The Use of Remote Sensing Indices for Land Cover Change Detection

Detalhes bibliográficos
Autor(a) principal: Dourado, Gustavo Facincani
Data de Publicação: 2019
Outros Autores: Motta, Jaíza Santos, Filho, Antonio Conceição Paranhos, Scott, David Findlay, Gabas, Sandra Garcia
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Anuário do Instituto de Geociências (Online)
Texto Completo: https://revistas.ufrj.br/index.php/aigeo/article/view/30051
Resumo: Remote sensing technology has been applied to monitor anthropogenic changes in the landscape that produce impacts on natural resources, such as environmental degradation, changes in the hydrological cycle and in ecosystems structure and functioning. As digital change detection may be a difficult task to perform, this study proposes a simple and logical technique to display land cover changes using Landsat imagery. Open source geoprocessing tools were used to acquire information for mapping changes on the land surface. The Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) derived from satellite images of four dates between 1984 and 2016 were used in RGB composites. The method was used to map gains and losses of vegetation cover and liquid water content in a spatiotemporal scale. The results indicate that this change detection method can effectively reflect the variations occurred over the years. Although both indices have similar responses, NDWI may provide opposite information to NDVI in certain areas, such as in wetlands and riparian zones, presenting wetness losses even in places that exhibit gains in vegetation. This method has applicability to other regions for deriving historical changes.
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spelling The Use of Remote Sensing Indices for Land Cover Change DetectionLandsat; Multi-temporal; NDVI; NDWI; QGIS; Time-seriesRemote sensing technology has been applied to monitor anthropogenic changes in the landscape that produce impacts on natural resources, such as environmental degradation, changes in the hydrological cycle and in ecosystems structure and functioning. As digital change detection may be a difficult task to perform, this study proposes a simple and logical technique to display land cover changes using Landsat imagery. Open source geoprocessing tools were used to acquire information for mapping changes on the land surface. The Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) derived from satellite images of four dates between 1984 and 2016 were used in RGB composites. The method was used to map gains and losses of vegetation cover and liquid water content in a spatiotemporal scale. The results indicate that this change detection method can effectively reflect the variations occurred over the years. Although both indices have similar responses, NDWI may provide opposite information to NDVI in certain areas, such as in wetlands and riparian zones, presenting wetness losses even in places that exhibit gains in vegetation. This method has applicability to other regions for deriving historical changes.Universidade Federal do Rio de JaneiroDourado, Gustavo FacincaniMotta, Jaíza SantosFilho, Antonio Conceição ParanhosScott, David FindlayGabas, Sandra Garcia2019-12-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://revistas.ufrj.br/index.php/aigeo/article/view/3005110.11137/2019_2_72_85Anuário do Instituto de Geociências; Vol 42, No 2 (2019); 72-85Anuário do Instituto de Geociências; Vol 42, No 2 (2019); 72-851982-39080101-9759reponame:Anuário do Instituto de Geociências (Online)instname:Universidade Federal do Rio de Janeiro (UFRJ)instacron:UFRJenghttps://revistas.ufrj.br/index.php/aigeo/article/view/30051/16959Copyright (c) 2019 Anuário do Instituto de Geociênciashttp://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccess2019-12-10T15:04:16Zoai:www.revistas.ufrj.br:article/30051Revistahttps://revistas.ufrj.br/index.php/aigeo/indexPUBhttps://revistas.ufrj.br/index.php/aigeo/oaianuario@igeo.ufrj.br||1982-39080101-9759opendoar:2019-12-10T15:04:16Anuário do Instituto de Geociências (Online) - Universidade Federal do Rio de Janeiro (UFRJ)false
dc.title.none.fl_str_mv The Use of Remote Sensing Indices for Land Cover Change Detection

title The Use of Remote Sensing Indices for Land Cover Change Detection
spellingShingle The Use of Remote Sensing Indices for Land Cover Change Detection
Dourado, Gustavo Facincani
Landsat; Multi-temporal; NDVI; NDWI; QGIS; Time-series
title_short The Use of Remote Sensing Indices for Land Cover Change Detection
title_full The Use of Remote Sensing Indices for Land Cover Change Detection
title_fullStr The Use of Remote Sensing Indices for Land Cover Change Detection
title_full_unstemmed The Use of Remote Sensing Indices for Land Cover Change Detection
title_sort The Use of Remote Sensing Indices for Land Cover Change Detection
author Dourado, Gustavo Facincani
author_facet Dourado, Gustavo Facincani
Motta, Jaíza Santos
Filho, Antonio Conceição Paranhos
Scott, David Findlay
Gabas, Sandra Garcia
author_role author
author2 Motta, Jaíza Santos
Filho, Antonio Conceição Paranhos
Scott, David Findlay
Gabas, Sandra Garcia
author2_role author
author
author
author
dc.contributor.none.fl_str_mv

dc.contributor.author.fl_str_mv Dourado, Gustavo Facincani
Motta, Jaíza Santos
Filho, Antonio Conceição Paranhos
Scott, David Findlay
Gabas, Sandra Garcia
dc.subject.none.fl_str_mv
dc.subject.por.fl_str_mv Landsat; Multi-temporal; NDVI; NDWI; QGIS; Time-series
topic Landsat; Multi-temporal; NDVI; NDWI; QGIS; Time-series
description Remote sensing technology has been applied to monitor anthropogenic changes in the landscape that produce impacts on natural resources, such as environmental degradation, changes in the hydrological cycle and in ecosystems structure and functioning. As digital change detection may be a difficult task to perform, this study proposes a simple and logical technique to display land cover changes using Landsat imagery. Open source geoprocessing tools were used to acquire information for mapping changes on the land surface. The Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) derived from satellite images of four dates between 1984 and 2016 were used in RGB composites. The method was used to map gains and losses of vegetation cover and liquid water content in a spatiotemporal scale. The results indicate that this change detection method can effectively reflect the variations occurred over the years. Although both indices have similar responses, NDWI may provide opposite information to NDVI in certain areas, such as in wetlands and riparian zones, presenting wetness losses even in places that exhibit gains in vegetation. This method has applicability to other regions for deriving historical changes.
publishDate 2019
dc.date.none.fl_str_mv 2019-12-01
dc.type.none.fl_str_mv

dc.type.driver.fl_str_mv info:eu-repo/semantics/article
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dc.identifier.uri.fl_str_mv https://revistas.ufrj.br/index.php/aigeo/article/view/30051
10.11137/2019_2_72_85
url https://revistas.ufrj.br/index.php/aigeo/article/view/30051
identifier_str_mv 10.11137/2019_2_72_85
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv https://revistas.ufrj.br/index.php/aigeo/article/view/30051/16959
dc.rights.driver.fl_str_mv Copyright (c) 2019 Anuário do Instituto de Geociências
http://creativecommons.org/licenses/by/4.0
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Copyright (c) 2019 Anuário do Instituto de Geociências
http://creativecommons.org/licenses/by/4.0
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Universidade Federal do Rio de Janeiro
publisher.none.fl_str_mv Universidade Federal do Rio de Janeiro
dc.source.none.fl_str_mv Anuário do Instituto de Geociências; Vol 42, No 2 (2019); 72-85
Anuário do Instituto de Geociências; Vol 42, No 2 (2019); 72-85
1982-3908
0101-9759
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