Spatiotemporal Characteristics and Driving Factors of Ecosystem Regulation Services Value at the Plot Scale

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The selection of only 9 ESs and the exclusion of urban noise reduction among others result in a limitation in capturing the total ERSV. For future studies, a broader array of ESs should be considered to provide a more comprehensive assessment of ERSV.

Furthermore, the inverse relationship between human activity intensity (HAI), construction land proportion (CLP), and ERSV, alongside the nuanced interaction between temperature (LST), vegetation index (NDVI), and ERSV, offers a not too deep understanding of the ecosystem dynamics. The study should provide a more rigorous statistical analysis to underpin these relationships.

Nonetheless, this study makes significant contributions to understanding the spatio-temporal evolution of ERSV in Yunyang District, providing important insights into the driving factors and constraints and therefore is recommended for publication in this journal after the following adjustments:

 

1. The differentiation between anthropogenic and natural factors as drivers of ERSV spatial differentiation is a critical point. The study effectively utilizes the OPGD and constraint line extraction method.

- the discussions should be expanded to benefit from a more detailed exploration of how these methods were applied and the statistical validation of their findings.

2. The identification of a temporal trend in ERSV, characterized by an initial decrease followed by a continuous increase, is supported by quantitative analysis, showing an overall growth rate of 3.68%.

- the attribution of this growth to climate regulation and water retention services should be better explained.

3. The manuscript lists several anthropogenic and natural factors affecting ERSV but does not explicitly discuss the rationale behind the selection of these specific factors over others

- provide a more detailed justification for the selection of the driving factors using either literature citation if needed or, alternatively, preliminary analysis demonstrating their relevance to ERSV in the context of Yunyang District.

4. The impact of socio-economic factors such as the consumer price index (CPI) and agricultural practices on ERSV is mentioned but not integrated into the spatial analysis framework

- incorporate a discussion of the potential impact of not integrating socio-economic factors into the ERSV accounting model and the assessment of their direct and indirect impacts on ERSV (e.g. through a multi-layered regression analysis or structural equation modeling)

5. The use of bilinear interpolation to achieve a finer resolution of 5m×5m is technically fine but could be questioned about the compatibility of this resolution with the original data resolution and the possibility of interpolation errors.

- introduce a brief discussion about the potential impact of interpolation errors on the ERSV estimates and comment on alternative interpolation methods that might be more suitable given the nature of the data and the spatial scale of analysis

6. The decision to use OPGD and classification methods for data discretization are reasonable. However, the authors do not provide a detailed comparison of the performance of different classification methods or justify the choice of the optimal parameter set other than the maximization of the q value.

- Provide a brief analysis of how different classification methods may affect the results of the geographical detector analysis.

Comments on the Quality of English Language

English language is acceptable.

Author Response

Response to Reviewer 1 Comments

 

Dear reviewer：

Thank you very much for taking the time to review this manuscript, and putting forward a lot of suggestions for us, so we are here to express our sincere gratitude to you!

Please find the detailed responses below and the corresponding revisions in the re-submitted files.

 

 

Comments 1: The selection of only 9 ESs and the exclusion of urban noise reduction among others result in a limitation in capturing the total ERSV. For future studies, a broader array of ESs should be considered to provide a more comprehensive assessment of ERSV.

Furthermore, the inverse relationship between human activity intensity (HAI), construction land proportion (CLP), and ERSV, alongside the nuanced interaction between temperature (LST), vegetation index (NDVI), and ERSV, offers a not too deep understanding of the ecosystem dynamics. The study should provide a more rigorous statistical analysis to underpin these relationships.

Nonetheless, this study makes significant contributions to understanding the spatio-temporal evolution of ERSV in Yunyang District, providing important insights into the driving factors and constraints and therefore is recommended for publication in this journal after the following adjustments.

Response 1: Thanks for your careful review for us. We have made revisions according to your comments.

 

 

Comments 2: The differentiation between anthropogenic and natural factors as drivers of ERSV spatial differentiation is a critical point. The study effectively utilizes the OPGD and constraint line extraction method.

