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.

Anoplophora glabripennis Motschulsky, 1854 (Asian longhorned beetle) does serious damage to forests. It has a long history and wide distribution area in China and is spreading there and elsewhere. Extreme climate events, such as cold surges and droughts, have had a promotive impact on Anoplophora glabripennis occurrence, but the spatial spillover effect of extreme climate events and other environmental factors on the occurrence of this pest has not yet been clarified. Two indices, namely, Standardized Precipitation Evapotranspiration Index (SPEI) and Low Temperature Index (LTI), were used to quantify the effects of drought and low-temperature freezing damage. Based on spatial panel data modeling, this study calculated the spatial spillover effect of environmental factors on the incidence of Anoplophora glabripennis in 666 counties in China’s central plains from 2002 to 2009. The factors examined included LTI, SPEI, average wind speed, hours of sunlight, Gross Domestic Product (GDP) of regional primary industry, population density, Normalized Difference Vegetation Index (NDVI), and pest control rate. Study results indicated that the impacts of environmental factors on the incidence rate of Anoplophora glabripennis are different. Low-temperature freezing damage, drought, wind speed, and pest control rate had a driving impact on pest incidence rates. Overall, the direct effect accounts for about 85% of the total effect, while the indirect effect accounts for about 15% of the total effect.