2010
Passalacqua, Paola; Trung, Tien Do; Foufoula-Georgiou, Efi; Sapiro, Guillermo; Dietrich, William E.
A geometric framework for channel network extraction from lidar: Nonlinear diffusion and geodesic paths Journal Article
In: Journal of Geophysical Research-Earth Surface, vol. 115, no. F1, 2010.
Abstract | Links | BibTeX | Tags: Channel network, geodesic paths, LiDAR, nonliner diffusion
@article{Passalacqua2010,
title = {A geometric framework for channel network extraction from lidar: Nonlinear diffusion and geodesic paths},
author = {Paola Passalacqua and Tien Do Trung and Efi Foufoula-Georgiou and Guillermo Sapiro and William E. Dietrich},
url = {https://angelo.berkeley.edu/wp-content/uploads/sites/59/Passalacqua_2010_JoGeophysRes.pdf},
doi = {10.1029/2009JF001254},
year = {2010},
date = {2010-01-07},
journal = {Journal of Geophysical Research-Earth Surface},
volume = {115},
number = {F1},
abstract = {A geometric framework for the automatic extraction of channels and channel networks from high-resolution digital elevation data is introduced in this paper. The proposed approach incorporates nonlinear diffusion for the preprocessing of the data, both to remove noise and to enhance features that are critical to the network extraction. Following this preprocessing, channels are defined as curves of minimal effort, or geodesics, where the effort is measured on the basis of fundamental geomorphological characteristics such as flow accumulation area and isoheight contours curvature. The merits of the proposed methodology, and especially the computational efficiency and accurate localization of the extracted channels, are demonstrated using light detection and ranging (lidar) data of the Skunk Creek, a tributary of the South Fork Eel River basin in northern California.},
keywords = {Channel network, geodesic paths, LiDAR, nonliner diffusion},
pubstate = {published},
tppubtype = {article}
}
A geometric framework for the automatic extraction of channels and channel networks from high-resolution digital elevation data is introduced in this paper. The proposed approach incorporates nonlinear diffusion for the preprocessing of the data, both to remove noise and to enhance features that are critical to the network extraction. Following this preprocessing, channels are defined as curves of minimal effort, or geodesics, where the effort is measured on the basis of fundamental geomorphological characteristics such as flow accumulation area and isoheight contours curvature. The merits of the proposed methodology, and especially the computational efficiency and accurate localization of the extracted channels, are demonstrated using light detection and ranging (lidar) data of the Skunk Creek, a tributary of the South Fork Eel River basin in northern California.