A novel approach for high-quality drainage network extraction in flat terrains by using a priori knowledge of hydrogeomorphic features to extend DEMs: A case study in the Hoh Xil region of the Qinghai-Tibetan Plateau

Abstract

Being a fundamental type of hydrogeomorphic parameter, drainage networks have been widely applied in the fields of geographic cartography, hydrologic and hydraulic modeling, water resource management, flood risk analysis, and so on. The rapid developments of modern remote sensing technologies have greatly improved the resolution and precision of spatial data providing a plenty of microtopographic information that could have significant impacts on the surface runoff, but most of the drainage network extraction methods have paid less attention to it especially for flat terrains. In order to deal with this problem, we propose a novel approach for high-quality drainage network extraction in flat terrains, in which the hydrogeomorphic features with the function of obstruction, guidance or collection to the runoff are considered as a priori knowledge used to extend digital elevation models (DEMs). Firstly, the hydrogeomorphic features that have obvious influences on surface runoff are classified, and used as a priori knowledge to extract drainage networks. After mathematically described by reorganizing their data into raster form and semanticizing their responses to runoff, this prior knowledge is then incorporated into a DEM to construct a Digital elevation-eXtended Model (DXM). Secondly, in order to solve the problem of erroneous or indeterminate flow directions in drainage network extraction based on the DEM alone, a method of drainage network extraction based on DXM is thereby developed. Finally, the proposed approach is applied and validated in a case study in Yanhu Lake area of Hoh Xil in the Qinghai–Tibet Plateau. The DEMs and digital orthophoto maps (DOMs) with sub-meter resolution were collected by employing unmanned aerial vehicle-based (UAV-based) structure-from-motion (SFM) photogrammetry. The hydrogeomorphic features with runoff responses were extracted from the DOMs and used as a priori knowledge. Various DXMs are constructed based on the extracted hydrogeomorphic features and DEMs to extract the drainage networks of the study area. The extraction results are qualitatively and quantitatively evaluated by visual comparisons and statistical analysis to validate the proposed approach. Additionally, the effects of DEM resolution and the presence of a priori knowledge on the quality of drainage network extraction were investigated. It is found that the DXM can clearly determine the flow directions by adding useful auxiliary information into the conventional D8 algorithm. This approach provides a new high-efficient way to extract the drainage networks in flat terrains.