Accuracy analysis of mapping land use and occupation using Sentinel-2 and CBERS-4 images in the surroundings of a reservoirs

Detecting changes in land cover helps policymakers understand the dynamics of environmental changes to ensure sustainable development in the Caatinga biome. Thus, the identification of spatial characteristics by Remote Sensing has emerged as an important aspect of research, and, therefore, adequate...

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Authors: Silva Júnior, Juarez Antônio da, Silva Junior, Ubiratan Joaquim da, Almeida, Débora Natália Oliveira de, Paiva, Anderson Luiz Ribeiro de, Santos, Ester Milena dos, Santos, Sylvana Melo dos, Oliveira, Leidjane Maria Maciel de
Format: article
Status:Published version
Publication Date:2024
Country:Brasil
Institution:Universidade Federal de Santa Maria (UFSM)
Repository:Revista Ciência e Natura (Online)
Language:Portuguese
OAI Identifier:oai:ojs.pkp.sfu.ca:article/84730
Online Access:https://periodicos.ufsm.br/cienciaenatura/article/view/84730
Access Level:Open access
Keyword:Sensoriamento Remoto
Aprendizagem de Máquinas
Reservatório Pedro Mauro Junior
Remote Sensing
Machine Learning
Pedro Mauro Junior Reservoir
id BR_534ce6d8fb76fe220d2a8d8093c2c921
oai_identifier_str oai:ojs.pkp.sfu.ca:article/84730
network_acronym_str BR
network_name_str Brasil
repository_id_str
dc.title.none.fl_str_mv Accuracy analysis of mapping land use and occupation using Sentinel-2 and CBERS-4 images in the surroundings of a reservoirs
Análise de acurácia do mapeamento do uso e ocupação do solo utilizando imagens Sentinel-2 e CBERS-4 no entorno de reservatórios
title Accuracy analysis of mapping land use and occupation using Sentinel-2 and CBERS-4 images in the surroundings of a reservoirs
spellingShingle Accuracy analysis of mapping land use and occupation using Sentinel-2 and CBERS-4 images in the surroundings of a reservoirs
Silva Júnior, Juarez Antônio da
Sensoriamento Remoto
Aprendizagem de Máquinas
Reservatório Pedro Mauro Junior
Remote Sensing
Machine Learning
Pedro Mauro Junior Reservoir
title_short Accuracy analysis of mapping land use and occupation using Sentinel-2 and CBERS-4 images in the surroundings of a reservoirs
title_full Accuracy analysis of mapping land use and occupation using Sentinel-2 and CBERS-4 images in the surroundings of a reservoirs
title_fullStr Accuracy analysis of mapping land use and occupation using Sentinel-2 and CBERS-4 images in the surroundings of a reservoirs
title_full_unstemmed Accuracy analysis of mapping land use and occupation using Sentinel-2 and CBERS-4 images in the surroundings of a reservoirs
title_sort Accuracy analysis of mapping land use and occupation using Sentinel-2 and CBERS-4 images in the surroundings of a reservoirs
dc.creator.none.fl_str_mv Silva Júnior, Juarez Antônio da
Silva Junior, Ubiratan Joaquim da
Almeida, Débora Natália Oliveira de
Paiva, Anderson Luiz Ribeiro de
Santos, Ester Milena dos
Santos, Sylvana Melo dos
Oliveira, Leidjane Maria Maciel de
author Silva Júnior, Juarez Antônio da
author_facet Silva Júnior, Juarez Antônio da
Silva Junior, Ubiratan Joaquim da
Almeida, Débora Natália Oliveira de
Paiva, Anderson Luiz Ribeiro de
Santos, Ester Milena dos
Santos, Sylvana Melo dos
Oliveira, Leidjane Maria Maciel de
author_role author
author2 Silva Junior, Ubiratan Joaquim da
Almeida, Débora Natália Oliveira de
Paiva, Anderson Luiz Ribeiro de
Santos, Ester Milena dos
Santos, Sylvana Melo dos
Oliveira, Leidjane Maria Maciel de
author2_role author
author
author
author
author
author
dc.subject.por.fl_str_mv Sensoriamento Remoto
Aprendizagem de Máquinas
Reservatório Pedro Mauro Junior
Remote Sensing
Machine Learning
Pedro Mauro Junior Reservoir
topic Sensoriamento Remoto
Aprendizagem de Máquinas
Reservatório Pedro Mauro Junior
