Analysis of Eco-Environmental Quality and Driving Forces in Opencast Coal Mining Area Based on GWANN Model: A Case Study in Shengli Coalfield, China
Round 1
Reviewer 1 Report
Analysis on eco-environmental quality and driving forces of human activities in opencast coal mining area based on GWANN model: A case study in Shengli Coalfield, China is an interesting study point. However, this paper has some obvious flaws:
The temporal logic in this study is confusing. Did you study the RSEI in the years of 2005, 2010, 2015 and 2021, or in the period from 2005 to 2021 annually?
What driving forces of human activities did you use to analyze the eco-environmental quality in opencast coal mining area? Are they temperature, precipitation, topography, and mining activity data? If so, they do not match.
It seems arbitrary to set seven auxiliary lines in 3.4. Analysis of the contribution rate of the driving factors.
The language organizing and English writing are poor, which should be improved urgently.
Some figures are not set reasonably, e.g., Figure 3 (a) and (b) can be merged as one graph, and Figure 9 are too small to display the corresponding information.
Who proposed the Remote Sensing Ecological Index (RSEI)? The reference should be provided.
The language organizing and English writing are poor.
Author Response
Response to Reviewer 1 Comments
Major comments:
Point 1: The temporal logic in this study is confusing. Did you study the RSEI in the years of 2005, 2010, 2015 and 2021, or in the period from 2005 to 2021 annually?
Response 1: Thank you for your comments! We have completed the calculation of the RSEI in the period from 2005 to 2021 annually, which can be seen in Figure 5. Although, according to the development of ecological restoration work in the mining area, the results of RSEI inversion, and the reasonable distinction of years, we finally chose four years 2005, 2010, 2015, and 2021 to carry out the analysis of RSEI and the driving forces in the study area.
Point 2: What driving forces of human activities did you use to analyze the eco-environmental quality in opencast coal mining area? Are they temperature, precipitation, topography, and mining activity data? If so, they do not match.
Response 2: The main driving forces of human activity we use here is mining activity, because for the mining scale, the main human activity is mining. We are also actively thinking whether human-made ecological management such as vegetation planting can also be included in the driver analysis, but there is no better solution at present because vegetation restoration is difficult to quantify. Temperature, precipitation, topography as natural drivers are also our key driving forces. In the future, to better match the main idea of the article with the drivers of our study. In order to better match the main idea of the paper with the RSEI driving forces analysis, we have changed the title of the manuscript and the corresponding expression of the manuscript from “Analysis on eco-environmental quality and driving forces of human activities...” to “ Analysis on eco-environmental quality and driver analysis”.
Point 3: It seems arbitrary to set seven auxiliary lines in 3.4. Analysis of the contribution rate of the driving factors.
Response 3: The auxiliary lines are mainly set by combining the direction of significant changes in the contribution of each factor and the center of gravity of the area such as the mining district and dump site where human activities are most typical, and can cover all the major directions of the Shengli mining areas. We have supplemented the information about the selection of auxiliary lines, seeing the marked revision in lines 391–395.
Point 4: The language organizing and English writing are poor, which should be improved urgently.
Response 4: We have carefully checked and improved the English writing in the revised manuscript; see the marked revision.
Point 5: Some figures are not set reasonably, e.g., Figure 3 (a) and (b) can be merged as one graph, and Figure 9 are too small to display the corresponding information.
Response 5: We have merged Figure 3 (a) and (b) as one graph, and re-depicted all unclear images in the manuscript.
Point 6: Who proposed the Remote Sensing Ecological Index (RSEI)? The reference should be provided.
Response 6: We have supplemented the reference; see the marked revision in lines 49–50.
Comments on the Quality of English Language
Point 7: The language organizing and English writing are poor.
Response 7: We have carefully checked and improved the English writing in the revised manuscript; see the marked revision.
