The outbreak of Pine Shoot Beetle (PSB, Tomicus spp.) posed a significant threat to the health of Yunnan pine forests, necessitating the development of an efficient and accurate remote sensing monitoring method. The integration of unmanned aerial vehicle (UAV) imagery and deep learning algorithms shows great potential for monitoring forest-damaged trees. Previous studies have utilized various deep learning semantic segmentation models for identifying damaged trees in forested areas; however, these approaches were constrained by limited accuracy and misclassification issues, particularly in complex forest backgrounds. This study evaluated the performance of five semantic segmentation models in identifying PSB-damaged trees (UNet, UNet++, PAN, DeepLabV3+ and FPN). Experimental results showed that the FPN model outperformed the others in terms of segmentation precision (0.8341), F1 score (0.8352), IoU (0.7239), mIoU (0.7185) and validation accuracy (0.9687). Under the pure Yunnan pine background, the FPN model demonstrated the best segmentation performance, followed by mixed grassland-Yunnan pine backgrounds. Its performance was the poorest in mixed bare soil-Yunnan pine background. Notably, even under this challenging background, FPN still effectively identified diseased trees, with only a 1.7% reduction in precision compared to the pure Yunnan pine background (0.9892). The proposed method in this study contributed to the rapid and accurate monitoring of PSB-damaged trees, providing valuable technical support for the prevention and management of PSB.

Invasive pests cause major ecological and economic damages to forests around the world including reduced carbon sequestration and biodiversity and loss of forest revenue. In this study, we used Random Forest to model forest mortality resulting from a 2015-2017 Spongy moth outbreak in the temperate deciduous forests of Rhode Island (northeastern U.S.). Mortality was modeled with a 100 m spatial resolution based on Landsat-derived defoliation maps and geospatial data representing soil characteristics, drought condition, and forest characteristics as well as proximity to coast, development, and water. Random Forest was used to model forest mortality with two classes (low/high) and three classes (low/med/high). The best models had overall accuracies of 82% and 65% for the two-class and three-class models, respectively. The most important predictors of forest mortality were defoliation, distance to coast, and canopy cover. Model performance improved only slightly with the inclusion of more than three variables. The models classified 35% of forests as having canopy mortality >5 trees/ha and 21% of Rhode Island forests having mortality >11 trees/ha. The study shows the benefit of Random Forest models that use both defoliation maps and geospatial environmental data for classifying forest mortality caused by Spongy moth.