Characterizing Spatial Patterns of Pine Wood Nematode Outbreaks in Subtropical Zone in China

Pine wood nematode (PWN), <i>Bursaphelenchus xyophilus</i>, originating from North America, has caused great ecological and economic hazards to pine trees worldwide, especially affecting the coniferous forests and mixed forests of masson pine in subtropical regions of China. In order to...

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Autores principales:	Yahao Zhang, Yuanyong Dian, Jingjing Zhou, Shoulian Peng, Yue Hu, Lei Hu, Zemin Han, Xinwei Fang, Hongxia Cui
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	the pine wood nematode susceptibility spatial pattern remote sensing random forest Science Q
Acceso en línea:	https://doaj.org/article/c69bf7cceaed4f92a747b9ae31ee6912
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spelling	oai:doaj.org-article:c69bf7cceaed4f92a747b9ae31ee69122021-11-25T18:55:21ZCharacterizing Spatial Patterns of Pine Wood Nematode Outbreaks in Subtropical Zone in China10.3390/rs132246822072-4292https://doaj.org/article/c69bf7cceaed4f92a747b9ae31ee69122021-11-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/22/4682https://doaj.org/toc/2072-4292Pine wood nematode (PWN), <i>Bursaphelenchus xyophilus</i>, originating from North America, has caused great ecological and economic hazards to pine trees worldwide, especially affecting the coniferous forests and mixed forests of masson pine in subtropical regions of China. In order to prevent PWN disease expansion, the risk level and susceptivity of PWN outbreaks need to be predicted in advance. For this purpose, we established a prediction model to estimate the susceptibility and risk level of PWN with vegetation condition variables, anthropogenic activity variables, and topographic feature variables across a large-scale district. The study was conducted in Dangyang City, Hubei Province in China, which was located in a subtropical zone. Based on the location of PWN points derived from airborne imagery and ground survey in 2018, the predictor variables were conducted with remote sensing and geographical information system (GIS) data, which contained vegetation indices including normalized difference vegetation index (NDVI), normalized difference moisture index (NDMI), normalized burn ratio (NBR), and normalized red edge index (NDRE) from Sentinel-2 imagery in the previous year (2107), the distance to different level roads which indicated anthropogenic activity, topographic variables in including elevation, slope, and aspect. We compared the fitting effects of different machine learning algorithms such as random forest (RF), K-neighborhood (KNN), support vector machines (SVM), and artificial neural networks (ANN) and predicted the probability of the presence of PWN disease in the region. In addition, we classified PWN points to different risk levels based on the density distribution of PWN sites and built a PWN risk level model to predict the risk levels of PWN outbreaks in the region. The results showed that: (1) the best model for the predictive probability of PWN presence is the RF classification algorithm. For the presence prediction of the dead trees caused by PWN, the detection rate (DR) was 96.42%, the false alarm rate (FAR) was 27.65%, the false detection rate (FDR) was 4.16%, and the area under the receiver operating characteristic curve (AUC) was equal to 0.96; (2) anthropogenic activity variables had the greatest effect on PWN occurrence, while the effects of slope and aspect were relatively weak, and the maximum, minimum, and median values of remote sensing indices were more correlated with PWN occurrence; (3) modeling analysis of different risk levels of PWN outbreak indicated that high-risk level areas were the easiest to monitor and identify, while lower incidence areas were identified with relatively low accuracy. The overall accuracy of the risk level of the PWN outbreak was identified with an AUC value of 0.94. From the research findings, remote sensing data combined with GIS data can accurately predict the probability distribution of the occurrence of PWN disease. The accuracy of identification of high-risk areas is higher than other risk levels, and the results of the study may improve control of PWN disease spread.Yahao ZhangYuanyong DianJingjing ZhouShoulian PengYue HuLei HuZemin HanXinwei FangHongxia CuiMDPI AGarticlethe pine wood nematodesusceptibilityspatial patternremote sensingrandom forestScienceQENRemote Sensing, Vol 13, Iss 4682, p 4682 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	the pine wood nematode susceptibility spatial pattern remote sensing random forest Science Q
