Spatiotemporal : Spatiotemporal dynamics in the land cover and land use in a river basin in southern Brazil: analysis based on remote sensing and big data
Autor(a) principal: | |
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Data de Publicação: | 2024 |
Outros Autores: | , , , , , , |
Tipo de documento: | Artigo |
Idioma: | por eng |
Título da fonte: | Revista de Geociências do Nordeste |
Texto Completo: | https://periodicos.ufrn.br/revistadoregne/article/view/34886 |
Resumo: | The exploitation of natural resources is of concern because economic growth results in negative impacts on environmental balance. This study analyzed the spatio-temporal changes in land cover and land use (LULC) in the Araranguá River Watershed (ARW), southern of Santa Catarina state, south Brazil, in the period of 2016-2023. Images from the Sentinel-2A satellite were used, the RGB, NIR and SWIR 1 bands were selected and the EVI2, MNDWI, NDBI indices were applied, which resulted in the selection of eight LULC classes. The orbital images were classified using programming routines in Google Earth Engine (GEE) and validation was performed by obtaining data generated by the platform. The overall accuracy was 93% for both years assessed. The Native Forest class was the most representative and increased by 1.62% in the last seven years. The Built Area class grew the most, and Pasture/Herbaceous Vegetation class decreased by 5.6%. The results revealed slight changes in the landscape, with areas with native forests being maintained and urban expansion occurring. These data can help public policy makers and decision makers to manage the basin territory with a bias towards the conservation and preservation of natural resources. Keywords: environmental degradation; machine learning; decision trees. |
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Spatiotemporal : Spatiotemporal dynamics in the land cover and land use in a river basin in southern Brazil: analysis based on remote sensing and big dataDinâmica espaço-temporal na cobertura e uso da terra em uma bacia hidrográfica no sul do Brasil: análise baseada em sensoriamento remoto e big data: Spatiotemporal dynamics in the land cover and land use in a river basin in southern Brazil: analysis based on remote sensing and big dataThe exploitation of natural resources is of concern because economic growth results in negative impacts on environmental balance. This study analyzed the spatio-temporal changes in land cover and land use (LULC) in the Araranguá River Watershed (ARW), southern of Santa Catarina state, south Brazil, in the period of 2016-2023. Images from the Sentinel-2A satellite were used, the RGB, NIR and SWIR 1 bands were selected and the EVI2, MNDWI, NDBI indices were applied, which resulted in the selection of eight LULC classes. The orbital images were classified using programming routines in Google Earth Engine (GEE) and validation was performed by obtaining data generated by the platform. The overall accuracy was 93% for both years assessed. The Native Forest class was the most representative and increased by 1.62% in the last seven years. The Built Area class grew the most, and Pasture/Herbaceous Vegetation class decreased by 5.6%. The results revealed slight changes in the landscape, with areas with native forests being maintained and urban expansion occurring. These data can help public policy makers and decision makers to manage the basin territory with a bias towards the conservation and preservation of natural resources. Keywords: environmental degradation; machine learning; decision trees.A exploração dos recursos naturais é alvo de preocupação visto que o crescimento econômico interfere no equilíbrio ambiental. O estudo analisou as mudanças espaço-temporais na cobertura e uso da terra (CUT) na Bacia Hidrográfica do Rio Araranguá (BHRA), sul de Santa Catarina, Brasil, no período de 2016-2023. Foram utilizadas imagens do satélite