digital aerial imagery, 203
image processing subtasks, 176
lidar derived information, analysis
land cover classification, 202
mapping
alpine geomorphology
airborne lidar data, 203
multiseasonal ASTER data, 203
urban sprawl, 201
SPOT images, 201
methodology, 175–176
at multiple scales, 73
multiscale image segmentation
comparison, 206
object based change detection, 198,
202
optimum scales in, 204–205
resources, 199–201
scale optimization, 199
subpixel mapping approach, 202
time-series images, 202
urban changes based on scale-space
filtering embedded in, 202
very high resolution data, 177
visual interpretation, objects, 199
vs. traditional pixel-based
approaches, 198
Object-based metrics, 198
Object pixel based classification, 202
Object-specific upscaling, 148
Observational scale, 65
of remote sensing, 62
Operational scale, 62, 67, 68
Optimal scale, 3, 6, 8, 62, 67, 73, 148,
174
Optimal segmentation scale, 203
Panchromatic (PAN), 148
band, 64
channel, 148
IKONOS image, 18
image, 8, 148
sensor, 13
Patch dynamics theory (PDT), 144
Per-pixel classifications, 66
Phenocams, 86–88
camera images, uploaded via, 87
excess greenness index, 86
installation, 87
NDVI values, 86–88
Photosynthesis, 126
Photosynthetic capacity, 127
Pigment quantification in canopy, 127
Pinus massoniana, 114
Pixel aggregation, 68
Pixel and subpixel approaches, 62
Pixel-based analysis, 197–198, 201
Point simple cokriging block cosimulation
(PSCBS), 112, 114,
119–122
Point simple cokriging point cosimulation
(PSCPS), 112, 114, 119–122
Population estimation
difference between MRE and
MedRE, 72
DMSP-OLS, suitable for, 71
impervious surface, 72
Landsat TM/ETM, 71
models developed
with remote sensing and GIS
techniques, 71
models for Indianapolis at each census
level, 72
remotely sensed images, scale
dependent, 71
sensors and spatial resolutions, for
estimation, 71
Terra ASTER, 71
Precipitation measurements, 254
error propagation into hydrological
prediction, 255
remote sensing measurements, 254
in situ measurements, 254
spatiotemporal scales of
precipitation, 254–255
uncertainty propagation
from precipitation data to hydrological
prediction, 257–262
uncertainty quantification, 255
framework, 255–256
Precipitation scale, 253
Precipitation variability, 253
Primitives, 175
edge and line, 175
extracting, 175
image, 171, 175
importance of, 175
PSCBS. See Point simple cokriging block
cosimulation (PSCBS)
INDEX
331
image processing subtasks, 176
lidar derived information, analysis
land cover classification, 202
mapping
alpine geomorphology
airborne lidar data, 203
multiseasonal ASTER data, 203
urban sprawl, 201
SPOT images, 201
methodology, 175–176
at multiple scales, 73
multiscale image segmentation
comparison, 206
object based change detection, 198,
202
optimum scales in, 204–205
resources, 199–201
scale optimization, 199
subpixel mapping approach, 202
time-series images, 202
urban changes based on scale-space
filtering embedded in, 202
very high resolution data, 177
visual interpretation, objects, 199
vs. traditional pixel-based
approaches, 198
Object-based metrics, 198
Object pixel based classification, 202
Object-specific upscaling, 148
Observational scale, 65
of remote sensing, 62
Operational scale, 62, 67, 68
Optimal scale, 3, 6, 8, 62, 67, 73, 148,
174
Optimal segmentation scale, 203
Panchromatic (PAN), 148
band, 64
channel, 148
IKONOS image, 18
image, 8, 148
sensor, 13
Patch dynamics theory (PDT), 144
Per-pixel classifications, 66
Phenocams, 86–88
camera images, uploaded via, 87
excess greenness index, 86
installation, 87
NDVI values, 86–88
Photosynthesis, 126
Photosynthetic capacity, 127
Pigment quantification in canopy, 127
Pinus massoniana, 114
Pixel aggregation, 68
Pixel and subpixel approaches, 62
Pixel-based analysis, 197–198, 201
Point simple cokriging block cosimulation
(PSCBS), 112, 114,
119–122
Point simple cokriging point cosimulation
(PSCPS), 112, 114, 119–122
Population estimation
difference between MRE and
MedRE, 72
DMSP-OLS, suitable for, 71
impervious surface, 72
Landsat TM/ETM, 71
models developed
with remote sensing and GIS
techniques, 71
models for Indianapolis at each census
level, 72
remotely sensed images, scale
dependent, 71
sensors and spatial resolutions, for
estimation, 71
Terra ASTER, 71
Precipitation measurements, 254
error propagation into hydrological
prediction, 255
remote sensing measurements, 254
in situ measurements, 254
spatiotemporal scales of
precipitation, 254–255
uncertainty propagation
from precipitation data to hydrological
prediction, 257–262
uncertainty quantification, 255
framework, 255–256
Precipitation scale, 253
Precipitation variability, 253
Primitives, 175
edge and line, 175
extracting, 175
image, 171, 175
importance of, 175
PSCBS. See Point simple cokriging block
cosimulation (PSCBS)
INDEX
331