Index

Accessory information, satellite imagery,68-69, 73-78

Aerial photography, Aero-neg film, 133black and white film, 132-33black and white infra-red film, 132-133colour film, 133colour infra-red film, 134, 181forest classification, 67mapping, 34, 61, 131sampling floristic composition, 54small scale, 137-138vegetation boundaries, 35vegetation classification, 12vegetation identification, 131see a/so Remote sensing

Agricultural land, change of use, 6deterioration, 101-102

Agricultural species, identification, 149Albedo changes, monitoring arid and

semi-arid regions, 187Alfisols, 94, 95, 97, 98, 99Alpine regions, soil carboR content, 113Allophane, 116Amazonia, forest conversion, 103Apollo-9, photographic studies, 138, 193Architectural classifications, 52Area deliniation, SAR, 176Area identification, LANDSAT, 232-234Areal-geographic-floristic classification,

25,27Argentine, crop production monitoring,

211Arid regions, vegetation monitoring, 187Aridosols, 94, 95, 97, 98, 99Asia, forest conversion, 103-104

Association types,24

Atmospheric haze, remote sensing, 182penetration by colour infra-red film,

134Atmospheric transmittance bands, 165Australia, mapping tropical rain forests,

62-63crop production monitoring, 211

Beard's formation series approach,vegetation classification, 6

Bioclimatic analysis, vegetation zones, 42Bioclimatic classification systems, 24-25

tropical vegetation, 58-59Bioclimatic mapping, 36-39, 63Biogeoclimatic zonation scheme, 39Biogeocoenotic approach, potential

vegetation mapping, 36Biogeographic separation, Ellenberg

scheme, 42Biotic impoverishment, 6Biotic residue decay, carbon dioxide

release, 4Boreal forest, field studies, 121

soil carbon content, 112, 113, 115Brazil, crop production monitoring, 211British Columbia, potential vegetation

mapping, 39-40Braun-Blanquet association scheme, 26,

27,54Brockmann-Jerosch map, 29, 30, 31, 39

Canada, wheat production monitoring,192,211

Canopy texture, radar signal, 15214Carbon, atomic weapon testing, 118Carbon storage and release, 5-8

241

242 The role of terrestrial vegetation in the global carbon cycle

Carbon dioxide, atmospheric, 3-5, 7, 11,14,111-123,229

Carbon pools, 4-5see also Soil carbon pools

Carbon release, deforestation, 228-232Cell turgor, identification by infra-red

film, 181Chernozem soils, carbon content, 114China, crop production monitoring, 211Chlorophyll absorption bands, 122,

140-141, 162-164Classification error estimator, 167, 169Classification systems, 10,21-79

limitations, 25-28, 223-224see also Computer classification;

Multispectral classificationClimate, vegetation mapping, 69-71Climate-diagrams, Hawaii, 70

potential vegetation mapping, 38Climatic changes, effect on atmospheric

carbon dioxide, 7Cloud cover, LANDSAT systems, 149,

175, 215radar systems, 153-154

Cluster analysis technique, seeDendrogram cluster data analysis

Clustering technique, computer systemtraining, 145

Coastal regions, SEASATSAR data, 176Coastal zone scanner, 162Commercial purposes, forest classifi-

cation, 67Committee on Remote Sensing for

Agricultural Purposes, 193Computer-aided analysis, MSS data, 139,

144-148, 153Computer classification, multispectral

scanner data, 144, 147-148Cover types, spectral responses, 146-147Crop growth stages, evaluation of

LACIE models, 207identification, 184, 192, 197

Crop land, soil carbon content, 97, 98soil orders, 95

Crop production monitoring, 191-217Crop stress, detection by colour infra-red

aerial photography, 135-136, 181detection by LANSAT systems, 204detection by radar systems, 152see also Vegetation stress

Crop yield, in relation to weather, 193models, 186, 188, 206-207, 215

Crops, identification by SLAR, 152Cultivation, soil organic matter loss, 116Cultivation practices, remote sensing,