- the discussions should be expanded to benefit from a more detailed exploration of how these methods were applied and the statistical validation of their findings.

Response 2: Thanks for your careful review and suggestion. We have carefully improved the relevant expressions in the manuscript, and added relevant discussions. (Lines 374-404). In addition, we have also examined and refined other parts of the manuscript.

 

 

Comments 3: The identification of a temporal trend in ERSV, characterized by an initial decrease followed by a continuous increase, is supported by quantitative analysis, showing an overall growth rate of 3.68%.

- the attribution of this growth to climate regulation and water retention services should be better explained.

Response 3: Thanks for your suggestion. We have made revisions as shown below.

“From 2016 to 2018, the ESV decreased by 0.16%, primarily due to a decrease in the value of water retention by 1.034 billion Yuan. However, in the following years, the ERSV showed an increase of 2.70% in 2018-2020 and 1.11% in 2020-2021. The ESs that contributed the most to these increases were flood mitigation and water retention, respectively.” (Lines 236-239).

 

 

Comments 4: The manuscript lists several anthropogenic and natural factors affecting ERSV but does not explicitly discuss the rationale behind the selection of these specific factors over others.

- provide a more detailed justification for the selection of the driving factors using either literature citation if needed or, alternatively, preliminary analysis demonstrating their relevance to ERSV in the context of Yunyang District.

Response 4: Thanks for your careful review and suggestion. We have carefully improved the relevant expressions in the manuscript.

“In this study, we have selected both anthropogenic and natural drivers that may im-pact the spatial differentiation of regional ERSV. We have referenced previous research on the drivers of ESV in Hubei Province (Li et al., 2023; Zheng et al., 2023), and have taken into account the specific circumstances of Yunyang District, as well as the availability of continuous data on driving factors, we have identified three anthropogenic and five natural driving factors. The anthropogenic factors include human activity index (HAI), population density (POPD), and construction land proportion (CLP). The natural factors include annual precipitation (PRE), digital elevation model (DEM), average annual normalized difference vegetation index (NDVI), average annual land surface temperature (LST), and annual evapotranspiration (ET).” (Lines 121-130).

 

 

Comments 5: The impact of socio-economic factors such as the consumer price index (CPI) and agricultural practices on ERSV is mentioned but not integrated into the spatial analysis framework.

- incorporate a discussion of the potential impact of not integrating socio-economic factors into the ERSV accounting model and the assessment of their direct and indirect impacts on ERSV (e.g. through a multi-layered regression analysis or structural equation modeling)

Response 5: Thanks for your careful review. We have made explanations and improvements as shown below.

Socio-economic data such as the CPI and agricultural practices mentioned in this article are required for ERSV accounting, not as driver data. However, this study does not include economic factors (such as GDP, etc.) in the spatial analysis framework, which may affect the integrity of the study, and corresponding discussions are carried out in the discussion section (Lines 424-432). In addition, we have also checked other parts of the manuscript.

 

 

Comments 6: The use of bilinear interpolation to achieve a finer resolution of 5m×5m is technically fine but could be questioned about the compatibility of this resolution with the original data resolution and the possibility of interpolation errors.

- introduce a brief discussion about the potential impact of interpolation errors on the ERSV estimates and comment on alternative interpolation methods that might be more suitable given the nature of the data and the spatial scale of analysis?

Response 6: Thanks for your careful review for us. We have made explanations and improvements as shown below.

In this study, the minimum ecological plot area was used as the basis for bilinear interpolation of raster parameter data with varying resolutions, resulting in a resolution of 5m×5m, which was the basis of realizing the calculation and analysis of ERSV at plot scale, but did not consider the nature of the data and conduct analysis and verification, which may increase the uncertainty of the calculation results. This is also briefly discussed in the discussion section. (Lines 411-420).

 

 

Comments 7: The decision to use OPGD and classification methods for data discretization are reasonable. However, the authors do not provide a detailed comparison of the performance of different classification methods or justify the choice of the optimal parameter set other than the maximization of the q value.

