Automated monitoring of river ice processes using shore-based imagery

Ansari, Saber; Rennie, Colin D.; Seidou, Ousmane; Malenchak, Jarrod; Zare, Soheil Ghareh Aghaji

doi:10.1016/j.coldregions.2017.06.011

Cited by 27 publications

(13 citation statements)

References 24 publications

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“…Since there is far too much data for manual analysis to be practical, one of the objectives of this work is to estimate this concentration from digital images and videos of the river surface in an automated or semi-automated manner using deep learning. Distinguishing ice from water is relatively straightforward and has been accomplished fairly successfully using simple techniques like thresholding [2] as well as classic machine learning methods like SVM with handcrafted features [22], [21], [20]. The main goal of this work is therefore to be able to distinguish between frazil and anchor ice pans with high accuracy using state of the art deep learning techniques.…”

Section: Introductionmentioning

confidence: 99%

River Ice Segmentation With Deep Learning

Singh

Kalke

Ray

et al. 2020

IEEE Trans. Geosci. Remote Sensing

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This paper deals with the problem of computing surface ice concentration for two different types of ice from digital images of river surface. It presents the results of attempting to solve this problem using several state of the art semantic segmentation methods based on deep convolutional neural networks (CNNs). This task presents two main challenges -very limited availability of labeled training data and presence of noisy labels due to the great difficulty of visually distinguishing between the two types of ice, even for human experts. The results are used to analyze the extent to which some of the best deep learning methods currently in existence can handle these challenges. The code and data used in the experiments are made publicly available to facilitate further work in this domain.

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Section: Introductionmentioning

confidence: 99%

River Ice Segmentation With Deep Learning

Singh

Kalke

Ray

et al. 2020

IEEE Trans. Geosci. Remote Sensing

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“…Beltaos and Kääb (2014) used successive satellite images to assess the flow velocity and discharge during ice break-up, illustrating the ability of using remote sensing for studying ice process dynamics. Ansari et al (2017) developed algorithms to automatically derive ice phenology data from bankside photography, and produced time series of these ice data for the lower Nelson River, Canada. Most satellite-based approaches described in the literature are applied to large rivers and may not apply to smaller rivers and streams due to the coarse resolution of the satellite imagery.…”

Section: Introductionmentioning

confidence: 99%

Brief Communication: Mapping river ice using drones and structure from motion

Alfredsen

Haas²,

Tuhtan

et al. 2018

The Cryosphere

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Abstract. In cold climate regions, the formation and breakup of river ice is important for river morphology, winter water supply, and riparian and instream ecology as well as for hydraulic engineering. Data on river ice is therefore significant, both to understand river ice processes directly and to assess ice effects on other systems. Ice measurement is complicated due to difficult site access, the inherent complexity of ice formations, and the potential danger involved in carrying out on-ice measurements. Remote sensing methods are therefore highly useful, and data from satellite-based sensors and, increasingly, aerial and terrestrial imagery are currently applied. Access to low cost drone systems with quality cameras and structure from motion software opens up a new possibility for mapping complex ice formations. Through this method, a georeferenced surface model can be built and data on ice thickness, spatial distribution, and volume can be extracted without accessing the ice, and with considerably fewer measurement efforts compared to traditional surveying methods. A methodology applied to ice mapping is outlined here, and examples are shown of how to successfully derive quantitative data on ice processes.

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“…The real world coordinates of the pixels are used in the final step to find the real world velocity of the surface boils. The detailed explanation of the employed image rectification method is presented in [3] and [4]. The digital elevation model (DEM) of the camera field of view was superimposed on the pixels of the reference image in order to assign real-world coordinates of the pixels (Figure. 2).…”

Section: Geo-rectificationmentioning

confidence: 99%

“…Development of different monitoring systems has also led to the application of these cost-effective methods in quantified data collection e.g. [4]. Extensive application of the monitoring methods was also developed to the open channel flow measurement era.…”

Section: Introductionmentioning

confidence: 99%

Shore-based monitoring of flow dynamics in a steep bedrock canyon river

et al. 2018

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The pace of landscape evolution is set by bedrock erosion in canyons. This phenomenon occurs by various geological processes including plucking of bedrock blocks and abrasion by saltating bedload and suspended load in highly turbulent flows. For a better understanding of the river flow characteristics in bedrock rivers, a comprehensive study of flow dynamics was undertaken in Black Canyon in the Fraser River, British Columbia. We used shore-based video imagery of the river to study surface flow dynamics. The shore-based monitoring system consisted of a Campbell Scientific camera mounted at the top of the canyon walls. We monitored the water surface boils due to upwelling and determined river surface flow velocities from the shore-based imagery. Automatic detection of the upwelling surface boils leads to a better understanding of the secondary circulation patterns and flow structures in this large steep river bedrock canyon. The data collection and analytical procedures developed in this research are cost-effective tools for remotely determining flow dynamics, which can be applied to other rivers.

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Automated monitoring of river ice processes using shore-based imagery

Cited by 27 publications

References 24 publications

River Ice Segmentation With Deep Learning

River Ice Segmentation With Deep Learning

Brief Communication: Mapping river ice using drones and structure from motion

Shore-based monitoring of flow dynamics in a steep bedrock canyon river

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