Early Monitoring of Forest Wood-Boring Pests with Remote Sensing

Automatic detection of standing dead trees based on improved YOLOv7 from airborne remote sensing imagery

Detecting and localizing standing dead trees (SDTs) is crucial for effective forest management and conservation. Due to challenges posed by mountainous terrain and road conditions, conducting a swift and comprehensive survey of SDTs through traditional manual inventory methods is considerably difficult. In recent years, advancements in deep learning and remote sensing technology have facilitated real-time and efficient detection of dead trees. Nevertheless, challenges persist in identifying individual dead trees in airborne remote sensing images, attributed to factors such as small target size, mutual occlusion and complex backgrounds. These aspects collectively contribute to the increased difficulty of detecting dead trees at a single-tree scale. To address this issue, the paper introduces an improved You Only Look Once version 7 (YOLOv7) model that incorporates the Simple Parameter-Free Attention Module (SimAM), an unparameterized attention mechanism. This improvement aims to enhance the network's feature extraction capabilities and increase the model's sensitivity to small target dead trees. To validate the superiority of SimAM_YOLOv7, we compared it with four widely adopted attention mechanisms. Additionally, a method to enhance model robustness is presented, involving the replacement of the Complete Intersection over Union (CIoU) loss in the original YOLOv7 model with the Wise-IoU (WIoU) loss function. Following these, we evaluated detection accuracy using a self-developed dataset of SDTs in forests. The results indicate that the improved YOLOv7 model can effectively identify dead trees in airborne remote sensing images, achieving precision, recall and mAP@0.5 values of 94.31%, 93.13% and 98.03%, respectively. These values are 3.67%, 2.28% and 1.56% higher than those of the original YOLOv7 model. This improvement model provides a convenient solution for forest management.

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

The Asian longhorn beetle (ALB) Anoplophora glabripennis is one of the most successful and most feared invasive insect species worldwide. This review covers recent research concerning the distribution of and damage caused by ALB, as well as major efforts to control and manage ALB in China. The distribution and destruction range of ALB have continued to expand over the past decade worldwide, and the number of interceptions has remained high. Detection and monitoring methods for the early discovery of ALB have diversified, with advances in semiochemical research and using satellite remote sensing in China. Ecological control of ALB in China involves planting mixtures of preferred and resistant tree species, and this practice can prevent outbreaks. In addition, strategies for chemical and biological control of ALB have achieved promising results during the last decade in China, especially the development of insecticides targeting different stages of ALB, and applying Dastarcus helophoroides and Dendrocopos major as biocontrol agents. Finally, we analyze recommendations for ALB prevention and management strategies based on native range and invasive area research. This information will hopefully help some invaded areas where the target is containment of ALB.