Many debris-covered glaciers are widely distributed on the Qinghai–Tibet Plateau. Glaciers are important freshwater resources and cause disasters such as glacier collapse and landslides. Therefore, it is of great significance to monitor the movement characteristics of large active glaciers and analyze the process of mass migration, which may cause serious threats and damage to roads and people living in surrounding areas. In this study, we chose a glacier with strong activity in Lulang County, Tibet, as the study area. The complete 4-year time series deformation of the glacier was estimated by using an improved small-baseline subset InSAR (SBAS-InSAR) technique based on the ascending and descending Sentinel-1 datasets. Then, the three-dimensional time series deformation field of the glacier was obtained by using the 3D decomposition technique. Furthermore, the three-dimensional movement of the glacier and its material migration process were analyzed. The results showed that the velocities of the Lulang glacier in horizontal and vertical directions were up to 8.0 m/year and 0.45 m/year, and these were basically consistent with the movement rate calculated from the historical optical images. Debris on both sides of the slope accumulated in the channel after slipping, and the material loss of the three provenances reached 6–9 × 10³ m³/year, while the volume of the glacier also decreased by about 76 × 10³ m³/year due to snow melting and evaporation. The correlation between the precipitation, temperature, and surface velocity suggests that glacier velocity has a clear association with them, and the activity of glaciers is linked to climate change. Therefore, in the context of global warming, the glacier movement speed will gradually increase with the annual increase in temperature, resulting in debris flow disasters in the future summer high-temperature period.

The ecological environment is an important context for geohazards formation. Traditional geohazards risk assessment mostly focuses on the physical characteristics and pays insufficient attention to long-term impact on the ecological environment. This paper considers environmental fragility from a 'current-sensitive-responsive' perspective, comparing the entropy power, projection pursuit, and random forest methods to identify the optimal model. It also proposes a 3D matrix to establish an adaptive framework for geohazards ecological risk, tailored to scenarios of frequent geohazards in environmental fragile mountainous regions. The results were validated using Gaizhou City: Random forest achieves better zoning performance and accuracy, making it the optimal model for environmental fragility. The area classified as very high risk zoning is only 28.35 km² using 3D matrix, with a high geohazard areal density of 0.35 point/km². With less than 0.3 % of the total area, it accommodates 10.6 % of the region's geohazard inventory, fully demonstrating its effectiveness. The townships of Kuangdonggou and Shizijie were key control units, and it was recommended that a combination of engineering and ecological measures be prioritized. The improvement method may provide a new perspective on the synergistic management of the ecological environment and geohazards.