187-188

Dansereau's profile diagram method,vegetation mapping, 44,52,61,62

Data accuracy, satellite imagery, 173Data correlation, 162, 173-174, 176-177Data delay, 162Data processing, 144-145, 195Data reformatting, 144Data sampling, 162-177Deciduous species, identification with

colour infra-red photography, 135Deforestation, see Forest clearanceDendrogram cluster, data analysis, 22,

54, 56-57Desert, soil carbon content, 113Direct gradient analysis, classification of

tropical rain forest, 64Discrimination process, remote sensing,

182Disturbance, effect on soil carbon

content, 224-226effect on vegetation carbon content,

224-226Disturbed land areas, 122Dominance types, forest classification,

24,28,67Dominant growth forms, 23Drought conditions, LANDSAT data,

201Dynamic-floristic classification, 25Dystic histosol, 114

Earth Resources Technology Satellite, 67,193

Ecological land classification, 36Ecological maps, 74Ecological series approach, potential

vegetation, 36Economic viability, repetitive remote

sensing, 191, 195Ecosystem types, distribution of soil

carbon pool, 113Ecosystems, 41, 42

remotely sensed imagery, 183Ellenberg's classification of world

ecosystems, 35, 36, 41, 52, 59Enhancement techniques, LANDSAT

data analysis, 144

Index

Entisols, 94, 95, 97, 98, 99Environmental classifications, 25, 27Environmental criteria, 24-25Environmental gradient analysis, 26, 27,

63~5EROS Data centre, 143Erosion, soil organic matter loss, 116,

117Europe, dominant tree species, 28

vegetation classification, 27

Feature colour, 182Fertilizer, remote sensing, 187-188Field surveys, role in remote sensing, 182Fire susceptibility, 176Floristic association system, 27Floristic criteria, 23Floristic dominance-type classifications,

28Floristic-structural classifications, 26-27Food and Agriculture Organization, 121

soil map, 94, 105Forbs, identification with colour infra-red

photography, 135Forest acreage, CAAT estimates, 148Forest area measurement, LANDSAT

systems, 149, 161Forest biomass, estimation, 228-229Forest classification, non-technical

classifications, 67Forest clearance, 102-104,223, 226

monitoring, 78radar imagery, 153remotely sensed data, 144, 149,

230-238soil carbon loss, 4-8, 104-107, 116,

228-231,233Forest damage, assessment by satellite,

67Forest fire, forest destruction, 103Forest floor disturbance, oxidation of

soil organic matter, 117, 229Forest harvesting, see Forest clearanceForest land, soil orders, 95Forest mapping, LANDSAT systems,

161Forest re-establishment, 4, 223Forest species, identification and

mapping, 149Formation systems, 23,26Fosberg's classification, 46,54,61,62,66Fossil fuels, carbon dioxide release, 3, 4

243

Fuel requirements, forest destruction, 103

Gaussen's regional landscape system, 42,59

Geographic criteria, 25Global crop production, LACIE

experiment, 191-217Global model, carbon cycle, 224-226Global temperature, effect on global

carbon cycle, 123Global sampling plan, 232Global vegetation monitoring by

LANDSAT, 153Global wheat production, monitoring,

196Gradient analysis, potential vegetation,

36Grain, identification problems in LACIE,

197Grassland, soil orders, 95

soil carbon content, 97, 98Grasses, identification with colour

infra-red photography, 135Grazing land, expansion, 6Green vegetation, spectral reflectance

characteristics, 140-142, 145Grid cell size, remote sensing, 186Ground observations, correlation with

remotely sensed data, 181-188Groundwater, transfer of soil carbon,

117-118

Habitat-type mapping, potentialvegetation, 36

Hawaii, carbon dioxide measurement,3-5

climate diagram, 70large scale maps, 34layer diagrams of tropical montane

forest, 48profile diagram of tropical montane

forest, 48, 50topographic map, 75vegetation and soil mapping, 32-33,

52-53,62,71-73Heirarchical approach, classification of

vegetation architecture, 45Histosols, 94, 95, 97, 98, 99, 105Hodridge's life zone mapping method,

