the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 10 Feb 2021
Technical Note: Low cost stage-camera system for continuous water level monitoring in ephemeral streams
Abstract. 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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Interactive discussion
Status: closed
-
RC1: 'Comment on hess-2021-36', Nils Kaplan, 27 Feb 2021
Interactive comment on „Technical Note: Low cost stage-camera system for continuous water level monitoring in ephemeral streams“
General comments:
This paper presents a promising re-interpretation of an image-based water level monitoring system for intermittent rivers and ephemeral streams (IRES) using a consumer wildlife camera with integrated time-lapse function. The continuous image-based monitoring of water level adds valuable information of the temporal dynamics of flow in IRES that were traditionally only monitored sporadically during field campaigns or with intermittency or EC sensors. However, those sensors cannot provide the visual information of an image, which allows for the evaluation of automated water level detection, the presence/absence of water in the channel or clogging of the stream channel. The presented method has a high potential to support future monitoring campaigns in the IRES research. However, some minor corrections and supplementary information needs to be added to the manuscript. Thus, I recommend the publication after minor revisions of this manuscript.
Specific comments:
Line 10: What are the “severe hydrometeorological conditions”? Only strong precipitation events or also extreme dry conditions? Please clarify.
Line 23: […]“uppermost” catchment “areas”
Line 23: “posit” seems to be an unusual wording in this context
Line 26: I suggest to use “Conventional” in stead of “Traditional”
Line 35: The citation of Kaplan et al., 2019 is a bit misleading as the presented dataset achieves it spatial resolution of an combination of EC-sensors, time-lapse imagery and conventional gauging.
Line 38: I would not recognize the noise of the sensor as biggest thread for EC-data accuracy for presence/absence of water but the accurate position of the sensor at the deepest point in the channel cross-section. The noise introduced by clogged material at the sensor is rather well to handle by using a little large thresholds for the EC-values.
Line 45: is very close to the sentence in Line 1. Maybe consider small changes to the wording.
Line 110: I experimented with a similar setup using a white pole, but the reflection of the pole through a clear water surface were too bright to extract the water line. Thus, I am interested in the paint you used for the pole (special matt color used?) and if the paint had any ability to prevent growth of algae, which would potentially affect the image processing algorithm. This would add valuable information to the manuscript.
Line 112: A little more details on the mounting system would be beneficial. Were specific measures be taken to prevent theft and/or camera movement?
Line 119: In this section a note on programming language and potentially packages used to write the image processing software would be great.
Line 122: Is the ROI static or set by an algorithm? Is the algorithm capable to respond to issues with movement of either the pole or the camera?
Line 124: Is the number thresholds individually calibrated for each site or automatically set according to the illumination conditions?
Line 137: Please add the information about the size of the moving average window already in this sentence to avoid confusion.
Line 138: […] “difference” between moving average and raw value […]?
Line 138: […] “set to the 90% quantile” of the moving window.
Line 138: The 90% quantile threshold might be a little bit too low to capture the fast dynamics of extreme events in ephemeral streams.
Line 163: May consider to use “Light scatter” or “Scattered sunlight” instead of “sunflecks“.
Line 182: Due to the specific dedication of the method to monitoring IRES it would be very beneficial to add an analysis for the MAE for the dry states of the channel compared to the flowing conditions. In case only the site “C” had dry conditions, it would help already to add this.
Line 195: Many image sequences during severe rainfall events were acquired as NIR images in the study of Kaplan et al., 2019. Thus, the difference of MAE between RGB and NIR images might be also an interesting information to add (they might be a reason for the higher MAE here).
Line 202: Simple image saturation statistics might already be sufficient to remove some of the blurriest images before the actual analysis.
Line 210: From an image processing point of view a white pole should be the brightest object compared to other colors. However, the difference between glossy vs. matt paint could be interesting.
Line 211: Additionally, to the debris that could get stuck at larger poles, they may have also a larger potential to get eroded.
Line 241: The advantages of the system could also be stated earlier; potentially at the end of the introduction
Line 242: “with minimal flow disturbance through the pole” instead of “without deploying any sensors in the flow”
Technical corrections:
Line 223: “frequency of time-lapse image acquisition” or “image acquisition frequency” instead of “camera acquisition frequency”.