Author Response File: 
Author Response.pdf
Reviewer 2 Report
sustainability-2409060-review
Analysis on eco-environmental quality and driving forces of human activities in opencast coal mining area based on GWANN model: A case study in Shengli Coalfield, China
In this paper, the mining area in the southwest of Shengli coalfield, a typical ore concentration area in eastern Inner Mongolia, was selected as the research object, and the remote sensing ecological index (RSEI) was calculated by using Google Earth Engine (GEE) platform to analyze the eco-environmental quality in the mining area and its surrounding 2km from 2005 to 2021. This is an interesting and valuable topic. My opinions and suggestions are as follows:
1. The engineering and hydrogeological conditions of the study area should be briefly described.
2. The description of methods and models should be more detailed.
3. The clarity of all images should be significantly improved, Especially in Figure 9 and 10.
4. The language needs to be carefully checked, about references part. There are some grammar and formatting errors.
5. To sum up, this is an interesting and valuable topic. Can be accepted after minor revision.
The language needs to be carefully checked, about references part. There are some grammar and formatting errors.
Author Response
Response to Reviewer 2 Comments
Point 1: The engineering and hydrogeological conditions of the study area should be briefly described.
Response 1: Thank you for your comments! More detailed information about the engineering and hydrogeological conditions of the study area have been described in lines 107–118.
Point 2: The description of methods and models should be more detailed.
Response 2: We have modified and supplemented the description of methods and models, seeing the marked revision in section 2.3.
Point 3: The clarity of all images should be significantly improved, Especially in Figure 9 and 10.
Response 3: We have re-depicted all unclear images in the manuscript.
Point 4: The language needs to be carefully checked, about references part. There are some grammar and formatting errors.
Response 4: We have carefully checked and improved the English writing in the revised manuscript; see the marked revision.
Point 5: To sum up, this is an interesting and valuable topic. Can be accepted after minor revision.
Response 5: Thank you for your comments!
Comments on the Quality of English Language
Point 6: The language needs to be carefully checked, about references part. There are some grammar and formatting errors.
Response 6: We have carefully checked and improved the English writing in the revised manuscript; see the marked revision.
Author Response File: Author Response.pdf
Reviewer 3 Report
According to the authors this article discusses the ecological impacts of human activities in opencast coal mines, particularly in semi-arid steppe regions. The main focus is on the southwest area of the Shengli coalfield in eastern Inner Mongolia, which serves as a typical case study. The authors aim to analyze the ecological quality of the mining area and its surrounding region from 2005 to 2021 using remote sensing data and the Google Earth Engine platform. They also utilize the Geographically Weighted Artificial Neural Network model (GWANN) to understand the driving factors behind the changes in eco-environmental quality in the study area.
The article highlights the significance of studying the ecological impacts of coal mining activities, especially in semi-arid steppe regions. These regions are particularly vulnerable to environmental disturbances, and the development and utilization of large coal bases in such areas can have a direct and significant impact on the ecosystem. Therefore, conducting an in-depth analysis of the ecological impacts and understanding the driving forces behind these impacts is crucial for the protection and restoration of the fragile steppe ecosystem in the region.
The authors employed the remote sensing ecological index (RSEI) calculated using the Google Earth Engine platform to assess the eco-environmental quality of the mining area and its 2km surrounding area over a period of 16 years. This index provides a measure of the ecological conditions based on remote sensing data. By analyzing the changes in the eco-environmental quality, the authors found that the area proportion of excellent and good eco-environmental quality in the study area increased slightly from 20.96% to 23.93% during the study period. The area ratios of other grades fluctuated significantly.
The article also highlights the relationship between specific factors and the changes in eco-environmental quality. The reclamation of the dump site and the migration of the mining area were found to be closely related to the changes in eco-environmental quality within the mining area. Additionally, the authors discuss the external factors influencing eco-environmental quality, with the mining activity factor found to have the highest contribution rate of 43.33%. As the distance from the mining area increases, the contribution of mining activity to the external eco-environmental quality gradually decreases.
The results are seeming to be interesting and original and can be recommended for publication after the following minor changes:
It should be clearly stated that “GWANN model” is used for the first time in such a study or there are several articles.
Punctuations are missing after equations.
The equations and equations numbering are not aligning.
Should explain how accurate the results as compared the earlier results (with other methods).
The graphical visualization is not clear I am trying to understand the behaviour of the graphs but the given information according to my point of view is not sufficient share the source how we visualize the graphs and check their accuracy. So, paste all computational work here.
