Estimating Ground Elevation and Vegetation Characteristics in Coastal Salt Marshes Using UAV-Based LiDAR and Digital Aerial Photogrammetry
Daniele Pinton, Alberto Canestrelli, Benjamin Wilkinson, Peter Ifju, Andrew Ortega
Abstract
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: MAEVH = 12.6 cm and RMSEVH = 17.5 cm; density: MAEVD = 6.9 stems m−2 and RMSEVD = 9.4 stems m−2) and morphology (MAEM = 4.2 cm; RMSEM = 5.9 cm) than Digital Aerial Photogrammetry (DAP) (MAEVH = 31.1 cm; RMSEVH = 38.1 cm; MAEVD = 12.7 stems m−2; RMSEVD = 16.6 stems m−2; MAEM = 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 (MAEVH = 6.9 cm; RMSEVH = 9.4 cm; MAEVD = 10.0 stems m−2; RMSEVD = 14.0 stems m−2). However, it improves when used together with the DAP-UAV point cloud (MAEVH = 21.7 cm; RMSEVH = 25.8 cm; MAEVD = 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.