对arp图像和机器学习的评估,以检测巴西南部火炬松的黑松攻击

IF 0.7 4区 农林科学 Q3 FORESTRY
Cerne Pub Date : 2023-06-23 DOI:10.1590/01047760202329013208
Carla Talita Pertille, M. B. Schimalski, V. Liesenberg, Vilmar Picinatto Filho, Mireli Moura Pitz, Fabiani das Dores Abati Miranda
{"title":"对arp图像和机器学习的评估,以检测巴西南部火炬松的黑松攻击","authors":"Carla Talita Pertille, M. B. Schimalski, V. Liesenberg, Vilmar Picinatto Filho, Mireli Moura Pitz, Fabiani das Dores Abati Miranda","doi":"10.1590/01047760202329013208","DOIUrl":null,"url":null,"abstract":"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, the trees 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","PeriodicalId":50705,"journal":{"name":"Cerne","volume":"1 1","pages":""},"PeriodicalIF":0.7000,"publicationDate":"2023-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Appraisal of arp images and machine learning to detect Sapajus nigritus attacks on loblolly’s pine stands in Southern Brazil\",\"authors\":\"Carla Talita Pertille, M. B. Schimalski, V. Liesenberg, Vilmar Picinatto Filho, Mireli Moura Pitz, Fabiani das Dores Abati Miranda\",\"doi\":\"10.1590/01047760202329013208\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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, the trees 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\",\"PeriodicalId\":50705,\"journal\":{\"name\":\"Cerne\",\"volume\":\"1 1\",\"pages\":\"\"},\"PeriodicalIF\":0.7000,\"publicationDate\":\"2023-06-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Cerne\",\"FirstCategoryId\":\"97\",\"ListUrlMain\":\"https://doi.org/10.1590/01047760202329013208\",\"RegionNum\":4,\"RegionCategory\":\"农林科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"FORESTRY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cerne","FirstCategoryId":"97","ListUrlMain":"https://doi.org/10.1590/01047760202329013208","RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"FORESTRY","Score":null,"Total":0}
引用次数: 0

摘要

本文章由计算机程序翻译,如有差异,请以英文原文为准。
Appraisal of arp images and machine learning to detect Sapajus nigritus attacks on loblolly’s pine stands in Southern Brazil
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, the trees 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
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Cerne
Cerne 农林科学-林学
CiteScore
1.60
自引率
0.00%
发文量
2
审稿时长
6-12 weeks
期刊介绍: Cerne is a journal edited by the Federal University of Lavras, Minas Gerais state, Brazil, which quarterly publishes original articles that represent relevant contribution to Forestry Science development (Forest ecology, Forest Management, Silviculture, Technology of Forest Products).
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信