Detection of forest pest outbreaks can help in controlling outbreaks and provide accurate information for forest management decision-making. Although some needle injuries occur at the beginning of the attack, the appearance of the trees does not change significantly from the condition before the attack. These subtle changes cannot be observed with the naked eye, but usually manifest as small changes in leaf reflectance. Therefore, hyperspectral remote sensing can be used to detect the different stages of pest infection as it offers high-resolution reflectance. Accordingly, this study investigated the response of a larch forest to Jas’s Larch Inchworm (Erannis jacobsoni Djak) and performed the different infection stages detection and identification using ground hyperspectral data and data on the forest biochemical components (chlorophyll content, fresh weight moisture content and dry weight moisture content). A total of 80 sample trees were selected from the test area, covering the following three stages: before attack, early-stage infection and middle- to late-stage infection. Combined with the Findpeaks-SPA function, the response relationship between biochemical components and spectral continuous wavelet coefficients was analyzed. The support vector machine classification algorithm was used for detection infection. The results showed that there was no significant difference in the biochemical composition between healthy and early-stage samples, but the spectral continuous wavelet coefficients could reflect these subtle changes with varying degrees of sensitivity. The continuous wavelet coefficients corresponding to these stresses may have high potential for infection detection. Meanwhile, the highest overall accuracy of the model based on chlorophyll content, fresh weight moisture content and dry weight moisture content were 90.48%, 85.71% and 90.48% respectively, and the Kappa coefficients were 0.85, 0.79 and 0.86 respectively.

Utilizing UAV remote sensing technology to acquire information on forest pests is a crucial technical method for determining the health of forest trees. Achieving efficient and precise pest identification has been a major research focus in this field. In this study, Dendrolimus superans (Butler) was used as the research object to acquire UAV multispectral, LiDAR, and ground-measured data for extracting sensitive features using ANOVA and constructing a severity-recognizing model with the help of random forest (RF) and support vector machine (SVM) models. Sixteen sensitive feature sets (including multispectral vegetation indices and LiDAR features) were selected for training the recognizing model, of which the normalized differential greenness index (NDGI) and 25% height percentile were the most sensitive and could be used as important features for recognizing larch caterpillar infestations. The model results show that the highest accuracy is SVM_VI+LIDAR (OA = 95.8%), followed by SVM_VI, and the worst accuracy is RF_LIDAR. For identifying healthy, mild, and severely infested canopies, the SVM_VI+LIDAR model achieved 90%–100% for both PA and UA. The optimal model chosen to map the spatial distribution of severity at the single-plant scale in the experimental area demonstrated that the severity intensified with decreasing elevation, especially from 748–758 m. This study demonstrates a high-precision identification method of larch caterpillar infestation severity and provides an efficient and accurate data reference for intelligent forest management.

Erannis jacobsoni Djak (Lepidoptera, Geometridae) is a leaf-feeding pest unique to Mongolia. Outbreaks of this pest can cause larch needles to shed slowly from the top until they die, leading to a serious imbalance in the forest ecosystem. In this work, to address the need for the low-cost, fast, and effective identification of this pest, we used field survey indicators and UAV images of larch forests in Binder, Khentii, Mongolia, a typical site of Erannis jacobsoni Djak pest outbreaks, as the base data, calculated relevant multispectral and red–green–blue (RGB) features, used a successive projections algorithm (SPA) to extract features that are sensitive to the level of pest damage, and constructed a recognition model of Erannis jacobsoni Djak pest damage by combining patterns in the RGB vegetation indices and texture features (RGB_VI&TF) with the help of random forest (RF) and convolutional neural network (CNN) algorithms. The results were compared and evaluated with multispectral vegetation indices (MS_VI) to explore the potential of UAV RGB images in identifying needle pests. The results show that the sensitive features extracted based on SPA can adequately capture the changes in the forest appearance parameters such as the leaf loss rate and the colour of the larch canopy under pest damage conditions and can be used as effective input variables for the model. The RGB_VI&TF-RF₄₄₀ and RGB_VI&TF-CNN₇₄₀ models have the best performance, with their overall accuracy reaching more than 85%, which is a significant improvement compared with that of the RGB_VI model, and their accuracy is similar to that of the MS_VI model. This low-cost and high-efficiency method can excel in the identification of Erannis jacobsoni Djak-infested regions in small areas and can provide an important experimental theoretical basis for subsequent large-scale forest pest monitoring with a high spatiotemporal resolution.

While unmanned aerial vehicle (UAV) remote sensing technology has been successfully used in crop vegetation pest monitoring, a new approach to forest pest monitoring that can be replicated still needs to be explored. The aim of this study was to develop a model for identifying the degree of damage to forest trees caused by Erannis jacobsoni Djak. (EJD). By calculating UAV multispectral vegetation indices (VIs) and texture features (TF), the features sensitive to the degree of tree damage were extracted using the successive projections algorithm (SPA) and analysis of variance (ANOVA), and a one-dimensional convolutional neural network (1D-CNN), random forest (RF), and support vector machine (SVM) were used to construct damage degree recognition models. The overall accuracy (OA), Kappa, Macro-Recall (R_macro), and Macro-F1 score (F1_macro) of all models exceeded 0.8, and the best results were obtained for the 1D-CNN based on the vegetation index sensitive feature set (OA: 0.8950, Kappa: 0.8666, R_macro: 0.8859, F1_macro: 0.8839), while the SVM results based on both vegetation indices and texture features exhibited the poorest performance (OA: 0.8450, Kappa: 0.8082, R_macro: 0.8415, F1_macro: 0.8335). The results for the stand damage level identified by the models were generally consistent with the field survey results, but the results of SVM_VIs+TF were poor. Overall, the 1D-CNN showed the best recognition performance, followed by the RF and SVM. Therefore, the results of this study can serve as an important and practical reference for the accurate and efficient identification of the damage level of forest trees attacked by EJD and for the scientific management of forest pests.

New data on the distribution, bionomy, and taxonomy of the longhorned beetles that occur in the poorly studied region of southeastern Mongolia (Ömnögovi, Dornogovi, and Sükhbaatar aimags) are presented together with a list of all taxa that are known from this area. The literature records for all known species from this area were summarized, verified, and mapped. Chlorophorus caragana Xie & Wang, 2012 is recorded from Mongolia for the first time. New localities of some little-known taxa that are endemic to Mongolia and adjacent territories, such as Anoplistes halodendri minutus Hammarström, 1892, Anoplistes kaszabi Karpiński, 2020, and Eodorcadion gorbunovi Danilevsky, 2004 are given. High-quality stacked images of several unique species, e.g., Anoplistes gobiensis (Namkhaidorzh, 1973), Ch. caragana, and Chlorophorus obliteratus (Ganglbauer, 1889) are presented for the first time along with photographs of their habitats. New remarks on highly complex taxonomic issues concerning some species in the genera Anoplistes, Chlorophorus, and Eodorcadion are also provided.

Physicochemical variables of aquatic environments are the main factors that influence aquatic organism distributions. In the present study, we aimed to assess the relationship between environmental variables and dytiscid assemblages in different aquatic basins of Mongolia. In total, 4193 diving beetle specimens of 66 species belonging to 15 genera were collected from 146 sites under various environmental and ecological conditions. Clear differences were observed in species richness and community composition among surveyed basins, subbasins, and habitat types. Environmental variables, particularly altitude and dissolved oxygen, exerted a strong influence on dytiscid communities in subbasins of the country. This large-scale study is the first to examine the relationship between dytiscids and their natural habitats in Mongolia at a species level, and it provides useful baseline information for future studies that aim to understand the factors that affect dytiscid communities.