- Provide a brief analysis of how different classification methods may affect the results of the geographical detector analysis.

Response 7: Thanks for your careful review and suggestion. We have carefully improved the relevant expressions in the manuscript, and added relevant discussions. (Lines 374-404).

 

 

Kind regards

 

Qingcheng Long; Yawen He

May 16, 2024

 

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

It is a very interesting and high-quality manuscript. The authors recognized the importance of the problem and dealt with this topic concisely and systematically. I suggest to the authors small corrections that are not necessary if they think that they will not significantly affect the quality of the paper.

Comments for author File: Comments.pdf

Author Response

Response to Reviewer 2 Comments

 

Dear reviewer：

Thank you very much for taking the time to review this manuscript, and putting forward a lot of suggestions for us, so we are here to express our sincere gratitude to you!

Please find the detailed responses below and the corresponding revisions in the re-submitted files. 

 

Comments 1: It is a very interesting and high-quality manuscript. The authors recognized the importance of the problem and dealt with this topic concisely and systematically. I suggest to the authors small corrections that are not necessary if they think that they will not significantly affect the quality of the paper.

Response 1: Thanks for your careful review for us.

 

Comments 2: In Line 80, the section "2. Study area" has a need for improvement.

Response 2: Thanks for your careful review and reminder. We have found the problem in "Figure 1" in chapter "2-Study area" of the manuscript, and we have made revisions as shown below. (Lines 100-101).

 

Comments 3: In Line 126, the source of NPP data needs to be improved.

Response 3: Thanks for your careful review for us. We have replaced the term "The NPP data was obtained from the MODIS/MOD17A3HGF dataset with a spatial resolution of 500 m" with " The NPP data was obtained from the MOD17A3HGF Version 6.0 product (https://lpdaac.usgs.gov/products/mod17a3hgfv006/) with a spatial resolution of 500 m, and the vector border of the Yunyang District was used to extract the mask." (Lines 133-135). In addition, we have also checked other parts of the manuscript.

 

Comments 4: In Lines 215-222, "Our findings show that…, The main contributors to these increases were flood regulation and water conservation.", which needs to be improved.

Response 4: Thanks for your suggestion. We have made revisions as shown below.

“Our findings show that the annual value output of climate regulation was at least 20.069 billion Yuan, making it the largest contributor to the ERSV in the district. The second highest contributor was water retention, with an annual output value of over 8.852 billion Yuan. However, air purification and water purification had the lowest value contribution. From 2016 to 2018, the ESV decreased by 0.16%, primarily due to a decrease in the value of water retention by 1.034 billion Yuan. However, in the following years, the ERSV showed an increase of 2.70% in 2018-2020 and 1.11% in 2020-2021. The ESs that contributed the most to these increases were flood mitigation and water retention, respectively.” (Lines 232-240).

 

Comments 5: In Lines 347-348, "The contribution of climate regulation and water retention was significant.", which needs to be improved.

Response 5: Thanks for your suggestion. We have made revisions as shown below.

"The contribution values of climate regulation function and water retention function to ERSV was significant". (Lines 440-441).

 

 

Kind regards

 

Qingcheng Long; Yawen He

May 16, 2024

 

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

Dear Authors,

The study has the value to estimate the economic relevance of regulating ecosystem services (ERSV) in China. Based on similar studies, the study focuses on the Yunyang District located in the Hubei Province. It aims at calculating the monetary value of specific ERS, their spatio-temporal evolution and also the impact of different driving factors on their spatial differentiation. With the support of specific methods, the above-mentioned results have been produced. Relevant aspects have been highlighted in this study.

Here below just a few observations and considerations.

Introduction

Line 42: Please, consider to further contextualise the reference to the General Secretary Xi Jinping’s theory, which can be for sure considered just part of a broader debate and research strand, initiated long ago.

