constant (1.43883 cm K),

CWN4 is the central wave number for band4 (cm-1), and e1 is the

pixel-average longwave emissivity. Band 4 central wave







68

numbers were obtained from the tabular values published by

NOAA (1991) for each TIROS AVHRR sensor and each surface-

temperature range. The highest surface-temperature range was

used to find the CWN4 for afternoon LAC images, and the sea-

surface range was used to find the CWN4 for nighttime and

early-morning images. Scaling of the output Tkin images to an

8-bit format was performed using the formula


S5 (Tin + 10), for low Tkin images
DN = 1[25]
6 (Tkin 15), for high Tkin images


where Tkin is the kinetic temperature (C), low-temperature

images were defined as those having a range of land surface

temperatures between -10.0 and 41.0 C, and high-temperature

images were defined as those having a range of land surface

temperatures between 15.0 and 57.0 C. In either case, a

precision of 0.2 C was kept by the scaling. It should be

noted that the AVHRR sensor saturation limit of 47 C applies

only to at-satellite radiant temperatures; a surface such as

a cleared sandy field or an urban area can have an afternoon

kinetic surface temperature well above 47 C and yet, due to

emissivity and atmospheric effects, it can easily have an at-

satellite radiant temperature under 47 C.


Accuracy Assessment of Kinetic Temperature Images


The water-body temperature data used to calculate the

atmospheric correction served as a validation data set. The







69

water-body temperature data from other stations served as a

verification data set. Procedures and results of validation

and verification are described below.

Validation of kinetic temperature. Validation assessment

was performed using the previously mentioned near-surface

water temperature data from the three permanent instrument

stations located within Lake Okeechobee (Figure 1). This was

done by comparing the processed image kinetic temperature

values with the corresponding water-body temperature data.

Errors indicated by the validation data set for the AVHRR

kinetic temperature images ranged from 0.0 to 1.1 C; the

average error was 0.5 C. This range of single-pixel basis

validation error for lake surface temperature values is even

lower than expected from the uncertainty associated with the

twin-band emissivity correction method, and indicates that the

station water-temperature (thermistor-based) data were

themselves very well calibrated. Validation details of

individual images are given in Table 140 of Appendix A.

Verification of kinetic temperature. Verification

assessment was performed using near-surface (0.5 m) water

temperature data from the permanent instrument station located

within Lake Apopka, and from periodic sampling by boat in Lake

Sampson and Tampa Bay (Figure 1). The Lake Apopka data were

obtained from the St. Johns River Water Management District

(SJRWMD); the Lake Sampson data were obtained from the

Suwannee River Water Management District (SRWMD); the Tampa

Bay data were obtained from the Environmental Protection







70
Commission of Hillsborough County (EPCHC). The processed

image kinetic temperature value for each of these verification

sites was compared with the corresponding near-surface water

temperature data (thermistor-based values reported to nearest

0.1 C). Errors indicated by the verification data set for the

AVHRR kinetic temperature images ranged from 0.4 to 3.4 C; the

average error was 1.9 C. This range of single-pixel basis

verification error values for atmospheric-corrected lake-

surface temperature is slightly higher than that (0.2 to 0.8

C) reported for atmospheric-corrected sea-surface temperature

under ideal clear-sky conditions (Llewellyn-Jones et al.,

1984), but far lower than would be the case (up to 20 C)

without atmospheric correction. Because the verification

stations are located in a different zone (north) from the

validation stations (south), they provide for each image a

conservative check on the error due to statewide spatial

variation in the atmospheric correction factor. Verification

details for individual images are given in Table 140 of

Appendix A.


Water and Cloud Masking


A masking procedure was used to exclude kilometer scale

water bodies and clouds from the surface temperature analyses

in this study. The mask was prepared from a normalized

difference vegetation index (NDVI) image (Figure 9). While

NDVI is commonly applied to vegetation vigor studies (Fischer,

1994; Teillet and Fedosejevs, 1994; Wade et al., 1994), it


























































Figure 9. Water/cloud mask (NDVI) image.







72

also distinguishes water and cloud surfaces (NOAA, 1990). The

NDVI was calculated from bands 1 and 2 (red and near-infrared)

of the AVHRR image (NOAA, 1990):


NIR R
NDVI = [26]
NIR + R


where NIR is the band 2 DN, and R is the band 1 DN. This NDVI

was scaled for 8-bit storage according to the standard global

vegetation index (GVI) procedure (NOAA, 1990):


255, for NDVI < -0.05

NDVIs = 0, for NDVI > 0.60 [27]

240-350(NDVI+0.05), otherwise


where NDVI, is the scaled NDVI. Values of NDVI, above 219 were

found by inspection to indicate water and cloud surfaces. A

separate NDVI image was prepared for each diurnal image pair,

using the daytime image band 1 and 2 data. This accounted for

any changes in kilometer-scale cloud or water body extent--

such as drifting clouds and floods. Each pixel of a surface

temperature image which corresponded with a water/cloud pixel

was then assigned a value of zero, creating a masked surface

temperature image (Figure 10). When each GIS coverage was

later analyzed using the ERDAS module "BSTATS," the option to

exclude zero values from statistical computations was

selected.























































Figure 10. Daytime (spring) masked surface temperature image.




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,' RLaa 81,9(56,7< 2) )/25,'$


APPLICATION OF REMOTELY SENSED DATA TO A GEOGRAPHIC
INFORMATION SYSTEM FOR MICROCLIMATE CHANGE ANALYSIS
By
JONATHAN DAVID JORDAN
A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY
UNIVERSITY OF FLORIDA
1994

To my parents,
Don and Dorothy Jordan

ACKNOWLE DGMENTS
This research was performed with the help of the staff
and facilities of the Remote Sensing Application Laboratory
(RSAL) located at the Agricultural Engineering Department of
the University of Florida. The author is grateful for the
assistance provided by the RSAL director, Dr. Sun F. Shih;
RSAL manager, Orlando Lanni; departmental computer technician
Curtis Weldon; and RSAL assistants Chih-Hung Tan, Yu Rong Tan,
and Bruce E. Myhre.
Acknowledgments are also due to additional persons and
agencies for key assistance in various portions of this study.
Technical information concerning the Advanced Very High
Resolution Radiometer (AVHRR) satellite imagery used in this
research was provided by Mary Hughes, Emily Harrod, Dr. Andrew
Horvitz, Dr. Katherine Kidwell, Dr. Carolyn Ng, Richard
DeRycke, and others of the National Oceanic and Atmospheric
Administration (NOAA). Both technical information and data
tapes concerning the Heat Capacity Mapping Mission (HCMM)
imagery used in this research were provided by Dr. William L.
Barnes, Barbara Pope, Locke M. Stuart, and others of the
National Space Science Data Center (NSSDC).
Essential water-body surface temperature data were
provided by Dr. Leslie Wedderburn, Brian Turkotte, Ernest
Gallego, and others of the South Florida Water Management
iii

District (SFWMD); William L. Osburn, Gail Gallagher, and
others of the St. Johns River Water Management District
(SJRWMD); David Hornsby and others of the Suwannee River Water
Management District (SRWMD); Kenneth Romie, Mark Rials, and
others of the Southwest Florida Water Management District
(SWFWMD); Thomas Cardenel, R. Malloy, and others of the
Environmental Protection Commission of Hillsborough County
(EPCHC); Donald D. Moores of the Pinellas County Department of
Environmental Management (PCDEM); and Dr. David Gowan of the
Florida Department of Environmental Protection (DEP). Both
water-body surface temperature data and statewide land-cover
maps were supplied by John Steyes and others of the Florida
Game and Freshwater Fish Commission (FGFFC). Land-cover
information and maps of the lower Lake Wales Ridge area were
provided by the Archbold Biological Station.
Aerial photographs used in this research were made
available by Dr. Helen J. Armstrong of the University of
Florida Map Library. Statewide aquaculture information and
maps were provided by Dr. Edward P. Lincoln and Dr. C. Direlle
Baird of the University of Florida Agricultural Engineering
Department. Phosphate mine and mine reclamation information
was made available by Dr. Lawrance N. Shaw of the University
of Florida Agricultural Engineering Department. Crop
information and assistance with site visits to St. Johns River
agricultural areas were provided by Dr. Dale R. Hensel of the
Hastings Agricultural Research and Education Center (AREC).
Crop information, maps, and assistance with site visits to the
IV

Everglades Agricultural Area (EAA) were provided by Dr. George
H. Snyder of the Everglades Research and Education Center
(EREC). Assistance with site visits to south Florida citrus
orchards, pastures, and Lake Okeechobee water-temperature
stations was given by Michael Piper, David Soballe, and others
of the SFWMD; assistance with site visits to the Lake Apopka
water-temperature station and marsh restoration project was
given by J. Palenkas and others of the SJRWMD. Land-cover
information and assistance with wetland site visits in north
and panhandle Florida were provided by Jay L. Johnson of the
NWFWMD. An infrared radiometer was made available by Dr.
Donald J. Pitts of the Immokalee AREC. Use of soil sampling
and analysis equipment was provided by Dr. Donald L. Myhre and
Joseph Nguyen of the University of Florida Soil and Water
Science Department. Thanks are also given to professors Sun
F. Shih, Jerome J. Gaffney, Dorota Z. Haman, Edward P.
Lincoln, Byron E. Ruth, and George H. Snyder of my supervisory
committee for their help and support.
v

TABLE OF CONTENTS
page
ACKNOWLEDGMENTS iii
LIST OF TABLES xiii
LIST OF FIGURES xxiv
ABSTRACT xxvi
INTRODUCTION 1
Purpose and Objectives 1
Importance of Surface Temperature in Climatology 2
Importance of Surface Temperature in Hydrology 4
Importance of Surface Temperature in Agriculture and
Forestry 5
Factors Affecting Surface Temperature Patterns 6
Temperature Impacts of Changes in Land-Cover 9
Temporal Inter-Relation of Forcing Factors 13
Spatial Inter-Relation of Forcing Factors 19
Potential for Future Changes in Soil Type 21
REVIEW OF LITERATURE 22
Difficulties of Surface Temperature Measurement 22
Atmospheric Correction Techniques 24
Emissivity Correction Techniques 26
Previous Studies 3 0
MATERIALS AND METHODS 39
Study Area 3 9
Panhandle Zone 39
vi

North Zone 41
South Zone 41
Geographic Information System 4 2
Raster Datasets 42
Vector Datasets 42
Geographic Referencing 43
GIS Analyses in Raster Environment 44
AVHRR Image Processing 45
AVHRR Data Types 4 6
Calibration to At-Satellite Radiant Temperature 49
Geographic Correction and Registration 51
Conversion from Radiant to Kinetic Temperature 61
Accuracy Assessment of Kinetic Temperature Images... 68
Water and Cloud Masking 70
Final Forms of Images in the GIS Database 74
HCMM Historical-Image Processing 74
Geographic Correction of HCMM Images 77
Calibration of HCMM At-Satellite Radiant
Temperature 78
Ground-Based DSTV/Soil-Moisture Work 79
Mineral Soil Investigation 79
Organic Soil Investigation 83
Vegetated Soil Investigation 85
Soil Type Data 8 6
Mineral Soils 87
Organic Soils 89
Artificial Soil Type Change 90
Vll

Land-Cover Data
91
Natural Land-Cover 91
Agricultural Land-Cover 115
Urban/Industrial Land-Cover 123
Special Land-Cover Conditions 129
RESULTS AND DISCUSSION 136
Analyses Across Macroclimate Zones 136
Analyses Within Macroclimate Zones 147
Spring Afternoon Natural Land-Cover Thermal
Patterns 147
Spring Afternoon Agricultural Land-Cover Thermal
Patterns 154
Spring Afternoon Urban/Industrial Land-Cover Thermal
Patterns 163
Spring Afternoon Change Analyses—Natural to
Agricultural Land-Cover 172
Spring Afternoon Change Analyses—Natural to Urban/
Industrial Land-Cover 178
Spring Afternoon Change Analyses—Agricultural to
Urban/Industrial Land-Cover 185
Spring Afternoon Comparison of Agricultural to
Natural Heat Islands 193
Spring Afternoon Comparison of Urban/Industrial to
Natural Heat Islands 196
Spring Afternoon Change Analyses—Special Factors... 199
Spring Nighttime Natural Land-Cover Thermal
Patterns 203
Spring Nighttime Agricultural Land-Cover Thermal
Patterns 2 08
Spring Nighttime Urban/Industrial Land-Cover Thermal
Patterns 218
Spring Nighttime Change Analyses—Natural to
Agricultural Land-Cover 227
viii

Spring Nighttime Change Analyses—Natural to Urban/
Industrial Land-Cover 232
Spring Nighttime Change Analyses—Agricultural to
Urban/Industrial Land-Cover 238
Spring Nighttime Comparison of Agricultural to
Natural Cold Islands 242
Spring Nighttime Comparison of Urban/Industrial to
Natural Cold Islands 245
Spring Nighttime Change Analyses—Special Factors... 246
Spring Diurnal Natural Land-Cover Thermal Patterns.. 252
Spring Diurnal Agricultural Land-Cover Thermal
Patterns 259
Spring Diurnal Urban/Industrial Land-Cover Thermal
Patterns 267
Spring Diurnal Change Analyses—Natural to
Agricultural Land-Cover 276
Spring Diurnal Change Analyses—Natural to Urban/
Industrial Land-Cover 281
Spring Diurnal Change Analyses—Agricultural to
Urban/Industrial Land-Cover 286
Spring Diurnal Comparison of Agricultural to
Natural Extreme Islands 294
Spring Diurnal Comparison of Urban/Industrial to
Natural Extreme Islands 296
Spring Diurnal Change Analyses—Special Factors 299
Winter Afternoon Natural Land-Cover Thermal
Patterns 303
Winter Afternoon Agricultural Land-Cover Thermal
Patterns 310
Winter Afternoon Urban/Industrial Land-Cover Thermal
Patterns 317
Winter Afternoon Change Analyses—Natural to
Agricultural Land-Cover 326
Winter Afternoon Change Analyses—Natural to Urban/
Industrial Land-Cover 330
IX

Winter Afternoon Change Analyses—Agricultural to
Urban/Industrial Land-Cover 330
Winter Afternoon Comparison of Agricultural to
Natural Heat Islands 335
Winter Afternoon Comparison of Urban/Industrial to
Natural Heat Islands 340
Winter Afternoon Change Analyses—Special Factors... 344
Winter Nighttime Natural Land-Cover Thermal
Patterns 34 8
Winter Nighttime Agricultural Land-Cover Thermal
Patterns 353
Winter Nighttime Urban/Industrial Land-Cover Thermal
Patterns 359
Winter Nighttime Change Analyses—Natural to
Agricultural Land-Cover 366
Winter Nighttime Change Analyses—Natural to Urban/
Industrial Land-Cover 370
Winter Nighttime Change Analyses—Agricultural to
Urban/Industrial Land-Cover 376
Winter Nighttime Comparison of Agricultural to
Natural Cold Islands 382
Winter Nighttime Comparison of Urban/Industrial to
Natural Cold Islands 384
Winter Nighttime Change Analyses—Special Factors... 387
Winter Diurnal Natural Land-Cover Thermal Patterns.. 390
Winter Diurnal Agricultural Land-Cover Thermal
Patterns 395
Winter Diurnal Urban/Industrial Land-Cover Thermal
Patterns 405
Winter Diurnal Change Analyses—Natural to
Agricultural Land-Cover 414
Winter Diurnal Change Analyses—Natural to Urban/
Industrial Land-Cover 414
Winter Diurnal Change Analyses—Agricultural to
Urban/Industrial Land-Cover 419
x

Winter Diurnal Comparison of Agricultural to
Natural Extreme Islands 423
Winter Diurnal Comparison of Urban/Industrial to
Natural Extreme Islands 429
Winter Diurnal Change Analyses—Special Factors 432
Analyses of Micro-Scale Maritime Effects 436
Hammock Comparisons 436
Marsh Comparisons 437
Maritime Micro-Scale Thermal Moderation 438
Analyses of Seasonal Effects on Deciduous Vegetation.. 438
Historical HCMM-Based Analyses 439
HCMM Analyses Across Macroclimate Zones 439
HCMM Historical Special Condition Change Analyses... 443
Results of Ground-Based DSTV/Soil-Moisture 4 50
Mineral Soil Results 451
Organic Soil Results 453
Vegetated Soil Results 453
SUMMARY AND CONCLUSIONS 455
Principal Findings 456
Importance of Soil Type and Land-Cover 456
Differences Among Natural Land-Cover Types 457
Differences Among Agricultural Land-Cover Types 457
Differences Among Urban/Industrial Land-Cover
Types 4 58
Potential for Soil Moisture Monitoring 458
Recommendations for Future Research 4 59
Ground-Based Data Collection Improvement 459
Satellite System Improvement 4 60
xi

Direction of Future Research 4 62
GLOSSARY 4 64
APPENDIX A IMAGE DOCUMENTATION 468
APPENDIX B WATER-BODY TEMPERATURE MEASUREMENT STATIONS. 475
APPENDIX C LAND-COVER POLYGON DETAILS 476
REFERENCES 4 92
BIOGRAPHICAL SKETCH 515
Xll

LIST OF TABLES
Table page
1 Spring afternoon surface temperature
across-zone differences among natural
land-cover types 137
2 Spring nighttime surface temperature
across-zone differences among natural
land-cover types 139
3 Spring diurnal surface temperature variation
across-zone differences among natural
land-cover types 141
4 Winter afternoon surface temperature
across-zone differences among natural
land-cover types 143
5 Winter nighttime surface temperature
across-zone differences among natural
land-cover types 144
6 Winter diurnal surface temperature variation
across-zone differences among natural
land-cover types 145
7 Spring afternoon surface temperature
differences among natural land-cover
types in panhandle zone 148
8 Spring afternoon surface temperature
differences among natural land-cover
types in north zone 150
9 Spring afternoon surface temperature
differences among natural land-cover
types in south zone 152
10 Spring afternoon surface temperature
differences among agricultural land-cover
types in panhandle zone 155
11 Spring afternoon surface temperature
differences among agricultural land-cover
types in north zone 157
xm

12
13
14
15
16
17
18
19
20
21
22
23
24
160
164
166
170
173
175
177
179
181
184
186
188
191
Spring afternoon surface temperature
differences among agricultural land-cover
types in south zone
Spring afternoon surface temperature
differences among urban/industrial land-
cover types in panhandle zone
Spring afternoon surface temperature
differences among urban/industrial land-
cover types in north zone
Spring afternoon surface temperature
differences among urban/industrial land-
cover types in south zone
Spring afternoon surface temperature change
from natural to agricultural in panhandle
zone
Spring afternoon surface temperature change
from natural to agricultural in north
zone
Spring afternoon surface temperature change
from natural to agricultural in south
zone
Spring afternoon surface temperature change
from natural to urban/industrial in
panhandle zone
Spring afternoon surface temperature change
from natural to urban/industrial in
north zone
Spring afternoon surface temperature change
from natural to urban/industrial in
south zone
Spring afternoon surface temperature change
from agricultural to urban/industrial in
panhandle zone
Spring afternoon surface temperature change
from agricultural to urban/industrial in
north zone
Spring afternoon surface temperature change
from agricultural to urban/industrial in
south zone
xiv

25
26
27
28
29
30
31
32
33
34
35
36
37
194
197
200
204
206
209
211
213
215
219
221
225
228
Spring afternoon surface temperature of
agricultural land-cover types vs hottest
natural land-cover
Spring afternoon surface temperature of
urban/industrial land-cover types vs
hottest natural land-cover
Spring afternoon surface temperature change
for special conditions
Spring nighttime surface temperature
differences among natural land-cover
types in panhandle zone
Spring nighttime surface temperature
differences among natural land-cover
types in north zone
Spring nighttime surface temperature
differences among natural land-cover
types in south zone
Spring nighttime surface temperature
differences among agricultural land-cover
types in panhandle zone
Spring nighttime surface temperature
differences among agricultural land-cover
types in north zone
Spring nighttime surface temperature
differences among agricultural land-cover
types in south zone
Spring nighttime surface temperature
differences among urban/industrial land-
cover types in panhandle zone
Spring nighttime surface temperature
differences among urban/industrial land-
cover types in north zone
Spring nighttime surface temperature
differences among urban/industrial land-
cover types in south zone
Spring nighttime surface temperature change
from natural to agricultural in panhandle
zone
xv

38
39
40
41
42
43
44
45
46
47
48
49
50
229
231
233
234
237
239
240
243
244
247
249
253
255
Spring nighttime surface temperature change
from natural to agricultural in north
zone
Spring nighttime surface temperature change
from natural to agricultural in south
zone
Spring nighttime surface temperature change
from natural to urban/industrial in
panhandle zone
Spring nighttime surface temperature change
from natural to urban/industrial in
north zone
Spring nighttime surface temperature change
from natural to urban/industrial in
south zone
Spring nighttime surface temperature change
from agricultural to urban/industrial in
panhandle zone
Spring nighttime surface temperature change
from agricultural to urban/industrial in
north zone
Spring nighttime surface temperature change
from agricultural to urban/industrial in
south zone
Spring nighttime surface temperature of
agricultural land-cover types vs coldest
natural land-cover
Spring nighttime surface temperature of
urban/industrial land-cover types vs
coldest natural land-cover
Spring nighttime surface temperature change
for special conditions
Spring diurnal surface temperature variation
differences among natural land-cover
types in panhandle zone
Spring diurnal surface temperature variation
differences among natural land-cover
types in north zone
xvi

51
52
53
54
55
56
57
58
59
60
61
62
63
257
260
262
264
268
270
274
277
279
280
282
284
287
Spring diurnal surface temperature variation
differences among natural land-cover
types in south zone
Spring diurnal surface temperature variation
differences among agricultural land-cover
types in panhandle zone
Spring diurnal surface temperature variation
differences among agricultural land-cover
types in north zone
Spring diurnal surface temperature variation
differences among agricultural land-cover
types in south zone
Spring diurnal surface temperature variation
differences among urban/industrial land-
cover types in panhandle zone
Spring diurnal surface temperature variation
differences among urban/industrial land-
cover types in north zone
Spring diurnal surface temperature variation
differences among urban/industrial land-
cover types in south zone
Spring diurnal surface temperature variation
change from natural to agricultural in
panhandle zone
Spring diurnal surface temperature variation
change from natural to agricultural in north
zone
Spring diurnal surface temperature variation
change from natural to agricultural in south
zone
Spring diurnal surface temperature variation
change from natural to urban/industrial in
panhandle zone
Spring diurnal surface temperature variation
change from natural to urban/industrial in
north zone
Spring diurnal surface temperature variation
change from natural to urban/industrial in
south zone
xvii

64
65
66
67
68
69
70
71
72
73
74
75
76
Spring diurnal surface temperature variation
change from agricultural to urban/industrial
in panhandle zone 288
Spring diurnal surface temperature variation
change from agricultural to urban/industrial
in north zone 290
Spring diurnal surface temperature variation
change from agricultural to urban/industrial
in south zone 293
Spring diurnal surface temperature variation
of agricultural land-cover types vs highest-
DSTV natural land-cover 295
Spring diurnal surface temperature variation
of urban/industrial land-cover types vs
highest-DSTV natural land-cover 297
Spring diurnal surface temperature variation
change for special conditions 300
Winter afternoon surface temperature
differences among natural land-cover
types in panhandle zone 304
Winter afternoon surface temperature
differences among natural land-cover
types in north zone 306
Winter afternoon surface temperature
differences among natural land-cover
types in south zone 308
Winter afternoon surface temperature
differences among agricultural land-cover
types in panhandle zone 311
Winter afternoon surface temperature
differences among agricultural land-cover
types in north zone 313
Winter afternoon surface temperature
differences among agricultural land-cover
types in south zone 315
Winter afternoon surface temperature
differences among urban/industrial land-
cover types in panhandle zone 319
xviii

77
78
79
80
81
82
83
84
85
86
87
88
89
320
324
327
328
329
331
332
334
336
337
339
341
342
Winter afternoon surface temperature
differences among urban/industrial land-
cover types in north zone
Winter afternoon surface temperature
differences among urban/industrial land-
cover types in south zone
Winter afternoon surface temperature change
from natural to agricultural in panhandle
zone
Winter afternoon surface temperature change
from natural to agricultural in north
zone
Winter afternoon surface temperature change
from natural to agricultural in south
zone
Winter afternoon surface temperature change
from natural to urban/industrial in
panhandle zone
Winter afternoon surface temperature change
from natural to urban/industrial in
north zone
Winter afternoon surface temperature change
from natural to urban/industrial in
south zone
Winter afternoon surface temperature change
from agricultural to urban/industrial in
panhandle zone
Winter afternoon surface temperature change
from agricultural to urban/industrial in
north zone
Winter afternoon surface temperature change
from agricultural to urban/industrial in
south zone
Winter afternoon surface temperature of
agricultural land-cover types vs hottest
natural land-cover
Winter afternoon surface temperature of
urban/industrial land-cover types vs
hottest natural land-cover
xix

90
91
92
93
94
95
96
97
98
99
100
101
102
345
349
351
354
357
358
360
363
364
367
369
371
372
Winter afternoon surface temperature change
for special conditions
Winter nighttime surface temperature
differences among natural land-cover
types in panhandle zone
Winter nighttime surface temperature
differences among natural land-cover
types in north zone
Winter nighttime surface temperature
differences among natural land-cover
types in south zone
Winter nighttime surface temperature
differences among agricultural land-cover
types in panhandle zone
Winter nighttime surface temperature
differences among agricultural land-cover
types in north zone
Winter nighttime surface temperature
differences among agricultural land-cover
types in south zone
Winter nighttime surface temperature
differences among urban/industrial land-
cover types in panhandle zone
Winter nighttime surface temperature
differences among urban/industrial land-
cover types in north zone
Winter nighttime surface temperature
differences among urban/industrial land-
cover types in south zone
Winter nighttime surface temperature change
from natural to agricultural in panhandle
zone
Winter nighttime surface temperature change
from natural to agricultural in north
zone
Winter nighttime surface temperature change
from natural to agricultural in south
zone
xx

103
104
105
106
107
108
109
110
111
112
113
114
115
373
374
377
378
379
381
383
385
388
391
393
396
399
Winter nighttime surface temperature change
from natural to urban/industrial in
panhandle zone
Winter nighttime surface temperature change
from natural to urban/industrial in
north zone
Winter nighttime surface temperature change
from natural to urban/industrial in
south zone
Winter nighttime surface temperature change
from agricultural to urban/industrial in
panhandle zone
Winter nighttime surface temperature change
from agricultural to urban/industrial in
north zone
Winter nighttime surface temperature change
from agricultural to urban/industrial in
south zone
Winter nighttime surface temperature of
agricultural land-cover types vs coldest
natural land-cover
Winter nighttime surface temperature of
urban/industrial land-cover types vs
coldest natural land-cover
Winter nighttime surface temperature change
for special conditions
Winter diurnal surface temperature variation
differences among natural land-cover
types in panhandle zone
Winter diurnal surface temperature variation
differences among natural land-cover
types in north zone
Winter diurnal surface temperature variation
differences among natural land-cover
types in south zone
Winter diurnal surface temperature variation
differences among agricultural land-cover
types in panhandle zone
xxi

