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

Abstract

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.

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

“…This was also confirmed by the fact that the most important features in the RF model and the most reactive VIs at the early stages of a PWD infestation were mainly red edge-based variables ( Figure 11 ). These results are consistent with other studies of forest pests and diseases ( Dennison et al., 2010 ; Fassnacht et al., 2014 ; Bárta et al., 2021 ; Einzmann et al., 2021 ; Huo et al., 2021 ; Zhou et al., 2021 ; Yu et al., 2021c ; Bárta et al., 2022 ). In a PWD study, red edge indices from needles and UAV-based hyperspectral data produced the highest accuracy for B. xylophilus early detection ( Yu et al., 2021d ).…”

Current and future control of the wood‐boring pest Anoplophora glabripennis

“…Recent studies have used satellite remote-sensing imaging to assess the damage of wood-boring pests to individual trees in China (Luo et al, 2022). Zhou et al (2021a) developed a novel approach of combining Multispectral WorldView-2 imaging data and tree physiological factors for the semiautomatic classification of 3 stages of poplar damage from ALB infestation (green, yellow, and gray). This approach was also used to determine if the canopy color was abnormal, which could be directly assessed by remote-sensing images at the tree level to predict tree damage.…”

“…Accurate and up-to-date location and health information at the single tree scale with a high spatial resolution (0.5 m) can be provided, which is important for managing tree damage due to ALB infestation in northwest China. Moreover, different physiological factors underlying the damage stages (green, yellow, and gray) can also be clarified, reducing the cost of field data collection and increasing management measure accuracy and applicability (Zhou et al, 2021a). The generated maps represent the spatial and single tree damage data required to implement prevention and control measures, thereby reducing the largescale harm from ALB in the shelter forest in northwest China.…”

A Classification-Based Machine Learning Approach to the Prediction of Cyanobacterial Blooms in Chilgok Weir, South Korea

“…It can be a problem in statistical analysis such as regression as it distorts the prediction results of the model [27,28]. For classification-based machine learning, the multi-collinearity problem can be addressed as part of feature selection (e.g., [27,[37][38][39]). In this study, features with weak inter-correlation are candidates to be selected.…”