CHANGE DETECTION IN FORESTS AND SAVANNAS USING STATISTICAL ANALYSIS BASED ON GEOGRAPHICAL OBJECTS
Autor(a) principal: | |
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Data de Publicação: | 2017 |
Outros Autores: | , |
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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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 |