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www.hydrol-earth-syst-sci-discuss.net/7/9327/2010/
doi:10.5194/hessd-7-9327-2010
© Author(s) 2010. This work is distributed
under the Creative Commons Attribution 3.0 License.
Channel network identification from high-resolution DTM: a statistical approach
1Department of Land and Agroforest Environments, University of Padova, Agripolis, viale dell'Università 16, 35020 Legnaro, PD, Italy
2Institute of Inland Waters, Hellenic Center for Marine Research, Anavyssos, Greece
3Department of Agriculture and Environmental Science, University of Udine, via delle Scienze 208, 33100 Udine, UD, Italy
Abstract. A statistical approach to LiDAR derived topographic attributes for the automatic extraction of channel network is presented in this paper. The basis of this approach is to use statistical descriptors to identify channel where terrain geometry denotes significant convergences. Two case study areas of different morphology and degree of organization are used with their 1 m LiDAR Digital Terrain Models (DTMs). Topographic attribute maps (curvature and openness) for different window sizes are derived from the DTMs in order to detect surface convergences. For the choice of the optimum kernel size, a statistical analysis on values distributions of these maps is carried out. For the network extraction, we propose a three-step method based (a) on the normalization and overlapping of openness and minimum curvature in order to highlight the more likely surface convergences, (b) a weighting of the upslope area according to such normalized maps in order to identify drainage flow paths and flow accumulation consistent with terrain geometry, (c) the z-score normalization of the weighted upslope area and the use of z-score values as non-subjective threshold for channel network identification. As a final step for optimal definition and representation of the whole network, a noise-filtering and connection procedure is applied. The advantage of the proposed methodology, and the efficiency and accurate localization of extracted features are demonstrated using LiDAR data of two different areas and comparing both extractions with field surveyed networks.
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Notice on Discussion StatusCitation: Sofia, G., Tarolli, P., Cazorzi, F., and Dalla Fontana, G.: Channel network identification from high-resolution DTM: a statistical approach, Hydrol. Earth Syst. Sci. Discuss., 7, 9327-9365, doi:10.5194/hessd-7-9327-2010, 2010. Bibtex EndNote Reference Manager XML
