The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLII-4
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4, 687–692, 2018
https://doi.org/10.5194/isprs-archives-XLII-4-687-2018
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4, 687–692, 2018
https://doi.org/10.5194/isprs-archives-XLII-4-687-2018

  19 Sep 2018

19 Sep 2018

THE COMPARISON OF DRAINAGE NETWORK EXTRACTION BETWEEN SQUARE AND HEXAGONAL GRID-BASED DEM

L. Wang and T. Ai L. Wang and T. Ai
  • Hexagonal grid-based DEM, Square grid-based DEM, D6 algorithm, Drainage networks extraction

Keywords: Hexagonal grid-based DEM, Square grid-based DEM, D6 algorithm, Drainage networks extraction

Abstract. The automatic extraction of valleys or ridges from DEM is a long term topic in the GIS and hydrology fields and a number of algorithms have been developed. The quality of drainage networks extraction depends on many impacts such as data source, DEM resolution and extraction algorithms. However, little consideration has been paid to the influence of different tessellation of grid-based DEM construction in terrain surface representation. Actually, hexagonal grid has been proved to be advantageous over square grid due to its consistent connectivity, isotropy of local neighbourhoods, higher symmetry, visual advantage, and so on. This study tries to explore the impact of different tessellation scheme for grid-based DEM on the accuracy of terrain representation reflected by the results of drainage networks extraction. The contour line data model is applied to grid-based DEM generation. Then, by analogy with traditional D8 algorithm, the D6 algorithm is introduced to extract drainage networks by calculating flow direction of each gird with the steepest slope neighbour criteria. From the comparison between D8 algorithm and D6 algorithm, we conclude that hexagonal grid-based DEM has a superior capability in maintaining the detailed shape and the characteristics of extracted drainage networks in coarser resolution.