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Article
Peer-Review Record

Long-Term Dynamics of Sandy Vegetation and Land in North China

Remote Sens. 2023, 15(19), 4803; https://doi.org/10.3390/rs15194803
by Zhaosheng Wang
Reviewer 1:
Reviewer 2: Anonymous
Reviewer 4: Anonymous
Remote Sens. 2023, 15(19), 4803; https://doi.org/10.3390/rs15194803
Submission received: 2 September 2023 / Revised: 26 September 2023 / Accepted: 27 September 2023 / Published: 2 October 2023
(This article belongs to the Special Issue Machine Learning in Global Change Ecology: Methods and Applications)

Round 1

Reviewer 1 Report

Comments:

The authors concentrate on long-term dynamics of sandy vegetation and land changes in North China from 1982-2018. I find this topic highly interesting. For example, the authors introduce a novel method for large-scale assessment of the distribution of various sandy land types using FVC data, which is based on the concept of sand-land type succession. This method considers the interconnectedness of sand types that undergoing a succession process, rather than abrupt interruptions or transitions. It will enhance the accuracy of estimating large-scale changes in sandy land areas, including mobile, semi-mobile, semi-fixed, and fixed sandy lands. Additionally, the novel sandy classification and monitoring methods proposed in this study will contribute to enhancing remote sensing monitoring of large-scale sand dynamics and provide fresh insights into monitoring desertification at a large scale using remote sensing techniques. I truly appreciate their good works. However, the study in some aspects that need further attention and there are still some minor issues. Hence, I recommend this paper be considered for publication after minor reversions.

 

Minor issues:

(1) Line 14, the abbreviations (NDVI and FVC) are not initially spelled out in full.

(2) In Section 3.31., which sandy area is used to verify the total sandy land area investigated by CD FVC?

(3) In Figure 6, what does “R-Desertification” represent?

(4) Do the annual trends of the five sets of NDVI exhibit consistency during the same time period?

The author should improve their English to enhance the authenticity and logical clarity of their expression.

Author Response

Please see the attached file.

Author Response File: Author Response.docx

Reviewer 2 Report

I congratulate the author for the research carried out, the information provided is of interest and up-to-date and also very useful. Good luck in your future studies.

Author Response

Many thanks to the professional reviewer for their highly positive evaluation and support of this study.

Reviewer 3 Report

The present study is focused on the “Long-term dynamics of sandy vegetation and land in North China”.

In summary, from the findings of this research, it appears that desertification in sandy areas of northern China has been mitigated. The sand classification and monitoring methods proposed in this study for tracking sand location changes will contribute to the enhancement of remote sensing monitoring of large-scale sand dynamics and provide novel insights into remote sensing monitoring of desertification on a large scale.

I therefore suggest its publication precisely in view of the need to proceed with concrete actions concerning a basis for ecological protection.

This publication establishes a foundation for further research.

Author Response

Many thanks to the professional reviewer for their highly positive evaluation and support of this study.

Reviewer 4 Report

This study examined the spatial and temporal variations in vegetation over sandy land using NDVI and FVC in North China. Although the overall analysis and methods presented in this manuscript are reasonable, I believe this manuscript lacks novelty. Many studies with very similar research focus and methods have already been conducted in this region. Therefore, I recommend rejecting this manuscript.

None

Author Response

Thank you very much for the hard work of the review expert. Your comments will greatly help improve the author's future research level.

However, I cannot agree with your current comments. Firstly, the use of long-term continuous 250m high-resolution NDVI and FVC data for 37 years (1982-2018) to investigate large-scale sandy land changes has filled the current gap in understanding short-term remote sensing monitoring sandy land changes. Due to the limited working life of remote sensing satellites, a set of 37 consecutive years of remote sensing Earth observation data has not been formed, so most of the existing studies focused on 1982-2015 or 2000-2018, which belong to short-term research on sandy land change. Second, a new classification monitoring method based on the idea of ecosystem evolution was proposed. Thirdly, the large-scale quantitative monitoring of the changes of sandy land distribution positions from long-term remote sensing monitoring data provides solid new evidence for an in-depth and objective understanding of sandy land change characteristics, which is a piece of important information that short-term monitoring methods cannot provide and has important value for large-scale desertification monitoring. It is worth noting that two other professional reviewers also gave positive comments on this study. Of course, at first glance, the title of this study does not seem to be novel. From the point of view of the research content, the current topic is the most suitable topic to summarize the content of this research. In conclusion, this study is not an outdated study, but an important study that can promote the current research on desert ecosystems.

Round 2

Reviewer 4 Report

Accepted

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