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O’Callaghan, J.F. and Mark, D.M. (1984) The Extraction of Drainage Networks from Digital Elevation Data. Computer Vision, Graphics and Image Processing, 28, 328-344.
http://dx.doi.org/10.1016/S0734-189X(84)80011-0

has been cited by the following article:

  • TITLE: Mapping Potential Infiltration Patterns Using Digital Elevation Model

    AUTHORS: Haris Hasan Khan, Arina Khan, P. D. Sreedevi, Shakeel Ahmed

    KEYWORDS: DEM, Permeability, Infiltration Capacity, Drainage Density, Autocorrelation

    JOURNAL NAME: Journal of Geographic Information System, Vol.6 No.4, August 12, 2014

    ABSTRACT: This study attempts to simulate the spatial heterogeneity of infiltration in a drainage basin using digital elevation models. Infiltration capacity is one of the controlling factors in the formation of stream channels. Channel formation is also a function of the slope and the contributing area. Natural stream channels, if properly graded and adjusted to the present climate, reflect the interactions of local slope, contributing area, and permeability of surface materials. Channel networks can be delineated from a Digital Elevation Model (DEM) using a variety of algorithms using different thresholds for channel initiation. These algorithms delineate a channel network on the basis of local slope, curvature, and contributing area, without considering the permeability of surface cover. Hence, the difference in the structure of the two drainage networks,i.e.the surveyed drainage network obtained from field observation and the simulated network generated from the DEM, is indicative of the spatial heterogeneities in the permeability of the surface cover as shown in this paper. Spatially variable drainage density maps corresponding to the two networks have been used here to obtain normalized difference maps that characterize the potential infiltration anomalies within the catchment. The simulated spatial pattern is compared with the actual infiltration measurements in the field using infiltration tests. Strong positive correlation between the observed and modeled infiltration confirms the effectiveness of this technique in the rapid assessment of potential infiltration variability.