An Approach to Orbital Image Classification for the Assessment of Potato Plantation Areas
Autor(a) principal: | |
---|---|
Data de Publicação: | 2013 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Labor & Engenho (Online) |
DOI: | 10.20396/lobore.v7i4.164 |
Texto Completo: | https://periodicos.sbu.unicamp.br/ojs/index.php/labore/article/view/164 |
Resumo: | In the city of Bueno Brandão, South of Minas Gerais State, Brazil, the Watershed of Rio das Antas is located prior to the public water supply and is susceptible to hydro-degradation due to the intensive agricultural activities developed in the area. The potato plantation is the most significant cropping in the city. Because of the possibility of interfering in the preservation areas, mainly the ones surrounding water courses and springs, it is very important to do the assessment of the plantation sites, in order to avoid the risk of water contamination. The procedures adopted by the agro activity farmers generally present the following features: intensive use of agro-chemicals, cropping in places with slopes which are higher than 20%, close to or in permanent preservation areas. The scope of this study was to develop the proper methodology for the assessment of the plantation areas, regarding the short time of procedure, as the period between the plantation and the harvest occurs in six months the furthest. These areas vary year in year out, as the plantation sites often change due to the land degradation. Because of that, geotechnologies are recommended to detect the plantation areas by the use of satellite images and accurate data processing. Considering the availability of LANDSAT medium resolution images, methods for their appropriate classification were approached to provide effective target detection. |
id |
UNICAMP-15_b4f1bfc59a1d00739153746d9669539b |
---|---|
oai_identifier_str |
oai:ojs.periodicos.sbu.unicamp.br:article/164 |
network_acronym_str |
UNICAMP-15 |
network_name_str |
Labor & Engenho (Online) |
spelling |
An Approach to Orbital Image Classification for the Assessment of Potato Plantation AreasPixel-based ClassificationOBIAAssessment of PlantationsPreservation Area InterferenceWater Contamination Risk.Pixel-based ClassificationOBIAAssessment of plantationsPreservation area interferenceWater contamination riskIn the city of Bueno Brandão, South of Minas Gerais State, Brazil, the Watershed of Rio das Antas is located prior to the public water supply and is susceptible to hydro-degradation due to the intensive agricultural activities developed in the area. The potato plantation is the most significant cropping in the city. Because of the possibility of interfering in the preservation areas, mainly the ones surrounding water courses and springs, it is very important to do the assessment of the plantation sites, in order to avoid the risk of water contamination. The procedures adopted by the agro activity farmers generally present the following features: intensive use of agro-chemicals, cropping in places with slopes which are higher than 20%, close to or in permanent preservation areas. The scope of this study was to develop the proper methodology for the assessment of the plantation areas, regarding the short time of procedure, as the period between the plantation and the harvest occurs in six months the furthest. These areas vary year in year out, as the plantation sites often change due to the land degradation. Because of that, geotechnologies are recommended to detect the plantation areas by the use of satellite images and accurate data processing. Considering the availability of LANDSAT medium resolution images, methods for their appropriate classification were approached to provide effective target detection.Universidade Estadual de Campinas2013-12-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionMétodo de classificação de imagens orbitais.application/pdfhttps://periodicos.sbu.unicamp.br/ojs/index.php/labore/article/view/16410.20396/lobore.v7i4.164Labor e Engenho; Vol. 7 No. 4 (2013): out./dez.; 21-30Labor e Engenho; Vol. 7 Núm. 4 (2013): out./dez.; 21-30Labor e Engenho; v. 7 n. 4 (2013): out./dez.; 21-302176-8846reponame:Labor & Engenho (Online)instname:Universidade Estadual de Campinas (UNICAMP)instacron:UNICAMPenghttps://periodicos.sbu.unicamp.br/ojs/index.php/labore/article/view/164/pdf_78BrasilCopyright (c) 2015 Labor & Engenhoinfo:eu-repo/semantics/openAccessBoulomytis, Vassiliki Terezinha GalvãoAlves, Claudia Durand2019-07-29T05:28:18Zoai:ojs.periodicos.sbu.unicamp.br:article/164Revistahttp://periodicos.sbu.unicamp.br/ojs/index.php/laborePUBhttps://periodicos.sbu.unicamp.br/ojs/index.php/labore/oai||argollo@fec.unicamp.br2176-88461981-1152opendoar:2019-07-29T05:28:18Labor & Engenho (Online) - Universidade Estadual de Campinas (UNICAMP)false |
dc.title.none.fl_str_mv |
An Approach to Orbital Image Classification for the Assessment of Potato Plantation Areas |
title |
An Approach to Orbital Image Classification for the Assessment of Potato Plantation Areas |
spellingShingle |
