BLM Unmanned Aircraft Systems (UAS) Resource Management Operations

The Department of the Interior Bureau of Land Management is funding research at the University of Alaska Fairbanks to study Unmanned Aircraft Systems (UAS) Resource Management Operations. In August 2015, the team conducted flight research at UAF's Toolik Field Station (TFS). The purpose was to determine the most efficient use of small UAS to collect low-altitude airborne digital stereo images, process the stereo imagery into close-range photogrammetry products, and integrate derived imagery products into the BLM's National Assessment, Inventory and Monitoring (AIM) Strategy. The AIM Strategy assists managers in answering questions of land resources at all organizational levels and develop management policy at regional and national levels. In Alaska, the BLM began to implement its AIM strategy in the National Petroleum Reserve-Alaska (NPR-A) in 2012. The primary goals of AIM-monitoring at the NPR-A are to implement an ecological baseline to monitor ecological trends, and to develop a monitoring network to understand the efficacy of management decisions. The long-term AIM strategy also complements other ongoing NPR-A monitoring processes, collects multi-use and multi-temporal data, and supports understanding of ecosystem management strategies in order to implement defensible natural resource management policy. The campaign measured vegetation types found in the NPR-A, using UAF's TFS location as a convenient proxy. The vehicle selected was the ACUASI Ptarmigan, a small hexacopter (based on DJI S800 airframe and 3DR autopilot) capable of carrying a 1.5 kg payload for 15 min for close-range environmental monitoring missions. The payload was a stereo camera system consisting of Sony NEX7's with various lens configurations (16/20/24/35 mm). A total of 77 flights were conducted over a 4 ½ day period, with 1.5 TB of data collected. Mission variables included camera height, UAS speed, transect overlaps, and camera lenses/settings. Invaluable knowledge was gained as to limitations and opportunities for field deployment of UAS relative to local conditions and vegetation type. Future efforts will focus of refining data analysis techniques and further optimizing UAS/sensor combinations and flight profiles.