Materials and Methods

Line 99: here the third land survey is mentioned. According to Chen et al. (2022), the third land survey in China was announced in 2017, could you briefly clarify this aspect? This is not expected to be the case, but does the Hubei Province conduct a different survey?

Lines 99-102: could you please further explain the main features of ecological plots in your study? The focus of the the study is on the contribution to ESV of different type of land uses: cultivated land, garden, forest and grassland. Would it be possible to explain which among these, is more preponderant?

Lines 106-107: please provide some more explanation about the manual modification that was implemented.

Line 152: please consider to add some more explanation about the determination of the price for the calculation of the value of the selected ES is really relevant.

Results

Line 215/Table 3: it is clear that the list of services in this study is based on previous studies. However, could you please explain possible relation between the service "climate regulation" and other services, such as "air purification", "carbon sequestration" and "oxygen production"?

 Thank you and all the best for your future research.

Author Response

Response to Reviewer 3 Comments

 

Dear reviewer：

Thank you very much for taking the time to review this manuscript, and putting forward a lot of suggestions for us, so we are here to express our sincere gratitude to you!

Please find the detailed responses below and the corresponding revisions in the re-submitted files.

 

Comments 1: The study has the value to estimate the economic relevance of regulating ecosystem services (ERSV) in China. Based on similar studies, the study focuses on the Yunyang District located in the Hubei Province. It aims at calculating the monetary value of specific ERS, their spatio-temporal evolution and also the impact of different driving factors on their spatial differentiation. With the support of specific methods, the above-mentioned results have been produced. Relevant aspects have been highlighted in this study.

Response 1: Thanks for your careful review for us. We have made revisions according to your comments.

 

Comments 2: Line 42: Please, consider to further contextualise the reference to the General Secretary Xi Jinping’s theory, which can be for sure considered just part of a broader debate and research strand, initiated long ago.

Response 2: Thanks for your careful review and suggestion. We have made revisions as shown below. “In recent years, under the guidance of General Secretary Xi Jinping's theory that "lucid waters and lush mountains are invaluable assets", China has made...”. (Line 39)

 

Comments 3: Line 99: here the third land survey is mentioned. According to Chen et al. (2022), the third land survey in China was announced in 2017, could you briefly clarify this aspect? This is not expected to be the case, but does the Hubei Province conduct a different survey?

Response 3: Thanks for your careful review for us. The third land survey in China was indeed announced in 2017. According to our field visit and investigation, the research area (Yunyang District) has always maintained a sound land use status survey and iterative update work. Although the annual data are closely related, this study includes 2016 land use data, and the expression in the manuscript about direct use of the third Land Survey data is indeed inappropriate. Therefore, we have improved the relevant expressions in the manuscript. “Based on the land use survey data provided by the land resources department of Yunyang District, detailed ecological plot data was obtained”. (Lines 104-105)

 

Comments 4: Lines 99-102: could you please further explain the main features of ecological plots in your study? The focus of the the study is on the contribution to ESV of different type of land uses: cultivated land, garden, forest and grassland. Would it be possible to explain which among these, is more preponderant?

Response 4: Thanks for your careful review and suggestion. We have made instructions and revisions as shown below.

The ecological plots in this study are obtained through the detailed processing of the land use survey data provided by the land resources department, and each plot has geographical entity information, so it can store and display more detailed information. The contribution to ecosystem regulation services value (ERSV) of different types of land use is different, the contribution of forest land and wetland is higher, the contribution of construction land is lower, and the corresponding expression is also found in the manuscript.

“The areas with high ERSV were primarily woodland plots, such as Baoxia Town, Canglangshan Forest Farm, Yeda Township in the southwest, and Daliu Township in the north, as well as the wetland distribution area of Yunyang District. These areas had higher unit ERSV values. In contrast, the areas with low ERSV were mainly located in Chengguan Town, Chadian Town, Liupi Town in the central region, and Meipu Town in the northeast, which are urban areas with high levels of human activity. These areas had lower unit ERSV values”. (Lines 252-258)

 

Comments 5: Lines 106-107: please provide some more explanation about the manual modification that was implemented.

