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.
Rapid technological progress has made mobile devices increasingly valuable for scientific research. This paper outlines a versatile camera‐based water gauging method, implemented on smartphones, which is usable almost anywhere if 3D data is available at the targeted river section. After analysing smartphone images to detect the present water line, the image data is transferred into object space. Using the exterior orientation acquired by smartphone sensor fusion, a synthetic image originating from the 3D data is rendered that represents the local situation. Performing image‐to‐geometry registration using the true smartphone camera image and the rendered synthetic image, image parameters are refined by space resection. Moreover, the water line is transferred into object space by means of the underlying 3D information. The algorithm is implemented in the smartphone application “Open Water Levels”, which can be used on both high‐end and low‐cost devices. In a comprehensive investigation, the methodology is evaluated, demonstrating both its potential and remaining issues.
Knowledge about the interior and exterior camera orientation parameters is required to establish the relationship between 2D image content and 3D object data. Camera calibration is used to determine the interior orientation parameters, which are valid as long as the camera remains stable. However, information about the temporal stability of low-cost cameras due to the physical impact of temperature changes, such as those in smartphones, is still missing. This study investigates on the one hand the influence of heat dissipating smartphone components at the geometric integrity of implemented cameras and on the other hand the impact of ambient temperature changes at the geometry of uncoupled low-cost cameras considering a Raspberry Pi camera module that is exposed to controlled thermal radiation changes. If these impacts are neglected, transferring image measurements into object space will lead to wrong measurements due to high correlations between temperature and camera’s geometric stability. Monte-Carlo simulation is used to simulate temperature-related variations of the interior orientation parameters to assess the extent of potential errors in the 3D data ranging from a few millimetres up to five centimetres on a target in X- and Y-direction. The target is positioned at a distance of 10 m to the camera and the Z-axis is aligned with camera’s depth direction.
Abstract. Thanks to the rapid technological progress in the field of mobile devices, smartphones are increasingly becoming valuable for science. They can serve as photogrammetric measurement devices with built-in cameras, micro-electro-mechanical systems for orientation and position assessment, as well as powerful processing units allowing field-based data acquisition and processing. This paper outlines a comprehensive investigation focusing on the accuracy and stability of smartphone camera rotation parameters determined by built-in smartphone sensors. For that purpose, the rotation parameters were measured under a range of different conditions. Four test scenarios were defined considering indoor- and outdoor measurements using three different devices being in static and dynamic modes. Furthermore, the influence of magnetic perturbations was investigated. The rotation parameters were determined from the measurements applying different sensor fusion approaches. Reference values for accuracy assessment were provided by a superior precision inertial measurement unit that measured the rotation parameters simultaneously to the smartphone in each experiment. The analysis of the smartphone-based rotation parameters, separated in the Euler angles azimuth, pitch and roll, shows average accuracies below 2° for pitch and roll. In comparison, azimuth shows significantly lower accuracies of more than 30° especially when the smartphone is in motion and when it is exposed to magnetic perturbations. In this regard, advanced multi-sensor fusion approaches were examined that handle such interferences to considerably improve the accuracy of azimuth measurements. In conclusion, a summary of accuracies and stabilities to be expected from smartphone sensors is given referring to ambient conditions and investigated sensor fusion strategies.
<p align="justify">Historical aerial photographs captured since the early 1900s and spy satellite photographs from the 1960s onwards have long been used for military, civil, and research purposes in natural sciences. These historical photographs have the unequalled potential for documenting and quantifying past environmental changes caused by anthropogenic and natural factors.</p><p align="justify">The increasing availability of historical photographs as digitized/scanned images, together with the advances in digital photogrammetry, have heightened the interest in these data in the scientific community for reconstructing long-term surface evolution from local to regional scale.</p><p align="justify">However, despite the available volume of historical images, their full potential is not yet widely exploited. Currently, there is a lack of knowledge of the types of information that can be derived, their availability over the globe, and their applications in geoscience. There are no standardized photogrammetric workflows to automatically generate 3D (three-dimensional) products, in the form of point clouds and digital elevation models from stereo images (i.e. images capturing the same scenery from at least two positions), as well as 2D products like orthophotos. Furthermore, influences on the quality and the accuracy of the products are not fully understood as they vary according to the image quality (e.g. photograph damage or scanning properties), the availability of calibration information (e.g. focal length or fiducial marks), and data acquisition (e.g. flying height or image overlap).</p><p align="justify">We reviewed many articles published in peer reviewed journals from 2010 to 2021 that explore the potential of historical images, covering both photogrammetric reconstruction techniques (methodological papers) and the interpretation of 2D and 3D changes in the past (application papers) in different geoscience disciplines such as geomorphology, cryosphere, volcanology, bio-geosciences, geology and archaeology. We present an overview of these published studies and a summary of available image archives. In addition, we compare the main methods used to process historical aerial and satellite images, highlighting new approaches. Finally, we provide our advice on image processing and accuracy assessment.</p>
Global climate change leads to an increase in local heavy rainfall events causing nearly unpredictable flash floods worldwide. This paper introduces a novel and flexible low-cost water gauging technology, called Open Water Levels, using smartphones as low-cost measuring devices enabling the crowdsourcing of water levels on demand with accuracies of a few centimetres. This merely requires smartphone camera images of a riverbank and approximate values of the camera position and rotation measured by smartphone sensors. The images are analysed for the water line that is further projected into object space and intersected with a 3D model, e.g. from a GIS database, to derive water level information.
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