Geostatistics and remote sensing methods in the classification of images of areas cultivated with citrus
Autor(a) principal: | |
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Data de Publicação: | 2013 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1590/S0100-69162013000600017 http://hdl.handle.net/11449/110179 |
Resumo: | This study compares the precision of three image classification methods, two of remote sensing and one of geostatistics applied to areas cultivated with citrus. The 5,296.52ha area of study is located in the city of Araraquara - central region of the state of São Paulo (SP), Brazil. The multispectral image from the CCD/CBERS-2B satellite was acquired in 2009 and processed through the Geographic Information System (GIS) SPRING. Three classification methods were used, one unsupervised (Cluster), and two supervised (Indicator Kriging/IK and Maximum Likelihood/Maxver), in addition to the screen classification taken as field checking.. Reliability of classifications was evaluated by Kappa index. In accordance with the Kappa index, the Indicator kriging method obtained the highest degree of reliability for bands 2 and 4. Moreover the Cluster method applied to band 2 (green) was the best quality classification between all the methods. Indicator Kriging was the classifier that presented the citrus total area closest to the field check estimated by -3.01%, whereas Maxver overestimated the total citrus area by 42.94%. |
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Repositório Institucional da UNESP |
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Geostatistics and remote sensing methods in the classification of images of areas cultivated with citrusGeoestatística e sensoriamento remoto na classificação de imagens em áreas cultivadas com citrosKrigagem IndicativaClusterMaxversatélite CBERS-2Bclassificação espacialIndicator KrigingClusterMaxverCBERS-2B satellitespatial classificationThis study compares the precision of three image classification methods, two of remote sensing and one of geostatistics applied to areas cultivated with citrus. The 5,296.52ha area of study is located in the city of Araraquara - central region of the state of São Paulo (SP), Brazil. The multispectral image from the CCD/CBERS-2B satellite was acquired in 2009 and processed through the Geographic Information System (GIS) SPRING. Three classification methods were used, one unsupervised (Cluster), and two supervised (Indicator Kriging/IK and Maximum Likelihood/Maxver), in addition to the screen classification taken as field checking.. Reliability of classifications was evaluated by Kappa index. In accordance with the Kappa index, the Indicator kriging method obtained the highest degree of reliability for bands 2 and 4. Moreover the Cluster method applied to band 2 (green) was the best quality classification between all the methods. Indicator Kriging was the classifier that presented the citrus total area closest to the field check estimated by -3.01%, whereas Maxver overestimated the total citrus area by 42.94%.O objetivo deste trabalho foi comparar a precisão de métodos de classificação de imagens orbitais na determinação de áreas cultivadas com citros, por métodos de sensoriamento remoto e de geoestatística. A área de estudo utilizada nesta pesquisa está localizada na cidade de Araraquara, região central do Estado de São Paulo, com 5.296,52 ha. Foi utilizada a imagem multiespectral do satélite CCD/CBERS 2B, adquirida em 2009 e processada no Sistema de Informações Geográficas (SIG) SPRING. Três métodos de classificação foram utilizados, sendo um não supervisionado (Cluster) e dois supervisionados (Krigagem Indicativa/KI e Máxima verossimilhança/Maxver), além da classificação em tela tida como verdade terrestre. A fidedignidade das classificações foi avaliada pelo índice Kappa. A Krigagem Indicativa foi o método que melhor classificou as áreas de citros na banda 4, com forte grau de concordância, conforme a estatística kappa, para as bandas 2 e 4. Pela validação, o Cluster para a banda 2 (verde) foi o classificador que obteve a melhor qualidade. A Krigagem Indicativa foi o classificador que apresentou área total de citros mais próxima da verdade terrestre em -3,01%, enquanto o Maxver foi o classificador que mais superestimou a área total de citros em 42,94 %.GEPAG - Georeferenced Agricultural Research GroupDepartment of Natural Resources - Soil SciencesGraduate Program in Agronomy - Energia na Agricultura, FCA / UNESP BotucatuCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)UNESP FCAUNESP FCA Depto de Solo e Recursos AmbientaisUNESP Instituto de Geociências e Ciências Exatas Departamento de Geologia AplicadaUNESP FCAUNESP FCA Depto de Solo e Recursos AmbientaisUNESP Instituto de Geociências e Ciências Exatas Departamento de Geologia AplicadaAssociação Brasileira de Engenharia Agrícola (SBEA)Universidade Estadual Paulista (Unesp)Silva, Alessandra F. [UNESP]Barbosa, Ana Paula [UNESP]Zimback, Célia Regina Lopes [UNESP]Landim, Paulo