Spatiotemporal dynamics in the land cover and land use in a river basin in southern Brazil: analysis based on remote sensing and big data

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Autor(es): dc.contributorPostgraduate Program in Environmental Sciences-
Autor(es): dc.contributorUniversidade de São Paulo (USP)-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorTechnology in Environment and Water Resources-
Autor(es): dc.creatorScussel, Cristiane-
Autor(es): dc.creatorde Lima, Sylvia Christina-
Autor(es): dc.creatorMendes, Amanda Letícia de Meneses-
Autor(es): dc.creatorSantander, Marina Barros-
Autor(es): dc.creatorFerreira, Anderson Targino da Silva-
Autor(es): dc.creatorZocche, Jairo José-
Autor(es): dc.creatorGrohmann, Carlos Henrique-
Autor(es): dc.creatorQuintanilha, José Alberto-
Data de aceite: dc.date.accessioned2025-08-21T19:13:26Z-
Data de disponibilização: dc.date.available2025-08-21T19:13:26Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2024-01-02-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.21680/2447-3359.2024v10n1ID34886-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/307449-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/307449-
Descrição: dc.descriptionThe exploitation of natural resources is of concern because economic growth results in negative impacts on environmental balance. This study analyzed the spatiotemporal 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.-
Descrição: dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)-
Descrição: dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)-
Descrição: dc.descriptionUniversity of the Extremo Sul Catarinense Postgraduate Program in Environmental Sciences, SC-
Descrição: dc.descriptionUniversity of São Paulo Institute of Energy and Environment, SP-
Descrição: dc.descriptionPaulista State University Júlio de Mesquita Filho Postgraduate Program in Environmental Sciences, SP-
Descrição: dc.descriptionUniversity of São Paulo Department of Geography, SP-
Descrição: dc.descriptionTechnological College of the State of São Paulo Technology in Environment and Water Resources, SP-
Descrição: dc.descriptionUniversity of Extremo Sul Catarinense Postgraduate Program in Environmental Sciences, SC-
Descrição: dc.descriptionPaulista State University Júlio de Mesquita Filho Postgraduate Program in Environmental Sciences, SP-
Descrição: dc.descriptionCNPq: 305188/2020-8-
Descrição: dc.descriptionCNPq: 311209/2021-1-
Formato: dc.format124-137-
Idioma: dc.languageen-
Idioma: dc.languagept_BR-
Relação: dc.relationRevista de Geociencias do Nordeste-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectDecision trees-
Palavras-chave: dc.subjectEnvironmental degradation-
Palavras-chave: dc.subjectMachine learning-
Título: dc.titleSpatiotemporal dynamics in the land cover and land use in a river basin in southern Brazil: analysis based on remote sensing and big data-
Título: dc.titleDinâmica espaço-temporal na cobertura e uso da terra em uma bacia hidrográfica no sul do Brasil: análise baseada em sensoriamento remoto e big data-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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