Utilizing UAS to Support Wildlife Hazard Management Efforts by Airport Operators

Abstract

The Federal Aviation Administration requires airports operating under the Code of Federal Regulations Part 139 to conduct a wildlife hazard assessment (WHA) when some wildlife-strike events have occurred at or near the airport. The WHA should be conducted by a Qualified Airport Wildlife Biologist (QAWB) and must contain several elements, including the identification of the wildlife species observed and their numbers; local movements; daily and seasonal occurrences; and the identification and location of features on and near the airport that could attract wildlife. Habitats and land-use practices at and around the airport are key factors affecting wildlife species and the size of their populations in the airport environment. The purpose of this ongoing study is to investigate how UAS technologies could be safely and effectively applied to identify hazardous wildlife species to aviation operations as well as potential wildlife hazard attractants within the airport jurisdiction. Researchers have used a DJI Mavic 2 Enterprise Dual drone with visual and thermal cameras to collect data. Data have been collected in a private airport in a “Class G†airspace. We have applied different risk mitigation strategies to mitigate risks associated with drone operations in an airport environment, including a visual observer during data collection, and an ADS-B flight box to obtain information of manned aircraft at and around the airport. Multiple flights were conducted in different days of the week as well as different times of the day. Noteworthy to mention we have had the technical support of QAWB throughout this study. Preliminary findings suggest that UAS can facilitate the observations made by a QAWB during a WHA, including the identification and assessment of potential wildlife attractants (e.g., wetlands), and the identification of wildlife species (e.g., White ibis). Additionally, initial findings indicate that UAS facilitates data collection in areas that are difficult to access by ground-based means (e.g., wetlands). Another key finding of this study was that our team could observe, and with the assistance of the QAWB identify different wildlife species and habitats simultaneously during each UAS flight. In different words, from a single image (video and/or picture) a QAWB could obtain valuable information about different wildlife species and related habitats. Lastly, results suggest that the versatility and speed of UAS (including their high-quality cameras and sensors) ensure that data can be collected more thoroughly and faster over large areas during a WHA.