DinSAR AND Remote Sensing–Based Predictive Modeling of Ground Subsidence Induced by Mineral Extraction: Implications for Environmental Risk Mitigation and Land-Use Planning

Abstract

This study examined how DInSAR and remote sensing based predictive modeling can improve the assessment of ground subsidence induced by mineral extraction and strengthen environmental risk mitigation and land use planning in mining affected areas. The problem addressed in this research is that although mining related subsidence creates serious threats to infrastructure, ecosystems, settlements, and planning systems, its monitoring and predictive use in decision making are often fragmented and insufficiently integrated. The purpose of the study was therefore to test whether stronger DInSAR and remote sensing capability improves predictive modeling accuracy and whether such predictive strength contributes to better environmental mitigation and planning effectiveness. The study adopted a quantitative, cross sectional, case-based design using cloud and enterprise oriented geospatial and institutional case contexts in mineral extraction environments, with a purposive sample of 162 professional respondents drawn from mining engineering, geology, GIS and remote sensing, environmental management, planning, and regulatory fields. The key variables were DInSAR and Remote Sensing Capability, Predictive Modeling Accuracy, Environmental Risk Mitigation, and Land Use Planning Effectiveness. Data were collected through a 5-point Likert scale questionnaire and analyzed using descriptive statistics, Cronbach’s alpha, Pearson correlation, and regression analysis in SPSS. The findings showed high mean scores across all constructs, including DInSAR and Remote Sensing Capability (M = 4.18, SD = 0.61), Predictive Modeling Accuracy (M = 4.09, SD = 0.66), Environmental Risk Mitigation (M = 4.14, SD = 0.58), and Land Use Planning Effectiveness (M = 4.11, SD = 0.63). Regression results revealed that DInSAR capability significantly predicted predictive modeling accuracy (β = 0.652, R² = 0.425, p < .001), predictive modeling significantly predicted environmental risk mitigation (β = 0.718, R² = 0.516, p < .001) and land use planning effectiveness (β = 0.681, R² = 0.478, p < .001), while environmental risk mitigation also significantly predicted land use planning effectiveness (β = 0.603, p < .001). The study implies that integrating satellite-based deformation monitoring with predictive analytics can provide a stronger evidence base for safer environmental governance, hazard prioritization, infrastructure protection, and sustainable land use planning in subsidence prone mining regions.