A combined algorithm for automated drainage network extraction from digital elevation models |
| |
| Authors: | Yanzi Yan Jing Tang Petter Pilesjö |
| |
| Affiliation: | 1. Department of Physical Geography and Ecosystem Science, Lund University, Lund, Sweden;2. GIS Centre, Lund University, Lund, Sweden;3. Terrestrial Ecology Section, Department of Biology, University of Copenhagen, Copenhagen, Denmark;4. Center for Permafrost, University of Copenhagen, Copenhagen, Denmark;5. Center for Middle Eastern Studies, Lund University, Lund, Sweden |
| |
| Abstract: | Drainage networks are the basis for segmentation of watersheds, an essential component in hydrological modelling, biogeochemical applications, and resource management plans. With the rapidly increasing availability of topographic information as digital elevation models (DEMs), there have been many studies on DEM‐based drainage network extraction algorithms. Most of traditional drainage network extraction methods require preprocessing of the DEM in order to remove “spurious” sink, which can cause unrealistic results due to removal of real sinks as well. The least cost path (LCP) algorithm can deal with flow routing over sinks without altering data. However, the existing LCP implementations can only simulate either single flow direction or multiple flow direction over terrain surfaces. Nevertheless, terrain surfaces in the real world are usually very complicated including both convergent and divergent flow patterns. The triangular form‐based multiple flow (TFM) algorithm, one of the traditional drainage network extraction methods, can estimate both single flow and multiple flow patterns. Thus, in this paper, it is proposed to combine the advantages of the LCP algorithm and the TFM algorithm in order to improve the accuracy of drainage network extraction from the DEM. The proposed algorithm is evaluated by implementing a data‐independent assessment method based on four mathematical surfaces and validated against “true” stream networks from aerial photograph, respectively. The results show that when compared with other commonly used algorithms, the new algorithm provides better flow estimation and is able to estimate both convergent and divergent flow patterns well regarding the mathematical surfaces and the real‐world DEM. |
| |
| Keywords: | digital elevation models drainage networks extraction hydrology multiple flow patterns sink treatment watershed |
|
|
