Progress and prospects of multi-source remote sensing monitoring technology for coal mining subsidence in mining areas of the western Loess Plateau

The western loess-covered area is an important coal production base in China, with complex geomorphology and geological mining conditions, huge thickness of loess layer, mining subsidence with special characteristics different from other mining areas, large surface subsidence rate, small starting distance, more serious discontinuous damage such as mining cracks, and more complicated and changeable characteristics of the shape of the surface subsidence basin and the distribution of subsidence deformation. Under the current technical conditions, it is still difficult to carry out efficient monitoring and quantitative evaluation of surface subsidence. In recent years, multi-source remote sensing technologies such as Interferometric Synthetic Aperture Radar(InSAR), Unmanned Aerial Vehicle photogrammetry(UAV photogrammetry), and Light Detection and Ranging(LiDAR) have been developing rapidly, and with its features of non-contact observation, wide coverage, and high spatial and temporal resolution, it is able to realize the continuity, dynamics and comprehensive monitoring of surface subsidence. Through the fusion of multi-source observation data, it can provide a new technological way for the efficient monitoring of the mining subsidence in the western mining area. This paper summarizes the progress of the application of multi-source remote sensing technology in mining subsidence monitoring. The practical effects of lnSAR, UAV photogrammetry, and LiDAR for subsidence monitoring in mining areas on the Loess Plateau are explored through field experiments, and it is found that under the conditions of complex geomorphology, undulating topography, and vegetation cover, as well as large-gradient deformation of the ground surface, the lnSAR interferometric loss of correlation, atmospheric delays and other kinds of errors are too large. The 3D model constructed from UAV photogrammetry is affected by vegetation and terrain, resulting in insufficient elevation accuracy for the ground model. UAV laser scanning is affected by the combined effects of terrain slope, point cloud density, and horizontal movement of surface points leading to significant DEM modeling errors. As a result, multi-source remote sensing techniques under the current conditions all have their own advantages, limitations and technical bottlenecks. On this basis, the algorithmic process of ground point cloud extraction and surface 3D deformation information acquisition in the loess gully area is proposed; the technical path to realize accurate and efficient monitoring of mine subsidence 3D displacement based on the fusion of multi-source remote sensing data is pointed out; and the prospect of the application of constructing an intelligent system of multi-source remote sensing monitoring of mine subsidence is envisioned.