Even though the topic is really interesting and the paper have the potential to be a nice add to the literature, I found the manuscript really difficult to read. Basically, the paper needs an extensive text editing and a re-organization of the data presented. Some of the equations are not correctly defined. There are several typos, errors, etc. Foremost, the English language needs to be deeply improved.
The graphical visualization is not clear I am trying to understand the behaviour of the graphs but the given information according to my point of view is not sufficient share the source how we visualize the graphs and check their accuracy. So, paste all computational work here.
Even though the topic is really interesting and the paper have the potential to be a nice add to the literature, I found the manuscript really difficult to read. Basically, the paper needs an extensive text editing and a re-organization of the data presented. Some of the equations are not correctly defined. There are several typos, errors, etc. Foremost, the English language needs to be deeply improved.
Author Response
Response to Reviewer 3 Comments
Point 1: It should be clearly stated that “GWANN model” is used for the first time in such a study or there are several articles.
Response 1: Thank you for your comments! Li et al [1] used the "GWDF-ANN model" in the study area to analyze the driving factors of the change of vegetation coverage within the range of 2 km around the mining areas. However, there is a lack of research on using GWANN model to fit and analyze the driving factor of RSEI. We have stated in lines 83–89.
Point 2: Punctuations are missing after equations.
Response 2：We have added correct punctuation after all equations.
Point 3: The equations and equations numbering are not aligning.
Response 3：We have modified it in all the equations.
Point 4: Should explain how accurate the results as compared the earlier results (with other methods).
Response 4：We added the detailed information about the accuracy of the study in this paper and the results of previous studies based on GWR, linear regression, ordinary least squares fitting of ecological quality [2–3], seeing the marked revision in lines 363–369.
Point 5: The graphical visualization is not clear I am trying to understand the behaviour of the graphs but the given information according to my point of view is not sufficient share the source how we visualize the graphs and check their accuracy. So, paste all computational work here.
Response 5：The graphical visualization process of each figure is as follows: For Figures 5–7, the equations used for the calculation of RSEI, NDVI, Wet, LST, and NDSI are in Figure 2 and equations (2) and (3), where Figure 7 used the ridge regression model in the GEE platform to perform the trend analysis of the three time periods of RSEI. For Figure 8, firstly, 3000 random points were selected as training samples using ArcGis 10.5 platform and the GWANN model was used to fit the predicted values of RSEI by four factors: mining activity, temperature, precipitation, and topography, and finally the predicted values were fitted as shown in Figure 8.
For Figure 9, the computational work was based on the geographically weighted differential factors-artificial neural network model proposed by Li et al [1]. First, we use the RSEI and four driving factors (xi) to build the GWANN model and predict RSEI0 xi. Second, a bias (∆xi) is added to the xi, which is calculated using Equation (1) as follows:
, (1)
The bias is added to a specific driving factor for all input neurons, while the other driving factors for each input neuron are kept constant. The bias does not affect the learning in the hidden layer of other inputs in computing the contribution of one factor. The setting of the bias is carried out in a series of experiments. If the bias is greater than 0.001, the contribution of the driving factor always changes. But if the bias is less than 0.001, the contribution of the driving factor hardly varies. From experiments, we found that 0.001 is the threshold value at which the contribution of the driving factor tends to stabilize. The bias is added separately for each driving factor and then input into the model to predict RSEI. The partial derivative for each driving factor is then calculated separately, using Equation (2) as follows:
, (2)
Finally, the partial derivative results are normalized using Equation (3), and the contribution of each driving factor to RSEI is obtained.
. (3)
The above calculation processes are all in RStudio, and the final raster image is formed in Figure 8.
For Figures 10–11, we counted the contribution values of the four factors in Figure 9 for different directions and different distances. For Figure 12, we extracted the center of gravity coordinates for different RSEI classes for different years in Figure 6.
Point6: Even though the topic is really interesting and the paper have the potential to be a nice add to the literature, I found the manuscript really difficult to read. Basically, the paper needs an extensive text editing and a re-organization of the data presented. Some of the equations are not correctly defined. There are several typos, errors, etc. Foremost, the English language needs to be deeply improved.
