High Spatial Resolution Images of Unmanned Aerial Vehicle (UAV) in Land Use and Occupancy Planning
Autor(a) principal: | |
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Data de Publicação: | 2017 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | por |
Título da fonte: | Anuário do Instituto de Geociências (Online) |
Texto Completo: | https://revistas.ufrj.br/index.php/aigeo/article/view/7853 |
Resumo: | The creation, improving and use of remote sensing techniques focused on suborbital images are increasing, as they present several advantages in geographical and ecological analysis, producing high spatial resolution data. The aim of this work was to test the supervised and unspervised classification techniques in aerial digital images with high spatial resolution obtained by Unmanned Aerial Vehicle (UAV), using the softwares SPRING and ArcGis. The aerial images have spatial resolution in approximately 10 cm, covering around 45% of the floor area. They were obtained in June, 2011, and overlies a stretch of the headwaters of the São Lourenço river in Campo Verde, Mato Grosso. Th aerial photographs were georeferenced and then the classification tests were performed, which presented better results the ones by region. At this stage, about 100 segmentation tests were performed with distinguished similarity parameters and areas, until finding a routine that would fit better to the study area. The classification that better delimited the different features present in the images was the supervised by region, whose segmentation had 20 pixels of similarity and 200 of area. To prove statistically the efficiency of classification, a cluster test was performed and the validation was done through Kappa index and overall accuracy. The presented results along with the use of UAVs are great tools and liable to use in several areas, including environmental expertising routines and recovery of degraded areas monitoring, under the Brazilian Forest Code. |
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High Spatial Resolution Images of Unmanned Aerial Vehicle (UAV) in Land Use and Occupancy PlanningImagens de Alta Resolução Espacial de Veículos Aéreos Não Tripulados (VANT) no Planejamento do Uso e Ocupação do SoloPermanent Preservation Areas; Plan for Recovery of degraded Areas; DRONEÁrea de Preservação Permanente (APP); Plano de Recuperação de Áreas Degradadas (PRAD); DRONEThe creation, improving and use of remote sensing techniques focused on suborbital images are increasing, as they present several advantages in geographical and ecological analysis, producing high spatial resolution data. The aim of this work was to test the supervised and unspervised classification techniques in aerial digital images with high spatial resolution obtained by Unmanned Aerial Vehicle (UAV), using the softwares SPRING and ArcGis. The aerial images have spatial resolution in approximately 10 cm, covering around 45% of the floor area. They were obtained in June, 2011, and overlies a stretch of the headwaters of the São Lourenço river in Campo Verde, Mato Grosso. Th aerial photographs were georeferenced and then the classification tests were performed, which presented better results the ones by region. At this stage, about 100 segmentation tests were performed with distinguished similarity parameters and areas, until finding a routine that would fit better to the study area. The classification that better delimited the different features present in the images was the supervised by region, whose segmentation had 20 pixels of similarity and 200 of area. To prove statistically the efficiency of classification, a cluster test was performed and the validation was done through Kappa index and overall accuracy. The presented results along with the use of UAVs are great tools and liable to use in several areas, including environmental expertising routines and recovery of degraded areas monitoring, under the Brazilian Forest Code.A criação, aperfeiçoamento e uso de técnicas de sensoriamento remoto com foco em imagens suborbitais vêm aumentando, por apresentarem uma série de vantagens na análise geográfica e ecológica, produzindo dados com alta resolução espacial. O objetivo desse trabalho foi testar técnicas de classificação supervisionada e não supervisionada em imagens aéreas digitais de alta resolução espacial obtidas por veículo aéreo não tripulado (VANT), empregando dois softwares, SPRING e ArcGis. As imagens aéreas possuem resolução espacial de aproximadamente 10 cm, com área útil de recobrimento em torno de 45%. Foram obtidas em junho de 2011 e recobrem um trecho da cabeceira do rio São Lourenço, Campo Verde-MT. As fotografias aéreas foram georreferenciadas e posteriormente foram realizados os testes de classificação, dentre os quais apresentaram melhores resultados as classificações por região. Nessa etapa foram realizados aproximadamente 100 testes de segmentação com parâmetros de similaridade e área diferenciados, até encontrar uma rotina que melhor se adequasse a área de estudo. A classificação que melhor delimitou as diferentes feições presentes na imagem foi a supervisionada por região, cuja segmentação possuía 20 pixels de similaridade e 200 de área. Para comprovar estatisticamente a eficiência da classificação foi realizado teste de cluster e a validação foi realizada por meio do índice kappa e exatidão global. Os resultados apresentados assim como o uso de VANT, são ótimas ferramentas e passiveis de utilização em diversas áreas, incluindo rotina de perícia ambiental e monitoramento de recuperação de áreas degradadas, no âmbito do Código Florestal Brasileiro.Universidade Federal do Rio de Janeiro2017-02-15info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://revistas.ufrj.br/index.php/aigeo/article/view/785310.11137/2015_1_147_156Anuário do Instituto de Geociências; Vol 38, No 1 (2015); 147-156Anuário do Instituto de Geociências; Vol 38, No 1 (2015); 147-1561982-39080101-9759reponame:Anuário do Instituto de Geociências (Online)instname:Universidade Federal do Rio de Janeiro (UFRJ)instacron:UFRJporhttps://revistas.ufrj.br/index.php/aigeo/article/view/7853/6334Copyright (c) 2016 Anuário do Instituto de Geociênciashttp://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessCândido, Anny Keli Aparecida AlvesSilva, Normandes Matos daParanhos Filho, Antonio Conceição2017-02-15T17:59:16Zoai:www.revistas.ufrj.br:article/7853Revistahttps://revistas.ufrj.br/index.php/aigeo/indexPUBhttps://revistas.ufrj.br/index.php/aigeo/oaianuario@igeo.ufrj.br||1982-39080101-9759opendoar:2017-02-15T17:59:16Anuário do Instituto de Geociências (Online) - Universidade Federal do Rio de Janeiro (UFRJ)false |
