Spatial Modelling of the Terrestrial Environment (Kelly/Spatial Modelling of the Terrestrial Environment) || Index

WU088-IND WU088/Kelly March 1, 2004 19:19 Char Count= 0

Index

Table in Bold, figure in Italic.

ablation, estimates of mass loss by 31ablation rates 20Advanced Microwave Scanning Radiometer EOS

(AMSR-E) 43, 44, 54, 253, 259Advanced Spaceborne Thermal Emission and

Reflection Radiometer (ASTER) 185, 186Advanced Very High Resolution Radiometer

(AVHRR) 158, 266cloud-free images, Lake Tanganyika 164–5, 168

air pollution, fire-related 177air quality modelling 233air quality monitoring 228–9airborne laser altimetry (LiDAR) see LiDARairborne stereo-photogrammetry 86Airborne Visible/IR Imaging Spectrometer (AVIRIS)

186albedo-ice positive feedback 14Along Track Scanning Radiometer (ATSR-2) 158,

164–5, 168Antarctic Ice Sheet 14, 20–1

balance velocities of grounded portion 26DEM(s)

satellite-derived 31for whole ice sheet 21, 23

east Antarctica, ice rheology 30iceberg fluxes 25–6mass budget 13

anthropogenic disturbance 138assimilation techniques 70Assimilation Value Index 69ASTER see Advanced Spaceborne Thermal Emission

and Reflection Radiometer (ASTER)atmospheric dust 137, 138–9atmospheric forcing data 251–2

European Centre for Medium-Range WeatherForecasts (ECMWF) products 252

Global Data Assimilation System (GDAS) weatherforecast model 251

NASA/Goddard Earth Observing System (GEOS)251–2

automated data correction methods 133–4automated data generation 114automatic mesh generation 95, 96automation, in error identification 121AVHRR see Advanced Very High Resolution

Radiometer (AVHRR)

balance velocities 20–1, 26Antarctica and Greenland 26, 27, 28

basal sliding 20best estimates 11Bi-spectral InfraRed Detection (BIRD) satellite, Hot

Spot Recognition System (HSRS) 187comparison with MODIS 188, 189–91

Spatial Modelling of the Terrestrial Environment. Edited by R. Kelly, N. Drake, S. Barr.C© 2004 John Wiley & Sons, Ltd. ISBN: 0-470-84348-9.

267


268 Index

Bi-spectral InfraRed Detection (BIRD) (cont.)usefulness of higher spatial resolution 189–90,

190bias, in statistical analysis 116–17, 120biodiversity, Lake Tanganyika catchment 158biodiversity monitoring, of remote locations 5biomass burning 177biomass combustion estimates 182, 184, 184boundary friction 79built-form connectivity models 5, 204–11

Kruger’s original model 204–8recognition of built-form constellations 211–18a region-based, graph-topological implementation

209–11built-form spatial structure 210–11built-form connectivity model 209representation of regional morphology/spatial

structure 209–10pre-processing 211

built-form constellation structure 218–23built-form unit area 220, 221

mixed land-use categories 220built-form unit compactness 220–3built-form unit packing and density 218–19

built-form constellations 211–18containment relation

analysis of for corresponding node set in XRAG211–12, 213

summary information on for scene as a whole213–15, 214

depth-first graph-searching algorithm, use of215–16

relations encapsulating hierarchical containmentpatterns 216–18

Cambridgeshire, UKbuilding the Cambridgeshire model 235–7

calibration data sources 235land use model 236transport model 236–7, 236

examination of planning strategy (1996–2000) 229formulating and testing a sustainable policy

package 237–9environmental problems needing solutions 238planning problems of rapid growth 237–8

modelling emissions impact in 235–41emissions impact of the policy scenarios 239–41reference case and policy case and scenarios 235,

238–9wide area estimates of emissions concentrations

required 234–5Canadian Meteorological Center 37catchment-based LSM 250, 259river channel routing 93Chavenet principle 121

climate change studies, and snow 35climate prediction and snow extent/volume 37climate system, impacts of ice sheets on 13–14cloud masking technique 165, 159–60coastal zone colour scanner 110Cold Lands Processes Experiment (CLPX) 54, 267combustion

chemical equation for 178combustion efficiency 180process in a spreading fire 179in wildfires 178–9

