Reconstructing Three Decades of Land Use and Land Cover Changes in Brazilian Biomes with Landsat Archive and Earth Engine

Detalhes bibliográficos
Autor(a) principal: Souza Junior, Carlos
Data de Publicação: 2020
Outros Autores: Shimbo, Julia Zanin, Rosa, Marcos Reis, Parente, Leandro L., Alencar, Ane A., Rudorff, Bernardo Friedrich Theodor, Hasenack, Heinrich, Matsumoto, Marcelo, Ferreira, Laerte G., Souza Filho, Pedro Walfir Martins e, Oliveira, Sergio W. de, Rocha, Washington, Fonseca, Antônio Victor, Marques, Camila B., Diniz, Cesar G., Costa, Diego Pereira, Monteiro, Dyeden, Rosa, Eduardo R., Vélez Martin, Eduardo, Weber, Eliseu Jose, Lenti, Felipe Eduardo Brandão, Paternost, Fernando F., Pareyn, Frans Germain Corneel, Siqueira, João, Viera, José L., Ferreira Neto, Luiz Carlos, Saraiva, Marciano M., Sales, Marcio H., Salgado, Moisés P. G., Vasconcelos, Rodrigo, Galano, Soltan, Mesquita, Vinicius Vieira, Azevedo, Tasso
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UFRGS
Texto Completo: http://hdl.handle.net/10183/215029
Resumo: Brazil has a monitoring system to track annual forest conversion in the Amazon and most recently to monitor the Cerrado biome. However, there is still a gap of annual land use and land cover (LULC) information in all Brazilian biomes in the country. Existing countrywide efforts to map land use and land cover lack regularly updates and high spatial resolution time-series data to better understand historical land use and land cover dynamics, and the subsequent impacts in the country biomes. In this study, we described a novel approach and the results achieved by a multi-disciplinary network called MapBiomas to reconstruct annual land use and land cover information between 1985 and 2017 for Brazil, based on random forest applied to Landsat archive using Google Earth Engine. We mapped five major classes: forest, non-forest natural formation, farming, non-vegetated areas, and water. These classes were broken into two sub-classification levels leading to the most comprehensive and detailed mapping for the country at a 30 m pixel resolution. The average overall accuracy of the land use and land cover time-series, based on a stratified random sample of 75,000 pixel locations, was 89% ranging from 73 to 95% in the biomes. The 33 years of LULC change data series revealed that Brazil lost 71 Mha of natural vegetation, mostly to cattle ranching and agriculture activities. Pasture expanded by 46% from 1985 to 2017, and agriculture by 172%, mostly replacing old pasture fields. We also identified that 86 Mha of the converted native vegetation was undergoing some level of regrowth. Several applications of the MapBiomas dataset are underway, suggesting that reconstructing historical land use and land cover change maps is useful for advancing the science and to guide social, economic and environmental policy decision-making processes in Brazil.
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spelling Souza Junior, CarlosShimbo, Julia ZaninRosa, Marcos ReisParente, Leandro L.Alencar, Ane A.Rudorff, Bernardo Friedrich TheodorHasenack, HeinrichMatsumoto, MarceloFerreira, Laerte G.Souza Filho, Pedro Walfir Martins eOliveira, Sergio W. deRocha, WashingtonFonseca, Antônio VictorMarques, Camila B.Diniz, Cesar G.Costa, Diego PereiraMonteiro, DyedenRosa, Eduardo R.Vélez Martin, EduardoWeber, Eliseu JoseLenti, Felipe Eduardo BrandãoPaternost, Fernando F.Pareyn, Frans Germain CorneelSiqueira, JoãoViera, José L.Ferreira Neto, Luiz CarlosSaraiva, Marciano M.Sales, Marcio H.Salgado, Moisés P. G.Vasconcelos, RodrigoGalano, SoltanMesquita, Vinicius VieiraAzevedo, Tasso2020-11-13T04:21:34Z20202072-4292http://hdl.handle.net/10183/215029001117848Brazil has a monitoring system to track annual forest conversion in the Amazon and most recently to monitor the Cerrado biome. However, there is still a gap of annual land use and land cover (LULC) information in all Brazilian biomes in the country. Existing countrywide efforts to map land use and land cover lack regularly updates and high spatial resolution time-series data to better understand historical land use and land cover dynamics, and the subsequent