This work addresses the topic of flow direction and flow accumulation simulations in urban areas over digital surface models derived from light detection and ranging (LiDAR) data and multispectral high-resolution imag...
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This work addresses the topic of flow direction and flow accumulation simulations in urban areas over digital surface models derived from light detection and ranging (LiDAR) data and multispectral high-resolution imagery. LiDAR data are very dense point clouds that include many objects that, in a 2 1/2-dimensional model, may become false obstacles for runoff, such as power lines or treetops. The presence of such obstacles is a problem for the flow paths simulation, especially in urban areas. We describe a methodology to produce a surface model more suitable for runoff modeling, by filtering objects that are above the surface and should not influence the flow paths. In a first step, thin obstacles are suppressed by applying mathematical morphology to a raster surface model. In a second step, satellite multispectral data and LiDAR data are classified using a support vector machine to identify trees, which are also removed from the digital model, and produce a more coherent surface model for runoff simulation. To simulate and evaluate the results, the flow-routing algorithmdinfinity was used. The results show that the filtering is necessary to achieve a better characterization of runoff paths and allows identifying places where runoff may accumulate, causing floods or other problems. (C) 2014 Society of Photo-Optical Instrumentation Engineers (SPIE)
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