116
117
118
119
120
121
122
123
124
125
126
127
128
400
403
406
408
412
415
416
417
418
420
422
424
425
Winter diurnal surface temperature variation
differences among agricultural land-cover
types in north zone
Winter diurnal surface temperature variation
differences among agricultural land-cover
types in south zone
Winter diurnal surface temperature variation
differences among urban/industrial land-
cover types in panhandle zone
Winter diurnal surface temperature variation
differences among urban/industrial land-
cover types in north zone
Winter diurnal surface temperature variation
differences among urban/industrial land-
cover types in south zone
Winter diurnal surface temperature variation
change from natural to agricultural in
panhandle zone
Winter diurnal surface temperature variation
change from natural to agricultural in north
zone .
Winter diurnal surface temperature variation
change from natural to agricultural in south
zone
Winter diurnal surface temperature variation
change from natural to urban/industrial in
panhandle zone
Winter diurnal surface temperature variation
change from natural to urban/industrial in
north zone
Winter diurnal surface temperature variation
change from natural to urban/industrial in
south zone
Winter diurnal surface temperature variation
change from agricultural to urban/industrial
in panhandle zone
Winter diurnal surface temperature variation
change from agricultural to urban/industrial
in north zone
xxii

129
130
131
132
133
134
135
136
137
138
139
140
Winter diurnal surface temperature variation
change from agricultural to urban/industrial
in south zone 427
Winter diurnal surface temperature variation
of agricultural land-cover types vs highest-
DSTV natural land-cover 428
Winter diurnal surface temperature variation
of urban/industrial land-cover types vs
highest-DSTV natural land-cover 430
Winter diurnal surface temperature variation
change for special conditions 433
HCMM Winter approximate afternoon surface
temperature across-zone differences among
natural land-cover types 440
HCMM Winter approximate nighttime surface
temperature across-zone differences among
natural land-cover types 441
HCMM Winter approximate diurnal surface
temperature variation across-zone
differences among natural land-cover types . 442
HCMM Winter approximate afternoon surface
temperature change for special conditions... 444
HCMM Winter approximate nighttime surface
temperature change for special conditions... 447
HCMM Winter approximate diurnal surface
temperature variation change for special
conditions 449
DSTV/soil-moisture relation for soil types.... 452
Image accuracy evaluation details 4 69
xxiii

LIST OF FIGURES
Figure page
1 Study area with climate zones and water-body
temperature stations (see Appendix B for
details) 40
2 AVHRR image without geographic correction
(polygon outlines true position of Florida). 52
3 AVHRR image with first-stage (ELP-based)
geographic correction (polygon outlines true
position of Florida) 54
4 Example of explosive extrapolation of third-
order global polynomial surface model
outside of control points 56
5 AVHRR image with second-stage (GCP-based)
geographic correction (polygon outlines true
position of Florida) 59
6 At-satellite radiant temperature image 62
7 Emissivity image 63
8 Surface temperature image 64
9 Water/cloud mask (NDVI) image 71
10 Daytime (spring) masked surface temperature
image 73
11 Nighttime (spring) masked surface temperature
image 75
12 DSTV (spring) image 76
13 HCMM daytime (winter) at-satellite radiant
temperature image 80
14 HCMM nighttime (winter) at-satellite radiant
temperature image 81
15 HCMM approximate-DSTV (winter) image 82
xxiv

16 Natural land-cover polygons (see Appendix C
for details) 93
17 Agricultural land-cover polygons (see
Appendix C for details) 116
18 Urban/industrial land-cover polygons (see
Appendix C for details) 124
xxv

Abstract of Dissertation Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Doctor of Philosophy
APPLICATION OF REMOTELY SENSED DATA TO A GEOGRAPHIC
INFORMATION SYSTEM FOR MICROCLIMATE CHANGE ANALYSIS
By
Jonathan David Jordan
December 1994
Chairman: Sun F. Shih
Major Department: Agricultural Engineering
This study demonstrates the monitoring of surface
temperature pattern impacts of meso-scale changes in land-
cover and hydrology through the use of a geographic
information system (GIS) incorporating remotely sensed data.
Advanced Very High Resolution Radiometer (AVHRR) thermal-
infrared images, land-cover maps, and soil-type maps were
assembled as GIS database layers for a 1-km spatial resolution
study of Florida. Seasonal and diurnal surface temperature
patterns of many combinations of land-cover and soil type were
analyzed quantitatively. Effects of changes in land-cover and
hydrology due to both artificial (agriculture, urbanization,
wetland disturbance, and exotic-plant introduction) and
natural factors (drought, freeze, and hurricane) were studied.
The thermal-infrared images were calibrated to at-
satellite radiant temperature and geographically corrected for
importation into the GIS, then corrected for atmospheric
xxv i

effects and surface emissivity to produce surface kinetic
temperature. Seven soil types (from maps) and 32 land-cover
types (from maps, aerial photographs, and site visits) were
imported into the GIS as digitized polygons.
Results of analyses performed using the GIS indicated
that both land-cover and soil type, as well as soil moisture
and season, were significant factors influencing surface
temperature patterns in Florida. Surface temperature effects
(daytime "heat island", nighttime "cold island", and diurnal
variation "extreme island") of several agricultural and urban
soil type/season combinations matched or exceeded those found
among natural land-cover types. The surface temperature
effects of certain agricultural soil type/season combinations
matched or exceeded those of urban counterparts. Drought,
freeze damage, hurricane damage, wetland disturbance, and
exotic-plant introduction all produced significant changes in
surface temperature.
xxv 11

INTRODUCTION
Surface temperature is a parameter relevant to many
fields. It is in demand for studies and models of
climatology, hydrology, agriculture, and forestry. There are
difficulties to be overcome in obtaining and applying surface
temperature data, which might be solved with new techniques
involving remote sensing and geographic information systems
(GIS). These matters are discussed below, with emphasis on
their importance to the state of Florida.
Purpose and Objectives
The purpose of this research was to use satellite imagery
together with a geographic information system to
quantitatively investigate the kilometer-scale relationship
between changes in land cover and those in surface
temperature. The specific objectives were 1) to obtain
remotely sensed thermal-infrared data at 1-km spatial
resolution for the state of Florida, using the National
Oceanic and Atmospheric Administration (NOAA) Television
Infrared Observation Satellite (TIROS) Advanced Very High
Resolution Radiometer (AVHRR) thermal-infrared imagery; 2) to
calibrate this thermal-infrared data to at-satellite radiant
temperature and to geographically correct the resulting
temperature imagery for inclusion in a GIS; 3) to calculate
1

2
surface kinetic temperature from at-satellite radiant
temperature through atmospheric correction and emissivity
correction; 4) to digitize land-cover and soil-type data
(obtained from maps, aerial photography, and site visits) into
polygons for inclusion in a GIS; 5) to build a GIS database
containing as coverage layers the surface temperature images,
soil type, land-cover (natural, agricultural, urban, and
industrial types), and special condition (drought, hydrologic
disturbance, freeze-damage, storm-damage); 6) to utilize the
GIS database in performing a quantitative analysis of seasonal
and diurnal relationships between land-cover, soil type, and
surface temperature; and 7) to utilize the GIS database in
performing a quantitative analysis of seasonal and diurnal
relationships between changes in land-cover type and changes
in surface temperature.
There were two additional objectives to supplement the
AVHRR work. These were 1) to perform a historical (1979)
surface temperature study of Florida based on the at-satellite
radiant temperature data of the National Aeronautics and Space
Administration (NASA) Heat Capacity Mapping Mission (HCMM)
satellite, and 2) to perform a ground-based evaluation of the
DSTV/soil moisture relationship for both mineral and organic
soil types.
Importance of Surface Temperature in Climatology
Surface temperature is one of critical parameters of
climate at macro-scale, meso-scale (Davis and Giles, 1990;

3
Giorgi and Mearns, 1991; Barron, 1992, Lewis and Wang, 1992;
McCabe and Wolock, 1992), and micro-scale (Auer, 1978; Lewis,
1984; Rourke, 1985; French and Krajewski, 1994). Macro-scale
climate (100 km to global spatial resolution, multi-year
temporal resolution) includes the basic macroclimate, or
"average weather", with its global weather systems,
atmospheric/oceanic/topographic influences, and long-term
perturbations from factors such as the El Niño/Southern
Oscillation (ENSO), volcanic dust plumes, and greenhouse gases
(McClain et al., 1985; Harries, 1990; Mather and Sdasyuk,
1991). Macro-scale climate is not directly concerned with
near-surface (< 2 m above surface) processes (Akin, 1991).
Meso-scale climate (1 to 100 km spatial resolution, 1 day
to 1 year temporal resolution) includes local weather and is
directly concerned with near-surface processes (Henderson-
Sellers and Robinson, 1986; Harries, 1990; Hostetler and
Giorgi, 1993; Johannessen et al., 1993). Micro-scale climate
(1 km or finer spatial resolution, 1 day or finer temporal
resolution) is directly concerned with highly localized
effects, which are nested within (and do not influence) the
meso-scale climate (Henderson-Sellers and Robinson, 1986;
Harries, 1990; Akin, 1991).
Meso-scale is the smallest scale at which surface factors
have potential to force weather patterns; land-cover and/or
surface moisture organized at this scale produces an organized
atmospheric response (Shuttleworth, 1991). This response
includes air convection and turbulence (Henderson-Sellers and

4
Robinson, 1986), and even shifts in rainfall patterns due to
urban "heat-islands", irrigated-desert "oases", drained-marsh
"heat-plateaus", etc. (Auer, 1978; Dickinson, 1988; Abtew and
Khanal, 1994). In combination with soil moisture and acidity,
surface temperature controls soil-biogenic greenhouse-gas
emissions (Schimel et al., 1988; Yienger and Levy, 1994). Due
to these effects on weather patterns and greenhouse gas
emission, surface temperature is a meso-scale parameter which
must be linked to the current macro-scale global-circulation
models for further study of the global-change/greenhouse-
effect (Bolin, 1988; Risser et al., 1988; MacCracken et al.,
1990; Mather and Sdasyuk, 1991).
Importance of Surface Temperature in Hydrology
Surface temperature is an important parameter of surface
hydrology and its components such as evapo-transpiration (ET)
and soil moisture (Soer, 1980; Haan et al., 1982; Heimberg et
al., 1982; Price, 1984; Reiniger and Seguin, 1986; Ottlé et
al., 1989; Sucksdorff and Ottlé, 1990; Novak, 1991; Rodriguez-
Iturbe et al., 1991a, 1991b; Brutsaert and Parlange, 1992;
Chang et al., 1992; Nikolaidis et al., 1993; Zelt and Dugan,
1994). It is also an important factor in water conservation
topics such as lake and reservoir evaporation (Miller and
Millis, 1989; Hondzo and Stefan, 1991; Steinhorn, 1991; Mahrer
and Assouline, 1993), streamflow (Cayan, 1993), and effects of
oil spills on ocean evaporation (Mather and Sdasyuk, 1991).

5
A typical application of surface temperature in hydrology
is in the computation of the Bowen ratio (Linsley et al.,
1982) :
P = 0.00066 p (Ts - TJ / (eQ - ea) [1]
where p is the ratio of sensible heat transport to latent heat
transport, p is the atmospheric pressure (mbar) , Ts is the
surface temperature, Ta is the air temperature (C) , e0 is the
air saturation vapor pressure (mbar) at Ts, and ea is the air
vapor pressure (mbar). The Bowen ratio is used in the study
of reservoir evaporation and vegetation evapotranspiration
(Linsley et al., 1982).
Importance of Surface Temperature in Agriculture and
Forestry
Surface temperature is important to agriculture and
forestry as a factor of water-stress and growing-region for
crops and trees (Henderson-Sellers and Robinson, 1986; Seguin,
1989). Meso-scale surface temperature is of interest to
studies involving agricultural topics such as regional crop
condition and yield prediction (Idso et al., 1979, 1981?
Reginato, 1983; Taconet et al., 1986b; Hope and Jackson,
1989). It is a major factor involved in soil conservation
issues—such as soil subsidence (Lucas, 1982) and soil
degradation (Kilmer, 1982) , which are critical to long-term
agricultural planning. Surface temperature is also of

6
interest in the assessment of forest-fire risk (Waters, 1976;
Chuvieco and Martin, 1994).
Together with soil moisture, meso-scale patterns and
changes of surface temperature are a major factor in outbreaks
of pests, parasites, and diseases in agricultural crops,
forest trees, and livestock (Uvarov, 1931; Geiger, 1950; Akin,
1991). Examples of such impacts include locust swarm-
behavior, Dutch elm disease, chestnut blight, tobacco blue
mold, Japanese beetle, Colorado potato beetle, potato blight,
cotton leaf worm, seedling-scald, and liver fluke (Uvarov,
1931; Rourke, 1985; Akin, 1991).
Factors Affecting Surface Temperature Patterns
Land surface temperature patterns at meso-scale are
forced by several factors which can change spatially and
temporally (Hillel, 1980; Risser et al., 1988; Lewis and Wang,
1992). These include macro-scale climate, solar irradiation,
geothermal heat sources, maritime effects, orogenic effects,
vegetation transpiration, root-zone soil moisture, soil type,
and land cover type.
Macro-scale climate has been discussed previously; its
impact on meso-scale surface temperature takes the form of
annual cyclic changes in average values of precipitation and
air temperature, which are well-documented for most places.
The macro-scale climate effect on surface temperature can be
estimated from comparison of data from similar natural land-
cover type pairs across macro-scale climate zones (temperate-

7
zone pine forest and subtropical-zone pine forest, temperate-
zone marsh and subtropical marsh, etc.)* Advection effects
(strong winds, precipitation, etc.) from transient weather
phenomena such as storms, winter frontal systems, and
especially desert-winds (harmattan, etc.) can have a
substantial impact on meso-scale temperature (and relative-
humidity) , but these effects are sporadic and transient
outside of continental interiors (Hillel, 1980; Haan et al.,
1982), and are not addressed in this study. Solar irradiation
influences surface temperature through daily cyclic changes,
and is a primarily a function of latitude, date, and hour.
Geothermal heat sources are common at micro-scale—such as hot
springs, subterranean steam-lines (Axelsson, 1988) , and
artesian wells (Jordan and Shih, 1988), but rare at meso-scale
(volcano and geyser areas).
Maritime effects operate at micro-scale (air advection
immediately adjacent to the coast) and at meso-scale
(increased humidity further inland) (Henderson-Sellers and
Robinson, 1986; Dickinson, 1988). The positions of meso-scale
water bodies and wetlands are well documented. Florida, due
to its proximity (at meso-scale) to the sea on every side, its
near sea-level elevation, and its lack of topographic
obstructions (mountains), is free from sources of major
variation in the meso-scale maritime effect. The micro-scale
maritime effect on surface temperature can be estimated from
comparison of data from coastal/inland pairs of similar

8
natural land-cover type, such as saltmarsh and freshwater
marsh, coastal hammock and inland hammock, etc.
Orogenic effects on meso-scale surface temperature can be
very pronounced in mountainous regions (Atkinson, 1985;
Henderson-Sellers, 1986; Barros and Lettenmaier, 1994) . They
are nonexistent in level-terrain regions such as Florida.
Vegetation transpiration, root-zone soil moisture, soil
type, and land cover type are forcing factors of meso-scale
surface temperature which are strongly inter-related.
Vegetation transpiration is a daily cyclic phenomenon. Root-
zone soil moisture can change over hours or days; it is
artificially controlled in urban and agricultural areas, and
is a function of the soil type and macroclimate in natural
areas. Soil type is generally constant over time, and is
documented to various degrees in most of the world; it is a
particularly important surface temperature factor for cleared
areas.
Land-cover type is subject to both annual cyclic changes
(seasonal tree leaf cover, agricultural crop seasons) and
sudden changes (agricultural/urban development, natural
disasters). Land-cover type is well-documented at meso-scale
for most of the world, although available maps are often
overly simplistic in land-cover distinction—particularly for
agricultural land-cover. Meso-scale land-cover information
for application to meso-scale surface temperature work should
contain distinctions comparable to Level-Ill designations in
the United States Geological Survey (USGS) land use

9
classification system (Anderson et al., 1976)—for example,
"pasture" or "cropland" instead of simply the Level-II
"cropland and pasture" or Level-I "agriculture" designations.
Temperature Impacts of Changes in Land-Cover
Land-cover is a forcing factor of surface temperature
pattern which can experience meso-scale changes that are non-
cyclic and discontinuously-distributed both spatially and
temporally. These changes can be due either to natural causes
(volcanoes, storms, floods, droughts, wildfires, pests, etc.)
or artificial causes (related to agricultural and urban/
industrial development). International attention has mounted
in recent years concerning the worldwide extent of
deforestation (Mather and Sdasyuk, 1991) , compared to the very
few regions currently experiencing a significant degree of
reforestation (Ireland, Senegal, England, and Algeria as of
1984) .
Urban effects. Previous research has established the
concept of the urban heat island, which is characterized by
increased surface and air temperature (by 5 to 10 C) and
decreased relative-humidity in an urbanized area relative to
its surroundings (Eagleman, 1974; Lewis, 1984; Atkinson, 1985;
Balling and Brazel, 1988; Henry et al., 1989; Akin, 1991).
The more high buildings, more smog, and fewer trees, the more
pronounced the effect (Henderson-Sellers and Robinson, 1986).
The heat island is primarily a daytime effect within the
urbanized area, and has been shown to have a cellular

10
topology, rather than a smooth dome shape, due to the peak and
canyon geometry of the urban skyline (Atkinson, 1985) . There
is a similar, but lesser, nighttime heat-island effect
(Eagleman, 1974; Henry et al., 1989). The importance of urban
temperatures to human well-being has been noted (Lewis, 1984;
Henderson-Sellers and Robinson, 1986; Meerow and Black, 1988).
There is a need for more detailed investigation of the
heat island effect. The heat island of urban areas has
usually been compared simply to non-urbanized areas nearby;
the influence of soil type has often been ignored. Thus, it
may be that a well-drained soil area selected for urban use
has always had an associated heat island relative to
surrounding areas of different soil type, even under its
natural cover. In addition, there is the possibility that the
inclusion of water-bodies, as is common in certain Florida
suburbs (finger canals) and mines (tailings ponds), may
counteract the urban heat island effect. The presence of
windbreak trees, as in golf-course suburb communities,
restricts the lateral flow of near-surface air, leading to
higher daytime temperatures in the open area between trees
than would be the case for a completely open field (Geiger,
1950; Crowe, 1971; Meerow and Black, 1988a; McCarty et al.,
1990). In closed-canopy parkland, the removal of undergrowth
and low tree branches increases the lateral flow of near¬
surface air, producing a moderating effect on temperature
compared to open urban areas, but a decrease in humidity
compared to a natural forest (Lewis, 1984).

11
Agricultural effects. Agricultural effects on surface
temperature patterns have received less attention than urban
effects, which is undeserved, considering the vastly greater
areal extent of agricultural land-cover compared to urban
land-cover. For irrigated areas in deserts, a measurable
daytime oasis effect characterized by decreased surface and
air temperature and increased relative-humidity compared to
the surroundings has been noted (Hillel, 1980; Haan et al.,
1982; Henderson-Sellers and Robinson, 1986). For agricultural
land-cover in forest/wetland areas, a measurable daytime heat-
plateau effect characterized by increased surface and air
temperature and decreased relative-humidity compared to the
natural surroundings has been observed (Ghuman and Lai, 1987),
together with a corresponding nighttime cold-plateau of
decreased surface and air temperature compared to the natural
surroundings (Chen, 1979). The presence of belt-planted
trees, as in agricultural field windbreaks, restricts the
lateral flow of near-surface air, leading to higher daytime
temperatures in the open area between trees than would be the
case for a completely open field (Geiger, 1950; Crowe, 1971;
Meerow and Black, 1988a; McCarty et al., 1990).
Florida land-cover change. In Florida, natural causes of
meso-scale land-cover change have consisted of storms
(hurricanes), droughts, wildfires, and freezes. Artificial
factors of land-cover change have consisted of agricultural
development, urban development, industrial development, water-

12
control projects, naturalization projects, and invasion by
exotic vegetation.
Agricultural development in previous times (pre-Columbian
to early 1900s for panhandle and north; primarily from 1910 to
1950 for south) displaced much of the natural land-cover in
Florida (Fernald and Patton, 1984) ; in recent times it has
consisted primarily of changes in the type of agriculture—
especially between row-crops, pasture, and citrus orchard—on
the same land. Urban/industrial development in previous times
(primarily in the form of urban centers and strip mines)
displaced much of the natural and agricultural land-cover; in
recent times this development is continuing—particularly in
the form of suburbs.
Water-control projects are located in the Everglades
Agricultural Area (EAA), Kissimmee River basin, and Upper
Suwannee River basin (north-central Florida as well as south-
central Georgia). Naturalization projects include the
restoration of marshes (Payne's Prairie, Lake Apopka,
Kissimmee River basin, Lake Jessup, and the activity of
beavers naturally re-colonizing parts of the panhandle),
saltmarshes (Indian River Lagoon, Tampa Bay), and scrub (state
parks, local parks, and Archbold Biological Station). In
addition, efforts have begun in recent decades to reclaim
mined land for naturalization and even agriculture (Blakey,
1973).
Invasion by exotic vegetation has occurred at meso-scale
in south Florida (EPPC, 1990). This vegetation consists of

13
evergreen trees which have a very fast growth rate, and
transpire enough to decrease the root-zone soil moisture—
which is precisely why some of them were introduced in earlier
decades—to dry out wetlands (EPPC, 1990). The spread of
exotic forest has been beyond effective human control since
the 1950s (Barrett, 1956; EPPC, 1990) , and has now reached
proportions of serious ecological and hydrological concern to
south Florida.
Temporal Inter-Relation of Forcing Factors
The net surface energy flux, soil type, and root-zone
soil moisture are forcing factors of the surface temperature
pattern which are inter-related temporally (Geiger, 1950;
Crowe, 1971; Kahle, 1977; Hillel, 1980; Bolin, 1988; Poliak,
1992). A bare-soil, flat-terrain, conductive heat-transfer
(non-advective) surface energy balance can be modeled
temporally by the harmonic equation (Mulders, 1987):
T(z,t) = Tavg + [F0/(Pw1/2) ]e~z/dsin(wt-z/d-7r/4) [2]
where T(z,t) is the soil temperature (K) at depth z (m) and
time t (t = 0 s at 0000 h) expressed as local solar time
(LST) , Tavg is the average (treated as constant) soil
temperature (K) at a depth of 2 to 3 m, F0 is the amplitude (W
m'2) of the net surface energy flux F, P is the soil thermal
inertia (J m'2 K'1 s~1/2) , d is the damping depth (m) , and w is
the Earth angular rotation frequency (7.27xl0'5 s'1). The net

14
surface energy flux, modeled in the form F = F0sin(wt) , is
primarily a function of solar irradiation and near-surface air
temperature (Budyko, 1974; Henderson-Sellers and Robinson,
1986; Lewis and Wang, 1992). Soil thermal inertia is
expressed by the formula (Price, 1982):
P = (Ape)1/2 [3]
where A is the soil thermal conductivity (W m"1 K'1) , p is the
soil density (kg m'3) , and c is the soil heat capacity (J kg'1
K'1) . The components A, p, and c are directly related to the
soil moisture content for a given soil type (Lillesand and
Kiefer, 1979; Carlson et al., 1981; Price, 1984; Curran,
1985). Therefore, the higher the soil moisture content, the
warmer the soil temperature during the night and the cooler
the soil temperature during the day, as has been noted in
numerous studies (Shih et al., 1986; Taconet et al., 1986b;
Sugita and Brutsaert, 1992). The above equation can be solved
for surface temperature, Ts, yielding the equation (Mulders,
1987) :
Ts(t) = Tavg + [F0/ (Pcj1/z) ] sin (U)t-7T/4) [4]
where the quantities are described as before. The ir/4 term
translates (by 27r = 24 h) to a 3-hour time lag between maximum
F0 (1200 h LST) and maximum surface temperature (1500 h LST).
Non-conductive components of soil heat transfer (dew
evaporation) can cause the surface temperature to vary

15
somewhat from that indicated by equation 4 (Hinkel and
Outcalt, 1993) , but only for a short period in the early
morning (Schmugge, 1978; Price, 1982).
Diurnal surface temperature variation. Taking the
diurnal surface temperature variation (DSTV) of equation 4
leads to the equation (Mulders, 1987):
DSTV = Tmax - Tmin = 2 F0/(PCJ1/Z) [5]
where Tmax is the maximum surface temperature (K) at t = 1500h
LST, Tmin is the minimum surface temperature (K) at t = 0300h
LST, and P, to, and F0 are defined as before. Thus, DSTV is
inversely related to the root-zone soil moisture content, and
is a strong indicator of relative root-zone soil moisture
conditions for different locations of the same soil type on
the same day (Engman and Gurney, 1991). The fact that DSTV is
an indicator of daily-average root-zone soil moisture makes it
particularly useful to hydrologic modeling studies (Parlange
et al., 1992). The presence of a clay hardpan or bedrock
within the root-zone depth of shallow soils will lead to
deviations from the predicted DSTV of equation 5 (Hillel,
1980). This influence of foreign bodies within the root-zone
soil depth on DSTV has in fact been used at micro-scale to
locate buried objects/features such as abandoned mine tunnels
and bombs (Cloud, 1992).
Estimated seasonal DSTV. Seasonal values of bare-soil
DSTV can be estimated based on soil parameters. A typical

16
Florida value of F0 in summer is 0.003941 (cal cm'2 s'1) and in
winter is 0.001433 (cal cm'2 s'1) (ASHRAE, 1981). For a
mineral soil (sand or clay with typical 4 0% porosity) , the
value of pc is 0.3 (cal cm"3 K"1) under dry condition and 0.7
under saturated condition, and the value of A is 0.0007 (cal
cm'1 s'1 K'1) under dry condition and 0.0052 under saturated
condition (Hillel, 1980). For an organic soil (peat with
typical 80% porosity), the value of pc is 0.35 (cal cm'3 K'1)
under dry condition and 1.15 under saturated condition, and
the value of A is 0.00014 (cal cm'1 s"1 K'1) under dry condition
and 0.0012 under saturated condition (Hillel, 1980). Plugging
these values into equations 3 and 5 produces estimated summer
DSTV (K) ranging from 15 (saturated) to 64 (dry) for mineral
soil, and from 25 (saturated) to 132 (dry) for organic soil;
it produces estimated winter DSTV (K) ranging from 6
(saturated) to 23 (dry) for mineral soil, and from 9
(saturated) to 48 (dry) for organic soil. Agricultural land-
cover values of DSTV can be expected to lie somewhere between
those of the saturated condition and those of the totally dry
condition.
Relevant depth of surface temperature/soil moisture
relation. The depth of soil to which the soil moisture
content is relevant to the surface temperature is a function
of the damping depth, d, which is given by the equation
(Hillel, 1980);
d = [2A/PCW) ]1/2
[6]

17
where p, w, and c are defined as before. The values of A,
p, and c are functions of both soil moisture and soil type.
For a mineral soil (sand or clay with typical 40% porosity)
under a totally-dry condition, the value of d is about 8 cm;
for an organic soil (peat with typical 80% porosity) under a
dry condition, the value of d is about 3 cm (Hillel, 1980).
The attenuation factor of equation 2 is e'z/d, so that an
attenuation (100 - e'z/d)% of 95% (relevant depth limit for
estimated soil-moisture) is reached at a depth of 3d,
corresponding to 24 cm for a mineral soil and 9 cm for an
organic soil. Under saturated soil condition, the value of d
increases to about 14 cm for mineral soil and about 5 cm for
organic soil (Hillel, 1980), increasing the respective
relevant depth limits to 42 cm and 15 cm. The moisture
content of such root-zone depths is of importance to surface
hydrologic modeling (Rourke, 1985; Risser et al., 1988; Milly,
1994; Zelt and Dugan, 1994) and climatological modeling
(Gillies and Carlson, 1994; Salvucci and Entekhabi, 1994;
Smith et al., 1994).
Vegetation influence on DSTV. Vegetated land cover
affects both the maximum and minimum surface-temperature
components of DSTV (Geiger, 1950; Luval et al. , 1990).
Minimum surface temperature is raised by nighttime reflection
of soil-emitted energy back to the surface. The radiation
contribution from vegetation foliage at night is negligible—
foliage quickly reaches equilibrium with air temperature
(Hillel, 1980; Chen et al., 1982; Reiniger and Seguin, 1986;

18
Seguin, 1989; van de Griend and van Boxel, 1989). The
nighttime vegetation effect will decrease during winter for
deciduous vegetation species.
Maximum surface temperature is lowered by the daytime
evapotranspiration (ET) from vegetation. ET rate is a
function of vegetation type (exact species or cultivar),
ambient water-vapor pressure deficit, air temperature,
vegetation species, and vegetation water stress (Idso et al.,
1981a, 1981b; Wetzel et al., 1984; Reiniger and Seguin, 1986;
Taconet et al., 1986a, 1986b; van de Griend and van Boxel,
1989; Doyle, 1992). It should be noted that the transpiration
component of ET is present for most vegetation only from late
morning to afternoon (Bolin, 1988) . If the source of
vegetation water stress is limited to root-zone soil moisture
(rather than salinity or damage from pests, diseases, wind,
hail, etc.), and the other ET factors are measured, the soil
moisture condition can be calculated. The relevant depth in
this case is dependent on the vertical distribution of the
root system (according to plant species and maturity), not on
the d-value of equation 6 (Rubin and Or, 1993) . This relation
is the basis for the Crop Water Stress Index (CWSI), which is
widely used for scheduling the irrigation of agricultural
fields (Howell et al., 1983; Reginato, 1983; SOEMC, 1987; El,
1991). Again, the daytime vegetation effect will decrease
during winter for deciduous vegetation species.
The diurnal effect on the net heat flux of the vegetated
surface, G (W m~2) can be represented (for a non-advective

19
situation) in form (Haan, 1982):
G = F0 sin(wt) - LE [7]
where LE is the latent heat flux (W m'2) caused by the
vegetation. The term LE can be approximated using the Bowen
ratio, /3, producing the equation (Haan, 1982) :
F0 sin(wt)
LE = [8]
1 + 0
where /3 typically ranges from 0.1 to 0.3 for humid-climate
conditions. Assuming /3 = 0.2, and substituting the LE from
equation 8 into equation 7, it follows that the DSTV for the
vegetated surface, DSTVv is
DSTVv = (0.3334) F0/(P which is a reduced-amplitude version of equation 5. The
assumption of full canopy closure is made here; the estimation
of exact effects of partial canopy (as in many forms of
agricultural land-cover) are a complex matter for study at
very fine spatial and temporal scales (Taconet et al., 1986a,
1986b; Massman, 1992).
Spatial Inter-Relation of Forcing Factors
The soil type, root-zone soil moisture, and land-cover
type are forcing factors of the surface temperature pattern
which are inter-related spatially (Akin, 1991). Different

20
types of agriculture require the maintenance of different
levels of soil moisture (Ziegler and Wolfe, 1961; Snyder,
1978; Henley, 1983; McCarty and Cisar, 1990)—standing water
for rice, taro, and fish-farm; high water-table for sod-farm;
medium water-table for winter-vegetables, sugarcane, potato,
strawberry, blueberry, blackberry, and pasture; and relatively
low water-table for leatherleaf fern, citrus, and most other
fruit trees.
Likewise, different soil types require different
agricultural management practices—irrigation of deep sands
and loams; drainage of organic soils and marls; and both
irrigation and drainage of spodosols and rockland soils
(Jones, 1948; Stewart et al., 1963; Hochmuth and Hanlon,
1989). Soil type and seasonal soil-moisture levels dictate
the natural land-cover type (scrub, forest, swamp, marsh,
etc.) and limit the possibilities of agricultural land-cover
types (citrus primarily to mineral soil, sugarcane primarily
to organic soil, blueberry to acid soil, atemoya to sub-
alkaline soil, etc.) (Critchfield, 1960; Schimel, 1988; Akin,
1991).
Urban development, however, is less impeded by soil type.
This is particularly evident in Florida, where coastal sands,
flatwoods sands, marls, and even mucks have been urbanized, as
well as the more conventional deep sands, upland loamy sands,
and sandy rockland.