Response 6: Thank you for your precious advice. We have revised the manuscript in accordance with your comments and those of the other reviewers, and the definitions and descriptions of the equations have been revised, seeing the marked revision. We have carefully checked and improved the English writing in the revised manuscript.
References
- Li, J.; Qin, T.; Zhang, C.; Zheng, H.; Guo, J.; Xie, H.; Zhang, C.; Zhang, Y. A New Method for Quantitative Analysis of Driving Factors for Vegetation Coverage Change in Mining Areas: GWDF-ANN. Remote Sens. 2022, 14, 1579.
- Huang, J.; Chen, Y.; Liang, Y.; Yang, J.; Yang, M. Analysis of spatial-temporal variation and driving factors of ecological quality in typical mining areas of Daye City. Industrial Minerals & Processing. Available online: https://kns.cnki.net/kcms/detail/detail.aspx?FileName=HGKJ20220914000&DbName=CAPJ2022 (accessed on 17 June 2022).
- Chen, N.; Cheng, G.; Yang, J.; Ding, H.; He, S. Evaluation of Urban Ecological Environment Quality Based on Improved RSEI and Driving Factors Analysis. Sustainability. 2023, 15, 8464.
Author Response File: Author Response.pdf
Reviewer 4 Report
The study: "Analysis on eco-environmental quality and driving forces of human activities in opencast coal mining area based on GWANN model: A case study in Shengli Coalfield, China", aims to assess ecological change around a mining area over a 15-year period. Nowadays it would be very important to use geolocation tools, artificial intelligence and ecology together. This study shows that they can be used perfectly well to determine the ecological viability of a geographical region where mining has been practised. I believe it qualifies for publication.
- It seems to me that the line numbering format has not been used, it should be,
- The results mention that the study covers the years 2005 and 2021. However, in the legend of figure 3 it says 2000 and 2021.
Author Response
Response to Reviewer 4 Comments
Point 1: It seems to me that the line numbering format has not been used, it should be.
Response 1: Thank you for your comments! We have supplemented the use of the line numbering format.
Point 2: The results mention that the study covers the years 2005 and 2021. However, in the legend of figure 3 it says 2000 and 2021.
Response 2: We have modified it, and mistakes like this are all corrected.
Author Response File: Author Response.pdf
Reviewer 5 Report
This manuscript predominantly scrutinizes the proportional influence of key determinants - including mining activities, climatic conditions (temperature, precipitation), and topographic features - on the ecological and environmental health of a mining region. The study deploys the GWANN model in conjunction with the RSEI index to conduct a quantitative evaluation of principal elements impacting the ecological management of the mining area over an extended timeline (2005, 2010, 2015, and 2021). The research findings bear notable practical significance. Recommendation: Acceptance post minor revisions.
Major comments:
1. The manuscript would greatly benefit from an exhaustive discourse and analytical assessment of the findings, especially with regards to their implications on policy-making and practical application.
2. It is imperative to elucidate on the precision of the GWANN model, in addition to elaborating on its validation procedure.
3. An extended exposition is required on the calculation method employed to determine the proportional influence of each key determinant.
Minor comments:
1. Line 63: It would be more suitable to utilize the acronym GEE directly.
2. Line 76: The term "nonliner" should be corrected to "nonlinear".
3. There is a significant necessity for the enhancement of English articulation.
4. The visuals in Figure 1a are rather faint and challenging to discern.
5. Certain illustrations do not adhere to the specified guidelines, with the font size being excessively diminutive for clear visibility.
This manuscript predominantly scrutinizes the proportional influence of key determinants - including mining activities, climatic conditions (temperature, precipitation), and topographic features - on the ecological and environmental health of a mining region. The study deploys the GWANN model in conjunction with the RSEI index to conduct a quantitative evaluation of principal elements impacting the ecological management of the mining area over an extended timeline (2005, 2010, 2015, and 2021). The research findings bear notable practical significance. Recommendation: Acceptance post minor revisions.
Major comments:
1. The manuscript would greatly benefit from an exhaustive discourse and analytical assessment of the findings, especially with regards to their implications on policy-making and practical application.
2. It is imperative to elucidate on the precision of the GWANN model, in addition to elaborating on its validation procedure.
3. An extended exposition is required on the calculation method employed to determine the proportional influence of each key determinant.