Response 5: Thanks for your careful review for us. We have made explanations as shown below.

The manual processing of the land use survey data refinement process involves the screening of large area plots and final repair. This study was all processed based on ArcGIS10.8 software. The land use data we obtained all contained basic information such as area and land use type, and we completed large area plots selection according to the area attribute. After the initial refinement, we conduct a final inspection of the various blocks to ensure the quality of the ecological plot data.

 

Comments 6: Line 152: please consider to add some more explanation about the determination of the price for the calculation of the value of the selected ES is really relevant.

Response 6: Thanks for your careful review for us. We have some explanations on this issue in the second half of Section 2.3.1 (Lines 179-184). The value accounting of each ES is carried out in strict accordance with the existing relevant norms and standards. In addition, in order to eliminate the impact of price fluctuations, we use the CPI index of the study area to revise the value of each ES to the comparable price in 2021, so as to achieve the unity of the comparative analysis scale of value between each accounting year.

 

Comments 7: Line 215/Table 3: it is clear that the list of services in this study is based on previous studies. However, could you please explain possible relation between the service "climate regulation" and other services, such as "air purification", "carbon sequestration" and "oxygen production"?

Response 7: Thanks for your careful review and suggestion. The ES selected in our study is based on previous studies, and we have read many articles about the relationship between ES (such as the tradeoff and synergistic relationship of ES). However, the object of this study is total ERSV, and the focus is to analyze the spatio-temporal characteristics and driving factors of ERSV. Therefore, this paper did not carry out relevant research on the relationship between ES, and the subsequent research will focus on it.

 

 

Kind regards

Qingcheng Long; Yawen He

May 16, 2024

 

Author Response File: Author Response.pdf

Reviewer 4 Report

Comments and Suggestions for Authors

Review comments on the paper “Spatio-Temporal Characteristics and Driving Factors of Ecosystem Regulation Services Value at Plot Scale”

 

General Comments:

Understanding the spatio-temporal evolution characteristics and driving factors of ERSV is essential for efficient management of regional ecosystems. The authors investigated the spatio-temporal evolution characteristics, main driving factors, and constraint rules of ERSV in Yunyang District, Hubei Province, by using the ERSV accounting model, and the barycentric analysis method. As the author clarified, the results can potentially provide a targeted and scientific basis for spatial optimization, ecological environment protection and restoration, and value realization of ecological products in this region. However, some major concerns remain to be solved before it can be published. Please see below my detailed comments.

 

Detailed Comments:

1.      Line 15. “The contribution of climate regulation and water retention was significant”. I believe climate regulation and water retention are not driving factors at the same scale. So, how to understand their contributions to ERSV?

2.      Lines 18-19. “The center of gravity of the ERSV increase shifted to the southwest by 12455.42 m, while the center of gravity of the reduction shifted to the southwest by 3582.79 m”, these changes happened during what period?

3.      Line 20. anthropogenic factors include what? please clarify.

4.      Line 21. “HAI and CLP were the leading anthropogenic factor”. What are the HAI and CLP, and other abbreviations throughout the Abstract?

5.      Line 29-31. “These results can provide a targeted and scientific basis for spatial optimization, ecological environment protection and restoration,..” but how and why?

6.      Line 70-71. “To address these issues and highlight the importance of ERSV” Why you think it work can solve the issues you mentioned before? What is the novelty of this work? Why you choose Yunyang District. Please clarify here. Also, the abstract section does not show the novelty of this research.

7.      Study area. This section can be part of “3. Material and methods”.

8.      The authors mentioned the source of the data, but does not explicitly tell the authors how the original data was obtained, please clarify the method that used to produce the data.

9.      Figure 1. Not well organized.

10.  Line 118-121. How did you calculate the indexes (i.e., HAI, POPD, CLP. and others) you mentioned here? Please clarify, may be in Table 2.

11.  Figure 7. How did you determine the Boundary points and the constraint lines, I guess you need to briefly mention it in the caption.