21
Potential for Future Changes in Soil Type
General soil type, which is a constant forcing factor of
meso-scale surface temperature for mineral-soil areas, can
change for organic-soil areas, or even for mineral-soil areas
where mass-wasting occurs (Risser et al., 1988). Organic
soils drained for conventional agricultural use tend to
subside and eventually disappear (Snyder, 1978; Kilmer, 1982;
Lucas, 1982; Abtew and Khanal, 1994). This effect was well
understood even at the time the EAA water-control system was
being planned and implemented (Jones, 1948). The current rate
of organic soil subsidence in the EAA is about 1 inch per year
(Snyder, 1978) ; the natural temporal scale of soil type change
is typically of the order of 10,000 years (Dickinson, 1988;
Lucas, 1982). The potential mesoscale surface-temperature
impact of such a soil-type change in Florida is greatest in
the EAA, where organic soil will likely be replaced in the
near future by sandy/marly rockland if the agricultural land-
cover does not change to aquatic crops or restored marsh.

REVIEW OF LITERATURE
The techniques involved in the remote sensing of land
surface temperature are described. Previous studies, their
methods, and their results are discussed.
Difficulties of Surface Temperature Measurement
Unlike air temperature, surface temperature cannot be
interpolated between point measurements on land surfaces (due
to differences in several spatially-variable factors); to
obtain it synoptically at meso-scale requires some form of
satellite-based radiometry. Thermal-infrared and passive-
microwave radiometry are the two types applicable to the
typical Earth-surface range of temperatures (Lillesand and
Kiefer, 1979; Owe and Chang, 1988; Engman and Gurney, 1991).
Both of these are available from various satellite platforms.
Passive-microwave sensor data have the advantage of cloud
penetration, but are much more limited in spatial resolution
and temperature accuracy than are thermal-infrared sensor data
at this temperature range, and are sensitive to extraneous
factors such as surface microwave-roughness and radio¬
communication interference (Lillesand and Kiefer, 1979; Owe
and Chang, 1988; Harries, 1990; Engman and Gurney, 1991; Owe
et al., 1992). Passive microwave data are typically used at
meso-scale (from satellite platform) for ice/snow water-
22

23
content mapping (Mather and Sdasyuk, 1991) or atmospheric
sounding (Miller et al., 1994), or at micro-scale (reguiring
an aircraft platform) for surface temperature or soil moisture
mapping (Ijjas and Rao, 1992; Appleby et al., 1993; Paloscia
et al., 1993). Due to the above considerations, thermal-
infrared radiometry was selected as the source of surface
temperature data in this study.
The extraction of land surface temperature data from
satellite thermal-infrared radiometer measurements involves
three different processes—sensor calibration, atmospheric
correction, and emissivity correction. Errors incurred in any
of these processes will degrade the accuracy of the surface
temperature data (Marlatt, 1967; Llewellen-Jones et al., 1984;
Schott, 1989; Ben-Dor et al. , 1994). Each satellite-based
thermal-infrared sensor has its own level of radiometric
precision and its own standardized calibration technique,
which the user must consult (Wolfe and Zizzis, 1978; Tebo,
1994a). A typical order of magnitude of radiometric precision
for thermal-infrared radiometers is 0.1 C (Myhre et al. , 1988;
NOAA, 1991).
Ignoring the atmospheric correction can result in surface
temperature underestimates of up to 20 C (Llewellyn-Jones et
al., 1984; Price, 1984; Sobrino et al., 1991); this source of
error is primarily of concern in comparison of surface
temperatures from more than one time or date (in addition,
substantial spatial variation in the atmospheric effect can be
expected for images covering continental distances). Ignoring

24
emissivity correction over land surfaces can result in
underestimates of up to 10 C (Barton and Takashima, 1986) ;
these errors will vary spatially within an individual image—
resulting in an inability to accurately compare the surface
temperature
of
one location to the next.
Therefore, if
atmospheric
or
emissivity corrections
are
not
used, the
remotely-sensed
surface temperatures
may
be
in error
(conservative) by at least two orders of magnitude compared to
the sensor radiometric precision.
Atmospheric Correction Techniques
Atmospheric correction is needed for remotely sensed
thermal data, due to the combined effects of absorption,
scattering, and in-path radiance by the atmosphere upon
thermal-infrared imagery, even in "atmospheric window" bands
(Lillesand and Kiefer, 1979; Ben-Dor et al., 1994). The net
effect on satellite thermal-infrared radiometry is an
attenuation. Three general types of atmospheric correction
techniques have been developed.
Atmospheric modeling. Detailed modeling of the
atmosphere has been used successfully for atmospheric
correction of thermal-infrared data (Henry et al., 1989;
Wukelic et al., 1989; Luval et al., 1990; Gillies and Carlson,
1994; Sadot et al., 1994). It requires atmospheric data,
which in most instances has been provided by radiosondes,
although laser-based techniques are currently being developed
for this purpose (Tebo, 1994b). Atmospheric sensor

25
instruments exist on most current satellite platforms, but
these have very crude spatial resolution and are used for
global atmospheric research rather than meso-scale atmospheric
modeling (Zhang, 1993; Aumann and Pagano, 1994; Ellingson et
al., 1994). The collection of atmospheric data by radiosondes
is relatively expensive, and cannot be substituted with non¬
local radiosonde data, non-simultaneous radiosonde data, or
estimated atmospheric values (Kerr et al., 1992).
Split-window techniques. Empirical "split-window"
techniques, based on differential atmospheric absorption
effects in different thermal-infrared bands, have long been
used successfully in atmospheric correction of thermal-
infrared data (Price, 1984; Llewellyn-Jones et al., 1984;
McClain et al., 1985; Cornillon et al., 1987; Cooper and
Asrar, 1989; Vidal, 1991). They require a sensor that
possesses multiple thermal-infrared bands, which is
fortunately available from various satellite platforms. They
also require ground-based surface-temperature measurements to
allow the initial calculation of their coefficients, but these
have already been established and reported for the individual
techniques (McClain et al., 1985; Di and Rundquist, 1994).
There are inherent difficulties in applying these techniques
to land surface studies (Becker, 1987; Sobrino et al., 1991),
the most important of which are the assumptions that the
surface emissivity is homogeneous within the instantaneous
field of view (IFOV) of the sensor, and that it is equal to 1.
Therefore, split-window techniques are primarily restricted to

26
sea-surface studies, where these assumptions are usually
valid.
Water-body reference technique. The water-body reference
technique requires one or more large water bodies, with near¬
surface water temperature measurements taken simultaneously
with the thermal-infrared image (Lillesand and Kiefer, 1979).
These measurements must be taken for each image date and time,
which can pose a logistical problem for studies involving a
temporal series of images. Fortunately, such water-body data
are collected and made readily available in Florida by various
environmental agencies.
This technique includes the assumption that within the
sensor IFOV the water-body surface temperature and emissivity
(not necessarily equal to 1) are homogeneous. This assumption
is generally valid. It also assumes that the water body is
not under conditions such as very hot and dry (desert-climate)
ambient air or strong winds, which would produce a difference
between skin and near-surface bulk temperature of the water.
This assumption is valid for non-desert water bodies under low
wind conditions (Cornillon et al., 1987).
Emissivity Correction Techniques
Radiant (blackbody) temperature values can be converted
to kinetic (surface) temperature values if the emissivity (in
the sensor bandwidth) is known with sufficient accuracy. The
physical foundation for kinetic temperature calculation by

27
radiometry is described by the Planck function (Saito et al.,
1992) :
E(X, T)
[10]
exp [C2/(X T) ] - 1
where E is the measured energy (W m'2 ¿m'1) , X is wavelength
(/m) , Cj is a constant (3.74x10® W m'2 /im4) , C2 is the Boltzmann
constant (14,388 p K), T is the kinetic temperature (K) , and
e is the emissivity. A commonly encountered formula for
kinetic temperature calculation is the Stefan-Boltzmann
equation (Lillesand and Kiefer, 1979):
[11]
where Tkin is the kinetic temperature (K) , Trad is the radiant
temperature (K) , and eb is the broadband emissivity (0 to 1) .
However, this formula was designed for application to
laboratory situations of broadband (all wavelengths)
radiometry, rather than the "atmospheric window" band
radiometry performed by satellite sensors (midwave thermal-
infrared window at 3-5 /¿m, longwave thermal-infrared window at
8-14 ¿un) . To account for this, some researchers (Davies et
al., 1971; Price, 1983) have used a version of the Stefan-
Boltzmann equation modified for use with longwave thermal-
infrared sensor bands (Price, 1983):
[12]

28
where Tkin and Trad are defined as before, and elw is the
longwave emissivity. This modified Stefan-Boltzmann formula
still contains simplifying approximations which limit its
accuracy in application to remotely sensed thermal data. The
integration of equation 10 over the bandwidth of given sensor
provides a better formula for quantitative longwave thermal-
infrared radiometry (Singh, 1985; Driggers et al., 1992; Saito
et al., 1992). It is given by the equation (Singh, 1985);
k CWNi
Tkin = [13]
In [1 - elw + elw exp(k CWN, / Trad i) ]
where Tkin is defined as before, k is the Boltzmann constant
(1.43883 cm K) , Trad d is the radiant temperature (K) in band i,
CWNi is the central wave number of band i (cm'1) , and elw is the
longwave emissivity. The central wave number is generally
documented for each band of a given sensor.
Emissivity presents the difficulty of being a property
that is calculable, rather than directly measurable (Fuchs and
Tanner, 1968; Friedman, 1969; Hejazi et al., 1992). Two
different methodologies have been employed to obtain
emissivity for processing remotely sensed thermal image data.
Emissivity by assignment. This commonly used technique
is based on longwave emissivity values determined in the field
or laboratory from close-range, longwave thermal-infrared
radiometry of small samples (Buettner and Kern, 1965; Fuchs
and Tanner, 1968; Friedman, 1969; Taylor, 1979; Barton and

29
Takashima, 1986; Rees, 1990; van de Griend et al., 1991;
Vidal, 1991; Salisbury and D'Aria, 1992). An emissivity value
is then assigned to a particular sensor IFOV according to
land-cover information or the normalized-difference vegetation
index (NDVI) (Gervin et al., 1985; Henry et al., 1989; Kerr et
al., 1992; Brown et al., 1993; van de Griend and Owe, 1993).
This method is primarily limited to aircraft or ground
radiometry, since the laboratory-determined emissivity values
are fine-resolution quantities bearing little relation to the
pixel-average emissivity value corresponding to a satellite
sensor IFOV (Curran, 1985; Jupp et al., 1988; Masuda et al.,
1988) . An additional problem with this technique in
agricultural areas is that bare sand emissivity is controlled
by the moisture content of a very thin surface layer, and can
change over a short period of time (Fuchs and Tanner, 1968).
Twin-band technique. The physically-based "twin-band"
technique allows the calculation of pixel-average emissivity
from a thermal-infrared image (Artis and Carnahan, 1982):
«ij = exp {k (Tradii-TradJ) / [Tradi Tradij (Xi-Aj)]} [14]
where is the emissivity over the wavelengths from band i
to band j_, Trad d and Tradj are the radiant temperatures (K) in
bands i and j_, k is the Boltzmann constant (1.43883xl0'2 m K) ,
and Ad and A¿ are the respective central wavelengths (m) of
bands i and This method requires a sensor possessing two
thermal-infrared bands that are synoptic, spectrally close,

30
and spectrally narrow. This makes it inapplicable to the
single-band thermal-infrared data acquired by most of the
current satellite sensors (GOES-VISSR, Nimbus-7, Landsat-TM,
Meteor). Fortunately, the twin-band requirement is met by the
NOAA TIROS satellite series AVHRR sensor longwave bands 4 and
5. More sophisticated emissivity correction techniques
involving three thermal-infrared bands have been developed
(Hejazi et al., 1992), but few current satellites produce
imagery containing triplet thermal bands (AVHRR band 3 does
not form a triplet with bands 4 and 5, since it is a midwave
thermal-infrared band). Such multi-band techniques will
likely be the methods of choice for emissivity correction of
satellite-based surface temperatures from the more advanced
sensors aboard satellites of the future international Earth
Observing System (EOS) program.
Previous Studies
Previous studies involving remotely sensed land surface
temperature have been hampered by various factors, including
sensor limitations, lack of adequate processing techniques,
lack of consideration for one or more of the surface
temperature forcing factors, and lack of an adequate
manipulation technique for large quantities of spatial and
temporal data. In particular, there has been a longstanding
difficulty in the mixing of raster (remotely sensed) data with
vector (map) data in climatological studies (Mather and
Sdasyuk, 1991). Consistency of procedural attention to the

31
various components of meso-scale land-surface temperature
research needs to be improved so that individual project
databases can be made mutually compatible.
Chen (1979. 1980). Chen (1979, 1980) used NOAA
Geostationary Operational Environmental Satellite (GOES)
Vertical Infrared Spin-Scan Radiometer (VISSR) thermal-
infrared, winter, nighttime images of peninsular Florida in a
multi-year, meso-scale study of the feasibility of monitoring
agricultural areas for potentially crop-damaging cold
temperatures. Geographic correction was performed by NOAA
using satellite orbital telemetry; the resulting images
required manually-fitted offsets of up to 3 pixels (VISSR
thermal-infrared has 8 km nominal resolution at nadir).
Emissivity by assignment was used for both agricultural and
natural land-cover. Comparison of calibrated at-satellite
temperatures to ground-measured surface temperatures (from
hand-held thermal-infrared radiometer) and near-ground air
temperatures (from thermometry) indicated a range of error of
up to 5 C (for both sets of data), which nonetheless allowed
for determination of statistically significant differences in
temperature between broad categories of land-cover
(agriculture, marsh) and soil (organic). This study indicated
the potential for detailed research into meso-scale,
satellite-based, land-surface temperature impacts of land-
cover type and its relevance to agriculture, as well as the
difficulties of image registration, and especially the

32
importance of full correction for both atmospheric and
emissivity effects.
Cornillon et al. (1987). Cornillon et al. (1987) used
NOAA TIROS AVHRR thermal-infrared images to construct an
archive of quality-assured meso-scale sea-surface (Atlantic
ocean) temperature maps. Geographic correction was performed
based on TIROS satellite orbital telemetry data and ground-
control points; accuracy of pixel registration was to the
nearest 1.5 km (AVHRR has 1.1 km nominal resolution at nadir).
Because entire AVHRR scans (including image extremities or
"limbs”) across continental distances were used, the images
were corrected for scan-angle effects. Ordinarily, AVHRR
longwave thermal-infrared images do not require correction for
scan-angle effects (Masuda et al., 1988; Kerr et al., 1992).
The water-body atmospheric correction technique was used, and
verification datasets indicated an accuracy to the nearest
0.51 C for sea-surface temperatures. This study illustrated
the value of accuracy assessment for both image registration
and surface temperature, and the surface temperature accuracy
attainable through atmospheric correction by the water-body
method.
Balling and Brazel (1988). Balling and Brazel (1988)
used NOAA TIROS AVHRR thermal-infrared images in a meso-scale
study of the urban heat-island effect in Phoenix, Arizona.
Images were geographically corrected based on satellite
orbital telemetry data. No atmospheric correction procedure
was reported. Emissivity correction was performed by

33
assignment (several values), and the Stefan-Boltzmann equation
was used to compute surface temperature. A lack of GIS
capability led to analyses based on linear transects of image
features, rather than on areal extractions. No accuracy
evaluation for geographic registration or surface temperature
was reported, but statistically significant differences in
urban center and suburb heat island effects were observed.
This study demonstrated the feasibility of measuring the
surface temperature impact of a purely meso-scale land-cover
type with AVHRR images, and the need for both accuracy
evaluation and a GIS-based analytical technique.
Cooper and Asrar (1989). Cooper and Asrar (1989) used
NOAA TIROS AVHRR thermal-infrared images in a meso-scale study
of land surface temperature in Kansas. Geographic correction
was performed by NOAA using satellite orbital telemetry. No
registration accuracy analysis was reported. Lack of GIS
overlay-analysis capability required the use of triangulation
between image features (lakes) to delineate study areas.
Atmospheric correction was performed by several techniques—
including both atmospheric modeling methods (with radiosonde
data) and split-window techniques. Ground-based radiometer
measurements of temperature were used to evaluate the accuracy
of the satellite-based surface temperature values. These
ground-based measurements themselves had a variance of about
6 (C2) , due to the high variability of surface temperature at
their very micro-scale (1 m), even though the land-cover was
uniform (prairie grassland). Emissivity correction was

34
performed by assignment of a single value for the entire land
surface. The atmospheric modeling methods were found to
produce acceptable surface temperature accuracies (to nearest
3 C) ; all but one of the split-window techniques produced
unacceptable surface temperature accuracies. This study
indicated the difficulties associated with non-GIS
manipulation of remote sensing data, as well as the
fundamental unsuitability of ground-based point measurements
of land-surface température as a basis for evaluating
satellite-based meso-scale average land-surface temperature.
Henrv et al. (1989). Henry et al. (1989) used NASA HCMM
satellite thermal-infrared images in a meso-scale, GIS-based
study of the urban heat-island effect of Gainesville, Florida.
Detailed land-cover information corresponding to the United
States Geological Survey (USGS) classification system
(Anderson et al., 1976) came from maps and aerial photographs.
Geographic correction was performed based on ground control
points and a first-order polynomial surface model;
registration accuracy was estimated solely by the root-mean-
square (rms) error of the fitted points (± 0.6 pixel).
Atmospheric correction was performed by the atmospheric
modeling method, with radiosonde data. Emissivity correction
was performed by assignment of a single value for all
urban/suburban land-cover. Quality evaluation was very
limited—based on near-surface air temperature measurements
collected non-simultaneously (different year) from the
satellite data; the authors acknowledged that even

35
simultaneous measurements of surface and near-surface air
temperature could vary on the order of 10 C. Despite this
problem, general heat-island impact differences were noted for
several urban and rural land-cover types through GIS-based
analyses. This study showed the power of GIS as an analytical
tool for linking satellite image data and map data, the value
of geographic registration accuracy evaluation, the necessity
of temperature accuracy evaluation, and the unsuitability of
using near-surface air temperature to evaluate surface
temperature accuracy.
Luvall et al. (1990). Luvall et al. (1990) used airborne
thermal-infrared sensor images in a micro-scale study of Costa
Rican rainforest canopy temperature and ET. Atmospheric
correction was performed by the atmospheric modeling method,
with radiosonde data. No emissivity correction was reported,
but the surface-temperature study was limited to full-canopy
vegetation surfaces having emissivity near unity. Lack of
both geographic correction and GIS capability led to eyeball
estimates of image subsets corresponding to polygons on aerial
photographs. Verification data in the form of thermocouple
leaf-temperature measurements at the top of the canopy
indicated an average remotely-sensed surface temperature
accuracy to the nearest 1.1 C. This study indicated the
potential for remote measurement of surface temperature over
forest canopy, the suitability of vegetation surface
temperature measurements for evaluating the accuracy of
remotely-sensed vegetation surface temperature, the need for

36
GIS-based analysis, and the need for geographic registration
and accuracy assessment.
Sucksdorff and Ottlé (1990). Sucksdorff and Ottlé (1990)
used NOAA TIROS AVHRR thermal-infrared images in a meso-scale
study of ET in Finland. Geographic correction was performed
by registration to a base map. Atmospheric correction was
performed by the atmospheric modeling technique, using
radiosonde data. No accuracy evaluation was reported for the
geographic correction or the temperature data. This study
demonstrated the use of base-map image registration to
construct a raster GIS database, as well as the need for
evaluation of geographic and temperature accuracy.
Leshkevich et al. (1993). Leshkevich et al. (1993) used
NOAA TIROS AVHRR images to construct an archive of quality-
assurred Great Lakes water-surface images. Geographic
correction was performed based on satellite orbital telemetry
data; manual offsets of up to 10 km were required for image
registration to allow construction of a raster GIS. No
further evaluation of geographic accuracy was reported.
Atmospheric correction was performed by a split-window
technique. Lake surface temperatures (day and night) were
determined to be accurate to the nearest 1 C, based on
comparisons with near-surface water-body temperature
verification data. This study illustrated the value of GIS as
an analytical tool, and the suitability of water-body
temperature measurements in accuracy assessment of remotely
sensed water surface temperature values.

37
Gillies and Carlson (1994). Gillies and Carlson (1994)
used NOAA TIROS AVHRR images in a study to estimate meso-scale
surface moisture-availability (ratio of soil moisture content
to that at field capacity) in northeast England. Four
afternoon spring and summer images from 1989 to 1990 were
calibrated to at-satellite radiant temperature. Atmospheric
correction was performed by the atmospheric modeling method,
using radiosonde data. No emissivity correction was reported.
Geographic correction was performed based on ground control
points, and the positional accuracy was determined to be
acceptable (0.8 km maximum rms error). Moisture-availability
estimation was evaluated based on point measured data, and was
found to be accurate to the nearest 5 to 7%. This study
indicated the potential for quantitative analyses of surface
parameters obtained from satellite images, and the increased
geographic accuracy resulting from the use of ground control
points, rather than relying solely on satellite orbital
telemetry for image geographic registration.
Current directions in land surface temperature research.
A methodology for detailed, meso-scale, quantitative study of
land-surface temperature patterns involving a GIS containing
the full set of forcing factors has been called for by several
researchers (Taconet et al., 1986a; Henderson-Sellers and
McGuffie, 1987; Dickinson, 1988; MacCracken et al., 1990;
Lagouarde, 1991; Mather and Sdasyuk, 1991; Sobrino et al.,
1991; Dozier, 1992; Kerr et al., 1992; Brown et al. , 1993;
Lindsey et al., 1993; Wheeler, 1993). This methodology would

38
necessarily involve careful attention to the matters of
atmospheric correction, emissivity correction, geographic
correction, and quality analysis of both temperature and pixel
registration (Henderson-Sellers and Robinson, 1986; Harries,
1990; Mather and Sdasyuk, 1991; Peters et al., 1992). These
recommendations were put forth in simplest form by Heimburg et
al. (1982, p. 128):
An estimation procedure can be no more accurate than
allowed by its weakest part. From this perspective, the
most important area for future research is development of
operational methods to determine surface temperature and
net radiation from satellite data. This development
includes solutions to the problems of image registration
and atmospheric absorption corrections. The ability to
accurately overlay visible and [thermal] infrared data
collected at different times from the same area on the
earth's surface is critical to all remote-sensing methods
[for evapotranspiration estimation], as is the ability to
correct temperature and net radiation estimates for
atmospheric effects.
Satellite data suitable for such work now exist (Sader et al.,
1990; NOAA, 1991; Di and Rundquist, 1994; Gillies and Carlson,
1994), as do geographic information systems for performing
sophisticated analyses (ESRI, 1990; Lo and Shipman, 1990; Tan
and Shih, 1990; ERDAS, 1991; Wood, 1991; Rutchey and Vilcheck,
1994; Srinivasan and Engel, 1994; Wong, 1994).

