Reconstrução histórica de mudanças na cobertura florestal em várzeas do baixo amazonas utilizando o algoritmo landtrendr
Autor(a) principal: | |
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Data de Publicação: | 2016 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1590/1809-4392201500835 http://hdl.handle.net/11449/172156 |
Resumo: | The Amazon várzeas are an important component of the Amazon biome, but anthropic and climatic impacts have been leading to forest loss and interruption of essential ecosystem functions and services. The objectives of this study were to evaluate the capability of the Landsat-based Detection of Trends in Disturbance and Recovery (LandTrendr) algorithm to characterize changes in várzea forest cover in the Lower Amazon, and to analyze the potential of spectral and temporal attributes to classify forest loss as either natural or anthropogenic. We used a time series of 37 Landsat TM and ETM+ images acquired between 1984 and 2009. We used the LandTrendr algorithm to detect forest cover change and the attributes of “start year”, “magnitude”, and “duration” of the changes, as well as “NDVI at the end of series”. Detection was restricted to areas identified as having forest cover at the start and/or end of the time series. We used the Support Vector Machine (SVM) algorithm to classify the extracted attributes, differentiating between anthropogenic and natural forest loss. Detection reliability was consistently high for change events along the Amazon River channel, but variable for changes within the floodplain. Spectral-temporal trajectories faithfully represented the nature of changes in floodplain forest cover, corroborating field observations. We estimated anthropogenic forest losses to be larger (1.071 ha) than natural losses (884 ha), with a global classification accuracy of 94%. We conclude that the LandTrendr algorithm is a reliable tool for studies of forest dynamics throughout the floodplain. |
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Reconstrução histórica de mudanças na cobertura florestal em várzeas do baixo amazonas utilizando o algoritmo landtrendrReconstructing historical forest cover change in the lower amazon floodplains using the landtrendr algorithmFlooded forestLand use changeLandsatMonitoringWetlandsThe Amazon várzeas are an important component of the Amazon biome, but anthropic and climatic impacts have been leading to forest loss and interruption of essential ecosystem functions and services. The objectives of this study were to evaluate the capability of the Landsat-based Detection of Trends in Disturbance and Recovery (LandTrendr) algorithm to characterize changes in várzea forest cover in the Lower Amazon, and to analyze the potential of spectral and temporal attributes to classify forest loss as either natural or anthropogenic. We used a time series of 37 Landsat TM and ETM+ images acquired between 1984 and 2009. We used the LandTrendr algorithm to detect forest cover change and the attributes of “start year”, “magnitude”, and “duration” of the changes, as well as “NDVI at the end of series”. Detection was restricted to areas identified as having forest cover at the start and/or end of the time series. We used the Support Vector Machine (SVM) algorithm to classify the extracted attributes, differentiating between anthropogenic and natural forest loss. Detection reliability was consistently high for change events along the Amazon River channel, but variable for changes within the floodplain. Spectral-temporal trajectories faithfully represented the nature of changes in floodplain forest cover, corroborating field observations. We estimated anthropogenic forest losses to be larger (1.071 ha) than natural losses (884 ha), with a global classification accuracy of 94%. We conclude that the LandTrendr algorithm is a reliable tool for studies of forest dynamics throughout the floodplain.Instituto Nacional de Pesquisas Espaciais, Divisão de Sensoriamento Remoto, Avenida dos Astronautas, 1758, Jardim da GranjaInstituto de Geociências e Ciências Exatas, UNESP - Universidade Estadual Paulista, Campus de Rio Claro, Departamento de Geografia, Av. 24A, 1515Instituto de Geociências e Ciências Exatas, UNESP - Universidade Estadual Paulista, Campus de Rio Claro, Departamento de Geografia, Av. 24A, 1515Instituto Nacional de Pesquisas Espaciais, Divisão de Sensoriamento RemotoUniversidade Estadual Paulista (Unesp)Fragal, Everton HafemannSilva, Thiago Sanna Freire [UNESP]Novo, Evlyn Márcia Leão de Moraes2018-12-11T16:58:58Z2018-12-11T16:58:58Z2016-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article13-24application/pdfhttp://dx.doi.org/10.1590/1809-4392201500835Acta Amazonica, v. 46, n. 1, p. 13-24, 2016.0044-5967http://hdl.handle.net/11449/17215610.1590/1809-4392201500835S0044-596720160001000132-s2.0-84945291652S0044-59672016000100013.pdfScopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengActa Amazonica0,360info:eu-repo/semantics/openAccess2023-11-13T06:11:30Zoai:repositorio.unesp.br:11449/172156Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T17:34:16.872274Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Reconstrução histórica de mudanças na cobertura florestal em várzeas do baixo amazonas utilizando o algoritmo landtrendr Reconstructing historical forest cover change in the lower amazon floodplains using the landtrendr algorithm |
