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

DOI	10.1186/s40663-021-00314-y
	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
通讯作者	Luo, YQ (corresponding author), Beijing Forestry Univ, Beijing Key Lab Forest Pest Control, Beijing 100083, Peoples R China. ; Luo, YQ (corresponding author), Beijing Forestry Univ, Sino French Joint Lab Invas Forest Pests Eurasia, French Natl Res Inst Agr Food & Environm INRAE, Beijing 100083, Peoples R China.
来源期刊	FOREST ECOSYSTEMS
ISSN	2095-6355
EISSN	2197-5620
出版年	2021
卷号	8 期号:1
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. Conclusions A novel method was developed by combining WV-2 and tree physiological index for semi-automatic classification of three damage stages of P. gansuensis infested with ALB. The canopy color was determined as an abnormal phenotype that could be directly assessed using remote-sensing images at the tree level to predict the damage degree. These tools are highly applicable for driving quick and effective measures to reduce damage to pure poplar forests in Gansu Province, China.
英文关键词	Worldview-2 Anoplophora glabripennis Populus gansuensis Infestation Degree of damage Canopy color Classification
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000657304400001
WOS关键词	WORLDVIEW-2 IMAGERY ; VEGETATION INDEX ; REFLECTANCE ; FORESTS
WOS类目	Forestry
WOS研究方向	Forestry
来源机构	北京林业大学
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/350239
作者单位	[Zhou, Quan; Zhang, Xudong; Yu, Linfeng; Ren, Lili; Luo, Youqing] Beijing Forestry Univ, Beijing Key Lab Forest Pest Control, Beijing 100083, Peoples R China; [Ren, Lili; Luo, Youqing] Beijing Forestry Univ, Sino French Joint Lab Invas Forest Pests Eurasia, French Natl Res Inst Agr Food & Environm INRAE, Beijing 100083, Peoples R China
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]. 北京林业大学,2021,8(1).
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(1).
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 8.1(2021).