The Jinsha River Basin in Yunnan serves as a crucial ecological barrier in southwestern China. Objective ecological assessment and identification of key driving factors are essential for the region’s sustainable development. The Remote Sensing Ecological Index (RSEI) has been widely applied in ecological assessments. In recent years, interpretable machine learning (IML) has introduced novel approaches for understanding complex ecological driving mechanisms. This study employed Google Earth Engine (GEE) to calculate three vegetation indices—NDVI, SAVI, and kNDVI—for the study area from 2000 to 2022, along with their corresponding RSEI models (NDVI-RSEI, SAVI-RSEI, and kNDVI-RSEI). Additionally, it analyzed the spatiotemporal variations of these RSEI models and their relationship with vegetation indices. Furthermore, an IML model (XGBoost-SHAP) was employed to interpret the driving factors of RSEI. The results indicate that (1) the RSEI levels in the study area from 2000 to 2022 were primarily moderate; (2) compared to NDVI-RSEI, SAVI-RSEI is more susceptible to soil factors, while kNDVI-RSEI exhibits a lower saturation tendency; and (3) potential evapotranspiration, land cover, and elevation are key drivers of RSEI variations, primarily affecting the ecological environment in the western, southeastern, and northeastern parts of the study area. The XGBoost-SHAP approach provides valuable insights for promoting regional sustainable development.

Scanning forests with LiDAR is an efficient method for conducting forest resource surveys, including estimating tree diameter at breast height (DBH), canopy height, and segmenting individual trees. This study uses three-dimensional (3D) forest test data and point cloud data simulated by the Helios++ V1.3.0 software, and proposes a voxelized trunk extraction algorithm to determine the trunk location and the vertical structure of single tree trunks in forest areas. Firstly, the voxel-based shape recognition algorithm is used to extract the trunk structure of tree point clouds, then the random sample consensus (RANSAC) algorithm is used to solve the vertical structure connectivity problem of tree trunks generated by the above method, and the Alpha Shapes algorithm is selected among various point cloud surface reconstruction algorithms to reconstruct the surface of tree point clouds. Then, building on the tree surface model, a light projection scene is introduced to locate the tree trunk coordinates at different heights. Finally, the convex hull of the trunk bottom is solved by the Graham scanning method. Accuracy assessments show that the proposed single-tree extraction algorithm and the forest vertical structure recognition algorithm, when applied within the light projection scene, effectively delineate the regions where the vertical structure distribution of single tree trunks is inconsistent.

Terrestrial vegetation productivity is vital for carbon sequestration and food provision. In Yunnan Province, complex terrain and diverse climates cause abrupt and nonlinear vegetation changes that linear trend analyses fail to capture, while existing attribution methods rarely distinguish causal drivers from spurious correlations or quantify thresholds with uncertainty. Using MODIS NDVI data (2002–2022), we developed an integrated framework combining multi-model trajectory diagnosis, land-use spatial analysis, GCCM causal screening, XGBoost-SHAP attribution, and GAM threshold refinement. Positive changes dominated 76.50% of the area; positive abrupt changes peaked in 2011–2014, negative in 2008–2009. GCCM excluded NL_change, retaining 12 factors. The positive model achieved F1 = 0.521 and AUC = 0.689, with climate factors accounting for 62.8% of mean absolute SHAP importance. The negative model achieved AUC = 0.858 (random CV) and 0.821 (spatial block CV), with human activity accounting for 17.9%—nearly double its positive share. Carbon_change ranked 10th for positive but 3rd for negative changes, consistent with an asymmetric association pattern. GAM-smoothed SHAP curves identified model-response thresholds with 95% confidence intervals: PRE_trend showed a stable zero-crossing near −8 mm/yr and a peak at 0–5 mm/yr (R² = 0.72); DEM showed a monotonic negative relationship with no stable zero-crossing (R² = 0.83); TEM_trend exhibited a stable zero-crossing near 0.02 °C/yr (R² = 0.57). Spatial block cross-validation confirmed acceptable ranking ability and stable SHAP rankings. This framework links change identification, model-based attribution, and model-response threshold identification, offering a transferable paradigm for complex terrain regions.