Deep learning-enhanced extraction of drainage networks from digital elevation models

Author(s):  
M.A.O. Xin ◽  
Jun Kang Chow ◽  
Zhaoyu Su ◽  
W.A.N.G. Yu-Hsing ◽  
Jiaye LI ◽  
...  
2013 ◽  
Vol 59 ◽  
pp. 116-123 ◽  
Author(s):  
Antonio Rueda ◽  
José M. Noguera ◽  
Carmen Martínez-Cruz

1991 ◽  
Vol 27 (5) ◽  
pp. 709-717 ◽  
Author(s):  
John Fairfield ◽  
Pierre Leymarie

2016 ◽  
Vol 92 ◽  
pp. 21-37 ◽  
Author(s):  
Henrique Rennó de Azeredo Freitas ◽  
Corina da Costa Freitas ◽  
Sergio Rosim ◽  
João Ricardo de Freitas Oliveira

2006 ◽  
Vol 42 (8) ◽  
Author(s):  
Adriano Rolim Paz ◽  
Walter Collischonn ◽  
André Luiz Lopes da Silveira

Hydrology ◽  
2018 ◽  
Vol 5 (3) ◽  
pp. 34 ◽  
Author(s):  
Elisabete Monteiro ◽  
Cidália Fonte ◽  
João Lima

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.


Water ◽  
2021 ◽  
Vol 13 (23) ◽  
pp. 3347
Author(s):  
Bo Chen ◽  
Chunying Ma ◽  
Yao Xiao ◽  
Hanxin Gao ◽  
Peijun Shi ◽  
...  

This study presents an enhanced variant of the priority-flood based algorithm proposed by Wang and Liu for treating depressions in digital elevation models (DEMs). The enhanced variant redefines spill elevation, the key concept of the original algorithm, as the lowest elevation that a pixel needs to have to ensure a non-ascending path toward the border of the DEM, plus the larger of a small number (~0.001) and the difference between the unaltered elevation values of the focal pixel and its immediate downhill neighbor. This redefinition is adopted to obtain an intermediate elevation surface to direct flow and ultimately to carve the original DEM. Each carving starts from a depression bottom and propagates downstream until a downhill cell is guaranteed in the original DEM. Tests of these algorithms on a complex terrain of the 260,000 km2 Sichuan structural basin in China shows that the enhanced algorithm maximally preserves the original flow directions and extracts realistic drainage networks. Retaining the relative heights, and therefore flow directions, of cells within depressions allows the new algorithm to offer a depressionless DEM with small modification of its origin for further hydrologic applications. The enhanced depression treatment algorithm is provided as the freely available tool BNUSinkRemv.


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