doi: 10.4304/jsw.6.8.1611-1618
Automatic Extraction of Drainage Networks from DEMs Base on Heuristic Search
- 1 Jilin University/College of Computer Science and Technology, Changchun, China
- 2 Northeast Normal University/School of Computer Science and Information Technology, Changchun, China
- 3 Changchun Normal University/College of Computer Science and Technology, Changchun, China
- 4 Jinggangshan University/Art School, Jinggangshan, China
Abstract
Computer aided automatic Extraction of drainage networks from DEMs can promote the efficiency of regional water resource prospecting and assessment in GIS. Extracting drainage networks from raster DEMs is a necessary requirement in many applications of GIS, and determining surface water flow direction is a fundamental problem. In a raster environment, surface water flow direction of each cell can be directed to the neighboring cell with the steepest downslope drop (The basic D8, deterministic eight-neighbour method), which is inadequate for routing flow over pits and flat areas. Several improved methods are proposed to assign flow direction of pits and flats, which typically use entirely different procedures for processing pits and flats without heuristic information. Being different from others, a method to treat pits and flats is proposed in this paper. The method is based on heuristic search, which can effectively handle both conditions. This method is implemented in Pascal and experiments are carried out on actual DEM data. The experimental results show that this approach are robust, computationally efficient, and avoid many of the problems associated with other methods.
Keywords
- GIS
- pit
- flat areas
- drainage networks
How to Cite
Kun Hou, Jigui Sun, Wei Yang, Tieli Sun, Ying Wang, and Sijun Ma, "Automatic Extraction of Drainage Networks from DEMs Base on Heuristic Search," Journal of Software, vol. 6, no. 8, pp. 1611-1618, 2011. https://doi.org/ 10.4304/jsw.6.8.1611-1618
Copyright & License
Copyright © 2011 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).