干旱区生态环境知识资源中心(Arid): Combining WV-2 images and tree physiological factors to detect damage stages of Populus gansuensis by Asian longhorned beetle (Anoplophora glabripennis) at the tree level

Arid

	Combining WV-2 images and tree physiological factors to detect damage stages of Populus gansuensis by Asian longhorned beetle (Anoplophora glabripennis) at the tree level
	Zhou Quan; Zhang Xudong; Yu Linfeng; Ren Lili; Luo Youqing
来源期刊	Forest Ecosystems
ISSN	2095-6355
出版年	2021
卷号	8 期号:3
英文摘要	Background: Anoplophora glabripennis (Motschulsky), commonly known as Asian longhorned beetle (ALB), is a wood-boring insect that can cause lethal infestation to multiple borer leaf trees. In Gansu Province, northwest China, ALB has caused a large number of deaths of a local tree species Populus gansuensis. The damaged area belongs to Gobi desert where every single tree is artificially planted and is extremely difficult to cultivate. Therefore, the monitoring of the ALB infestation at the individual tree level in the landscape is necessary. Moreover, the determination of an abnormal phenotype that can be obtained directly from remote-sensing images to predict the damage degree can greatly reduce the cost of field investigation and management. Methods: Multispectral WorldView-2 (WV-2) images and 5 tree physiological factors were collected as experimental materials. One-way ANOVA of the tree's physiological factors helped in determining the phenotype to predict damage degrees. The original bands of WV-2 and derived vegetation indices were used as reference data to construct the dataset of a prediction model. Variance inflation factor and stepwise regression analyses were used to eliminate collinearity and redundancy. Finally, three machine learning algorithms, i.e., Random Forest (RF), Support Vector Machine (SVM), Classification And Regression Tree (CART), were applied and compared to find the best classifier for predicting the damage stage of individual P. gansuensis. Results: The confusion matrix of RF achieved the highest overall classification accuracy (86.2%) and the highest Kappa index value (0.804), indicating the potential of using WV-2 imaging to accurately detect damage stages of individual trees. In addition, the canopy color was found to be positively correlated with P. gansuensis' damage stages.
类型	Article
语种	英语
开放获取类型	Green Submitted, gold
收录类别	CSCD
WOS类目	Forestry
CSCD记录号	CSCD:7059609
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/377415
作者单位	Zhou Quan, Beijing Forestry University, Beijing Key Laboratory for Forest Pest Control, Beijing 100083, China.; Zhang Xudong, Beijing Forestry University, Beijing Key Laboratory for Forest Pest Control, Beijing 100083, China.; Yu Linfeng, Beijing Forestry University, Beijing Key Laboratory for Forest Pest Control, Beijing 100083, China.; Ren Lili, Beijing Forestry University;;Sino-French Joint Laboratory for Invasive Forest Pests in Eurasia,Beijing Forestry University-French National Research Institute for Agriculture,Food and Environment (INRAE), Beijing Key Laboratory for Forest Pest Control;;, ;;, Beijing;;Beijing 100083;;100083.; Luo Youqing, Beijing Forestry University;;Sino-French Joint Laboratory for Invasive Forest Pests in Eurasia,Beijing Forestry University-French National Research Institute for Agriculture,Food and Environment (INRAE), Beijing Key Laboratory for Forest Pest Control;;, ;;, Beijing;;Beijing 100083;;100083.
推荐引用方式 GB/T 7714	Zhou Quan,Zhang Xudong,Yu Linfeng,et al. Combining WV-2 images and tree physiological factors to detect damage stages of Populus gansuensis by Asian longhorned beetle (Anoplophora glabripennis) at the tree level[J],2021,8(3).
APA	Zhou Quan,Zhang Xudong,Yu Linfeng,Ren Lili,&Luo Youqing.(2021).Combining WV-2 images and tree physiological factors to detect damage stages of Populus gansuensis by Asian longhorned beetle (Anoplophora glabripennis) at the tree level.Forest Ecosystems,8(3).
MLA	Zhou Quan,et al."Combining WV-2 images and tree physiological factors to detect damage stages of Populus gansuensis by Asian longhorned beetle (Anoplophora glabripennis) at the tree level".Forest Ecosystems 8.3(2021).