Multitemporal spectral mixture analysis for Amazonian land-cover change detection.
Autor(a) principal: | |
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Data de Publicação: | 2004 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) |
Texto Completo: | http://www.alice.cnptia.embrapa.br/alice/handle/doc/994980 |
Resumo: | The complex landscape and environmental conditions in the moist tropical region often result in poor land-cover change detection accuracy using traditional change detection methods. This paper explores linear spectral mixture analysis (LSMA) of multitemporal thematic mapper (TM) images to detect land-cover change in Rondônia, Brazilian Amazon basin. Three image endmembers (shade, green vegetation, and soil) were developed based on a combination of field data and image scatterplots. An unconstrained least-squares solution was used to unmix the multitemporal TM images into three fractions. Then, fraction image differencing results were used to analyze land-cover change/non-change detection. The detailed ?from-to? change detection was implemented using a pixel-by-pixel comparison of classified images, which were developed using a decision tree classifier on the multitemporal fraction images. This study indicates that LSMA is a powerful image processing tool for land-cover classification and change detection. The multitemporal fraction images can be effectively used for land-cover change detection. The stable and reliable multitemporal fraction images developed using LSMA make the change detection possible without the use of training sample datasets for historical remotely sensed data. This characteristic is particularly valuable for the land-cover change detection in the Amazon basin. |
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Multitemporal spectral mixture analysis for Amazonian land-cover change detection.Tropical regionRemote sensingThe complex landscape and environmental conditions in the moist tropical region often result in poor land-cover change detection accuracy using traditional change detection methods. This paper explores linear spectral mixture analysis (LSMA) of multitemporal thematic mapper (TM) images to detect land-cover change in Rondônia, Brazilian Amazon basin. Three image endmembers (shade, green vegetation, and soil) were developed based on a combination of field data and image scatterplots. An unconstrained least-squares solution was used to unmix the multitemporal TM images into three fractions. Then, fraction image differencing results were used to analyze land-cover change/non-change detection. The detailed ?from-to? change detection was implemented using a pixel-by-pixel comparison of classified images, which were developed using a decision tree classifier on the multitemporal fraction images. This study indicates that LSMA is a powerful image processing tool for land-cover classification and change detection. The multitemporal fraction images can be effectively used for land-cover change detection. The stable and reliable multitemporal fraction images developed using LSMA make the change detection possible without the use of training sample datasets for historical remotely sensed data. This characteristic is particularly valuable for the land-cover change detection in the Amazon basin.DENGSHENG LU, INDIANA UNIVERSITY; MATEUS BATISTELLA, CNPM; EMILIO MORAN, INDIANA UNIVERSITY.LU, D.BATISTELLA, M.MORAN, E.2014-09-15T11:11:11Z2014-09-15T11:11:11Z2014-09-1520042014-09-15T11:11:11Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleCanadian Journal of Remote Sensing, v. 30, n. 1, p. 87-100, 2004.http://www.alice.cnptia.embrapa.br/alice/handle/doc/994980enginfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice)instname:Empresa Brasileira de Pesquisa Agropecuária (Embrapa)instacron:EMBRAPA2014-09-17T00:49:44Zoai:www.alice.cnptia.embrapa.br:doc/994980Repositório InstitucionalPUBhttps://www.alice.cnptia.embrapa.br/oai/requestopendoar:21542014-09-17T00:49:44falseRepositório InstitucionalPUBhttps://www.alice.cnptia.embrapa.br/oai/requestcg-riaa@embrapa.bropendoar:21542014-09-17T00:49:44Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) - Empresa Brasileira de Pesquisa Agropecuária (Embrapa)false |
dc.title.none.fl_str_mv |
Multitemporal spectral mixture analysis for Amazonian land-cover change detection. |
title |
Multitemporal spectral mixture analysis for Amazonian land-cover change detection. |
spellingShingle |
Multitemporal spectral mixture analysis for Amazonian land-cover change detection. LU, D. Tropical region Remote sensing |
title_short |
Multitemporal spectral mixture analysis for Amazonian land-cover change detection. |
title_full |
Multitemporal spectral mixture analysis for Amazonian land-cover change detection. |
title_fullStr |
Multitemporal spectral mixture analysis for Amazonian land-cover change detection. |
title_full_unstemmed |
Multitemporal spectral mixture analysis for Amazonian land-cover change detection. |
title_sort |
Multitemporal spectral mixture analysis for Amazonian land-cover change detection. |
author |
LU, D. |
author_facet |
LU, D. BATISTELLA, M. MORAN, E. |
author_role |
author |
author2 |
BATISTELLA, M. MORAN, E. |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
DENGSHENG LU, INDIANA UNIVERSITY; MATEUS BATISTELLA, CNPM; EMILIO MORAN, INDIANA UNIVERSITY. |
dc.contributor.author.fl_str_mv |
LU, D. BATISTELLA, M. MORAN, E. |
dc.subject.por.fl_str_mv |
Tropical region Remote sensing |
topic |
Tropical region Remote sensing |
description |
The complex landscape and environmental conditions in the moist tropical region often result in poor land-cover change detection accuracy using traditional change detection methods. This paper explores linear spectral mixture analysis (LSMA) of multitemporal thematic mapper (TM) images to detect land-cover change in Rondônia, Brazilian Amazon basin. Three image endmembers (shade, green vegetation, and soil) were developed based on a combination of field data and image scatterplots. An unconstrained least-squares solution was used to unmix the multitemporal TM images into three fractions. Then, fraction image differencing results were used to analyze land-cover change/non-change detection. The detailed ?from-to? change detection was implemented using a pixel-by-pixel comparison of classified images, which were developed using a decision tree classifier on the multitemporal fraction images. This study indicates that LSMA is a powerful image processing tool for land-cover classification and change detection. The multitemporal fraction images can be effectively used for land-cover change detection. The stable and reliable multitemporal fraction images developed using LSMA make the change detection possible without the use of training sample datasets for historical remotely sensed data. This characteristic is particularly valuable for the land-cover change detection in the Amazon basin. |
publishDate |
2004 |
dc.date.none.fl_str_mv |
2004 2014-09-15T11:11:11Z 2014-09-15T11:11:11Z 2014-09-15 2014-09-15T11:11:11Z |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
Canadian Journal of Remote Sensing, v. 30, n. 1, p. 87-100, 2004. http://www.alice.cnptia.embrapa.br/alice/handle/doc/994980 |
identifier_str_mv |
Canadian Journal of Remote Sensing, v. 30, n. 1, p. 87-100, 2004. |
url |
http://www.alice.cnptia.embrapa.br/alice/handle/doc/994980 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
reponame:Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) instname:Empresa Brasileira de Pesquisa Agropecuária (Embrapa) instacron:EMBRAPA |
instname_str |
Empresa Brasileira de Pesquisa Agropecuária (Embrapa) |
instacron_str |
EMBRAPA |
institution |
EMBRAPA |
reponame_str |
Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) |
collection |
Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) |
repository.name.fl_str_mv |
Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) - Empresa Brasileira de Pesquisa Agropecuária (Embrapa) |
repository.mail.fl_str_mv |
cg-riaa@embrapa.br |
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1794503394096840704 |