37,58Hubbard Brook ExperimentalForest,

118

244 The role of terrestrial vegetation in the global carbon cycle

Hueck, mapping vegetationof SouthAmerica, 40

Hueck and Siebert, vegetation map ofSouth America, 30-32, 36, 64

vegetation map of Venezuela, 40Human roles, Ellenberg's classification

scheme, 41Humus, 96, 115

IBP, see International BiologicalProgram

Ice structure, effect on return radarsignal, 177

Identification process, remote sensing,182

India, crop production monitoring, 211potential vegetation mapping, 42

Indirect gradient analysis, 26Inceptisols, 94, 95, 97, 98, 99Industry, role in LACIE, 194Information tabulation and display, 147Instrument accuracy, remote sensing, 182Insect infestations, detection by colour

infra-red aerial photography, 136Intensity resolution, 162International Biological Program, 26, 46,

112, 114International Committee for Vegetation

Mapping, 40

K-band radar, 152Krajina, British Columbia map, 36,

29-40, 59Kuchler, vegetation map of United

States, 30, 32, 36vegetation map of Kansas, 32

Kuchler's formula, classification ofvegetation architecture, 45-46, 61, 62

L-band radar, 152, 176LACIE, see Large Area Crop Inventory

ExperimentLand cultivation, 119-121Land use, non-agricultural, 101Land use changes, 100

LANDSAT data, 149soil carbon content, 97see also Forest clearance

Land use types, soil orders, 95LANDSAT-1,138LANDSAT-2, LACIE, 197

LANDSAT-D, 174, 178thematic mapper, 174-175, 184-185

LANDSAT multispectral scanner system,139-150,171,183,186,197,221-222

capability testing, 191, 195correlation with radar data, 176crop production monitoring, 191-217data analysis, 192, 206-208 .limitations, 175, 197, 201resolution limits, 192, 209, 212-213,

215sampling plan, 233-238study of local effects, 187vegetation classification, 9, 65vegetation mapping, 65, 122, 138, 161,

222vegetation stress monitoring, 14, 138,

153, 161, 173,204,226-227see also Multispectral systems; Remote

sensing; Satellite imageryLandscape classifications, 39, 59Landscape mapping, potential vegetation,

36-39Large Area Crop Inventory Experiment,

13-14,149,184,191-217Layer-diagram method, 47Life form combinations, 23Life zone mapping, tropical America, 37Linear array sensors, 164-165, 178Litter, 113Look angle, radar systems, 151Low temperatures, effect on decom-

position of organic matter, 113

Meteorological data, remote sensing ofcrop production, 187-188

Microwave sensing, 175-177Moisture content of vegetation, radar

signal, 152Moisture stress, LANDSAT detection,

204Mollisols, 94, 95, 97, 98Montane tropical rain forests, mapping,

34soil organic matter, 113

Mosaic analysis, 26Mountain soils, 94, 95, 97, 98, 99MSS, see Multispectral scanner systemsMueller-Bombois classification system,

34, 35, 52Multiband photography, 133Multiband linear array sensor, 164, 178

Index

Multicluster blocks technique, 146Multidimensional ordination technique,

22-23Multiple observations, remote sensing,

183Multispectral classification, 166-169Multispectral scanner band, 163Multispectral scanner systems, 139-150

vegetation classification, 12-14,67see also LANDSAT, Remote sensing,

Satellite imageryMultitemporal data, 168Multivariate analysis techniques, 22

Noda approach, floristic classification, 54Noise, 164, 166-169Non-supervised technique, see Clustering

technique

Ocean waves, SEASATSAR data, 176Optical-mechanical mechanisms, data

collection, 139Ordination diagrams, 22Organic matter, soil content, see Soil

organic matterOxidation, soil carbon loss, 106, 117,

229-230Oxisols, 94, 95, 97, 98, 99Ozone-absorbing band, 165

Paired images, vegetation changedetection, 9, 226-228

Periodicity, 23Phenological model, interpretation of

remotely sensed data, 183, 184, 186Photographic data, advantages, 154Physiognomic classification, 23, 25-26Physiognomic-ecological classification, 65Physiognomic-environmental classifi-