Community Land Model (CLM) 251coupled land surface and microwave emission models

60–3MICRO-SWEAT 61–2

emission component based on Wilheit coherentmodel 61–2

Dobson et al model 62time series of modelled and measured brightness

temperatures 62–3, 62Cryosat 14, 32, 265cryosphere, study of, primary objective for satellite

missions, Cryosat and ICESat 14, 31–2

Darcy-Weisbach friction factor 87, 94data

accuracy, reliability and precision in terms of errors116

distinction between accuracy and bias 116–17data assimilation 11, 246, 247, 254–6

data assimilation theory 254hydrologic data assimilation 255in meterology and oceanography 253–4see also Land Data Assimilation SystemsPhysical-Space Statistical Analysis System (PSAS)

252, 254–5soil moisture estimation 255

data qualitydetermination of the SDE important 117–18local vs. global measurements of 118–19, 135

description of surface quality needs carefulthought 118–19

three main issues 118measured by the RMSE (Root Mean Square Error)

117deforestation, Lake Tanganyika’s 158DEM quality 113–14DEMs 111

coarse resolution 124–6, 125, 126accuracy and error characterization 167, 265

NASA Shuttle Radar Topographic Mission data265

InSAR-derived 19, 24error in 116–21SRA-derived 30, 31


Index 269

dense media radiative transfer (DMRT) model 50–1,52

desert dust 139–40digital elevation models see DEMsdistributed process models 10

Earth System Modeling Framework (ESMF) 4economic multiplier effect 232eddy viscosity parameter 86emissions model 233–5

DRMB 233–4validation 233

environmental modelling 3, 4environmental problems, Cambridgeshire, needing

solutions 238equifinality 80, 98, 99–100, 99erosion

modelling in large catchments 158–9controlling parameters derived from remotely

sensed imagery 110–11error correction 121–6, 134–5error identification 121–7

localized error 121–6systematic surface error: banding 126–7

error management 111DEMs 114

aerial photography with photo-control points114–15

assessment of DEM quality 115–16banding 120DEMs of study reach produced 115, 119–20,

119error 119–21

error propagationspatial modelling 265DEM-derived geo morphological and hydrological

parameters 113analytical approaches 54

error(s) 5associated with remote sensing products 11LiDAR data 92in DEMs 116–18

systematic error, blunders (gross errors) andrandom errors 116, 118, 134

explanation of 127–33localized

causes 128–9identification 121–6and severe, may be masked by global indicator of

precision 118in surface temperature remote sensing

253in traditional hydraulic investigation 80Waimakariri study

associated with proximity to wet areas 128

significantly greater along channel margins 128,128

systematic error 119–21, 119, 129, 135see also misclassification errors/problems

ERS-1 and ERS-2 17, 83, 84eucalyptus

high fire intensities 181high heat yield 180

European Centre for Medium-Range WeatherForecasts (ECMWF) 252, 256

European Space Agency (ESA), Soil Moisture OceanSalinity (SMOS) mission see SMOS mission

exhaust emissions 228extinction coefficient 65

Famine Early Warning System (FEWS) 158filters, local topography-based 121–3fire ecology 177–8fire intensity 179, 180–2

fireline intensities 181–2, 190–1wild-land fires 180–1

fire modelling 111–12fire power see fire intensityfire propagation modelling

potential use of fire radiative energy (FRE) in 192–3useful measures from 193

fire radiative energy (FRE) 178, 182derivation of from MIR spectral radiance 186–92

geostationary satellite imagery 191–2polar-orbiting satellite imagery 186–91

fire propagation modelling 192remote sensing of 183–6

firesheat lost to convection/ conductive transfer 191heat generation in 179–80

heat yield parameter 180, 181fuel moisture content 179–80

effects across multiple scales in time and space177

excessive release of particulates and gases 177self-sustaining 179spreading, combustion process in 179wild-land 176

flood envelopeinundation extent prediction 80–1

fuzzy maps 81, 98flood inundation modelling 79–106

development of spatial fields for 81–92integration of spatial data with hydraulic models