12.  Line 340. I never saw a title of “Conclusion and discussion”, Maybe “Discussion and Conclusion” is better. Also, no discussion were detected in this section.

 

Comments on the Quality of English Language

Please see my comments

 

Author Response

Response to Reviewer 4 Comments

 

Dear reviewer：

Thank you very much for taking the time to review this manuscript, and putting forward a lot of suggestions for us, so we are here to express our sincere gratitude to you!

Please find the detailed responses below and the corresponding revisions in the re-submitted files.

 

Comments 1: Understanding the spatio-temporal evolution characteristics and driving factors of ERSV is essential for efficient management of regional ecosystems. The authors investigated the spatio-temporal evolution characteristics, main driving factors, and constraint rules of ERSV in Yunyang District, Hubei Province, by using the ERSV accounting model, and the barycentric analysis method. As the author clarified, the results can potentially provide a targeted and scientific basis for spatial optimization, ecological environment protection and restoration, and value realization of ecological products in this region. However, some major concerns remain to be solved before it can be published. Please see below my detailed comments.

Response 1: Thanks for your careful review for us. We have made revisions according to your comments.

 

Comments 2: Line 15. “The contribution of climate regulation and water retention was significant”. I believe climate regulation and water retention are not driving factors at the same scale. So, how to understand their contributions to ERSV?

Response 2: Thanks for your careful review and suggestion. We have made explanations and revisions as shown below.

The function of climate regulation and water retention are two important indicators to calculate the value of ecosystem regulation services (ERSV). "The contribution of climate regulation and water retention was significant" here means that "The contribution of climate regulation function value and water retention function value to ERSV in Yunyang District was significant", but it is undeniable that there are some problems in our expression. Therefore, we revised it to "The contribution values of climate regulation function and water retention function to ERSV was significant". (Lines 16-17)

 

Comments 3: Lines 18-19. “The center of gravity of the ERSV increase shifted to the southwest by 12455.42 m, while the center of gravity of the reduction shifted to the southwest by 3582.79 m”, these changes happened during what period?

Response 3: Thanks for your suggestion. We have made revisions as shown below.

“The center of gravity of the ERSV increase shifted to the southwest by 12455.42 m, while the center of gravity of the reduction shifted to the southwest by 3582.79 m from 2016 to 2021”. (Lines 19-21)

 

Comments 4: Line 20. anthropogenic factors include what? please clarify.

Response 4: Thanks for your careful review and suggestion. The anthropogenic factors include human activity index (HAI), population density (POPD), and construction land proportion (CLP). The natural factors include annual precipitation (PRE), DEM, NDVI, average annual land surface temperature (LST), and annual evapotranspiration (ET). In addition, we have also made revisions as shown below.

“(3) The interaction of any two driving factors had greater explanatory power to the spatial differentiation of ERSV than that of a single driving factor, and all of them showed nonlinear or double factor enhancement characteristics. The human active index (HAI) and construction land proportion (CLP) were the leading anthropogenic factors, while land surface temperature (LST) and NDVI were the leading natural factors. (4) The ERSV could maintain a high and stable value output when HAI was less than 0.3, CLP was less than 15%, LST was between 18-22 ℃, and NDVI was greater than 0.5.” (Lines 21-27)

 

 

Comments 5: Line 21. “HAI and CLP were the leading anthropogenic factor”. What are the HAI and CLP, and other abbreviations throughout the Abstract?

Response 5: Thanks for your careful review. We have already explained this in Response 4. In addition, we have also checked other parts of the manuscript.

 

 

Comments 6: Line 29-31. “These results can provide a targeted and scientific basis for spatial optimization, ecological environment protection and restoration, ...” but how and why?

Response 6: Thanks for your careful review for us. We have made instructions and revisions as shown below.

In this study, we found that the contribution value of climate regulation function, water retention function, forest and wetland ecosystem to ERSV of Yunyang District was significant. We can maintain the high yield of climate regulation function value and water conservation function value by protecting and restoring the environmental conditions of forests and wetlands, and realize the high-quality development of ERSV.

