An efficient approach to monitoring pine wilt disease severity based on random sampling plots and UAV imagery

Abstract

The mapping of forest pests and diseases using remote sensing presents a critical challenge in forest management and conservation. Forestry professionals have shown particular concern about the rapid decline of pine trees caused by pine wilt disease, conducting annual inspections to monitor its impact. However, achieving a balance between large-scale monitoring areas and the identification of individual trees poses a challenging issue. To tackle this issue, we employed a random sampling plan and utilized an unmanned aerial vehicle (UAV) to capture high-resolution images on two separate occasions. This methodology facilitated the comparison of temporal variations in tree health. We applied three object detection algorithms (YOLOv5, Faster-RCNN, CenterNet) to identify diseased trees in the images and introduced an enhanced vision of YOLOv5 for better performance. The improved YOLOv5 demonstrated higher precision of average precision (AP) (0.8806, 0.9004) compared to the other algorithms across two datasets. The improved YOLOv5 generated predictions that identified the topographic regions most severely affected by pine wilt nematode infestation. Geographically, the highest disease incidence was observed at altitudes between 200 and 299 m, slopes ranging from 10 to 15 degrees, and locations facing a sunny slope direction. The primary source of infection is pre-existing diseased trees that are not properly controlled. Additionally, the density of infected trees contributes to the spread, with the most severe outbreaks occurring in densely clustered over large areas. By aggregating the cumulative sampling data, we derived an estimation of the total tree mortality in the two towns. This study introduces an effective monitoring approach for pine wilt disease at the district level by integrating a random sampling protocol, unmanned aerial vehicle (UAV) strategies, and a deep learning algorithm. Our approach can assist forest managers in timely and cost-effective detection and control of pine wilt disease.