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.

Introduction: Erannis jacobsoni Djak.(EJD) is one of the major pests that severely threatens forest health. Its damage predominantly affects pine species, resulting in significant changes to the biochemical composition of needle leaves. Needle leaf water content exhibits a clear response to these changes and is highly sensitive in reflecting the degree of tree damage.Methods: In this work, we combine vegetation indices with machine learning algorithms to estimate the water content of needles at a large scale. Multiple vegetation indices are screened via recursive feature elimination cross validation (RFECV), and then support vector regression (SVR) and back-propagation neural network (BP) models are used to predict the leaf weight content fresh (LWCF) and leaf weight content dry (LWCD) of needles over a large area. The water content ranges were then classified based on the severity of damage derived from actual sampling data. These ranges were used to categorize the estimated water content, thereby assessing the degree of tree damage. The accuracy of the method is verified by comparing the estimation results with field measurements, and the results are combined with the classifications of the leaf loss rate(LLR) to assess the severity of infestation.Results: The results indicate that: 1) When estimating LWCD and LWCF using the SVR and BP models, the SVR model demonstrated superior accuracy and stability (MAE for LWCF = 0.1477, RMSE = 0.17314; MAE for LWCD = 0.10507, RMSE = 0.14760). 2) The classification accuracies of LWCD and LWCF were notably higher in areas with light and medium damage, suggesting that these indices are effective indicators for assessing damage caused by Erannis jacobsoni Djak. and can serve as valuable tools for monitoring pest infestation and its progression. 3) Through precision evaluation and supplementary validation, the results show that LWCD is more stable and reliable than LWCF, demonstrating greater credibility, particularly in terms of MAE and RMSE, where LWCD exhibits lower values (MAE for LWCD = 0.10507, RMSE = 0.14760). This method’s high reliability provides an effective approach for estimating leaf weight content, both fresh and dry (LWCF and LWCD), and underscores its significant potential for the early monitoring and management of forest pests.

In recent years, Jas’s Larch Inchworm (Erannis jacobsoni Djak, EJD) outbreaks have frequently occurred in forested areas of Mongolia, causing significant damage to forest ecosystems, and rapid and effective monitoring methods are urgently needed. This study focuses on a typical region of EJD infestation in the larch forests located in Binder, Khentii, Mongolia. Initial super-resolution enhancement was performed on Sentinel-2 images, followed by the calculation of vegetation indices and first-order spectral derivatives. The Kruskal–Wallis H test (KW test), Dunn’s multiple comparison test (Dunn’s test), and the RF-RFECV algorithm were then employed to identify sensitive features. Using support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost) machine learning algorithms, along with field survey data and UAV remote sensing data, multiple models were developed to assess the severity of EJD infestation and the corresponding spatial distribution characteristics. Seven sensitive combined features were obtained from high-quality super-resolution Sentinel-2 images. Then, a high-precision monitoring model was constructed, and it was revealed that the areas prone to EJD infestation are located at elevations of 1171–1234 m, on gentle slopes, and in semi-shady or semi-sunny areas. The super-resolution processing of Sentinel-2 satellite data can effectively refine monitoring results. The combination of the first-order spectral derivatives and vegetation indices can improve the monitoring accuracy and the discrimination of light and moderate damage. D8a and NDVI_swir can be used as important indicators for assessing the severity of EJD infestation. EJD has an adaptive preference for certain environments, and environmental factors directly or indirectly affect the diffusion and distribution of EJD.

Leaf loss caused by pest infestations poses a serious threat to forest health. The leaf loss rate (LLR) refers to the percentage of the overall tree-crown leaf loss per unit area and is an important indicator for evaluating forest health. Therefore, rapid and accurate acquisition of the LLR via remote sensing monitoring is crucial. This study is based on drone hyperspectral and LiDAR data as well as ground survey data, calculating hyperspectral indices (HSI), multispectral indices (MSI), and LiDAR indices (LI). It employs Savitzky–Golay (S–G) smoothing with different window sizes (W) and polynomial orders (P) combined with recursive feature elimination (RFE) to select sensitive features. Using Random Forest Regression (RFR) and Convolutional Neural Network Regression (CNNR) to construct a multidimensional (horizontal and vertical) estimation model for LLR, combined with LiDAR point cloud data, achieved a three-dimensional visualization of the leaf loss rate of trees. The results of the study showed: (1) The optimal combination of HSI and MSI was determined to be W11P3, and the LI was W5P2. (2) The optimal combination of the number of sensitive features extracted by the RFE algorithm was 13 HSI, 16 MSI, and hierarchical LI (2 in layer I, 9 in layer II, and 11 in layer III). (3) In terms of the horizontal estimation of the defoliation rate, the model performance index of the CNNR_HSI model (MPI = 0.9383) was significantly better than that of RFR_MSI (MPI = 0.8817), indicating that the continuous bands of hyperspectral could better monitor the subtle changes of LLR. (4) The I-CNNR_HSI+LI, II-CNNR_HSI+LI, and III-CNNR_HSI+LI vertical estimation models were constructed by combining the CNNR_HSI model with the best accuracy and the LI sensitive to different vertical levels, respectively, and their MPIs reached more than 0.8, indicating that the LLR estimation of different vertical levels had high accuracy. According to the model, the pixel-level LLR of the sample tree was estimated, and the three-dimensional display of the LLR for forest trees under the pest stress of larch caterpillars was generated, providing a high-precision research scheme for LLR estimation under pest stress.