At the same time, we also found that human active index (HAI), construction land proportion (CLP), NDVI and land surface temperature (LST) were the major driving factors affecting ERSV in Yunyang District. ERSV could maintain a high and stable value output when HAI was less than 0.3, CLP was less than 15%, LST was in the range of 18-22 ℃ and NDVI was greater than 0.5. We can use these driver value ranges to empower regional production and life, and contribute to the high-quality and sustainable development of regional economy and ecology.

Therefore, we say that these results can provide a targeted and scientific basis for spatial optimization, ecological environment protection and restoration, and value realization of ecological products in Yunyang District, Hubei Province.

In addition, we also improved the relevant content in the manuscript.

 

 

Comments 7: Line 70-71. “To address these issues and highlight the importance of ERSV” Why you think it work can solve the issues you mentioned before? What is the novelty of this work? Why you choose Yunyang District. Please clarify here. Also, the abstract section does not show the novelty of this research.

Response 7: Thanks for your careful review and suggestion. We have carefully improved the relevant expressions in the manuscript, and made explanations and revisions as shown below.

In this study, we have realized the calculation of ERSV at plot scale, and systematically explored the spatio-temporal characteristics and driving factors of ERSV in Yunyang District, Hubei Province based on the barycentric analysis method, the optimal parameters-based geographical detector model (OPGD), and constraint line extraction method. On the one hand, the study of ERSV at plot scale is more conducive to exploring the internal spatial difference information of ERSV. On the other hand, compared with the land use raster data obtained by human-computer combined interpretation, the ecological plot data obtained based on land use survey data is more realistic and has smaller errors, so the accounting results have higher accuracy and credibility. In addition, we also refined the ecological plot data and obtained a more refined accounting unit, which can also help us better complete the analysis and study of the spatio-temporal characteristics and driving factors of ERSV.

For the first question: By carrying out the ERSV study of the plot scale, we can avoid the shortcomings of the mentioned different accounting methods and achieve more accurate ERSV accounting and analysis. But to say “To address these issues and” is really not appropriate here, so we have improved the corresponding content. “In view of this, and consider the important role of ERSV and the impact of continuous data access, this study focuses on …”. (Lines 74-75)

The novelty of this work: The red section of Response 7 points out the advantages of this study as some responses to this question. In addition, many important conclusions and rules were also found in our study, which provided important insights into the spatio-temporal evolution, driving factors and constraints of ERSV in Yunyang District, Hubei Province. For example, “Response 6” replies you in detail. (The contribution value of climate regulation function, water retention function, forest and wetland ecosystem to ERSV of Yunyang District was significant; human active index (HAI), construction land proportion (CLP), NDVI and land surface temperature (LST) were the major driving factors affecting ERSV in Yunyang District. ERSV could maintain a high and stable value output when HAI was less than 0.3, CLP was less than 15%, LST was in the range of 18-22 ℃ and NDVI was greater than 0.5, et.)

We choose Yunyang District as the study area. On the one hand, Yunyang District itself has rich ecological resources such as forests. According to previous studies, ERSV of this area plays an extremely important role in GEP; on the other hand, it is due to project requirements.

In addition, we have improved the relevant content of the abstract and introduction of the manuscript according to your suggestions.

 

 

Comments 8: Study area. This section can be part of “3. Material and methods”.

Response 8: Thanks for your suggestion. We have adjusted the chapter content of the manuscript. (Lines 85-99)

 

 

Comments 9: The authors mentioned the source of the data, but does not explicitly tell the authors how the original data was obtained, please clarify the method that used to produce the data.

Response 9: Thanks for your suggestion. We have made revisions as shown below.

First, we supplemented the sources and processing of ecological plot data and NPP data in the manuscript (Lines 133-135). Secondly, we improved the data processing of each driving factor in Table 2  (Lines 148-151). Finally, we also added to explain the use of different data, “The raster data of each driving factor and indicator parameter was resampled and the statistical data was spatialized, and different data were unified into each plot unit to realize ERSV calculation and driving factors exploration”. (Lines 143-146).

