This tool can be used to extract channel networks from digital elevation models (DEMs) based on the Lindsay (2006) 'lower-quartile' method. The algorithm is a type of 'valley recognition' method. Other channel mapping methods, such as the Johnston and Rosenfeld (1975) algorithm, experience problems because channel profiles are not always ?v?-shaped, nor are they always apparent in small 3 x 3 windows. The lower-quartile method was developed as an alternative and more flexible valley recognition channel mapping technique. The lower-quartile method operates by running a filter over the DEM that calculates the percentile value of the centre cell with respect to the distribution of elevations within the filter window. The roving window is circular, the diameter of which should reflect the topographic variation of the area (e.g. the channel width or average hillslope length). The user specifies the filter size, in pixels, and this value should be an odd number (e.g. 3, 5, 7, etc.). The appropriateness of the selected window diameter will depend on the grid resolution relative to the scale of topographic features. Cells that are within the lower quartile of the distribution of elevations of their neighbourhood are flagged. Thus, the algorithm identifies grid cells that are in relatively low topographic positions at a local scale. This approach to channel mapping is only appropriate in fluvial landscapes. In regions containing numerous lakes and wetlands, the algorithm will pick out the edges of features.
This method is best applied to DEMs that are relatively smooth and do not exhibit high levels of short-range roughness. As such, it may be desirable to use a smoothing filter before applying this tool.
This method of extracting valley networks results in line networks that can be wider than a single grid cell. It is often desirable to thin the resulting network using a line-thinning algorithm. The option to perform line-thinning is provided by the tool as a post-processing step.
The following is an example of a Python script that uses this tool:
wd = pluginHost.getWorkingDirectory()
demFile = wd + "DEM.dep"
outputFile = wd + "output.dep"
filterSize = "11"
lineThinning = "true"
args = [demFile, outputFile, filterSize, lineThinning]
pluginHost.runPlugin("ExtractValleysLowerQuartile", args, False)
This is a Groovy script also using this tool:
def wd = pluginHost.getWorkingDirectory()
def demFile = wd + "DEM.dep"
def outputFile = wd + "output.dep"
def filterSize = "11"
def lineThinning = "true"
String args = [demFile, outputFile, filterSize, lineThinning]
pluginHost.runPlugin("ExtractValleysLowerQuartile", args, false)