impacts in the country biomes. In this study, we described a novel approach and the results achieved by a multi-disciplinary network called MapBiomas to reconstruct annual land use and land cover information between 1985 and 2017 for Brazil, based on random forest applied to Landsat archive using Google Earth Engine. We mapped five major classes: forest, non-forest natural formation, farming, non-vegetated areas, and water. These classes were broken into two sub-classification levels leading to the most comprehensive and detailed mapping for the country at a 30 m pixel resolution. The average overall accuracy of the land use and land cover time-series, based on a stratified random sample of 75,000 pixel locations, was 89% ranging from 73 to 95% in the biomes. The 33 years of LULC change data series revealed that Brazil lost 71 Mha of natural vegetation, mostly to cattle ranching and agriculture activities. Pasture expanded by 46% from 1985 to 2017, and agriculture by 172%, mostly replacing old pasture fields. We also identified that 86 Mha of the converted native vegetation was undergoing some level of regrowth. Several applications of the MapBiomas dataset are underway, suggesting that reconstructing historical land use and land cover change maps is useful for advancing the science and to guide social, economic and environmental policy decision-making processes in Brazil.application/pdfengRemote Sensing. Basel. Vol. 12, n. 17 (2020), 27 p.Uso da terraSoloBiomasLandsatBrasilReconstructing Three Decades of Land Use and Land Cover Changes in Brazilian Biomes with Landsat Archive and Earth EngineEstrangeiroinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da UFRGSinstname:Universidade Federal do Rio Grande do Sul (UFRGS)instacron:UFRGSTEXT001117848.pdf.txt001117848.pdf.txtExtracted Texttext/plain98203http://www.lume.ufrgs.br/bitstream/10183/215029/2/001117848.pdf.txt642192b4feeb349d7668982df17905a0MD52ORIGINAL001117848.pdfTexto completo (inglês)application/pdf5877771http://www.lume.ufrgs.br/bitstream/10183/215029/1/001117848.pdf2d18b93e5b93fb4b54e91109cce57ec6MD5110183/2150292023-08-23 03:30:10.636417oai:www.lume.ufrgs.br:10183/215029Repositório de PublicaçõesPUBhttps://lume.ufrgs.br/oai/requestopendoar:2023-08-23T06:30:10Repositório Institucional da UFRGS - Universidade Federal do Rio Grande do Sul (UFRGS)false
dc.title.pt_BR.fl_str_mv Reconstructing Three Decades of Land Use and Land Cover Changes in Brazilian Biomes with Landsat Archive and Earth Engine
title Reconstructing Three Decades of Land Use and Land Cover Changes in Brazilian Biomes with Landsat Archive and Earth Engine
spellingShingle Reconstructing Three Decades of Land Use and Land Cover Changes in Brazilian Biomes with Landsat Archive and Earth Engine
Souza Junior, Carlos
Uso da terra
Solo
Biomas
Landsat
Brasil
title_short Reconstructing Three Decades of Land Use and Land Cover Changes in Brazilian Biomes with Landsat Archive and Earth Engine
title_full Reconstructing Three Decades of Land Use and Land Cover Changes in Brazilian Biomes with Landsat Archive and Earth Engine
title_fullStr Reconstructing Three Decades of Land Use and Land Cover Changes in Brazilian Biomes with Landsat Archive and Earth Engine
title_full_unstemmed Reconstructing Three Decades of Land Use and Land Cover Changes in Brazilian Biomes with Landsat Archive and Earth Engine
title_sort Reconstructing Three Decades of Land Use and Land Cover Changes in Brazilian Biomes with Landsat Archive and Earth Engine
author Souza Junior, Carlos
author_facet Souza Junior, Carlos
Shimbo, Julia Zanin
Rosa, Marcos Reis
Parente, Leandro L.
Alencar, Ane A.
Rudorff, Bernardo Friedrich Theodor
Hasenack, Heinrich
Matsumoto, Marcelo
Ferreira, Laerte G.
Souza Filho, Pedro Walfir Martins e
Oliveira, Sergio W. de
Rocha, Washington
Fonseca, Antônio Victor
Marques, Camila B.
Diniz, Cesar G.
Costa, Diego Pereira
Monteiro, Dyeden
Rosa, Eduardo R.
Vélez Martin, Eduardo
Weber, Eliseu Jose
Lenti, Felipe Eduardo Brandão
Paternost, Fernando F.
Pareyn, Frans Germain Corneel
Siqueira, João
Viera, José L.
Ferreira Neto, Luiz Carlos
Saraiva, Marciano M.
Sales, Marcio H.
Salgado, Moisés P. G.