Line 224: “time-lapse interval” instead of “frame frequency”
Figures:
I suggest to include a figure describing the processing algorithm in a flow chart.
Citation: https://doi.org/10.5194/hess-2021-36-RC1 - AC3: 'Reply on RC1', Salvatore Grimaldi, 13 Apr 2021
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RC2: 'Comment on hess-2021-36', Anonymous Referee #2, 09 Mar 2021
The application of a camera approach as a low-cost system for water level observations is certainly interesting. However, my rather fundamental issue with this manuscript is the added value of this approach. These days, one can buy water level sensors for 100-200 USD which provide measurements with a millimetre resolution. As the described system utilizes a pole installed in the stream (which is a neat idea), there is also not the argument that no in-stream installations are needed for the camera approach. So, as cool as the described approach is, I am not sure about its practical value. At the end of the manuscript, the authors mention the importance of having pictures of the streambed. I could agree, but if this is the added value, it should be addressed before and in more detail.
My other major concern is the study design. First of all, I am afraid I have to disagree that manual inspection of the images should be the sole comparison. Here a fully independent approach should be used, i.e. a ‘real’ water level sensor. Second, the observed level variations (Fig 5) are really small. For evaluation, there should be larger changes, especially also in the ‘lab setting’ of Test A. Using a constant level here limits the evaluation.
Approach
(fig 2): wouldn’t it be advantageous to rotate the image so that the pole is exactly vertical? I am not sure I understood how the tilting of the pole, in reality, is considered. Please clarify.
Fig 4: why is the pole so long? This seems to make things rather unstable
Minor comments:
Sometimes long lists of references are given, e.g. P1L18-19, please try to be more specific about the contributions of the individual papers.
P2L41: here ‘only’ should be added for clarification
Often hydrologists are interested in flows rather than in levels. Please comment on the use of level data without a rating curve.
I would prefer to have results and discussion in two separate sections for better readability.
Citation: https://doi.org/10.5194/hess-2021-36-RC2 - AC4: 'Reply on RC2', Salvatore Grimaldi, 13 Apr 2021
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CC1: 'Comment on hess-2021-36', Kenneth Chapman, 15 Mar 2021
Just want to post a note on your really nice work. We have been working on this problem for about a decade now and your innovation of using a pole and an "in the water--out of the water" approach to measuring water in the settings described in your paper is very nice and novel in our experience. This is significantly different from using a calibration target (our method) or with a staff gauge. Its power is the ability to install something really simple in a stream and still get measurements that are accurate enough to be of interest with fairly simple image processing algorithms and calibration techniques. The balance between simplicity and ease of use vs. precision of measurement is a difficult one. I am looking forward to following your work.
Citation: https://doi.org/10.5194/hess-2021-36-CC1 - AC5: 'Reply on CC1', Salvatore Grimaldi, 13 Apr 2021
- AC1: 'Authors' reply to CC, RC1, and RC2', Salvatore Grimaldi, 18 Mar 2021
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RC3: 'Comment on hess-2021-36', Anonymous Referee #3, 02 Apr 2021
This manuscript presents a method to automatically measure water level in streams using NIR-cameras and image processing techniques. The paper is generally well written and the results are promising. However, I do think that the paper should be improved before it can be considered for publication in HESS. First, the structure of the paper is somewhat unbalanced. The introduction is relatively extensive, making the results and discussion section seem rather marginal. I think the latter section would benefit from a stronger and more elaborated evaluation of the presented method, and include a more detailed outlook to future work and potential applications. Second, the data availability statement is not in line with HESS policy. This should be updated before the manuscript can be considered. Finally, the paper would benefit from additional support for and clarification about the setup and choices made, detailed in the general and specific comments below.
1. General comments
Introduction
- Another reason why ephemeral streams are so relevant is perhaps that the onset of flow may result in the mobilization of (anthropogenic) debris and sediment as well?
