Land cover mapping (Continued )
from hyperspectral remote sensing
imagery, 305
case studies, 308–309
object-based classification, 306
pixel-based techniques, 305–306
spectral unmixing, 307
Land imaging, 63
Landsat ETM+ imagery, 8, 203
Landsat Multispectral Scanner (MSS), 8,
233–235, 239, 243, 245
landsat, 247, 249, 250
Landsat satellites, 13, 82
Landsat Thematic Mapper (TM), 8, 30, 86,
115, 233–235, 239, 248
Landscape-biome-biosphere, 146
Landscape ecology, 34
Landscape mapping, 5
Land surface energy budgets,
estimation, 38–40
land cover indices, use of, 39
landsat TM and ETM data, 38
MODIS and ASTER sensors, 39
satellite TIR data, 38
scaling approaches, to compare LSTs for
MODIS and ASTER data, 39
SMACEX, ASTER data to detect and
discern variations, 40
surface energy flux models, 40
surface energy balance algorithm for
land (SEBAL), 40
two-source energy balance (TSEB),
40
temperature–vegetation index method
(TVX), 39
Land surface moisture (LSM), 46
Land surface temperature (LST), 73
and landscape pattern, scaling issues in
studying relationship
between, 218–227
data processing, 219–222
scaling-up effect on class-level
landscape metrics, 220–224
scaling-up effect on landscape-level
landscape metrics, 224–227
study area, 218–219
spatial variations, 69
in urban environments, 218
variability, 6
Land use and land cover (LULC), 68,
219–220, 224, 226, 234, 239,
241–242, 249
classification, 63, 64
maps, 220, 224, 239, 248
patterns, 217
Language IDL (interactive data
language), 157
Leaf angle distribution, 128
Leaf area index (LAI), 82, 95, 98–100,
127
Leaf scale, 127
Lidar data, 268
Lidar sensing, 5, 61
Light–foliar interactions, 127
Lin-An County, ZheJiang Province
above-ground forest carbon map at spatial
resolution of
using PSCBS, 119
biomass conversion coefficient, 115–116
block variances and probabilities
reflect, 122
climate, 114
empirical regression models, use of, 115
estimation map of above-ground forest
carbon at spatial resolution, 119
using sequential Gaussian
cosimulation, 119
using window averaging, 119
forest types, 114
image-based indicator cosimulation, 123
landsat enhanced TM Plus (ETM+)
image, 116
Landsat TM band 3, and inversion, 117
Landsat TM images, 115
national permanent sample plots, 114
Pearson product moment correlation
coefficients of images, 116–117
root mean square error (RMSE), 122
spatial autocorrelation models,
117–118
goodness of fit, 118
study area, features, 114
temporary forest inventory sample
plots, 115
variances of block predicted values, for
above-ground forest carbon
at, 121
by PSCPS and PSCBS, 121
328
INDEX
from hyperspectral remote sensing
imagery, 305
case studies, 308–309
object-based classification, 306
pixel-based techniques, 305–306
spectral unmixing, 307
Land imaging, 63
Landsat ETM+ imagery, 8, 203
Landsat Multispectral Scanner (MSS), 8,
233–235, 239, 243, 245
landsat, 247, 249, 250
Landsat satellites, 13, 82
Landsat Thematic Mapper (TM), 8, 30, 86,
115, 233–235, 239, 248
Landscape-biome-biosphere, 146
Landscape ecology, 34
Landscape mapping, 5
Land surface energy budgets,
estimation, 38–40
land cover indices, use of, 39
landsat TM and ETM data, 38
MODIS and ASTER sensors, 39
satellite TIR data, 38
scaling approaches, to compare LSTs for
MODIS and ASTER data, 39
SMACEX, ASTER data to detect and
discern variations, 40
surface energy flux models, 40
surface energy balance algorithm for
land (SEBAL), 40
two-source energy balance (TSEB),
40
temperature–vegetation index method
(TVX), 39
Land surface moisture (LSM), 46
Land surface temperature (LST), 73
and landscape pattern, scaling issues in
studying relationship
between, 218–227
data processing, 219–222
scaling-up effect on class-level
landscape metrics, 220–224
scaling-up effect on landscape-level
landscape metrics, 224–227
study area, 218–219
spatial variations, 69
in urban environments, 218
variability, 6
Land use and land cover (LULC), 68,
219–220, 224, 226, 234, 239,
241–242, 249
classification, 63, 64
maps, 220, 224, 239, 248
patterns, 217
Language IDL (interactive data
language), 157
Leaf angle distribution, 128
Leaf area index (LAI), 82, 95, 98–100,
127
Leaf scale, 127
Lidar data, 268
Lidar sensing, 5, 61
Light–foliar interactions, 127
Lin-An County, ZheJiang Province
above-ground forest carbon map at spatial
resolution of
using PSCBS, 119
biomass conversion coefficient, 115–116
block variances and probabilities
reflect, 122
climate, 114
empirical regression models, use of, 115
estimation map of above-ground forest
carbon at spatial resolution, 119
using sequential Gaussian
cosimulation, 119
using window averaging, 119
forest types, 114
image-based indicator cosimulation, 123
landsat enhanced TM Plus (ETM+)
image, 116
Landsat TM band 3, and inversion, 117
Landsat TM images, 115
national permanent sample plots, 114
Pearson product moment correlation
coefficients of images, 116–117
root mean square error (RMSE), 122
spatial autocorrelation models,
117–118
goodness of fit, 118
study area, features, 114
temporary forest inventory sample
plots, 115
variances of block predicted values, for
above-ground forest carbon
at, 121
by PSCPS and PSCBS, 121
328
INDEX