Vasconcelos, Rodrigo
Galano, Soltan
Mesquita, Vinicius Vieira
Azevedo, Tasso
author_role author
author2 Shimbo, Julia Zanin
Rosa, Marcos Reis
Parente, Leandro L.
Alencar, Ane A.
Rudorff, Bernardo Friedrich Theodor
Hasenack, Heinrich
Matsumoto, Marcelo
Ferreira, Laerte G.
Souza Filho, Pedro Walfir Martins e
Oliveira, Sergio W. de
Rocha, Washington
Fonseca, Antônio Victor
Marques, Camila B.
Diniz, Cesar G.
Costa, Diego Pereira
Monteiro, Dyeden
Rosa, Eduardo R.
Vélez Martin, Eduardo
Weber, Eliseu Jose
Lenti, Felipe Eduardo Brandão
Paternost, Fernando F.
Pareyn, Frans Germain Corneel
Siqueira, João
Viera, José L.
Ferreira Neto, Luiz Carlos
Saraiva, Marciano M.
Sales, Marcio H.
Salgado, Moisés P. G.
Vasconcelos, Rodrigo
Galano, Soltan
Mesquita, Vinicius Vieira
Azevedo, Tasso
author2_role author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
dc.contributor.author.fl_str_mv Souza Junior, Carlos
Shimbo, Julia Zanin
Rosa, Marcos Reis
Parente, Leandro L.
Alencar, Ane A.
Rudorff, Bernardo Friedrich Theodor
Hasenack, Heinrich
Matsumoto, Marcelo
Ferreira, Laerte G.
Souza Filho, Pedro Walfir Martins e
Oliveira, Sergio W. de
Rocha, Washington
Fonseca, Antônio Victor
Marques, Camila B.
Diniz, Cesar G.
Costa, Diego Pereira
Monteiro, Dyeden
Rosa, Eduardo R.
Vélez Martin, Eduardo
Weber, Eliseu Jose
Lenti, Felipe Eduardo Brandão
Paternost, Fernando F.
Pareyn, Frans Germain Corneel
Siqueira, João
Viera, José L.
Ferreira Neto, Luiz Carlos
Saraiva, Marciano M.
Sales, Marcio H.
Salgado, Moisés P. G.
Vasconcelos, Rodrigo
Galano, Soltan
Mesquita, Vinicius Vieira
Azevedo, Tasso
dc.subject.por.fl_str_mv Uso da terra
Solo
Biomas
Landsat
Brasil
topic Uso da terra
Solo
Biomas
Landsat
Brasil
description Brazil has a monitoring system to track annual forest conversion in the Amazon and most recently to monitor the Cerrado biome. However, there is still a gap of annual land use and land cover (LULC) information in all Brazilian biomes in the country. Existing countrywide efforts to map land use and land cover lack regularly updates and high spatial resolution time-series data to better understand historical land use and land cover dynamics, and the subsequent impacts in the country biomes. In this study, we described a novel approach and the results achieved by a multi-disciplinary network called MapBiomas to reconstruct annual land use and land cover information between 1985 and 2017 for Brazil, based on random forest applied to Landsat archive using Google Earth Engine. We mapped five major classes: forest, non-forest natural formation, farming, non-vegetated areas, and water. These classes were broken into two sub-classification levels leading to the most comprehensive and detailed mapping for the country at a 30 m pixel resolution. The average overall accuracy of the land use and land cover time-series, based on a stratified random sample of 75,000 pixel locations, was 89% ranging from 73 to 95% in the biomes. The 33 years of LULC change data series revealed that Brazil lost 71 Mha of natural vegetation, mostly to cattle ranching and agriculture activities. Pasture expanded by 46% from 1985 to 2017, and agriculture by 172%, mostly replacing old pasture fields. We also identified that 86 Mha of the converted native vegetation was undergoing some level of regrowth. Several applications of the MapBiomas dataset are underway, suggesting that reconstructing historical land use and land cover change maps is useful for advancing the science and to guide social, economic and environmental policy decision-making processes in Brazil.
publishDate 2020
dc.date.accessioned.fl_str_mv 2020-11-13T04:21:34Z
dc.date.issued.fl_str_mv 2020
dc.type.driver.fl_str_mv Estrangeiro
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dc.identifier.issn.pt_BR.fl_str_mv 2072-4292
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dc.language.iso.fl_str_mv eng
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dc.relation.ispartof.pt_BR.fl_str_mv Remote Sensing. Basel. Vol. 12, n. 17 (2020), 27 p.
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eu_rights_str_mv openAccess
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