spellingShingle	the pine wood nematode susceptibility spatial pattern remote sensing random forest Science Q Yahao Zhang Yuanyong Dian Jingjing Zhou Shoulian Peng Yue Hu Lei Hu Zemin Han Xinwei Fang Hongxia Cui Characterizing Spatial Patterns of Pine Wood Nematode Outbreaks in Subtropical Zone in China
description	Pine wood nematode (PWN), <i>Bursaphelenchus xyophilus</i>, originating from North America, has caused great ecological and economic hazards to pine trees worldwide, especially affecting the coniferous forests and mixed forests of masson pine in subtropical regions of China. In order to prevent PWN disease expansion, the risk level and susceptivity of PWN outbreaks need to be predicted in advance. For this purpose, we established a prediction model to estimate the susceptibility and risk level of PWN with vegetation condition variables, anthropogenic activity variables, and topographic feature variables across a large-scale district. The study was conducted in Dangyang City, Hubei Province in China, which was located in a subtropical zone. Based on the location of PWN points derived from airborne imagery and ground survey in 2018, the predictor variables were conducted with remote sensing and geographical information system (GIS) data, which contained vegetation indices including normalized difference vegetation index (NDVI), normalized difference moisture index (NDMI), normalized burn ratio (NBR), and normalized red edge index (NDRE) from Sentinel-2 imagery in the previous year (2107), the distance to different level roads which indicated anthropogenic activity, topographic variables in including elevation, slope, and aspect. We compared the fitting effects of different machine learning algorithms such as random forest (RF), K-neighborhood (KNN), support vector machines (SVM), and artificial neural networks (ANN) and predicted the probability of the presence of PWN disease in the region. In addition, we classified PWN points to different risk levels based on the density distribution of PWN sites and built a PWN risk level model to predict the risk levels of PWN outbreaks in the region. The results showed that: (1) the best model for the predictive probability of PWN presence is the RF classification algorithm. For the presence prediction of the dead trees caused by PWN, the detection rate (DR) was 96.42%, the false alarm rate (FAR) was 27.65%, the false detection rate (FDR) was 4.16%, and the area under the receiver operating characteristic curve (AUC) was equal to 0.96; (2) anthropogenic activity variables had the greatest effect on PWN occurrence, while the effects of slope and aspect were relatively weak, and the maximum, minimum, and median values of remote sensing indices were more correlated with PWN occurrence; (3) modeling analysis of different risk levels of PWN outbreak indicated that high-risk level areas were the easiest to monitor and identify, while lower incidence areas were identified with relatively low accuracy. The overall accuracy of the risk level of the PWN outbreak was identified with an AUC value of 0.94. From the research findings, remote sensing data combined with GIS data can accurately predict the probability distribution of the occurrence of PWN disease. The accuracy of identification of high-risk areas is higher than other risk levels, and the results of the study may improve control of PWN disease spread.
format	article
author	Yahao Zhang Yuanyong Dian Jingjing Zhou Shoulian Peng Yue Hu Lei Hu Zemin Han Xinwei Fang Hongxia Cui
author_facet	Yahao Zhang Yuanyong Dian Jingjing Zhou Shoulian Peng Yue Hu Lei Hu Zemin Han Xinwei Fang Hongxia Cui
author_sort	Yahao Zhang
title	Characterizing Spatial Patterns of Pine Wood Nematode Outbreaks in Subtropical Zone in China
title_short	Characterizing Spatial Patterns of Pine Wood Nematode Outbreaks in Subtropical Zone in China
title_full	Characterizing Spatial Patterns of Pine Wood Nematode Outbreaks in Subtropical Zone in China
title_fullStr	Characterizing Spatial Patterns of Pine Wood Nematode Outbreaks in Subtropical Zone in China
title_full_unstemmed	Characterizing Spatial Patterns of Pine Wood Nematode Outbreaks in Subtropical Zone in China
title_sort	characterizing spatial patterns of pine wood nematode outbreaks in subtropical zone in china
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/c69bf7cceaed4f92a747b9ae31ee6912
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Characterizing Spatial Patterns of Pine Wood Nematode Outbreaks in Subtropical Zone in China

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