With the development of remote sensing technology, the estimation of the chlorophyll content (CHLC) of vegetation via satellite data has become an important means of monitoring vegetation health, and high-precision estimation has been the focus of research in this field. In this study, we used larch affected by Yarl’s larch looper (Erannis jacobsoni Djak) in the boundary region of Mongolia as the research object, simulated the multispectral reflectance, downscaled Sentinel-2A satellite data, performed mixed-pixel decomposition, analyzed the potential of Sentinel-2A satellite data for estimating the chlorophyll content by calculating the spectral indices (SIs) and spectral derivatives (SDFs) of images, and then extracted sensitive spectral features as the model training set. Spectral features sensitive to the chlorophyll content were extracted to establish the training set, and, finally, the chlorophyll content estimation model for larch was constructed on the basis of the partial least squares algorithm (PLSR). The results revealed that SI and SDF based on simulated remote sensing data were highly sensitive to the chlorophyll content under the influence of pests, with the SAVI and EVI2 spectral indices as well as the D_B2 and D_B5 spectral derivatives being the most sensitive to the chlorophyll content. The estimation models based on simulated data performed significantly better than models without simulated data in terms of accuracy, especially those based on SDF-PLSR. The simulated spectral reflectance well reflected the spectral characteristics of the larch canopy and was sensitive to damaged larch, especially in the green light, red edge, and near-infrared bands. The proposed approach improves the accuracy of chlorophyll content estimation via Sentinel-2A data and enhances the ability to monitor changes in the chlorophyll content under complex forest conditions through simulations, providing new technical means and a theoretical basis for forestry pest monitoring and vegetation health management.

In the context of global climate change, a comprehensive understanding of the spatiotemporal impacts of drought on vegetation productivity is essential for assessing terrestrial ecosystem stability. Utilizing outputs from six global climate models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6), this study systematically assessed historical and projected drought probability, the drought vulnerability of Net Primary Productivity (NPP), and overall drought risk across the Mongolian Plateau under three Shared Socioeconomic Pathway scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5). Results revealed that the Standardized Precipitation Evapotranspiration Index (SPEI) exhibited a declining trend, whereas NPP showed an overall increasing trend. These changes were most pronounced under the SSP5-8.5 scenario, with the SPEI decreasing at a rate of −0.39/10a and NPP increasing at 25.8/10a. Drought severity exhibited strong spatial heterogeneity, intensifying from northeast to southwest, whereas NPP demonstrated an inverse spatial pattern. The spatial distribution of high-drought-risk zones varied markedly across scenarios: the southwestern region was most affected under SSP1-2.6, the northwestern region under SSP2-4.5, and the southeastern region under SSP5-8.5. Based on 12-month SPEI values and NPP derived from the Carnegie–Ames–Stanford Approach (CASA) model, SSP2-4.5 presented the highest overall drought risk, despite lower emissions. The annual mean NPP drought vulnerability ranked as follows: SSP2-4.5 (0.60 $\mathrm{g}\mathrm{C}{\mathrm{m}}^{-2}{\mathrm{y}\mathrm{r}}^{-1}$) > SSP1-2.6 (−1.03 $\mathrm{g}\mathrm{C}{\mathrm{m}}^{-2}{\mathrm{y}\mathrm{r}}^{-1}$) > SSP5-8.5 (−1.24 $\mathrm{g}\mathrm{C}{\mathrm{m}}^{-2}{\mathrm{y}\mathrm{r}}^{-1}$). Projections indicated a substantial increase in drought occurrence probability during the period 2061–2100, particularly under SSP2-4.5 and SSP5-8.5. Under higher emissions, the spatial extent of areas with negative drought vulnerability values was expected to expand 68%. Wind speed was the dominant factor influencing drought risk under SSP1-2.6 and SSP2-4.5, whereas precipitation became the primary driver (45.34%) under SSP5-8.5. These findings offer critical insights for early drought warning systems and for strengthening ecosystem resilience across the Mongolian Plateau.