Remote Sensing of Mine Site Rehabilitation for Ecological Outcomes: A Global Systematic Review

The mining industry has been operating across the globe for millennia, but it is only in the last 50 years that remote sensing technology has enabled the visualization, mapping and assessment of mining impacts and landscape recovery. Our review of published literature (1970–2019) found that the number of ecologically focused remote sensing studies conducted on mine site rehabilitation increased gradually, with the greatest proportion of studies published in the 2010– 2019 period. Early studies were driven exclusively by Landsat sensors at the regional and landscape scales while in the last decade, multiple earth observation and drone‐based sensors across a diverse range of study locations contributed to our increased understanding of vegetation development post‐mining. The Normalized Differenced Vegetation Index (NDVI) was the most common index, and was used in 45% of papers; while research that employed image classification techniques typically used supervised (48%) and manual interpretation methods (37%). Of the 37 publications that conducted error assessments, the average overall mapping accuracy was 84%. In the last decade, new classification methods such as Geographic Object‐Based Image Analysis (GEOBIA) have emerged (10% of studies within the last ten years), along with new platforms and sensors such as drones (15% of studies within the last ten years) and high spatial and/or temporal resolution earth observation satellites. We used the monitoring standards recommended by the International Society for Ecological Restoration (SER) to determine the ecological attributes measured by each study. Most studies (63%) focused on land cover mapping (spatial mosaic); while comparatively fewer studies addressed complex topics such as ecosystem function and resilience, species composition, and absence of threats, which are commonly the focus of field‐based rehabilitation monitoring. We propose a new research agenda based on identified knowledge gaps and the ecological mon