Separation of ground and low vegetation signatures in LiDAR measurements of salt-marsh environments

Abstract—Light detection and ranging (LiDAR) has been shown to have a great potential in the accurate characterization of forest systems; however, its application to salt-marsh environments is challenging because the characteristic short vegetation does not give rise to detectable differences between first and last LiDAR returns. Furthermore, the lack of precisely identifiable references (e.g., buildings, roads, etc.) in marsh areas makes the registration and bias correction of the LiDAR data much more difficult than in conventional urban- or forested-area applications. In this paper, we introduce reliable methods to remove random and systematic errors and to register raw data, as well as a new procedure, to determine the optimal filter window size to separate ground and canopy returns. A limited amount of field observations is used to determine the size of the filtering window which produces the minimally biased estimates of the digital terrain model (DTM)