92–100, 101research needs 102value of spatial data 102–3

flood inundation modelsintegration in a GIS 93raster-based 93


270 Index

floodplain maps, UK 92flow velocity 85form drag 86–7friction data, spatially distributed 94–5

benchmark validation dataset 94–5friction, in hydraulic models 86–92

skin friction for in-channel flows 87vegetation biophysical attributes 88–92vegetation classification 87–8

gauging stations, national spacing defined by floodwarning role 81–2

Geosat 17Geostationary Operational Environmental Satellites

(GOES:US)GOES Precipitation Index (GPI) 162remotely sensed FRE from 191, 192

GEWEX Global Soil Wetness Project 251GIS 3, 4, 264

integrated, and policy measures 241–2and remote sensing, in fire modelling 112see also SHIRE 2000 GIS

Glen’s flow law 15, 20and diagnostic velocities 26

Global Data Assimilation System (GDAS), weatherforecast model 251

GOES Precipitation Index (GPI) 162gravitational driving force, and ice sheet dynamics

14–15, 30Gravity Recovery and Climate Experiment (GRACE)

253–4Greenland Ice Sheet 14, 20–1, 31

balance velocities calculated for grounded portion26

DEMs for 16, 21, 22, 31–2InSAR-derived, north-east Greenland ice stream

24ice divides 23–5, 25uncertainty in mass budget 13

heat yield parameter 180, 181Helsinki University of Technology (HUT) snow

emission model 51–2, 51, 52hydraulic model calibration and validation, practical

consequences for flood envelope estimationand flood risk maps 80–1

hydraulic modellers, predictive uncertainty asignificant problem 80

hydraulic modelsfriction 86–7integration of spatial data with 92–100, 101inundation extent 82physically-based parameterization using LiDAR

data 94, 95of reach scale flood inundation 81–2

parameterization 86of friction 94

trials against consistent inundation datasets 96use of LiDAR and airborne stereo-photogrammetry

for automated broad-area mapping 86hydraulic resistance 79–80

a lumped term 86hydrological cycle fluxes 246hydrological models, attempt to represent explicitly

mass or energy transfers 10hydrology

land surface modelling at large regional scales5–6

spatial modelling in 9–12models constantly evolving 10physically-based models 9

HYDROS (HYDROspheric States Mission) (NASA)66, 71, 73

ice deformation 27ice divides 23–4

for Greenland and Antarctica 24–5, 25ice flow models 11ice mass geometry 20ice sheet dynamics

derived datasets 23–6fast-flow features

ice sheet interior 26mechanisms 20, 27

force-budget approach 30numerical models 19–21relationship between thickness and rheology 15,

30thermo-mechanical models, in situ rheology of the

ice 28, 30validation of models 26–30

Greenland Ice Sheet, balance velocities cf.diagnostic velocities 26–9

representation of thermodynamics 28–9isothermal cases 27–8limitations in model resolution 29shallow ice approximation 29use of accurate surface DEMs for 30, 31

ice sheet models 19–21key feedbacks 20

ice sheet topography 4, 31derived from satellite radar altimetry 16–18from InSAR 18–19, 19, 22–3, 24from SRA 21–2parameterization in numerical modelling 14–15

ice sheetsimpacts on the climate system 13–14surface profile, dependence on ice rheology 30thickness of 15

iceberg fluxes 25–6


Index 271

ICESatestimation of topographic variables 265study of cryosphere a primary objective 14, 31–2

IFOV (instantaneous field of view) 43, 44image banding

causes of 129–33simulation of random error 129, 130, 131

main problem, how to deal with 131–3adding additional tie points, main effects 131–2,

131method based on using ground control points

132–3, 133refinement of stitching process 132, 132

in systematic surface error 126–7interferometric Synthetic Aperture Radar (InSAR)

18–19topography from 22–3, 24

inundation extent 82–5remote sensing systems 82–5, 83

air photo datasets 82airborne optical platforms 82satellite optical platforms 82Synthetic Aperture Radar (SAR) 83–5

hydraulic models 82validation 96

Jornada del Muerto Basin, New Mexicowind erosion 138–9, 139Chihuahuan Desert 140–1, 140mesquite dunelands 143, 152wind erosion and dust flux 152, 153SWEMO 145, 146–53

model, operation 148–53soil data 145, 146, 146vegetation data 146–7, 147, 147wind data 147–8, 148