cations, 25, 26Phytomass, estimation, 78

mapping, 10Plant biomass, small scale vegetation

maps, 32Plant diseases, detection by colour

infra-red aerial photography, 136Plant life-form spectrum, 48, 51Plant pests, remote sensing, 187-188Plant succession, 224-225Polar ordination, data analysis, 54-57Polarization, radarsystems, 151

245

Population growth, effect on forest area,104

Potential evapotranspiration, 37Potential vegetation, mapping, 36-43, 65Potato blight, detection by colour

infra-red aerial photography, 136Prehistory, soil carbon content, 99Primary production, estimation, 78

mapping, 10Profile diagrams, 22, 27, 47-48, 61, 74-78

Hawaii,74-77Push broom sensor, 164

Radar data, correlation with LANDSATimages, 176

Radar shadow, SLAR systems, 151Radar systems, vegetation mapping and

classification, 12, 150-153see also Remote sensing; Side-Looking

Airborne RadarRaunkiaer plant life-form classification,

48,52Reciprocal averaging, data analysis, 57Refractory carbon compounds, 116Regional landscape system, 42Remote sensing, measurement of

vegetation changes, 8, 12-14,131-319,221-238

Repetitive measurements, LANDSAT,221

Respiration, effect of temperature, 6-7

Sampling strategy, LACIE, 197,208Satellite imagery, forest classification,

65-78intermediate scale maps, 33-34soil order mapping, 73vegetation mapping, 8-10, 65-78,

161-178,221-238see also LANDSAT; Multispectral

scanner systems; Remote sensingSatellite orbit patterns, 171-173Scalar approach, potential vegetation

mapping, 36Schmithiisen map, 29SEASAT synthetic aperture radar, 176Seasonal behaviour, classification

criterion, 48Secondary forest, soil carbon content,

97-98Semi-arid regions, monitoring scheme,

187

246 The role of terrestrial vegetation in the global carbon cycle

Sequential image inventory, 9Shifting cultivation, forest destruction,

102Shrubs. identification with colour

infrared photography, 135Shuttle imaging radar, 176-177Side-looking Airborne Radar, 12,67,

151-153Single image inventory, 8-9Site selection, remote sensing, 186-189Skip orbit, satellite imagery, 171Skylab, photographic data, 138

vegetation mapping, 148SLAR, see Side-looking Airborne RadarSnow structure, effect on return radar

signal, 177Soil carbon content, 91-98, 112-113, 122,

224-226,231interpretation, 222

Soil carbon dynamics, hypotheticalmodel, 119

Soil carbon loss, 10-12,99-107, 114-119,123

Soil carbon pool, 111-114, 122Soil changes, effect on carbon dioxide

levels, 224-226Soil groups, areal extent, 122Soil moisture, radar measurement, 177Soil orders, land use types, 95

mapping, 71-72world land area, 94

Soil organic matter, 111-123oxidation, 229-230

Soil parameter interactions, 177Soil profiles, 112, 121-122Soil variation, classification schemes, 64South America, potential vegetation

mapping, 40South Pole, carbon dioxide measure-

ment, 3Spacecraft photography, 138Spatial resolution, radar systems, 151Spatial sampling, 167-170Specht's classification scheme, 46, 62Species differentiation, mapping, 149, 176Species distributions, floristic classifi-

cations, 53Species populations, structural analysis,

57-58Spectral differences, detection by colour

infra-red film, 134

Spectral reflectance characteristics,vegetation, 140-141, 146

Spectral sampling, 162-167Spectral data analysis, 166-167Spodosols, 94, 95, 97, 98, 99Sri Lanka, potential vegetation mapping,