An Approach to Orbital Image Classification for the Assessment of Potato Plantation Areas An Approach to Orbital Image Classification for the Assessment of Potato Plantation Areas Boulomytis, Vassiliki Terezinha Galvão Pixel-based Classification OBIA Assessment of Plantations Preservation Area Interference Water Contamination Risk. Pixel-based Classification OBIA Assessment of plantations Preservation area interference Water contamination risk Boulomytis, Vassiliki Terezinha Galvão Pixel-based Classification OBIA Assessment of Plantations Preservation Area Interference Water Contamination Risk. Pixel-based Classification OBIA Assessment of plantations Preservation area interference Water contamination risk |
title_short |
An Approach to Orbital Image Classification for the Assessment of Potato Plantation Areas |
title_full |
An Approach to Orbital Image Classification for the Assessment of Potato Plantation Areas |
title_fullStr |
An Approach to Orbital Image Classification for the Assessment of Potato Plantation Areas An Approach to Orbital Image Classification for the Assessment of Potato Plantation Areas |
title_full_unstemmed |
An Approach to Orbital Image Classification for the Assessment of Potato Plantation Areas An Approach to Orbital Image Classification for the Assessment of Potato Plantation Areas |
title_sort |
An Approach to Orbital Image Classification for the Assessment of Potato Plantation Areas |
author |
Boulomytis, Vassiliki Terezinha Galvão |
author_facet |
Boulomytis, Vassiliki Terezinha Galvão Boulomytis, Vassiliki Terezinha Galvão Alves, Claudia Durand Alves, Claudia Durand |
author_role |
author |
author2 |
Alves, Claudia Durand |
author2_role |
author |
dc.contributor.author.fl_str_mv |
Boulomytis, Vassiliki Terezinha Galvão Alves, Claudia Durand |
dc.subject.por.fl_str_mv |
Pixel-based Classification OBIA Assessment of Plantations Preservation Area Interference Water Contamination Risk. Pixel-based Classification OBIA Assessment of plantations Preservation area interference Water contamination risk |
topic |
Pixel-based Classification OBIA Assessment of Plantations Preservation Area Interference Water Contamination Risk. Pixel-based Classification OBIA Assessment of plantations Preservation area interference Water contamination risk |
description |
In the city of Bueno Brandão, South of Minas Gerais State, Brazil, the Watershed of Rio das Antas is located prior to the public water supply and is susceptible to hydro-degradation due to the intensive agricultural activities developed in the area. The potato plantation is the most significant cropping in the city. Because of the possibility of interfering in the preservation areas, mainly the ones surrounding water courses and springs, it is very important to do the assessment of the plantation sites, in order to avoid the risk of water contamination. The procedures adopted by the agro activity farmers generally present the following features: intensive use of agro-chemicals, cropping in places with slopes which are higher than 20%, close to or in permanent preservation areas. The scope of this study was to develop the proper methodology for the assessment of the plantation areas, regarding the short time of procedure, as the period between the plantation and the harvest occurs in six months the furthest. These areas vary year in year out, as the plantation sites often change due to the land degradation. Because of that, geotechnologies are recommended to detect the plantation areas by the use of satellite images and accurate data processing. Considering the availability of LANDSAT medium resolution images, methods for their appropriate classification were approached to provide effective target detection. |
publishDate |
2013 |
dc.date.none.fl_str_mv |
2013-12-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Método de classificação de imagens orbitais. |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://periodicos.sbu.unicamp.br/ojs/index.php/labore/article/view/164 10.20396/lobore.v7i4.164 |
url |
https://periodicos.sbu.unicamp.br/ojs/index.php/labore/article/view/164 |
identifier_str_mv |
10.20396/lobore.v7i4.164 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
https://periodicos.sbu.unicamp.br/ojs/index.php/labore/article/view/164/pdf_78 |
dc.rights.driver.fl_str_mv |
Copyright (c) 2015 Labor & Engenho info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2015 Labor & Engenho |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.coverage.none.fl_str_mv |
Brasil |
dc.publisher.none.fl_str_mv |
Universidade Estadual de Campinas |
publisher.none.fl_str_mv |
Universidade Estadual de Campinas |
dc.source.none.fl_str_mv |
Labor e Engenho; Vol. 7 No. 4 (2013): out./dez.; 21-30 Labor e Engenho; Vol. 7 Núm. 4 (2013): out./dez.; 21-30 Labor e Engenho; v. 7 n. 4 (2013): out./dez.; 21-30 2176-8846 reponame:Labor & Engenho (Online) instname:Universidade Estadual de Campinas (UNICAMP) instacron:UNICAMP |
instname_str |
Universidade Estadual de Campinas (UNICAMP) |
instacron_str |
UNICAMP |
institution |
UNICAMP |
reponame_str |
Labor & Engenho (Online) |
collection |
Labor & Engenho (Online) |
repository.name.fl_str_mv |
Labor & Engenho (Online) - Universidade Estadual de Campinas (UNICAMP) |
repository.mail.fl_str_mv |
||argollo@fec.unicamp.br |
_version_ |
1822219038950948864 |
dc.identifier.doi.none.fl_str_mv |
10.20396/lobore.v7i4.164 |