High-efficient extraction of drainage networks from digital elevation models constrained by enhanced flow enforcement from known river maps

摘要

Drainage network extraction plays an important role in geomorphologic analyses, hydrologic modeling, and non-point source pollutant simulation, among others. Flow enforcement, by imposing information of known river maps to digital elevation models (DEMs), leads to improved drainage network extraction. However, the existing flow enforcement methods (e.g., the elevation-based stream-burning method) have certain limitations, as they may cause unreal longitudinal profiles, lead to unintended topological errors, and even misinterpret the overall drainage patterns. The present study proposes an enhanced flow enforcement method without elevation modification towards an accurate and efficient drainage network extraction. In addition to preserving the Boolean-value information as to whether a DEM pixel belongs to a stream, the proposed method can also well preserve and fully utilize the topological relations among mapped streamlines and morphological information of each mapped streamline. The method involves two important steps: (1) proposal of an improved rasterization algorithm of mapped streamlines to yield continuous, unambiguous, and collision-free raster equivalent of stream vectors for flow enforcement; and (2) realization of the enhanced flow enforcement in a modified Priority-Flood procedure - in this way, flows are enforced to completely follow the mapped streamlines, and hence, channel short-circuits and spurious confluences of adjacent streams are avoided. An efficient implementation of the method is made based on a size-balanced binary search tree. The method is also tested over the Rogue River Basin in the United States, using DEMs with various resolutions. Visual and statistical analyses of the results indicate three major advantages of the proposed method: (1) significant reduction in the misinterpretation of drainage patterns; (2) maximum channel displacement of one pixel to the river map at various resolutions; and (3) high computational efficiency. (C) 2019 The Author(s). Published by Elsevier B.V.