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Registro Completo |
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Biblioteca(s): |
Embrapa Territorial. |
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Data corrente: |
02/03/2009 |
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Data da última atualização: |
25/08/2014 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
LU, D.; BATISTELLA, M.; MIRANDA, E. E. de. |
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Afiliação: |
DENGSHENG LU, Indiana University; MATEUS BATISTELLA, CNPM; EVARISTO EDUARDO DE MIRANDA, CNPM. |
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Título: |
A comparative study of landsat TM and SPOT HRG images of vegetation classification in the brazilian Amazon. |
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Ano de publicação: |
2008 |
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Fonte/Imprenta: |
Photogrammetric Engineering & Remote Sensing, v. 74, n. 3, p. 311-321, mar. 2008. |
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Volume: |
v. 74 |
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ISBN: |
0099-1112 |
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Idioma: |
Inglês |
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Conteúdo: |
Complex forest structure and abundant tree species in the moist tropical regions often couse difficulties in classifying vegetation classes with remotely sensed data. This paper explores improvement in vegetation classification accuracies through a comparative study of different image combinations based on the integration of Landsat Thematic Mapper (TM) and SPOT High Resolution Geometric (HRG) instrument data, as well as the combination of spectral signatures and textures. A maximum likelihood classifier was used to classify the different image combinations into thematic maps. This research indicated that data fusion based on HBG multispectral and panchromatic data slightly improved vegetation classification accuracies: a 3.1 to 4.6 percent increase in the kappa coefficient compared with the classification results based on original HRG of TM multispectral images. A combination of HRG spectral signatures and two textural images improved the kappa coefficient by 6.3 percent compared with pure HRG multispectral images. The textural images based on entropy or second-moment texture measures with a window size of 9 pixels X 9 pixels played an important role in improving vegetation classification accuracy. Overall, optical remote-sensing data are still insufficient for accurate vegetation classifications in the Amazon basin. |
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Palavras-Chave: |
Brazilian Amazon; Comparative study; Landsat TM and SPOT HRG Images; Machadinho d´Oeste; Moist tropical regions. |
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Categoria do assunto: |
P Recursos Naturais, Ciências Ambientais e da Terra |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/107253/1/2284.pdf
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Marc: |
LEADER 02068naa a2200241 a 4500
001 1031577
005 2014-08-25
008 2008 bl uuuu u00u1 u #d
022 $a0099-1112
100 1 $aLU, D.
245 $aA comparative study of landsat TM and SPOT HRG images of vegetation classification in the brazilian Amazon.
260 $c2008
300 $av. 74
490 $vv. 74
520 $aComplex forest structure and abundant tree species in the moist tropical regions often couse difficulties in classifying vegetation classes with remotely sensed data. This paper explores improvement in vegetation classification accuracies through a comparative study of different image combinations based on the integration of Landsat Thematic Mapper (TM) and SPOT High Resolution Geometric (HRG) instrument data, as well as the combination of spectral signatures and textures. A maximum likelihood classifier was used to classify the different image combinations into thematic maps. This research indicated that data fusion based on HBG multispectral and panchromatic data slightly improved vegetation classification accuracies: a 3.1 to 4.6 percent increase in the kappa coefficient compared with the classification results based on original HRG of TM multispectral images. A combination of HRG spectral signatures and two textural images improved the kappa coefficient by 6.3 percent compared with pure HRG multispectral images. The textural images based on entropy or second-moment texture measures with a window size of 9 pixels X 9 pixels played an important role in improving vegetation classification accuracy. Overall, optical remote-sensing data are still insufficient for accurate vegetation classifications in the Amazon basin.
653 $aBrazilian Amazon
653 $aComparative study
653 $aLandsat TM and SPOT HRG Images
653 $aMachadinho d´Oeste
653 $aMoist tropical regions
700 1 $aBATISTELLA, M.
700 1 $aMIRANDA, E. E. de
773 $tPhotogrammetric Engineering & Remote Sensing$gv. 74, n. 3, p. 311-321, mar. 2008.
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Embrapa Territorial (CNPM) |
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