What question did this study set out to answer?

The study aims to improve the accuracy of monitoring surface deformation in coal mining areas using multi-source remote sensing technologies.

May 27, 2026Open Access

Dynamic Weighted Monitoring of Surface Deformation in Mining Areas Based on Multi-Source Remote Sensing from Space, Airborne, and Ground Platforms

Key Points

The study aims to improve the accuracy of monitoring surface deformation in coal mining areas using multi-source remote sensing technologies.
Utilized 168 Sentinel-1A images for Small Baseline Subset InSAR technology to analyze surface deformation.
Proposed a Satellite Aerial-Prior Weighting method that integrates satellite InSAR, aerial Pixel Offset Tracking, and UAV LiDAR.
Constructed a dynamic priority weighting model for synergistic inversion of high-precision surface deformation parameters.
SA-PW method achieved a 10% improvement in parameter accuracy compared to SBAS-InSAR alone.
On the A line, MAE and RMSE values were 60 mm and 90 mm, respectively; on the H line, 57 mm and 83 mm.
SA-PW data fusion led to higher accuracy in monitoring surface deformation compared to traditional methods.

Abstract

Coal mines constitute a vital component of the national security system, where the extraction and utilisation of coal resources directly impact mine stability and engineering safety. Therefore, addressing the surface deformation issues caused by repeated mining activities across multiple coal seams at the Daliuta Mine, this study proposes a multi-source remote sensing monitoring technology system, which aims to improve the accuracy of surface deformation in the mining area. At the mining area scale, optimised Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technology utilised 168 Sentinel-1A image scenes to generate 789 interferometric image pairs. This extracted the long-term surface deformation field of the Daliuta mining area, revealing the spatiotemporal evolution patterns of surface subsidence under repeated mining activities. To further enhance monitoring accuracy and reliability, this study proposed a Satellite Aerial-Prior Weighting (SA-PW) method. This approach integrated satellite-based time-series InSAR, aerial Pixel Offset Tracking (POT), and unmanned aerial vehicle light detection and ranging (UAV LiDAR) data through a dynamic priority weighting model. This enabled the synergistic inversion of high-precision surface deformation parameters for repeatedly mined areas. Results demonstrated that compared to SBAS-InSAR alone, the SA-PW method achieved a 10% improvement in surface deformation parameter accuracy. By constructing a dynamic priority-weighted model, this approach systematically integrated multi-source data to achieve collaborative inversion of high-precision surface deformation parameters in repeatedly mined areas. Results demonstrated that compared to SBAS-InSAR and UAV LiDAR methods, SA-PW data fusion yielded higher accuracy, with MAE and RMSE values of 60 mm and 90 mm on the A line, and 57 mm and 83 mm on the H line, respectively. This method facilitates the establishment of integrated air–space–ground real-time monitoring networks for mining areas, enables subsidence hazard early warning and management, identifies key zones for ecological restoration, explores synergistic mechanisms between repeated mining and ecological rehabilitation, and promotes safe and green mining operations and development.

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