Towards Better Visualisation of Alpine Quaternary Landform Features on High-Resolution Digital Elevation Models

Round 1

Reviewer 1 Report

The manuscript has been significantly improved, all unclear issues have been clarified by the authors.

Author Response

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Reviewer 2 Report

The manuscript "Best practice for visualising alpine Quaternary landform features on high-resolution digital elevation models" is an interesting example of how to use the increasingly available LiDAR data and the methods of visualizing these data in presenting complex forms of landforms on the example of high-mountain areas. The authors present and compare several different techniques of LiDAR data visualization in order to answer the question which way allows to perceive and visualize the complex forms of the terrain in the best possible way. The manuscript was written correctly, using good English, it is also well structured and consistent. Nevertheless, in my opinion, the article does not introduce any scientific novelty. The authors did not outline the research niche and did not indicate whether and how the presented research could be used in other research areas. For this reason, the research is purely local and has little scientific aspect. Nevertheless, due to the lack of serious substantive comments and the general good preparation of the manuscript, I believe that it can be published in Remote Sensing and will be an interesting source of knowledge on LiDAR data visualization in Alpine regions.

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Reviewer 3 Report

October 5, 2021
Review
for Remote Sensing
Best practice for visualising alpine Quaternary landform features on
high-resolution digital elevation models
ID: remotesensing-1413379
Major comments
1. Key results
Presented work is a subjective evaluation of visualization methods of alpine Quaternary
landform features from high resolution (ALS based) digital elevation models. Hillshading
from multiple directions resulted to be the most universal method across the mountainous
and hilly terrains. Other methods (8-bit sky view factor and 8-bit slope visualizations)
facilitated interpretation of subtle and small-scale sedimentary and morphological features.
The authors' results can help in the selection of methods for visualization of alpine landforms.
It is a pity that the results and conclusions were referred to only three pre-selected methods,
and not to all eleven at the level of individual landforms.
2. Clarity and context
The abstract, introduction and conclusions are appropriate. The analyzes presented are
subjective, but clear and persuasive.
I spend couple of minutes to gure out why numbering of squares on Figure 1 is so strange.
The current description is unclear and not obvious. It must be improved.
Also sample locations should be described more explicitly:
Figure 3. Examples of produced visualisations (for location see Figure 1)
can be changed to for example
Figure 3. Examples of produced visualisations (see rectangle 3 on Figure 1)
3. Data and methodology
From the eleven visualisations produced, we selected those that were most suitable for detailed
analysis of the studied area following the criteria of [27] (e.g. clear visibility of small-scale
features, intuitive visualisation, no articially produced artifacts, etc.) and compared them
with analytical hillshading.
Why not analyse all the methods?
Why not rank usability of all visualization methods for all six landform types?
Maybe some are useful (better than the others) just for one specic landform type.
Especially since the authors write:
As shown in previous studies, there is no general visualisation that could be provided as a
universal surface representation across all landscape types.
In fact authors compared just three visualization methods.
Why was the analysis limited only to the default parameters? They may be suboptimal for
specic landforms.
4. Statistics and uncertainties
How many locations/features were analysed for each landform? This information is unclear.
If only those shown in Figure 1, one should consider whether it is possible to generalize
conclusions on the basis of such a small number.
How representative are they for alpine Quaternary landforms?
What is the probability that presented examples are biased?
1
5. Conclusions
By comparing the produced DEM visualisations to eld mapping [47] we estimate that more
than 90% of sedimentary bodies and features can be successfully identied.
There is no such outcome in results.
The remaining 10% represent areas challenging to identify, which need to be eld mapped or
investigated using other remote sensing techniques.
More results data and discussion on this aspect will improve the paper.
6. Suggested improvements
The title is a bit exaggerated, as the term "best practice" promises much more than can be
found in the article. We only learn that three of the eleven visualization methods are better
for visualizing the studied objects.
I would expect additional table with more details compared to Table 3. The authors could
assign points (0-10) for each pair (visualization method vs. landform feature). At the moment
the outcome from the paper is that from eleven methods only three are useful, and the rest
is not suitable.
Detailed comments
 <Line 173> Table 1
Missing units of some parameters. The same problem is with Figure 3.
Lack of consistency (Number, No, No.)
What is (also 8-bit version)? There is no information about unknown-bit versions.
 <493-494>
For research articles with several authors, a short paragraph specifying their individual con-
tributions must be provided. The following statements should be used
Text to remove.
2

Author Response

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This manuscript is a resubmission of an earlier submission. The following is a list of the peer review reports and author responses from that submission.

