Ehlers fusion, 6, 15–16
Electro-optical data, 27
Electro-optical (EO) sensing, 26
Electro-optical sensors GSD, 6, 13, 28
Energy fluxes, 38
Enhanced Thematic Mapper Plus
(ETM+), 233
ENVI FX software, 147
ET models, 44–45
Euclidean geometry, 231
European Space Agency (ESA), 37, 54
Euthamia graminifolia (L.), 128
Evaporation, 40–45
Evapotranspiration (ET), 40–45, 83, 85, 86
Explorer VII, 36
Extrapolation
2D discrete case, 281
information across spatial scales, 3
information along hierarchical scaling
ladder, 144
to other scales, 61
preserving surface slope beyond, 281
FDI. See Forest disturbance index (FDI)
Feature shape, 65
Festuca rubra L., 128
Field spectral library, development, 292
continuum-removed spectral
library, 293–294
first- and second-derivative spectral
library, 294–295
spectral libraries available from field
spectra data sets, 295–296
ASTER spectral library, 296
JHU spectral library, 296
JPL spectral library, 296
SPECCHIO database system, 296
Vegetation Spectral Library, 296
unaltered reflectance spectral library, 293
Field spectroradiometry, 285–289
applications related to land cover
mapping, 298–302
Cenchurus ciliaris, 299
statistical approaches for, 299
Vitis vinifera, 301
bidirectional reflectance distribution
function (BRDF), 288
factors affecting measurements, 289
environmental factors, 290–291
instrumentation factors, 289–290
measurement errors, 291–292
signal-to-noise ratio, 290
irradiance intensity in visible
spectrum, 288
properties, 285–286
spectral fingerprint, 286
spectral reflectance factor, 288
statistical approaches, for vegetation
discrimination, 296
basic assumptions, 296–297
canonical discriminant analysis, 298
cluster analysis/clustering, 298
Kruskal–Wallis nonparametric
ANOVA, 297
Mann–Whitney U-test, 298
multivariate statistical techniques,
298
principal-component analysis
(PCA), 298
univariate statistical techniques,
297–298
SWIR reflectance, 288
variable in, 287
Filtering methods, 274
ground mask and DTM generation,
279–280
MDHT with directional erosion, 275–277
parameter selection, 277–280
point-to-raster conversion, 274–275
repeat pass and point labeling, 280
Filtering tests, 281
accuracy assessment, 281
filtering result
for samples, overall accuracy, 282
for urban and forest sites, 282
ISPRS data sets, 281
selected filter parameters for each test
site, 281
FNEA. See Fractal net evolution approach
(FNEA)
Focal size, 65
Foliar nitrogen levels, as proxy for forest
damage, 84
Forest carbon, 108
global variance, 113
mapped by combining forest inventory
sample plot data
and remotely sensed image, 111
324
INDEX
Electro-optical data, 27
Electro-optical (EO) sensing, 26
Electro-optical sensors GSD, 6, 13, 28
Energy fluxes, 38
Enhanced Thematic Mapper Plus
(ETM+), 233
ENVI FX software, 147
ET models, 44–45
Euclidean geometry, 231
European Space Agency (ESA), 37, 54
Euthamia graminifolia (L.), 128
Evaporation, 40–45
Evapotranspiration (ET), 40–45, 83, 85, 86
Explorer VII, 36
Extrapolation
2D discrete case, 281
information across spatial scales, 3
information along hierarchical scaling
ladder, 144
to other scales, 61
preserving surface slope beyond, 281
FDI. See Forest disturbance index (FDI)
Feature shape, 65
Festuca rubra L., 128
Field spectral library, development, 292
continuum-removed spectral
library, 293–294
first- and second-derivative spectral
library, 294–295
spectral libraries available from field
spectra data sets, 295–296
ASTER spectral library, 296
JHU spectral library, 296
JPL spectral library, 296
SPECCHIO database system, 296
Vegetation Spectral Library, 296
unaltered reflectance spectral library, 293
Field spectroradiometry, 285–289
applications related to land cover
mapping, 298–302
Cenchurus ciliaris, 299
statistical approaches for, 299
Vitis vinifera, 301
bidirectional reflectance distribution
function (BRDF), 288
factors affecting measurements, 289
environmental factors, 290–291
instrumentation factors, 289–290
measurement errors, 291–292
signal-to-noise ratio, 290
irradiance intensity in visible
spectrum, 288
properties, 285–286
spectral fingerprint, 286
spectral reflectance factor, 288
statistical approaches, for vegetation
discrimination, 296
basic assumptions, 296–297
canonical discriminant analysis, 298
cluster analysis/clustering, 298
Kruskal–Wallis nonparametric
ANOVA, 297
Mann–Whitney U-test, 298
multivariate statistical techniques,
298
principal-component analysis
(PCA), 298
univariate statistical techniques,
297–298
SWIR reflectance, 288
variable in, 287
Filtering methods, 274
ground mask and DTM generation,
279–280
MDHT with directional erosion, 275–277
parameter selection, 277–280
point-to-raster conversion, 274–275
repeat pass and point labeling, 280
Filtering tests, 281
accuracy assessment, 281
filtering result
for samples, overall accuracy, 282
for urban and forest sites, 282
ISPRS data sets, 281
selected filter parameters for each test
site, 281
FNEA. See Fractal net evolution approach
(FNEA)
Focal size, 65
Foliar nitrogen levels, as proxy for forest
damage, 84
Forest carbon, 108
global variance, 113
mapped by combining forest inventory
sample plot data
and remotely sensed image, 111
324
INDEX