- The link with citizen science makes sense, as this offers an unprecedented opportunity for upscaling of data collection. However, how would this work for the locations of interested in this manuscript, i.e. ungauged headwater catchments? These may not be the locations where many citizens may be available to contribute with data collection.
- The introduction in general is well-written. I do think it is a bit long and goes on a tangent here and there. Perhaps the authors can reduce the length a bit and focus more on the potential of their approach, and why this is a promising addition to the existing suite of monitoring techniques.
Methods
- Perhaps a sketch of the monitoring setup can be included in addition to Fig. 1.
- What is the motivation for taking images every 30 minutes? What is the relevant timescale for ephemeral streams? I’d argue that a single to a couple of images per day would suffice, drastically reducing the required storage. With the current setup someone needs to read out the data every two weeks, which I would personally find quite much for ungauged headwater catchments.
- 1: I find this figure a bit unclear. Perhaps some additional headings to complete the workflow makes it a bit clearer.
- Please include some more details about the setup. How long is the pole? How is the pole robustly placed in what looks to be a rather “wild” environment? What is the distance between the pole and the camera? How is the camera fixed? What is the estimated pixel length (mm, cm)?
- Maybe a overview map can be included to show the outdoor testing locations.
- For the data validation, was the water level identification done by the same person? Or by a group of people? If the latter, was there any bias between the observers? Also, I was wondering if there was a reason to not measure the water level with an accurate water level logger.
Results and discussion
- Why was Test A done with the same water level for each image? As this method is most valuable to detect changes in water level, would it not have been valuable to test the method for the full ranges of values?
- The method seems to work quite well for Test C, which includes quite some dynamic behaviour. For Test D and E, the dynamics seem not to be captured completely. Can the authors elaborate on this, including the implications for what that would mean for long-term monitoring?
- The discussion is rather limited. I would encourage the authors to include a critical synthesis and more elaborated outlook on future work. What are the next steps for this method? How do the authors envision application in the field? Only for measurements of a couple of days, or also for seasonal or even multi-year monitoring efforts?
- When reading the paper I partially get very enthusiastic about this method, because it offers a nice new method for automatic monitoring. On the other hand, I keep on wondering what the added value of this method is over a traditional water level logger with millimetre accuracy, at more or less the same price. Such sensors are very robust, don’t need frames, and additional constructions, have a very long battery life (weeks, months), and don’t need any further processing.
- What I also wonder is whether this approach may be expanded with detection and monitoring of (anthropogenic) debris, such as woody debris, plastic pollution, or otherwise (van Lieshout et al., 2020). Then there’s a clear added value over more traditional sensing equipment.
Conclusions
- In the conclusions the authors sate that their method allows for “supervising the stream area and banks”. This is not elaborated on in the paper, so I suggest to either omit this statement or actually provide some additional analyses to support this in the paper.
Data and code availability
- The data availability is not in line with HESS policy: https://www.hydrology-and-earth-system-sciences.net/policies/data_policy.html. I would strongly suggest to make the data openly available through one a repository. And otherwise follow HESS’ policy to include a statement on why there are not available (“if the data are not publicly accessible, a detailed explanation of why this is the case is required”).
2. Specific comments:
- Line 18-21: Maybe omit some references, seems a bit much.
- Line 26-48: Useful summary of other techniques and drawbacks, but can maybe be written more concisely.
- Line 85: Although not “purely hydrological”, van Lieshout et al. (2020) recently demonstrated the potential of using cameras and deep learning for automatic plastic monitoring in rivers. Quite some lessons learned and practical considerations may be relevant for this manuscript as well.
- Line 122: How is the ROI automatically trimmed around it?
- Line 137: What moving average is used? E.g. how many datapoints? How does the length of the window influence the accuracy?
- Line 138: Is the 90% quantile based on the entire dataseries? Or a subset (e.g. without outliers)?
References
van Lieshout, Colin, et al. "Automated river plastic monitoring using deep learning and cameras." Earth and space science 7.8 (2020): e2019EA000960.
