| Abstract Detail
Recent Topics Posters Lehmitz, Matthew [1], Brown, Gregory [2], Sivanpillai, Ramesh [1]. Novel UAV based methods for remote sensing of epiphytes. Studying high-canopy vascular ephiphytes pose challenges mostly due to access constraints. Recent advances in Unmanned Aerial Vehicles (UAVs) technology could help overcome some of those access constraints. UAVs have found use in a very diverse array of applications, with a profound impact on improving the ease, access, and safety in viewing hard to inspect target-objects. The goal of this research was to develop a framework for close-up remote sensing of epiphytic bromeliads. Using a Phantom-3 Advance (DJI), we acquired close range digital images of tree branches and examined them for the presence of epiphytes. Initial studies were conducted in a controlled environment of the Williams Conservatory, University of Wyoming, Laramie, WY. The second study involved the use of UAV to locate Tillandsia utriculata individuals growing in trees within a segment of Marie Selby Botanical Gardens, Sarasota, FL. We imaged epiphytes growing in different trees and our results demonstrate the efficacy of UAV systems to gather data on these difficult to reach areas. This poster will report our findings from these studies and include preliminary recommendations for UAV use and acquiring photos of canopy epiphytes. Our findings will provide valuable insights for plant taxonomists considering use of UAVs for acquiring images of plants growing in inaccessible environments. Log in to add this item to your schedule
1 - University of Wyoming, Botany, 1000 E. University Ave., Laramie, Wy, 82071, USA 2 - UNIVERSITY OF WYOMING, Department Of Botany, BOX 3165, LARAMIE, WY, 82071-3165, USA
Keywords: Drone epiphyte Bromeliad Remote Sensing UAV photography Video Detailed Canopy Rapid Access.
Presentation Type: Recent Topics Poster Session: P, Recent Topics Posters Location: Exhibit Hall/Savannah International Trade and Convention Center Date: Monday, August 1st, 2016 Time: 5:30 PM Number: PRT020 Abstract ID:1212 Candidate for Awards:None |