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Assessment of land use and cover in the Sucuru Watershed using Google Earth Engine

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Detalhes bibliográficos
Resumo:Human activities modify the natural characteristics of numerous watersheds worldwide. Google Earth Engine provides tools for the analysis of land use and natural resources. In this work, we classify current land use and cover in the Sucuru watershed, Paraíba, Brazil. We compared the accuracy of five supervised classification algorithms of Google Earth Engine. Classifiers based on Decision Trees, such as the Classification and Regression Trees (CART) and Random Forest (RF), showed the best accuracy and visual inspection values. The Google Earth Engine is a powerful tool for analysis of large-scale environmental data, monitoring land use changes, and providing information for sustainable management.
Autores principais:Sousa, José Hugo Simplicio de
Outros Autores:Moreira, Amanda Rezende; Nascimento, Abdalan Andrade do; Ribeiro, George do Nascimento; Oliveira Neto, Jose Nunes de; Prado Júnior, Leonardo Souza do
Assunto:Geotechnologies Remote sensing Supervised classification Geotecnologias Sensoriamento remoto Classificação supervisionada Geotecnologias Sensoriamento remoto Classificação supervisionada
Ano:2022
País:Brasil
Tipo de documento:artigo
Tipo de acesso:unknown
Instituição associada:IBICT
Idioma:inglês
Origem:Oasisbr
Descrição
Resumo:Human activities modify the natural characteristics of numerous watersheds worldwide. Google Earth Engine provides tools for the analysis of land use and natural resources. In this work, we classify current land use and cover in the Sucuru watershed, Paraíba, Brazil. We compared the accuracy of five supervised classification algorithms of Google Earth Engine. Classifiers based on Decision Trees, such as the Classification and Regression Trees (CART) and Random Forest (RF), showed the best accuracy and visual inspection values. The Google Earth Engine is a powerful tool for analysis of large-scale environmental data, monitoring land use changes, and providing information for sustainable management.

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