MATERIALS AND METHODS
This research was performed on hardware consisting of a
personal computer (PC), high-resolution monitor, and
digitizing tablet. Software used included the PC versions of
the Earth Resources Laboratory Application Software (ELAS),
Earth Resources Data Acquisition System (ERDAS), and ARC/INFO.
The ground-based DSTV/soil-moisture work utilized soil augers,
hand-held radiometer, thermistors, and the gravimetric soil-
moisture analysis equipment of the University of Florida Soil
and Water Science Department.
Study Area
The Florida study area is shown in Figure 1. There are
three macroclimate zones—panhandle, north, and south (Fernald
and Patton, 1984; Schmidt, 1992).
Panhandle Zone
The panhandle zone includes the Florida Panhandle, which
for the purpose of this study is defined as the region to the
west of the St. Marks river. Its macroclimate is warm-
temperate, with a relatively wet winter (Fernald and Patton,
1984). Vegetation is limited to warm-temperate species
(Clewell, 1985). The natural forest trees include evergreen
broadleaf types and palms, as well as deciduous broadleaf
39

40
Panhandle
Figure 1. Study area with climate zones and water-body
temperature stations (see Appendix B for details).

41
types and both evergreen and deciduous conifers. The
agricultural vegetation types include deciduous orchards and
nearly year-round pasture and crops.
North Zone
The north zone, for the purpose of this study, includes
the region to the east of the St. Marks river and to the north
of Lake Okeechobee. It is a transition zone, with a
macroclimate that is warm-temperate to sub-tropical, with a
winter that is relatively drier than that of the panhandle,
but not as dry as that of the south (Fernald and Patton, 1984;
Schmidt, 1992). Vegetation is limited to warm-temperate and
sub-tropical species. The natural forest trees include
evergreen broadleaf types and palms, as well as deciduous
broadleaf types and both evergreen and deciduous conifers.
The agricultural vegetation types include both evergreen and
deciduous orchards, and nearly year-round pasture and crops.
South Zone
The south zone, for the purpose of this study, includes
the region from Lake Okeechobee southwards. Its macroclimate
is sub-tropical, with a distinctly dry winter (Fernald and
Patton, 1984; Schmidt, 1992). Vegetation includes warm-
temperate, sub-tropical, and tropical species (Barrett, 1956;
Elias, 1980; Morton, 1982; FDNR, 1990). The natural forest
trees include evergreen broadleaf types and palms, deciduous
broadleaf types (some tropical), and both evergreen and

42
deciduous conifers. The agricultural vegetation types include
both evergreen and deciduous (some tropical) orchards, year-
round pasture and crops, and multi-season field crops.
Geographic Information System
The GIS database used in this research was assembled from
both raster and vector components, or "layers". The raster
layers included satellite images; the vector layers included
land-cover and soil type data.
Raster Datasets
A raster dataset consists of lines and elements ("rows"
and "columns") . The number of elements per line is a
constant, forming in concept a rectangular grid, each
identically-sized unit (picture element or "pixel") of which
is assigned data in the form of digital numbers (DNs). There
is one DN for each data type (sensor band, etc.) included in
the raster. ELAS and ERDAS were used to store and manipulate
the image raster files. Further information about raster
datasets can be found in ERDAS (1991).
Vector Datasets
A vector dataset consists of nodes and arcs which make up
individual polygons. One or more polygons may be included
within a "coverage" of a particular geographic region. For
example, a pasture-on-muck-soil coverage might consist of
several individual polygons distributed across Florida. Each

43
polygon can be assigned an attribute file, which contains one
or more types of data (such as ownership, water-quality
parameters, etc.)* In this research, attribute files were not
constructed for the polygons, since the polygons were intended
for importation into the raster environment. ARC/INFO
(modules "ADS", "CREATE", and "TABLES") was used to record the
digitized map polygons as vector files. Further information
about vector datasets can be found in ESRI (1990).
Geographic Referencing
Both raster and vector datasets must be geographically
referenced in order to be included in a GIS (ESRI, 1990;
Connin, 1994; Wong, 1994). Raster datasets are geographically
referenced by knowledge of pixel size and the position
(measured either from pixel center or a pixel corner) of one
raster-corner pixel (typically the upper-left). Vector
datasets are geographically referenced by knowledge of the
position of each node. A consistent geographic coordinate
system must be used for all of the datasets in the GIS. The
Universal Transverse Mercator (UTM) system was selected for
use throughout this study. It is commonly used on maps having
conformal projection (suitable for navigation) published by
the United States Geological Survey (USGS) and other agencies.
Further information about geographic referencing and map
projections can be found in ERDAS (1990).

44
GIS Analyses in Raster Environment
Analyses in this study were performed in a raster
environment. Polygon files in ARC/INFO vector format were
converted to ERDAS raster format ("DIG" file) equivalents.
This conversion was performed using ARC/INFO modules
"TRANSFORM" and "UUNGEN", and ERDAS module "DXIN".
Statistical data (mean and standard deviation) were then
extracted from the raster data (temperature values)
corresponding to each polygon. This extraction process was
performed using the ERDAS modules "CUTTER", "STITCH" (for
assembling coverages of more than one polygon), and "BSTATS".
Where a given land-cover polygon contained more than one soil
type, it was subdivided (using ERDAS module "DIGSCRN") into
two or more final polygons having a single land-cover and a
single soil type.
Analyses of surface temperature patterns were performed
using the mean, standard deviation, and sample-size data
extracted from the GIS. Separate within-zone analyses were
performed for the three macroclimate zones. T-tests were run
at a = 0.05 and a = 0.01. The test statistic was given by the
separate-variance formula (Ott, 1988) :
- x2
T = [15]
(Si2/ni + s22/n2)05
where xx and x2 are the respective means of samples 1 and 2,
sx and s2 are the respective standard deviations of samples 1

45
and 2, and nj and n2 are the respective sample sizes of samples
1 and 2. The difference of two sample means was considered
statistically significant if T < -ta/2 or T > ta/2. In the
analyses reported in this study, nx + n2 » 30, so that the
critical values were t0 025 = 1.960 and t0 005 = 2.5 7 6 (Walpole
and Myers, 1978).
AVHRR Image Processing
NOAA TIROS-AVHRR images of surface temperature were
obtained for two seasons and two times-of-day. Two day/night
pairs of images (14 December 1989 and 12 December 1992) were
required for complete winter coverage of Florida, due to
partial cloud contamination. A single day/night pair of
images (11 April 1993) was adequate for spring coverage of the
state. Details of individual images are provided in Appendix
A.
Winter (December) images allowed the analysis of
differences in surface temperature patterns due to natural
defoliation (for deciduous vegetation types) and agricultural
management practices (winter-crop season). Deciduous
vegetation in Florida includes both temperate and tropical
species, so that seasonal effects could be studied in all
three climate zones.
Spring (April) images allowed the analysis of differences
in surface temperature patterns due to growth flush (for
natural and many cultivated vegetation types) and agricultural

46
management practices (spring-crop season). It is a season of
particularly high irrigation demand in agricultural and
suburban areas in all three climate zones.
Time-of-day for the images included nighttime (late
night/early morning) and daytime (afternoon). Repeat coverage
of a given spot at nearly the same LST occurs every 9 days
(Kerr et al., 1992) for TIROS satellites, so that a minor
variation in coverage time occurs from day to day within this
period. There are also long-term changes in repeat-coverage
time for TIROS satellites; these are very gradual (years),
compared to changes for other weather-satellites such as the
Russian Meteor series (weeks). Nighttime (c. 0300 h LST),
surface temperature images represented the minimum values in
the diurnal cycle. Daytime (c. 1500 h LST) surface
temperature images represented the maximum values in the
diurnal cycle. The difference between these two values was
the DSTV, which indicated relative values of daily-average
root-zone soil moisture for a given land-cover/soil type
combination.
AVHRR Data Types
The NOAA TIROS-satellite AVHRR images were obtained from
the National Environmental Satellite Data and Information
Service (NESDIS) in the form of local area coverage (LAC)
level lb packed format data on computer-compatible tapes
(CCTs). Each tape was down-loaded, and then one raster file
of image data, one tabular file of Earth Location Points

47
(ELPs), and one tabular file of calibration coefficients were
extracted.
LAC-format AVHRR. AVHRR LAC images have the full spatial
resolution (1.1 km nominal at nadir) of the AVHRR; the
thermal-infrared bands have the full AVHRR radiometric
precision of 0.1 C, stored in 10-bit (0-1024 DN) data
precision (NOAA, 1991). The AVHRR bands are described (NOAA,
1988) as follows: band 1 (red) at 0.58 to 0.68 /¿m, band 2
(near-infrared) at 0.725 to 11.1 /¿m, band 3 (midwave thermal-
infrared) at 3.55 to 3.93 /¿m, band 4 (longwave thermal-
infrared) at 10.3 to 11.3 /¿m, and band 5 (longwave thermal-
infrared) at 11.5 to 12.5 /¿m. For typical earth surfaces (not
hot lava flows, fires, etc.), AVHRR bands 1 and 2 measure
reflected energy (daytime only), band 3 measures both
reflected (in daytime) and emitted energy, and bands 4 and 5
measure emitted energy (daytime or nighttime).
Other AVHRR formats. There are other forms of AVHRR
image which do not retain full spatial resolution nor full
radiometric precision, but are made available with greater
frequency than LAC. For each TIROS satellite, a single 10-
minute AVHRR High Resolution Picture Transmission (HRPT) image
per 102-minute orbit can be stored on-board for later
transmission to a NOAA ground reception station (NOAA, 1991),
but there are usually only two orbital coverages of a given
location per day, and not every transmitted HRPT image is
selected for inclusion in the NOAA LAC archive. More frequent
availability is provided by the NOAA-archived global area

48
coverage (GAC) format AVHRR images, which have reduced spatial
resolution (4 km nominal at nadir), but keep the original data
precision (10 bits); a complete orbital path of GAC data can
be stored on-board per orbit for later transmission to a NOAA
station (NOAA, 1991). Users with their own digital-signal
ground station can receive HRPT images directly, and achieve
a coverage freguency of at least twice per day from each
operational satellite, but they also have to calculate their
own ELPs and calibration coefficients from the HRPT telemetry
(Brush, 1985; Emery et al., 1989; Klaes and Georg, 1992).
Users with their own analog-signal ground station can
receive automatic picture transmission (APT) format AVHRR
images, with reduced spatial resolution (4 km nominal at
nadir) and reduced data precision (8 most significant bits
pre-analog), and achieve a coverage frequency of at least
twice per day from each operational satellite every day (NOAA,
1982b). APT images contain their own calibration information
(NOAA, 1982b, 1988; Olivier, 1990), but have no ELPs nor the
telemetry information to calculate them (NOAA, 1988). They
are limited to two NOAA-selected AVHRR bands—typically bands
2 and 4 in daytime, and bands 3 and 4 at nighttime.
Use of TIROS/Meteor APT archive. During the course of
this research, the APT ground station located at the Remote
Sensing Application Laboratory (RSAL) of the University of
Florida Agricultural Engineering Department was utilized to
obtain APT images for purposes of building a browse file for
selecting dates and times for ordering NOAA LAC images. These

49
APT images included multiple daily coverages by the four
current NOAA TIROS satellites (NOAA-9, -10, -11, -12) and the
short-lived but productive NOAA-13, and also the Meteor-APT of
various Russian Meteor-series weather satellites (Meteor 2-21,
3-3, 3-4, and 3-5). The Meteor APT consists of daytime
panchromatic (0.5 to 0.7 ¿¿m) images at somewhat finer spatial
resolution (2 km nominal at nadir). It should be noted by
users of weather-satellite data that imagery from the NOAA
TIROS-series satellites (as well as the Russian Meteor-series
satellites) is subject to temporary suspension on rare
occasions due to participation in the international Search and
Rescue Satellite (SARSAT) program in cases of emergencies at
sea (WMO, 1989; NOAA, 1991).
Calibration to At-Satellite Radiant Temperature
The thermal-infrared, 10-bit, image data of AVHRR bands
4 and 5 were calibrated to at-satellite radiant temperature by
the method of NOAA (1991). One pair of calibration
coefficients (scaled slope and intercept of the sensor
internal calibration) was extracted from the level lb LAC CCT
for each scan line of the image. At-satellite radiant
temperature was then calculated for the pixels of each line by
the equation (NOAA, 1991):
C2 CWNi
^rad.sat.i — [10]
In [1 + Cx CWNi3 / (Si DN + IJ ]
where Trad sat i is the at-satellite radiant temperature (K) in

50
band i, is a constant = 1.1910659xl0'5 (mW m'2 ster'1 cmA) , C2
is a constant = 1.438833 (cm K) , CWNi is the central wave
number (cm'1) for band i in one of three discrete target-
temperature ranges, St is the scaled calibration slope (mW m'2
ster'1 cm) for band i, and is the scaled calibration
intercept (mW m'2 ster'1 cm) for band i, and DN is the 10-bit
digital number (0 to 1023).
This calibration technique is based on a linear fit of
AVHRR sensor response to target temperature within three
discrete target-temperature ranges (180 to 225 K, 225-275 K,
275-320 K) . It greatly reduces the at-satellite radiant
temperature errors (up to 4.3 K at extremities) that would
result from a simple two-point calibration over the entire
target-temperature range (180 to 320 K) of AVHRR data (NOAA,
1988). It should be noted that a although a single target-
temperature range (270-310 K) is generally employed in AVHRR
calibration for sea-surface work (NOAA, 1991), all three
standard target-temperature ranges have to be addressed in
daytime land surface work. In addition, there is an upper
limit of 320 K (47 C) for target temperature; the AVHRR
longwave thermal-infrared band sensors saturate (DN = maximum)
at this limit, and higher at-satellite radiant temperatures
cannot be recorded (NOAA, 1988; Chuvieco and Martin, 1994).
Fortunately, none of the images used in this study contained
at-satellite radiant temperature data reaching this saturation
limit.

51
Scaling of the output radiant temperature values was
performed in order to retain as high a level of radiometric
precision as possible (0.2 C) in the 8-bit (0 to 255 DN) data
storage format to be used in the GIS of this research. The
scaling was given by the equations:
DN, = 5 (Trad sat ¿ + 10) [17]
and
DNi = 6 (Trad sat ¿ - 15) [18]
where Trad sat>i is the at-satellite radiant temperature (C) in
band i, and DNi is the 8-bit scaled digital number output for
band i. Eguation 17 was used for nighttime and winter
afternoon images; equation 18 was used for spring afternoon
images.
Geographic Correction and Registration
A base-map image was constructed to allow registration of
the AVHRR images prior to their importation into the GIS.
Weather-satellite images without any geographic correction are
completely unusable in a GIS (Figure 2) . Base-map
construction, and subsequent AVHRR image registration, was
performed using a two-stage geographic correction process, as
is recommended (Thomas et al., 1987; Chen and Lee, 1992;
Peters et al., 1992) for highly warped (containing distortions
requiring a second-order or higher global polynomial surface
model) imagery.

52
Figure 2. AVHRR image without geographic correction (polygon
outlines true position of Florida).

53
The first stage involved the use of the extracted ELP
data from the AVHRR LAC image. These ELPs, which are provided
in the form of latitude/longitude coordinates, are imbedded in
the raw image at every 40th pixel along each scan line. They
form an evenly-distributed network of known geographic
coordinates throughout the entire image, which is the most
desirable situation for application of geographic correction
techniques. The accuracy of these coordinates is dependent
upon the accuracy of the satellite orbital telemetry data used
by NOAA to calculate them (NOAA, 1991). Early in the course
of this study, it was found that geographic correction based
solely on the ELPs produced output images with positional
errors of up to 10 km (Figure 3) ; this problem has been
reported in several AVHRR-based studies (Cornillon et al.,
1987; Nelson, 1989; Leshkevich et al., 1993). It should be
noted that the positional error shown in Figure 3 is not a
simple offset; there are still second-order distortions
present in the image.
In order to keep output positional errors closer to the
nominal spatial resolution (1.1 km) of the raw AVHRR LAC
images, a second stage of refined geographic correction was
performed based on ground control points (GCPs). The picking
of these GCPs simultaneously from monitor displays of the
AVHRR image (band 2 in daytime, band 4 at night) and from maps
was greatly facilitated by the first stage of correction,
which had removed most of the Earth-curvature and view-angle
distortions. Picking of GCPs directly from the raw image is

54
Figure 3. AVHRR image with first-stage (ELP-based) geographic
correction (polygon outlines true position of Florida).

55
not advisable, since the application of the high-order global
polynomial surface model required for such a distorted image,
combined with the relatively poor distribution of GCP network
obtainable from most images, can easily result in instability
of pixel fits interpolated between the GCPs (Thomas et al.,
1987), and "explosion" of pixel fits extrapolated outside of
the GCP network (Figure 4).
First-stage geographic correction of base-map image.
First-stage geographic correction was performed using the ELP
data extracted from the raw AVHRR LAC image. These ELP data
formed a 240-ELP grid (consisting of 16 grid lines with 15
ELPs each) having a 40 x 40 pixel spacing. The latitude/
longitude values in this ELP grid were converted to Universal
Transverse Mercator (UTM) northing/easting values (UTM zone 17
format) and entered into the ERDAS module "GCP". A second-
order global polynomial surface model was fitted to the ELPs.
The Nearest-Neighbor resampling technique was then applied to
the image; this is the only resampling technique that does not
corrupt (smooth or average) image data values (Lillesand and
Kiefer, 1979; Peters et al., 1992). These two operations were
performed using ERDAS modules "COORDN" and "NRECTIFY." The
polynomial fit (mapping equation) was as follows:
E = 1000 a0 + a3 E + a2 L + 0.001 a3 E2 + 0.001 a^ E L +
0.001 a5 L2
L' = 1000 b0 + bx E + b2 L + 0.001 b3 E2 + 0.001 b4 E L +
0.001 b5 L2 [19]
and

56
Figure 4. Example of explosive extrapolation of third-order
global polynomial surface model outside of control
points.

57
where E was the original element number, L was the original
line number, E’ was the fitted element number, L’ was the
fitted line number, and the values of the coefficients were a0
= 1.371406, a3 = -0.1608168E-2, a2 = -0.3716236E-3, a3 =
0.4853050E-6, a4 = 0.1728803E-6, a5 = 0.2920270E-7, b0 =
-2.333373, b3 = -0.1690915E-3, b2 = 0.8806317E-3, b3 =
-0.2704551E-8, bA = 0.2105952E-7, and b5 = 0.3205131E-8, which
are unitless. Resampling was done to an output pixel size of
1 km. Details concerning geographic correction techniques for
satellite images can be found in remote sensing literature
(Lillesand and Kiefer, 1979; Gonzalez and Wintz, 1987; Thomas
et al., 1987; ERDAS, 1990; Novak, 1992; Peters et al., 1992;
Di and Rundquist, 1994).
Second-stage geographic correction of base-map image.
The first-stage output image was then imported into ELAS. A
set of 115 well-distributed GCPs (coastal features and lakes
of size appropriate to the spatial resolution of the image)
was picked simultaneously from a monitor display of the image
and from 1:500,000 scale UTM maps of Florida (DMAAC, 1987).
ELAS module "CPPP" and a digitizing tablet were the tools used
for this process. Panhandle GCP coordinates, located within
UTM zone 16, were converted to their equivalents in UTM zone
17 format. The first-stage output image was then re-imported
into ERDAS along with the GCP set mentioned above. A second-
order global polynomial surface model was fitted to the GCPs.
The Nearest-Neighbor resampling technique was then applied to

58
the raster. The polynomial fit (mapping equation) was as
follows:
£ = 1000 a0 + a3 E + a2 L + 0.001 a3 E2 + 0.001 a4 E L +
0.001 a5 L2
and
L’ = 1000 b0 + b1 E + b2 L + 0.001 b3 E2 + 0.001 b4 E L +
0.001 b5 L2 [20]
where E was the original element number, L was the original
line number, E' was the fitted element number, L' was the
fitted line number, and the values of the coefficients were
a0 = 0.1952093, ax = 0.9931500E-3, a2 = -0.3316682E-5, a3 =
0.1140539E-7, a4 = 0.3552246E-8, a5 = 0.8012727E-9, b0 =
3.655660, b3 = -0.5229729E-4, b2 = -0.1038144E-2, b3 =
0.1049909E-7, b4 = 0.1479114E-7, and b5 = 0.5142951E-8,
which are unitless. Second-stage resampling was done to an
output pixel size of 1 km, to form a base-map image of 1000
elements by 1000 lines covering the entire Florida study area
and small portions of southern Alabama and Georgia (Figure 5) .
Location of the first pixel (element 1, line 1) was at
-187,000 m east and 3,584,000 m north (UTM zone 17 format) in
the ERDAS raster GIS reference system.
Accuracy assessment of base-map image. The positional
accuracy of the base-map image was carefully evaluated, since
all future images would be registered to it. ERDAS module
"COORDN" reported that the root-mean-square (rms) error for

59
Figure 5. AVHRR image with second-stage (GCP-based)
geographic correction (polygon outlines true position of
Florida).

60
the second-stage fit in the element direction was 0.648 km,
and in the line direction was 0.597 km; the overall rms error
for the fit was 0.881 km. These figures apply only to the GCP
pixels, not to other resampled pixels; they represent a form
of validation check. The potential for the user to be misled
by these GCP-based rms values can be seen in the
extrapolation-exploded image of Figure 4, which had a GCP-
based rms error of only 1.2 km, even though the image was
clearly rendered useless. In order to verify the positional
accuracy of the entire base-map image, a set of 91 well-
distributed GCPs (different from the 115 used to build the
base-map image) were digitized in the manner described above.
The resulting rms error was found to be 0.495 km in the
element direction, and 0.566 km in the line direction; the
overall rms error was 0.752 km. Considering both the overall
rms error of the fit (0.881 km) and that of the verification
(0.752 1cm), the base map was demonstrated to be spatially
accurate to within 1 km.
Registration of subsequent images to base-map image.
Subsequent AVHRR LAC images were registered to the base-map
image by a two-stage process with ELP-based first-stage
geographic correction similar to that described above. The
only change was in the manner of picking the GCPs for the
second-stage geographic correction; they were picked from
simultaneous monitor displays of the first-stage corrected
image and the base-map image (rather than maps) . The ELAS
module "OCON" was used to simultaneously display these images

61
and pick the GCPs. These GCPs and the AVHRR LAC image being
registered were then imported into ERDAS for the second-stage
geographic correction. Second-order global polynomial surface
models were used whenever possible, but third-order models
were used if second-order models were insufficient to produce
registration rms errors below 1.2 km. Registered AVHRR images
had rms errors ranging from 1.09 to 1.2 km. Registration
details of individual images are given in Table 140 of
Appendix A.
Conversion from Radiant to Kinetic Temperature
In order to produce kinetic surface temperature images
from the at-satellite radiant temperature images, a three-
stage process was implemented. First, atmospheric correction
of an at-satellite radiant temperature image (Figure 6) was
performed by the water-body calibration technique. Second, an
instantaneous pixel-average emissivity image (Figure 7) was
constructed by the twin-band method. Third, the atmospheric-
corrected radiant temperature image and emissivity image were
used to produce a kinetic temperature image (Figure 8) based
on the analytical solution of the Planck equation. The
overall conservative bias due to the atmospheric effect, and
the "hiding away" of high surface temperature in urban and
agricultural areas due to the emissivity effect, are both
evident in the uncorrected image of Figure 6, when it is
compared to the fully corrected image of Figure 8.

62
Figure 6. At-satellite radiant temperature image

63
Figure 7
Emissivity image

64
Figure 8. Surface temperature image

65
Atmospheric correction. The water-body calibration
technique described by Lillesand and Kiefer (1979) was
performed on the at-satellite radiant temperature images from
bands 4 and 5. This technique is based on the near-uniform
emissivity and relatively stable temperature (over the
satellite overpass time) of water-body pixels. Hourly near¬
surface (0.5 m) water temperature data from three permanent
instrument stations located within Lake Okeechobee were
obtained from the South Florida Water Management District
(SFWMD) "DBHYDRO" database (Figure 1). These kinetic
temperatures (thermistor-based values reported to nearest 0.1
C) were converted to AVHRR band 4 and 5 radiant temperature
equivalents by rearranging equation 13 and plugging in the
water temperatures:
k CWNi
Trad.i = [21]
In (ew_1 [exp (k CWN, / Tkin) + ew - 1] }
where Trad * is the radiant temperature (K) calculated in band
i for a station pixel, Tkin is the measured water-surface
kinetic temperature (K) , k is the Boltzmann constant (1.438883
cm K) , CWNi is the central wave number for band i (cm'1) , and
ew is the longwave emissivity of the lake water. A well-
established value of 0.99 was used for ew (Buettner and Kern,
1965; Barton and Takashima, 1986; Saunders, 1986; Masuda et
al., 1988; Wukelic et al., 1989; Salisbury and D'Aria, 1992).
Central wave numbers were obtained from the tabular values

66
published by NOAA (1991) for each TIROS satellite AVHRR
sensor, each band i, and each surface-temperature range. The
highest surface-temperature range was used to find the CWNi
for afternoon LAC images, and the sea-surface range was used
to find the CWNi for nighttime and early-morning images. The
differences between the at-satellite radiant temperatures and
the atmospherically-corrected radiant temperatures were
averaged (from up to 3 values, according to station data
availability) to obtain atmospheric correction factors which
were applied to the at-satellite radiant temperature data from
bands 4 and 5 of each LAC image.
Emissivitv correction. The atmospheric-corrected radiant
temperature images from bands 4 and 5 were used to produce a
longwave thermal-infrared emissivity image for each AVHRR
image by the twin-band technique of Artis and Carnahan (1982).
Instantaneous pixel-average emissivity calculation was
performed by plugging appropriate AVHRR values into equation
14:
eiw — exP (k (Trad 5~Trad,«) / [Trad 5 Trad 4 (A5-A4) ] ) [22]
where elw is the pixel-average longwave thermal-infrared
emissivity, k is the Boltzmann constant (1.43883 cm K) , Trad 4
is the atmospheric-corrected band 4 radiant temperature (K),
Trad5 is the atmospheric-corrected band 5 radiant temperature
(K) , and X4 and A5 are the respective central wavelengths
(inverse of central wavelength number, m) of AVHRR bands 4 and

67
5. The last two values come from NOAA (1991) look-up tables
for the TIROS satellite, band, and temperature range, as
described previously. An 8-bit scaling of the calculated
emissivity values preserved the precision to the nearest 0.01:
DN = 100 elw [23]
where the parameters are described as before. The assumption
in this method that surface emissivity values in AVHRR bands
4 and 5 are identical is a simplification (Price, 1984) ;
slight differences in emissivity values between these bands
(up to 0.01) will result in an uncertainty of 1 C when the
calculated longwave-band emissivity is applied to kinetic
temperature calculation.
Kinetic temperature calculation. The emissivity image
was used with the atmospheric-corrected radiant temperature
image from band 4 to calculate the kinetic temperature image
for each AVHRR image. This computation was performed by
plugging appropriate AVHRR band 4 values and the pixel-average
emissivity values into equation 13:
k CWNa
Tki„ = [24]
In [1 - elw + elw exp (k CWN, / Trad>J ]
where Tkin is the pixel-average kinetic temperature (K) ; Trad 4
is the atmosphere-corrected, pixel-average, band 4 radiant
temperature (K); k is the Boltzmann constant (1.43883 cm K),
CWN* is the central wave number for band* (cm'1) , and elw is the
pixel-average longwave emissivity. Band 4 central wave

68
numbers were obtained from the tabular values published by
NOAA (1991) for each TIROS AVHRR sensor and each surface-
temperature range. The highest surface-temperature range was
used to find the CWN4 for afternoon LAC images, and the sea-
surface range was used to find the CWN¡, for nighttime and
early-morning images. Scaling of the output Tkin images to an
8-bit format was performed using the formula
DN =
5 (Tkin + 10) ,
6 (Tkin - 15) ,
for low Tkin images
for high Tkin images
[25]
where Tkin is the kinetic temperature (C) , low-temperature
images were defined as those having a range of land surface
temperatures between -10.0 and 41.0 C, and high-temperature
images were defined as those having a range of land surface
temperatures between 15.0 and 57.0 C. In either case, a
precision of 0.2 C was kept by the scaling. It should be
noted that the AVHRR sensor saturation limit of 47 C applies
only to at-satellite radiant temperatures; a surface such as
a cleared sandy field or an urban area can have an afternoon
kinetic surface temperature well above 47 C and yet, due to
emissivity and atmospheric effects, it can easily have an at-
satellite radiant temperature under 47 C.
Accuracy Assessment of Kinetic Temperature Images
The water-body temperature data used to calculate the
atmospheric correction served as a validation data set. The

69
water-body temperature data from other stations served as a
verification data set. Procedures and results of validation
and verification are described below.
Validation of kinetic temperature. Validation assessment
was performed using the previously mentioned near-surface
water temperature data from the three permanent instrument
stations located within Lake Okeechobee (Figure 1). This was
done by comparing the processed image kinetic temperature
values with the corresponding water-body temperature data.
Errors indicated by the validation data set for the AVHRR
kinetic temperature images ranged from 0.0 to 1.1 C; the
average error was 0.5 C. This range of single-pixel basis
validation error for lake surface temperature values is even
lower than expected from the uncertainty associated with the
twin-band emissivity correction method, and indicates that the
station water-temperature (thermistor-based) data were
themselves very well calibrated. Validation details of
individual images are given in Table 140 of Appendix A.
Verification of kinetic temperature. Verification
assessment was performed using near-surface (0.5 m) water
temperature data from the permanent instrument station located
within Lake Apopka, and from periodic sampling by boat in Lake
Sampson and Tampa Bay (Figure 1). The Lake Apopka data were
obtained from the St. Johns River Water Management District
(SJRWMD) ; the Lake Sampson data were obtained from the
Suwannee River Water Management District (SRWMD); the Tampa
Bay data were obtained from the Environmental Protection

70
Commission of Hillsborough County (EPCHC). The processed
image kinetic temperature value for each of these verification
sites was compared with the corresponding near-surface water
temperature data (thermistor-based values reported to nearest
0.1 C) . Errors indicated by the verification data set for the
AVHRR kinetic temperature images ranged from 0.4 to 3.4 C; the
average error was 1.9 C. This range of single-pixel basis
verification error values for atmospheric-corrected lake-
surface temperature is slightly higher than that (0.2 to 0.8
C) reported for atmospheric-corrected sea-surface temperature
under ideal clear-sky conditions (Llewellyn-Jones et al.,
1984) , but far lower than would be the case (up to 20 C)
without atmospheric correction. Because the verification
stations are located in a different zone (north) from the
validation stations (south), they provide for each image a
conservative check on the error due to statewide spatial
variation in the atmospheric correction factor. Verification
details for individual images are given in Table 140 of
Appendix A.
Water and Cloud Masking
A masking procedure was used to exclude kilometer scale
water bodies and clouds from the surface temperature analyses
in this study. The mask was prepared from a normalized
difference vegetation index (NDVI) image (Figure 9). While
NDVI is commonly applied to vegetation vigor studies (Fischer,
1994; Teillet and Fedosejevs, 1994; Wade et al. , 1994), it

71
Figure 9. Water/cloud mask (NDVI) image

72
also distinguishes water and cloud surfaces (NOAA, 1990). The
NDVI was calculated from bands 1 and 2 (red and near-infrared)
of the AVHRR image (NOAA, 1990):
NIR - R
NDVI = [26]
NIR + R
where NIR is the band 2 DN, and R is the band 1 DN. This NDVI
was scaled for 8-bit storage according to the standard global
vegetation index (GVI) procedure (NOAA, 1990):
NDVIS =
255, for NDVI < -0.05
• 0, for NDVI >0.60
240-350(NDVI+0.05), otherwise
[27]
where NDVIS is the scaled NDVI. Values of NDVIS above 219 were
found by inspection to indicate water and cloud surfaces. A
separate NDVI image was prepared for each diurnal image pair,
using the daytime image band 1 and 2 data. This accounted for
any changes in kilometer-scale cloud or water body extent—
such as drifting clouds and floods. Each pixel of a surface
temperature image which corresponded with a water/cloud pixel
was then assigned a value of zero, creating a masked surface
temperature image (Figure 10) . When each GIS coverage was
later analyzed using the ERDAS module "BSTATS," the option to
exclude zero values from statistical computations was
selected.

