Supervised classification of irrigated area using spectral indexes from landsat-8 images with google earth engine
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Tipo de documento: | Artigo |
Idioma: | por |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.15809/irriga.2020v25n1p160-169 http://hdl.handle.net/11449/200535 |
Resumo: | Identifying irrigation areas using satellite images is a challenge that finds great potential in cloud computing solutions as the Google Earth Engine (GEE) tool, which facilitates the process of searching, filtering and manipulating large volumes of remote sensing data without the need for paid software or image downloading. The present work presents an implementation of the supervised classification of irrigated and rain-fed areas in the region of Sorriso and Lucas do Rio Verde/MT with the Classification and Regression Trees (CART) algorithm in GEE environment using bands 2-7 of the Landsat-8 and the NDVI, NDWI and SAVI indices. The accuracy of the supervised classification was 99.4% when using NDWI, NDVI and SAVI indices and 98.7% without using these indices, which were considered excellent. The average processing time, redone 10 times, was 52 seconds, considering all the source code developed from the filtering of the images to the conclusion of the classification. The developed source code is available in the appendix in order to disseminate and encourage the use of GEE for studies of spatial intelligence in irrigation and drainage due to its usability and easy manipulation. |
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Supervised classification of irrigated area using spectral indexes from landsat-8 images with google earth engineClassificação supervisionada de área irrigada utilizando índices espectrais de imagens landsat-8 com google earth engineCloud computingHydrologyModelingRemote sensingIdentifying irrigation areas using satellite images is a challenge that finds great potential in cloud computing solutions as the Google Earth Engine (GEE) tool, which facilitates the process of searching, filtering and manipulating large volumes of remote sensing data without the need for paid software or image downloading. The present work presents an implementation of the supervised classification of irrigated and rain-fed areas in the region of Sorriso and Lucas do Rio Verde/MT with the Classification and Regression Trees (CART) algorithm in GEE environment using bands 2-7 of the Landsat-8 and the NDVI, NDWI and SAVI indices. The accuracy of the supervised classification was 99.4% when using NDWI, NDVI and SAVI indices and 98.7% without using these indices, which were considered excellent. The average processing time, redone 10 times, was 52 seconds, considering all the source code developed from the filtering of the images to the conclusion of the classification. The developed source code is available in the appendix in order to disseminate and encourage the use of GEE for studies of spatial intelligence in irrigation and drainage due to its usability and easy manipulation.Departamento de Engenharia Rural Faculdade de Ciências Agronômicas Universidade Estadual Paulista (UNESP), Campus de Botucatu. Avenida Universitária, n° 3780, Altos do ParaísoDepartamento de Engenharia Rural Faculdade de Ciências Agronômicas Universidade Estadual Paulista (UNESP), Campus de Botucatu. Avenida Universitária, n° 3780, Altos do ParaísoUniversidade Estadual Paulista (Unesp)Silva, César De Oliveira Ferreira [UNESP]2020-12-12T02:09:11Z2020-12-12T02:09:11Z2020-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article160-169http://dx.doi.org/10.15809/irriga.2020v25n1p160-169IRRIGA, v. 25, n. 1, p. 160-169, 2020.1808-37651413-7895http://hdl.handle.net/11449/20053510.15809/irriga.2020v25n1p160-1692-s2.0-85085559798Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPporIRRIGAinfo:eu-repo/semantics/openAccess2024-04-30T14:01:51Zoai:repositorio.unesp.br:11449/200535Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T19:30:37.476640Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Supervised classification of irrigated area using spectral indexes from landsat-8 images with google earth engine Classificação supervisionada de área irrigada utilizando índices espectrais de imagens landsat-8 com google earth engine |
title |
Supervised classification of irrigated area using spectral indexes from landsat-8 images with google earth engine |
spellingShingle |
Supervised classification of irrigated area using spectral indexes from landsat-8 images with google earth engine Silva, César De Oliveira Ferreira [UNESP] Cloud computing Hydrology Modeling Remote sensing |
title_short |
Supervised classification of irrigated area using spectral indexes from landsat-8 images with google earth engine |
title_full |
Supervised classification of irrigated area using spectral indexes from landsat-8 images with google earth engine |
title_fullStr |
Supervised classification of irrigated area using spectral indexes from landsat-8 images with google earth engine |
title_full_unstemmed |
Supervised classification of irrigated area using spectral indexes from landsat-8 images with google earth engine |
title_sort |
Supervised classification of irrigated area using spectral indexes from landsat-8 images with google earth engine |
author |
Silva, César De Oliveira Ferreira [UNESP] |
author_facet |
Silva, César De Oliveira Ferreira [UNESP] |
author_role |
author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) |
dc.contributor.author.fl_str_mv |
Silva, César De Oliveira Ferreira [UNESP] |
dc.subject.por.fl_str_mv |
Cloud computing Hydrology Modeling Remote sensing |
topic |
Cloud computing Hydrology Modeling Remote sensing |
description |
Identifying irrigation areas using satellite images is a challenge that finds great potential in cloud computing solutions as the Google Earth Engine (GEE) tool, which facilitates the process of searching, filtering and manipulating large volumes of remote sensing data without the need for paid software or image downloading. The present work presents an implementation of the supervised classification of irrigated and rain-fed areas in the region of Sorriso and Lucas do Rio Verde/MT with the Classification and Regression Trees (CART) algorithm in GEE environment using bands 2-7 of the Landsat-8 and the NDVI, NDWI and SAVI indices. The accuracy of the supervised classification was 99.4% when using NDWI, NDVI and SAVI indices and 98.7% without using these indices, which were considered excellent. The average processing time, redone 10 times, was 52 seconds, considering all the source code developed from the filtering of the images to the conclusion of the classification. The developed source code is available in the appendix in order to disseminate and encourage the use of GEE for studies of spatial intelligence in irrigation and drainage due to its usability and easy manipulation. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-12-12T02:09:11Z 2020-12-12T02:09:11Z 2020-01-01 |
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/10.15809/irriga.2020v25n1p160-169 IRRIGA, v. 25, n. 1, p. 160-169, 2020. 1808-3765 1413-7895 http://hdl.handle.net/11449/200535 10.15809/irriga.2020v25n1p160-169 2-s2.0-85085559798 |
url |
http://dx.doi.org/10.15809/irriga.2020v25n1p160-169 http://hdl.handle.net/11449/200535 |
identifier_str_mv |
IRRIGA, v. 25, n. 1, p. 160-169, 2020. 1808-3765 1413-7895 10.15809/irriga.2020v25n1p160-169 2-s2.0-85085559798 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.none.fl_str_mv |
IRRIGA |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
160-169 |
dc.source.none.fl_str_mv |
Scopus 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_ |
1808129079377920000 |