Kalman filter 70ensemble Kalman filter 255–6extended Kalman filter 255, 256, 258–9

kernel-based reclassification techniques 203

L-band radar, use in canopy penetration 84L-band (satellite) missions, potential 60, 66land cover, and land use 202land data assimilation systems (LDAS) 59–60, 69–70,

247–9, 247multiple land surface models under one system

248–9, 249, 252applications 256–9

precipitation forcing data 256–8, 258SMMR-derived surface soil moisture data

258–9atmospheric forcing fields 256, 257

components of 249–56, 252

data assimilation 254–6land surface simulations 249–51observations and observation-based data

251–4uncoupled vs.coupled modelling systems 251

future directions 259global LDAS (GLDAS) 248–9, 251–2land information system 249North American LDAS (NLDAS) 248, 251

use of existing Land Surface Models 248primary goal 248

land form change, application of remote sensing to110

land information system (LIS) 249, 251land surface models 12

assimilation of soil moisture remote sensing datainto 255

coupled with microwave emission models 60–3to be used in LDAS 248, 249, 250–1

catchment-based LSM 250, 259Community Land Model (CLM) 251Mosaic LSM 250NOAH LSM 250–1

use of probability density function (PDF) with 71land surface state data 253–4

snow variables 254soil moisture remote sensing 253surface temperature remote sensing 253

land use, and atmospheric mineral dust 139land use and transport modelling framework 231–3

demand elements 232important features for monitoring environmental

impact of traffic emissions 233land use system 231–2transport model, based on trade pattern between

zones 232–3modal split procedure 233

Landsat Thematic Mapper (TM) 82, 203, 229large catchments, problems of modelling erosion and

deposition in 158LDAS see land data assimilation systems (LDAS)LiDAR

high spatial-density airborne LiDAR 201topographical mapping 86r.m.s. errors 92LiDAR data, variogram analysis of 92–3LiDAR and SAR data

coupled to a numerical flood model 5used in flood inundation modelling 12,

79–106LiDAR segmentation system 88, 89, 90–1, 90, 96

advantage of segmentation 91LISFLOOD-FP inundation model 93, 96, 97

application of 98–100, 99model uncertainty 100


272 Index

Long-Term Ecological Research (LTER) Network,National Science Foundation 138, 141

loss of lock (SRA) 17LSM see land surface models

METEOSAT 138METEOSAT-based rainfall estimates 158

warm cloud precipitation estimation 162–3derivation of overland flow from curve number

maps 162–3MICRO-SWEAT 61–2

microwave component used in exploratory SMOSretrieval algorithm 71– 2, 72

prediction of surface soil-deeper soil moisturerelationship 69–70, 69

Microwave Doppler radar 252for determining water velocities 85

microwave emission model of layered snowpacks(MEMLS) 52

SNTHERM and Crocus 50microwave emission models 50, 52, 53

coupled with land surface models 60–3from soil, using coherent or non-coherent models

61when soil surface is rough, use of Mo and

Schmugge model 62microwave remote sensing algorithms, to 49, 50microwave sensors

useful in polar regions 15interferometric synthetic aperture radar (InSAR)

18–19satellite radar altimetry 16–18

misclassification errors/problems 83–4national land cover map 87–8

Moderate Resolution Imaging Spectroradiometer(MODIS) Airborne Simulator 182, 186, 266

Moran’s I 210, 220values of for built-form compactness 222, 223

NASA/Goddard Earth Observing System (GEOS)251–2

incorporates Physical-Space Statistical AnalysisSystem (PSAS) data assimilation techniques252

national censuses, suitability of forstudying/modelling urban systems 200

national land cover map 87–8NOAH LSM 250–1Normalized Difference Vegetation Index (NDVI)

67Lake Tanganyika catchment 160–1

north-east Greenland ice stream (NEGIS) 26InSAR-derived DEM 22–3, 24

Numerical Weather Prediction land surface forcingbiases, in coupled modelling 251

optical satellite images 201potential for urban land-use mapping 203–4pattern recognition techniques 204, 205

Orpington, Bromley (London)identification of built-form constellations 217–18,

217land-cover containment hierarchy 215–16used to evaluate the built-form connectivity

approach 211

passive microwave remote sensingof soil moisture 45–46, 45, 72–3of snow volume 36validation 53

photogrammetric data collection, some postprocessing with local variance based filter121–3

bilinear interpolation 126, 127effects of different DEM resolutions and elevation

tolerance values 125–6, 126main parameters: radius of search area 123main parameters: SD tolerance 123–4tolerance levels tested 123–4

