Landslides have become a common global concern because of their widespread nature and destructive power. The Gaizi Valley section of the Karakorum Highway is located in an alpine mountainous area with a high degree of geological structure development, steep terrain, and severe regional soil erosion, and landslide disasters occur frequently along this section, which severely affects the smooth flow of traffic through the China-Pakistan Economic Corridor (CPEC). In this study, 118 views of Sentinel-1 ascending- and descending-orbit data of this highway section are collected, and two time-series interferometric synthetic aperture radar (TS-InSAR) methods, distributed scatter InSAR (DS-InSAR) and small baseline subset InSAR (SBAS-InSAR), are used to jointly determine the surface deformation in this section and identify unstable slopes from 2021 to 2023. Combining these data with data on sites of historical landslide hazards in this section from 1970 to 2020, we constructed 13 disaster-inducing factors affecting the occurrence of landslides as evaluation indices of susceptibility, carried out an evaluation of regional landslide susceptibility, and identified high-susceptibility unstable slopes (i.e., potential landslides). The results show that DS-InSAR and SBAS-InSAR have good agreement in terms of deformation distribution and deformation magnitude and that compared with single-orbit data, double-track SAR data can better identify unstable slopes in steep mountainous areas, providing a spatial advantage. The landslide susceptibility results show that the area under the curve (AUC) value of the artificial neural network (ANN) model (0.987) is larger than that of the logistic regression (LR) model (0.883) and that the ANN model has a higher classification accuracy than the LR model. A total of 116 unstable slopes were identified in the study, 14 of which were determined to be potential landslides after the landslide susceptibility results were combined with optical images and field surveys. These 14 potential landslides were mapped in detail, and the effects of regional natural disturbances (e.g., snowmelt) and anthropogenic disturbances (e.g., mining projects) on the identification of potential landslides using only SAR data were assessed. The results of this research can be directly applied to landslide hazard mitigation and prevention in the Gaizi Valley section of the Karakorum Highway. In addition, our proposed method can also be used to map potential landslides in other areas with the same complex topography and harsh environment.

Low vegetation cover and patchy distribution in arid regions make vegetation spectral signals susceptible to interference from bare-soil backgrounds, while differences in canopy structure among herbaceous vegetation, shrubs, and trees further increase the difficulty of accurate aboveground biomass (AGB) estimation. This study investigated heterogeneous vegetation in the middle and lower plains of the Yarkand River Basin, China, using field-measured AGB, UAV multispectral imagery, and LiDAR point clouds. Spectral (S), canopy height (H), and texture (T) features were derived, and four combinations (S, S + H, S + T, and S + H + T) were evaluated with SVR, GPR, XGBoost, and RF under repeated nested five-fold cross-validation. The results showed that the optimal texture window size varied among models, with the 7 × 7 pixel window providing a reasonable balance between prediction accuracy and stability. Adding canopy height to spectral-only models increased R² by 0.282–0.455 and reduced RMSE by 28.2–41.9%, exceeding the incremental contribution of texture. S + H + T achieved the best overall performance (R² = 0.643–0.806; RMSE = 1.189–1.629 kg·m⁻²). XGBoost yielded the highest mean accuracy, whereas RF was more stable. SHAP analysis attributed 63.6%, 30.2%, and 6.2% of the overall contribution to canopy height, spectral, and texture features, respectively. Overall, multisource feature integration improved AGB estimation, with canopy height providing the main incremental information; however, accurately retrieving canopy height for low-stature vegetation in arid regions remains a key challenge for further improving estimation reliability.