A combined algorithm for automated drainage network extraction from digital elevation models

Abstract

Drainage networks are the basis for segmentation of watersheds, an essential component in hydrological modelling, biogeochemical applications, and resource management plans. With the rapidly increasing availability of topographic information as digital elevation models (DEMs), there have been many studies on DEM‐based drainage network extraction algorithms. Most of traditional drainage network extraction methods require preprocessing of the DEM in order to remove “spurious” sink, which can cause unrealistic results due to removal of real sinks as well. The least cost path (LCP) algorithm can deal with flow routing over sinks without altering data. However, the existing LCP implementations can only simulate either single flow direction or multiple flow direction over terrain surfaces. Nevertheless, terrain surfaces in the real world are usually very complicated including both convergent and divergent flow patterns. The triangular form‐based multiple flow (TFM) algorithm, one of the traditional drainage network extraction methods, can estimate both single flow and multiple flow patterns. Thus, in this paper, it is proposed to combine the advantages of the LCP algorithm and the TFM algorithm in order to improve the accuracy of drainage network extraction from the DEM. The proposed algorithm is evaluated by implementing a data‐independent assessment method based on four mathematical surfaces and validated against “true” stream networks from aerial photograph, respectively. The results show that when compared with other commonly used algorithms, the new algorithm provides better flow estimation and is able to estimate both convergent and divergent flow patterns well regarding the mathematical surfaces and the real‐world DEM.

Reconstruction of the Water Cultivation Paleoenvironment Dating Back to the Han and Tang Dynasties Surrounding the Yangguan Frontier Pass Using X- and L-Band SAR Data

“…Flow calculations were conducted for the downstream and upstream directions. The Strahler algorithm was used to acquire the drainage network with six orders, and the watershed segmentation was conducted for each flow distribution [38,39]. The results were compared with the current drainage network and the targeted dried-up watercourses were extracted.…”

Precision mapping of boundaries of flood plain river basins using high-resolution satellite imagery: A case study of the Varuna river basin in Uttar Pradesh, India

“…Drainage networks are the basis of watershed delineation, an essential component in hydrological modelling, biogeochemical applications and resource management plans (Yan et al 2018). With the advent of remotely sensed digital elevation models (DEMs), there has been a spurt in studies on DEM-based drainage network extraction algorithms (Purinton and Bookhagen 2017;Yan et al 2018). Many factors such as the scale, DEM quality, physical characteristics of the river basin and the algorithms used influence the accuracy of drainage networks that are derived from DEMs (Martins et al 2017).…”

Improving the Accuracy of Open Source Digital Elevation Models with Multi-Scale Fusion and a Slope Position-Based Linear Regression Method

“…DEMs are the fundamental tools of geo-analysis and the essential input variables for many models. They have been widely used in science and engineering fields such as water resource management [1][2][3], agriculture [4,5], and ecology [6][7][8]. DEMs can be derived not only from scanned/vectorized contour lines of existing topographic sheets obtained by field measurement [9], but also from remote sensing techniques.…”