No abstract

[1] We perform an analysis of the observational morphological structure of a tidal landscape aimed at examining key assumptions on the geomorphological evolution of wetlands, lagoons, estuarine areas and tidal environments in general. The issues addressed pertain to the statistical measures and the morphodynamic implications of topological or metric properties of the observed landforms, in particular their scale-dependent (or invariant) characters that might suggest self-organized dynamical origins. Field surveys and remote sensing are employed here to accurately characterize different morphodynamic features of a lagoonal environment. Of particular novelty and interest is the structure of landscape-forming shear stresses (properly calculated in unchanneled portions of the landscape) which suggests the viability of threshold models of incision for the formation of tidal channel networks. Distinctive geomorphic indicators, suitable for comparative purposes with modeling of the long-term evolution of tidal systems, are also pointed out. We finally discuss space-distributed analyses of ecogeomorphological properties which strongly suggest the dominance of subvertical processes in the control of the distribution of halophytic vegetation, a key morphodynamic factor.

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). The digital surface model (DSM, representing the canopy top) is then obtained in a similar manner, and the digital vegetation model (DVM, representing the vegetation height) is computed as the difference between the DSM and the DTM. We apply this procedure to a study marsh within the Venice Lagoon, Italy, and obtain a high-accuracy DTM. The error (z_LiDAR − z_f ield) is 2.2 cm, with a standard deviation of 6.4 cm. The comparison of the estimated DVM with field observations shows an underestimation of the height of the canopy top (17.7 cm, on average). The height of the lowest canopy elements (e.g., basal leaves), however, is significantly correlated to the LiDAR-derived DVM, showing that this contains useful information on the canopy structure.

No abstract

No abstract