Construction of High-Precision and Complete Images of a Subsidence Basin in Sand Dune Mining Areas by InSAR-UAV-LiDAR Heterogeneous Data Integration

Author:

Wang Rui123,Huang Shiqiao1,He Yibo4,Wu Kan2,Gu Yuanyuan5,He Qimin6ORCID,Yan Huineng1,Yang Jing1

Affiliation:

1. School of Resources and Civil Engineering, Gannan University of Science and Technology, Ganzhou 341000, China

2. School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China

3. Ganzhou Key Laboratory of Remote Sensing for Resource and Environment, Ganzhou 341000, China

4. Institute of Land Reclamation and Ecological Restoration, China University of Mining and Technology, Beijing 100083, China

5. College of Surveying and Geoinformatics, Tongji University, Shanghai 200092, China

6. School of Geography Science and Geomatics Engineering, Suzhou University of Science and Technology, Suzhou 215009, China

Abstract

Affected by geological factors, the scale of surface deformation in a hilly semi-desertification mining area varies. Meanwhile, there is certain dense vegetation on the ground, so it is difficult to construct a high-precision and complete image of a subsidence basin by using a single monitoring method, and hence the laws of the deformation and inversion of mining parameters cannot be known. Therefore, we firstly propose conducting collaborative monitoring by using InSAR (Interferometric Synthetic Aperture Radar), UAV (unmanned aerial vehicle), and 3DTLS (three-dimensional terrestrial laser scanning). The time-series complete surface subsidence basin is constructed by fusing heterogeneous data. In this paper, SBAS-InSAR (Small Baseline Subset) technology, which has the characteristics of reducing the time and space discorrelation, is used to obtain the small-scale deformation of the subsidence basin, oblique photogrammetry and 3D-TLS with strong penetrating power are used to obtain the anomaly and large-scale deformation, and the local polynomial interpolation based on the weight of heterogeneous data is used to construct a complete and high-precision subsidence basin. Compared with GNSS (Global Navigation Satellite System) monitoring data, the mean square errors of 1.442 m, 0.090 m, 0.072 m are obtained. The root mean square error of the high-precision image of the subsidence basin data is 0.040 m, accounting for 1.4% of the maximum subsidence value. The high-precision image of complete subsidence basin data can provide reliable support for the study of surface subsidence law and mining parameter inversion.

Funder

National Natural Science Foundation of China

Ningxia Key R & D Project

Publisher

MDPI AG

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