Evaluation and enhancement of unmanned aircraft system photogrammetric data quality for coastal wetlands

Understanding the impacts of flight configuration and post-mission data processing techniques on unmanned aircraft system (UAS) photogrammetric data quality is essential for employing this popular technique in coastal wetland ecosystems. In this study, we systematically evaluated the effects of flight configuration (flying altitude, image overlap, and lighting conditions) on UAS photogrammetric level 1 products: orthoimagery and point clouds, and level 2 products: digital terrain models (DTM) and canopy height models (CHM). We also developed an object-based machine learning approach to correct UAS DTMs 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 DTMs for two study sites. This study informs UAS photogrammetric survey design and data enhancement for applications in coastal wetlands.