Tree species information is an important factor in forest resource surveys, and light detection and ranging (LiDAR), as a new technical tool for forest resource surveys, can quickly obtain the 3D structural information of trees. In particular, the rapid and accurate classification and identification of tree species information from individual tree point clouds using deep learning methods is a new development direction for LiDAR technology in forest applications. In this study, mobile laser scanning (MLS) data collected in the field are first pre-processed to extract individual tree point clouds. Two downsampling methods, non-uniform grid and farthest point sampling, are combined to process the point cloud data, and the obtained sample data are more conducive to the deep learning model for extracting classification features. Finally, four different types of point cloud deep learning models, including pointwise multi-layer perceptron (MLP) (PointNet, PointNet++, PointMLP), convolution-based (PointConv), graph-based (DGCNN), and attention-based (PCT) models, are used to classify and identify the individual tree point clouds of eight tree species. The results show that the classification accuracy of all models (except for PointNet) exceeded 0.90, where the PointConv model achieved the highest classification accuracy for tree species classification. The streamlined PointMLP model can still achieve high classification accuracy, while the PCT model did not achieve good accuracy in the tree species classification experiment, likely due to the small sample size. We compare the training process and final classification accuracy of the different types of point cloud deep learning models in tree species classification experiments, further demonstrating the advantages of deep learning techniques in tree species recognition and providing experimental reference for related research and technological development.

Forest carbon flux is critical to climate change, and the accurate modeling of forest carbon flux is an extremely challenging task. The remote sensing model (the MODIS MOD_17 gross primary productivity (GPP) model (MOD_17)) has strong practicability and is widely used around the world. The ecological process (the Biome-BioGeochemical Cycles Multilayer Soil Module model (Biome-BGCMuSo)) model can describe most of the vegetation’s environmental and physiological processes on fine time scales. Nevertheless, complex parameters and calibrations pose challenges to the application and development of models. In this study, we optimized all the input parameters of the MOD_17 model for the calibration of the Biome-BGCMuSo model to obtain GPP with continuous spatiality. To determine the contribution of input parameters to the GPP of different forest types, an Extended Fourier Amplitude Sensitivity Test (EFAST) was performed on the Biome-BGCMuSo model firstly. Then, we selected the sample points of each forest type and its different ecological gradients (30 for each type), using the GPP simulation value of the optimized MOD_17 model corresponding to the time and space scale to calibrate the Biome-BGCMuSo model, to drive the calibrated Biome-BGCMuSo, and we simulated the different forest types’ net primary productivity (NPP). According to dendrochronological measurements, the NPP simulation results were verified on the whole regional scale. The results showed that the GPP values of different forest types were highly sensitive to C:N_leaf (C:N of leaf), SLA1 (canopy average specific leaf area in phenological phase 1), and FLNR (fraction of leaf N in Rubisco). The coefficient of determination (R²) between the simulated forest NPP and the measured NPP was 0.64, and the root-mean-square (RMSE) was 26.55 g/C/m²/year. Our study aims to reduce uncertainty in forest carbon fluxes simulated by the Biome-BGCMuSo model, providing feedback for understanding forest ecosystem carbon cycling, vegetation productivity, and climate change.

Although planted forests (PFs) contribute greatly to soil and water conservation in the Yangtze River Basin and are central to afforestation efforts in China, their resilience to compound drought and heatwave (CDHW) events remains poorly understood. The unprecedented CDHW event in 2022 provided a unique opportunity to assess the responses of PFs and natural forests (NFs) to such extreme climatic disturbances. Using kernel‐normalized difference vegetation index (kNDVI), gross primary productivity (GPP), and solar‐induced chlorophyll fluorescence (GOSIF), we found that PFs experienced larger declines than NFs during the event year, but recovered more rapidly in the following year under alleviated climatic conditions, revealing a clear resistance–recovery trade‐off. For both kNDVI and GPP, NFs showed weaker declines and PFs showed faster recovery in more than 70% of grids. NFs, with their taller canopies, greater biomass, and higher species diversity, exhibited stronger resistance, whereas PFs, dominated by simpler and younger stands, demonstrated higher recovery potential. Recovery differences between the two forest types became most evident under extreme and exceptional CDHW conditions. XGBoost coupled with SHAP analysis further showed that structural differences between forest types, particularly canopy height differences, were the strongest predictors of recovery divergence, while the effects of edaphic, compositional, and climatic variables varied among indicators. These findings provide new empirical evidence that NFs and PFs play complementary roles in ecosystem stability and recovery, offering critical implications for water resource management under the projected intensification of CDHWs.