Citation: https://doi.org/10.5194/hess-2021-36-RC3 - AC6: 'Reply on RC3', Salvatore Grimaldi, 13 Apr 2021
- AC2: 'Authors' reply to RC3', Salvatore Grimaldi, 07 Apr 2021
Interactive discussion
Status: closed
-
RC1: 'Comment on hess-2021-36', Nils Kaplan, 27 Feb 2021
Interactive comment on „Technical Note: Low cost stage-camera system for continuous water level monitoring in ephemeral streams“
General comments:
This paper presents a promising re-interpretation of an image-based water level monitoring system for intermittent rivers and ephemeral streams (IRES) using a consumer wildlife camera with integrated time-lapse function. The continuous image-based monitoring of water level adds valuable information of the temporal dynamics of flow in IRES that were traditionally only monitored sporadically during field campaigns or with intermittency or EC sensors. However, those sensors cannot provide the visual information of an image, which allows for the evaluation of automated water level detection, the presence/absence of water in the channel or clogging of the stream channel. The presented method has a high potential to support future monitoring campaigns in the IRES research. However, some minor corrections and supplementary information needs to be added to the manuscript. Thus, I recommend the publication after minor revisions of this manuscript.
Specific comments:
Line 10: What are the “severe hydrometeorological conditions”? Only strong precipitation events or also extreme dry conditions? Please clarify.
Line 23: […]“uppermost” catchment “areas”
Line 23: “posit” seems to be an unusual wording in this context
Line 26: I suggest to use “Conventional” in stead of “Traditional”
Line 35: The citation of Kaplan et al., 2019 is a bit misleading as the presented dataset achieves it spatial resolution of an combination of EC-sensors, time-lapse imagery and conventional gauging.
Line 38: I would not recognize the noise of the sensor as biggest thread for EC-data accuracy for presence/absence of water but the accurate position of the sensor at the deepest point in the channel cross-section. The noise introduced by clogged material at the sensor is rather well to handle by using a little large thresholds for the EC-values.
Line 45: is very close to the sentence in Line 1. Maybe consider small changes to the wording.
Line 110: I experimented with a similar setup using a white pole, but the reflection of the pole through a clear water surface were too bright to extract the water line. Thus, I am interested in the paint you used for the pole (special matt color used?) and if the paint had any ability to prevent growth of algae, which would potentially affect the image processing algorithm. This would add valuable information to the manuscript.
Line 112: A little more details on the mounting system would be beneficial. Were specific measures be taken to prevent theft and/or camera movement?
Line 119: In this section a note on programming language and potentially packages used to write the image processing software would be great.
Line 122: Is the ROI static or set by an algorithm? Is the algorithm capable to respond to issues with movement of either the pole or the camera?
Line 124: Is the number thresholds individually calibrated for each site or automatically set according to the illumination conditions?
Line 137: Please add the information about the size of the moving average window already in this sentence to avoid confusion.
Line 138: […] “difference” between moving average and raw value […]?
Line 138: […] “set to the 90% quantile” of the moving window.
Line 138: The 90% quantile threshold might be a little bit too low to capture the fast dynamics of extreme events in ephemeral streams.
Line 163: May consider to use “Light scatter” or “Scattered sunlight” instead of “sunflecks“.
Line 182: Due to the specific dedication of the method to monitoring IRES it would be very beneficial to add an analysis for the MAE for the dry states of the channel compared to the flowing conditions. In case only the site “C” had dry conditions, it would help already to add this.
Line 195: Many image sequences during severe rainfall events were acquired as NIR images in the study of Kaplan et al., 2019. Thus, the difference of MAE between RGB and NIR images might be also an interesting information to add (they might be a reason for the higher MAE here).
Line 202: Simple image saturation statistics might already be sufficient to remove some of the blurriest images before the actual analysis.
Line 210: From an image processing point of view a white pole should be the brightest object compared to other colors. However, the difference between glossy vs. matt paint could be interesting.
Line 211: Additionally, to the debris that could get stuck at larger poles, they may have also a larger potential to get eroded.
Line 241: The advantages of the system could also be stated earlier; potentially at the end of the introduction
Line 242: “with minimal flow disturbance through the pole” instead of “without deploying any sensors in the flow”
Technical corrections:
Line 223: “frequency of time-lapse image acquisition” or “image acquisition frequency” instead of “camera acquisition frequency”.
