Identification of geological faults in vegetation-covered regions using Jilin-1 and ALOS DEM data

Fault structure interpretation is fundamental to structural geology and geological hazard assessment, yet remains challenging in vegetated regions where extensive vegetation obscures surface fault signatures. Integrating Jilin-1 high-resolution optical imagery with ALOS PALSAR Digital Elevation Model (DEM) data, this study develops a comprehensive approach for fault mapping through indirect indicators and demonstrates its application in southwestern Africa. By analyzing vegetation anomalies, terrain features, and drainage patterns, this method establishes interpretation criteria for identifying faults in vegetation-covered areas. The results show that: (1) Fault structures dominate the study area, with 18 faults interpreted, predominantly oriented NW and NNW parallel to the river courses, followed by NNEand NE-trending faults that obliquely cut across the valleys; (2) Vegetation anomaly features play an important indicative role in fault identification; (3) Integrated analysis of multi-source data significantly improves the reliability of fault structure interpretation in vegetation-covered areas. This study can be applied to fault structure extraction in vegetation-covered areas and provides a reference for regional geological surveys and structural framework analysis.