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Peer-Review Record

Spatiotemporal Changes in Vegetation Cover during the Growing Season and Its Implications for Chinese Grain for Green Program in the Luo River Basin

Forests 2024, 15(9), 1649; https://doi.org/10.3390/f15091649
by Xuning Qiao 1,2, Jing Zhang 1,*, Liang Liu 1, Jinyuan Zhang 1 and Tongqian Zhao 3,*
Reviewer 1: Anonymous
Reviewer 2: Anonymous
Forests 2024, 15(9), 1649; https://doi.org/10.3390/f15091649
Submission received: 21 August 2024 / Revised: 9 September 2024 / Accepted: 14 September 2024 / Published: 19 September 2024
(This article belongs to the Special Issue Application of Remote Sensing in Vegetation Dynamic and Ecology)

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The manuscript “Spatiotemporal Changes of Vegetation Cover during the Growing Season and its implications for Chinese Grain for Green 3 Programme in the Luo River Basin” presents the results quantifying the spatiotemporal variations of land cover and its driving factors. In general, paper provided an interesting analysis on the variation of land cover in the Luo River Basin using different statistical analysis method. However, the following points should be addressed before considering for publication.

-         The methodology was not well presented. The diagram to implement the spatiotemporal analysis should be added and each step in the diagram with associated methods used should be clearly described.

-          The novelty of combining the OPGD with constraint line analysis to access the nonlinear constraints between kNDVI and its driving factors compared to other previous methods were not described in the methodology and the results. How it helped to improve the analysis the contribution of each driving factors and capture the nonlinear relationship between these factors and kNDVI?

-          Please explain why the Hurst index can predict the future change of land cover? Why the Hurst index and Theil-Sen and Mann-Kendall trend analysis does not present a similar trend while both of these methods tested the temporal trend?

-          Reconsider if equations are correct. Many terms in the equations and in figures (UF, UB, q-values…) were not explained. Especially, explain in more details the Hurst index method.

-          Improve the captions of all figures with more information (Figures 2, 6…).

-          Carefully check if all descriptions closely follow the figures. For example, texts in lines 294-297 are not suitable with Figure 2, etc.

Comments on the Quality of English Language

Quality of English Language could be improved, expecially in the methodology and result sections.

Author Response

We sincerely thank you once again for reviewing our manuscript and providing invaluable suggestions, which have been essential for its improvement. We have made the necessary revisions based on your feedback. Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

The paper “Spatiotemporal Changes of Vegetation Cover during the Growing Season and its implications for Chinese Grain for Green Programme in the Luo River Basin” is of interest to the readers but needs few improvements for further visibility

(a) Abstract is too longs need to be shortened quantitative

(b) The study needs to be connected to Sustainable Development Goals of UN

(c) What is the novelty behind the work needs to be clearly mentioned in the abstract as well as the introduction section

 (d) Why modeled surface temperature and soil moisture is being used instead of derived products

(e)A Methodology diagram needs to be introduced so that the readers can easily understand the flow of analysis

(f) What method was used to explain the explanatory  power in figure 6

(g) Round off the numbers in the co-relation hot spot diagram to two digits

(b) Figure 7 needs readability improvement

 (d) Define the range in Pattern of Cv spatial distribution

 

(c) In figure kNDVI remove the hill shade map from behind

 (h) Team is suggested to go through below papers for further improvements

Monitoring vegetation degradation using remote sensing and machine learning over India–a multi-sensor, multi-temporal and multi-scale approach." Frontiers in Forests and Global Change 7 (2024): 1382557.

Spatio-temporal changes in vegetation activity and its driving factors during the growing season in China from 1982 to 2011. Remote Sensing, 7(10), 13729-13752.

"Spatiotemporal vegetation cover variations associated with climate change and ecological restoration in the Loess Plateau." Agricultural and Forest Meteorology 209 (2015): 87-99.

 

Dynamic trend of land degradation/restoration along Indira Gandhi Canal command area in Jaisalmer District, Rajasthan, India: a case study. Environmental Earth Sciences, 78(15), 472.

Comments on the Quality of English Language

English is presentable.

Author Response

We sincerely thank you once again for reviewing our manuscript and providing invaluable suggestions, which have been essential for its improvement. We have made the necessary revisions based on your feedback. Please see the attachment.

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

All my comments have been addressed. The paper can be published as it is.

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