Line 224: “time-lapse interval” instead of “frame frequency”
Figures:
I suggest to include a figure describing the processing algorithm in a flow chart.
Citation: https://doi.org/10.5194/hess-2021-36-RC1 - AC3: 'Reply on RC1', Salvatore Grimaldi, 13 Apr 2021
-
RC2: 'Comment on hess-2021-36', Anonymous Referee #2, 09 Mar 2021
The application of a camera approach as a low-cost system for water level observations is certainly interesting. However, my rather fundamental issue with this manuscript is the added value of this approach. These days, one can buy water level sensors for 100-200 USD which provide measurements with a millimetre resolution. As the described system utilizes a pole installed in the stream (which is a neat idea), there is also not the argument that no in-stream installations are needed for the camera approach. So, as cool as the described approach is, I am not sure about its practical value. At the end of the manuscript, the authors mention the importance of having pictures of the streambed. I could agree, but if this is the added value, it should be addressed before and in more detail.
My other major concern is the study design. First of all, I am afraid I have to disagree that manual inspection of the images should be the sole comparison. Here a fully independent approach should be used, i.e. a ‘real’ water level sensor. Second, the observed level variations (Fig 5) are really small. For evaluation, there should be larger changes, especially also in the ‘lab setting’ of Test A. Using a constant level here limits the evaluation.
Approach
(fig 2): wouldn’t it be advantageous to rotate the image so that the pole is exactly vertical? I am not sure I understood how the tilting of the pole, in reality, is considered. Please clarify.
Fig 4: why is the pole so long? This seems to make things rather unstable
Minor comments:
Sometimes long lists of references are given, e.g. P1L18-19, please try to be more specific about the contributions of the individual papers.
P2L41: here ‘only’ should be added for clarification
Often hydrologists are interested in flows rather than in levels. Please comment on the use of level data without a rating curve.
I would prefer to have results and discussion in two separate sections for better readability.
Citation: https://doi.org/10.5194/hess-2021-36-RC2 - AC4: 'Reply on RC2', Salvatore Grimaldi, 13 Apr 2021
-
CC1: 'Comment on hess-2021-36', Kenneth Chapman, 15 Mar 2021
Just want to post a note on your really nice work. We have been working on this problem for about a decade now and your innovation of using a pole and an "in the water--out of the water" approach to measuring water in the settings described in your paper is very nice and novel in our experience. This is significantly different from using a calibration target (our method) or with a staff gauge. Its power is the ability to install something really simple in a stream and still get measurements that are accurate enough to be of interest with fairly simple image processing algorithms and calibration techniques. The balance between simplicity and ease of use vs. precision of measurement is a difficult one. I am looking forward to following your work.
Citation: https://doi.org/10.5194/hess-2021-36-CC1 - AC5: 'Reply on CC1', Salvatore Grimaldi, 13 Apr 2021
- AC1: 'Authors' reply to CC, RC1, and RC2', Salvatore Grimaldi, 18 Mar 2021
-
RC3: 'Comment on hess-2021-36', Anonymous Referee #3, 02 Apr 2021
This manuscript presents a method to automatically measure water level in streams using NIR-cameras and image processing techniques. The paper is generally well written and the results are promising. However, I do think that the paper should be improved before it can be considered for publication in HESS. First, the structure of the paper is somewhat unbalanced. The introduction is relatively extensive, making the results and discussion section seem rather marginal. I think the latter section would benefit from a stronger and more elaborated evaluation of the presented method, and include a more detailed outlook to future work and potential applications. Second, the data availability statement is not in line with HESS policy. This should be updated before the manuscript can be considered. Finally, the paper would benefit from additional support for and clarification about the setup and choices made, detailed in the general and specific comments below.
1. General comments
Introduction
- Another reason why ephemeral streams are so relevant is perhaps that the onset of flow may result in the mobilization of (anthropogenic) debris and sediment as well?
- The link with citizen science makes sense, as this offers an unprecedented opportunity for upscaling of data collection. However, how would this work for the locations of interested in this manuscript, i.e. ungauged headwater catchments? These may not be the locations where many citizens may be available to contribute with data collection.
