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 SfM, employing terrestrial as well as UAV 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 SfM. In future, adding tracking algorithms can help to densify existing gauging networks. https://opara.zih.tu-dresden.de/xmlui/handle/123456789/1308 2026-04-21T07:34:02Z 2026-04-21T07:34:02Z Eltner, Anette https://opara.zih.tu-dresden.de/xmlui/handle/123456789/1333 2018-10-02T09:03:21Z 2018-01-01T00:00:00Z

StageDetect - An image-based tool for automatic water stage detection - image dataset Eltner, Anette The dataset includes image sets and data to reference the image measurements. The image sets and datasets are used for figure 3 in the in the WRR manuscript 'Automatic image-based water stage measurement for long-term observations in ungauged catchments'. The image sets comprises the master image of the image sequences captured in frequent interval with Raspberry Pi cameras at the river Wesenitz and Wernersbach in Saxony, Germany. If entire image sequences (instead of single masters) are requested (they are not provided here due to large data size), please contact Anette.Eltner@tu-dresden.de

2018-01-01T00:00:00Z