Sentinel 2A, selecionadas as bandas RGB, NIR e SWIR 1 e, aplicados os índices EVI2, MNDWI e NDBI, o que resultou na seleção de oito classes de CUT. As imagens orbitais foram classificadas por meio de rotinas de programação no Google Earth Engine (GEE) e a validação foi realizada a partir da obtenção de dados gerados pela plataforma. Os resultados evidenciaram acurácia geral de 93% para os dois anos. A classe Floresta Nativa foi a mais representativa e aumentou cerca de 1,62% nos últimos sete anos. Área Construída foi a classe que mais cresceu e a classe Pastagem/Vegetação Herbácea teve redução de 5,6%. Os resultados revelaram mudanças tênues na paisagem, mantendo áreas com florestas nativas e incremento da expansão urbana. Esses dados podem auxiliar as políticas públicas e as tomadas de decisão no gerenciamento do território da bacia com viés para a conservação e preservação dos recursos naturais. Palavras-chave: degradação ambiental; aprendizado de máquina; árvores de decisão.Universidade Federal do Rio Grande do Norte2024-03-08info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttps://periodicos.ufrn.br/revistadoregne/article/view/3488610.21680/2447-3359.2024v10n1ID34886Notheast Geoscience Journal; Vol. 10 No. 1 (2024); 124-137Revista de Geociências do Nordeste; v. 10 n. 1 (2024); 124-1372447-335910.21680/2447-3359.2024v10n1reponame:Revista de Geociências do Nordesteinstname:Universidade Federal do Rio Grande do Norte (UFRN)instacron:UFRNporenghttps://periodicos.ufrn.br/revistadoregne/article/view/34886/18376https://periodicos.ufrn.br/revistadoregne/article/view/34886/18377Copyright (c) 2024 Revista de Geociências do Nordestehttps://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessScussel, Cristiane de Lima, Sylvia Christina de Meneses Mendes, Amanda LetíciaBarros Santander, MarinaTargino da Silva Ferreira, AndersonZocche, Jairo JoséGrohmann, Carlos HenriqueQuintanilha, José Alberto2024-03-13T13:50:40Zoai:periodicos.ufrn.br:article/34886Revistahttps://periodicos.ufrn.br/revistadoregne/indexPUBhttps://periodicos.ufrn.br/revistadoregne/oairegneufrn@gmail.com || periodicos@bczm.ufrn.br2447-33592447-3359opendoar:2024-03-13T13:50:40Revista de Geociências do Nordeste - Universidade Federal do Rio Grande do Norte (UFRN)false |
dc.title.none.fl_str_mv |
Spatiotemporal : Spatiotemporal dynamics in the land cover and land use in a river basin in southern Brazil: analysis based on remote sensing and big data Dinâmica espaço-temporal na cobertura e uso da terra em uma bacia hidrográfica no sul do Brasil: análise baseada em sensoriamento remoto e big data: Spatiotemporal dynamics in the land cover and land use in a river basin in southern Brazil: analysis based on remote sensing and big data |
title |
Spatiotemporal : Spatiotemporal dynamics in the land cover and land use in a river basin in southern Brazil: analysis based on remote sensing and big data |
spellingShingle |
Spatiotemporal : Spatiotemporal dynamics in the land cover and land use in a river basin in southern Brazil: analysis based on remote sensing and big data Scussel, Cristiane |
title_short |
Spatiotemporal : Spatiotemporal dynamics in the land cover and land use in a river basin in southern Brazil: analysis based on remote sensing and big data |
title_full |
Spatiotemporal : Spatiotemporal dynamics in the land cover and land use in a river basin in southern Brazil: analysis based on remote sensing and big data |
title_fullStr |
Spatiotemporal : Spatiotemporal dynamics in the land cover and land use in a river basin in southern Brazil: analysis based on remote sensing and big data |
title_full_unstemmed |
Spatiotemporal : Spatiotemporal dynamics in the land cover and land use in a river basin in southern Brazil: analysis based on remote sensing and big data |
title_sort |
Spatiotemporal : Spatiotemporal dynamics in the land cover and land use in a river basin in southern Brazil: analysis based on remote sensing and big data |