M. B. [UNESP]2014-10-01T13:08:53Z2014-10-01T13:08:53Z2013-12-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article1245-1256application/pdfhttp://dx.doi.org/10.1590/S0100-69162013000600017Engenharia Agrícola. Associação Brasileira de Engenharia Agrícola, v. 33, n. 6, p. 1245-1256, 2013.0100-6916http://hdl.handle.net/11449/11017910.1590/S0100-69162013000600017S0100-69162013000600017WOS:000331653400018S0100-69162013000600017.pdfSciELOreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengEngenharia Agrícola0.3870,305info:eu-repo/semantics/openAccess2024-04-30T19:29:57Zoai:repositorio.unesp.br:11449/110179Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-06T00:03:38.869213Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Geostatistics and remote sensing methods in the classification of images of areas cultivated with citrus Geoestatística e sensoriamento remoto na classificação de imagens em áreas cultivadas com citros |
title |
Geostatistics and remote sensing methods in the classification of images of areas cultivated with citrus |
spellingShingle |
Geostatistics and remote sensing methods in the classification of images of areas cultivated with citrus Silva, Alessandra F. [UNESP] Krigagem Indicativa Cluster Maxver satélite CBERS-2B classificação espacial Indicator Kriging Cluster Maxver CBERS-2B satellite spatial classification |
title_short |
Geostatistics and remote sensing methods in the classification of images of areas cultivated with citrus |
title_full |
Geostatistics and remote sensing methods in the classification of images of areas cultivated with citrus |
title_fullStr |
Geostatistics and remote sensing methods in the classification of images of areas cultivated with citrus |
title_full_unstemmed |
Geostatistics and remote sensing methods in the classification of images of areas cultivated with citrus |
title_sort |
Geostatistics and remote sensing methods in the classification of images of areas cultivated with citrus |
author |
Silva, Alessandra F. [UNESP] |
author_facet |
Silva, Alessandra F. [UNESP] Barbosa, Ana Paula [UNESP] Zimback, Célia Regina Lopes [UNESP] Landim, Paulo M. B. [UNESP] |
author_role |
author |
author2 |
Barbosa, Ana Paula [UNESP] Zimback, Célia Regina Lopes [UNESP] Landim, Paulo M. B. [UNESP] |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) |
dc.contributor.author.fl_str_mv |
Silva, Alessandra F. [UNESP] Barbosa, Ana Paula [UNESP] Zimback, Célia Regina Lopes [UNESP] Landim, Paulo M. B. [UNESP] |
dc.subject.por.fl_str_mv |
Krigagem Indicativa Cluster Maxver satélite CBERS-2B classificação espacial Indicator Kriging Cluster Maxver CBERS-2B satellite spatial classification |
topic |
Krigagem Indicativa Cluster Maxver satélite CBERS-2B classificação espacial Indicator Kriging Cluster Maxver CBERS-2B satellite spatial classification |
description |
This study compares the precision of three image classification methods, two of remote sensing and one of geostatistics applied to areas cultivated with citrus. The 5,296.52ha area of study is located in the city of Araraquara - central region of the state of São Paulo (SP), Brazil. The multispectral image from the CCD/CBERS-2B satellite was acquired in 2009 and processed through the Geographic Information System (GIS) SPRING. Three classification methods were used, one unsupervised (Cluster), and two supervised (Indicator Kriging/IK and Maximum Likelihood/Maxver), in addition to the screen classification taken as field checking.. Reliability of classifications was evaluated by Kappa index. In accordance with the Kappa index, the Indicator kriging method obtained the highest degree of reliability for bands 2 and 4. Moreover the Cluster method applied to band 2 (green) was the best quality classification between all the methods. Indicator Kriging was the classifier that presented the citrus total area closest to the field check estimated by -3.01%, whereas Maxver overestimated the total citrus area by 42.94%. |
publishDate |
2013 |
dc.date.none.fl_str_mv |
2013-12-01 2014-10-01T13:08:53Z 2014-10-01T13:08:53Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1590/S0100-69162013000600017 Engenharia Agrícola. Associação Brasileira de Engenharia Agrícola, v. 33, n. 6, p. 1245-1256, 2013. 0100-6916 http://hdl.handle.net/11449/110179 10.1590/S0100-69162013000600017 S0100-69162013000600017 WOS:000331653400018 S0100-69162013000600017.pdf |
url |
http://dx.doi.org/10.1590/S0100-69162013000600017 http://hdl.handle.net/11449/110179 |
identifier_str_mv |
Engenharia Agrícola. Associação Brasileira de Engenharia Agrícola, v. 33, n. 6, p. 1245-1256, 2013. 0100-6916 10.1590/S0100-69162013000600017 S0100-69162013000600017 WOS:000331653400018 S0100-69162013000600017.pdf |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Engenharia Agrícola 0.387 0,305 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
1245-1256 application/pdf |
dc.publisher.none.fl_str_mv |
Associação Brasileira de Engenharia Agrícola (SBEA) |
publisher.none.fl_str_mv |
Associação Brasileira de Engenharia Agrícola (SBEA) |
dc.source.none.fl_str_mv |
SciELO reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1808129578049208320 |