Analysing the Potential of OpenStreetMap Data to Improve the Accuracy of SRTM 30 DEM on Derived Basin Delineation, Slope, and Drainage Networks

Abstract

Abstract:Terrain slope and drainage networks are useful components to the basins morphometric characterization as well as to hydrologic modelling. One way to obtain the slope, drainage networks, and basins delineation is by their extraction from Digital Elevation Models (DEMs) and, therefore, their accuracy depends on the accuracy of the used DEM. Regional DEMs with high detail and accuracy are produced in many countries by National Mapping Agencies (NMA). However, the use of these products usually has associated costs. An alternative to those DEMs are the Global Digital Elevation Models (GDEMs) that can be accessed freely and cover almost the entire surface of the world. However, they are not as accurate as the regional DEMs obtained with other techniques. This study intends to assess if generating new, modified DEMs using altimetric data from the original GDEMs and the watercourses available for download in the collaborative project OpenStreetMap (OSM) improves the accuracy of the rebuilt DEMs, the slope derived from them, as well as the delineation of basins and the horizontal and vertical accuracy of the extracted drainage networks. The methodology is presented and applied to a study area located in the United Kingdom. The GDEMs used are of 30 m spatial resolution from the Shuttle Radar Topography Mission (SRTM 30). The accuracy of the original data and the data obtained with the proposed methodology is compared with a reference DEM, with a spatial resolution of 50 m, and the rivers network available at the Ordnance Survey website. The results mainly show an improvement of the horizontal accuracy of the drainage networks, but also a decrease of the systematic errors of the new DEMs, the derived slope, and the vertical position of the drainage networks, as well as the basin's identification for a set of pour points.

Remote sensing-aided rainfall–runoff modeling in the tropics of Costa Rica

“…Several issues were found during the delineation of catchments on flat terrain, where computed water courses differed from the actual river network. We corrected the computed river network using the vector layers of the main rivers from Open-StreetMap (OSM), forcing the water courses following Monteiro et al (2018). The final catchments ranged from 3 to 500 km 2 with a median value of 65 km 2 and a main river length from 2.5 to 75 km with a median value of 15.2 km.…”

Novel Ensembles of Deep Learning Neural Network and Statistical Learning for Flash-Flood Susceptibility Mapping

“…Slope angle has been confirmed as a key factor for flash-flood studies because it greatly controls the speed of the floodwaters [57][58][59][60]. This was derived from the Digital Elevation Model (DEM) with a cell size of 30 m. The DEM was extracted from Shuttle Radar Topography Mission (SRTM) database at a spatial resolution of 30 m. It should be noted that SRTM is an international project coordinated by the U.S. National Geospatial-Intelligence Agency (NGA) and the U.S. Aeronautics and Space Administration (NASA) [61]. According to Zaharia et al [62] the following slope classes have been established: 0°-3°, 3°-7°, 7°-15°, 15°-25° and > 25° (Figure 5a).…”

Automatic Extraction and Filtering of OpenStreetMap Data to Generate Training Datasets for Land Use Land Cover Classification

“…OSM data is structured into four data types, namely: Nodes, ways, relations and tags [34], which are described as: The completeness and types of features available in OSM may vary considerably from region to region, as they depend upon the volunteer's contributions [35]. Usually, features such as waterways and the road network are the first to be inserted in OSM [36]. It can be seen (Figure 1c) that for study area A, most of the missing data (shown in beige) is outside the urban areas.…”