Waimakariri River case study 114–15discrepancies between check data and

photogrammetric data 128–9errors and the stero-matching process 129correction methods 133–4, 133

planning policy development 5policy scenarios, Cambridgeshire, emissions impact

239–41index of emissions impact for Reference and Policy

cases 240–1, 240, 241precision, theoretical 134probability density function (PDF) 71Program for Integrated Earth System Modelling

(PRISM) 4pyrogenic energy emissions

combustion 178–9fire intensity 180–2heat generation 179–80

pyrolysis 179

radar data, images of flooding 84radar interferometry 18RADARSAT 83, 84–5radiative transfer models (emission models) 50, 51radiative transfer theory 63–6

discrete, simple and extended Wilheit models63–4

discrete model 64extended Wilheit model 65–6simple model 64–5

random function and regionalized variable 39range window, satellite radar altimeter 17, 17


Index 273

raster grid representation 10reaction intensity/reaction intensity curve 193Region Search Map (RSM) 209remote sensing 264

coupled with spatial hydrological models 11–12estimates of snow depth/SWE

recent approaches and limits to accuracy 43–7spatial representivity of SSM/I snow depth

estimates: example 47–9of fire radiative energy (FRE) 183–6

spectral-matching technique to satellite imagery184–5

small-scale experiment using spectroradiometer183–4

shortwave IR wavebands on ASTER 185–6of floodplain environments 79measuring fireline intensity 182methods to retrieve snow depth/SWE 44–7

passive microwave estimates cf. visible/infraredglobal snow maps 44–6, 45

of near surface sediment concentrations, LakeTanganyika 164–5, 172

atmospheric correction and cloud maskingessential 165

passive microwave systems 43–4total water storage variations 253–4coupled to hydrological models 10–11of soil moisture 253spatial resolution 266

regional scale application 48, 53of surface temperature 253of SWE and snow areal coverage 254of erosion 110see also satellite remote sensing

remote sensing data 2, 3, 4, 5reproductive biology, of species in fire-prone systems

177river channel research 5

remotely sensed topographic data for 113–36river reaches, real, traditional methods of hydraulic

investigation 79–80

saltation, negative physical consequences forvegetation 138

saltation equation 141–2SAR see Synthetic Aperture Radar (SAR)satellite radar altimetry (SRA) 16–18

corrections to 17–18topography from 21–2

satellite remote sensing 14development of new applications 110–11see also remote sensing

scale, and spatial modelling 3Scanning Multichannel Microwave Radiometer

(SMMR) 43, 44, 253

SCS (Soil Conservation Service) modelestimating overland flow using FEWS rainfall data

161–3problem implementing with FEWS rainfall data 163

Seasat 16–17sediment plumes, Lake Tanganyika

buoyancy of 164detection by remote sensing 164, 168, 169–71

the Malagarasi River plume 167–8mapping 158Ruzizi river mouth plume 168

sediment transport and routing, Lake Tanganyika163–4, 167–8, 172

alternative method to model deposition 163–4routed delivery ratio 164sediment transport capacity 163

seedbank depletion 139Severn Basin, and LiDAR segmentation system 88,

89, 90–1, 90Severn River

finite-element hydraulic model, computationalmesh 94–5, 95

floods (1998 and 2000) 84, 84–5observed and simulated inundated areas 93

shallow ice approximation 29Shallow Water models 94SHIRE 2000 GIS 229–30, 231

creation of framework for modelling emissionsimpact 230, 231

emissions model 230‘hot spots’ of traffic emissions 239key assumptions 229land use and transport models 230visualization of traffic emissions

concentrations-settlement pattern relationship239

Shuffled Complex Evolution optimization procedure71

slope-induced error 18, 18smoothing functions, and error in DEMs 127SMOS mission 66, 73

downscaled estimates of soil moisture 71opacity coefficient 67–8, 68estimates of optical depth 67–8use of MICRO-SWEAT microwave emission

