Authors: Sara Durgan*, Florida Atlantic University, Caiyun Zhang, Florida Atlantic University
Topics: UAS / UAV, Remote Sensing, Coastal and Marine
Keywords: UAS, coastal wetland, photogrammetry, Structure from Motion, UAS data quality
Session Type: Paper
Start / End Time: 9:35 AM / 10:50 AM
Room: Terrace, Sheraton, IM Pei Tower, Terrace Level
Presentation File: No File Uploaded
Understanding the impacts of flight configuration and post-mission data processing techniques on UAS photogrammetric data quality is essential for employing this popular technique in coastal wetland ecosystems. We systematically evaluated the effects of flight configuration (flying altitude, image overlap, and lightning conditions) on UAS photogrammetric level 1 products: orthoimagery and point clouds, and level 2 products: digital elevation models (DEM) and canopy height models (CHM). We also developed an object-based machine learning approach to correct UAS DEMs to mitigate data uncertainties caused by flight configuration and dense vegetation. Flying altitude was identified as the leading parameter in the quality of level 1 products, while image overlap was the most influential determinant for the quality of level 2 products. The correction approach effectively reduced the vertical error of the DEM from a suboptimal flight. This study informs UAS photogrammetric survey design and data enhancement for applications in coastal wetlands.