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(searched for: doi:10.1016/j.cad.2010.11.006)
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, Ruofei Zhong, Jisheng Xia, Shucheng Tan
Published: 10 March 2020
Geosphere, Volume 16, pp 806-816; https://doi.org/10.1130/ges02188.1

Abstract:
With the advent of digital elevation models (DEMs) and geographic information systems (GIS), several methods have been proposed to extract channels from raster DEMs. Light detection and ranging (lidar) can produce high-resolution DEMs and poses new challenges to existing methods for channel extraction. This paper introduces a semi-automated method for extracting stream channels and channel profiles from high-resolution DEMs using image processing techniques. Based on user-specified approximate locations of start and end points and a few simple parameters, the method implements five automated steps: (1) channel detection using a local minimum value search; (2) channel delineation using Bresenham’s line algorithm and mathematical morphological operation; (3) vectorization; (4) profile generation; and (5) accuracy assessment. The method is implemented as an ArcGIS Python add-in toolbar named Channel Extraction. The application of the toolbar is demonstrated using a lidar-derived DEM in a study area along the San Andreas fault in California, USA. The software and test data are freely available for download (see Supplemental Files1). The demonstrated samples suggest that this new semi-automated method for extracting channels and channel profiles is flexible and user-friendly and can produce accurate results to support geomorphic studies.
Yumin Zhang, Steven Garcia, Weiwei Xu, Tianjia Shao, Yin Yang
Published: 1 November 2018
Graphical Models, Volume 100, pp 61-70; https://doi.org/10.1016/j.gmod.2017.06.004

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