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Deep Learning Segmentation of Satellite Imagery Identifies Aquatic Vegetation Associated with Snail Intermediate Hosts of Schistosomiasis in Senegal, Africa
 
 
Article
Peer-Review Record

Satellite Imagery-Based Identification of High-Risk Areas of Schistosome Intermediate Snail Hosts Spread after Flood

Remote Sens. 2022, 14(15), 3707; https://doi.org/10.3390/rs14153707
by Juan Qiu 1, Dongfeng Han 1,2, Rendong Li 1, Ying Xiao 3, Hong Zhu 3, Jing Xia 3, Jie Jiang 1, Yifei Han 1,2, Qihui Shao 1,2, Yi Yan 4 and Xiaodong Li 1,*
Reviewer 1:
Reviewer 2: Anonymous
Reviewer 3:
Reviewer 4:
Remote Sens. 2022, 14(15), 3707; https://doi.org/10.3390/rs14153707
Submission received: 27 May 2022 / Revised: 18 July 2022 / Accepted: 1 August 2022 / Published: 2 August 2022
(This article belongs to the Special Issue Remote Sensing and Infectious Diseases)

Round 1

Reviewer 1 Report

Satellite Imagery-based Identification of High-risk Areas of

Schistosome Intermediate Snail Hosts Spread after Flood

-In my opinion this paper is lack of contribution and novelty. The author should clearly state their main contribution. 

-In academic work, comparing the obtained results to some related/recently published works under the same conditions (i.e., databases + protocols of evaluation) is necessary. The objective is to show the superiority of the presented work against the existing ones. 

-What is the different of your research with the previous research result? 

-Explain more about your proposed method. 

-The author should explain Figure 2 in detailed.

 

Author Response

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Author Response File: Author Response.docx

Reviewer 2 Report

Original Submission

Recommendation

Minor revision

Comments to Author:

 

Title:  

 

 

Satellite Imagery-based Identification of High-risk Areas of Schistosome Intermediate Snail Hosts Spread after Flood

 

 

Overview and general recommendation.

 

This paper is demonstrating the usage of SAR methodology and some important applications. This technique is very important and somehow accurate and has lots of important applications. First of all, as a person with more than 20 years of familiarity with SAR/InSAR/RADAR data, I like this paper very much; but as a scientist, I have to say the truth about the material and to be honest.

The Abstract is OK: summarizing the idea and concepts inside the paper is OK, but I feel you need to write it again; English is a bit problem!! I think it is better/must to give it to a native person to review (it is a big must). I feel also like the abstract is raw: pls think more about what you have done in the paper and do the abstract again (it is a must).

The introduction is very good. But, I think in some positions, some important corrections must be done; but, overall merit is good.

Pls improve the quality of Figs> they are very bad; unacceptable.

Results are needed to be reconsidered seriously, but overall merit is OK.

The conclusion is very bad to write it again pls.

I like this paper very much: good experiments have been done; however, I think this work must be improved and lots of things to do; agreed? I think the paper is very raw and must be improved tremendously, but overall merit is acceptable.

 

Detailed comments:

 

Fig.7 I cannot see anything; pls è Improve the qualities of all Figs!

 

 

 

 

 

 

 

 

 

 

 

 

 

Author Response

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Author Response File: Author Response.docx

Reviewer 3 Report

My comments:

1. please check if all acronyms are explained in text if you used their first time.

2. equation (1), please explain the symbols.

3. equation (2), see comment 2.

4. conclusions, please add information about obtained results from research test and your findings from paper.

 

Author Response

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Author Response File: Author Response.docx

Reviewer 4 Report

In this study, multi-source remote sensing data are used to solve the actual problems in infectious diseases, namely, the prediction and early warning of the spread of the intermediate snail host of schistosomiasis caused by floods. The authors have summarized three novelties in this paper (Lines 67-88). My comments about the novelties are:

(1)    Novelty-1: From the data perspective, this paper used SAR images, which can reduce the impact of clouds and shadows, in mapping inundation areas for snail spread. It is better than using the optical image in mapping inundation areas, and a more accurate inundation map can be generated.

(2)    Novelty-2: From the data&method perspective, using filed survey snail distribution data is very important. Previous studies usually assume all inundation areas have snail spread risk (i.e., ‘all the flooded areas carry snails’), while this paper refines the snail spread area using the filed survey data and multiple environmental factors. I think this is the main contribution of this paper, because a different ‘assumption’ is proposed and used (i.e., ‘only the refined regions are snail spread risky areas’). This paragraph (Lines 74-78) is introduced very shortly in the current paper, which should be augmented with more details.

(3)    Novelty-3: From the data&method perspective, the authors highlight using multiple environmental factors, including NDVI, DEM, land use, and wetness, is essential to identifying snail survival and reproduction. previous studies usually use a single variable, such as NDVI, as the environmental factor which may be insufficient to fully represent the snail’s spread. This paper used multiple factors for this analysis, and used logistic regression to better filter which factors are more important in this study area (Table 1). I think it has a novelty to the relevant methodology studies.

In summary, I think Novelties 2-3 can be combined and highlighted with more detailed introductions and comparisons, especially in the introduction and conclusion sections. Novelty-1 is good, but it is a remote sensing issue and can be just introduced briefly. 

Major issues:

1. line 85-86, you mentioned digital elevation model (DEM) is related to snail habitats. It’s necessary to explain why DEM is not considered and used in your study region.

2. line 114, snail field surveys are in 2016. How to match the acquisition time of remote sensing data with other environmental factors in 2020?
3. The Discussion about the results and influencing factors in the study site can be strengthened.

Minor issues:

1. lines 167-168 seem to duplicate lines 170-173.

2. line 193, “…as the snail spread area., With with the” should be “…as the snail spread area. With the”

3. line 274, Figure 7, not Figure 4.

4. line 291, please explain the width of which factor is 5 meters.

5. In the abstract, it’s necessary to directly point out the novelty or theoretical contribution of this paper.

6. Fig. 2 introduced many details. The novelty part, i.e., the ‘Identify’ and ‘Snail spread areas’ can be highlighted. I strongly suggest the authors use example maps (data flow) including inputs, outputs, and a series of maps from each step, to show the flowchart and how the data are processed.

7. Figs 4-5 can be combined, and the result of bi-temporal water maps should be added. For the comparison purpose, is it possible to add a snail’s spread risk map merely using the inundation map and compare it with Fig. 8? I think this is essential to highlight the paper’s novelty.

8. The languages can be polished by native speakers.

 

 

Author Response

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Author Response File: Author Response.docx

Round 2

Reviewer 1 Report

1. Please add your future research studies in the conclusion.

2. Discuss more about your limitations and challenges while doing this experiment.

3. Check all your English grammar mistakes before next submission.

4. Revise all images with good resolution.

Author Response

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Author Response File: Author Response.docx

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