Advances in remote sensing of hydraulic roughness

Riparian vegetation plays a crucial role in affecting the floodplain hydraulic roughness, which in turn significantly influences the dynamics of flood waves. Systematic detection, identification and assessment of flow resistance factors using conventional field sampling is often unfeasible as these techniques are time-consuming and expensive. As in many other environmental monitoring problems, remote sensing may provide unprecedented mapping capabilities. In this article we present an overview focusing on the different methods that can be used to remotely derive floodplain hydraulic roughness. The overview is based on an extensive literature review on recent estimation techniques of riparian roughness using remote sensing data from different platforms. The outlined methods of floodplain roughness parameterization include: (1) classification-derived hydraulic roughness maps and (2) estimation of vegetation hydrodynamic properties. Possible directions for a multiscale analysis of riparian flow resistance are also described in a short section by focusing on the potential of data assimilation methods for the estimation of floodplain roughness. The literature reveals that many valuable remote-sensing techniques have been developed for riparian corridor parameterization. Methodologies based on the fusion of multispectral/temporal imagery with data of different origin, such as light detection and ranging (LiDAR) and radar/microwave, appear to be powerful tools for characterizing riparian ecosystems for hydraulic purposes.