essd-2023-94
Multitemporal characterisation of a proglacial system: a multidisciplinary approach
I carefully read the authors' response to my comments and looked at the manuscript with the tracked changes. The authors have made a great effort to respond to my comments, and the article has benefited greatly from this work. As I pointed out in my previous paper, I consider this work a very good contribution and it demonstrates the importance of studying the functioning of proglacial margins with a multidisciplinary approach. The results presented here will allow further investigation of the Rutor Glacier and its proglacial margin, and the papers that follow will certainly be of interest to the geomorphological community.
The paper is now stronger, more informative, and better structured. However, I still have some concerns about the error assessment in the DSMs, but it may be that I am too picky.
The authors present a first DSM assessment in Table 4. They calculate the vertical and horizontal RMS of the GCPs and checkpoints, but it would have been more informative to include the mean error and the standard deviation of error as well (this would allow characterizing systematic and random errors in the DSMs). Then, the authors calculate such statistics in Table 5 by using the delta of Z of a number of stable points, however these results are not informative since those stable points are not well scattered across the study zone.
By looking at the DoD in Figure 9a, it appears that the DSMs are of good quality, and no apparent systematic deformation is occurring. However, without a deeper error assessment some doubts might arise. Building on my own experience in error assessment in DSMs and DoDs, I would suggest two paths to assess the DSMs.
1. Assess the DSMs by using your independent checkpoints, which are widely distributed across the study area, although their number is low (but this is OK in such extreme and dangerous environments). This analysis would inform if your DSMs are impacted by random errors (std of error) and systematic errors (mean error). In the presence of systematic errors, check if those errors have some spatial structure (e.g., systematic tilt, which I formerly named datum shift), and if any correct it in respect to your checkpoint network (if possible). Once the presence of systematic errors is assessed (and corrected), use the std of error to calculate your Limit of Detection (LoD), either at 68 or 95 confidence limits. In this way, you do not violate the assumption of the error propagation theory which states that the errors in your DSM should be random, independent and gaussian. I suspect that this approach would provide a large LoD, but this is fine.
2. Assess your reference DSM (i.e., 2021) by using your independent checkpoints, and check for systematic errors. In the presence of systematic errors, check if those errors have some spatial structure (e.g., systematic tilt), and if any correct it in respect to your checkpoint network (if possible). This would inform about the quality of your reference. Then create a new set of checkpoints based on a number of widely distributed stable points (e.g., bed-rock, etc.). Calculate the shift between the reference (2021) and the DSM of 2020, and assess the presence of random and systematic errors. In the presence of systematic deformations such as tilt, correct them. Once the presence of systematic errors is assessed (and corrected), use the std of error of your stable points to calculate the LoD.
Both ways are widely accepted, although (1) is probably more robust although your checkpoint population is somehow little. I would suggest discussing this with the editors, and get to know what their expectations in terms of error assessment are. I want to emphasize that my suggestions should not be seen as criticism, but as genuine suggestions to improve the quality of the article, which - as I have already said - in my opinion is already good. I am of the opinion that the authors can respond to my concerns fairly quickly and I don't think another round of review by the referees is needed.
I know the challenges of collecting (and processing) this amount of data in these extreme environments, therefore I compliment the authors for having produced and discussed this remarkable dataset.
Some specific comments are listed down below.
Line 192: "During the 2021 field activities, a total of 32 artificial photogrammetric markers"
Following Table 2, you deployed 33 markers. Either the text or the table are wrong.
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Lines 223 – 225: "Outwash plains, which may have been affected by geomorphological changes (e.g. due to erosion and water deposits) between the time of the surveys, were also considered stable areas."
I do not see the point of using such points if you want to assess the error in your datasets. Outwash plains are extremely unstable as you pointed out. What is the rationale of choosing points within the outwash plain? You bring in this point later in the paragraph, stating that using such points may have worsen your statistics, which again questions the rationale of choosing them. Could you please clarify? How many stable points did you select?
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Lines 352 – 354: "A standard Structure-from-Motion (SfM) photogrammetric approach was adopted, following a consolidated workflow (i.e. interior and exterior orientation, camera calibration, dense point cloud generation, DSM and orthomosaic generation) using the software Agistoft Metashpe."
I would move this sentence to methods. I am a bit picky here, but could you please clarify what consolidated workflow means. I guess you mean the estimation of the internal/external orientation parameters through the alignment in Metashape, then markers, re-fitting through a bundle adjustment (camera calibration), and so on. Is this the workflow? Perhaps, it would be useful for future readers to specify the parameters you used within the bundle adjustment (e.g., focal length, principal point offset, etc.).
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Table 4: See my general comments about including the mean error and std of error.
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Line 365: "about 0.2 m"
Consider using 20 cm instead of 0.2 m.
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Lines 377 – 379: "Glacier surface elevation differences were estimated by subtracting 2021 DSM to 2020 one, to quantify ablation and displacement (Table 5)"
I suspect that the reference to Table 5 is wrong here.
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Lines 380 – 385: The introduction of a LoD is indeed very good, however there is no reference to the works of Brasington et al. (2000) and Lane et al. (2003), both published in ESPL, that are the foundation of the LoD theory in DoDs (in my opinion). Furthermore, you use the RMS of Table 4 that was previously noted as accuracy and here as precision. When calculating the LoD, one should use precision i.e. the standard deviation of error as illustrated in both Brasington et al. (2000) and Lane et al. (2003) papers.
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Table 5: I appreciate the choice of including Table 5 in the results, however I think that the way it is presented now does not provide an informative assessment since the stable points cover a fraction of the study area and they are not widely distributed across the study zone. I acknowledge the difficulty of selecting stable points in such unstable environments, but you might find stable zones even in close proximity of the glacier (e.g., bed rock) so that your assessment becomes more informative. Please refer to my general comments at the beginning of this report, and if possible calculate the statistics again to leave no room for any doubt.
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Line 386: "Figure 9 (a) shows the differences between the 2021 and 2020 DSMs adopting a LoD threshold of 95% = 40 cm"
Since you used the 95% LoD in your DoD (Figure 9a) I would remove the description of the 68%. I do not see any point of including the calculation of the 68% if you do not use it in further analysis.
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Figure 9a: The DoD is now clearer with the bivariate scale and the LoD.
I would suggest using the color white for changes that fall within the LoD or even use the orthomosaic as background (with LoD range in transparent), so that significant changes are easier to see. Furthermore, consider using more classes to be more informative, and perhaps use better contrasting colors. I would also suggest reversing the scale, since now it shows that the glacier termini gained in Z between 2020 and 2021. This results from subtracting the DSM of 2021 from the DSM of 2020 instead of doing the opposite, that is subtracting 2020 from 2021. |