Comparing a simple performance of wetland vegetation structure using Structure from Motion and LiDAR derived point clouds

Elijah Dalton

Abstract

Wetlands are considered one of the most productive ecosystems, but they are at a high risk of degradation from anthropogenic climate change, especially in coastal regions. The introduction of LiDAR technology and the SfM–MVS algorithms based on UAS data have extended our ability to detect, identify, and map wetlands remotely. UAS derived images provide a means to produce 3D point clouds using photogrammetric mapping. UAS mounted LiDAR sensors can provide more dense 3D point clouds that can both be used to improve wetland classification and monitoring. Point clouds derived from these methods vary in data collection methodologies and processing, therefore, a detailed comparison of a simple performance of wetland vegetation structure using these methods is necessary. In this research, point clouds produced by UAS photogrammetry and UAS-borne LiDAR data collected over multiple wetland study sites along the southeastern NC coastal region, providing for a range of wetland types from estuarine, to riverine and palustrine. We compare the estimated RMSE and CHM over four sites and report the results of statistical tests of the difference between photogrammetric- and LiDAR-derived point clouds.

Faculty Advisor: Dr. Narcisa Pricope

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