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Automatic Image‐Based Water Stage Measurement for Long‐Term Observations in Ungauged Catchments
Author(s) -
Eltner Anette,
Elias Melanie,
Sardemann Hannes,
Spieler Diana
Publication year - 2018
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1029/2018wr023913
Subject(s) - stage (stratigraphy) , scale (ratio) , computer science , environmental science , remote sensing , image resolution , reliability (semiconductor) , artificial intelligence , hydrology (agriculture) , computer vision , geology , geography , cartography , paleontology , power (physics) , physics , geotechnical engineering , quantum mechanics
Small‐scale and headwater catchments are mostly ungauged, even though their observation could help to improve the understanding of hydrological processes. However, it is expensive to build and maintain conventional measurement networks. Thus, the heterogeneous characteristics and behavior of catchments are currently not fully observed. This study introduces a method to capture water stage with a flexible low‐cost camera setup. By considering the temporal signature of the water surface, water lines are automatically retrieved via image processing. The image coordinates are projected into object space to estimate the actual water stage. This requires high‐resolution 3D models of the river bed and bank area, which are calculated in a local coordinate system with structure from motion, employing terrestrial as well as unmanned aerial vehicle imagery. A medium‐ and a small‐scale catchment are investigated to assess the accuracy and reliability of the introduced method. Results reveal that the average deviation between the water stages measured with the camera gauge and a reference gauge are below 6 mm in the medium‐scale catchment. Trends of water stage changes are captured reliably in both catchments. The developed approach uses a low‐cost camera design in combination with image‐based water level measurements and high‐resolution topography from structure from motion. In future, adding tracking algorithms can help to densify existing gauging networks.