component for exploratory retrieval algorithm71–2, 72

snow representation in climate models 35–6snow cover

and climate 35distribution 37global, quantitative maps using satellite-derived

snow cover estimates 37McKay and Gray classification 37, 38–9

snow cover area, qualitative maps of 36–7


274 Index

snow depth measurement networks 37–8, 38U.S. COOP station network 38, 38, 40, 40, 41FSU network 38, 38, 40, 41WMO GTS network 37, 38, 38, 40, 41

snow depth/SWE 11estimation using pysically based models 42–3,

50improving estimates of at all scales

combining models and observations 49–52validation frameworks 53methodological approaches to 50, 51modelling spatial variations of using in situ snow

measurements 36–42remote sensing estimates

recent approaches and limits to accuracy 43–7spatial representivity of SSM/I snow depth

estimates 47–9retrieval schemes based on empirical formulations

46–7spatial dependency of 37spatial variability investigated using variograms

39–42use of terrain and meteorological variables in

spatial modelling 42snow hydrology models 42–3, 53

combined with microwave emission models 53snow maps, uses of 37snow pack energy balance models 49snow packs

layering 46interaction with vegetation cover 266

snow variables and land data assimilation 254snow volume

global, retrieval of 4satellite passive microwave estimates 36

snow water equivalent (SWE)general estimation approach 53–4changes at hemispheric level 35regionally calibrated approaches 36

SNTHERM model, coupled with dense mediaradiative transfer (DMRT) model 50–1,54

soil erosion 109required parameters 111models of erosion by water 109–10regional scale, near real-time modelling of

157–73Sediment flux 168, 172source areas of erosion 168

soil erosion model, applied to Lake Tanganyika 158,159–63

overland flow estimated using SCS model withFEWS data 161–3

soil erodibility computed from soil properties maps161, 162

vegetation cover estimated using LAC AVHRR159–61

relationship between vegetation cover and NDVI160–1, 160

scaling problem, overcome by use of Polyafunction 161

soil moisture, improving accuracy of in models 59soil moisture estimates 11

downscalingfour-dimensional assimilation algorithm 71modified fractal interpolation technique 71

extending estimates from deeper within the profile69–70

use of statistical methods 69–70using assimilation techniques 70

from ground-based and aircraft radiometer systems4–5

remotely sensed observations using L-band passivemicrowave radiometer 60

subpixel heterogeneity 70–2Soil Moisture Ocean Salinity (SMOS) mission see

SMOS missionsoil moisture retrieval algorithms

effects of vegetation in 66–9ancillary information to estimate optical depth

66–7use of NDVI 67

quantifying errors due to assumptions about thevegetation 68–9

space syntax theory 204spatial autocorrelation 223

of snow depth 39spatial data

incorporation into environmental models 102–3integration with hydraulic models 92–100, 101

automatic mesh generation 96high resolution topographic data 92–4model calibration and validation studies 96–8spatially distributed friction data 94–5uncertainty estimation using spatial data and

distributed mapping 98–100, 101spatial models/modelling 2–4

the future 267in hydrology 9–12importance of 264of the terrestrial environment, key research issues

264–7DEMs: improved accuracy and error

characterization 265spatial resolution: scales of variation and size of

support 266–7vegetation cover: improved characterization

265–6spatial reclassification techniques 203spatial resolution 267


Index 275

higher, usefulness of 189–90, 190passive microwave systems 43–4scales of variation and size of support 266–7

spatial variability, of environmental parameters 267spatially explicit wind erosion and dust flux model

143–6issues in integration of parameters for 144–6, 145

processing stream for 145predicting wind erosion and dust flux at the Jornada

Basin 146–53data sources and model inputs 145, 146–8, 146,

147, 147, 148relations between model parameters and

vegetation/soil parameters 144, 144Special Sensor Microwave Imager (SSM/I) 43, 44,

253SPOT-HVR XS images 203SSM/I snow depth estimates, spatial representivity of,

an example 47–9standard deviation (SD), relationship with error 122,

123–4, 124Stefan’s Law 187stereo-photogrammetry, limitations over ice sheets 15Structural Analysis and Mapping System (SAMS)

