CHANGE DETECTION IN FORESTS AND SAVANNAS USING STATISTICAL ANALYSIS BASED ON GEOGRAPHICAL OBJECTS

Detalhes bibliográficos
Autor(a) principal: Leite, lLucilia Rezende
Data de Publicação: 2017
Outros Autores: Carvalho, Luis Marcelo Tavares de, Silva, Fortunato Menezes da
Tipo de documento: Artigo
Idioma: por
Título da fonte: Boletim de Ciências Geodésicas
Texto Completo: https://revistas.ufpr.br/bcg/article/view/52782
Resumo: The aim of this work was to assess techniques of land cover change detection in areas of Brazilian Forest and Savanna, using Landsat 5/TM images, and two iterative statistical methodologies based on geographical objects. The sensitivity of the methodologies was assessed in relation to the heterogeneity of the input data, the use of reflectance data and vegetation indices, and the use of different levels of confidence. The periods analyzed were from 2000 to 2006, and from 2006 to 2010. After the segmentation of images, the descriptive statistics average and standard deviation of each object were extracted. The determination of change objects was realized in an iterative way based on the Mahalanobis Distance and the chi-square distribution. The results were validated with an early visual detection and analyzed according to Receiver Operating Characteristic (ROC) Curve. Significant gains were obtained by using vegetation masks and bands 3 and 4 for both areas tested with 94,67% and 95,02% of the objects correctly detected as changes, respectively for the areas of Forest and Savanna. The use of the NDVI and different images were not satisfactory in this study.
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spelling CHANGE DETECTION IN FORESTS AND SAVANNAS USING STATISTICAL ANALYSIS BASED ON GEOGRAPHICAL OBJECTSGeociências; GeodésiaBrazilian Savanna, Amazon Forest, remote sense, segmentation of images, Distance of Mahalanobis.The aim of this work was to assess techniques of land cover change detection in areas of Brazilian Forest and Savanna, using Landsat 5/TM images, and two iterative statistical methodologies based on geographical objects. The sensitivity of the methodologies was assessed in relation to the heterogeneity of the input data, the use of reflectance data and vegetation indices, and the use of different levels of confidence. The periods analyzed were from 2000 to 2006, and from 2006 to 2010. After the segmentation of images, the descriptive statistics average and standard deviation of each object were extracted. The determination of change objects was realized in an iterative way based on the Mahalanobis Distance and the chi-square distribution. The results were validated with an early visual detection and analyzed according to Receiver Operating Characteristic (ROC) Curve. Significant gains were obtained by using vegetation masks and bands 3 and 4 for both areas tested with 94,67% and 95,02% of the objects correctly detected as changes, respectively for the areas of Forest and Savanna. The use of the NDVI and different images were not satisfactory in this study.Boletim de Ciências GeodésicasBulletin of Geodetic SciencesCAPESLeite, lLucilia RezendeCarvalho, Luis Marcelo Tavares deSilva, Fortunato Menezes da2017-07-31info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://revistas.ufpr.br/bcg/article/view/52782Boletim de Ciências Geodésicas; Vol 23, No 2 (2017)Bulletin of Geodetic Sciences; Vol 23, No 2 (2017)1982-21701413-4853reponame:Boletim de Ciências Geodésicasinstname:Universidade Federal do Paraná (UFPR)instacron:UFPRporhttps://revistas.ufpr.br/bcg/article/view/52782/32442Copyright (c) 2017 lLucilia Rezende Leite, Luis Marcelo Tavares de Carvalho, Fortunato Menezes da Silvahttp://creativecommons.org/licenses/by-nc/4.0info:eu-repo/semantics/openAccess2017-07-31T16:00:12Zoai:revistas.ufpr.br:article/52782Revistahttps://revistas.ufpr.br/bcgPUBhttps://revistas.ufpr.br/bcg/oaiqdalmolin@ufpr.br|| danielsantos@ufpr.br||qdalmolin@ufpr.br|| danielsantos@ufpr.br1982-21701413-4853opendoar:2017-07-31T16:00:12Boletim de Ciências Geodésicas - Universidade Federal do Paraná (UFPR)false
dc.title.none.fl_str_mv CHANGE DETECTION IN FORESTS AND SAVANNAS USING STATISTICAL ANALYSIS BASED ON GEOGRAPHICAL OBJECTS
title CHANGE DETECTION IN FORESTS AND SAVANNAS USING STATISTICAL ANALYSIS BASED ON GEOGRAPHICAL OBJECTS
spellingShingle CHANGE DETECTION IN FORESTS AND SAVANNAS USING STATISTICAL ANALYSIS BASED ON GEOGRAPHICAL OBJECTS
Leite, lLucilia Rezende
Geociências; Geodésia
Brazilian Savanna, Amazon Forest, remote sense, segmentation of images, Distance of Mahalanobis.
title_short CHANGE DETECTION IN FORESTS AND SAVANNAS USING STATISTICAL ANALYSIS BASED ON GEOGRAPHICAL OBJECTS
title_full CHANGE DETECTION IN FORESTS AND SAVANNAS USING STATISTICAL ANALYSIS BASED ON GEOGRAPHICAL OBJECTS
title_fullStr CHANGE DETECTION IN FORESTS AND SAVANNAS USING STATISTICAL ANALYSIS BASED ON GEOGRAPHICAL OBJECTS
title_full_unstemmed CHANGE DETECTION IN FORESTS AND SAVANNAS USING STATISTICAL ANALYSIS BASED ON GEOGRAPHICAL OBJECTS
title_sort CHANGE DETECTION IN FORESTS AND SAVANNAS USING STATISTICAL ANALYSIS BASED ON GEOGRAPHICAL OBJECTS
author Leite, lLucilia Rezende
author_facet Leite, lLucilia Rezende
Carvalho, Luis Marcelo Tavares de
Silva, Fortunato Menezes da
author_role author
author2 Carvalho, Luis Marcelo Tavares de
Silva, Fortunato Menezes da
author2_role author
author
dc.contributor.none.fl_str_mv CAPES
dc.contributor.author.fl_str_mv Leite, lLucilia Rezende
Carvalho, Luis Marcelo Tavares de
Silva, Fortunato Menezes da
dc.subject.por.fl_str_mv Geociências; Geodésia
Brazilian Savanna, Amazon Forest, remote sense, segmentation of images, Distance of Mahalanobis.
topic Geociências; Geodésia
Brazilian Savanna, Amazon Forest, remote sense, segmentation of images, Distance of Mahalanobis.
description The aim of this work was to assess techniques of land cover change detection in areas of Brazilian Forest and Savanna, using Landsat 5/TM images, and two iterative statistical methodologies based on geographical objects. The sensitivity of the methodologies was assessed in relation to the heterogeneity of the input data, the use of reflectance data and vegetation indices, and the use of different levels of confidence. The periods analyzed were from 2000 to 2006, and from 2006 to 2010. After the segmentation of images, the descriptive statistics average and standard deviation of each object were extracted. The determination of change objects was realized in an iterative way based on the Mahalanobis Distance and the chi-square distribution. The results were validated with an early visual detection and analyzed according to Receiver Operating Characteristic (ROC) Curve. Significant gains were obtained by using vegetation masks and bands 3 and 4 for both areas tested with 94,67% and 95,02% of the objects correctly detected as changes, respectively for the areas of Forest and Savanna. The use of the NDVI and different images were not satisfactory in this study.
publishDate 2017
dc.date.none.fl_str_mv 2017-07-31
dc.type.none.fl_str_mv

