SBAS-InSAR-Based Spatiotemporal Characteristics, Driving Factors, and Land Use Conflict Detection of Land Subsidence: A Case Study of Huainan City

Abstract

Highlights: What are the main findings? We propose an integrated assessment framework combining SBAS-InSAR, GeoDetector, and spatial conflict detection for studying land subsidence evolution, driving mechanisms, and the response of land-use planning to subsidence risk. We apply the proposed framework in Huainan and results demonstrate that land subsidence in Huainan was generally mild but pronounced in mining areas from 2017 to 2024 and mainly driven by soil type, annual rainfall, and mining activity, and land-use planning partially accounted for subsidence risk compared with current land use, though insufficient consideration remained in some areas. What are the implication of the main finding? This framework enables the identification of land subsidence risk zones, dominant driving factors, and conflict areas between land subsidence and land use planning. The proposed framework provides solid scientific support for land subsidence risk management and spatial planning optimization, and has strong potential for application in other subsidence-prone regions. Land subsidence (LS) is a major global geo-environmental issue that profoundly affects the suitability and safety of land use planning (LUP). However, existing LUP systems generally neglect the dynamic evolution of LS and lack a systematic framework for assessing conflicts between land use and subsidence. To address this gap, this study develops an integrated evaluation framework that combines SBAS-InSAR, GeoDetector, and a spatial conflict detection model. A total of 166 Sentinel-1A images acquired from 2017 to 2024 were processed using SBAS-InSAR to derive the spatiotemporal characteristics of LS. GeoDetector was subsequently applied to identify the dominant driving factors and their interactions. A sensitivity classification scheme for current land use (CLU) and LUP types with respect to LS hazards was then developed, and a spatial conflict detection model was constructed to delineate conflict zones and quantify conflict intensity. Using Huainan City as a case study, the results show the following: (1) from 2017 to 2024, LS was generally characterized by slight or negligible subsidence, with severe subsidence mainly concentrated in coal mining areas; ongoing and recently suspended mines exhibited pronounced LS, whereas early-closed and unmined areas showed an overall uplift trend. (2) LS in Huainan was primarily driven by soil type, annual rainfall, and mining activities, and two-factor interactions generally exhibited enhancement effects. (3) Compared with CLU, LUP has, to some extent, incorporated LS risk considerations and implemented corresponding mitigation measures, although certain areas still insufficiently account for LS risks. (4) The proposed framework demonstrates strong rationality and applicability in LS monitoring, driving factor identification, and spatial conflict assessment, providing scientific support for LS risk management and land use spatial optimization.