73
Figure 10. Daytime (spring) masked surface temperature image.

74
Final Forms of Images in the GIS Database
Examples of final-form masked daytime and nighttime
surface temperature images are shown respectively in Figures
10 and 11. The nighttime image values were subtracted from
the daytime image values using the capabilities of the GIS,
producing a DSTV image (Figure 12).
HCMM Historical-Image Processing
Analysis of historical temperature patterns was performed
in order to supplement the previously described analysis of
contemporary Florida temperature patterns. It was
particularly desired to obtain meso-scale thermal-infrared
images from a date preceding the freezes of the mid 1980s,
which resulted in permanent changes in the citrus orchard
component of agricultural land-cover. Although the NOAA-TIROS
satellite series has been in operation since 1978, only 6% of
the pre-1985 world-wide set of AVHRR-LAC images have been
preserved (the reduced-resolution GAC was given priority for
preservation) in the current NOAA archive (NOAA, 1991); the
AVHRR-LAC archive extends only from 1985 to present.
Therefore, the imagery used for historical analysis came from
the short-lived NASA HCMM satellite program of 1979.
The NASA HCCM data were obtained from the National Space
Science Data Center (NSSDC) in the form of CCTs. Each tape
was down-loaded, and then one raster file of image data was
extracted. These HCMM images had been calibrated and scaled

75
Figure 11. Nighttime (spring) masked surface temperature
image.

76
Figure 12. DSTV (spring) image.

77
by NASA, and had a nominal at-nadir spatial resolution (in the
thermal-infrared band) of 600 m. The radiometric precision of
the HCMM longwave thermal-infrared band (10.5-12.5 /¿m) in the
calibrated and scaled 8-bit form was 0.4 K (NASA, 1980).
Because HCMM was designed for land-surface thermal imaging,
rather than cloud-top/sea-surface work, its at-satellite
radiant temperature sensitivity ranged from 260 to 340 K.
The orbit of the HCMM satellite was chosen so as to
provide 12-hour repeat coverage for all of the globe, except
for two narrow belts of latitude (15 to 35 degrees N and S)
(NASA, 1980). Unfortunately, Florida lies within the northern
12-hour repeat-coverage exclusion belt, so that only one HCMM
image per 24-hour period was possible. A set of relatively
cloud-free HCMM images was selected which provided day/night
repeat coverage of Florida within a 3-day period. These were
dated 1 February 1979 (night) and 3 February 1979 (afternoon) .
Details concerning the HCMM images are provided in Appendix A.
Geographic Correction of HCMM Images
HCMM images from 12-hour repeat coverage areas were
geographically corrected by NASA (NASA, 1980), but the HCMM
images from the 12-hour exclusion belt were not. The HCMM
images of Florida used in this study were geographically
corrected using a single-stage (GCP-based) process similar to
the second-stage (GCP-based) correction described previously
for AVHRR images, since HCMM CCTs contain no ELP data.
Registration to the previously described base-map image was

78
accomplished using 3rd-order global polynomial surface models
and nearest-neighbor resampling to 500 m pixel size; rms
errors were 1.19 km for both HCMM images. Each registered
HCMM output consisted of an image of 2000 elements by 2000
lines covering the entire Florida study area and small
portions of southern Alabama and Georgia. Location of the
first pixel (element 1, line 1) was at -187,250 m east and
3,584,250 m north (UTM zone 17 format) in the ERDAS raster GIS
reference system. Details of HCMM image geographic correction
are given in Appendix A.
Calibration of HCMM At-Satellite Radiant Temperature
The HCMM image data were calibrated to at-satellite
radiant temperature by the method of NASA (1980):
1251.1591
Trad sat h = [28]
In [14421.587 / (DN+118.21378) + 1]
where Trad sat h is the at-satellite radiant temperature (K) in
the HCMM thermal-infrared band, and DN is the 8-bit digital
number (0-255). It should be noted that the NASA-processed
HCMM thermal-infrared data had already been radiometrically
calibrated using a two-point (space and internal-target)
technique with full non-linearity correction (NASA, 1980) .
No atmospheric correction was performed on the HCMM
images, due to lack of any existing water-body temperature
stations at the time of the images. No emissivity correction
was performed on the HCMM images for the same reason, plus the

79
fact that HCMM possessed only a single thermal-infrared band
(which spanned the wavelength ranges of AVHRR bands 4 and 5) .
The afternoon and night HCMM images are shown in Figures 13
and 14.
An approximation of DSTV, shown in Figure 15, was
calculated by subtraction of night HCMM temperatures from
afternoon HCMM temperatures. It should be noted that this
approximate-DSTV is not as rigorously defined as the DSTV from
the AVHRR images, since the HCMM images were separated in time
by more than 1 day, and no corrections for atmospheric or
emissivity effects were made.
Ground-Based DSTV/Soil-Moisture Work
Ground-based investigations of the DSTV/soil-moisture
relation were conducted for the cases of mineral and organic
soil types. The methodology of this work is described below.
Mineral Soil Investigation
Mineral soil (Ellzey fine sand) effects on DSTV were
investigated at the Hastings Agricultural Research and
Education Center (AREC) located near Hastings, Florida. Soil
type and drainage conditions of the experimental potato fields
are typical of those found in the rest of the Hastings/Spuds/
Tocoi spodosol row-crop area (Appendix C, agricultural polygon
number 38).
From 11 to 12 March 1991, five sites consisting of bare
sand soil under the widest available variety of moisture

80
Figure 13. HCMM
temperature
daytime
image.
(winter)
at-satellite radiant

81
Figure 14. HCMM
temperature
nighttime
image.
(winter)
at-satellite radiant

82
Figure 15. HCMM approximate-DSTV (winter) image.

83
conditions were sampled. Sampling times were from 0750 to
0940 h LST (early morning) , and from 1230 to 1450 h LST
(afternoon). Weather conditions were clear and sunny.
Surface temperature was measured with a set of three
thermistor probes calibrated to ± 0.1 C. These were placed
just beneath the soil surface, according to the technique
described by Fuchs and Tanner (1968).
Soil samples were taken, just after the temperature
measurements, with a sand auger in 8 cm increments to a depth
of 40 cm, and placed in moisture-sealed plastic bags for later
laboratory analysis. The increment of 8 cm was chosen to
match the damping depth, d of equation 6, predicted for
mineral soil under dry condition—the conservative case, which
produces the smallest value of d. Gravimetric soil-moisture
analysis was performed with laboratory oven and scale.
Because all of the sampling sites were in cleared and freshly-
planted fields with no appreciable amount of vegetation
nearby, no normalization for variation in vegetation effects
was applied to the analysis of the DSTV/soil-moisture
relation.
Organic Soil Investigation
Organic soil (Pahokee muck) effects on DSTV were
investigated at the Everglades Research and Education Center
(EREC) located near Belle Glade, Florida. Soil type and
drainage conditions of the experimental sugarcane fields are

84
typical of those found in the rest of the EAA (Appendix C,
agricultural polygon number 49).
From 28 to 30 April 1992, twelve sites consisting of bare
muck soil under the widest available variety of moisture
conditions were sampled. Sampling times were from 0740 to
0940 h LST (early morning) , and from 1440 to 1640 h LST
(afternoon). Weather conditions were clear and sunny (south
Florida dry season).
Surface temperature was measured with a set of three
thermistor probes calibrated to ± 0.1 C. These were placed
just beneath the soil surface, according to the technique
described by Fuchs and Tanner (1968). Simultaneous air
temperature was taken at 1 m height with the thermistor air-
temperature sensor of an Everest Interscience model 210
radiometer.
Soil samples were taken, just after the temperature
measurements, with a muck auger in 3 cm increments to a depth
of 18 cm, and placed in moisture-sealed plastic bags for later
laboratory analysis. The increment of 3 cm was chosen to
match the damping depth, d of equation 6, predicted for
organic soil under dry condition. Gravimetric soil-moisture
analysis was performed with laboratory oven and scale.
Because many of the sampling sites were in clearings
within or near fields of various crops such as mature
sugarcane, young ratoon sugarcane, sod, rice, and vegetables,
the well-known air-temperature normalization technique (Idso
et al., 1976; Millard et al., 1978) was applied to the surface

85
temperature data to reduce the influence of variation in the
transpiration of surrounding vegetation on the analysis of the
DSTV/soil-moisture relation. The normalization used the
equation (Millard et al., 1978):
DATVavg
DSTVn = (DSTV) - DATVavg [29]
DATV
where DSTVn is the normalized DSTV, DATV is the difference (C)
between afternoon and morning air temperature for a given
sample, and DATVavg is the average DATV from the air
temperature data of all of the samples.
Vegetated Soil Investigation
Vegetation (grass) effects on DSTV were investigated at
the Energy Research and Education Park and adjoining Biogas
Production Research Park located at Gainesville, Florida. The
soil was of the upland loamy sand type (Blichton sand) and
drainage conditions were typical of those found in surrounding
pasture areas.
On 13, 14, and 2 6 November and 5 and 6 December 1991,
seven sites consisting of grass-covered soil under the widest
available variety of moisture conditions were sampled.
Sampling times were from 0830 to 0930 h LST (early morning),
and from 1430 to 1540 h LST (afternoon). Weather conditions
were clear and sunny.
Surface temperature was measured with a set of three
thermistor probes calibrated to ± 0.1 C. These were placed

86
just beneath the vegetation surface. Simultaneous air
temperature was taken at 1 m height with the thermistor air-
temperature sensor of an Everest Interscience model 210
radiometer.
Soil samples were taken, just after the temperature
measurements, with a sand auger in 8 cm increments to a depth
of 48 cm, and placed in moisture-sealed plastic bags for later
laboratory analysis. The increment of 8 cm was chosen to
match the damping depth, d of equation 6, predicted for
mineral soil under dry condition. Gravimetric soil-moisture
analysis was performed with laboratory oven and scale.
Because the sampling sites had grass cover, the
previously described air-temperature normalization technique
was applied to the surface temperature data to reduce the
influence of variation in grass transpiration on the analysis
of the DSTV/soil-moisture relation.
Soil Type Data
Polygons of soil type were digitized from a soil type map
(USDA, 1982) for inclusion in the GIS. Broadly-defined soil
types occurring at kilometer scale were selected for this
research. There are two general soil types of potential
interest to surface temperature studies—mineral and organic.
In this research, further distinctions have been addressed
based on drainage properties (especially the presence of very
low, very high, or seasonally fluctuating water-table) and the
presence of hardpan or limestone within root-zone depths.

87
One to three coverages (corresponding to the three
climate zones) of each soil type were assembled from these
digitized polygons. Certain soil types did not exist in all
three climate zones. Soil types were of seven basic kinds—
deep sand, upland loamy sand, flatwoods sand, coastal sand,
sandy rockland, marly rockland, and organic. Their individual
descriptions are given below. Further details concerning soil
types can be obtained from various sources (Stewart et al.,
1963; Brady, 1984; Sodek et al., 1990; Akin, 1991; Salisbury
and D'Aria, 1992).
Mineral Soils
Deep sand. Deep sand includes deep, excessively-drained
mineral soils that consist of sand throughout the root-zone
and some distance below (a form of Entisol); the water-table
is well below the root-zone. They are locally referred to as
"sugar sands" (if white) or "buff sands" (if not). Except for
preserves of the natural scrub, the land-cover has been
changed to agricultural, urban, or mine use.
Upland loamy sand. Upland loamy sand includes deep,
moderately-drained to well-drained mineral soils that consist
of loamy sand with clayey subsurface horizons that are either
high in base-saturation (Alfisols) or low in base-saturation
(Ultisols); the water-table is well below the root-zone.
Except for preserves of the natural upland mixed forest, the
land-cover has been changed to agricultural and some urban
use.

88
Flatwoods sand. Flatwoods sand includes shallow, poorly-
drained, mineral soils that consist of acid-leached sand with
subsurface horizons containing organic matter and aluminum
oxides, bounded within the root-zone by a cemented-sand
hardpan (Aquod Spodosols). Because the hardpan is an
aquiclude, the water-table under natural conditions
experiences a pronounced seasonal fluctuation—high (saturated
near or at surface) in summer/fall, to low in winter/spring
(Ziegler and Wolfe, 1961; Mullahey et al. , 1992). Except for
preserves of flatwoods forest and pine plantations, the land-
cover has been changed to agricultural, urban, and mine use.
Coastal sand. Coastal sand includes the deep, variably-
drained, miscellaneous coastal mineral soil associations of
sand and/or shell with no subsurface horizons (Entisols).
Portions of this coastal soil which are poorly drained
(including those tidally submerged) remain under their natural
cover of saltmarsh and mangrove swamp; those which are better-
drained are now mostly under agricultural and especially urban
use, except for preserves of coastal hammock and strand.
Sandy rockland. Sandy rockland includes shallow, well to
poorly drained mineral soils consisting of sand (Entisols) or
loamy sand (Ultisols, Alfisols) over limestone. The water-
table can be either high (saturated near or at surface) or
low, depending on local topography. Except for preserves of
rockland hammock (high water-table) and calcareous hammock
(low water-table), the land cover has been changed to
agricultural or urban use.

89
Marly rockland. Marly rockland includes shallow, poorly
drained mineral soils consisting of marl (a form of Entisol)
over limestone. Marl is a gray, alkaline, clayey material
composed of calcium carbonate that was precipitated on the
foliage of certain aquatic plants—algae such as stonewort
(Chara spp.) and angiosperms such as coontail-moss
fCeratophvllum demersum L.)—which grew in the area when it
was covered by relatively deep fresh water? it is often mixed
with fine sand and/or freshwater mollusk shells. The water-
table can be either seasonally high (saturated near surface or
submerged in summer/fall, drier in winter/spring) or
relatively low (not seasonally submerged), depending on local
topography. Agricultural and urban use has been limited to
the low water-table areas, but has displaced much of the
natural cover except in preserves of rockland hammock. The
vast areas having a high water-table are mostly under the
natural land-cover of wet-prairie and dwarf-cypress swamp.
Organic Soils
Organic soil includes poorly drained soils consisting of
a shallow to deep layer of muck (highly decomposed vegetation)
or peat (slightly decomposed vegetation) over limestone or
sand (Histosols) . The substrate is not considered in this
surface-temperature study, due to the previously mentioned
thermal properties of organic soil (d of equation 6). Under
natural conditions, these soils are submerged (muck) or
saturated (peat). Except for preserves of natural marsh or

90
bay swamp, the land-cover of organic soils has been changed to
mostly agricultural use.
Artificial Soil Type Change
There are two forms of artificial soil type change
occuring at kilometer scale in Florida. One involves organic
soil, and the other involves mineral soil.
Organic soil decrease. Subsidence of organic soil, due
to drainage for agriculture, takes place due to dewatering and
microbial breakdown (Jones, 1948; Snyder, 1978; Lucas, 1982).
The present rate of subsidence of muck soils under
agricultural use in the EAA is about 1 inch per year (Snyder,
1978). If the organic layer is completely lost in the EAA,
the result will be a meso-scale change from organic to mineral
(rockland) soil type. In this study, the surface temperature
patterns of organic soil were analyzed under several land-
cover conditions.
Wet clay increase. A phosphatic clay "soil" is gradually
produced from phosphate mine settling ponds. If this
situation remains unchanged (no future re-working), a wet clay
soil sharing many of the characteristics of marl will have
been deposited over large areas of former flatwoods sand.
Depending upon the success of reclamation technigues, this
soil may be used for row-crop or sod-farm agriculture, or
managed as wet-prairie parkland. In this study, the surface
temperature patterns of phosphate mines were compared to those

91
of other land-cover types, including titanium mines and
agricultural subtypes.
Land-Cover Data
Polygons of land-cover type were digitized from land-
cover maps (CFW, 1973; USDA, 1980b; ABS, 1992a; FGFWFC, 1992)
for inclusion in the GIS. Land-cover types occurring at meso-
scale were selected for this research. Where map land-cover
information was in doubt, aerial black and white photography
from the University of Florida Map Library was photo-
interpreted. This photography was taken from 1980 to 1985 at
scales ranging from 1:20,000 to 1:40,000 by the United States
Department of Agriculture. Ground visits were also made to
many of the sites (especially the agricultural ones) during
the course of this research. Details concerning the land-
cover polygons are given in Appendix C.
One to three coverages (corresponding to the three
climate zones) of each land-cover type were assembled from
these digitized polygons. Certain land-cover types did not
exist in all three climate zones. Land-cover types were of
three basic kinds—natural, agricultural, and urban/
industrial. There were also some land-cover types under
special conditions.
Natural Land-Cover
Many different natural land-cover types have been
described in Florida (Jones, 1948; CFW, 1973; USDA, 1980b;

92
Morton, 1982; Clewell, 1985; USDA, 1989; FDNR, 1990). This
research included 13 general types which occur at meso-scale
(Figure 16) . These include scrub, upland mixed forest,
flatwoods forest, rockland hammock, coastal hammock, hardwood
swamp, cypress swamp, bay swamp, mixed swamp, mangrove swamp,
shrubby marsh, herbaceous freshwater marsh, and saltmarsh.
Some of these contain subtypes (evergreen, deciduous, etc.)
which were also studied.
Scrub forest. Scrub is a xeric type of forest found
throughout the state on dry soils. Two subtypes are present
in Florida—mixed scrub and evergreen scrub.
Mixed scrub is a dense to open xeric forest of mixed pine
and deciduous broadleaf trees; it occurs on gently rolling
terrain with well-drained to excessively-drained deep sand
soil of the "buff sand" sort. It experiences frequent to
occasional ground fire. Mixed scrub has an upper story of
longleaf pine (Pinus palustris Mill.) and/or Choctawhatchee
sand pine (Pinus clausa imuginata Ward)—in panhandle—or
Ocala sand pine (Pinus clausa Chapm. ex Engelm.)—in north,
mixed with the deciduous turkey oak (Ouercus laevis Walt.),
bluejack oak (Ouercus incana Bartr.), Margaret oak (Ouercus
maroaretta Ashe), and persimmon (Diospyros viroiniana L.).
There is an understory of deciduous Chapman oak (Ouercus
chaomanii Sarg.), scrub hickory (Carva floridana Sarg.)—in
north, and Hercules'-club (Zanthoxvlum clava-herculis L.) .
The typical composition is a mixed upper story of longleaf
pine, turkey oak, and blue jack oak, an understory of mixed

93
Figure 16. Natural land-cover polygons (see Appendix C for
details).

94
deciduous Chapman oak, scrub hickory, and a mixed undergrowth
including evergreen shrubs such as staggerbush (Lyonia spp.),
weakleaf yucca (Yucca flaccida Haw.)/ and prickly-pear cactus
(Qpuntia spp.); deciduous shrubs such as sparkleberry
(Vaccineum arboreum Marsh.), huckleberry (Gavlussacia spp.),
Chickasaw plum (Prunus angustifolia Marsh.), dwarf hawthorn
(Crataegus uniflora Muenchh.), sand holly (Ilex ambigua
Michx.), tread-softly (Cnidoscolus stimulosus Michx.), and
flag paw-paw (Asimina spp.); and wiregrass (Aristida stricta
Michx.).
Evergreen scrub is a dense to open xeric forest of pine;
it occurs on gently rolling terrain with acid, excessively-
drained, deep sand soil of the "sugar sand" sort. It
experiences occasional canopy fire. Evergreen scrub has an
upper story of Ocala sand pine and/or south Florida slash pine
(Pinus elliotii densa Little)—in south Florida only. There
is an understory consisting of evergreen sand live oak
(Ouercus geminata Small) and/or dwarf live oak (Ouercus pumila
Walt.). The typical composition is Ocala sand pine upper
story with an undergrowth of shrubs dominated by the evergreen
Florida-rosemary (Ceratiola ericoides Michx.), Archbold oak
(Ouercus inopina Swingle), scrub oak (Ouercus minima Sarg.),
myrtle oak (Ouercus mvrtifolia Willd.), scrub palmetto (Sabal
etonia Swingle), saw-palmetto (Serenoa repens Bartr.),
staggerbush, shiny deerberry (Vaccineum rovrsinites Lam.),
tallow-wood (Ximenia americana L.)—in south Florida only,
prickly-pear cactus, weakleaf yucca, gopher-apple (Licania

95
xnichauxii Prance) , greenbrier (Smilax spp.) , and ground lichen
(Cladonia spp).
Upland mixed forest. Upland mixed forest is a dense to
open forest of mixed pine and both deciduous and evergreen
broadleaf trees; it is found on gently rolling terrain with
well-drained to moderately well-drained deep loamy sand soil.
It consists, at kilometer-scale, of a mixture of two
components—"pine-oak-hickory woods" and "mesic hammock." The
open pine-oak-hickory woods occupies the well-drained soil and
experiences frequent ground fire; the dense mesic hammock
occupies the moderately well-drained soil and seldom
experiences any fire. The upper story consists of longleaf
pine, loblolly pine (Pinus taeda L.), slash pine (Pinus
elliotii Engelm.), or spruce pine (Pinus glabra Walt.), mixed
with the deciduous southern red oak (Ouercus falcata Michx.),
post oak (Ouercus stellata Wang.), Margaret oak, beech (Fagus
qrandifolia Ehrh.)—in panhandle, tulip-tree (Liriodendron
tulipifera L.)—in panhandle, white ash (Fraxinus americana
L.) , southern sugar maple (Acer saccharum Marsh.), mockernut
hickory (Carva tomentosa Poir.), pignut hickory (Carva glabra
Mill.), sweetgum (Liquidambar stvraciflua L.) , sugarberry
(Celtis laevigata Willd.), and basswood (Tilia caroliniana
Mill.), and the evergreen laurel oak (Ouercus hemisphaerica
Bartr.), water oak (Ouercus nigra L.) , live oak (Ouercus
virqiniana Mill.), southern magnolia (Magnolia grandiflora
L.), and red bay (Persea borbonia L.). There is an understory
consisting of the deciduous dogwood (Cornus florida L.),

96
fringe-tree (Chionanthus virginicus L.), red mulberry (Morus
rubra L.), hornbeam (Carpinus caroliniana Walt.)/ and hop-
hornbeam (Ostrya virginiana Mill.), and the evergreen
sweetleaf (Svmplocos tinctoria L.) and American holly (Ilex
opaca Ait.). The typical composition is an upper story of
mixed longleaf pine, loblolly pine, southern red oak, Margaret
oak, laurel oak, live oak, mockernut hickory, pignut hickory,
sweetgum, and sugarberry, with an understory of dogwood,
hornbeam, and hop-hornbeam, and an undergrowth of mixed shrubs
such as the evergreen yaupon holly (Ilex vomitoria Ait.), wax-
myrtle (Mvrica cerifera L.) , and greenbrier, and the deciduous
hawthorn (Crataegus spp.), pawpaw (Asimina parviflora Michx.) ,
trailing chinquapin (Castanea alnifolia Nutt.), beautyberry
(Callicarpa americana L.), witch-hazel (Hamamelis virginiana
L.), wild bunch grape (Vitis aestivalis Michx.), wild
muscadine grape (Vitis rotundifolia Michx.), leatherleaf
clematis (Clematis reticulata Walt.), wild-indigo (Baptisia
spp.), milk-pea (Galactia spp.), butterfly-pea (Clitoria
mariana L.), poison-oak (Toxicodendron toxicarium Salisb.),
and Virginia creeper (Parthenocissus guinguefolia L.).
Flatwoods forest. Flatwoods forest is a dense to open
evergreen forest of pine; it occurs on very level terrain with
flatwoods sand soil having a hardpan. It experiences large
seasonal water-table fluctuations, frequent ground fire, and
occasional canopy fire.
In panhandle and north Florida, flatwoods forest has a
dense upper story consisting of longleaf pine, loblolly pine,

