Using high-resolution remote sensing data to identify infected trees is an important method for controlling pine wilt disease (PWD). Currently, single-date image classification methods are widely used for PWD detection in pure stands of pine. However, they often yield false detections caused by deciduous trees, brown herbaceous, and sparsely vegetated regions in complex landscapes, resulting in low user accuracies. Due to the limitations on the bands of the high-resolution imagery, it is difficult to distinguish wilted pine trees from such easily confused objects when only using the optical spectral characteristics. This paper proposes a spatiotemporal change detection method to reduce false detections in tree-scale PWD monitoring under a complex landscape. The framework consisted of three parts, which represent the capture of spectral, temporal, and spatial features: (1) the Normalized Green–Red Difference Index (NGRDI) was calculated as a descriptor of canopy greenness; (2) two NGRDI images with similar dates in adjacent years were contrasted to obtain a bitemporal change index that represents the temporal behaviors of typical cover types; and (3) a spatial enhancement was performed on the change index using a convolution kernel matching the spatial patterns of PWD. Finally, a set of criteria based on the above features were established to extract the wilted pine trees. The results showed that the proposed method effectively distinguishes wilted pine trees from other easily confused objects. Compared with single-date image classification, the proposed method significantly improved user’s accuracy (81.2% vs. 67.7%) while maintaining the same level of producer’s accuracy (84.7% vs. 82.6%).

Pine wood nematode disease is a devastating pine disease that poses a great threat to forest ecosystems. The use of remote sensing methods can achieve macroscopic and dynamic detection of this disease; however, the efficiency and accuracy of traditional remote sensing image recognition methods are not always sufficient for disease detection. Deep convolutional neural networks (D-CNNs), a technology that has emerged in recent years, have an excellent ability to learn massive, high-dimensional image features and have been widely studied and applied in classification, recognition, and detection tasks involving remote sensing images. This paper uses Gaofen-1 (GF-1) and Gaofen-2 (GF-2) remote sensing images of areas with pine wood nematode disease to construct a D-CNN sample dataset, and we train five popular models (AlexNet, GoogLeNet, SqueezeNet, ResNet-18, and VGG16) through transfer learning. Finally, we use the “macroarchitecture combined with micromodules for joint tuning and improvement” strategy to improve the model structure. The results show that the transfer learning effect of SqueezeNet on the sample dataset is better than that of other popular models and that a batch size of 64 and a learning rate of 1 × 10⁻⁴ are suitable for SqueezeNet’s transfer learning on the sample dataset. The improvement of SqueezeNet’s fire module structure by referring to the Slim module structure can effectively improve the recognition efficiency of the model, and the accuracy can reach 94.90%. The final improved model can help users accurately and efficiently conduct remote sensing monitoring of pine wood nematode disease.

Pine wilt disease (PWD) is the most dangerous biohazard of pine species and poses a serious threat to forest resources. Coupling satellite remote sensing technology and deep learning technology for the accurate monitoring of PWD is an important tool for the efficient prevention and control of PWD. We used Gaofen-2 remote sensing images to construct a dataset of discolored standing tree samples of PWD and selected three semantic segmentation models—DeepLabv3+, HRNet, and DANet—for training and to compare their performance. To build a GAN-based semi-supervised semantic segmentation model for semi-supervised learning training, the best model was chosen as the generator of generative adversarial networks (GANs). The model was then optimized for structural adjustment and hyperparameter adjustment. Aimed at the characteristics of Gaofen-2 images and discolored standing trees with PWD, this paper adopts three strategies—swelling prediction, raster vectorization, and forest floor mask extraction—to optimize the image identification process and results and conducts an application demonstration study in Nanping city, Fujian Province. The results show that among the three semantic segmentation models, HRNet was the optimal conventional semantic segmentation model for identifying discolored standing trees of PWD based on Gaofen-2 images and that its MIoU value was 68.36%. Additionally, the GAN-based semi-supervised semantic segmentation model GAN_HRNet_Semi improved the MIoU value by 3.10%, and its recognition segmentation accuracy was better than the traditional semantic segmentation model. The recall rate of PWD discolored standing tree monitoring in the demonstration area reached 80.09%. The combination of semi-supervised semantic segmentation technology and high-resolution satellite remote sensing technology provides new technical methods for the accurate wide-scale monitoring, prevention, and control of PWD.

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.

Chinese forests, particularly the coniferous forest ecosystems represented by pines, play a crucial role in the global carbon cycle, significantly contributing to mitigating climate change, regulating regional climates, and maintaining ecological balance. However, pine wilt disease (PWD), caused by the pine wood nematode (PWN), has become a major threat to forest carbon stocks in China. This study evaluates the impact of PWN invasion on forest carbon stocks in China using multi-source data and an optimized MaxEnt model, and the study analyzes this invasion’s spread trends and potential risk areas. The results show that the high-suitability area for PWN has expanded from 68,000 km² in 2002 to 184,000 km² in 2021, with the spread of PWN accelerating, especially under warm and humid climate conditions and due to human activities. China’s forest carbon stocks increased from 111.34 billion tons of carbon (tC) to 168.05 billion tC, but the carbon risk due to PWN invasion also increased from 87 million tC to 99 million tC, highlighting the ongoing threat to the carbon storage capacity. The study further reveals significant differences in tree species’ sensitivity to PWN, with highly sensitive species such as Masson’s pine and black pine mainly concentrated in the southeastern coastal regions, while less sensitive species such as white pine and larch show stronger resistance in the northern and southwestern areas. This finding highlights the vulnerability of high-sensitivity tree species to PWN, especially in high-risk areas such as Guangdong, Guangxi, and Guizhou, where urgent and effective control measures are needed to reduce carbon stock losses. To address this challenge, the study recommends strengthening monitoring in high-risk areas and proposes specific measures to improve forest management and policy interventions, including promoting cross-regional joint control, enhancing early warning systems, and utilizing biological control measures, while encouraging local governments and communities to actively participate. By strengthening collaboration and implementing control measures, the health and sustainable development of forest ecosystems can be ensured, safeguarding the forests’ important role in climate regulation and carbon sequestration and contributing to global climate change mitigation.

Larch shoot blight, caused by the fungus Neofusicoccum laricinum, threatens larch (Larix spp.) forests across northeastern China, jeopardizing both timber productivity and ecological stability. This study aimed to investigate the genomic diversity, population structure, and potential adaptive mechanisms of N. laricinum across contrasting climatic regions. To achieve this, we conducted whole-genome resequencing of 23 N. laricinum isolates collected from three major provinces—Heilongjiang, Inner Mongolia, and Jilin—that represent distinct climatic zones ranging from cold-temperate to relatively warmer regions. We identified ~219.1 K genetic variants, offering a detailed portrait of the pathogen’s genomic diversity. Population structure analyses, including principal component analysis and phylogenetic tree, revealed clear genetic differentiation aligning with geographic origin and climate. Functional annotation (GO and KEGG) highlighted enrichment in metabolic, stress-response, and membrane transport pathways, suggesting potential adaptation to varied temperature regimes and environmental pressures. Moreover, region-specific variants—particularly missense and stop-gain mutations—were linked to genes involved in ATP binding, oxidoreductase activity, and cell division, underscoring the fungus’s capacity for rapid adaptation. Collectively, these findings fill a critical gap in the population genetics of N. laricinum and lay a foundation for future disease management strategies to larch shoot blight under changing climatic conditions.