Using unmanned aerial vehicle multispectral data for monitoring the outcomes of ecological restoration in mining areas

The effective and efficient monitoring of revegetation outcomes is a key component of ecosystem restoration. Monitoring often involves labor-intensive manual methods, which are difficult to deploy when sites are inaccessible or involve large areas of revegetation. This study aimed to identify plant species and quantify alpha-diversity index on a sub-meter scale at Manlailiang Mine Site in Northwestern China using unmanned aerial vehicles (UAVs) as a means to semiautomate large-scale vegetation monitoring. UAVs equipped with multispectral sensors were combined with three industry-standard supervised classification algorithms (support vector machine [SVM], maximum likelihood, and artificial neural network) to classify plant species. Spectral vegetation indices (normalized difference vegetation index [NDVI], difference vegetation index [DVI], visible-band difference vegetation index, soil-adjusted vegetation index, modified soil-adjusted vegetation index, and excess green-excess red) were used to assess vegetation diversity obtained from on-ground survey plot data (the Margalef, Pielou, Simpson, and Shannon-Wiener indices). Our results showed that SVM outperformed other algorithms in species identification accuracy (overall accuracy of 84%). Significant relationships were observed between vegetation indices and diversity indices, with the DVI performing significantly better than many more commonly used indices such as the NDVI. The findings highlight the potential of combining UAV multispectral data, spectral vegetation indices and ground surveys for effective and efficient fine-scale monitoring of vegetation diversity in the ecological restoration of mining areas. This has significant practical benefits for improving adaptive management of restoration through improved monitoring tools.