Remote Sensing
Machine Learning
Pedro Mauro Junior Reservoir
description Detecting changes in land cover helps policymakers understand the dynamics of environmental changes to ensure sustainable development in the Caatinga biome. Thus, the identification of spatial characteristics by Remote Sensing has emerged as an important aspect of research, and, therefore, adequate and efficient methodology for mapping the necessary land cover is a preponderant factor. In this study, data from the Sentinel-2 and CBERS-4 satellites captured by the MultiSpectral Instrument (MSI) and Panchromatic and Multispectral Camera (PAN) sensors, respectively, were used for classification and accuracy analysis for five land cover classes around dams located in the municipality of Belo Jardim, Pernambuco. The KNN algorithm (K-th nearest neighbor) with a value of k=1 was used for image training and classification. Recent high-resolution images from the European program WorldCover were used as a spatial and thematic reference image. After the Contingency Matrix analysis between the land cover maps and the reference data, an overall accuracy of 57.4% was obtained for the MSI and 54.5% for the PAN product. The results obtained showed that the MSI presented more satisfactory land cover maps than the PAN data. On the other hand, for the shrubby vegetation class, the PAN product presented an r of 0.5, while the MSI had an r of 0.47. Spatial and spectral characteristics of the images were the main causes of the variability found in the thematic accuracy coefficients.
publishDate 2024
dc.date.none.fl_str_mv 2024-08-23
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.ufsm.br/cienciaenatura/article/view/84730
10.5902/2179460X84730
url https://periodicos.ufsm.br/cienciaenatura/article/view/84730
identifier_str_mv 10.5902/2179460X84730
dc.language.iso.fl_str_mv por
language por
dc.relation.none.fl_str_mv https://periodicos.ufsm.br/cienciaenatura/article/view/84730/64215
dc.rights.driver.fl_str_mv Copyright (c) 2024 Ciência e Natura
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Copyright (c) 2024 Ciência e Natura
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Universidade Federal de Santa Maria
publisher.none.fl_str_mv Universidade Federal de Santa Maria
dc.source.none.fl_str_mv Ciência e Natura; Vol. 46 (2024): Publicação contínua; e84730
Ciência e Natura; v. 46 (2024): Publicação contínua; e84730
2179-460X
0100-8307
reponame:Revista Ciência e Natura (Online)
instname:Universidade Federal de Santa Maria (UFSM)
instacron:UFSM
instname_str Universidade Federal de Santa Maria (UFSM)
instacron_str UFSM
institution UFSM
reponame_str Revista Ciência e Natura (Online)
collection Revista Ciência e Natura (Online)
repository.name.fl_str_mv Revista Ciência e Natura (Online) - Universidade Federal de Santa Maria (UFSM)
repository.mail.fl_str_mv cienciaenatura@ufsm.br || centraldeperiodicos@ufsm.br
_version_ 1853664263166492672
spelling Accuracy analysis of mapping land use and occupation using Sentinel-2 and CBERS-4 images in the surroundings of a reservoirsAnálise de acurácia do mapeamento do uso e ocupação do solo utilizando imagens Sentinel-2 e CBERS-4 no entorno de reservatóriosSensoriamento RemotoAprendizagem de MáquinasReservatório Pedro Mauro JuniorRemote SensingMachine LearningPedro Mauro Junior ReservoirDetecting changes in land cover helps policymakers understand the dynamics of environmental changes to ensure sustainable development in the Caatinga biome. Thus, the identification of spatial characteristics by Remote Sensing has emerged as an important aspect of research, and, therefore, adequate and efficient methodology for mapping the necessary land cover is a preponderant factor. In this study, data from the Sentinel-2 and CBERS-4 satellites captured