GSA Annual Meeting in Seattle, Washington, USA - 2017

Paper No. 354-8
Presentation Time: 9:00 AM-6:30 PM


RASMUSSEN, Dirk, Colorado Mountain College, Natural Resource Management, 901 South US-24, Leadville, CO 80461 and MOHRMANN, Jacob, Colorado Mountain College, Natural Resource Management, 901 South Hwy 24, Leadville, CO 80461,

Unmanned aerial systems are an effective tool for gathering detailed spatiotemporal land surface information necessary for mine remediation projects. Data-driven approaches to quantifying the sources and magnitude of metal loading in a watershed are crucial for the success of remediation efforts. Historic mining in the Sugarloaf mining district near Leadville, Colorado, contributes to impaired water quality in the Lake Fork Creek and headwaters of the Arkansas River. Several abandoned mines in Little Frying Pan Gulch have been significant contributors of metals and contaminated sediment to the watershed. Negative impacts on downstream water quality and aquatic ecosystems have led to targeted remediation of abandoned mine workings in this area. As part of the remediation in Little Frying Pan Gulch several piles of mine waste and tailings were excavated from the drainage and buried in a waste rock repository. Throughout this process, we used a small-unmanned aerial vehicle (UAV) to monitor progress and collect photogrammetry datasets. Photographs were analyzed using Pix4D spatial modeling software to create orthomosaic imagery, digital elevation surfaces, and three-dimensional models of the site. Composite samples of tailings and waste rock were collected from material placed into the repository, and analyzed using a field portable XRF to determine metal content. We combined volumetric estimates from UAV photogrammetry with XRF data to estimate a minimum mass of metals and contaminated sediment sequestered into the repository. Unmanned aerial systems are a complimentary workflow to more traditional methods, and can serve as a cost-effective tool aiding a breadth of future project applications.