USING REMOTE SENSING AND ARCGIS TO FIND HISTORIC MINING PITS AND TAILING PILES IN THE SHAWANGUNK LEAD-ZINC DISTRICT, NEW YORK

Germanium, gallium, indium, and zinc have all been designated as Critical Minerals (U.S. Geological Survey, 2022). Finding domestic sources of these critical minerals is therefore a national priority and an important potential site for these might be in historical mining districts either through untapped ore in the ground or in tailings piles. One such locality of interest is the Shawangunk Ridge District that lies within three counties of New York (Orange, Sullivan, and Ulster) and has been historically mined for the zinc-lead (Sims, and Hotz, 1951). Galena and sphalerite were mined in this district in the late 18th and 19th centuries. While zinc is the only abundant “critical mineral”, gallium, germanium, and indium are often in high concentrations in sphalerite. Additionally, while not a critical metal, silver content is commonly elevated in galena and usage of it has increased dramatically in the last five years due to its use in photovoltaic cells and other electrical uses. Historical mines can often prove difficult to identify and locate, and reconnaissance field work alone is inadequate to find all sites. By utilizing Lidar (Light Detection and Ranging), and ArcGIS mapping, former commercial mines, tailing piles, small “non-commercial” pits, exploratory pits, and adits can be discovered. These sites can be more easily identified using Lidar since most of the mines are old, abandoned, and are overgrown with vegetation. Lidar is a wonderful tool since the vegetation, tree canopy, and other visual noise has already been removed. Once these formations are isolated using slope and elevation, the sites can then be sorted by land types (public, private, CERCLA designated, etc.). Additionally, historical maps can be used to further whittle down the number of possible tailing piles and pits. Using these techniques, several areas within the Shawangunks were identified as potential targets as either negative elevation features (pits) or positive elevation features (piles) and mapped out accordingly. Field reconnaissance was then conducted to test whether the predictive models were accurate and provide samples used by two paired geochemical studies (Kirschenmann et al., 2024; Homewood et al., 2024). Results indicate a high level of correlation between the predicted locations and those identified in the field.