author |
Scussel, Cristiane |
author_facet |
Scussel, Cristiane de Lima, Sylvia Christina de Meneses Mendes, Amanda Letícia Barros Santander, Marina Targino da Silva Ferreira, Anderson Zocche, Jairo José Grohmann, Carlos Henrique Quintanilha, José Alberto |
author_role |
author |
author2 |
de Lima, Sylvia Christina de Meneses Mendes, Amanda Letícia Barros Santander, Marina Targino da Silva Ferreira, Anderson Zocche, Jairo José Grohmann, Carlos Henrique Quintanilha, José Alberto |
author2_role |
author author author author author author author |
dc.contributor.author.fl_str_mv |
Scussel, Cristiane de Lima, Sylvia Christina de Meneses Mendes, Amanda Letícia Barros Santander, Marina Targino da Silva Ferreira, Anderson Zocche, Jairo José Grohmann, Carlos Henrique Quintanilha, José Alberto |
description |
The exploitation of natural resources is of concern because economic growth results in negative impacts on environmental balance. This study analyzed the spatio-temporal changes in land cover and land use (LULC) in the Araranguá River Watershed (ARW), southern of Santa Catarina state, south Brazil, in the period of 2016-2023. Images from the Sentinel-2A satellite were used, the RGB, NIR and SWIR 1 bands were selected and the EVI2, MNDWI, NDBI indices were applied, which resulted in the selection of eight LULC classes. The orbital images were classified using programming routines in Google Earth Engine (GEE) and validation was performed by obtaining data generated by the platform. The overall accuracy was 93% for both years assessed. The Native Forest class was the most representative and increased by 1.62% in the last seven years. The Built Area class grew the most, and Pasture/Herbaceous Vegetation class decreased by 5.6%. The results revealed slight changes in the landscape, with areas with native forests being maintained and urban expansion occurring. These data can help public policy makers and decision makers to manage the basin territory with a bias towards the conservation and preservation of natural resources. Keywords: environmental degradation; machine learning; decision trees. |
publishDate |
2024 |
dc.date.none.fl_str_mv |
2024-03-08 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://periodicos.ufrn.br/revistadoregne/article/view/34886 10.21680/2447-3359.2024v10n1ID34886 |
url |
https://periodicos.ufrn.br/revistadoregne/article/view/34886 |
identifier_str_mv |
10.21680/2447-3359.2024v10n1ID34886 |
dc.language.iso.fl_str_mv |
por eng |
language |
por eng |
dc.relation.none.fl_str_mv |
https://periodicos.ufrn.br/revistadoregne/article/view/34886/18376 https://periodicos.ufrn.br/revistadoregne/article/view/34886/18377 |
dc.rights.driver.fl_str_mv |
Copyright (c) 2024 Revista de Geociências do Nordeste https://creativecommons.org/licenses/by/4.0 info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2024 Revista de Geociências do Nordeste https://creativecommons.org/licenses/by/4.0 |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf application/pdf |
dc.publisher.none.fl_str_mv |
Universidade Federal do Rio Grande do Norte |
publisher.none.fl_str_mv |
Universidade Federal do Rio Grande do Norte |
dc.source.none.fl_str_mv |
Notheast Geoscience Journal; Vol. 10 No. 1 (2024); 124-137 Revista de Geociências do Nordeste; v. 10 n. 1 (2024); 124-137 2447-3359 10.21680/2447-3359.2024v10n1 reponame:Revista de Geociências do Nordeste instname:Universidade Federal do Rio Grande do Norte (UFRN) instacron:UFRN |
instname_str |
Universidade Federal do Rio Grande do Norte (UFRN) |
instacron_str |
UFRN |
institution |
UFRN |
reponame_str |
Revista de Geociências do Nordeste |
collection |
Revista de Geociências do Nordeste |
repository.name.fl_str_mv |
Revista de Geociências do Nordeste - Universidade Federal do Rio Grande do Norte (UFRN) |
repository.mail.fl_str_mv |
regneufrn@gmail.com || periodicos@bczm.ufrn.br |
_version_ |
1797052927587647488 |