A geometric framework for channel network extraction from lidar:Nonlinear diffusion and geodesic paths

摘要

A geometric framework for the automatic extraction of channels and channelnetworks from high-resolution digital elevation data is introduced in this paper. Theproposed approach incorporates nonlinear diffusion for the preprocessing of the data, bothto remove noise and to enhance features that are critical to the network extraction.Following this preprocessing, channels are defined as curves of minimal effort, orgeodesics, where the effort is measured on the basis of fundamental geomorphologicalcharacteristics such as flow accumulation area and isoheight contours curvature. Themerits of the proposed methodology, and especially the computational efficiency andaccurate localization of the extracted channels, are demonstrated using light detection andranging (lidar) data of the Skunk Creek, a tributary of the South Fork Eel River basin innorthern California.