97
pond pine (Pinus serótina Michx.), or slash pine (Mullahey and
Tanner, 1992). The typical composition is an upper story of
slash or longleaf pine, with an undergrowth dominated by
evergreen shrubs such as saw-palmetto, gallberry holly (Ilex
glabra L.), staggerbush, tar-flower (Befaria racemosa Vent.)»
and wax-myrtle, but also including the deciduous blazing-star
(Liatris spp.), beautyberry, winged sumac (Rhus copallina L.) ,
and huckleberry, as well as grasses such as three-awn
(Aristida spp.) and Indian-grass (Sorghastrum spp.). It also
includes scattered dome swamps and/or natural clearings of
marsh or wet-prairie.
In south Florida, flatwoods forest has an open upper
story consisting of south Florida slash pine (Mullahey and
Tanner, 1992). Undergrowth includes those species from north
Florida. The typical composition is an upper story of south
Florida slash pine, with an undergrowth dominated by saw-
palmetto, gallberry holly, and wax-myrtle, but also including
the deciduous blazing-star and huckleberry, as well as grasses
such as three-awn and Indian-grass. It also includes
scattered dome swamps and/or natural clearings of marsh or
wet-prairie.
Rockland hammock. Rockland hammock is a dense forest of
mixed pine and/or red-cedar and both evergreen and deciduous
broadleaf trees; it occurs on level or slightly elevated
terrain with well to poorly-drained shallow sandy or marly
soil over limestone. It experiences frequent to rare ground
fire. There are two subtypes—"rockland hammock" and

98
"calcareous hammock". The rockland hammock subtype occurs on
rockland with a relatively high water-table in north and south
florida; the calcareous hammock subtype occurs on rockland
with a relatively low water-table in north Florida.
In north Florida, the rockland hammock subtype has a
mixed upper story of slash pine, southern red-cedar (Juniperus
silicicola Small), and the evergreen live oak, cabbage palm
(Sabal palmetto Walt.), southern magnolia, sweetbay magnolia
(Magnolia virginiana L.), red bay, laurel oak, and water oak,
and the deciduous Florida elm (Ulmus americana floridana
Chapm.), basswood, and swamp ash (Fraxinus pauciflora Nutt.).
There is an understory consisting of evergreen dahoon holly
(Ilex cassine L.) , American holly, and laurelcherry (Prunus
caroliniana Mill.). The typical composition is a mixed upper
story of slash pine, red-cedar, live oak, laurel oak, Florida
elm, Carolina basswood, swamp ash, and cabbage palm, with an
understory of dahoon holly and laurelcherry, and an
undergrowth of mixed shrubs such as the evergreen bluestem
palm (Sabal minor Jacq.) and greenbrier, and the deciduous
climbing buckthorn (Sageretia minutiflora Michx.). This
subtype rarely experiences any fire.
In north Florida, the calcareous hammock subtype has a
mixed upper story of longleaf pine, loblolly pine, red-cedar,
and the deciduous bluff oak (Ouercus durandii Buckl.), Shumard
oak (Ouercus shumardii Buckl.), mockernut hickory, basswood,
sugarberry, and Florida elm. There is an understory
consisting of the deciduous Carolina buckthorn (Rhamnus

99
caroliniana Walt.)/ red buckeye (Aesculus pavia L.) , rusty
black-haw viburnum (Viburnum rufidulum Raf.) . dogwood, fringe-
tree, hornbeam, and hop-hornbeam, and the evergreen
laurelcherry, red bay, and gum-bumelia (Bumelia lanuginosa
Michx.). The typical composition is a mixed upper story of
longleaf pine, loblolly pine, red-cedar, bluff oak, and
mockernut hickory, with an understory of Carolina buckthorn,
rusty black-haw viburnum, dogwood, hornbeam, hop-hornbeam,
laurelcherry, red bay, and gum-bumelia, and a mixed
undergrowth including the evergreen needle palm
(Rhapidophvllum hvstrix Pursh), buckthorn bumelia (Bumelia
reclinata Michx.), snowberry (Symphoricarpos spp.), ebony
spleenwort fern (Asplenium platyneuron L.), greenbrier, and
coontie (Zamia floridana A. DC.), and the deciduous bracken
fern (Pteridium aouilinum L.) (Mullahey and Tanner, 1992).
This subtype experiences occasional ground fire. Although
calcareous hammock once covered substantial portions of
Alachua, Gilchrist, and Levy counties, it has been almost
completely replaced at kilometer scale by agriculture—unlike
the rockland hammock subtype, which has been partly preserved
at kilometer scale in south Florida parkland and in north
Florida undeveloped hammock-land. Calcareous hammock is
modeled in this study by substituting the thermal data of the
natural land-cover type having the most similar vegetation and
soil type—Upland Mixed Forest.
In south Florida, the rockland hammock subtype has two
components at kilometer scale—"tropical hammock" and

100
"Everglades flatwoods" (Mullahey and Tanner, 1992) . The
tropical hammock component occurs on sites that rarely
experience fire; the Everglades flatwoods component occurs on
sites that are subject to a seasonal flood and fire regime.
The typical composition of the tropical hammock component is
an upper story of south Florida slash pine mixed with the
evergreen live oak, laurel oak, cabbage palm, strangler fig
(Ficus aurea Nutt.), shortleaf fig (Ficus citrifolia Mill.),
false-mastic (Mastichodendron foetidissimum Cronq.), wild-
tamarind (Lvsiloma bahamensis L.), pigeon-plum (Coccoloba
diversifolia Jacq.), mahogany (Sweitenia mahogani L.) , willow-
bustic (Dipholis salicifolia L.), red bay, paradise-tree
(Simarouba glauca DC.), and poisonwood (Metopium toxiferum
Krug & Urban), and the deciduous peninsular persimmon
(Diospvros virginiana mosieri Sarg.), sugarberry, and gumbo-
limbo (Bursera simaruba L.) , with an understory of the
evergreen snakebark (Colubrina arborescens Sarg.), smooth
snakebark (Colubrina elliptica Brinz. & Stern), satinleaf
(Chrvsophvllum oliviforme L.), black ironwood (Kruqiodendron
ferreum Urb.) , wingleaf soapberry (Sapindus saponaria L.) . and
silver palm (Coccothrinax arqentata Bailey), and the deciduous
Geiger-tree (Cordia sebestena L.), and an undergrowth
consisting of the evergreen saw-palmetto, wax-myrtle, tough
bumelia (Bumelia tenax L.), saffron-plum (Bumelia celastrina
HBK.), blackbead (Pithecellobium spp.), marlberry (Ardisia
escallonioides Schiede & Deppe), glamberry (Bvrsonima lucida
DC.), balsamo (Psvchotria spp.), myrsine (Mvrsine quianensis

101
Kuntze), snowberry, tallow-wood, coontie, greenbrier, and
stopper (Eugenia spp.)/ and the deciduous winged sumac and
bracken fern. The typical composition of the Everglades
flatwoods component is an upper story of south Florida slash
pine mixed with cabbage palm, with no understory, and a fire-
trimmed undergrowth of saw-palmetto, wax-myrtle, coontie,
greenbrier, and bracken fern.
Coastal hammock. Coastal hammock is a dense to open,
dissected forest with an upper story of pine mixed with mostly
evergreen broadleaf trees; it occurs along both coasts. It
consists, at kilometer scale, of a mixture of three
components—a dense forest of "coastal hammock" on well to
poorly drained coastal sand or shell middens, a more open
"coastal strand" on well-drained stabilized dune sand, and a
non-forested "coastal swale" on poorly drained coastal sand.
These components rarely experience any fire. In the panhandle
and north, the typical composition is an upper story of slash
pine, sand pine, and southern red-cedar, mixed with the
evergreen wild olive (Osmanthus americanus L.), live oak,
southern magnolia, sand live oak, myrtle oak, cabbage palm,
red bay, and American holly, and the deciduous sweet acacia
(Acacia smallii Isely) and Hercules'-club, with an understory
of the evergreen dahoon holly, and an undergrowth of mostly
evergreen broadleaf shrubs such as yaupon holly, wax-myrtle,
saw-palmetto, myrsine, prickly-pear cactus, bayonet bush
(Yucca aloifolia L.) , greenbrier, and Florida-privet
(Forestiera segregata Jacq.), and the needle-leaved Florida-

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rosemary. In the south, the typical composition is an upper
story of south Florida slash pine and sand pine, mixed with
the evergreen sand live oak, myrtle oak, cabbage palm, red
bay, sea-grape (Coccoloba uvifera Jacq.), mahoe (Hibiscus
tiliaceus L.), portia-tree (Thespesia populnea Soland.)—an
exotic, pigeon-plum, strangler fig, wild-tamarind, paradise-
tree, mahogany, poisonwood, and false-mastic, and the
deciduous gumbo-limbo, with an understory of the evergreen
dahoon holly, blackbead, caper-tree (Capparis spp.), and
snakebark, and the deciduous Geiger-tree, and an undergrowth
of mostly evergreen broadleaf shrubs such as the cocoplum
(Chrvsobalanus icaco L.), tallow-wood, wax-myrtle, saw-
palmetto, marlberry, myrsine, Florida-privet, greenbrier,
prickly-pear cactus, prickly-apple cactus (Cereus spp.),
lantana (Lantana camara L.)—an exotic, bayonet bush, balsamo,
necklace-pod (Sophora spp.), and seven-year apple (Casasia
clusiaefolia Urban), and the needle-leaved Florida-rosemary.
Hardwood swamp. Hardwood swamp is a dense forest of
broadleaf trees; it occurs on depressed terrain with poorly-
drained (seasonally submerged) soil of variable type. It is
almost never subject to fire. There are two subtypes—
"evergreen hardwood swamp"—which occupies relatively drier
sites, and "deciduous hardwood swamp"—which occupies the
wetter sites.
In north Florida, the evergreen hardwood swamp subtype
has an upper story of the evergreen live oak, laurel oak, and
water oak, cabbage palm, and southern magnolia. There is an

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understory, consisting of the evergreen red bay, swamp bay
(Persea palustris Raf.), dahoon holly, myrtle-dahoon holly
(Ilex mvrtifolia Walt.)» American holly, sweetbay magnolia,
laurelcherry, and the deciduous pop ash (Fraxinus caroliniana
Mill.), wafer-ash (Ptelea trifoliata L.) . and basswood. There
are usually a few scattered specimens of the deciduous swamp
ash, black-gum túpelo (Nvssa biflora Walt.), and bald-cypress
(Taxodium distichum L.) . The typical composition is an upper
story of live oak, laurel oak, water oak, cabbage palm, and
southern magnolia, with an understory of myrtle-dahoon holly,
red bay, swamp bay, and an undergrowth of bluestem palm,
ferns, and mixed broadleaf shrubs.
In south Florida, the evergreen hardwood swamp subtype
has an upper story of the evergreen live oak, laurel oak,
cabbage palm, Florida royal palm (Rovstonea elata Harper),
false-mastic, shortleaf fig, strangler fig, willow-bustic,
mahogany, pigeon-plum, and wild-tamarind, and scattered
specimens of the deciduous gumbo-limbo, red maple (Acer rubrum
L.), Florida elm, swamp ash, persimmon, sugarberry, pignut
hickory, black-gum túpelo, and bald-cypress. There is an
understory, consisting of the evergreen Everglades palm
(Paurotis wrightii L.), dahoon holly, sweetbay magnolia, and
red bay, and the deciduous pond-apple (Annona glabra L.) and
red mulberry. The typical composition is an upper story of
live oak, laurel oak, cabbage palm, Florida royal palm, and
willow-bustic, with an understory of dahoon holly, red bay,

104
Everglades palm, and pond-apple, and an undergrowth of ferns
and predominantly evergreen broadleaf shrubs.
In panhandle and north Florida the deciduous hardwood
swamp subtype has an upper story of the deciduous water-tupelo
(Nvssa acruatica L.), red maple, boxelder maple (Acer nequndo
L.), swamp ash, green ash (Fraxinus pennsvlvanica. Marsh.)—in
panhandle, pumpkin ash (Fraxinus profunda Bush), cottonwood
(Populus spp.)—in panhandle, river birch (Betula niara L.),
water hickory (Carya aouatica Michx.), bitternut hickory
(Carva cordiformis Wang.)—in panhandle, pignut hickory, black
willow (Salix nigra Marsh.)—in panhandle, Carolina basswood,
soapberry (Sapindus marainatus Willd.), swamp-chestnut oak
(Ouercus michauxii Nutt.), overcup oak (Ouercus lvrata Walt.),
willow oak (Ouercus phellos L.)—in panhandle, water-locust
(Gleditsia aquatica Marsh.), black-gum túpelo, sourgum túpelo
(Nvssa svlvatica Marsh.), sweetgum, water-elm (Plañera
acruatica Walt.), tulip-tree, American sycamore (Platanus
occidentalis L.)—in panhandle, sugarberry, winged elm (Ulmus
alata Michx.), Florida elm, red mulberry, and persimmon.
There is usually a very narrow strip of bald-cypress along the
edges of rivers and lakes, and there are usually some
scattered specimens of the evergreen southern magnolia and
sweetbay magnolia. There is an understory consisting of the
deciduous pop ash, Carolina willow (Salix caroliniana Michx.),
silverbell (Halesia spp.)—in panhandle, snowbell (Stvrax
spp.), and wafer-ash. The typical composition is an upper
story of various deciduous hardwoods (dominated locally by

105
water-tupelo, red maple, green ash, cottonwood, willow oak,
black gum túpelo, river birch, or water-elm, but usually a
mixture of red maple, swamp ash, water hickory, basswood,
swamp-chestnut oak, water-locust, black-gum túpelo, sweetgum,
sugarberry, and Florida elm), with an understory of pop ash,
Carolina willow, silverbell—in panhandle, and wafer ash, and
an undergrowth of bluestem palm, switchcane (Arundinaria
spp.)/ arid mixed evergreen and deciduous broadleaf shrubs and
vines.
Cypress swamp. Cypress swamp is a dense to open forest
of deciduous cypress trees; it occurs on depressed terrain
with poorly-drained (seasonally submerged) soil of variable
type. It is seldom subject to fire. There are two subtypes—
"cypress swamp" and "dwarf-cypress swamp". The dense cypress
swamp subtype occurs on soil of variable type—sometimes a
shallow layer over limestone, or elongated pockets of deep
organic soil in "strand" limestone channels; the more open
dwarf-cypress swamp subtype occurs on marly rockland in south
Florida.
In north Florida, the cypress swamp subtype consists of
bald-cypress and/or pond-cypress (Taxodium ascendens Brongn.) ,
with some scattered specimens of the evergreen cabbage palm
and the deciduous green ash, pumpkin ash, swamp ash, red
maple, black-gum túpelo, persimmon, sugarberry, pignut
hickory, water hickory, and Florida elm. There is an
understory, consisting of the evergreen myrtle-dahoon holly,
sweetbay magnolia, red bay, swamp bay, loblolly-bay (Gordonia

106
lasianthus L.), and laurelcherry, and the deciduous pop ash
and Carolina willow. The typical composition is an upper
story dominated by bald-cypress or pond-cypress, with an
understory of myrtle-dahoon holly, red bay, pop ash, and
Carolina willow, and an undergrowth of bluestem palm, ferns,
mixed evergreen and deciduous broadleaf shrubs, and aquatic
herbs.
In south Florida, the cypress swamp subtype has an upper
story of bald-cypress or pond-cypress, with some scattered
specimens of the evergreen cabbage palm and Florida royal
palm, and the deciduous black-gum túpelo, gumbo-limbo, red
maple, persimmon, sugarberry, Florida elm, and pignut hickory.
There is an understory, consisting of the evergreen dahoon
holly, red bay, Everglades palm, sweetbay magnolia, and the
deciduous pond-apple, pop ash, and Carolina willow. The
typical composition is an upper story of bald-cypress, with an
understory of dahoon holly, red bay, pond-apple, pop ash, and
Carolina willow, and an undergrowth of ferns, mixed evergreen
and deciduous broadleaf shrubs, and aquatic herbs.
In south Florida, the dwarf-cypress subtype has a dense
to open upper story of pond-cypress, which is usually very
stunted. It has much less understory than the cypress swamp
subtype. There are scattered clearings of wet-prairie
vegetation (see under Marsh). The typical composition is an
upper story of pond-cypress, with an undergrowth dominated by
the evergreen shrubs tough bumelia and saffron-plum, as well

107
as ferns and aquatic herbs, with scattered clearings of wet-
prairie grasses.
Bay swamp. Bay swamp is a dense forest having some
combination of evergreen broadleaf trees with pine, white-
cedar, and/or deciduous cypress; it occurs on depressed
terrain with poorly-drained organic soil. It is seldom
subject to fire, but under drainage and/or drought impact, bay
swamp can suffer catastrophic soil and canopy fire.
Bay swamp has an upper story of scattered pond pine,
slash pine, loblolly pine, Atlantic white-cedar (Chamaecvoaris
thvoides L.)—in panhandle, southern red-cedar, and/or the
deciduous pond-cypress or bald-cypress, mixed with the
evergreen sweetbay magnolia, wild olive, cabbage palm,
southern magnolia, live oak, and laurel oak, and the deciduous
swamp ash, red maple, Carolina basswood, pignut hickory, and
sweetgum. There is an understory, consisting of the evergreen
myrtle-dahoon holly, red bay, swamp bay—in panhandle,
loblolly-bay—in north, and laurelcherry, and the deciduous
silky-Camellia. The typical composition is an upper story of
mixed slash pine, pond pine, white-cedar, pond-cypress,
sweetbay magnolia, southern magnolia, and wild olive, with an
understory of myrtle-dahoon holly, red bay, swamp bay, and
loblolly-bay, and a mixed undergrowth consisting of the
evergreen staggerbush, dog-hobble (Leucothpe spp.), titi
(Cvrilla racemiflora L.), and black-titi (Cliftonia monophvlla
Lam.), and the deciduous blueberry (Vaccineum corvmbosum L.) ,

108
red chokeberry fAronia arbutifolia L.)/ possum-haw viburnum
(Viburnum nudum L.), and ferns.
Mixed swamp. Mixed swamp is a dense forest having some
combination of pine with cypress swamp, hardwood swamp, and/or
bay swamp; it occurs on depressed terrain with poorly drained
(seasonally submerged) soil of variable type—at kilometer
scale in north Florida, a mixture of flatwoods sand and
pockets of organic soil; and in south Florida, sandy rockland.
It is not ordinarily subject to fire, but under drainage
and/or drought impact, mixed swamp can suffer catastrophic
canopy and/or soil fire. This has happened repeatedly in the
Okeefenokee Swamp area, but is more rare in south Florida.
In north Florida, mixed swamp has a mixed upper story of
slash pine, pond pine, loblolly pine, deciduous pond-cypress,
and the evergreen live oak, laurel oak, water oak, wild olive,
sweetbay magnolia, southern magnolia, and cabbage palm, and
the deciduous black-gum túpelo, sourgum túpelo, swamp ash, red
maple, Florida elm, and Ogeechee-lime túpelo (Nvssa ogeche
Bartr.). There is an understory, consisting of the evergreen
red bay, myrtle-dahoon holly, and loblolly-bay. The typical
composition is a mixed upper story of slash pine, pond pine,
pond-cypress, laurel oak, water oak, sweetbay magnolia,
southern magnolia, wild olive, cabbage palm, black-gum túpelo,
swamp ash, red maple, Florida elm, and Ogeechee-lime túpelo,
with an understory of myrtle-dahoon holly, red bay, and
loblolly bay, and an undergrowth of mixed evergreen and
deciduous broadleaf shrubs.

109
In south Florida, mixed swamp has a mixed upper story of
south Florida slash pine and deciduous bald-cypress, the
evergreen live oak, cabbage palm, and strangler fig, and the
deciduous red maple. It has an understory, consisting of the
evergreen dahoon holly and red bay, and the deciduous Carolina
willow. The typical composition is a mixed upper story of
south Florida slash pine, bald-cypress, cabbage palm, and live
oak, with an understory of dahoon holly and red bay, and an
undergrowth of evergreen broadleaf shrubs.
Mangrove swamp. Mangrove swamp is a dense forest of
evergreen broadleaf trees, dissected by a network of tidally
flooded channels; it occurs on level coastal terrain with
sandy/shelly soil. It is always wet, so is never subject to
fire. Mangrove swamp has an upper story of the evergreen red
mangrove (Rhizophora mangle L.), black mangrove (Avicennia
germinans Stearn), white mangrove (Laguncularia racemosa
Gaertn.), buttonwood (Conocarpus erecta L.), , blolly
(Torrubia longifolia L.), and sea-grape. The typical
composition is a solid stand of red mangrove (below high-tide
line), black mangrove (at high-tide line), white mangrove
(above high-tide line), and buttonwood (well above high-tide
line), with undergrowth (if any) dominated by leather fern
(Acrostichum danaeifolium Lansd. & Fisch.), coin vine
(Dalbergia ecastophvllum L. ) , coastal moonflower-vine (Ipomoea
tuba G. Don), and nickerbean (Caesalpinia spp.), and tidal
channels of saltmarsh grasses (or open).

110
Shrubby marsh. Shrubby freshwater marsh is a dense
growth of broadleaf shrubs; it occurs on poorly drained muck
soil, and is common at kilometer scale only in south Florida.
It experiences occasional to rare fire. There are two
subtypes—"evergreen shrubby marsh" and "deciduous shrubby
marsh".
The evergreen shrubby marsh subtype occupies sites with
a relatively lower water-table. It consists of the evergreen
wax-myrtle, red bay, saltbush (Baccharis spp.), elderberry
(Sambucus simosonii Rehder), Brazilian pepper-tree (Schinus
terebinthifolius Raddi)—an exotic, guava (Psidium quaiava
L.)—an exotic, strawberry guava (Psidium cattlevanum L.)—an
exotic), wild papaya (Carica papaya L.)—an exotic, lime
prickly-ash (Zanthoxvlum fragara L.) , castorbean (Ricinus
communis L.)—an exotic, Everglades cocoplum (Chrvsobalanus
icaco pellocarous DC.), and gallberry holly. The typical
composition is a mixture of wax-myrtle (often dominant),
elderberry, Brazilian pepper-tree, saltbush, guava, wild
papaya, Everglades cocoplum, and castorbean.
The deciduous shrubby marsh subtype occupies sites with
a relatively higher water-table. It consists of the deciduous
pond-apple, buttonbush (Ceohalanthus occidentalis L.) ,
Carolina willow, primrose-willow (Ludwigia peruviana L.)—an
exotic, rose mallow (Hibiscus moscheutos L.) , balloon-vine
(Cardiospermum halicacabum L.), bitter-gourd (Momordica
charantia abbreviata Ser.)—an exotic, creeping-cucumber
(Melothria péndula L.) , maypops passionflower (Passiflora

Ill
incarnata L.) , highbush blackberry (Rubus arqutus Link), wild-
yam (Dioscorea bulbifera L.)—an exotic, moonflower-vine
(Ipomea alba L.)—an exotic, and pipevine (Aristolochia spp.).
The typical composition is a mixture of Carolina willow (often
dominant), buttonbush, pond-apple, primrose-willow, rose
mallow, and vines.
Herbaceous freshwater marsh. Herbaceous freshwater marsh
is a dense, quasi-evergreen growth of aquatic and emergent
plants. It occurs on poorly drained soil. There are two
subtypes—"marsh" and "wet-prairie". The marsh subtype occurs
on submerged muck soil; the wet-prairie subtype occurs on
seasonally-submerged marly rockland. This land-cover type
seldom experiences fire.
In panhandle and north Florida, the marsh subtype
consists of grasses (Panicum spp., Paspalum spp., etc.),
sedges (Carex spp., Cvperus spp., etc.), bluestem/broomsedge
(Andropogon spp.), rushes (Juncus spp.), bulrushes (Scirpus
spp.), clubhead cutgrass (Leersia hexandra Sw.), toothache-
grass (Ctenium aromaticum Walt.), Muhly-grass (Muhlenbergia
spp.), reed (Phragmites communis Trin.), southern wild-rice
(Zizaniopsis miliacea Michx.), Atamasco lily (Zephyranthes
atamasco L.) , spider lily (Hvmenocallis rotata Ker-Gawl.),
swamp lily (Crinum americanum L.), bog-buttons (Lachnocaulon
spp.), fireflag (Thalia qeniculata L.), blue-flag iris (Iris
virginica L.), yellow canna (Canna flaccida Salisb.),
maidencane (Panicum hemitomon Schult.), sawgrass (Cladium
iamaicense Crantz), and cattail (Tvpha spp.), with some

112
broadleaf herbs such as pickerel-weed (Pontederia spp.)/
tuckahoe (Peltandra virginica Schott & Endl.), arrowhead
(Sagittaria spp.)/ wild taro (Colocasia esculenta L.)—an
exotic, water-lily (Nvmphaea spp. ) . water-hyacinth fEichhornia
crassipes Mart.)—an exotic, water-lettuce (Pistia stratiotes
L.), chain fern (Woodwardia spp.), cinnamon fern (Osmunda
cinnamomea L.) , alligator-weed (Alternanthera philoxeroides
Mart.)—an exotic, elodea (Egeria densa Planch.)—an exotic,
hydrilla (Hvdrilla verticillata L.f.)—an exotic, lotus
(Nelumbo spp.), bladderwort (Utricularia spp.), floating-
hearts (Nvmphoides spp.), smartweed (Polygonum spp.), bindweed
(Calvstegia spp.), pipevine, pennywort (Hvdrocotvle spp.),
spatterdock (Nuphar luteum L.), black-eyed Susan (Rudbeckia
spp.), coontail-moss, and filamentous algae (periphyton). The
typical composition is a mixture of grasses, sedges, rushes,
cattail, pickerel-weed, smartweed, arrowhead, wild taro,
water-lily, water-hyacinth, water-lettuce, lotus, spatterdock,
alligator-weed, floating-hearts, smartweed, and pennywort.
In south Florida, the marsh subtype consists of the marsh
plants listed above. There are also some scattered tree-
islands, which are small examples of shrubby marsh or hardwood
swamp. The typical composition in undisturbed areas is
sawgrass, bladderwort, coontail-moss, water-lily, and
spatterdock; the composition in hydrologically disturbed areas
is cattail, rushes, wild-sugarcane (Saccharum officinarum L.) ,
maidencane, pickerel-weed, arrowhead, water-hyacinth, water-
lettuce, wild taro, alligator-weed, and mats of periphyton.

113
In south Florida, the wet-prairie subtype consists of the
south Florida marsh plants, plus beakrush (Rhvnchospora spp.),
poverty-grass (Aristida purourescens Poir.), broomsedge,
switch-grass (Aristida patula Chapin, ex Nash) , maidencane,
foxtail grass (Setaria spp.), sand cordgrass (Spartina bakeri
Merr.), gulf-dune paspalum (Paspalum monostachvum L.) . little-
bluestem (Schizachyrium scoparium Michx.), little blue-
maidencane (Amphicarpum muhlenbergianum Schult.), spikerush
(Eleocharis spp.), bulrush, leather fern, cinnamon fern, and
St. Johns-wort (Hypericum spp.). There are also some
scattered tree-islands, which are small examples of shrubby
marsh or dwarf-cypress swamp. The typical composition is a
mixture of beakrush, poverty-grass, switch-grass, broomsedge,
foxtail-grass, gulf-dune paspalum, sand cordgrass, little-
bluestem, little blue-maidencane, spikerush, bulrush,
pickerel-weed, arrowhead, cattail, fireflag, and St. Johns-
wort.
Saltwater/brackish marsh. Herbaceous saltwater/brackish
marsh is a dense, quasi-evergreen growth of mostly grassy
aquatic plants along quieter parts of the sea-coast. There
are two subtypes—"saltmarsh" and "brackish marsh". The
saltmarsh subtype occurs on sand/mud soil in panhandle and
north Florida; the brackish marsh subtype occurs on marly
rockland in south Florida. This land-cover type rarely
experiences any fire.
In panhandle and north Florida, the saltmarsh subtype
consists primarily of grasslike plants such as sawgrass,

114
rushes, saltmarsh cordgrass (Spartina spp.), seashore
saltgrass (Distichlis spicata L.) , knotgrass fPaspalum
distichuxn L.) , seashore dropseed (Sporobolus virginicus L.)/
cattail, sedges, and broadleaf halophytic herbs such as
samphire (Salicornia spp.), sea-purslane (Sesuvium
portulacastrum L.) , saltwort (Batis maritima L.) , beach-carpet
(Philoxerus vermicularis R. Br.), sea-lavender (Limónium
carolinianum Walt.)/ sea-blite (Suaeda linearis Moq.),
matchflower (Lippia nodiflora L.), pennywort, wild-savory
(Calamintha coccínea Nutt.), matrimony-vine (Lvcium
carolinianum Walt.), marsh-elder (Iva spp.), and sea-oxeye
daisy (Borrichia frutescens L.) . The typical composition is
a mixture of saltmarsh cordgrass, seashore saltgrass,
knotgrass, seashore dropseed, rushes, sedges, sea-lavender,
samphire, pennywort, pony-foot, wild-savory, marsh-elder, and
glasswort.
In south Florida, the brackish marsh subtype consists of
the above saltmarsh plants plus leather fern, Virginia wild-
rye (Elymus virginicus L.), reed, marsh-mallow (Hibiscus
spp.), and Wedellia-vine (Wedelia glauca Ort.)—an exotic.
The typical composition is a mixture of spikerush, sawgrass,
leather fern, cattail, Virginia wild-rye, saltmarsh cordgrass,
glasswort, rushes, sedges, samphire, pennywort, swamp lily,
and marsh-mallow.

