1 Spatial Modelling of the Terrestrial Environment - Georeferencial

Index 271
ICESat
estimation of topographic variables 265
study of cryosphere a primary objective 14, 31–2
IFOV (instantaneous field of view) 43, 44
image banding
causes of 129–33
simulation of random error 129, 130, 131
main problem, how to deal with 131–3
adding additional tie points, main effects 131–2,
131
method based on using ground control points
132–3, 133
refinement of stitching process 132, 132
in systematic surface error 126–7
interferometric Synthetic Aperture Radar (InSAR)
18–19
topography from 22–3, 24
inundation extent 82–5
remote sensing systems 82–5, 83
air photo datasets 82
airborne optical platforms 82
satellite optical platforms 82
Synthetic Aperture Radar (SAR) 83–5
hydraulic models 82
validation 96
Jornada del Muerto Basin, New Mexico
wind erosion 138–9, 139
Chihuahuan Desert 140–1, 140
mesquite dunelands 143, 152
wind erosion and dust flux 152, 153
SWEMO 145, 146–53
model, operation 148–53
soil data 145, 146, 146
vegetation data 146–7, 147, 147
wind data 147–8, 148
Kalman filter 70
ensemble Kalman filter 255–6
extended Kalman filter 255, 256, 258–9
kernel-based reclassification techniques 203
L-band radar, use in canopy penetration 84
L-band (satellite) missions, potential 60, 66
land cover, and land use 202
land data assimilation systems (LDAS) 59–60, 69–70,
247–9, 247
multiple land surface models under one system
248–9, 249, 252
applications 256–9
precipitation forcing data 256–8, 258
SMMR-derived surface soil moisture data
258–9
atmospheric forcing fields 256, 257
components of 249–56, 252
data assimilation 254–6
land surface simulations 249–51
observations and observation-based data
251–4
uncoupled vs.coupled modelling systems 251
future directions 259
global LDAS (GLDAS) 248–9, 251–2
land information system 249
North American LDAS (NLDAS) 248, 251
use of existing Land Surface Models 248
primary goal 248
land form change, application of remote sensing to
110
land information system (LIS) 249, 251
land surface models 12
assimilation of soil moisture remote sensing data
into 255
coupled with microwave emission models 60–3
to be used in LDAS 248, 249, 250–1
catchment-based LSM 250, 259
Community Land Model (CLM) 251
Mosaic LSM 250
NOAH LSM 250–1
use of probability density function (PDF) with 71
land surface state data 253–4
snow variables 254
soil moisture remote sensing 253
surface temperature remote sensing 253
land use, and atmospheric mineral dust 139
land use and transport modelling framework 231–3
demand elements 232
important features for monitoring environmental
impact of traffic emissions 233
land use system 231–2
transport model, based on trade pattern between
zones 232–3
modal split procedure 233
Landsat Thematic Mapper (TM) 82, 203, 229
large catchments, problems of modelling erosion and
deposition in 158
LDAS see land data assimilation systems (LDAS)
LiDAR
high spatial-density airborne LiDAR 201
topographical mapping 86
r.m.s. errors 92
LiDAR data, variogram analysis of 92–3
LiDAR and SAR data
coupled to a numerical flood model 5
used in flood inundation modelling 12,
79–106
LiDAR segmentation system 88, 89, 90–1, 90, 96
advantage of segmentation 91
LISFLOOD-FP inundation model 93, 96, 97
application of 98–100, 99
model uncertainty 100