As one of the most devastating disasters to pine forests, pine wilt disease (PWD) has caused tremendous ecological and economic losses in China. An effective way to prevent large-scale PWD outbreaks is to detect and remove the damaged pine trees at the early stage of PWD infection. However, early infected pine trees do not show obvious changes in morphology or color in the visible wavelength range, making early detection of PWD tricky. Unmanned aerial vehicle (UAV)-based hyperspectral imagery (HI) has great potential for early detection of PWD. However, the commonly used methods, such as the two-dimensional convolutional neural network (2D-CNN), fail to simultaneously extract and fully utilize the spatial and spectral information, whereas the three-dimensional convolutional neural network (3D-CNN) is able to collect this information from raw hyperspectral data. In this paper, we applied the residual block to 3D-CNN and constructed a 3D-Res CNN model, the performance of which was then compared with that of 3D-CNN, 2D-CNN, and 2D-Res CNN in identifying PWD-infected pine trees from the hyperspectral images. The 3D-Res CNN model outperformed the other models, achieving an overall accuracy (OA) of 88.11% and an accuracy of 72.86% for detecting early infected pine trees (EIPs). Using only 20% of the training samples, the OA and EIP accuracy of 3D-Res CNN can still achieve 81.06% and 51.97%, which is superior to the state-of-the-art method in the early detection of PWD based on hyperspectral images. Collectively, 3D-Res CNN was more accurate and effective in early detection of PWD. In conclusion, 3D-Res CNN is proposed for early detection of PWD in this paper, making the prediction and control of PWD more accurate and effective. This model can also be applied to detect pine trees damaged by other diseases or insect pests in the forest.

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.

The sea buckthorn, Hippophae rhamnoides L., is a thorny, nitrogen-fixing, dioecious, and deciduous shrub which has been attacked by a catastrophic outbreak of Holcocerus hippophaecolus in the 'Three North Areas' of China recently. The behavioral responses of female individuals to their dioecious host sea buckthorn, H. rhamnoides ssp. sinensis, were tested by Y-tube bioassay, and intraspecific emission variations and the circadian rhythm of male and female sea buckthorn plants were compared, together with the electrophysiological responses of sea buckthorn carpenter moths to these parameters. Y-tube olfactometry indicated that mated female H. hippophaecolus individuals did not display a significant preference for either sex of sea buckthorns. Additionally, no unique chemical compound was found. Female antennae significantly responded to 1-octene, methyl salicylate, and (Z)-3-Hexen-1-ol acetate, among which methyl salicylate was more abundant in females than in males. In addition, the circadian variation of (Z)-3-Hexen-1-ol acetate suggested that it was an effective compound for host location.

In recent years, the red turpentine beetle (RTB) (Dendroctonus valens LeConte) has invaded the northern regions of China. Due to the short invasion time, the outbreak of tree mortality corresponded to a low level of damage. Important information about tree mortality, provided by remote sensing at both single-tree and forest stand scale, is needed in forest management at the early stages of outbreak. In order to detect RTB-induced tree mortality at a single-tree scale, we evaluated the classification accuracies of Gaofen-2 (GF2) imagery at different spatial resolutions (1 and 4 m) using a pixel-based method. We also simultaneously applied an object-based method to 1 m pan-sharpened images. We used Sentinel-2 (S2) imagery with different resolutions (10 and 20 m) to detect RTB-induced tree mortality and compared their classification accuracies at a larger scale—the stand scale. Three kinds of machine learning algorithms—the classification and regression tree (CART), the random forest (RF), and the support vector machine (SVM)—were applied and compared in this study. The results showed that 1 m resolution GF2 images had the highest classification accuracy using the pixel-based method and SVM algorithm (overall accuracy = 77.7%). We found that the classification of three degrees of damage percentage within the S2 pixel (0%, <15%, and 15% < x < 50%) was not successful at a forest stand scale. However, 10 m resolution S2 images could acquire effective binary classification (<15%: overall accuracy = 74.9%; 15% < x < 50%: overall accuracy = 81.0%). Our results indicated that identifying tree mortality caused by RTB at a single-tree and forest stand scale was accomplished with the combination of GF2 and S2 images. Our results are very useful for the future exploration of the patterns of spatial and temporal changes in insect pest transmission at different spatial scales.

In China, the number of end-of-life vehicles (ELVs) has reached an era of exponential growth because of continuous vehicle sales. The Chinese government has guided trends in the ELV recycling industry by implementing various recycling policies and expects most ELVs to be legally treated by licensed companies. The effects of subsidy policies are remarkable, and it was found that the effective adjustment of the subsidy is beneficial in increasing the recovery rate of ELVs without additional financial burden. Just as objects have their own end-of-life laws, different vehicle types have different life distribution curves and they are slightly influenced by government policies, especially subsidy policies. The aim of the study is to establish the logistics distribution functions for the passenger vehicles and commercial vehicles on the basis of the service years of 220,000 ELVs from 2012 to 2016 in Shanghai, and use a statistical model to predict and analyze the future trend of the number of the ELVs in China. Forecasts show that the number of ELVs in China will surpass 10 million in 2023.