Versatile Mobile and Stationary Low-Cost Approaches for Hydrological Measurements

Water Resources Research

Bressan

Akiyama

et al. 2021

Self Cite

Spatiotemporally dense hydrological observation networks are required to provide a suitable database for modeling and planning of water resources. One important hydrological observation is the water stage, which can be retrieved using several methods. Floating and pressure gauges are very common (Morgenschweis, 2010); however, they require the installation in the water and therefore entail the risk of losing them during severe flooding. Thus, remote observation techniques, such as those based on radar or ultrasonic devices (Herschy, 2008), might be preferred in such situations because they have the advantage that they can be installed remotely. Another remote option is the application of image-based methods. In general, they can be low-cost in terms of the device compared to conventional water stage retrieval approaches if, for instance, basic camera circuit boards are used in combination with single board computers or microcontrollers. These setups also have the advantage that data processing can be performed on the spot, and only small data amounts have to be transmitted if needed, for example, via IoT infrastructures (Da Xu et al., 2014). Furthermore, camera gauges provide the benefit of potentially measuring flow velocities simultaneously by capturing short videos and tracking particles at the water surface (e.g., Eltner et al., 2020), which allows quantifying discharge with the same device eventually and thereby avoiding the installation of additional equipment. The application of camera gauges is suitable at rivers with periodic heavy debris loads or low angle river banks with large river width fluctuations leading to failure of other gauging techniques. Observing the river reach with an optical device enables capturing qualitative and quantitative information beyond the water stage or flow velocities. For instance, ephemeral rivers and extended flooded areas become observable, and snow coverage and ice growth can be assessed. Furthermore, the vegetation evolution might be monitored, or the river cross-section changes determined to ensure a continuous and reliable water stage-discharge curve. Thus, camera observations can practically support hydrological monitoring.

Section: Methodsmentioning

confidence: 99%

Using Deep Learning for Automatic Water Stage Measurements

Water Resources Research

Bressan

Akiyama

et al. 2021

Self Cite

“…Image‐based systems could fill this gap and help to densify gauging networks in sparsely or unsupervised catchments. Webcams or basic camera circuit boards can serve as low‐cost platforms for image acquisition and processing (Kröhnert & Eltner, ). The development of image‐based solutions for measuring hydrometric parameters has increased recently, using terrestrial cameras (e.g., Muste et al, ), helicopters (Fujita et al, ), or unmanned aerial vehicles (UAVs) like Detert and Weibrecht () or Perks et al ().…”

Section: Introductionmentioning

confidence: 99%

Automatic Image‐Based Water Stage Measurement for Long‐Term Observations in Ungauged Catchments

Water Resources Research

Elias

Sardemann

et al. 2018

Self Cite

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.

“…Uncoupled low-cost cameras, e.g., RPi cameras, are assumed to be less affected by self-heating effects. Due to their potential for outdoor monitoring applications [21], they might be exposed to ambient temperature changes that can range from strong heat due to direct solar radiation to strong cold due to icing or snow.…”

Section: Introductionmentioning

confidence: 99%

Assessing the Influence of Temperature Changes on the Geometric Stability of Smartphone- and Raspberry Pi Cameras

Elias

Liebold

et al. 2020

Sensors

Self Cite

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.