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Development and Comparison of Approaches for Automated Mapping of Stream Channel Networks

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Abstract:

Accurate mapping of stream channel networks is important for measuring hydrologic parameters, for site planning in construction projects, and for use in hydrologic models. This article compares five existing and two new methods for extracting stream channel networks for use in topographic mapping. In order of increasing accuracy, these methods are: (1) blue lines on USGS 1:24,000 topographic maps (64.6 percent underrepresentation), (2) placing stream heads using a constant flow-accumulation area to mimic USGS blue lines (47.8 percent underrepresentation), (3) constant flow-accumulation area equal to the mean for identified channel heads (30.3 percent combined under- and overrepresentation), (4) variable flow-accumulation area estimated by multiple linear regression (28.9 percent combined under- and overrepresentation), (5) variable flow-accumulation area estimated by a slope-power relationship (23.6 percent combined under- and overrepresentation), (6) identifying stream cells using logistic regression (12.7 percent combined under- and overrepresentation), and (7) extracting stream channel head locations from digital orthophotoquads (DOQs) (nearly 100 percent accurate, but only applicable under ideal conditions). Methods 2–6 require 10 m resolution digital elevation models that can be acquired directly in many areas or can be derived from 1:24,000 hypsography where available; Methods 4 and 6 are new methods developed in this paper.

Using DOQs, while extremely accurate, is labor intensive and can be applied only in a small minority of locations where vegetation cover does not obscure channel head locations. We conclude that identifying stream cells using logistic regression has the broadest applicability because it can be implemented in an automated fashion using only DEMs while still achieving accuracies for mapping low-order streams that are far superior to existing USGS maps.

Keywords: digital elevation models; hydrography; logistic regression; stream channel heads; stream channel mapping

Document Type: Research Article

DOI: https://doi.org/10.1111/j.1467-8306.2004.00409.x

Affiliations: 1: Southern Illinois University Carbondale 2: McGill University

Publication date: 2004-09-01

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