Extracting a Connected River Network from DEM by Incorporating Surface River Occurrence Data and Sentinel-2 Imagery in the Danjiangkou Reservoir Area

Abstract

Accurate extraction of river network from the Digital Elevation Model (DEM) is a significant content in the application of a distributed hydrological model. However, the study of river network extraction based on DEM has some limitations, such as location offset, inaccurate parallel channel and short circuit of meandering channels. In this study, we proposed a new enhancement method for NASADEM V001 in the Danjiangkou Reservoir area. We used Surface Water Occurrence (SWO) and Sentinel-2 data to describe vertical limit differences between morphological units to complement actual flow path information from NASADEM data by a stream burning method. The differences between the extracted river network and the actual river network were evaluated in three different geographical regions. Compared with the actual river centerline, the location error of the river network extraction was significantly reduced. The average offset distances between river network extraction and the actual river network were 68.38, 36.99, and 21.59 m in the three test areas. Compared with NASADEM V001, the average offset distances in the three test areas were reduced by 7.26, 40.29, and 42.35%, respectively. To better estimate accuracy, we also calculated and compared the accuracy of the river network based on MERIT Hrdro and HydroSHEDS. The experimental results demonstrated that the method can effectively improve the accuracy of river network extraction and meet the needs of hydrological simulation.

Editorial for Special Issue: “Monitoring Terrestrial Water Resource Using Multiple Satellite Sensors”

“…Lyu et al [28] developed a MSAFNet (multiscale successive attention fusion network) model for extracting water bodies from remote sensing images; they tested their method on the Qinghai-Tibet Plateau Lake (QTPL) and the Land-cOVEr Domain Adaptive semantic segmentation (LoveDA) datasets and achieved a high mapping accuracy (i.e., overall accuracy of 98.97% on the QTPL dataset and 95.87% on the LoveDA dataset). Lu et al [29] proposed a model for extracting river networks by combining DEM, a Landsat-derived global surface water occurrence (GSWO) dataset, and Sentinel-2 imagery, and they applied the model to conduct a case study across the Danjiangkou Reservoir Area, finding results consistent with the actual river network. Wang et al [30] investigated the spatial-temporal variation in water coverages in the sub-lakes of Poyang Lake in accordance with multi-source remote sensing imagery (Landsat 8 data, MODIS data, GF-1 data, and GlobeLand30 data) by using the MNDWI (modified normalized difference water index) and the ISODATA (iterative self-organizing data analysis technique algorithm), achieving a good accuracy value of 97%.…”

Segmentation and Connectivity Reconstruction of Urban Rivers from Sentinel-2 Multi-Spectral Imagery by the WaterSCNet Deep Learning Model

“…Therefore, some widely used pixel-based flow detection algorithms, such as D8 [1] and D-infinity [2], use digital elevation data to extract the distribution and connectivity of rivers. Recently, a number of methods [3,4] have also been developed to integrate Sentinel-2 multi-spectral imagery [5] with digital elevation data to extract river distribution and connectivity. However, in complex urban environments, tall buildings and artificial lowlands can make it very difficult to extract rivers based on digital elevation data [3], and high spatial resolution urban digital elevation data are also very expensive [6].…”