title |
Reconstrução histórica de mudanças na cobertura florestal em várzeas do baixo amazonas utilizando o algoritmo landtrendr |
spellingShingle |
Reconstrução histórica de mudanças na cobertura florestal em várzeas do baixo amazonas utilizando o algoritmo landtrendr Fragal, Everton Hafemann Flooded forest Land use change Landsat Monitoring Wetlands |
title_short |
Reconstrução histórica de mudanças na cobertura florestal em várzeas do baixo amazonas utilizando o algoritmo landtrendr |
title_full |
Reconstrução histórica de mudanças na cobertura florestal em várzeas do baixo amazonas utilizando o algoritmo landtrendr |
title_fullStr |
Reconstrução histórica de mudanças na cobertura florestal em várzeas do baixo amazonas utilizando o algoritmo landtrendr |
title_full_unstemmed |
Reconstrução histórica de mudanças na cobertura florestal em várzeas do baixo amazonas utilizando o algoritmo landtrendr |
title_sort |
Reconstrução histórica de mudanças na cobertura florestal em várzeas do baixo amazonas utilizando o algoritmo landtrendr |
author |
Fragal, Everton Hafemann |
author_facet |
Fragal, Everton Hafemann Silva, Thiago Sanna Freire [UNESP] Novo, Evlyn Márcia Leão de Moraes |
author_role |
author |
author2 |
Silva, Thiago Sanna Freire [UNESP] Novo, Evlyn Márcia Leão de Moraes |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
Instituto Nacional de Pesquisas Espaciais, Divisão de Sensoriamento Remoto Universidade Estadual Paulista (Unesp) |
dc.contributor.author.fl_str_mv |
Fragal, Everton Hafemann Silva, Thiago Sanna Freire [UNESP] Novo, Evlyn Márcia Leão de Moraes |
dc.subject.por.fl_str_mv |
Flooded forest Land use change Landsat Monitoring Wetlands |
topic |
Flooded forest Land use change Landsat Monitoring Wetlands |
description |
The Amazon várzeas are an important component of the Amazon biome, but anthropic and climatic impacts have been leading to forest loss and interruption of essential ecosystem functions and services. The objectives of this study were to evaluate the capability of the Landsat-based Detection of Trends in Disturbance and Recovery (LandTrendr) algorithm to characterize changes in várzea forest cover in the Lower Amazon, and to analyze the potential of spectral and temporal attributes to classify forest loss as either natural or anthropogenic. We used a time series of 37 Landsat TM and ETM+ images acquired between 1984 and 2009. We used the LandTrendr algorithm to detect forest cover change and the attributes of “start year”, “magnitude”, and “duration” of the changes, as well as “NDVI at the end of series”. Detection was restricted to areas identified as having forest cover at the start and/or end of the time series. We used the Support Vector Machine (SVM) algorithm to classify the extracted attributes, differentiating between anthropogenic and natural forest loss. Detection reliability was consistently high for change events along the Amazon River channel, but variable for changes within the floodplain. Spectral-temporal trajectories faithfully represented the nature of changes in floodplain forest cover, corroborating field observations. We estimated anthropogenic forest losses to be larger (1.071 ha) than natural losses (884 ha), with a global classification accuracy of 94%. We conclude that the LandTrendr algorithm is a reliable tool for studies of forest dynamics throughout the floodplain. |
publishDate |
2016 |
dc.date.none.fl_str_mv |
2016-01-01 2018-12-11T16:58:58Z 2018-12-11T16:58:58Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1590/1809-4392201500835 Acta Amazonica, v. 46, n. 1, p. 13-24, 2016. 0044-5967 http://hdl.handle.net/11449/172156 10.1590/1809-4392201500835 S0044-59672016000100013 2-s2.0-84945291652 S0044-59672016000100013.pdf |
url |
http://dx.doi.org/10.1590/1809-4392201500835 http://hdl.handle.net/11449/172156 |
identifier_str_mv |
Acta Amazonica, v. 46, n. 1, p. 13-24, 2016. 0044-5967 10.1590/1809-4392201500835 S0044-59672016000100013 2-s2.0-84945291652 S0044-59672016000100013.pdf |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Acta Amazonica 0,360 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
13-24 application/pdf |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1808128828078292992 |