Tracking vegetation degradation and recovery in multiple mining areas in Beijing, China, based on time-series Landsat imagery

Monitoring of the vegetation change trajectory in mining areas is crucial to understand ecosystem degradation induced by mining activities and to evaluate the effectiveness of ecological restoration. Beijing, the capital of China, is rich in mineral resources and has had many small-scale mine sites. In this study, we aimed to propose a new method to automatically characterize vegetation degradation and recovery trajectory for multiple mine sites. First, the method constructs temporal composites of the Normalized Differential Vegetation Index (NDVI) on a pixel-by-pixel basis using Landsat satellite observations on the Google Earth Engine platform; second, the Pettitt test and Sen+Mann-Kendall analysis are used to identify the year of change and pre- and post-change trends; then, the time-series NDVI is classified into five vegetation trajectory types [recovery (R), degradation (D), degradation-recovery (D-R), recovery-degradation (R-D), and no change (NC)] and 13 subtypes; and finally, the recovery status of the mine sites is analyzed. The method was applied to track vegetation change in 500 mine sites in the Beijing mountainous area and compared to widely used the LandTrendr algorithm. The results showed that our method achieved satisfactory accuracies in trajectory type classification with an overall accuracy of 91.10%. The year of change detected by the Pettitt test was generally consistent with historical Google Earth high resolution imagery. Compared to the LandTrendr algorithm, our method yielded much less omission and commission errors of R, D, R-D, and D-R types and had better capability in identifying gradual recovery or degradation. As the method required few parameters, it is suitable for automatic trajectory monitoring of multiple mine sites. In the study area, 1469.07 ha out of 3746.25 ha of the mine sites have been recovered from 2000 to 2019. The recovery mainly occurred during 2009-2013, around the same time when the Green Mine Construction Campaign was launched in Beijing. 622.62 ha of the mine sites have experienced degradation probably due to illegal mining activities. Our results are expected to support evaluation of mine restoration effects and detection of illegal mine sites.

Automated summary

A new method was proposed to automatically characterize vegetation degradation and recovery trajectories for multiple mine sites, achieving satisfactory accuracy in trajectory type classification. In the study area, around one-third of the mine sites have recovered, while some have experienced degradation likely due to illegal mining activities.