- The introduction in general is well-written. I do think it is a bit long and goes on a tangent here and there. Perhaps the authors can reduce the length a bit and focus more on the potential of their approach, and why this is a promising addition to the existing suite of monitoring techniques.
Methods
- Perhaps a sketch of the monitoring setup can be included in addition to Fig. 1.
- What is the motivation for taking images every 30 minutes? What is the relevant timescale for ephemeral streams? I’d argue that a single to a couple of images per day would suffice, drastically reducing the required storage. With the current setup someone needs to read out the data every two weeks, which I would personally find quite much for ungauged headwater catchments.
- 1: I find this figure a bit unclear. Perhaps some additional headings to complete the workflow makes it a bit clearer.
- Please include some more details about the setup. How long is the pole? How is the pole robustly placed in what looks to be a rather “wild” environment? What is the distance between the pole and the camera? How is the camera fixed? What is the estimated pixel length (mm, cm)?
- Maybe a overview map can be included to show the outdoor testing locations.
- For the data validation, was the water level identification done by the same person? Or by a group of people? If the latter, was there any bias between the observers? Also, I was wondering if there was a reason to not measure the water level with an accurate water level logger.
Results and discussion
- Why was Test A done with the same water level for each image? As this method is most valuable to detect changes in water level, would it not have been valuable to test the method for the full ranges of values?
- The method seems to work quite well for Test C, which includes quite some dynamic behaviour. For Test D and E, the dynamics seem not to be captured completely. Can the authors elaborate on this, including the implications for what that would mean for long-term monitoring?
- The discussion is rather limited. I would encourage the authors to include a critical synthesis and more elaborated outlook on future work. What are the next steps for this method? How do the authors envision application in the field? Only for measurements of a couple of days, or also for seasonal or even multi-year monitoring efforts?
- When reading the paper I partially get very enthusiastic about this method, because it offers a nice new method for automatic monitoring. On the other hand, I keep on wondering what the added value of this method is over a traditional water level logger with millimetre accuracy, at more or less the same price. Such sensors are very robust, don’t need frames, and additional constructions, have a very long battery life (weeks, months), and don’t need any further processing.
- What I also wonder is whether this approach may be expanded with detection and monitoring of (anthropogenic) debris, such as woody debris, plastic pollution, or otherwise (van Lieshout et al., 2020). Then there’s a clear added value over more traditional sensing equipment.
Conclusions
- In the conclusions the authors sate that their method allows for “supervising the stream area and banks”. This is not elaborated on in the paper, so I suggest to either omit this statement or actually provide some additional analyses to support this in the paper.
Data and code availability
- The data availability is not in line with HESS policy: https://www.hydrology-and-earth-system-sciences.net/policies/data_policy.html. I would strongly suggest to make the data openly available through one a repository. And otherwise follow HESS’ policy to include a statement on why there are not available (“if the data are not publicly accessible, a detailed explanation of why this is the case is required”).
2. Specific comments:
- Line 18-21: Maybe omit some references, seems a bit much.
- Line 26-48: Useful summary of other techniques and drawbacks, but can maybe be written more concisely.
- Line 85: Although not “purely hydrological”, van Lieshout et al. (2020) recently demonstrated the potential of using cameras and deep learning for automatic plastic monitoring in rivers. Quite some lessons learned and practical considerations may be relevant for this manuscript as well.
- Line 122: How is the ROI automatically trimmed around it?
- Line 137: What moving average is used? E.g. how many datapoints? How does the length of the window influence the accuracy?
- Line 138: Is the 90% quantile based on the entire dataseries? Or a subset (e.g. without outliers)?
References
van Lieshout, Colin, et al. "Automated river plastic monitoring using deep learning and cameras." Earth and space science 7.8 (2020): e2019EA000960.
Citation: https://doi.org/10.5194/hess-2021-36-RC3 - AC6: 'Reply on RC3', Salvatore Grimaldi, 13 Apr 2021
- AC2: 'Authors' reply to RC3', Salvatore Grimaldi, 07 Apr 2021
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