115
Agricultural Land-Cover
Many different agricultural land-cover types exist in
Florida. This research included five general types which
occur at meso-scale (Figure 17). These consist of row-crops,
pasture/range, pasture/sod, citrus orchard, and mixed
agriculture.
Row-crops. Row-crops agriculture consists of seasonal
crops on tilled land; the crops provide only partial coverage
of the soil even at crop maturity. It is practiced on all
soil types (Hochmuth and Hanlon, 1989) . Row-crops are always
carefully drained, irrigated, and fertilized, and sometimes
planted in plastic mulch (Hochmuth and Hanlon, 1989; Clark et
al., 1993). Irrigation is performed mainly by seepage
(pumping into ditches) or sprinkler gun (travelling high-
pressure sprinkler), depending on local soil conditions and
groundwater supplies (Smajstrla et al., 1993). There are
spring, summer, fall, and winter crop seasons, and there is
usually a rotation of the exact crop species from season to
season. Spring row-crops consist of tomato (Lvcopersicon
esculentum Mill.), pepper (Capsicum annuum L.), potato
(Solanum tuberosum L.), eggplant (Solanum melongena L.),
cantaloupe (Cucumis meló L.), watermelon (Citrullis lanatus
Thunb.), sunflower (Helianthus annuus L.) , and strawberry
(Fragaria spp.). Summer row-crops consist of long-
season/heat-tolerant crops such as soybean (Glycine max L.),
peanut (Arachis hvpogaea L.), sweet-potato (Ipomoea batatas.

116
Figure 17. Agricultural
for details).
land-cover polygons
(see Appendix C

117
L.), okra (Hibiscus esculentus L.). tobacco (Nicotiana tabacum
L.) , and cotton (Gossvpium hirsutum L.) , or fallow cover-crops
such as hairy-indigo (Indigofera hirsuta Harv.) or sudangrass
(Sorghum spp. hybrid). Fall row-crops consist of peppers and
beans (Phaseolus spp.)* Winter row-crops consist of lettuce
(Lactuca sativa L.), cabbage (Brassica olerácea capitata L.),
kale/collard (Brassica olerácea acephala L.) , cucumber
(Cucumis sativa L.). squash/zucchini/calabaza (Cucúrbita pepo
L.), cauliflower/broccoli (Brassica olerácea botrvtis L.),
Chinese cabbage (Brassica spp.), carrot (Daucus carota L.),
endive/escarole (Cichorium endiva L.), radish (Raphanus
sativus L.) , celery (Apium graveolens L.) , parsley (Apium
petroselinum L.), onion (Allium spp.), and pepper. Row-crops
have been cultivated in north and panhandle Florida since pre-
Columbian times (Goggin, 1975).
Pasture/range. This type occurs primarily on flatwoods
sand and sandy rockland. Pasture consists of improved pasture
and paddocks; it is usually drained, sometimes fertilized, and
sometimes irrigated—mainly by seepage (Smajstria et al.,
1993). Pasture grasses include the cultivated bahiagrass
(Paspalum notatum Fluegge), centipedegrass (Eremochloa
ophiuroides Munro), and Bermudagrass (Cvnodon dactvlon L.).
Range consists of unimproved pasture, with many weeds and
scattered patches of forest, shrubs, and/or marsh; it is
usually drained, but little other maintenance is supplied
other than a burning every two to four years (Mullahey and
Tanner, 1992). Range grasses include a wide variety of wild

118
marsh and wet-prairie grasses in addition to the cultivated
grasses (Mullahey and Tanner, 1992).
Pasture and range are usually mixed at kilometer scale.
Most of the grasses involved are quasi-evergreen. Pasture/
range agriculture has been practiced in panhandle and north-
central Florida since colonial times (Goggin, 1975), and in
south-central Florida mainly since the 1870s (FDA, 1954).
Pasture/sod. This agriculture type occurs mainly on
organic soils (McCarty and Cisar, 1990). Pasture and sod-farm
are usually mixed at kilometer scale. Pasture component
consists of improved pasture; it is always drained, and
irrigated (when necessary) by seepage (Smajstria et al.,
1993) .
Sod-farm component consists of lawn-grasses grown for the
landscaping trade—turfs are periodically harvested in strips
which then regrow quickly (propagation by seeding is not
possible for sterile hybrid varieties of most lawn grasses).
Sod-farms are always drained, fertilized, and irrigated (when
necessary) by seepage (Smajstrla et al., 1993). Lawn grasses
in Florida consist of the quasi-evergreen St. Augustine-grass
fStenotaphrum secundatum Walt.), centipedegrass, bahiagrass,
and zoysiagrass (Zovsia tenuifolia L.) ; grass for golf-courses
and athletic fields is Bermudagrass (McCarty and Cisar, 1990).
The primary sod-farm grasses are St. Augustine-grass,
bahiagrass, and centipedegrass; Bermudagrass and zoysiagrass
are grown in smaller amounts (McCarty and Cisar, 1990).

119
Pasture/sod has been cultivated in Florida mainly since the
early 1900s.
Citrus orchard. Citrus orchard consists of kilometer-
scale groves of subtropical citrus trees; it is practiced in
north and south Florida, primarily on flatwoods sand, deep
sand, and upland loamy sand. It is drained on flatwoods sand,
and irrigated on deep sand and upland loamy sand. Irrigation
is (currently) provided mainly by micro-irrigation, in the
form of stationary low flow-rate emitters (Smajstrla et al.,
1993). Citrus trees grown in Florida at kilometer scale
include orange (Citrus sinensis L.), grapefruit (Citrus
paradisi Macf.), tangerine (Citrus reticulata Blanco), and
various hybrids of these, such as tángelo, tangor, and
Persian-lime (Ziegler and Wolfe, 1961) .
Citrus orchard has been cultivated in Florida since
colonial times. It was well-established commercially in
northeast Florida by 1763, but a succession of severe freezes
in 1894, 1895, and 1899 wiped out these early groves (Ziegler
and Wolfe, 1961); the land-cover of this former grove area now
consists of mixed agriculture and suburb. The center of
commercial citrus orchard agriculture moved to central
Florida, where it had been expanding since the 1870s, and the
previous levels of production were resumed there by 1910 (FDA,
1954). Heavy damage from a succession of freezes in the mid
1980s destroyed many of the northmost central Florida citrus
orchards on the central ridge (between Okahumpka and Orlando);
these were not replanted—most new groves have since been

120
planted further south in the Indian River and Allapattah Flats
areas. The only variety of citrus grown commercially before
1900 was orange (FDA, 1954). Commercial grapefruit orchards
began about 1910; later additions included tangerine and
various hybrids (mandarin, tángelo, etc.). The kilometer-
scale, conventional form of citrus orchard consists of the
subtropical trees described above; the smaller-scale groves of
the tropical key-lime (Citrus aurantifolia Swingle) in extreme
south Florida are included in the orchard component of the
mixed agriculture land-cover category.
Mixed agriculture. Mixed agriculture consists of a
kilometer-scale mixture of several agricultural types. These
can include row-crops, pasture, field-crops, hayfields,
orchards, woodlots, and (on flatwoods sand) ponds. At any
given season, some fields will be vegetated and others bare,
and the seasonal pattern will change for a given field each
year due to crop rotation. Mixed agriculture has been
practiced in north and panhandle Florida since colonial times;
it has been practiced in south Florida mainly since the late
1800s.
Row-crops, pasture, and citrus orchard have been
described previously. Field-crops are seasonal crops on
tilled land; the crops provide full coverage of the soil early
in the season. They consist of corn (Zea mays L.), sugarcane
(Saccharum officinarum L.), wheat (Triticum spp.), triticale
(Triticum spp. x Secale spp.), grain-sorghum (Sorghum bicolor
L.), rice (Orvza sativa L.), oats (Avena fatua L.), pearl-

121
millet (Pennisetum americanum L.), and some foxtail-millet
(Setaria itálica L.). Irrigation is performed by seepage,
sprinkler gun, or center-pivot (travelling low-pressure
sprinkler), depending on local soil conditions and groundwater
supplies (Smajstrla et al., 1993). Sugarcane and rice are
grown primarily on organic soil in the EAA, while corn is
grown on both organic and sandy/loamy soil types throughout
the state. Wheat, triticale, oats, and millet are grown on
loamy soil types in the north and panhandle. Corn is grown in
spring and fall; grain-sorghum and millet are grown in summer;
wheat, triticale, and oats are grown in winter; sugarcane and
rice have an extended growing season (more than one year for
sugarcane) with primary and secondary (ratoon) harvests.
Hayfields consist of fall and winter forage and silage
crops of winter-ryegrass (Lolium perenne L.) , alfalfa
(Medicago sativa L.), white-clover (Trifolium repens L.), and
red-clover (Trifolium pratense L.), and summer forage crops of
cowpea (Vigna spp.) and perennial-peanut (Arachis qlabrata
L.). Irrigation is performed by seepage, sprinkler gun,
center-pivot, or not at all, depending on local soil
conditions and groundwater supplies (Smajstrla et al., 1993).
Hayfields are located primarily in the north and panhandle.
In north Florida, commercial orchards other than
conventional citrus include smaller-scale groves of the
evergreen kumquat (Fortunella spp.) and the deciduous pecan
(Carva illinoensis Wang.), chestnut (Castanea spp.), peach
(Prunus pérsica L.), apple (Malus spp.), and oriental

122
persimmon (Diospyros kaki L.), as well as vineyards of bunch
grape (Vitis aestivalis Michx.) and muscadine grape (Vitis
rotundifolia Michx.)» and berry farms of the perennial
blueberry (Vaccineum corvmbosum L.) and blackberry (Rubus
argutus Link) bushes. Irrigation is performed mainly by
micro-irrigation or not at all, except for blueberries, which
are irrigated mainly by sprinkler gun (Smajstria et al.,
1993). A relatively small amount of central Florida tree-
related agriculture involves commercial production of
leatherleaf fern (Rumohra adiantiformis Forst) and ornamental
asparagus (Asparagus springeri Regel, Asparagus setaceus
Regel) on deep sand for the florist trade. About 40% of these
micro-irrigated ferneries operate beneath a closed canopy of
native live oak and sand live oak (Henley et al., 1985).
In south Florida, commercial orchards other than
conventional citrus include smaller-scale groves of the
evergreen mango (Mangifera indica L.), avocado (Persea
americana L.) , carambola (Averrhoa carambola L.) , white-sapote
(Casimiroa edulis L.), papaya (Carica papaya L.), guava
(Psidium quaiava L.) , canistel (Pouteria campechiana L.),
lychee (Litchi chinensis L.) . sapodilla (Manilkara zapota L.) .
key-lime, and specialty-bananas (Musa spp.), as well as the
deciduous sweetsop (Annona squamosa L.) , atemoya (Annona
squamosa x cherimola), and mamey-sapote (Pouteria sapota L).
Irrigation is performed mainly by micro-irrigation (Smajstrla
et al., 1993).

123
Woodlots consist of small (typically 10 to 40 acre)
stands of pine or hardwood trees; narrow bands of pine, red-
cedar, Australian-pine (Casuarina eauisetifolia L.)—in south,
and hardwoods along fence-lines and windbreaks; and narrow
bands of natural swamp forest along creeks, rivers, and ponds.
Woodlots are seldom deliberately irrigated.
In flatwoods and wetter rockland soil areas, mixed
agriculture usually contains the additional component of small
ponds. Some of these are holding ponds—artificial reservoirs
storing the water necessary for seasonal irrigation. Others
are aquacultural ponds for commercial growing of ornamental
fish—and to a lesser areal extent—aquarium plants,
alligators, catfish, crawfish, game fish (for stocking),
freshwater prawns, eels, and tilapia (FASS, 1990).
Urban/Industrial Land-Cover
Many different urban/industrial land-cover types exist in
Florida. The population of the state has increased from about
141,000 (an early census figure excluding American Indians) in
1860 (Blakey, 1973; Hoffman, 1993), to a total of 12,937,926
in 1990 (Hoffman, 1993). It exceeded 1,000,000 by 1930, and
had its fastest rate of 20th-century increase during the
period from 1950 to 1960 (USBOC, 1979; Hoffman, 1993). Prior
to 1900, only about a quarter of the state population was
located in the south (Blakey, 1973) . This research included
eight general urban/industrial types which occur at meso-scale
(Figure 18) . These consist of suburb, platted suburb, finger-

124
Figure 18. Urban/industrial land-cover polygons (see Appendix
C for details).

125
canal suburb, golf-course suburb, Indian reservation, urban
center, phosphate mine, and titanium mine.
Suburb. Suburb consists of urbanized areas that are
primarily low-density residential in character. It includes
a kilometer-scale mixture of houses, pavement, lawns, and
trees. Lawns include the quasi-evergreen grasses St.
Augustine-grass, bahiagrass, centipedegrass, and Bermudagrass;
in winter, lawns are often sown with winter-ryegrass. Lawns
are usually irrigated at night. Ornamental trees include a
mixture of both deciduous and evergreen trees whether in north
or south Florida (Meerow and Black, 1988b).
Suburbs have been constructed in Florida mainly since the
1950s. They have been built on almost every soil type.
Concern for the microclimate effects of variation in the
fraction of suburban land-cover components has appeared in
recent times, mainly for purposes of energy conservation
(Meerow and Black, 1988a). Due to restriction of lateral air
movement, the placement of tree windbreaks and walls can lead
to higher daytime and nighttime temperatures in enclosed lawn
areas than would be the case with no obstacles at all (Meerow
and Black, 1988a).
Platted suburb. Platted suburb consists of areas that
have streets and cleared (to some degree) lots, but few
houses. It includes a kilometer-scale mixture of clearings,
pavement, lawns, and trees. Usually it is a transitional
type, but in some places it lasts for years.

126
Finger-canal suburb. Finger-canal suburb consists of
urbanized coastal areas that are residential in character,
0 with a network of finger canals constructed to allow boat
access from each house to the sea. It includes a kilometer-
scale mixture of houses, pavement, lawns, trees, and seawater.
Golf-course suburb. Golf-course suburb consists of
urbanized areas that are both residential and recreational in
character, with numerous golf courses. It includes a
kilometer-scale mixture of houses, pavement, lawns, trees, and
golf-courses.
Grass species required for golf-courses include the
permanent Bermudagrass, which is over-seeded from fall to
spring with creeping bentgrass (Agrostis palustris Huds.),
roughstalk bluegrass (Poa trivialis L.), and winter-ryegrass
(McCarty et al., 1990, 1993). The placement of the relatively
few trees and shrubs on golf-courses tends to prevent lateral
air movement, leading to higher temperatures than for bare sod
alone (Meerow and Black, 1988a; McCarty et al., 1990). This
is a matter of concern in springtime, because surface
temperatures (top of soil) above 80 °F cause decline of
bentgrass, and surface temperatures above 100 °F will kill it
(McCarty et al., 1990).
Indian reservation. Indian reservation consists of areas
under traditional-style residential use by American Indians.
It includes a kilometer-scale mixture of trees, pasture/range,
and houses—many with traditional palm-thatched roofs. The
trees are primarily live oak and cabbage palm. Similar

127
communities have existed in Florida since pre-Columbian times
(Goggin, 1975).
Urban center. Urban center consists of areas having a
strongly urbanized character, with high-density residential
areas, transportation centers, and business/manufacturing
districts. It includes a kilometer-scale mixture of pavement,
buildings, and a variable fraction of trees. Urban centers
have been built in Florida since 1565 (Ziegler and Wolfe,
1961); most of their expansion has occured since the 1800s
(Hoffman, 1993). Only since the early 1900s has it been
possible to build urban centers on organic soil in the EAA
(Izuno, 1989).
Phosphate mine. Phosphate mine consists of areas
(primarily on flatwoods sand) strip-mined for phosphate. It
includes a kilometer-scale mixture of cleared land, mounds of
sand from overburden and tailings, and settling "slime" ponds
(Hochmuth et al., 1987). The older ponds contain a settled
phosphatic clay, and are generally covered either by de¬
watering cover crops (primarily alfalfa) or by wet-prairie
vegetation (primarily cattail, saltbush, and bluestem-grass).
Many phosphate deposits have been re-worked as new processing
techniques have been developed.
Phosphate has been mined commercially from tricalcium
phosphate ore in Florida since the 1890s (Blakey, 1973).
Large-scale mining involving on-site processing of phosphoric
acid and superphosphate began in 1948 in the present centers
of the industry in Florida, which are the pebble-phosphate ore

128
areas of north (Hamilton County) and west-central ("Bone
Valley" district of Polk and Hillsborough Counties) Florida
(Blakey, 1973; Yon and Oglesby, 1975).
There has been some effort in recent decades to reclaim
mined land for agriculture and naturalization (Yon and
Oglesby, 1975). The sand mounds are readily reclaimed for
either purpose, and for agriculture the uses have included
pasture and citrus orchard (Blakey, 1973) . Reclamation of the
settling ponds has been more problematic; although use for
row-crop, field-crop, hayfield, sod-farm, and ornamental-tree
nursery agricultural types has been investigated, concerns
involving vegetation uptake of radionuclides and especially
molybdenum from the phosphatic clay have yet to be answered
fully (Shibles and Strieker, 1990). Naturalization of
settling ponds has included marsh reclamation (Blakey, 1973).
Titanium mine. Titanium mine consists of areas
(primarily on deep sand) strip-mined for titanium. It
includes cleared land, mounds of sand from tailings, and ponds
(not for settling). Titanium has been mined commercially from
ilmenite ore in northeast Florida since 1916 (Martens, 1928).
Many former titanium mines near Jacksonville (Martens, 1928;
Yon and Oglesby, 1975) are now under urban land-cover. The
present center of the industry in Florida is the Trail Ridge
area in the northeast (Clay County) (Force, 1976). Types of
commercial mining other than titanium and phosphate exist in
Florida—limestone quarrying, clay mining, peat mining, and

129
sand/gravel mining, but these operate at a much smaller
spatial scale (Yon and Oglesby, 1975).
Special Land-Cover Conditions
Several special land cover conditions have been present
in Florida during the preparation of this research. These
have included both natural and artificial events. Natural
conditions included drought, freezes, and a hurricane.
Artificial conditions included wetland disturbance and exotic
forest invasion.
It should be noted in passing that the winter blizzard or
Century Storm of 13 March 1993, while costly in human life and
property damage from the Florida Big Bend northwards to most
of the southeastern United States, left little lasting
kilometer-scale impact in north Florida. This was because
natural and agricultural areas were quickly re-vegetated (the
results of such cold two weeks later might have been more
lasting in the natural areas) after the brief period of
damaging cold temperatures, and because most of the north
Florida wind damage was in the form of highly localized
tornado touchdowns. In non-touchdown areas, only one
component (laurel oak) of the diverse local forest and urban
tree community experienced a high rate of toppling.
Drought effect on swamp. Bay swamp and mixed swamp
within portions of the Suwannee river basin (Figure 16) were
under a drought condition during the span of AVHRR imagery
used in this research (winter of 1989 to spring of 1993) . The

130
HCMM data used in this research pre-dated this drought
condition (1979). These areas were compared respectively to
normal bay swamp and mixed swamp. The droughty mixed swamp
experienced wildfire two months (June 1993) after the spring
1993 image was taken.
Freeze damage effect on citrus orchard. An area of north
Florida citrus orchard on deep sand was destroyed by freeze
damage during the winters of 1983 and 1985 (Figure 17). This
area is now under an altered condition consisting of a meso-
scale mixture of (predominantly) abandoned land (wild grasses
and scrubby rootstock regrowth), with some sand pine
plantations and a few commercial vineyards of the deciduous
bunch and muscadine grapes (Halbrook, 1989). It was compared
to areas of normal citrus orchard on deep sand. The AVHRR
data used in this research post-dated this damaged condition;
the HCMM data pre-dated it.
Hurricane damage effect on urban center. The southern
tip of Florida experienced damage from Hurricane Andrew on 24-
25 August 1992. This hurricane lacked a major saltwater storm
surge; its effects were principally due to strong winds.
While natural and agricultural areas in the hurricane path
were quickly re-vegetated, the urban area took far longer to
recover. In the natural areas, forest trees were largely
defoliated, and some were toppled, but standing trees quickly
refoliated and subtropical growth quickly refilled any
temporary clearings (coastal mangrove swamps were an
exception); marsh, by its nature, suffered negligible lasting

131
wind-damage. In agricultural areas, most winter-vegetable
crops were not yet planted, although some laid-down plastic
mulch was obliterated; tropical fruit trees were largely
defoliated, and some were toppled, but standing trees guickly
refoliated and many toppled trees were righted; greenhouses
and shadehouses were obliterated. In urban areas, buildings
were largely obliterated and the trees defoliated and
(especially Ficus spp.) toppled, but toppled palms were
quickly righted, and lawns/shrubbery suffered few lasting
effects.
A heavily damaged urban center on sandy rockland
(Homestead/Leisure City) which was destroyed by the hurricane
(Figure 18) was compared to a normal urban center on sandy
rockland (Miami/Ft. Lauderdale). This damaged area consisted
of a kilometer-scale mixture of streets and lawns, with few
buildings. The spring AVHRR images dating from four months
(December 1992) and eight months (April 1993) after the
hurricane were used to investigate any seasonal components of
surface temperature change. The winter AVHRR images used in
this research, as well as the HCMM images, pre-dated the
hurricane damage.
Wetland disturbance effects. Two south Florida wetland
types were studied for the effects of artificial disturbance.
These included dwarf-cypress swamp and marsh.
An area of south Florida dwarf-cypress swamp has been
disturbed (since the 1950s) by the construction of a network
of roads for a large planned suburb (later suspended) on the

132
marly rockland. This area (Figure 16) was compared to normal
dwarf-cypress swamp.
Two areas of marsh in south Florida are located within
the EAA water-control system. The system of drainage canals
has existed in this part of the EAA since the 1920s (Izuno,
1989). The adjoining Holey Land/Rotenberger tracts, under
state management as Wildlife Management Areas, comprise the
less impacted of the two studied marsh areas. Water
Conservation Area 1 (WCA-1), under federal management as the
Loxahatchee National Wildlife Refuge, has been heavily
disturbed by altered hydrology related to back-pumping of EAA
drainage water (Izuno, 1989; Abtew and Khanal, 1994; Rutchey
and Vilcheck, 1994). The two disturbed marsh areas (Figure
16) were compared to normal marsh. Meso-scale disturbance of
wetlands by hydrologic alteration has also been of concern in
western Europe (Stein et al., 1991).
Exotic forest invasion. An area of exotic forest was
compared to normal flatwoods forest. The West Green Acres
exotic forest area (Figure 16) consists of former flatwoods
forest (with cypress domes) and abandoned agricultural land on
flatwoods sand, which has been completely overtaken by exotic
pest trees. The upper story includes the evergreen punk-tree
(Melaleuca quinquenervia Cav.) and Australian-pine (an
angiosperm that is not related to true pines). Understory
consists almost entirely of Brazilian pepper-tree, which is
not related to the cultivated red pepper (Capsicum spp.) or
black pepper (Piper spp.) plants.

133
These trees are all very fast-growing, exotic species
which have become naturalized pests in south Florida since the
1950s (Barrett, 1956; Elias, 1980; EPPC, 1990; FDNR, 1990;
Butts et al., 1991). The punk-tree was originally introduced
in the early 1900s in order to drain wetlands and also for
ornamental purposes (Barrett, 1956; EPPC, 1990) ; it is now
banned from deliberate planting in Florida. Australian-pine
was introduced in the 1890s as a timber and windbreak tree,
even though it topples easily (Barrett, 1956; Elias, 1980;
EPPC, 1990). Brazilian pepper-tree was introduced in the
1890s as an ornamental and windbreak tree (Barrett, 1956;
EPPC, 1990).
All three of these exotic pest-trees recover guickly from
mechanical and fire damage; punk-tree is not only fireproof
(flame-retardant bark, hence the name "punk"), it actually
releases seeds in response to fire (much like the native fire-
dependent pines) (EPPC, 1990). These species all tolerate
high and fluctuating water tables, and to a varying extent,
salinity. The leaf-litter of Australian-pine and Brazilian
pepper-tree contains substances which suppress the growth of
other vegetation species; Australian-pine has the additional
advantage of nitrogen-fixing microbes within its roots (EPPC,
1990). Punk-tree and Australian-pine often form pure, even-
aged stands. An area of approximately 3 million acres in
south Florida has become exotic-invaded forest; only a lack of
freeze-tolerance keeps exotic forest from extending its range
northward (EPPC, 1990). Mechanical removal (cutting and

134
pulling), followed by defoliant chemical application, is the
present countermeasure employed by various agencies;
biological countermeasures are still in the research stage
(EPPC, 1990).
Other areas of exotic forest in extreme south Florida
include many additional tree species (Meerow and Black, 1988b;
EPPC, 1990), such as the Asiatic colubrina (Colubrina asiatica
L.)/ jambolan (Svzvqium cumini L.), ear-tree (Enterolobium
cvclocaroum L.), toog (Bischoffia iavanica L.) , Eucalyptus
(Eucalyptus spp.), weeping fig (Ficus beniamina L.) , Cuban-
laurel fig (Ficus retusa L.)/ lofty fig (Ficus altissima L.)/
canistel, guava, strawberry guava (Psidium cattleianum L.),
papaya, rose-apple (Svzigium jambos L.), and sandbox-tree
(Hura crepitans L.). Asiatic colubrina was carried from the
Caribbean Islands to Florida by seeds floating on the sea
(EPPC, 1990). Several Eucalyptus species were introduced as
timber trees, and are still cultivated as such in commercial
plantations (FDF, 1988; USDA, 1989). Jambolan was introduced
as a fruit and ornamental tree (Barrett, 1956). The various
exotic figs, ear-tree, and toog were introduced as ornamentals
(Meerow and Black, 1988b). Canistel, guava, strawberry guava,
papaya, and rose-apple were introduced as fruit trees; the
guava and papaya are still cultivated as such in commercial
groves. Sandbox-tree, a south American timber species
introduced as a curiosity, has been banned from deliberate
planting in Dade County, due to the hazard associated with its
explosive pods of sharp poisonous seeds (Barrett, 1956).

