Appraisal of arp images and machine learning to detect Sapajus nigritus attacks on loblolly’s pine stands in Southern Brazil
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Cerne (Online) |
Texto Completo: | https://cerne.ufla.br/site/index.php/CERNE/article/view/3208 |
Resumo: | Background: This study aimed to evaluate UAV images of Pinus taeda L. stands for classifying trees attacked by Sapajus nigritus in Southern Brazil. UAV images were acquired on March 2018, using a DJI Phantom Pro 4 over 18.73 hectares. We evaluated different band compositions and vegetation indices. Using photo interpretation based on the color of the crown and field measurements, thetrees were manually labeled as not attacked, dead, and attacked. The classified trees were divided into training (75%) and validation (25%), considering three tree crown diameters (0.5, 1, and 1.5 m) and three region-oriented classification algorithms. The classification accuracy was assessed by overall accuracy and the kappa index.Results: A total of 3,773 trees were manually detected, of which 39% were attacked, 5% died and 56% were not attacked. The results also indicated that the best-chosen diameter was 0.5 meters, the best classifier algorithm was the SVM, and the highest accuracy was represented by the composition of the ExG index associated with the original spectral bands.Conclusion: We argue that the attacks can be monitored using UAV images and such results provide insights for forest management initiatives. |
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Cerne (Online) |
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Appraisal of arp images and machine learning to detect Sapajus nigritus attacks on loblolly’s pine stands in Southern BrazilUAVphotogrammetryforest healthBackground: This study aimed to evaluate UAV images of Pinus taeda L. stands for classifying trees attacked by Sapajus nigritus in Southern Brazil. UAV images were acquired on March 2018, using a DJI Phantom Pro 4 over 18.73 hectares. We evaluated different band compositions and vegetation indices. Using photo interpretation based on the color of the crown and field measurements, thetrees were manually labeled as not attacked, dead, and attacked. The classified trees were divided into training (75%) and validation (25%), considering three tree crown diameters (0.5, 1, and 1.5 m) and three region-oriented classification algorithms. The classification accuracy was assessed by overall accuracy and the kappa index.Results: A total of 3,773 trees were manually detected, of which 39% were attacked, 5% died and 56% were not attacked. The results also indicated that the best-chosen diameter was 0.5 meters, the best classifier algorithm was the SVM, and the highest accuracy was represented by the composition of the ExG index associated with the original spectral bands.Conclusion: We argue that the attacks can be monitored using UAV images and such results provide insights for forest management initiatives.CERNECERNE2023-05-26info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://cerne.ufla.br/site/index.php/CERNE/article/view/3208CERNE; Vol. 29 No. 1 (2023); e-103208CERNE; v. 29 n. 1 (2023); e-1032082317-63420104-7760reponame:Cerne (Online)instname:Universidade Federal de Lavras (UFLA)instacron:UFLAenghttps://cerne.ufla.br/site/index.php/CERNE/article/view/3208/1340http://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessPertille, Carla TalitaSchimalski, Marcos Benedito Liesenberg, VeraldoFilho, Vilmar Picinatto Pitz, Mireli Moura Miranda, Fabiani das Dores Abati 2023-06-16T13:00:02Zoai:cerne.ufla.br:article/3208Revistahttps://cerne.ufla.br/site/index.php/CERNEPUBhttps://cerne.ufla.br/site/index.php/CERNE/oaicerne@dcf.ufla.br||cerne@dcf.ufla.br2317-63420104-7760opendoar:2024-05-21T19:54:51.043124Cerne (Online) - Universidade Federal de Lavras (UFLA)true |
dc.title.none.fl_str_mv |
Appraisal of arp images and machine learning to detect Sapajus nigritus attacks on loblolly’s pine stands in Southern Brazil |
title |
