Using Unmanned Aerial Vehicle multispectral data for monitoring outcomes
of ecological restoration in mining areas
Abstract
The effective and efficient monitoring of revegetation outcomes is a key
component of ecosystem restoration. Monitoring often involves labour
intensive manual methods which can be difficult to deploy when sites are
inaccessible or involve large areas of revegetation. This study aimed to
identify plant species and quantify α-diversity index on a sub-meter
scale at Manlailiang Mine Site in Northwestern China using unmanned
aerial vehicles (UAVs) as a means to semi-automate 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 (NDVI, DVI, VDVI, SAVI, MSAVI, EXG - EXR) were used
to assess vegetation diversity obtained from on-ground survey plot data
(Margalef, Pielou, Simpson, Shannon indices). Our results showed that
SVM outperformed other algorithms in species identification accuracy
(overall accuracy 84%). Significant relationships were observed between
vegetation indices and diversity indices, with DVI performing
significantly better than many more commonly used indices such as 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.