TEMPORAL CHANGES ON ABANDONED MINE LANDS USING UNMANNED AERIAL SYSTEMS AND REMOTE SENSING TECHNOLOGY

Weathering of waste rock in abandoned mining sites can degrade downstream environments and contaminate surface and groundwater resources when pyrite bearing ore is exposed to air and water, producing acid mine drainage (AMD) and secondary sulfate minerals such as jarosite. Current abandoned mine land (AML) monitoring techniques rely on time intensive field measurements, which make it difficult to efficiently map temporal changes relative to contaminants on the waste rock, and environmental changes, such as vegetation health. We present a case study from the Perry Canyon, NV AML which explores the use of Unmanned Aerial Systems (UAS) as an easy-to-deploy remote sensing platform to map environmental changes and the temporal dispersion of AMD, jarosite, and iron oxides. Over a 13-month period, 5 UAS flights were conducted over one waste rock pile in the Perry Canyon, NV AML, part of the historical Pyramid Mining District. The UAS was equipped with a 5-band multispectral sensor from which we created 5 cm-scale orthomosaics using structure from motion photogrammetry techniques. We also obtained 4 mm-scale transects on one day using a 271-band hyperspectral from a fixed platform. Both sensors measure in the visible to near infrared (0.4 - 1.0 μm). We combined soil samples analyzed with XRD analysis and previous site characterization data to confirm the presence of AMD, iron-oxides, jarosite, and efflorescent mineral salts. Goals of the project include assessment of temporal change using the multispectral data and validation of techniques using the hyperspectral data. For the multispectral data we used standard image supervised classification methods of maximum likelihood (ML) and spectral angle mapper (SAM). We have classified and mapped 9 surfaces of interest, including AMD, jarosite, iron-oxides, healthy and burned vegetation, soil, efflorescent mineral salts, shadows, and water. ML is able to map the temporal dispersion of classes to an average overall accuracy of 70% and SAM to 73%. Final maps of distribution of AMD and related minerals over time will incorporate these techniques as well as band ratios. Our work demonstrates that UAS and remote sensing has much promise to increase the spatial coverage and the accuracy of environmental monitoring efforts over AML.