Minor comments:
1. Line 63: It would be more suitable to utilize the acronym GEE directly.
2. Line 76: The term "nonliner" should be corrected to "nonlinear".
3. There is a significant necessity for the enhancement of English articulation.
4. The visuals in Figure 1a are rather faint and challenging to discern.
5. Certain illustrations do not adhere to the specified guidelines, with the font size being excessively diminutive for clear visibility.
Author Response
Response to Reviewer 5 Comments
Major comments:
Point 1: The manuscript would greatly benefit from an exhaustive discourse and analytical assessment of the findings, especially with regards to their implications on policy-making and practical application.
Response 1: Thank you for your comments!
Point 2: It is imperative to elucidate on the precision of the GWANN model, in addition to elaborating on its validation procedure.
Response 2: We have elucidated the precision and validation procedure of the GWANN model in section 3.3, and supplemented the detailed information about the accuracy of the study in this paper and the results of previous studies based on GWR, linear regression, ordinary least squares fitting of ecological quality [1–2], seeing the marked revision in lines 363–369.
Point 3: An extended exposition is required on the calculation method employed to determine the proportional influence of each key determinant.
Response 3: The computational work of the proportional influence of each key determinant was based on the geographically weighted differential factors-artificial neural network model proposed by Li et al [3]. First, we use the RSEI and four driving factors (xi) to build the GWANN model and predict RSEI0 xi. Second, a bias (∆xi) is added to the xi, which is calculated using Equation (1) as follows:
, (1)
The bias is added to a specific driving factor for all input neurons, while the other driving factors for each input neuron are kept constant. The bias does not affect the learning in the hidden layer of other inputs in computing the contribution of one factor. The setting of the bias is carried out in a series of experiments. If the bias is greater than 0.001, the contribution of the driving factor always changes. But if the bias is less than 0.001, the contribution of the driving factor hardly varies. From experiments, we found that 0.001 is the threshold value at which the contribution of the driving factor tends to stabilize. The bias is added separately for each driving factor and then input into the model to predict RSEI. The partial derivative for each driving factor is then calculated separately, using Equation (2) as follows:
, (2)
Finally, the partial derivative results are normalized using Equation (3), and the contribution of each driving factor to RSEI is obtained.
. (3)
The above calculation processes are all in RStudio, and the final raster image is formed in Figure 8. We have made certain modifications in 2.2.3.
Minor comments:
Point 4: Line 63: It would be more suitable to utilize the acronym GEE directly.
Response 4: We have modified it.
Point 5: Line 76: The term "nonliner" should be corrected to "nonlinear".
Response 5: We have modified it, and mistakes like this are all corrected.
Point 6: There is a significant necessity for the enhancement of English articulation.
Response 6: We have carefully checked and improved the English writing in the revised manuscript; see the marked revision.
Point 7: The visuals in Figure 1a are rather faint and challenging to discern.
Response 7: We have re-depicted all unclear images in the manuscript.
Point 8: Certain illustrations do not adhere to the specified guidelines, with the font size being excessively diminutive for clear visibility.
Response 8: We have re-adjusted the font size in the figures.
References
- Huang, J.; Chen, Y.; Liang, Y.; Yang, J.; Yang, M. Analysis of spatial-temporal variation and driving factors of ecological quality in typical mining areas of Daye City. Industrial Minerals & Processing. Available online: https://kns.cnki.net/kcms/detail/detail.aspx?FileName=HGKJ20220914000&DbName=CAPJ2022 (accessed on 17 June 2022).
- Chen, N.; Cheng, G.; Yang, J.; Ding, H.; He, S. Evaluation of Urban Ecological Environment Quality Based on Improved RSEI and Driving Factors Analysis. Sustainability. 2023, 15, 8464.
- Li, J.; Qin, T.; Zhang, C.; Zheng, H.; Guo, J.; Xie, H.; Zhang, C.; Zhang, Y. A New Method for Quantitative Analysis of Driving Factors for Vegetation Coverage Change in Mining Areas: GWDF-ANN. Remote Sens. 2022, 14, 1579.
