Monitoring mining impacts using harmonic decomposition of time series vegetation indices

This is the authors' abstract. We don't add key points for this paper.

Global mining operations cause significant vegetation disturbance, yet their cumulative footprint remains poorly quantified. Remote monitoring of these changes over time is needed for environmental accounting and to estimate aggregated impacts across the thousands of mines that underpin global metal supply chains. In this contribution, we integrate vegetation index time series with a domain-adapted breakpoint detection approach, Seasonal Harmonic Anomaly Break Analysis (SHABA), to detect multiple abrupt and subtle vegetation changes associated with mining activities. We demonstrate the potential of this methodology with six geographically distributed mine sites: Cardinal River, Roșia Poieni, Trident, Carajás, Vametco, and Worsley. The resulting breakpoints accurately quantify cumulative changes associated with mine expansion and rehabilitation efforts and indicate where secondary changes in surrounding areas might occur. The mines’ footprint trajectories are varied and complex. In some of the cases illustrated in this study, primary footprints are dominated by stepwise vegetation clearing (2.11–5.77% yr 1), but also show early regrowth (1.58–2.68%yr 1). consistent with natural succession or rehabilitation. Secondary changes outside the direct mine footprint vary widely, but are temporally correlated with mine expansion. Based on these results, we hypothesise that the SHABA workflow could provide a robust approach to automated, global-scale quantification of changes in mine footprint over time and to better dialogue among stakeholders.