Landslides, like other natural hazards, such as avalanches, floods, and debris flows, may result in a lot of property damage and human casualties. The volume of landslide deposits is a key parameter for landslide studies and disaster relief. Using remote sensing and digital terrain model (DTM) data, this paper analyzes errors that can occur in calculating landslide volumes using conventional models. To improve existing models, the mechanisms and laws governing the material deposited by landslides are studied and then the mass balance principle and mass balance line are defined. Based on these ideas, a novel and improved model (Mass Balance Model, MBM) is proposed. By using a parameter called the “height adaptor”, MBM translates the volume calculation into an automatic search for the mass balance line within the scope of the landslide. Due to the use of mass balance constraints and the height adaptor, MBM is much more effective and reliable. A test of MBM was carried out for the case of a typical landslide, triggered by the Wenchuan Earthquake of 12 May 2008.

The formation mechanism of glacial debris flows in alpine gorge mountain areas is complex, with varying characteristics across different regions. Due to the influence of mountain shadows and the accumulation and ablation of ice and snow, accurately identifying and rapidly extracting glacial debris flows using optical images remains challenging. This study utilizes the Random Forest method to develop a multi-feature spatiotemporal information extraction model based on Landsat-8 images and a glacial debris flow gully identification model. These models were applied to the Songzong–Tongmai section of the Sichuan–Tibet Highway to identify glacial debris flows. The results showed that (1) the multi-feature spatiotemporal extraction model effectively eliminated the interference of mountain shadows and ice–snow phase changes, resulting in a higher accuracy for identifying and extracting glacial debris flows in areas with significant information loss due to deep shadows. The total accuracy was 93.6%, which was 8.9% and 4.2% higher than that of the Neural Network and Support Vector Machine methods, respectively. (2) The accuracy of the glacial debris flow gully identification model achieved 92.6%. The proposed method can accurately and rapidly identify glacial debris flows in alpine gorge mountain areas, facilitating remote sensing dynamic monitoring. This approach reduces the damage caused by debris flows to both transportation and the environment, ensuring the safe passage of highways and promoting the sustainable development of the region.

Road construction threatens habitat integrity and escalates landscape ecological risks. Evaluating a 20 km buffer zone along the Jiuzhi-Barkam (Jiuma) Expressway, this study constructed a landscape ecological risk index (LERI) model, predicted 2030 dynamics under four scenarios using the PLUS model, and quantified distance-decay thresholds via a Gaussian function. Results indicate the corridor is dominated by moderate risk, displaying a core-periphery gradient decreasing outward from intensive human activity. Pronounced spatial heterogeneity exists across sections: alpine grasslands experienced substantial shifts, while mid-high-mountain narrow-valleys shifted toward higher risk. By 2030, except for ecological conservation, alternative scenarios exhibited varying risk expansion; road development induced the highest risk, shrinking low-risk zones by 80.43% and expanding higher-risk zones by 73.85%. Roadside risk and distance demonstrate a significant "unimodal" relationship. Driven by increased road density, impacts became more concentrated, with 2030 thresholds peaking under road development (851.33 m) and minimizing under comprehensive development (645.63 m).

Arid cold mountain-oasis transition zones are increasingly affected by climate variability, soil salinization and human disturbance, but conventional remote-sensing ecological indices mainly describe greenness, wetness, dryness and heat. This study developed a human-activity and salinization-response ecological indicator (HARSEI) for the Altai Mountains, a transboundary ecological barrier in Central Asia. Multi-source Google Earth Engine datasets for 2000, 2005, 2010, 2015, 2020 and 2024 were used to integrate NDVI, WET, NDBSI, LST and a salinization/structure response index (SRSI) into an Altai weighted remote sensing ecological index (AWRSEI) by principal component analysis. A reversely standardized human activity intensity index (HAI), derived from population density, night-time lights and land-use disturbance, was then fused with AWRSEI using entropy weights. Grade transition, spatial autocorrelation, Theil-Sen/Mann-Kendall trend tests, Hurst analysis, GeoDetector and random forest-SHAP were used to diagnose change and drivers. HARSEI was strongly correlated with conventional RSEI (mean Pearson = 0.9123; mean Spearman = 0.9022) but identified saline and disturbed transition zones more strictly. Eco-environmental quality deteriorated most during 2010-2015 and partly recovered during 2015-2020. High-quality areas were concentrated in northern and northwestern high-elevation forest, grassland and water-conservation zones, whereas low-quality areas occurred mainly in southern and southeastern piedmont, desert-oasis and intensively used areas. Vegetation condition, land-use structure, dryness, water supply, soil conditions and salinization response jointly shaped the pattern, with strong interaction enhancement. HARSEI provides a transferable framework for monitoring ecological quality and salinization risk in arid cold mountain regions.