Round 1

Reviewer 1 Report

This is an interesting but mostly practical study, with a great potential to be used as a tool in other process-focused studies. The Introduction could be improved with some topic sentences that introduce the problem in a more general way (what if those sedimentary units are o readily recognized?; how a better detection allow to understand subtle processes?, etc.). After that the article could go into the technical content, and the limitations exposed by traditional analysis based on standard visualization of DEMs.

No specific comments; the text is straightforward  and easy to read.

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Reviewer 2 Report

The authors submitted the manuscript to the journal in which they describe: Best practice for visualising alpine Quaternary landform features on high-resolution digital elevation models

The entire article is about 23 pages of content, but nothing innovative is written in it.

The discussion was not conducted in accordance with the guidelines of journal.

There is no in-depth statistical analysis. The manuscript does not have any advanced statistical analysis and correlation e.g. with the use of ANOVA with the Duncan test. It will certainly enrich the manuscript.

An expansion of literature, conclusions and discussions is required.

In this form, I advise against publishing the article in this journal.

English needs a lot of improvement.

 

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Reviewer 3 Report

Interesting idea for qualitative testing of detection methods for landform features using visualisation techniques

while the idea is interesting the reviewer wonders why a) not other landform delineation techniques have been tested (needs to be addressed in the article) , the paper would be probably be more suitable for geomorphology or similar journal as the dependency on RS is rather limited and c) a statistical sampling approach with n number of samples across the survey areas should be performed -in that respect the paper falls short of current practices by simply describing it qualitatively (the reviewer expected to see a statement of 5 landform x out of 10 could be identified using vis technique 3).

Detailed comments

L42 + L90 several typos throughout the document: 1.  ..'the The large-scale alpine' and in 1.1  ...'exception for' - please revise document by native language editor

L152 in 2.2. Method description is not sufficient, for ESRI part which software version, which algorithm (reference), m2 (units need to be superscript), uncertainty of dem needs to be reported.Why was the cell size of 0.5m was selected, what happended if 5m was selected ? other studies have shown that detailed DEMs do not always deliver better results. Please address and rectify.

Table 1 where are the references to the algorithms deployed or formulas ? slope gradient is not really an view parameters. this is a derivative of elevation. as there is no reference/formulas for algorithms is difficult to assess which parameters might also not be view parameters (what is local dominance)  might want to see hengl & reuter geomorphometry book for inspiration. needs to be addressed.

L172 in 2.2. 'following the criteria of (27)' - please mention these criteria in the paper and cite 27

l354 the reviewer expects in this section not only a qualitative description of the testing of the various visualisation techniques, but also a table showing which forms with n greater 5-10 for which vis did work or not. 

L395 the reviewer wonders why no averaging of cell sizes have been tested. not everyone has access to these kind of high density flights.

 

 

 

 

 

Author Response

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Reviewer 4 Report

All comments in pdf-file

Comments for author File: Comments.pdf

Author Response

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Round 2

Reviewer 2 Report

The manuscript has a text, but nothing concrete has been specified in it. 

There are no appropriate conclusions

There is no in-depth statistical analysis. The article contains statistical data, but they are at the primary level. The statistical analysis of the factors was not supported by any posthoc test (eg Duncan), which would allow any more advanced conclusions to be drawn. The manuscript has very mediocre results.

In this form, I advise against publishing the article in this journal.

English needs a lot of improvement.

Author Response

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