Spatial Dynamics and Predictive Analysis of Vegetation Cover in the Ouémé River Delta in Benin (West Africa)

Round 1

Reviewer 1 Report

The MS investigates the changes in vegetation in Ouémé Delta with multiple ecosystem types. While the global-scale vegetation dynamics have been well documented by the current literature, a study that focuses the Africa appear to be indeed novel. For this reason, I think the MS deserves publication. I suggest Minor Revisions for this MS.

 

Comments:

 

1)      There have been a lot of publications in Nature or Science journals that have investigated the vegetation changes over the globe, which need to be discussed, e.g., The very famous paper by Zhu et al. 2016. Greening of the Earth and its drivers. Nature Climate Change. DOI: 10.1038/NCLIMATE3004. The authors are suggested to put their region-specific results in a broader picture, improving the importance of their own study.

2)      Line 59-64: Quantifying how vegetation change is indeed very critical to Earth system. Please add more importance about it. For example, plant plays a key role in global water (https://doi.org/10.1029/2021WR029691) and carbon (https://doi.org/10.1038/s41467-019-12257-8) cycle, which could also affect the Earth’s climate (https://www.science.org/doi/10.1126/science.aax0848). These materials could be incorporated into this paragraph to improve the research motivation of yours.

Author Response

Thanks for this valuable review.

Author Response File: Author Response.docx

Reviewer 2 Report

I was honored to be part of the review process for this manuscript. My comments may be a bit harsh, but this is how I really feel inside after reading it. This work is pointless and insufficient to be published in RS. It is not recommended that the authors spend additional time and effort to improve this work.

1.It is absurd for the authors to use images of specific time points from only three periods to reveal the characteristics of vegetation cover changes in the study area over the past 30 years.

2.The model used in this study for the simulation of LUCC is too traditional and uninnovative. It is recommended to learn more about related cutting-edge technologies, such as the PLUS model.

3.What is the model chosen for supervised classification? What is the sample distribution?

4.The driving analysis is too subjective and should be done with the help of scientific modeling methods, either linear or non-linear models.

5.From Figure 6, the simulation results of this paper are poor.

Author Response

Thanks for the valuable review. It helps improve the quality of the manuscript. 

Author Response File: Author Response.docx

Reviewer 3 Report

Comments to authors：

The manuscript classifies the land cover status of the Ouémé Delta region for the period 1990-2020 using the Landsat satellite. This study is in line with the scope of remote sensing. However, my main concern is the limited innovation of this manuscript. As a journal of international importance in remote sensing, work that can be published should either be an improvement in remote sensing methodology; or apply remote sensing techniques to identify important scientific questions to appeal to a wide audience. The manuscript currently includes the following issues:

Firstly, current remote sensing land use mapping tends to be large scale, high spatial and temporal resolution and high accuracy. The study area is only 9000km², I believe that the advantages of high-resolution satellites should be fully exploited for small areas.

Secondly, the authors have used the medium to high resolution Landsat instead of the higher resolution Sentinel satellite. However, the authors did not take full advantage of Landsat. Using Landsat data, the authors could have mapped earlier as well as more time series intensive land cover data. Unfortunately I only saw maps for the three periods 1990-2005-2020 and they were not consistent in time intervals.

Third, the classification tools are not advanced and the classification strategy is missing more information. For small area mapping, the authors' classification accuracy is not very satisfactory. The use of machine learning and deep learning algorithms could have yielded more accurate results. In addition, surprisingly, the authors do not give the necessary information on the fieldwork, modelling process and validation methods.

Fourth, the predicted future reliability of LULC is unknown. I do not consider the modelled 2020 land cover results in Table 6 to be reliable. The 2020 land cover results obtained using supervised classification do not represent the true values. There are significant uncertainties and biases in the comparison between the two. I still recommend using survey information to build confusion matrices.

At last, the drivers lack quantitative analysis. The limited methodological innovation of the manuscript is ignored, I would have liked to see the authors develop a qualitative and quantitative analysis of the state of vegetation cover in the area and the drivers. It is clear that the authors have much room for improvement.

*The end*

Author Response

Thanks for this valuable review. It helped a lot.

Author Response File: Author Response.docx

Reviewer 4 Report

see attached file

Comments for author File: Comments.pdf

Author Response

Thanks for your valuable review.

Author Response File: Author Response.docx

Round 2

Reviewer 2 Report

The author has solved most of my problems, but when citing relevant literature to answer my questions, the author can consider adding the following research papers to support

1.Xu D, Yang F, Yu L, et al. Quantization of the coupling mechanism between eco-environmental quality and urbanization from multisource remote sensing data[J]. Journal of Cleaner Production, 2021, 321: 128948.

 

 

Author Response

Thanks for spending your valuable time on our manuscript 

 

Author Response File: Author Response.docx

Reviewer 3 Report

Thanks to the authors for their responses. Despite some changes made by them, I am concerned about the lack of innovation in the manuscript (also mentioned by reviewer 4). In addition, I have read reviewer 2's comments. I also do not see much value in revising the manuscript for publication in Remote Sensing. Therefore, I suggest authors submit their manuscript to another journal instead.

Author Response

Thanks for all.

Author Response File: Author Response.docx