Research and application of karst cave detection technology based on LiDAR and high-resolution remote sensing

Karst landscapes significantly impact the global carbon cycle and water security, yet detecting hidden caves remains challenging. This study develops an integrated surface-subsurface framework for the Kongtong Mountain area (Zhangjiajie, China), combining GF-2 satellite imagery, DEMs, and handheld LiDAR. We reveal tectono-hydrologic coupling as the core control on cave morphology/distribution. A newly developed algorithm (VCCL with multi-criteria discrimination) identified 120 karst dolines with 93.54% accuracy. Key findings: (1) 89% of solution dolines align within 15° of fault strikes; collapse density increases near fault intersections. (2) 3D laser scanning confirms fault-fracture networks strictly control cave passage direction. (3) We propose a cascade model: tectonics govern fractures → fractures govern fluid flow → flow governs dissolution; fault activity accelerates dissolution via enhanced fracture density. By integrating surface doline parameters with subsurface cave characteristics, we establish a predictive framework for hidden passages, inferring deep conduit systems along fault zones whose feasibility is validated in this case study by electromagnetic surveys and engineering data. This research provides a rapid and low-cost technical paradigm for karst geohazard early warning, groundwater resource management, and site selection for major engineering projects.