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Early detection of pine wilt disease tree candidates using time-series of spectral signatures

Pine wilt disease (PWD), caused by pine wood nematode (PWN), poses a tremendous threat to global pine forests because it can result in rapid and widespread infestations within months, leading to large-scale tree mortality. Therefore, the implementation of preventive measures relies on early detectio...

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Autores principales: Yu, Run, Huo, Langning, Huang, Huaguo, Yuan, Yuan, Gao, Bingtao, Liu, Yujie, Yu, Linfeng, Li, Haonan, Yang, Liyuan, Ren, Lili, Luo, Youqing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9606806/
https://www.ncbi.nlm.nih.gov/pubmed/36311089
http://dx.doi.org/10.3389/fpls.2022.1000093
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author Yu, Run
Huo, Langning
Huang, Huaguo
Yuan, Yuan
Gao, Bingtao
Liu, Yujie
Yu, Linfeng
Li, Haonan
Yang, Liyuan
Ren, Lili
Luo, Youqing
author_facet Yu, Run
Huo, Langning
Huang, Huaguo
Yuan, Yuan
Gao, Bingtao
Liu, Yujie
Yu, Linfeng
Li, Haonan
Yang, Liyuan
Ren, Lili
Luo, Youqing
author_sort Yu, Run
collection PubMed
description Pine wilt disease (PWD), caused by pine wood nematode (PWN), poses a tremendous threat to global pine forests because it can result in rapid and widespread infestations within months, leading to large-scale tree mortality. Therefore, the implementation of preventive measures relies on early detection of PWD. Unmanned aerial vehicle (UAV)-based hyperspectral images (HSI) can detect tree-level changes and are thus an effective tool for forest change detection. However, previous studies mainly used single-date UAV-based HSI data, which could not monitor the temporal changes of disease distribution and determine the optimal detection period. To achieve these purposes, multi-temporal data is required. In this study, Pinus koraiensis stands were surveyed in the field from May to October during an outbreak of PWD. Concurrently, multi-temporal UAV-based red, green, and blue bands (RGB) and HSI data were also obtained. During the survey, 59 trees were confirmed to be infested with PWD, and 59 non-infested trees were used as control. Spectral features of each tree crown, such as spectral reflectance, first and second-order spectral derivatives, and vegetation indices (VIs), were analyzed to identify those useful for early monitoring of PWD. The Random Forest (RF) classification algorithm was used to examine the separability between the two groups of trees (control and infested trees). The results showed that: (1) the responses of the tree crown spectral features to PWD infestation could be detected before symptoms were noticeable in RGB data and field surveys; (2) the spectral derivatives were the most discriminable variables, followed by spectral reflectance and VIs; (3) based on the HSI data from July to October, the two groups of trees were successfully separated using the RF classifier, with an overall classification accuracy of 0.75–0.95. Our results illustrate the potential of UAV-based HSI for PWD early monitoring.
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spelling pubmed-96068062022-10-28 Early detection of pine wilt disease tree candidates using time-series of spectral signatures Yu, Run Huo, Langning Huang, Huaguo Yuan, Yuan Gao, Bingtao Liu, Yujie Yu, Linfeng Li, Haonan Yang, Liyuan Ren, Lili Luo, Youqing Front Plant Sci Plant Science Pine wilt disease (PWD), caused by pine wood nematode (PWN), poses a tremendous threat to global pine forests because it can result in rapid and widespread infestations within months, leading to large-scale tree mortality. Therefore, the implementation of preventive measures relies on early detection of PWD. Unmanned aerial vehicle (UAV)-based hyperspectral images (HSI) can detect tree-level changes and are thus an effective tool for forest change detection. However, previous studies mainly used single-date UAV-based HSI data, which could not monitor the temporal changes of disease distribution and determine the optimal detection period. To achieve these purposes, multi-temporal data is required. In this study, Pinus koraiensis stands were surveyed in the field from May to October during an outbreak of PWD. Concurrently, multi-temporal UAV-based red, green, and blue bands (RGB) and HSI data were also obtained. During the survey, 59 trees were confirmed to be infested with PWD, and 59 non-infested trees were used as control. Spectral features of each tree crown, such as spectral reflectance, first and second-order spectral derivatives, and vegetation indices (VIs), were analyzed to identify those useful for early monitoring of PWD. The Random Forest (RF) classification algorithm was used to examine the separability between the two groups of trees (control and infested trees). The results showed that: (1) the responses of the tree crown spectral features to PWD infestation could be detected before symptoms were noticeable in RGB data and field surveys; (2) the spectral derivatives were the most discriminable variables, followed by spectral reflectance and VIs; (3) based on the HSI data from July to October, the two groups of trees were successfully separated using the RF classifier, with an overall classification accuracy of 0.75–0.95. Our results illustrate the potential of UAV-based HSI for PWD early monitoring. Frontiers Media S.A. 2022-10-13 /pmc/articles/PMC9606806/ /pubmed/36311089 http://dx.doi.org/10.3389/fpls.2022.1000093 Text en Copyright © 2022 Yu, Huo, Huang, Yuan, Gao, Liu, Yu, Li, Yang, Ren and Luo https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Plant Science
Yu, Run
Huo, Langning
Huang, Huaguo
Yuan, Yuan
Gao, Bingtao
Liu, Yujie
Yu, Linfeng
Li, Haonan
Yang, Liyuan
Ren, Lili
Luo, Youqing
Early detection of pine wilt disease tree candidates using time-series of spectral signatures
title Early detection of pine wilt disease tree candidates using time-series of spectral signatures
title_full Early detection of pine wilt disease tree candidates using time-series of spectral signatures
title_fullStr Early detection of pine wilt disease tree candidates using time-series of spectral signatures
title_full_unstemmed Early detection of pine wilt disease tree candidates using time-series of spectral signatures
title_short Early detection of pine wilt disease tree candidates using time-series of spectral signatures
title_sort early detection of pine wilt disease tree candidates using time-series of spectral signatures
topic Plant Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9606806/
https://www.ncbi.nlm.nih.gov/pubmed/36311089
http://dx.doi.org/10.3389/fpls.2022.1000093
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