dc.title.none.fl_str_mv |
High Spatial Resolution Images of Unmanned Aerial Vehicle (UAV) in Land Use and Occupancy Planning Imagens de Alta Resolução Espacial de Veículos Aéreos Não Tripulados (VANT) no Planejamento do Uso e Ocupação do Solo |
title |
High Spatial Resolution Images of Unmanned Aerial Vehicle (UAV) in Land Use and Occupancy Planning |
spellingShingle |
High Spatial Resolution Images of Unmanned Aerial Vehicle (UAV) in Land Use and Occupancy Planning Cândido, Anny Keli Aparecida Alves Permanent Preservation Areas; Plan for Recovery of degraded Areas; DRONE Área de Preservação Permanente (APP); Plano de Recuperação de Áreas Degradadas (PRAD); DRONE |
title_short |
High Spatial Resolution Images of Unmanned Aerial Vehicle (UAV) in Land Use and Occupancy Planning |
title_full |
High Spatial Resolution Images of Unmanned Aerial Vehicle (UAV) in Land Use and Occupancy Planning |
title_fullStr |
High Spatial Resolution Images of Unmanned Aerial Vehicle (UAV) in Land Use and Occupancy Planning |
title_full_unstemmed |
High Spatial Resolution Images of Unmanned Aerial Vehicle (UAV) in Land Use and Occupancy Planning |
title_sort |
High Spatial Resolution Images of Unmanned Aerial Vehicle (UAV) in Land Use and Occupancy Planning |
author |
Cândido, Anny Keli Aparecida Alves |
author_facet |
Cândido, Anny Keli Aparecida Alves Silva, Normandes Matos da Paranhos Filho, Antonio Conceição |
author_role |
author |
author2 |
Silva, Normandes Matos da Paranhos Filho, Antonio Conceição |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Cândido, Anny Keli Aparecida Alves Silva, Normandes Matos da Paranhos Filho, Antonio Conceição |
dc.subject.por.fl_str_mv |
Permanent Preservation Areas; Plan for Recovery of degraded Areas; DRONE Área de Preservação Permanente (APP); Plano de Recuperação de Áreas Degradadas (PRAD); DRONE |
topic |
Permanent Preservation Areas; Plan for Recovery of degraded Areas; DRONE Área de Preservação Permanente (APP); Plano de Recuperação de Áreas Degradadas (PRAD); DRONE |
description |
The creation, improving and use of remote sensing techniques focused on suborbital images are increasing, as they present several advantages in geographical and ecological analysis, producing high spatial resolution data. The aim of this work was to test the supervised and unspervised classification techniques in aerial digital images with high spatial resolution obtained by Unmanned Aerial Vehicle (UAV), using the softwares SPRING and ArcGis. The aerial images have spatial resolution in approximately 10 cm, covering around 45% of the floor area. They were obtained in June, 2011, and overlies a stretch of the headwaters of the São Lourenço river in Campo Verde, Mato Grosso. Th aerial photographs were georeferenced and then the classification tests were performed, which presented better results the ones by region. At this stage, about 100 segmentation tests were performed with distinguished similarity parameters and areas, until finding a routine that would fit better to the study area. The classification that better delimited the different features present in the images was the supervised by region, whose segmentation had 20 pixels of similarity and 200 of area. To prove statistically the efficiency of classification, a cluster test was performed and the validation was done through Kappa index and overall accuracy. The presented results along with the use of UAVs are great tools and liable to use in several areas, including environmental expertising routines and recovery of degraded areas monitoring, under the Brazilian Forest Code. |
publishDate |
2017 |
dc.date.none.fl_str_mv |
2017-02-15 |
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.ufrj.br/index.php/aigeo/article/view/7853 10.11137/2015_1_147_156 |
url |
https://revistas.ufrj.br/index.php/aigeo/article/view/7853 |
identifier_str_mv |
10.11137/2015_1_147_156 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.none.fl_str_mv |
https://revistas.ufrj.br/index.php/aigeo/article/view/7853/6334 |
dc.rights.driver.fl_str_mv |
Copyright (c) 2016 Anuário do Instituto de Geociências http://creativecommons.org/licenses/by/4.0 info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2016 Anuário do Instituto de Geociências http://creativecommons.org/licenses/by/4.0 |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Universidade Federal do Rio de Janeiro |
publisher.none.fl_str_mv |
Universidade Federal do Rio de Janeiro |
dc.source.none.fl_str_mv |
Anuário do Instituto de Geociências; Vol 38, No 1 (2015); 147-156 Anuário do Instituto de Geociências; Vol 38, No 1 (2015); 147-156 1982-3908 0101-9759 reponame:Anuário do Instituto de Geociências (Online) instname:Universidade Federal do Rio de Janeiro (UFRJ) instacron:UFRJ |
instname_str |
Universidade Federal do Rio de Janeiro (UFRJ) |
instacron_str |
UFRJ |
institution |
UFRJ |
reponame_str |
Anuário do Instituto de Geociências (Online) |
collection |
Anuário do Instituto de Geociências (Online) |
repository.name.fl_str_mv |
Anuário do Instituto de Geociências (Online) - Universidade Federal do Rio de Janeiro (UFRJ) |
repository.mail.fl_str_mv |
anuario@igeo.ufrj.br|| |
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1797053538367438849 |