Appraisal of arp images and machine learning to detect Sapajus nigritus attacks on loblolly’s pine stands in Southern Brazil |
spellingShingle |
Appraisal of arp images and machine learning to detect Sapajus nigritus attacks on loblolly’s pine stands in Southern Brazil Pertille, Carla Talita UAV photogrammetry forest health |
title_short |
Appraisal of arp images and machine learning to detect Sapajus nigritus attacks on loblolly’s pine stands in Southern Brazil |
title_full |
Appraisal of arp images and machine learning to detect Sapajus nigritus attacks on loblolly’s pine stands in Southern Brazil |
title_fullStr |
Appraisal of arp images and machine learning to detect Sapajus nigritus attacks on loblolly’s pine stands in Southern Brazil |
title_full_unstemmed |
Appraisal of arp images and machine learning to detect Sapajus nigritus attacks on loblolly’s pine stands in Southern Brazil |
title_sort |
Appraisal of arp images and machine learning to detect Sapajus nigritus attacks on loblolly’s pine stands in Southern Brazil |
author |
Pertille, Carla Talita |
author_facet |
Pertille, Carla Talita Schimalski, Marcos Benedito Liesenberg, Veraldo Filho, Vilmar Picinatto Pitz, Mireli Moura Miranda, Fabiani das Dores Abati |
author_role |
author |
author2 |
Schimalski, Marcos Benedito Liesenberg, Veraldo Filho, Vilmar Picinatto Pitz, Mireli Moura Miranda, Fabiani das Dores Abati |
author2_role |
author author author author author |
dc.contributor.author.fl_str_mv |
Pertille, Carla Talita Schimalski, Marcos Benedito Liesenberg, Veraldo Filho, Vilmar Picinatto Pitz, Mireli Moura Miranda, Fabiani das Dores Abati |
dc.subject.por.fl_str_mv |
UAV photogrammetry forest health |
topic |
UAV photogrammetry forest health |
description |
Background: This study aimed to evaluate UAV images of Pinus taeda L. stands for classifying trees attacked by Sapajus nigritus in Southern Brazil. UAV images were acquired on March 2018, using a DJI Phantom Pro 4 over 18.73 hectares. We evaluated different band compositions and vegetation indices. Using photo interpretation based on the color of the crown and field measurements, thetrees were manually labeled as not attacked, dead, and attacked. The classified trees were divided into training (75%) and validation (25%), considering three tree crown diameters (0.5, 1, and 1.5 m) and three region-oriented classification algorithms. The classification accuracy was assessed by overall accuracy and the kappa index.Results: A total of 3,773 trees were manually detected, of which 39% were attacked, 5% died and 56% were not attacked. The results also indicated that the best-chosen diameter was 0.5 meters, the best classifier algorithm was the SVM, and the highest accuracy was represented by the composition of the ExG index associated with the original spectral bands.Conclusion: We argue that the attacks can be monitored using UAV images and such results provide insights for forest management initiatives. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-05-26 |
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://cerne.ufla.br/site/index.php/CERNE/article/view/3208 |
url |
https://cerne.ufla.br/site/index.php/CERNE/article/view/3208 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
https://cerne.ufla.br/site/index.php/CERNE/article/view/3208/1340 |
dc.rights.driver.fl_str_mv |
http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
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 |
CERNE CERNE |
publisher.none.fl_str_mv |
CERNE CERNE |
dc.source.none.fl_str_mv |
CERNE; Vol. 29 No. 1 (2023); e-103208 CERNE; v. 29 n. 1 (2023); e-103208 2317-6342 0104-7760 reponame:Cerne (Online) instname:Universidade Federal de Lavras (UFLA) instacron:UFLA |
instname_str |
Universidade Federal de Lavras (UFLA) |
instacron_str |
UFLA |
institution |
UFLA |
reponame_str |
Cerne (Online) |
collection |
Cerne (Online) |
repository.name.fl_str_mv |
Cerne (Online) - Universidade Federal de Lavras (UFLA) |
repository.mail.fl_str_mv |
cerne@dcf.ufla.br||cerne@dcf.ufla.br |
_version_ |
1799874944566296576 |