210, 215and built-form constellations 209

surface mass balance model 19–2020surface quality

description 118–19quantification 116–18

surface temperature remote sensing 253suspended sediment concentration, used to map flow

patterns 82sustainable planning policy 229sustainable urban development 200SWE see snow water equivalent (SWE)SWEMO see spatially explicit wind erosion and dust

flux modelSynthetic Aperture Radar (SAR)

airborne 84, 97–8dynamic flooding processes 83River Severn floods (1998 and 2000) 84–5polarimetric or multi-frequency and accurate flood

mapping 84new sensors for measurement of inundation extent

83–4statistical active contour methods (snakes) 83–4

Tanganyika, Lake 5, 157–8deficiency in sediment delivery model, Malagarasi

River sediment 167–8estimation of lake sediment concentrations 164–5

use of AVHRR and ATSR-2 imagery 164–5key regions prone to erosion, deforested and

degraded 166, 167

Lake Tanganyika Biodiversity Project (LTBP)Special Sediment Study 158

mapping of sediment plumes 158remote sensing of sediment plumes 164, 168,

169–71Ruzizi River plumes 168

sediment monitoring system 158estimation of lake sediment concentrations 164–5sediment transport and routing 163–4, 167–8, 172soil erosion modelling 159–63

sediment yield estimations 172sensitive to sedimentation problems 157, 158severe erosion in the northern catchment, Rwanda,

Burundi and eastern Zaire 166, 167, 167soil erosion model of the catchment 158

terrestrial environment 1connected to spatial modelling 1–2

topographic data, high resolution 92–4integration with standard hydraulic models 92

topographycontrolling sediment route through a catchment 164in hydraulic models 86ice sheet 4, 14–15, 31, 265

from InSAR and SRA 21–3, 24see also ice sheet topography; surface quality

Total Ozone Mapping Spectrometer (TOMS) 137–8,139

trace gases and aerosols, emissions from fires 177, 182tractability, in computer modelling 3traffic congestion, Cambridgeshire 238traffic emissions, modelling impact of on the urban

environment 228–43integrated land use and transport modelling

framework 231–3emissions model 233–5

integrating models within the framework of SHIRE2000 GIS 229–30, 231

modelling emmissions impact in Cambridgeshire235–41

emissions impact of the policy scenarios 239–41Tropical Rainfall Measuring Mission (TRMM)

Microwave Imager (TMI) 253

UKlocal authorities responsible for an ‘Air Quality

Strategy’ 228–9National Atmospheric Emissions Inventory (NAIE)

229uncertainty estimation

using spatial data and distributed risk mapping98–101

probability mapping 100, 101uniform flow theory 86universal soil loss equation (USLE) 109urban growth 200


276 Index

urban land usein remotely sensed images, automated analyses

using multi-stage approach 202–3use of kernel-based re-classification techniques

203urban land-use categories, recognised by human

photo-interpreters 202urban systems

graph-theoretic measures of built-form connectivity(Kruger) 207, 207, 208, 208

modelled by Kruger as structured trees 206–7, 206

variograms/semi-variograms 39–42, 52U.S. COOP data 40, 40different sampling scales 39WMO dataset, range, nugget and sill variance 40,

41vector representations of space 10vegetation

accounting for effects of in retrieval algorithms66–9

amount of heat released during burning 180biophysical attibutes 88–92burnt and unburnt components 193determining drag coefficient of 87effect on microwave emission from the soil

63–6, 73use of height data to specify an ‘effective’‚

individual friction factor 94, 95vegetation cover

estimated using LAC AVHRR data 159–60improved characterization 265–6

errors in the estimation 161relationship with NDVI 160–1, 160

vehicular emissions, exposure to harmful 228von Karmann’s constant 142

Waimakariri River case study 114–34water erosion modelling 110

Lake Tanganyika 111waveform retracking 17wildfire events

close to Sydney, Australia, MODIS and HSRS MIRimages compared 187, 188, 189–91

possible role in climate change/species extinction176

Wilheit (1978) model 64, 65–6multi-layered canopy 65dielectric constant for the canopy 65–6time series of modelled and measured brighteness

temperature 62, 66wind erosion

local averages 144–5wind speed 153

wind erosion and dust emission 5little information about occurrence of 137

wind erosion and dust flux model 141–2, 143see also spatially explicit wind erosion and dust

flux modelwind erosion modelling 110, 111

basic equations relating to 141–3

XRAG (eXtended Relational Attribute Graph) 209,210, 211

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Spatial Modelling of the Terrestrial Environment (Kelly/Spatial Modelling of the Terrestrial Environment) || Index