by the MultiSpectral Instrument (MSI) and Panchromatic and Multispectral Camera (PAN) sensors, respectively, were used for classification and accuracy analysis for five land cover classes around dams located in the municipality of Belo Jardim, Pernambuco. The KNN algorithm (K-th nearest neighbor) with a value of k=1 was used for image training and classification. Recent high-resolution images from the European program WorldCover were used as a spatial and thematic reference image. After the Contingency Matrix analysis between the land cover maps and the reference data, an overall accuracy of 57.4% was obtained for the MSI and 54.5% for the PAN product. The results obtained showed that the MSI presented more satisfactory land cover maps than the PAN data. On the other hand, for the shrubby vegetation class, the PAN product presented an r of 0.5, while the MSI had an r of 0.47. Spatial and spectral characteristics of the images were the main causes of the variability found in the thematic accuracy coefficients.A detecção de mudanças na cobertura do solo ajuda os formuladores de políticas a entender a dinâmica das mudanças ambientais para garantir o desenvolvimento sustentável no bioma Caatinga. Assim, a identificação de características espaciais por Sensoriamento Remoto surgiu como um importante aspecto de pesquisa e, dessa forma, metodologia adequada e eficiente para o mapeamento de cobertura do solo necessárias é fator preponderante. Neste estudo, os dados do satélite Sentinel-2 e CBERS-4 capturados pelos sensores MultiSpectral Instrument (MSI) e a Câmera Pancromática e Multiespectral (PAN), respectivamente, foram usados para a classificação e análise de acurácia para cinco classes de cobertura da terra no entorno de Barragens localizados no município de Belo Jardim, Pernambuco. O algoritmo KNN (K-ésimo vizinho mais próximo) com um valor de k=1 foi utilizado para o treinamento e classificação das imagens. As recentes imagens de alta resolução do programa europeu WorldCover foram utilizadas como imagem de referência espacial e temática. Após a análise por Matrix Contingência entre os mapas de cobertura do solo e os dados de referência, foram obtidos uma acurácia global de 57,4% para o MSI e 54,5% para o produto PAN. Os resultados obtidos mostraram que o MSI apresentou mapas de cobertura da terra mais satisfatórias do que os dados PAN. Por outro lado, para a classe de vegetação arbustiva para o produto PAN apresentou r de 0,5 enquanto o MSI de 0,47. Características espaciais e espectrais das imagens foram os principais causadores das variabilidades encontradas nos coeficientes de acurácia temática.Universidade Federal de Santa Maria2024-08-23info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://periodicos.ufsm.br/cienciaenatura/article/view/8473010.5902/2179460X84730Ciência e Natura; Vol. 46 (2024): Publicação contínua; e84730Ciência e Natura; v. 46 (2024): Publicação contínua; e847302179-460X0100-8307reponame:Revista Ciência e Natura (Online)instname:Universidade Federal de Santa Maria (UFSM)instacron:UFSMporhttps://periodicos.ufsm.br/cienciaenatura/article/view/84730/64215Copyright (c) 2024 Ciência e Naturainfo:eu-repo/semantics/openAccessSilva Júnior, Juarez Antônio daSilva Junior, Ubiratan Joaquim daAlmeida, Débora Natália Oliveira dePaiva, Anderson Luiz Ribeiro deSantos, Ester Milena dosSantos, Sylvana Melo dosOliveira, Leidjane Maria Maciel de2024-11-19T11:33:44Zoai:ojs.pkp.sfu.ca:article/84730Revistahttps://periodicos.ufsm.br/cienciaenatura/indexPUBhttps://periodicos.ufsm.br/cienciaenatura/oaicienciaenatura@ufsm.br || centraldeperiodicos@ufsm.br2179-460X0100-8307opendoar:2024-11-19T11:33:44Revista Ciência e Natura (Online) - Universidade Federal de Santa Maria (UFSM)false
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