Spectral‐ALS data fusion for different roughness parameterizations of forested floodplains |
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| Authors: | G Forzieri L Guarnieri E R Vivoni F Castelli F Preti |
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| Affiliation: | 1. Dipartimento di Ingegneria Civile e Ambientale, University of Florence, Florence, Italy;2. Department of Earth and Environmental Science, New Mexico Institute of Mining and Technology, Socorro, New Mexico, USA;3. Dipartimento di Ingegneria Agraria e Forestale, University of Florence, Florence, Italy;4. School of Earth and Space Exploration and School of Sustainable Engineering and the Built Environment, Arizona State University, Tempe, Arizona, USA |
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| Abstract: | Natural river floodplains and adjacent wetlands grow typically a diverse and heterogeneous combination of herbs, shrubs and trees, which play an essential role in determining the total flow resistance. Hydrodynamic effects of trees in forested floodplains can provide the majority of flow resistance during flood events. Nevertheless, ground‐based techniques to acquire vegetation parameters are expensive and difficult to apply over long reaches. This paper presents a novel method of automated roughness parameterization of riparian woody vegetation by fusion of Quickbird multi‐spectral image with airborne laser scanning (ALS) data. The data fusion approach includes: individual tree detection and estimation of vegetation metrics from light detection and ranging (LiDAR) data, assessment of predictive models for the vegetation parameters and spatial mapping of the vegetation parameters for the forest plants in the riparian corridor. The proposed method focuses on estimation of plant density (d), crown diameters (DC), tree height (h), stem diameter (DS), crown base height (cbh) and leaf area index (LAI). The procedure is tested along a 14‐km reach of the Sieve River (Tuscany, Italy) characterized by high woody plant density. Due to the complex study area, the data fusion approach explains with variable reliability the local vegetation properties (R2(DC) = 0.14, R2(h) = 0.84, R2(DS) = 0.25, R2(cbh) = 0.66). The generated structural parameter maps represent spatially explicit data layers that can be used as inputs to hydrodynamic models used to analyse flow resistance effects in different submergence conditions of vegetation. A simple flow resistance model was applied over a test area comparing the results of the proposed method and a traditional ground‐based approach. The modelling results showed that the new method is able to provide accurate output data to describe the interaction between water levels and bio‐mechanical characteristics of vegetation. The proposed methodology provides a fast, repeatable and accurate way of obtaining floodplain roughness, which enables regular updating of vegetation characteristics. Copyright © 2010 John Wiley & Sons, Ltd. |
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| Keywords: | hydraulic roughness flow resistance riparian corridor woody vegetation Sieve River LiDAR Quickbird remote sensing |
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