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://revistas.ufpr.br/bcg/article/view/52782
url https://revistas.ufpr.br/bcg/article/view/52782
dc.language.iso.fl_str_mv por
language por
dc.relation.none.fl_str_mv https://revistas.ufpr.br/bcg/article/view/52782/32442
dc.rights.driver.fl_str_mv http://creativecommons.org/licenses/by-nc/4.0
info:eu-repo/semantics/openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc/4.0
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Boletim de Ciências Geodésicas
Bulletin of Geodetic Sciences
publisher.none.fl_str_mv Boletim de Ciências Geodésicas
Bulletin of Geodetic Sciences
dc.source.none.fl_str_mv Boletim de Ciências Geodésicas; Vol 23, No 2 (2017)
Bulletin of Geodetic Sciences; Vol 23, No 2 (2017)
1982-2170
1413-4853
reponame:Boletim de Ciências Geodésicas
instname:Universidade Federal do Paraná (UFPR)
instacron:UFPR
instname_str Universidade Federal do Paraná (UFPR)
instacron_str UFPR
institution UFPR
reponame_str Boletim de Ciências Geodésicas
collection Boletim de Ciências Geodésicas
repository.name.fl_str_mv Boletim de Ciências Geodésicas - Universidade Federal do Paraná (UFPR)
repository.mail.fl_str_mv qdalmolin@ufpr.br|| danielsantos@ufpr.br||qdalmolin@ufpr.br|| danielsantos@ufpr.br
_version_ 1799771719413530624

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