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Predicting Wetland Location using UAS-derived LiDAR, Multispectral, and Thermal Imagery

Authors: Lauren Whitehouse*, University of North Carolina - Wilmington, Narcisa Pricope, University of North Carolina - Wilmington, Britton Baxley, University of North Carolina - Wilmington, Eileen Pye, University of North Carolina - Wilmington, James Wu, University of North Carolina - Wilmington
Topics: Drones, Remote Sensing, Environmental Science
Keywords: drone, UAS, wetland
Session Type: Poster
Presentation File: No File Uploaded


Wetlands provide numerous ecosystem services and support thousands of unique species in the US alone. They are highly sensitive to disturbances; for instance, trekking through a marsh to study it may leave a trail that endures for months. This research utilizes unmanned aerial systems (UAS) and satellite technology to collect images of wetland areas remotely. We will use structure-from-motion algorithms in Pix4D software to reconstruct three-dimensional models to refine relationships between vegetation type and wetland proximity, as a function of spectral characteristics of vegetation communities. In combination with satellite imagery, this workflow will provide a means to enhance wetland conservation by minimizing the environmental impact of manual surveying. Additionally, the disparate and combined merits of UAS-derived thermal, multispectral, and LiDAR imagery in evaluating wetland areas’ hydrologic, topographic, and vegetative characteristics remotely will be analyzed. As multispectral and LiDAR data have previously been demonstrated as effective for vegetation and topographic classifications respectively, thermal imagery will be investigated in greater depth. The results of this research will contribute to a larger wetlands prediction project funded by the Department of Transportation (NCDOT), enhancing our ability to more accurately predict wetland location and condition in low-lying coastal areas.

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