Author Response File: Author Response.pdf
Reviewer 6 Report
Dear Authors,
I believe that the recent paper contains interesting results and will attract many reader in environment, in case if published; but I believe you have to follow the revision part, point by point to come out with strong case and gap filling in opencast mining issue and get finally easy paper to follow by reader.
Kindly find my comments below and you will see a detailed correction, and feedback in uploaded file:
- Abstract should finalize with the a summary of the work and adding the novelty of work, if any.
- again long sentences and not useful sentences in abstract should …remove and instead use the concrete data, application, and summary of your work at end of abstract
- Introduction is missing the argument, controversy argument, quesion words for eco-environmental problem in an opencast coal mining, paragraph should include the gap of the work, addressed question, novelty of your work…it sounds like you are writing the methods and material…
- In Figure 1, the four location of the studied are not clear presented, and the image resolution for a, b, also the coordinations are enough.
- In Page 3, the authors mentioned “In recent years, the eco-environmental quality inside and around the mining area has formed a strong contrast (Figure 1d–e)”. It is important to show the contrast on the figures to see the human activities, the alteration on the studied area during the interval time.
- In 2.2. Data Resources and Pre-Processing section what do you mean by precipitation data, be clear in the text especially when you initiate the work? Precipitation rate??? Or sediments precipitation?….???However, in the next paragraph is become more clear.
- In the same section, you mentioned “The mining activity data were obtained using a combination of annual coal production at each mine and the shortest Euclidean distance of the grid cell from the center of the adjacent mine, which was calculated by Equation”…What do you mean by mine? Please be more professional when you address your results and data. mining location???
- In figure 4 and table 1, how could you explain Medium RESI Grade that increases from 2005 to 2010, and decreases from 2015 to 2021.
- In figure 7, The legend for mining location `s` are not well presented in the legend ..and not clear in the figure.
- Based on figure 8 in page 12, the authors mentioned that “….while the contribution of the topography did not vary significantly with the change in distance.” , in contrast I observed the sudden changes in locations C and D...
- The resolution of figure 9 not clear, I ca not read the inserted words….replace it.
- Figure 10 totally un readable…put them into 2 figures..
- At the beginning of discussion, it is the first time there I saw this words (Gravity), explain it well when you start with any significant word that present your scientific argument . And try to be consistent during presentation of your results and discussion..
- Because of distortion figures 9 and 10. I could not continue reviewing the MS
The English Language in places needs a strong editing ...otherwise the language will cause a closed way for transferring the looped data
Author Response
Response to Reviewer 6 Comments
Point 1: Abstract should finalize with a summary of the work and adding the novelty of work, if any.
Response 1: Thank you for your comments! We have modified and supplemented the final part of the abstract according to your comments; see the marked revision in lines 32–36.
Point 2: Again long sentences and not useful sentences in abstract should …remove and instead use the concrete data, application, and summary of your work at end of abstract.
Response 2: We have modified the abstract.
Point 3: Introduction is missing the argument, controversy argument, quesion words for eco-environmental problem in an opencast coal mining, paragraph should include the gap of the work, addressed question, novelty of your work…it sounds like you are writing the methods and material.
Response 3: We have added to the discussion of the shortcomings of the current work on ecological assessment of the ecology of mining sites and the environmental problems of the environment due to open pit mining, and in the third part of the introduction we have added the problems that our work can solve and the novelty of the work; see the marked revision in lines 56–67, 88–89, 98–100.
Point 4: In Figure 1, the four locations of the studied are not clear presented, and the image resolution for a, b, also the coordinations are enough.
Response 4: We have re-depicted Figure 1, in particular, to improve the image resolution of Figure 1a and b.
Point 5: In Page 3, the authors mentioned “In recent years, the eco-environmental quality inside and around the mining area has formed a strong contrast (Figure 1d–e)”. It is important to show the contrast on the figures to see the human activities, the alteration on the studied area during the interval time.
Response 5: We have added a new figure reflecting the ecological conditions within the mining area in different years; see Figure 3.
Point 6: In 2.2. Data Resources and Pre-Processing section what do you mean by precipitation data, be clear in the text especially when you initiate the work? Precipitation rate??? Or sediments precipitation?….???However, in the next paragraph is become more clear.
Response 6: We have modified it, seeing in lines 154–155.