Table 2. Construction of index system for driving force of spatial heterogeneity of ERSV in Yunyang District, Hubei Province.

Type	Factors	Resolution	Year	Data source and processing
Anthropogenic factor	HAI	100 m	2016 2018 2020 2021	The mathematical model constructed by Yan et al. (2014) was used for calculation based on the ecological plot data, and rasterized to 100 m × 100 m.
	POPD/(person·km-2)	1 km		Based on the LandScan Global Population Data (https://landscan.ornl.gov/), and the vector border of the Yunyang District was used to extract the mask.
	CLP/%	1 km		The proportion of construction land in the area of each 1 km × 1 km fishing net unit was calculated based on ArcGIS10.8 software.
Natural factor	DEM/m	30 m	2019	Based on the ASTER GDEM V3 data provided by the Geospatial Data Cloud (http://www.gscloud.cn/), and the vector border of the Yunyang District was used to extract the mask.
	PRE/mm	1 km	2016 2018 2020 2021	Based on the 1-km monthly precipitation dataset for China provided by the National Tibetan Plateau Scientific Data Center (https://data.tpdc.ac.cn/), and synthesized by pixel-by-pixel sum.
	NDVI	250 m		Based on the MODIS-NDVI monthly synthesis product provided by the PIESAT (https://engine.piesat.cn/), and synthesized by pixel-by-pixel average.
	LST/℃	1 km		Based on the NASA (https://earthdata.nasa.gov/) MOD11A2 Data Products, and synthesized by pixel-by-pixel average.
	ET/mm	500 m		Based on the NASA (https://earthdata.nasa.gov/) MOD16A2 Data Products, and synthesized by pixel-by-pixel sum.

Note: HAI, human active index; POPD, population density; CLP, construction land proportion; PRE, annual precipitation; DEM, digital elevation model; NDVI, annual average normalized difference vegetation index; LST, annual average land surface temperature; ET, annual evapotranspiration.

 

 

Comments 10: Figure 1. Not well organized.

Response 10: Thanks for your careful review and suggestion. We have found the problem in “Figure 1” and made revisions as shown below. (Lines 100-101).

 

 

Comments 11: Line 118-121. How did you calculate the indexes (i.e., HAI, POPD, CLP. and others) you mentioned here? Please clarify, may be in Table 2.

Response 11: Thanks for your careful review and suggestion. We have completed the processing. The source and processing process of each driving factor(i.e., HAI, POPD, CLP. and others) are shown in Table 2. (Lines 148-151)

 

 

Comments 12: Figure 7. How did you determine the Boundary points and the constraint lines, I guess you need to briefly mention it in the caption.

Response 12: Thanks for your careful review for us. We have perfected the determination of boundary points and the extraction method of constraint lines in the section "2.3.4. The extraction method of constraint lines" (Lines 212-219). We also mentioned in the beginning of the chapter in "Figure 7" that this part of content was implemented based on this method. (Lines 331-334)

“The quantile segmentation method was used to identify constraint lines in this study. First, we divided the range of major anthropogenic and natural factors on the x-axis of each scatter plot into 100 column parts with equal intervals. Secondly, we used the 99% quantile of each column as the boundary point to obtain 100 boundary points. Finally, we fitted the constraint line rela-tionship between the major driving factors and ERSV by combining the shape of the scatter points and the goodness of fit (Fang et al., 2021; Li et al., 2022a).”. (Lines 212-219).

 

 

Comments 13: Line 340. I never saw a title of “Conclusion and discussion”, Maybe “Discussion and Conclusion” is better. Also, no discussion were detected in this section.

Response 13: Thanks for your suggestion. We have adjusted the chapter content of the manuscript and improved the discussion section. (Lines 358-432).

 

 

Kind regards

 

Qingcheng Long; Yawen He

May 16, 2024

 

Author Response File: Author Response.pdf