Estimating Ground Elevation and Vegetation Characteristics in Coastal Salt Marshes Using UAV-Based LiDAR and Digital Aerial Photogrammetry

This study evaluates the skills of two types of drone-based point clouds, derived from LiDAR and photogrammetric techniques, in estimating ground elevation, vegetation height, and vegetation density on a highly vegetated salt marsh. The proposed formulation is calibrated and tested using data measured on a Spartina alterniflora-dominated salt marsh in Little Sapelo Island, USA. The method produces high-resolution (ground sampling distance = 0.40 m) maps of ground elevation and vegetation characteristics and captures the large gradients in the proximity of tidal creeks. Our results show that LiDAR-based techniques provide more accurate reconstructions of marsh vegetation (height: MAE(VH) = 12.6 cm and RMSEVH = 17.5 cm; density: MAE(VD) = 6.9 stems m(-2) and RMSEVD = 9.4 stems m(-2)) and morphology (MAE(M) = 4.2 cm; RMSEM = 5.9 cm) than Digital Aerial Photogrammetry (DAP) (MAE(VH) = 31.1 cm; RMSEVH = 38.1 cm; MAE(VD) = 12.7 stems m(-2); RMSEVD = 16.6 stems m(-2); MAE(M) = 11.3 cm; RMSEM = 17.2 cm). The accuracy of the classification procedure for vegetation calculation negligibly improves when RGB images are used as input parameters together with the LiDAR-UAV point cloud (MAE(VH) = 6.9 cm; RMSEVH = 9.4 cm; MAE(VD) = 10.0 stems m(-2); RMSEVD = 14.0 stems m(-2)). However, it improves when used together with the DAP-UAV point cloud (MAE(VH) = 21.7 cm; RMSEVH = 25.8 cm; MAE(VD) = 15.2 stems m(-2); RMSEVD = 18.7 stems m(-2)). Thus, we discourage using DAP-UAV-derived point clouds for high-resolution vegetation mapping of coastal areas, if not coupled with other data sources.