DocuBase  

 

TITLE: A geometric framework for channel network extraction from lidar: Nonlinear diffusion and geodesic paths
AUTHOR: Paola Passalacqua,1 Tien Do Trung,2 Efi Foufoula-Georgiou,1 Guillermo Sapiro,3 and William E. Dietrich4
NOTES: 1Saint Anthony Falls Laboratory, National Center for Earth Surface Dynamics, Department of Civil Engineering, University of Minnesota, Minneapolis, Minnesota, USA. 2De´partement de Mathe´matiques, Ecole Normale Supe´rieure de Cachan, Paris, France. 3
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.
COLLECTION: Dietrich
ID: 213

YOU CAN VIEW THIS DOCUMENT IN THE FOLLOWING WAYS:

  • PDF (from the DocuBase repository)



    Search DocuBase

    Edit this document

  • Back to DocuBase

    BNHM      University of California, Berkeley