135
Exotic vegetation invasion is a problem that is not
unique to Florida; it occurs anywhere that introduced exotic
species, in the absence of natural controls, aggressively
displace native species. North American examples include
kudzu vine (Pueraria lobata Willd.) and Chinese privet
(Liaustrum sinense Lour.) in the southern United States,
multiflora rose (Rosa multiflora Thunb.) in the midwestern
United States, and tamarisk (Tamarix aallica L.) and
tumbleweed (Salsola kali tenuifolia L.) in the western United
States. International examples include prickly-pear cactus
(Opuntia spp.) in Australia, blackberry (Rubus spp.) in New
Zealand, cinnamon (Cinnamomum zevlanicum L.) in southeast
Asia, and cardoon (Cvnara cardunculus L.) in the South
American pampas (Martin, 1972; Akin, 1991).

RESULTS AND DISCUSSION
Analyses of AVHRR-based afternoon surface temperature
(AST), night surface temperature (NST), and diurnal surface
temperature variation (DSTV) patterns were performed using the
GIS both across and within the three macro-climate zones—
Panhandle (P) , North (N) , and South (S) . All temperatures
reported here are in degrees C, rounded to the nearest 0.2 C.
Values of differences in means below 0.2 C were considered
insignificant (below precision of temperature data in the
GIS); those below 1.0 C were considered insubstantial (less
than the uncertainty of surface temperature due to emissivity
knowledge to the nearest 0.01).
Analyses Across Macroclimate Zones
Across-zone analyses were performed for the natural land-
cover types to assess the macroclimate component of across-
zone bias. Results are shown in Tables 1, 2, 3, 4, 5, and 6
for spring AST, spring NST, spring DSTV, winter AST, winter
NST, and winter DSTV. It should be noted that not all natural
land-cover types were present in all three zones.
Macroclimate influence produced substantial differences in
these six surface temperature patterns between the three
Florida macroclimate zones for most of the natural land-cover
types.
136

137
Table 1. Spring afternoon surface temperature across-zone
differences
among
natural
land-cover types.
Natural
cover type
Zones crossed
Difference in
AST means (C)a
Scrub, evergreen
N
to
S
1.6**
Scrub, mixed
P
to
N
1.2**
Upland mixed
P
to
N
-0.4**
forest
Flatwoods forest
P
to
N
*
*
•
o
N
to
S
1.0**
P
to
S
1.4**
Rockland hammock
N
to
s
3.8**
Coastal hammock
P
to
N
1.0**
N
to
s
0.8**
P
to
s
1.8**
Hardwood swamp,
N
to
s
-0.4**
evergreen
Hardwood swamp,
P
to
N
3.0**
deciduous
Cypress swamp
N
to
S
-0.6**
Bay swamp
P
to
N
o
•
o
Mixed swamp
N
to
S
0.0

138
Table 1—continued.
Natural
cover type
Zones crossed
Difference in
AST means (C)a
Marsh
P
to
N
0.2
N
to
S
0.2**
P
to
S
0.6**
Saltmarsh
P
to
N
0.4**
N
to
S
4.6**
P
to
S
4.8**
a difference
is positive
if
mean of
zone at right is larger than
mean of zone at left,
significant at a = 0.05.
significant at a = 0.01.

139
Table 2. Spring nighttime surface temperature across-zone
differences
among
natural
land-cover types.
Natural
cover type
Zones crossed
Difference in
NST means (C)a
Scrub, evergreen
N
to
S
0.0
Scrub, mixed
P
to
N
0.2**
Upland mixed
P
to
N
0.4**
forest
Flatwoods forest
P
to
N
-0.4**
N
to
S
4.2**
P
to
S
3.6**
Rockland hammock
N
to
S
-0.2*
Coastal hammock
P
to
N
-1.6**
N
to
S
2.0**
P
to
S
0.4*
Hardwood swamp,
N
to
s
2.2**
evergreen
Hardwood swamp,
P
to
N
-1.8**
deciduous
Cypress swamp
N
to
S
1.8**
Bay swamp
P
to
N
2.6**
Mixed swamp
N
to
S
1.0**

140
Table 2—continued.
Natural
cover type
Zones crossed
Difference in
NST means (C)a
Marsh
P
to
N
-0.6
N
to
S
4.0**
P
to
S
3.4**
Saltmarsh
P
to
N
•
O
N
to
S
0.8**
P
to
S
1.0**
a difference
is positive
if
mean of
zone at right is larger than
mean of zone at left,
significant at a = 0.05.
significant at a = 0.01.

141
Table 3. Spring diurnal surface temperature variation across-
zone differences among natural land-cover types.
Natural
cover type
Zones crossed
Difference
DSTV means
in
(C)a
Scrub, evergreen
N
to
s
1.8**
Scrub, mixed
P
to
N
0.8**
Upland mixed
forest
P
to
N
*
*
00
•
o
Flatwoods forest
P
to
N
1.0**
N
to
S
-3.2**
P
to
S
-2.2**
Rockland hammock
N
to
S
3.8**
Coastal hammock
P
to
N
2.6**
N
to
S
-1.2**
P
to
S
1.2**
Hardwood swamp,
evergreen
N
to
S
-2.6**
Hardwood swamp,
deciduous
P
to
N
4.8**
Cypress swamp
N
to
S
-2.4**
Bay swamp
P
to
N
-2.6**
Mixed swamp
N
to
S
-1.0“

142
Table 3—continued.
Natural Difference in
cover type Zones crossed DSTV means (C)a
Marsh
P
to
N
CO
•
o
N
to
S
-3.6“
P
to
S
-3.0“
Saltmarsh
P
to
N
o
•
to
N
to
S
3.6“
P
to
S
3.8“
a difference
is positive
if
mean of
zone at right is larger than
mean of zone at left,
significant at a = 0.05.
significant at a = 0.01.

143
Table 4. Winter afternoon surface temperature across-zone
differences
among
natural
land-cover types.
Natural
cover type
Zones crossed
Difference in
AST means (C)a
Scrub, evergreen
N
to
S
o
•
00
*
*
Scrub, mixed
P
to
N
0.8**
Upland mixed
forest
P
to
N
0.2**
Flatwoods forest
P
to
N
-0.4**
Coastal hammock
P
to
N
o
•
00
*
*
Hardwood swamp,
deciduous
P
to
N
2.2**
Bay swamp
P
to
N
1.2**
Marsh
P
to
N
1.6**
N
to
S
3.2**
Saltmarsh
P
to
N
0.6**
a difference is positive if mean of zone at right is larger than
mean of zone at left. Comparisons of zones P and N were
performed using 1992 image data; those of zones N and S were
performed using 1989 image data.
significant at a = 0.05.
significant at a = 0.01.

144
Table 5. Winter nighttime surface temperature across-zone
differences among natural land-cover types.
Natural
cover type
Zones crossed
Difference in
NST means (C)a
Scrub, evergreen
N
to
s
0.0
Scrub, mixed
P
to
N
3.6**
Upland mixed
forest
P
to
N
1.4**
Flatwoods forest
P
to
N
-0.4**
Coastal hammock
P
to
N
-2.2**
Hardwood swamp,
deciduous
P
to
N
-0.6**
Bay swamp
P
to
N
2.8**
Marsh
P
to
N
2.8**
N
to
S
5.6**
Saltmarsh
P
to
N
1.2**
a difference is positive
if
mean of
zone at right is larger than
mean of zone at left. Comparisons of zones P and N were
performed using 1992 image data; those of zones N and S were
performed using 1989 image data.
significant at a = 0.05.
significant at a = 0.01.

145
Table 6. Winter diurnal surface temperature variation across-
zone differences among natural land-cover types.
Natural Difference in
cover type Zones crossed DSTV means (C)a
Scrub, evergreen
N
to
s
1.0“
Scrub, mixed
P
to
N
-2.8“
Upland mixed
P
to
N
-1.0“
forest
Flatwoods forest
P
to
N
0.0
Coastal hammock
P
to
N
3.2“
Hardwood swamp,
P
to
N
2.8“
deciduous
Bay swamp
P
to
N
-1.6“
Marsh
P
to
N
-1.2“
N
to
S
-2.4“
Saltmarsh
P
to
N
-0.6“
a difference is positive
if
mean of
zone at right is larger than
mean of zone at left. Comparisons of zones P and N were
performed using 1992 image data; those of zones N and S were
performed using 1989 image data.
significant at a = 0.05.
significant at a = 0.01.

146
The AST and NST values were generally higher with
distance south in both spring and winter, as expected. The
largest across-zone differences were between zones P and S, as
expected. AST across-zone differences ranged to 3.2 C in
winter, and to 4.8 C in spring. NST across-zone differences
ranged to 5.6 C in winter, and to 4.2 C in spring.
The DSTV across-zone differences were less easy to
predict, since they serve as an indication of differences in
relative moisture conditions for a given natural land-cover
from zone to zone. DSTV across-zone differences ranged to 3.2
C in winter, and to 4.8 C in spring.
These results indicate that any comparisons of surface
temperature pattern differences across macroclimate zones
would have to be made with due consideration of the
macroclimate component of zone bias. For example, the thermal
pattern of a flatwoods forest in zone P cannot be compared
directly to that of a flatwoods forest in zone S, because of
differences in soil moisture (from differences in antecedent
precipitation) and in near-surface air temperature, both of
which are forcing factors for surface temperature (see
equation 2) . Agricultural and urban land-cover types were not
evaluated for across-zone differences, because of their
artificial control of soil moisture levels through drainage
and irrigation. However, they can be reasonably assumed to
have across-zone differences for a given land-cover and soil
type. These across-zone differences would be due to temporal
differences in the crop growing seasons for agricultural land-

147
cover, and in the irrigation of lawns and ornamental
vegetation for urban land-cover.
Analyses Within Macroclimate Zones
Within-zone analyses were performed to assess the surface
temperature pattern differences between different combinations
of land-cover and soil type, and the surface temperature
effects of land-cover changes and special conditions. Results
are discussed below.
Spring Afternoon Natural Land-Cover Thermal Patterns
Zone P. Within zone P in spring, eight natural land
cover types were present. Listed in order of decreasing mean
AST (C) , these included mixed scrub (27.4), upland mixed
forest (26.6), flatwoods forest (25.8), marsh (25.6), bay
swamp (24.0), deciduous hardwood swamp (23.2), coastal hammock
(22.8), and saltmarsh (22.8). Differences in mean AST among
zone P natural land cover types are given in Table 7; most
were significant, and many were substantial. The highest
difference (4.6 C) was between mixed scrub and saltmarsh. The
overall trend was a higher AST for the drier natural land-
cover types (mixed scrub, upland mixed forest), and lower AST
for the wetter types (marshes and swamps).
Zone N. Within zone N in spring, thirteen natural land
cover types were present. Listed in order of decreasing mean
AST (C) , these included mixed scrub (28.4), evergreen scrub
(27.8), flatwoods forest (26.4), upland mixed forest (26.4),

148
Table 7. Spring afternoon surface temperature differences
among natural land-cover types in panhandle zone.
Natural
cover
type
Difference in AST (C) mean
natural cover type8
for
SCM
FMX
FLW
MRF
SBY
SHD
HCO
FMX
0.6**
FLW
1.4**
0.8**
MRF
1.6**
1.0**
CM
•
o
SBY
3.4**
2.8**
2.0**
1.6**
SHD
4.0**
3.4**
2.6**
2.4**
0.6**
HCO
4.4**
3.8**
3.0‘*
2.8**
1.2**
0.4**
MRS
4.6**
4.0**
3.2**
2.8**
1.2**
0.6**
o
•
o
a Positive number indicates increase in spring afternoon surface
temperature mean for the natural cover type compared to that
of the left-column natural cover type. The natural cover
types are denoted as follows: SCM = mixed scrub, FMX = upland
mixed forest, FLW = flatwoods forest, MRF = marsh, SBY = bay
swamp, SHD = deciduous hardwood swamp, HCO = coastal hammock,
and MRS = saltmarsh. These natural cover types are further
described in the text.
Significant at a = 0.05.
Significant at a = 0.01.

149
deciduous hardwood swamp (26.2), cypress swamp (26.2), mixed
swamp (25.8), marsh (25.8), evergreen hardwood swamp (25.4),
rockland hammock (24.0), bay swamp (23.8), coastal hammock
(23.8), and saltmarsh (23.0). Differences in mean AST among
zone N natural land cover types are given in Table 8; most
were significant, and many were substantial. Among these
natural land cover types, the highest difference (5.4 C) in
AST was between mixed scrub and saltmarsh. The overall trend
was a higher AST for the drier natural land-cover types (mixed
scrub, evergreen scrub), and lower AST for the wetter types
(marshes and swamps). Differences between evergreen and
deciduous subtypes of scrub and hardwood swamp were
significant, but not substantial—as expected from a spring
date (deciduous subtypes in full foliage).
Zone S. Within zone S in spring, fourteen natural land
cover types were present. Listed in order of decreasing mean
AST (C), these included evergreen scrub (29.6), wet-prairie
(28.4), rockland hammock (27.6), brackish marsh (27.6),
flatwoods forest (27.4), dwarf cypress swamp (26.6), deciduous
shrubby marsh (26.4), marsh (26.2), mixed swamp (25.8),
cypress swamp (25.6), evergreen shrubby marsh (25.6), mangrove
swamp (25.4), evergreen hardwood swamp (25.0), and coastal
hammock (24.6). Differences in mean AST among zone S natural
land cover types are given in Table 9; most were significant,
and many were substantial. Among these natural land cover
types, the highest difference (5.0 C) in AST was between
evergreen scrub and coastal hammock. The overall trend was a

Table 8. Spring afternoon surface temperature differences among natural land-cover
types in north zone.
Natural
Difference
in AST
(C)
mean for natural cover type®
cover
type
SCM
SCE
FLW
FMX
SHD
SCY
SMX
MRF
SHE
HRO SBY
HCO
SCE
0.6**
FLW
2.2**
1.4**
FMX
2.1**
1.6**
0.0
SHD
2.2**
1.6**
0.0
0.0
SCY
2.4**
1.8**
0.2**
0.2*
0.2
SMX
2.6**
2.0**
0.4**
0.4**
0.4**
0.2**
MRF
2.6**
2.0**
0.6**
0.4**
0.4**
0.2**
0.0
SHE
3.0**
2.4**
1.0**
1.0**
0.8**
0.8**
o
•
*
*
0.4**
HRO
4.6**
4.0**
2.4**
2.4**
2.4**
2.2**
2.0**
2.0**
1.4**
SBY
4.6**
4.0**
2.4**
2.4**
2.4**
2.2**
2.0**
*
*
o
•
CM
1.6**
0.0
HCO
4.6**
4.0**
2.6**
2.6**
2.4**
2.4**
2.0**
2.0**
1.6**
0.2 0.0
MRS
5.4**
4.8‘*
3.2**
3.2**
3.2**
3.0**
2.8**
2.8**
2.4**
0.8** 0.8**
0.8**
a Positive number indicates increase in spring afternoon surface temperature mean for the
natural cover type compared to that of the left-column natural cover type. The natural
cover types are denoted as follows: SCM = mixed scrub, SCE = evergreen scrub, FLW =
flatwoods forest, FMX = upland mixed forest, SHD = deciduous hardwood swamp, SCY =
150

Table 8—continued.
cypress swamp, SMX = mixed swamp, MRF = marsh, SHE = evergreen hardwood swamp, HRO =
rockland hammock, SBY = bay swamp, HCO = coastal hammock, and MRS = saltmarsh. These
natural cover types are further described in the text.
Significant at a = 0.05.
Significant at a = 0.01.
151

Table 9. Spring afternoon surface temperature differences among natural land-cover
types in south zone.
Nat.
Difference
in AST (C)
mean
for natural
cover
type8
cov.
type
SCE
MRP
HRO
MRS
FLW
SCD
MSD
MRF
SMX
SCY
MSE
SMG
SHE
MRP
1.2**
HRO
1.8**
0.6“
MRS
2.0**
0.8“
0.0
FLW
2.2**
1.0“
0.4*
0.2“
SCD
3.0**
1.8“
1.2“
1.2“
0.8**
MSD
3.0**
1.8“
1.2“
1.2“
0.8“
0.0
MRF
3.4**
2.2“
1.6“
1.4“
1.2“
0.4“
0.4“
SMX
3.6**
2.4“
1.8“
1.8“
1.4“
0.6“
0.6“
0.4**
SCY
4.0**
2.8“
2.0**
2.0“
1.8“
1.0“
0.8“
0.6“
0.2“
MSE
4.0**
2.8“
2.2**
2.2“
1.8**
1.0“
1.0“
0.6“
0.4“
0.0
SMG
4.0**
2.8“
2.2**
2.2“
1.8“
1.0“
1.0“
0.6“
0.4“
0.2
0.0
SHE
4.4“
3.2“
2.6“
2.6“
2.2“
1.4“
1.4“
1.0“
0.8“
0.4**
0.4“
0.4“
HCO
5.0**
3.8“
3.2“
3.0“
2.8**
to
•
o
*
*
2.0“
1.6**
1.2“
1.0“
1.0“
1.0“
0.6“
Positive number indicates increase in spring afternoon surface temperature mean for the
natural cover type compared to that of the left-column natural cover type. The natural
152

Table 9—continued.
cover types are denoted as follows: SCE = evergreen scrub, MRP = wet-prairie, HRO =
rockland hammock, MRS = saltmarsh, FLW = flatwoods forest, SCD = dwarf-cypress swamp,
MSD = deciduous shrubby marsh, MRF = marsh, SMX = mixed swamp, SCY = cypress swamp, MSE
= evergreen shrubby marsh, SMG = mangrove swamp, SHE = evergreen hardwood swamp, and HCO
= coastal hammock. These natural cover types are further described in the text.
Significant at a = 0.05.
Significant at a = 0.01.
153

154
higher AST for the drier natural land-cover types (evergreen
scrub), and lower AST for the wetter types (marshes and
swamps). The relatively high AST of wet-prairie and brackish
marsh (both on marly rockland soil) reflects their April dry-
season hydrology; the marsh subtype (on muck soil) remains wet
throughout the year. Deciduous shrubby marsh had higher AST
(by 1.0 C) than did evergreen shrubby marsh, which can be
attributed to the more open canopy of deciduous shrubby marsh;
as will be discussed later, there was no substantial
difference in DSTV (relative soil moisture) for these shrubby
marsh subtypes. Dwarf-cypress swamp had higher AST than did
cypress swamp (by 1.0 C) , which can be attributed to the
combination of the more open canopy of the dwarf-cypress swamp
and its marly rockland soil type; as will be discussed later,
there was no substantial difference in DSTV (relative soil
moisture) for these swamp subtypes.
Spring Afternoon Agricultural Land-Cover Thermal Patterns
Zone P. Within zone P, there were two types of
agriculture distributed on three soil types. Listed in order
of decreasing mean AST (C) , these agricultural/soil type
combinations included mixed agriculture on deep sand (29.2),
mixed agriculture on upland loamy sand (27.8), and
pasture/range on flatwoods sand (25.0). Differences in mean
AST among zone P agricultural/soil type combinations are given
in Table 10; all were both significant and substantial. The
highest difference in AST was between mixed agriculture on

155
Table 10. Spring afternoon surface temperature differences
among agricultural land-cover types in panhandle zone.
Difference in
agricultural
AST (C) mean for
cover/soil type3
Agricultural
cover/soil type
AXS
AXU
AXU
1.6**
APF
4.4**
2.8“
a Positive number indicates increase in spring afternoon surface
temperature mean for the agricultural cover/soil type compared
to that of the left-column agricultural cover/soil type. The
agricultural cover/soil types are denoted as follows: AXS =
mixed agriculture on deep sand, AXU = mixed agriculture on
upland loamy sand, and APF = pasture/range on flatwoods sand.
These cover/soil types are further described in the text.
Significant at a = 0.05.
Significant at a = 0.01.

156
deep sand and pasture/range on flatwoods sand (4.4 C). Mixed
agriculture on deep sand had higher AST (by 1.6 C) than did
that on upland loamy sand.
Zone N. Within zone N in spring, there were five
agriculture types on up to five different soil types. Listed
in order of decreasing mean AST (C), these combinations
included mixed agriculture on deep sand (29.4) , citrus orchard
on deep sand (29.4), citrus orchard on upland loamy sand
(29.4), mixed agriculture on upland loamy sand (29.2), row-
crops on muck (28.8), mixed agriculture on sandy rockland
(28.8), citrus orchard on flatwoods sand (27.8), pasture/sod
on muck (27.4), pasture/range on flatwoods sand (27.0), and
mixed agriculture on flatwoods sand (26.6). Differences in
mean AST among zone N agricultural/soil type combinations are
given in Table 11; most were significant, and many were
substantial. Among these agricultural land cover types, the
highest difference in AST (2.8 C) was between mixed
agriculture on deep sand and mixed agriculture on flatwoods
sand.
Soil type impacts on agricultural type temperatures were
compared. Among the mixed agriculture combinations, that on
flatwoods sand had lower AST than that on other soil types.
The differences between mixed agriculture on deep sand, upland
loamy sand, and sandy rockland were significant, but not
substantial. Among the pasture combinations, the difference
in AST between pasture/sod on muck and pasture/range on
flatwoods sand was significant, but not substantial. Among

157
Table 11. Spring afternoon surface temperature differences
among agricultural land-cover types in north zone.
Difference in AST (C) mean for
Agrie.
cover/
soil
type
agricultural cover/soil type
a
AXS
ACS
ACU
AXU
ARM
AXR
ACF
ASM APF
ACS
0.0
ACU
0.2
0.0
AXU
0.2**
0.0
0.0
ARM
0.6**
0.2*
0.4*
0.4
AXR
0.8**
0.6**
0.6**
0.6**
0.2
ACF
1.6**
1.6**
1.6**
1.4**
1.2**
1.0“
ASM
2.0**
1.8**
1.8**
1.8**
1.4**
1.2“
0.2“
APF
2.4**
2.2**
2.2**
2.2**
1.8“
1.6“
0.6“
0.4“
AXF
2.8**
2.8**
2.8**
2.6**
2.4“
2.2“
1.2“
0.8“ 0.4“
a Positive number indicates increase in spring afternoon surface
temperature mean for the agricultural cover/soil type compared
to that of the left-column agricultural cover/soil type. The
agricultural cover/soil types are denoted as follows: AXS =
mixed agriculture on deep sand, ACS = citrus orchard on deep
sand, ACU = citrus orchard on upland loamy sand, AXU = mixed
agriculture on upland loamy sand, ARM = row-crops on muck, AXR
= mixed agriculture on sandy rockland, ACF = citrus orchard on
flatwoods sand, ASM = pasture/sod on muck, APF = pasture/range
on flatwoods sand, and AXF = mixed agriculture on flatwoods
sand. These cover/soil types are further described in the
text.
Significant at a = 0.05.
Significant at a = 0.01.

158
the citrus orchard combinations, that on flatwoods sand had
lower AST than did that on either deep sand or upland loamy
sand (by 1.6 C in both cases). There was no significant
difference in AST between citrus orchard on deep sand and that
on upland loamy sand. In zone N in spring, flatwoods sand
appears to lower the AST of a given agricultural type, while
other soil types appear to have no substantial effect on the
AST of mixed agriculture, pasture, or citrus orchard.
Agriculture type impacts on soil type temperatures were
compared. Among the deep sand combinations, there was no
significant difference in mean AST between mixed agriculture
and citrus orchard. Among the upland loamy sand combinations,
there was no significant difference between mixed agriculture
and citrus orchard. Among the flatwoods sand combinations,
citrus orchard had higher AST than did mixed agriculture,
while the difference between pasture/range and either citrus
orchard or mixed agriculture was significant, but not
substantial. Among the muck combinations, row-crops had
higher AST (by 1.4) than did pasture/sod. In zone N in
spring, differences in AST among certain agricultural types
were substantial on flatwoods sand and on muck, but not on
deep sand or upland loamy sand.
Zone S. Within zone S, there were five agriculture types
on up to six different soil types. Listed in order of
decreasing mean AST (C) , these combinations included mixed
agriculture on muck (32.0), row-crops on flatwoods sand
(30.8), mixed agriculture on sandy rockland (30.2), citrus

159
orchard on deep sand (30.0), pasture/sod on muck (29.0), mixed
agriculture on flatwoods sand (28.8), mixed agriculture on
marly rockland (28.8), pasture/range on sandy rockland (28.6),
citrus orchard on flatwoods sand (28.2), pasture/range on
flatwoods sand (27.8), row-crops on marly rockland (27.0), and
row-crops on coastal sand (26.0). Differences in mean AST
among zone S agricultural/soil type combinations are given in
Table 12. Among these agricultural land cover types, the
highest difference in AST (6.0 C) was between mixed
agriculture on muck and row-crops on coastal sand.
Soil type impacts on agricultural type temperatures were
compared. Among the mixed agriculture combinations, the
highest difference in AST (3.2 C) was between that on muck and
that on marly rockland. There was no significant difference
in AST between that on flatwoods sand and that on marly
rockland. Differences between other mixed agriculture
combinations were both significant and substantial. Among the
row-crop combinations, the highest difference in mean AST (4.8
C) was between that on flatwoods sand and that on coastal
sand. Differences between other row-crop combinations were
both significant and substantial. Among the pasture
combinations, the highest difference in AST (1.2 C) was
between pasture/sod on muck and pasture/range on flatwoods
sand. The differences in AST between pasture/sod on muck and
pasture/range on sandy rockland, and between pasture/range on
sandy rockland and that on flatwoods sand, were significant,
but not substantial. Among the citrus orchard combinations,

Table 12. Spring afternoon surface temperature differences among agricultural land-cover
types in south zone.
Agrie.
cover/
soil
type
Difference in
AST (C) mean for .
agricultural
cover/soil
type®
AXM
ARF
AXR
ACS
ASM
AXF
AXL
APR
ACF APF
ARL
ARF
1.2**
AXR
1.6**
0.4*
ACS
2.0**
0.8**
0.2
ASM
2.8**
1.8**
1.2**
1.0“
AXF
3.2**
2.0**
1.4**
1.2“
0.2
AXL
3.2**
2.0**
1.6**
1.4“
0.4*
0.2
APR
3.4**
2.2**
1.6**
1.4“
0.4*
0.2
0.0
ACF
3.8**
2.6**
2.2**
1.8“
1.0“
0.8“
0.6“
0.6*
APF
4.2**
3.0**
2.4“
2.2“
1.2“
1.0“
0.8**
0.8“
0.2*
ARL
5.0**
3.8**
3.2**
3.0“
2.0“
1.8“
1.6**
1.6“
1.0“ 0.8“
ARC
6.0**
4.8**
4.4“
4.0“
3.2“
3.0“
2.8“
2.6“
2.2“ 1.8**
1.2“
a Positive number indicates increase in spring afternoon surface temperature mean for the
agricultural cover/soil type compared to that of the left-column agricultural cover/soil
type. The agricultural cover/soil types are denoted as follows: AXM = mixed agriculture
on muck, ARF = row-crops on flatwoods sand, AXR = mixed agriculture on sandy rockland,
ACS = citrus orchard on deep sand, ASM = pasture/sod on muck, AXF = mixed agriculture
on flatwoods sand, AXL = mixed agriculture on marly rockland, APR = pasture/range on
160

Table 12—continued.
sandy rockland, ACF = citrus orchard on flatwoods sand, APF = pasture/range on flatwoods
sand, ARL = row-crops on marly rockland, and ARC = row-crops on coastal sand. These
cover/soil types are further described in the text.
Significant at a = 0.05.
Significant at a =
0.01.

162
that on deep sand had higher AST (by 1.8 C) than did that on
flatwoods sand. This was expected, due to the excessively-
drained character of the deep sand; as will be discussed
later, the DSTV indicated a relatively drier condition for the
deep sand citrus orchard. In zone S in spring, the AST of a
given agricultural type appears to be increased the most by
muck, followed respectively by sandy rockland, deep sand,
flatwoods sand, and marly rockland.
Agriculture type impacts on soil type temperatures were
compared. Among