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Panzhou City of Guizhou Province is a core coal-mining base in Southwest China. Long-term intensive underground coal exploitation has triggered widespread mining-induced ground subsidence, threatening regional ecological security and the stability of linear transportation infrastructures. Constrained by complex karst landforms, dense vegetation cover and persistent cloudy-foggy climate, conventional C-band and X-band InSAR suffer severe temporal decorrelation and low coherence in such environments, leading to insufficient monitoring accuracy and difficulties in capturing gradient mining-related deformation. To mitigate these bottlenecks, this study adopts China’s domestic LuTan-1 (LT-1) L-band SAR satellite together with ALOS-2 L-band observations and constructs a collaborative monitoring framework integrating cumulative D-InSAR and Stacking-InSAR techniques. Quantitative results show that the LT-1 dataset yields 80.99% high-coherence pixels (coherence >0.6) over heavily vegetated karst coal-mining zones, demonstrating superior vegetation penetration and anti-decorrelation performance compared with conventional SAR sensors. The maximum cumulative subsidence detected by LT-1 reaches 0.796 m with a maximum subsidence rate of 0.654 m/yr, which agrees well with ALOS-2 results (maximum cumulative subsidence 0.687 m, maximum rate 0.632 m/yr). Spatiotemporal analysis reveals heterogeneous deformation features and dynamic migration of subsidence bowls, which are highly coupled with the advance of underground mining working faces. Combined with field qualitative validation and multi-factor risk correction, a 1.245 km road segment with high subsidence hazard is identified. We explicitly emphasize that this output represents preliminary deformation-oriented hazard zoning rather than absolute engineering risk grading, as pavement structure and traffic-load parameters are not incorporated. This study systematically validates the applicability and advantages of the domestic LT-1 satellite for monitoring over vegetated karst mining terrain, providing a feasible technical paradigm for dynamic deformation surveillance and geological-hazard early warning in similarly complex mining regions.
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Chen et al. (2026) studied this question.
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