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Peer-Review Record

Applying Remote Sensing Methods to Estimate Alterations in Land Cover Change and Degradation in the Desert Regions of the Southeast Iberian Peninsula

Remote Sens. 2023, 15(16), 3984; https://doi.org/10.3390/rs15163984
by Emilio Ramírez-Juidias 1,*, Antonio Madueño-Luna 2, José Miguel Madueño-Luna 3, Miguel Calixto López-Gordillo 3 and Jorge Luis Leiva-Piedra 4
Reviewer 1:
Reviewer 2:
Reviewer 3:
Remote Sens. 2023, 15(16), 3984; https://doi.org/10.3390/rs15163984
Submission received: 3 July 2023 / Revised: 31 July 2023 / Accepted: 9 August 2023 / Published: 11 August 2023

Round 1

Reviewer 1 Report

This manuscript estimated land cover change and degradation in desert regions. In general, the findings are interesting, but some main concerns in methods and results should be solved before publication.

Some terms are confusing, for example, in the abstract, what are bright-degraded areas and what are darker surrounding areas?

The authors indicated this work is novel, since it has not been carried out before in the drylands of the southeast Iberian Peninsula. So, what similar work has been done in other regions? What is the special of drylands of the southeast Iberian Peninsula? What is the technically difference between previous studies and this work? All these should be clarified in the Introduction part in order to highlight the importance and novelty of this work.

Figure 1, what image product is used? What do the color represent for? A legend of this image should be added.

L151-157, the sentences are unclear.

It is unusual to define only one type of variogram model. The right way is to fit the sample plots using different models (exponential, spherical, Gaussian, etc.) or nested models and choose the one with the best effect.

Why choosing 2000, 2015 and 2020? There is a large span between 2000 and 2015.

Figure 3 should be improved using a colored map, the value of the legend should be marked. It seemed the BI of Sierra Alhamilla in 2000 has some processing problems.

I could not understand the change detection part. What is the method used for change detection, and where is the change detection map? BI differencing cannot detect changes.

What do those dots around boundaries mean in Figure 5?

BI differencing should not be like this in Figure 6. This image is a typical false-colored composited remote sensing image.

Why reversing the time range such as 2020-2016, and 2016-2015?

Author Response

Dear Reviewer.

I attach you the authors’ response to the Review Comments.

Thanks and best regards.

Corresponding author.

Author Response File: Author Response.docx

Reviewer 2 Report

The paper analyzed degradation by using semi-variograms. The authors found out the differences of variogram sill and variogram range among three time periods. The topic and method is new and interesting. There are two key steps in the paper. One is the semi-variograms and the other is the other is the interpolation. The data collection includes hundreds of images whose conclusion can be believable. Based on the following reasons, I choose “major revision” for the journal.

1.     There are types of semi-variograms like exponential models, spherical model and Gaussian model. But maybe the author can explain why they choose the exponential model for this study areas. Even though other researchers [1] used the same model, it might not fully persuade readers that the exponential was the best choice here. I hope the authors can compare two or three models using the relative nugget effects, C0/ (C0+C1) or other relevant indices to choose a best-fit model for this study. [2].

2.     This is also for the ordinary kriging interpolation. We also have the other options for the interpolation like the universal kriging or others (https://pro.arcgis.com/en/pro-app/3.0/tool-reference/3d-analyst/how-kriging-works.htm). Please use some ways to judge which approach is better.  

[1]Lu, C., Song, Z., Wang, W., Zhang, Y., Si, H., Liu, B., & Shu, L. (2021). Spatiotemporal variation and long-range correlation of groundwater depth in the Northeast China Plain and North China Plain from 2000 2019. Journal of Hydrology: Regional Studies, 37, 100888.

[2]Nielsen, A. A. (2009). Geostatistics and analysis of spatial data. Informatics and Mathematical Modelling, Technical University of Denmark, DTU, 7-12.

Author Response

Dear Reviewer.

I attach you the authors’ response to the Review Comments.

Thanks and best regards.

Corresponding author.

Author Response File: Author Response.docx

Reviewer 3 Report

It is a very interesting topic on the discovery of the changes of the land use and land degradation in desert regions.

The major questions need to concern as follows:
1 From the results and discussion, it concluded that the changes among three stages were caused by the 2007 economic crisis and the climate issue, respectively. I guess that drawing this conclusion might be weak if there were no more census of the livestock on grazing or farm activities to support. Not sure the authors can find more evidences.
2 About the grouping of years in three research periods, why three stages were defined as 2005-2010, 2014-2016, and 2019-2021 in your experimental design? Checking the fig.8, the aridity index has become very poor since 2014, but your BI map around 2015 (fig.5) show greening are much better than other two stages, meaning the recovery occurred within this period. In this case we have to guess grazing not developed. Perhaps, the positive impact of 2007 crisis are still there even after the 7-8 years it occurred. This guessing can be secured?
3 the three curves related to spatial dependence or spatial lag in the Fig.4 are very interesting.  Could you please explain more about it to illustrate some new things behind the curve because the pattern of three stages are so different.


In addition, some minor issues as follows:

4 The overview map in the up-left corner of the Fig.1 might not be a related quick-look to this research?
5 There are many circles along the boundary of two research areas. Perhaps somethings went wrong when making these maps.
6 A typo about high aridity index is in the line 366-367 as the low aridity index appears twice (with the values great than 1 and less than 1). Conflicted.

 

Author Response

Dear Reviewer.

I attach you the authors’ response to the Review Comments.

Thanks and best regards.

Corresponding author.

Author Response File: Author Response.docx

Round 2

Reviewer 1 Report

The revision addressed most concerns. Some minor issues can be further considered.

1.       The color illustration of Figure 7 should be illustrated in the figure caption.

2.       Does 2020-2016 means 2020 abstracts 2016? It should not use the connect symbol as it also means from 2020 to 2016, which would be strange.

3.       The source of land use map should be clarified.

Author Response

Dear reviewer.

I attach you the authors’ response to the review comments.

Thanks and best regards.

Author Response File: Author Response.docx

Reviewer 2 Report

The authors compared a couple of semi-variograms model which made their research more believable. Their improvement meets my requirements.

Author Response

Dear reviewer.

I attach you the authors’ response to the review comments.

Thanks and best regards.

Author Response File: Author Response.docx

Reviewer 3 Report

1 Regarding the BI maps (Figure 4),  the brightness of the Sierra Alhamilla on the mid-late 2000s is significantly different (or opposite) from the other two maps of the same region. I guess the brightness at this map was obviously influenced by topography and the other two were not. Can you clearly illustrate on the map the information related watering points?  as it is your key concept used to represent the grazing and land degradation in your review.  You might use a window to enlarge a specific area to show an example of the watering point.

2 About how to relate your findings to two control factors - the socio-economic background and climate changes, I think your explaination in your response is still weak to draw your conclusion in line 430-442.
The issues are that your BI maps was done with three user-defined stages while your aridity index was calculated in a continous way from 2005-2021. Looking Line 436-437, you mentioned the aridity index are lower than 1 in the second study peroid (2020-2016), but the index has been lower than 1 since 2014 if you check Fig. 8.  In this case, can we conclude that the climate has started to affect land change since 2014 (the first peroid 2016-2005)?  There are overlapped time between two control factors, I guess.

Author Response

Dear reviewer.

I attach you the authors’ response to the review comments.

Thanks and best regards.

Author Response File: Author Response.docx

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