The occurrence and prevalence of pine wood nematode disease has had a great impact on China’s forestry production and ecological environment, but the quantitative evaluation of the economic losses of this biological disease is lacking from a macro-geographic scale and long-term series perspective. This study establishes a pine wood nematode disease economic loss evaluation index system, covering the loss of natural ecology, landscape aesthetics and economic production, and quantitatively evaluates the economic losses of pine wood nematode disease in mainland China at a provincial level from 1998 to 2017. The results show that the economic losses of the pine wood nematode disease in China showed a fluctuant rising trend from 1998, since 2013, it has entered a full-scale outbreak phase, and since 2015, the economic loss has increased significantly, with a growth rate higher than 40%. The average annual total economic loss is CNY 7.17 billion, of which the direct economic losses is CNY 1.53 billion, and the indirect economic losses is CNY 5.64 billion. The loss of forest material resources, the expenditure of ineffective forest management expenditure and prevention and control expenditure were CNY 817 million, CNY 649 million, and CNY 67 million, respectively. Regulate service value loss, support service value loss and cultural service loss are CNY 3.95 billion, CNY 1.41 billion, CNY 276 million, respectively. East China and South China suffered the most due to pine wood nematode disease, and the economic loss of these regions accounted for 79.9% of the total national economic losses. Among them, Zhejiang Province, Guangdong Province and Jiangsu Province lost CNY 2.14 billion, CNY 1.81 billion, and CNY 1.22 billion, respectively, accounting for 26.8%, 22.7% and 15.3% of the total national economic loss.

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 is a major plant epidemic that has significantly impaired the ecological safety of pine wood, the national economy, and peoples’ livelihood. It is challenging to accurately assess the loss from pine wilt disease through academic research or field work. Based on the 342,000 subcompartments of epidemic data of pine wilt disease in China in 2020, this study builds a refined assessment indicator system and measurement model for economic loss from disasters at the subcompartment scale and assesses direct economic loss and ecological service value loss. The results show that through direct economic loss and ecological service value loss, China lost USD 7.40 billion in 2020, including a direct economic loss of USD 1.11 billion and ecological service value loss of USD 6.29 billion. Of the direct economic loss, the forest material resource loss and protection expense reached USD 0.17 billion and USD 0.94 billion, respectively; of the ecological system service losses, regulation service, supporting service, and cultural service losses reached USD 4.58 billion, USD 1.35 billion, and USD 0.36 billion. Spatial distribution analysis showed that the loss declined from southeast to northwest, with Shandong, Zhejiang, and Jiangxi suffering the greatest losses. Based on the subcompartment scale, this study employs a more refined assessment indicator system and measurement model to provide accurate real-world assessment results.

Tomicus yunnanensis and Tomicus minor have caused serious shoot damage in Yunnan pine forests in the Yunnan Province of China. However, very few remote sensing studies have estimated the shoot damage ratio (SDR). Thus, we used multi-date Landsat satellite imagery to quantify SDRs and assess the possibility of using spectral indices to determine the beetle outbreak time and spread direction. A new threshold-based classification method was proposed to identify damage levels (i.e., healthy, slightly to moderately infested, and severely infested forests) using time series of moisture stress index (MSI). Permanent plots and temporal field inspection data were both used as references for training and evaluation. Results show that a single threshold value can produce a total classification accuracy of 86.38% (Kappa = 0.80). Furthermore, time series maps detailing damage level were reconstructed from 2004 to 2016. The shoot beetle outbreak year was estimated to be 2013. Another interesting finding is the movement path of the geometric center of severe damage, which is highly consistent with the wind direction. We conclude that the time series of shoot damage level maps can be produced by using continuous MSI images. This method is very useful to foresters for determining the outbreak time and spread direction.

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.

The Yangtze River Basin is among the river basins with the strongest strategic support and developmental power in China. As an invasive species, the pinewood nematode (PWN) Bursaphelenchus xylophilus has introduced a serious obstacle to the high-quality development of the economic and ecological synchronization of the Yangtze River Basin. This study analyses the occurrence and spread of pine wilt disease (PWD) with the aim of effectively managing and controlling the spread of PWD in the Yangtze River Basin. In this study, statistical data of PWD-affected areas in the Yangtze River Basin are used to analyse the occurrence and spread of PWD in the study area using spatiotemporal visualization analysis and spatiotemporal scanning statistics technology. From 2000 to 2018, PWD in the study area showed an “increasing-decreasing-increasing” trend, and PWD increased explosively in 2018. The spatial spread of PWD showed a “jumping propagation-multi-point outbreak-point to surface spread” pattern, moving west along the river. Important clusters were concentrated in the Jiangsu-Zhejiang area from 2000 to 2015, forming a cluster including Jiangsu and Zhejiang. Then, from 2015–2018, important clusters were concentrated in Chongqing. According to the spatiotemporal scanning results, PWD showed high aggregation in the four regions of Zhejiang, Chongqing, Hubei, and Jiangxi from 2000 to 2018. In the future, management systems for the prevention and treatment of PWD, including ecological restoration programs, will require more attention.