A geometric framework for channel network extraction from lidar: Nonlinear diffusion and geodesic paths
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...
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| Veröffentlicht in: | Journal of Geophysical Research. B. Solid Earth 2010-01, Vol.115 (F1), p.n/a |
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| container_title | Journal of Geophysical Research. B. Solid Earth |
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| creator | Passalacqua, Paola Do Trung, Tien Foufoula-Georgiou, Efi Sapiro, Guillermo Dietrich, William E. |
| description | 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. |
| doi_str_mv | 10.1029/2009JF001254 |
| format | Article |
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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.</description><identifier>ISSN: 0148-0227</identifier><identifier>ISSN: 2169-9003</identifier><identifier>EISSN: 2156-2202</identifier><identifier>EISSN: 2169-9011</identifier><identifier>DOI: 10.1029/2009JF001254</identifier><language>eng</language><publisher>Washington, DC: Blackwell Publishing Ltd</publisher><subject>anisotropic diffusion ; Channels ; Curvature ; Diffusion ; Earth sciences ; Earth, ocean, space ; Exact sciences and technology ; Extraction ; filtering ; Freshwater ; Geomorphology ; high-resolution topography ; Hydrology ; Lidar ; Networks ; Nonlinearity ; Preprocessing ; River basins</subject><ispartof>Journal of Geophysical Research. B. 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B. Solid Earth</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Passalacqua, Paola</au><au>Do Trung, Tien</au><au>Foufoula-Georgiou, Efi</au><au>Sapiro, Guillermo</au><au>Dietrich, William E.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A geometric framework for channel network extraction from lidar: Nonlinear diffusion and geodesic paths</atitle><jtitle>Journal of Geophysical Research. B. Solid Earth</jtitle><addtitle>J. Geophys. Res</addtitle><date>2010-01-07</date><risdate>2010</risdate><volume>115</volume><issue>F1</issue><epage>n/a</epage><issn>0148-0227</issn><issn>2169-9003</issn><eissn>2156-2202</eissn><eissn>2169-9011</eissn><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.</abstract><cop>Washington, DC</cop><pub>Blackwell Publishing Ltd</pub><doi>10.1029/2009JF001254</doi><tpages>18</tpages><oa>free_for_read</oa></addata></record> |
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| subjects | anisotropic diffusion Channels Curvature Diffusion Earth sciences Earth, ocean, space Exact sciences and technology Extraction filtering Freshwater Geomorphology high-resolution topography Hydrology Lidar Networks Nonlinearity Preprocessing River basins |
| title | A geometric framework for channel network extraction from lidar: Nonlinear diffusion and geodesic paths |
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