It is normally difficult to identify the ground deformation of potential landslides in highly vegetation-covered areas in terms of field investigation or remote sensing interpretation. In order to explore a methodology to effectively identify potential landslides in highly vegetation-covered areas, this paper established an integrated identification method, including sliding prone area identification based on regional geological environment analysis, target area identification of potential landslides in terms of comprehensive remote sensing methods, and landslide recognition through engineering geological survey. The Miaoyuan catchment in Quzhou City, Zhejiang Province, southeastern China, was taken as an example to validate the identification methods. Particularly, the Shangfang landslide was successfully studied in terms of comprehensive methods, such as geophysical survey, drilling, mineral and chemical composition analysis, and microstructure scanning of the sliding zone. In order to assess the landslide risk, the potential runout of the Shangfang landslide was evaluated in a quantitative simulation. This paper suggests a methodology to identify potential landslides from a large area to a specific slope covered by dense vegetation.

Landslide hazard mapping is important to urban construction and landslide risk management. Dynamic landslide hazard mapping considers landslide deformation with changes in the environment. It can show more details of the landslide process state. Landslides in highly vegetated areas are difficult to observe directly, which makes landslide hazard mapping much more challenging. The application of multi-InSAR opens new ideas for dynamic landslide hazard mapping. Specifically, landslide susceptibility mapping reflects the spatial probability of landslides. For rainfall-induced landslides, the scale exceedance probability reflects the temporal probability. Based on the coupling of them, dynamic landslide hazard mapping further considers the landslide deformation intensity at different times. Zigui, a highly vegetation-covered area, was taken as the study area. The landslide displacement monitoring effect of different band SAR datasets (ALOS-2, Sentinel-1A) and different interpretation methods (D-InSAR, PS-InSAR, SBAS-InSAR) were studied to explore a combined application method. The deformation interpreted by SBAS-InSAR was taken as the main part, PS-InSAR data were used in towns and villages, and D-InSAR was used for the rest. Based on the preliminary evaluation and the displacement interpreted by fusion InSAR, the dynamic landslide hazard mappings of the study area from 2019 to 2021 were finished. Compared with the preliminary evaluation, the dynamic mapping approach was more focused and accurate in predicting the deformation of landslides. The false positives in very-high-hazard zones were reduced by 97.8%, 60.4%, and 89.3%. Dynamic landslide hazard mapping can summarize the development of and change in landslides very well, especially in highly vegetated areas. Additionally, it can provide trend prediction for landslide early warning and provide a reference for landslide risk management.