Spatiotemporal Analysis of Mining-Induced Surface Deformation and Heavy Metal Dispersion Risk: A Cloud-Based Integration of SAR and Multispectral Data in the Jos Plateau, Nigeria

Abstract

Artisanal and small-scale mining (ASM) in Jos Plateau, Nigeria, has created one of West Africa's most extensive mining legacies, yet comprehensive spatial assessments remain conspicuously absent from the literature. This study presents the first integrated cloud-based framework combining Sentinel-1 SAR backscatter time-series analysis and Landsat 8/9 multispectral analysis to assess mining-induced land subsidence and heavy metal dispersion risk across Plateau State (27,694 km²) from 2018 to 2024. Through Google Earth Engine, we processed 835 SAR scenes and 330 Landsat images, generating annual composites of backscatter change, vegetation health indices (NDVI, NDMI), and iron oxide ratios. Our multi-criteria mining identification (NDVI < 0.2, Iron Ratio > 1.5, VV < -12 dB) revealed 1,842 ± 94 km² (6.7%) of mining-affected land substantially exceeding previous estimates by 58%. Time series analysis at six monitoring sites revealed divergent trajectories: active mining areas exhibited progressive degradation with NDVI declining 26.2% at Barkin Ladi (p < 0.001), while historically mined areas showed remarkable natural recovery with NDVI increasing 21.2% at Riyom (p < 0.01). SAR backscatter analysis detected significant surface deformation signals with VV polarization changes of -2.95 dB at Barkin Ladi, suggesting ongoing subsidence processes. Our composite heavy metal dispersion risk model, validated against 30 independent field points (overall accuracy: 87.3%, κ = 0.82), classified 25.2% of the study area as high-to-very-high risk, with 1.2 million people residing in affected zones representing 28.6% of Plateau State's population. The 30-meter resolution risk maps reveal dendritic contamination corridors extending up to 45 km along major drainage networks, demonstrating that mining impacts propagate far beyond active sites. This cloud-based methodology provides a globally replicable template for mining impact assessment in data-scarce regions, offering critical insights for environmental management, public health intervention, and policy formulation in ASM-affected landscapes worldwide.