Satellite-Integrated Multi-Hazard Environmental Risk Assessment Framework for Mining-Adjacent U.S. Communities Using InSAR and GIS

Abstract

This study examined the problem of fragmented and reactive environmental hazard monitoring in mining-adjacent communities across the United States, where ground deformation, water contamination, air quality degradation, tailings-dam instability, and legacy-site contamination are frequently assessed in isolation rather than through an integrated, satellite-enabled framework. The purpose of the study was to assess how a Satellite-Integrated Multi-Hazard Environmental Risk Assessment Framework, built on Interferometric Synthetic Aperture Radar (InSAR) and Geographic Information Systems (GIS), influences the quality and reliability of environmental risk assessment outcomes for communities located near active, inactive, and abandoned mining operations. A quantitative, cross-sectional, case-based design was adopted, and data were collected through a structured five-point Likert-scale questionnaire from 141 valid respondents out of 160 distributed questionnaires, representing an 88.1% valid response rate. The sample included geospatial analysts, environmental scientists, mining and geotechnical engineers, remote-sensing specialists, and local planning and public-health officials, with 66.7% directly involved in hazard-monitoring or risk-assessment activities. The key variables were InSAR ground-deformation monitoring, GIS multi-hazard data integration, remote-sensing data quality and validation, hazard exposure and vulnerability mapping, community and institutional engagement, framework design quality, and environmental risk assessment performance. The analysis plan included descriptive statistics, reliability testing using Cronbach's alpha, Pearson correlation, regression modeling, a framework maturity index, and a hazard risk-control priority matrix. The headline findings showed that all major constructs were rated high, with environmental risk assessment performance recording the highest mean score of 4.17, followed by InSAR ground-deformation monitoring at 4.13 and framework design quality at 4.09. Reliability was acceptable to excellent, with Cronbach's alpha values ranging from 0.79 to 0.92. Correlation results showed significant positive relationships, including r = 0.76 between framework design quality and environmental risk assessment performance. Regression results confirmed that the model explained 69.1% of the variance in environmental risk assessment performance, R² = 0.691, adjusted R² = 0.678, F(6,134) = 49.87, p < 0.001. Framework design quality was the strongest predictor, β = 0.32, followed by InSAR ground-deformation monitoring, β = 0.25, and GIS multi-hazard data integration, β = 0.22. The findings imply that mining-adjacent U.S. communities and their supporting agencies should strengthen InSAR time-series processing, multi-source GIS integration, validation protocols, vulnerability mapping, and community engagement to improve the reliability of multi-hazard environmental risk assessment.