Registro Completo
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Biblioteca(s): |
Embrapa Territorial. |
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Data corrente: |
29/04/2004 |
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Data da última atualização: |
25/03/2019 |
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Tipo da produção científica: |
Artigo em Anais de Congresso |
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Autoria: |
LU, D.; BATISTELLA, M.; MORAN, E. |
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Afiliação: |
1: Indiana University-CIPEC; 2: Embrapa Monitoramento por Satélite; 3: Indiana University-ACT. |
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Título: |
Detecting Amazonian deforestation using multitemporal thematic mapper imageries and spectral mixture analysis. |
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Ano de publicação: |
2003 |
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Fonte/Imprenta: |
In: ASPRS ANNUAL CONFERENCE, 2003, Anchorage, Alaska-EUA. Proceedings... [S.l.]: ASPRS, 2003. |
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Páginas: |
12 p. |
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Descrição Física: |
folhas avulsas |
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Idioma: |
Inglês |
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Conteúdo: |
Linear spectral mixture analysis (LSMA) and multitemporal Thematic Mapper (TM) data were used to detect deforestation in Altamira and Machadinho, Brazilian Amazon. Standardized principal component analysis was used to transform TM data into uncorrelated principal components (PCs). Three endmembers were selected and an unconstrained least root-mean squared error solution was used to unmix the first four PCs into three fraction images. Mature forest classification was implemented using thresholds and deforestation detection using binary image overlay. This study indicates that LSMA is an effective method to identify mature forest and detect deforested areas with high accuracies. |
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Palavras-Chave: |
Altamira; Amazonas; Brasil; Machadinho d´Oeste; Mapeamento; Rondônia. |
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Thesagro: |
Floresta; Satélite. |
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Thesaurus NAL: |
Amazonia. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/194967/1/1145.pdf
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Marc: |
LEADER 01416nam a2200253 a 4500
001 1017038
005 2019-03-25
008 2003 bl uuuu u00u1 u #d
100 1 $aLU, D.
245 $aDetecting Amazonian deforestation using multitemporal thematic mapper imageries and spectral mixture analysis.
260 $aIn: ASPRS ANNUAL CONFERENCE, 2003, Anchorage, Alaska-EUA. Proceedings... [S.l.]: ASPRS$c2003
300 $a12 p.$cfolhas avulsas
520 $aLinear spectral mixture analysis (LSMA) and multitemporal Thematic Mapper (TM) data were used to detect deforestation in Altamira and Machadinho, Brazilian Amazon. Standardized principal component analysis was used to transform TM data into uncorrelated principal components (PCs). Three endmembers were selected and an unconstrained least root-mean squared error solution was used to unmix the first four PCs into three fraction images. Mature forest classification was implemented using thresholds and deforestation detection using binary image overlay. This study indicates that LSMA is an effective method to identify mature forest and detect deforested areas with high accuracies.
650 $aAmazonia
650 $aFloresta
650 $aSatélite
653 $aAltamira
653 $aAmazonas
653 $aBrasil
653 $aMachadinho d´Oeste
653 $aMapeamento
653 $aRondônia
700 1 $aBATISTELLA, M.
700 1 $aMORAN, E.
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Registro original: |
Embrapa Territorial (CNPM) |
