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Biblioteca(s): |
Embrapa Solos. |
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Data corrente: |
22/11/2002 |
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Data da última atualização: |
22/11/2002 |
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Autoria: |
ACCIOLY, L. J. de O. |
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Título: |
Applying spectral mixture analysis (SMA) for soil information extraction on the airborne visible/infrared imaging spectrometer (AVIRIS) data. |
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Ano de publicação: |
1997 |
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Fonte/Imprenta: |
s.l.: University of Arizona, 1997. |
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Páginas: |
233p. |
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Idioma: |
Inglês |
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Notas: |
Ph.D. Thesis. |
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Conteúdo: |
The research objectives of this study were formulated to produce the soil spectral maps using spectral mixture analysis on the AVIRIS data of the Walnut Gulch Experimental Watershed, Tombstone, Arizona. To accomplish this objective the spectral characteristics of eight soils of this Watershed were determined considering the effect of the source of illumination/sensor viewing geometry, degree of wetness (dry vs wet), surface roughness, and the source of the spectra (field, sieved samples and lab) on the selection of image and reference endmembers. The scale effect of the source of spectra was also studied in connection with AVIRIS spectral response. The soils presented anisotropic behavior which varied inversely with the wavelength, and it was reduced under wet conditions. Loss of information occurred when moving from large scale data set (lab, sieved sample, and field spectra) to small scale data (AVIRIS). Cluster analysis and factor analysis were used to extract information about how soil reference endmembers are grouped in relation to viewing angles, degree of wetness and the source of the spectra. Factor analysis was applied to identify the key set of bands that carried most of the information. Soil spectral classes varied as a result of scale effects, soil conditions (wet or dry), and viewing angles. Factor analysis showed that with four unique bands (located at 0.410, 1.310, 0650, and 2.400 um) it was possible to reconstruct the four basic soil spectral curves (Epitaph, Graham, McAllister, and Baboquivar) from the lab data AVIRIS image was modeled using mixture analysis on the basis of image endmembers and reference endmembers. Based on the four dimensions of the AVIRIS data image endmembers were defined by three soil spectra (McAllister, Stronghold-3, and Graham) and by one spectra of green vegetation. The shade fractions were separated from dark soils (Graham and Epitaph) on the basis of the spatial context. The target test identified at least seven reference endmembers in the AVIRIS scene of the Watershed however; mixture analysis failed in obtaining fraction images from these reference endmembers. Mixture analysis was able to produce fraction imagens with a relatively high error for a small set (3) of reference endmembers (McAllister and Graham soils, and walnut leaf). However when applied to a subset of pixed extracted from the AVIRIS image mixture analysis identified the reference endmembers and quantified their proportions. MenosThe research objectives of this study were formulated to produce the soil spectral maps using spectral mixture analysis on the AVIRIS data of the Walnut Gulch Experimental Watershed, Tombstone, Arizona. To accomplish this objective the spectral characteristics of eight soils of this Watershed were determined considering the effect of the source of illumination/sensor viewing geometry, degree of wetness (dry vs wet), surface roughness, and the source of the spectra (field, sieved samples and lab) on the selection of image and reference endmembers. The scale effect of the source of spectra was also studied in connection with AVIRIS spectral response. The soils presented anisotropic behavior which varied inversely with the wavelength, and it was reduced under wet conditions. Loss of information occurred when moving from large scale data set (lab, sieved sample, and field spectra) to small scale data (AVIRIS). Cluster analysis and factor analysis were used to extract information about how soil reference endmembers are grouped in relation to viewing angles, degree of wetness and the source of the spectra. Factor analysis was applied to identify the key set of bands that carried most of the information. Soil spectral classes varied as a result of scale effects, soil conditions (wet or dry), and viewing angles. Factor analysis showed that with four unique bands (located at 0.410, 1.310, 0650, and 2.400 um) it was possible to reconstruct the four basic soil spectral curves (Epitaph,... Mostrar Tudo |
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Categoria do assunto: |
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Marc: |
LEADER 02915nam a2200133 a 4500
001 1334252
005 2002-11-22
008 1997 bl uuuu m 00u1 u #d
100 1 $aACCIOLY, L. J. de O.
245 $aApplying spectral mixture analysis (SMA) for soil information extraction on the airborne visible/infrared imaging spectrometer (AVIRIS) data.
260 $as.l.: University of Arizona$c1997
300 $a233p.
500 $aPh.D. Thesis.
520 $aThe research objectives of this study were formulated to produce the soil spectral maps using spectral mixture analysis on the AVIRIS data of the Walnut Gulch Experimental Watershed, Tombstone, Arizona. To accomplish this objective the spectral characteristics of eight soils of this Watershed were determined considering the effect of the source of illumination/sensor viewing geometry, degree of wetness (dry vs wet), surface roughness, and the source of the spectra (field, sieved samples and lab) on the selection of image and reference endmembers. The scale effect of the source of spectra was also studied in connection with AVIRIS spectral response. The soils presented anisotropic behavior which varied inversely with the wavelength, and it was reduced under wet conditions. Loss of information occurred when moving from large scale data set (lab, sieved sample, and field spectra) to small scale data (AVIRIS). Cluster analysis and factor analysis were used to extract information about how soil reference endmembers are grouped in relation to viewing angles, degree of wetness and the source of the spectra. Factor analysis was applied to identify the key set of bands that carried most of the information. Soil spectral classes varied as a result of scale effects, soil conditions (wet or dry), and viewing angles. Factor analysis showed that with four unique bands (located at 0.410, 1.310, 0650, and 2.400 um) it was possible to reconstruct the four basic soil spectral curves (Epitaph, Graham, McAllister, and Baboquivar) from the lab data AVIRIS image was modeled using mixture analysis on the basis of image endmembers and reference endmembers. Based on the four dimensions of the AVIRIS data image endmembers were defined by three soil spectra (McAllister, Stronghold-3, and Graham) and by one spectra of green vegetation. The shade fractions were separated from dark soils (Graham and Epitaph) on the basis of the spatial context. The target test identified at least seven reference endmembers in the AVIRIS scene of the Watershed however; mixture analysis failed in obtaining fraction images from these reference endmembers. Mixture analysis was able to produce fraction imagens with a relatively high error for a small set (3) of reference endmembers (McAllister and Graham soils, and walnut leaf). However when applied to a subset of pixed extracted from the AVIRIS image mixture analysis identified the reference endmembers and quantified their proportions.
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