Point 7: In the same section, you mentioned “The mining activity data were obtained using a combination of annual coal production at each mine and the shortest Euclidean distance of the grid cell from the center of the adjacent mine, which was calculated by Equation”…What do you mean by mine? Please be more professional when you address your results and data. mining location???
Response 7: Thank you for your comments. We have modified it.
Point 8: In figure 4 and table 1, how could you explain Medium RESI Grade that increases from 2005 to 2010, and decreases from 2015 to 2021.
Response 8: The study area is located in a semi-arid ecologically fragile area of the steppe, where the ecological quality of the areas of concentrated non-human activity are strongly influenced by the natural environment [1–2], and the areas belonging to the Medium RESI Grade are mainly located outside the mining area. Thus, the quality of the ecological environment there depends more on natural climatic environmental elements, although mining activities also have some influence. Therefore, there will be some unregulated fluctuating changes.
Point 9: In figure 7, The legend for mining location `s` are not well presented in the legend ..and not clear in the figure.
Response 9: We have modified Figure 7 which has changed to Figure 9 and redrawn the legend of mining locations.
Point 10: Based on figure 8 in page 12, the authors mentioned that “….while the contribution of the topography did not vary significantly with the change in distance.” , in contrast I observed the sudden changes in locations C and D...
Response 10: In Figure 8, the contribution of topography to ecological quality is concentrated around 10% in direction C and around 20% in direction D, and the different contributions of topography in locations C and D are due to the different natural topographic environments. Thus, we proposed the contribution of the topography did not vary significantly with the change in distance.
Point 11: The resolution of figure 9 not clear, I ca not read the inserted words….replace it.
Response 11: We are very sorry, but we have re-depicted Figure 9 which has changed to Figure11.
Point 12: Figure 10 totally un readable…put them into 2 figures.
Response 12: We have re-depicted Figure 10 to make sure readers can read it clearly; see Figure 12.
Point 13: At the beginning of discussion, it is the first time there I saw this words (Gravity), explain it well when you start with any significant word that present your scientific argument. And try to be consistent during presentation of your results and discussion.
Response 13: Thank you for your comments. We have supplemented the explanation of Gravity and references in the manuscript, and the gravity center displacement model of RSEI can provide a better understanding the redistribution trends that have, and will continue to be [3-4]; see the marked revision in lines 458–461.
Point 14: Because of distortion figures 9 and 10. I could not continue reviewing the MS
Response 14: We are very sorry, and we have re-depicted all unclear images in the manuscript.
References
[1] Li, Q.; Hu, Z.; Zhang, F.; Song, D.; Liang, Y.; Yu, Y. Multispectral Remote Sensing Monitoring of Soil Particle-Size Distribution in Arid and Semi-Arid Mining Areas in the Middle and Upper Reaches of the Yellow River Basin: A Case Study of Wuhai City, Inner Mongolia Autonomous Region. Remote Sens. 2023, 15, 2137. https://doi.org/10.3390/rs15082137.
[2] Zhang, K.; Feng, R.; Zhang, Z.; Deng, C.; Zhang, H.; Liu, K. Exploring the Driving Factors of Remote Sensing Ecological Index Changes from the Perspective of Geospatial Differentiation: A Case Study of the Weihe River Basin, China. Int. J. Environ. Res. Public Health 2022, 19, 10930. https://doi.org/10.3390/ijerph191710930.
[3] Ariken, M.; Zhang, F.; Liu, K.; Fang, C.; Kung, H. Coupling coordination analysis of urbanization and eco-environment in Yanqi Basin based on multi-source remote sensing data. Ecol. Indic. 2020, 114, 106331.
[4] Li, R.; Chen, G.; Li, W.; Meng, R.; Wang, M.; Guo, Y. Spatiotemporal Analusis of Eco-environmental Benefits in Shenfu-Dongshen Mining Area During 1995-2020 Based on RSEI. Bulletin of Soil and Conservation 2021, 6, 143–151.
Author Response File: Author Response.pdf
Round 2
Reviewer 1 Report
This manuscript can be accepted.
The language can be improved further
Author Response
Response： We have carefully checked and improved the English writing in the revised manuscript; see the marked revision.