42Stand density, measurement, 175Structural elements, satellite imagery, 66Sun-synchronous satellite, repeat

patterns, 171-172Supervised technique, computer system

training, 145Surface material characteristics, radar

systems, 151Swamp, soil carbon content, 113Synthetic aperture radar systems, 150,

152,176, 178Synusial approach, classification of

vegetation architecture, 47,51-52

Target resolution, remote sensing, 182Telemetry, 139Temperate areas, soil carbon content,

113-114, 116vegetation maps, 35

Temperature, effect on soil carbon loss,123

effect on USSR wheat production,201-203

Temperature differences, association withvegetation stress, 165

Temporal factors, remotely sensedimagery, 182-183

Temporal resolution, instrument design,162

Temporal sampling, 170-173Terrain complexity, remote sensing crop

production measurement, 187-188Thematic mapper, 174-175, 184-185Thematic mapper bands, 162-163, 165Thermal infra-red scanner systems, 134Thornthwaite, bioclimatic classification

methods, 37, 58Topographic characteristics, radar

systems, 151Topographic ecosystem profile, map

interpretation, 74, 76-77Topography, vegetation mapping, 69-71Training statistics, definition, 145-146Tree height, SAR measurement, 176

Index

Tropical forests, SAR inventory, 176soil carbon content, 113

Tropical grasslands, 121soil carbon contents, 112-113

Tropical savannah, carbon con-centrations after cultivations, 116

Tropical trees, architectural studies, 52Tropical vegetation, mapping, 58--65,67Tropical wheat growth, measurement,

215Tundra, soil carbon content, 113-114Two-way tabulation, data analysis, 22,

54-55, 57

Ultisols, 94, 95, 97, 98, 99UN Food Conference (1974), 101UNESCO, classification scheme, 26,

4G-41,59,65Universities, role in LACIE, 194United States, change detection methods,

227-232Federal Agency involvement in

LACIE, 194loss of agricultural land, 101winter wheat production monitoring,

192,205-208,211USSR, wheat production monitoring,

192, 199-205,211

Vegetation, spectral reflectancecharacteristics, 140

Vegetation architecture, classificationschemes, 43-52

Vegetation boundaries, 35Vegetation changes, detection, 222-224

effect on carbon dioxide levels, 224-226

measurement by satellite imagery,221-238

Vegetation floristic classifications, 53-58Vegetation floristics, 43, 53Vegetation layers, 23Vegetation mapping, 22, 43-58

aerial photography, 131, 134-137CAAT, 148-150classification systems, 35--65interpretation, 222radar systems, 150--153remotely sensed analyses, 182satellite imagery, 71-72, 144, 150,222

247

Vegetation maps, 28-35, 74Hawaiian montane forests, 52-53

Vegetation monitoring, aircraft remotesensing, 131-154

satellite remote sensing, 161-178Vegetation sampling, classification

systems, 22remote sensing, 170

Vegetation stress, detection, 176temperature differences, 165USSR, 204see a/so Crop stress

Vegetation structure, mapping, 61Vegetation types, aerial photography

identification, 131satellite identification, 67

Vegetation units, comparison of maps forUSA, 30--31

comparison of maps for SouthAmerica, 31-32

Vegetational criteria, 25Venezuela, potential vegetation mapping,

40Vertisols, 94, 95, 97, 98, 99Virgin forest, soil carbon content, 97-98

Walter's climate diagram method, 58Water absorption bands, 140--141,

162-163Weather, in relation to crop yield, 193,

201-203microwave systems, 175

Weather data, correlation withLANDSAT data, 191-192, 197-198

Webb's physiognomic-architecturalclassification, 62--64

Wheat production, remote sensing,13-14,195-217

Wheat rust, detection by colour infra-redphotography, 135-136

Whitmore's classification of tropical rainforests, 60--61

Wisconsin watershed, soil carbon lossesdue to erosion, 117

Wood products, oxidation, 229-230Woodland, soil carbon content, 113World Atlas of Agriculture, 235World Meteorological Organization, 191,

198

X-band radar, 152