Mapping Shrimp Pond Dynamics: A Spatiotemporal Study Using Remote Sensing Data and Machine Learning
, Murali Krishna Gumma 2,*, Pranay Panjala 1, Ismail Mohammed 1 and Aya Suzuki 3
Round 1
Reviewer 1 Report
What is the software you used to run Supervised Classification approach by using Maximum Likelihood Classification.
Please insert how do you compute NDWI, MDWI, AWEInsh, AWEIsh, and WRI indices.
Author Response
Manuscript Details
Manuscript ID: Agriengineering-2492076
Title: Assessing Spatiotemporal differences in Shrimp ponds us-ing Remote sensing data and Machine learning algorithms
Reviewer: 1
Comments and Suggestions for Author
- What is the software you used to run the Supervised Classification approach by using Maximum Likelihood Classification?
Our response: ERDAS was Software used for Supervised Classification. Thanks for your encouraging feedback and recommendations. We have addressed each of your issues either within the text, or in the responses below.
- Please insert how do you compute NDWI, MDWI, AWEInsh, AWEIsh, and WRI indices.
Our response: Thanks for your recommendation. Edited Methods and approaches with computation equations of indices
Reviewer 2 Report
In 2.1. Study Area, improve the location map, it must include an image with more coverage that shows the study area, country, continent.
In 3. Methods and approaches, the use of Machine Learning is not required, more bibliographical references are required to justify this adopted methodology.
You must make a reference to the climate in this region.
It is necessary to establish the relationship of climatic variability with the variation of water bodies.
The reason for the variation in shrimp production is not clearly discussed. What factors influence this?
The discussion of results should be enriched, results could be discussed in other image resolutions and their impact in relation to what was done in this study. As it is ensured that natural calamities such as floods, sources of water contamination by chemicals used cause losses in shrimp farming.
The answer to the research question is not specified in the conclusions.
Must include author contributions
References do not comply with MDPI style
Author Response
Manuscript Details
Manuscript ID: Agriengineering-2492076
Title: Assessing Spatiotemporal differences in Shrimp ponds us-ing Remote sensing data and Machine learning algorithms
Reviewer: 2
Comments and Suggestions for Author
- In 2.1. Study Area, improve the location map, it must include an image with more coverage that shows the study area, country, and continent.
Our response: Thanks for your recommendations. We have updated the study area location map accordingly.
- In 3. Methods and approaches, the use of Machine Learning is not required, more bibliographical references are required to justify this adopted methodology.
Our response: Thanks for noticing, we have modified Methods and approaches slightly as per the comment. But previously it was used because k-Means clustering is also a machine learning algorithm.
- You must make a reference to the climate in this region.
Our response: Thanks for the recommendation. We have identified the necessity of the climate of the region and incorporated in study area. Also included references of suitable research papers.
- It is necessary to establish the relationship of climatic variability with the variation of water bodies.
Our response: Thanks for noticing. We have placed its relation according with support of research articles
- The reason for the variation in shrimp production is not clearly discussed. What factors influence this?
Our response: Thanks for the noticing. We have added in manuscript as Extreme weather events, Natural processes impacting LULC changes. Government initiatives to promote ecological restoration references to influencing factors.
- The discussion of results should be enriched, results could be discussed in other image resolutions and their impact in relation to what was done in this study. As it is ensured that natural calamities such as floods, sources of water contamination by chemicals used cause losses in shrimp farming.
Our response: Thanks for your recommendations. We have updated the discussions part with advantages of different image resolutions and periodic data in natural calamities impact on shrimp farming and contamination related to shrimp farming.
- The answer to the research question is not specified in the conclusions.
Our response: Thanks for noticing the error. We have updated conclusions.
- Must include author contributions
Our response: Thanks for your recommendations. We have updated in manuscript.
- References do not comply with MDPI style
Our response: Thanks for your suggestion. We have modified references accordingly.
Reviewer 3 Report
1) Please, add in “2.1. Study Area” the more detailed information about study area, that include climatic context of investigated time period and hydrology.
2) Figure 1 should be improved. Please,
a) include 2 images (minimum). One of these should show the study area location on the continent. The second one should provide a detail image of the study area with neighbor district and borderline of Cai Doi Vam Subdistrict;
b) use only English letters and words in the legends;
c) if you use a false-color image, write in the figure's caption used bands, satellite mission name, and acquisition time of the image.
3) Please, add the description how you compute all used indices (NDWI, MDWI, AWEInsh, AWEIsh, WRI). Or add references to literature with description of used methods with formulas.
4) Figure 7 should be improved. Please,
a) rename LULC “01_Shrimp_Ponds” to “Shrimp Ponds”;
b) change colors, since “Vegetation” and “Other LULC” are indistinguishable in the image.
5) Please, rename LULC in the Table 2 as the Table 3 (“01_Shrimp_Ponds” to “Shrimp Ponds”).
6) Please, presume some reasons of LULC changes from 2019 to 2022. Also, describe what events or processes have influenced on ponds' area decreasing and vegetation's area increasing.
Author Response
Manuscript Details
Manuscript ID: Agriengineering-2492076
Title: Assessing Spatiotemporal differences in Shrimp ponds us-ing Remote sensing data and Machine learning algorithms
Reviewer: 3
Comments and Suggestions for Authors
1) Please, add in “2.1. Study Area” the more detailed information about study area, that include climatic context of investigated time period and hydrology.
Our response: Thanks for the recommendation. We have identified the necessity of the climate of the region and incorporated in study area.
2) Figure 1 should be improved. Please,
a) include 2 images (minimum). One of these should show the study area location on the continent. The second one should provide a detail image of the study area with neighbor district and borderline of Cai Doi Vam Subdistrict;
b) use only English letters and words in the legends;
c) if you use a false-color image, write in the figure's caption used bands, satellite mission name, and acquisition time of the image.
Our response: Thanks for the precise recommendations. Updated figure 1 with two partly individual images and caption for second part also.
3) Please, add the description how you compute all used indices (NDWI, MDWI, AWEInsh, AWEIsh, WRI). Or add references to literature with description of used methods with formulas.
Our response: Thanks for your recommendation. Edited Methods and approaches with computation equations of indices
4) Figure 7 should be improved. Please,
a) rename LULC “01_Shrimp_Ponds” to “Shrimp Ponds”;
b) change colors, since “Vegetation” and “Other LULC” are indistinguishable in the image.
Our response: Thanks for the precise recommendations. Updated figure 7 with colour changes for classes and rename of LULC class.
5) Please, rename LULC in the Table 2 as the Table 3 (“01_Shrimp_Ponds” to “Shrimp Ponds”).
Our response: Thanks for the precise recommendations. Updated tables accordingly.
6) Please, presume some reasons of LULC changes from 2019 to 2022. Also, describe what events or processes have influenced on ponds' area decreasing and vegetation's area increasing.
Our response: Thanks for the recommendations. Presumed reasons based on various scientific articles was updated in manuscript.
Round 2
Reviewer 1 Report
accept
Reviewer 2 Report
The authors fully acknowledged the comments made. Thank you for implementing the recommendations.
Reviewer 3 Report
Many thanks to authors for their efforts in manuscript improvement. I am sure that this article will be interesting to the readers.
Good luck to you in future researchers.
Sincerely yours,
Reviewer
