Time-Lapse Photography: Low-Cost, Low-Tech Alternative for Monitoring Flow Depth

Schoener, Gerhard

doi:10.1061/(asce)he.1943-5584.0001616

Cited by 25 publications

(15 citation statements)

References 15 publications

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“…Furthermore, several studies have demonstrated their utility for calibration of hydrological models (e.g. van Meerveld et al, 2017;Seibert and Vis, 2016).…”

Section: Introductionmentioning

confidence: 99%

Deep learning for automated river-level monitoring through river-camera images: an approach based on water segmentation and transfer learning

Vandaele

Dance

Ojha

2021

Hydrol. Earth Syst. Sci.

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Abstract. River-level estimation is a critical task required for the understanding of flood events and is often complicated by the scarcity of available data. Recent studies have proposed to take advantage of large networks of river-camera images to estimate river levels but, currently, the utility of this approach remains limited as it requires a large amount of manual intervention (ground topographic surveys and water image annotation). We have developed an approach using an automated water semantic segmentation method to ease the process of river-level estimation from river-camera images. Our method is based on the application of a transfer learning methodology to deep semantic neural networks designed for water segmentation. Using datasets of image series extracted from four river cameras and manually annotated for the observation of a flood event on the rivers Severn and Avon, UK (21 November–5 December 2012), we show that this algorithm is able to automate the annotation process with an accuracy greater than 91 %. Then, we apply our approach to year-long image series from the same cameras observing the rivers Severn and Avon (from 1 June 2019 to 31 May 2020) and compare the results with nearby river-gauge measurements. Given the high correlation (Pearson's correlation coefficient >0.94) between these results and the river-gauge measurements, it is clear that our approach to automation of the water segmentation on river-camera images could allow for straightforward, inexpensive observation of flood events, especially at ungauged locations.

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“…Furthermore, several studies have demonstrated their utility for calibration of hydrological models (e.g. van Meerveld et al, 2017;Seibert and Vis, 2016).…”

Section: Introductionmentioning

confidence: 99%

Deep learning for automated river-level monitoring through river-camera images: an approach based on water segmentation and transfer learning

Vandaele

Dance

Ojha

2021

Hydrol. Earth Syst. Sci.

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“…For instance, in industrial applications, optics-based non-contact techniques are routinely constructed to estimate the depth of liquids in tanks (Chakravarthy et al, 2002;Reza and Riza, 2010;Yu, 2014). In river systems, imagery has opened novel capabilities toward water level measurement in ephemeral settings (Schoener, 2018). Since the pioneering work by Takagi et al (1998), most of these image processing approaches rely on the detection of the water line, that is, the "boundary" sub-horizontal line at the water-air interface.…”

Section: Introductionmentioning

confidence: 99%

Comment on hess-2021-36

2021

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Monitoring ephemeral and intermittent streams is a major challenge in hydrology. While direct field observations are best to detect spatial patterns of flow persistence, on site inspections are time and labor intensive and may be impractical in difficult-to-access environments. Motivated by latest advancements of digital cameras and computer vision techniques, in this work, we describe the development and application of a stage-camera system to monitor the water level in ungauged headwater streams. The system encompasses a consumer grade wildlife camera with near infrared (NIR) night vision capabilities and a white pole that serves as reference object in the collected images. Time-lapse imagery is processed through a computationally inexpensive algorithm featuring image quantization and binarization, and water level time series are filtered through a simple statistical scheme. The feasibility of the approach is demonstrated through a set of benchmark experiments performed in controlled and natural settings, characterized by an increased level of complexity. Maximum mean absolute errors between stage-camera and reference data are approximately equal to 2 cm in the worst scenario that corresponds to severe hydrometeorological conditions. Our preliminary results are encouraging and support the scalability of the stage-camera in future implementations in a wide range of natural settings.

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“…In recent years, a network video surveillance system has gradually become the standard configuration at all levels of hydrological bureaus and stations [10,11]. Ensuring daily monitoring, remote dispatching and disaster alert are important measures to make good use of and protect water resources and water conservancy facilities, which provide favorable conditions for water-level measurement based on video images [12,13,14]. There are also some automatic water-level monitoring systems based on image [15,16,17,18].…”

Section: Introductionmentioning

confidence: 99%

Visual Measurement of Water Level under Complex Illumination Conditions

Zhang

Zhou

Liu

et al. 2019

Sensors

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Image-based water level measurement is a visual-sensing technique which automatically inspects the reading of the water line via image processing instead of the human eye. It can be realized easily on an existing video surveillance system and has advantages like low cost, non-contact, as well as results that are verifiable. It has the potential to be widely used in flood and waterlogging monitoring, while facing the challenge that water-line detection under complex natural or artificial illumination conditions is quite difficult in field applications. To handle this problem, a method is proposed assuming that the water line is generally located on the row with the largest local change of gray or edge features in the image of the water gauge. The water line is determined by coarse-to-fine detection of the position of the maximum mean difference (MMD) of the horizontal projections of gray and edge images. Image-based flow-level measurement systems were developed at two measurement sites. In situ comparative experiments were conducted with the float-type stage gauge and other image-based methods. The results show that the fusion of gray and edge features can overcome the shortcomings of single feature methods under complex illumination conditions such as dim light, glares, shadows and artificial night lighting. A coarse-to-fine strategy utilizes the periodicity of the surface pattern distribution of the standard bicolor water gauge, which improves the reliability of water-line detection. The resolution and accuracy of water-level measurement are 1 mm and 1 cm, respectively. In particular, the MMD value is efficient at identifying extremely unfavorable conditions and reducing gross errors.

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Time-Lapse Photography: Low-Cost, Low-Tech Alternative for Monitoring Flow Depth

Cited by 25 publications

References 15 publications

Deep learning for automated river-level monitoring through river-camera images: an approach based on water segmentation and transfer learning

Deep learning for automated river-level monitoring through river-camera images: an approach based on water segmentation and transfer learning

Comment on hess-2021-36

Visual Measurement of Water Level under Complex Illumination Conditions

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