Pine wilt disease (PWD) is characterized by rapid transmission, high mortality rates, and difficulty in control, resulting in severe and destructive impacts on both the ecological environment and socioeconomic development in China. Due to the lack of significant symptoms in infected trees during the early stages of the disease, improving the accuracy of early detection has become a major challenge in PWD monitoring. In recent years, the rapid advancement of UAV-based hyperspectral remote sensing technology has provided a promising approach for the early detection of PWD. In this study, we selected classic canopy pigment and moisture content indices to construct a set of recognition indicators. The optimal canopy pigment index (CI) and canopy moisture content index (WASCOSBNDI) were then chosen through significance testing and derivative analysis. Based on the asynchronous variations in canopy moisture and pigment content during the development of PWD, the CI, WASCOSBNDI, and CI-WASCOSBNDI models were developed using a multi-threshold segmentation method to identify trees at different stages of infection. The results demonstrate that the CI-WASCOSBNDI model achieved the highest accuracy in detecting infection stages, with an overall classification accuracy of 92.78%. In comparison, the CI and WASCOSBNDI models achieved classification accuracies of 81.34% and 89.84%, respectively. For the early stage infected trees, which are the primary focus of this study, the CI-WASCOSBNDI model exhibited the best performance with an accuracy rate exceeding 70%, significantly outperforming the other models. Furthermore, the timing of infection in early stage trees significantly influenced the model’s detection accuracy, with trees closer to the disease outbreak period being more easily identified. These findings provide a reference for the accurate early monitoring of PWD using UAV hyperspectral imagery.

Pine wilt disease (PWD) is a devastating biological forest disturbance, making its large-scale and high-precision remote sensing monitoring crucial for epidemic prevention and control. However, the performance of existing deep learning methods in high-resolution imagery is often limited by the confusion of spectral features among disparate ground objects and the complexity of forest boundaries. To address these challenges, this study proposes an innovative, end-to-end deep learning architecture termed MBA-Former. Built upon the robust Swin Transformer V2 backbone, the model systematically integrates two highly adaptable functional modules: (1) a front-end intelligent fusion module designed to adaptively fuse heterogeneous features, and (2) a back-end boundary refinement module that refines segmentation contours via dual-task learning. To train and evaluate the model, fine-grained manual annotations were first performed on Gaofen-2 satellite imagery acquired from multiple typical epidemic areas across northern and southern China. Information-enhanced datasets were constructed by fusing the original spectral bands, typical vegetation indices, and texture features. A comprehensive performance evaluation was then conducted, specifically targeting typical challenging scenarios characterized by complex ground object boundaries. The experimental results demonstrate that the Multi-modal Boundary-Aware Transformer (MBA-Former) significantly outperforms current state-of-the-art models. It achieved a mean Intersection over Union (mIoU) of 81.74%, an IoU of 77.58% for the most critical infected tree category, and a Boundary F1-Score of 78.62%. Compared to the best-performing baseline model, Swin-Unet, these three metrics exhibited notable improvements of 2.88%, 3.55%, and 4.46%, respectively. These findings convincingly demonstrate that MBA-Former provides a highly accurate and robust solution for the large-scale, automated remote sensing monitoring of forest diseases, offering immense value in preventing significant economic losses and preserving forest ecosystem integrity.