USGS THawk UAS (image IMG_6800) by USGS Unmanned Aircraft Systems

The RQ-16A T-Hawk, is a Vertical Take-Off and Landing (VTOL) Air Vehicle (AV) capable of manual and autonomous flight that employs a gasoline engine to power a lift-augmented ducted fan and uses the global positioning system (GPS) for guidance. Interchangeable sensors allow the AV to be configured with either a gimbal mounted electro-optical (EO) camera or gimbal mounted infrared (IR) camera. The AV can provide a persistent stare capability while transmitting live airborne video images.

The gMAV system consists of two Air Vehicles, one Operator Control Unit, one Ground Data Terminal, an ancillary and support equipment (i.e. starter, operator repair kit, transportation carriers, fuel and batteries), and modular electro optical (EO) and modular infrared (IR) sensor. The total system weight of the current g-MAV design is approximately 110 plus lbs, while the aircraft vehicle itself weighs approximately 17 lbs.

The National Wildlife Refuge System has a legal mandate to monitor the status and trends of fish, wildlife and plant populations on refuges. To help refuges meet this mandate, the Region 8 Inventory and Monitoring Initiative is partnering with the USGS National Unmanned Aircraft Systems (UAS) Project to test the use of UAS technology for a variety of applications on refuges. The potential benefits of UAS technology vs. conventional aerial surveys using fixed-wing aircraft include: (1) reduced safety risks to staff; (2) reduced costs; and (3) increased data accuracy. Two proof-of-concept projects are being developed in Region 8, one at Ruby Lake NWR and one at Tomales Bay, California, to determine if UAS technology can be used to effectively survey waterbirds and/or their habitats.

The overall objectives of the Region 8 UAS surveys are:
1)To test the overall feasibility of using UAS technology for waterbird and habitat surveys;
2)To determine whether waterbird species of interest can be accurately differentiated and counted using processed imagery from UAS units;
3)To develop optimal sampling designs for imagery collection; and
4)To develop automated or semi-automated and cost-effective processes for image processing and accuracy assessment, including an investigation of online crowdsourcing as an alternate classification method.
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