Sustainable Development of Production–Living–Ecological Spaces: Insights from a 30-Year Remote Sensing Analysis
Round 1
Reviewer 1 Report
Comments and Suggestions for AuthorsThis paper leverages remote sensing technology to explore the spatial and temporal evolution of the production-living-ecological space (PLES) in Henan Province over the span of three decades. The study reveals substantial reductions in production space, consistent expansions in living space, and complex, non-linear changes in ecological space. By applying Principal Component Analysis (PCA) to identify the key drivers of PLES evolution, the study aims to establish a theoretical framework for sustainable land use planning and offer valuable insights for promoting sustainable development in rapidly urbanizing regions. The article serves as a scientific foundation for sustainable development policies and practices in Henan and similar regions. It analyzes the spatial and temporal characteristics of PLES evolution and presents the following observations:
1. In terms of overall conception, the article focuses on the spatial and temporal evolution of PLES and its underlying drivers. It exhibits a clear research direction and strong practical relevance. However, the study lacks suggestions for optimizing the system's evolution and achieving a sustainable balance among various land use resources.
2. From a research methodology perspective, the article employs a range of techniques, including remote sensing data, Geographic Information System (GIS) analysis, PCA, and linear regression. The technical approach is both reasonable and advanced, providing a solid quantitative foundation for the research. However, a more robust correlation analysis of geographic space would be beneficial. This could involve analyzing the correlation of land use evolution between different regions in Henan Province and studying the spatial-temporal evolution and dynamic changes in land use in surrounding areas due to rapid urbanization. Such an analysis would facilitate a deeper understanding of the intrinsic reasons behind the evolutionary pattern of PLES. Additionally, the significant multicollinearity of the independent variables suggests a high degree of association between them, making linear regression an unsuitable approach. Furthermore, the PLES classification system is an artificially delineated standard, and the study lacks clarity on how to achieve objective definition, vertical standard unity, and comparability.
3. The results showcase the evolution of PLES in Henan Province over the past thirty years and the underlying causes of the complex changes in ecological space. However, the findings do not fully account for the deeper social, political, and policy factors that have influenced these changes. For instance, the size of production land in Henan Province, a major grain-producing province in China, is a crucial factor in China's food security.
4. While the article includes a chart illustrating the change in mulberry area, it lacks an accompanying explanation. It would be beneficial to provide a description of the chart and a conclusion analysis. In the global impact analysis, considering other countries and regions as case studies would further elucidate the global implications of the findings.
5. This article examines the spatial and temporal evolution of PLES in Henan Province over the past three decades. It utilizes remote sensing technology to analyze the trends of change in production space, living space, and ecological space amidst rapid urbanization and industrialization. However, the article lacks an objective definition of PLES to ensure longitudinal comparability.
Author Response
REVIEWER #1: This paper leverages remote sensing technology to explore the spatial and temporal evolution of the production-living-ecological space (PLES) in Henan Province over the span of three decades. The study reveals substantial reductions in production space, consistent expansions in living space, and complex, non-linear changes in ecological space. By applying Principal Component Analysis (PCA) to identify the key drivers of PLES evolution, the study aims to establish a theoretical framework for sustainable land use planning and offer valuable insights for promoting sustainable development in rapidly urbanizing regions. The article serves as a scientific foundation for sustainable development policies and practices in Henan and similar regions. It analyzes the spatial and temporal characteristics of PLES evolution and presents the following observations:
We would like to thank Reviewer#1 for your invaluable and constructive suggestions, which have been applied to the revised manuscript. Responses to specific comments are provided below. All the changes made in the revised manuscript are highlighted in color to facilitate their identification.
COMMENT 1: In terms of overall conception, the article focuses on the spatial and temporal evolution of PLES and its underlying drivers. It exhibits a clear research direction and strong practical relevance. However, the study lacks suggestions for optimizing the system's evolution and achieving a sustainable balance among various land use resources.
Response 1: Thank you for the feedback. Policy recommendations have been added in section 4.5, aimed at optimizing PLES evolution and achieving a sustainable balance among land use resources. These recommendations are proposed to be applicable not only to Henan but also to similar regions and cities worldwide.
COMMENT 2: From a research methodology perspective, the article employs a range of techniques, including remote sensing data, Geographic Information System (GIS) analysis, PCA, and linear regression. The technical approach is both reasonable and advanced, providing a solid quantitative foundation for the research. However, a more robust correlation analysis of geographic space would be beneficial. This could involve analyzing the correlation of land use evolution between different regions in Henan Province and studying the spatial-temporal evolution and dynamic changes in land use in surrounding areas due to rapid urbanization. Such an analysis would facilitate a deeper understanding of the intrinsic reasons behind the evolutionary pattern of PLES. Additionally, the significant multicollinearity of the independent variables suggests a high degree of association between them, making linear regression an unsuitable approach. Furthermore, the PLES classification system is an artificially delineated standard, and the study lacks clarity on how to achieve objective definition, vertical standard unity, and comparability.
Response 2: We sincerely appreciate the reviewer’s valuable suggestion, and we fully agree on the importance of incorporating correlation analysis of land use evolution across different regions. Such analysis would indeed enhance our understanding of the intrinsic reasons behind the evolutionary patterns of PLES, especially regarding the spatial-temporal evolution and dynamic changes in surrounding areas under rapid urbanization. We plan to address this aspect in future research (see section 4.6), and we are currently in the process of collecting and processing relevant data to facilitate a more in-depth regional correlation analysis. We hope that this follow-up work will provide more comprehensive insights into spatial correlations. Thank you again for your constructive feedback, which has provided essential direction for our research.
Additionally, in response to the reviewer’s concerns, the following explanation provides a detailed account of our approach. Relevant content and reference have been added in sections 2.3.4 and 2.3.5. This study combines Principal Component Analysis (PCA) and linear regression to effectively analyze the driving forces behind PLES changes. Specifically, PCA is used to eliminate multicollinearity among independent variables, while linear regression captures the relationship between PLES changes and the main components. Here is how each step is applied: First, given the high multicollinearity among the independent variables, PCA transforms these correlated variables into a set of uncorrelated principal components, effectively removing multicollinearity. This dimension-reduction process allows the extraction of key components that retain most of the original information while reducing the number of variables, thereby improving the accuracy of subsequent regression analysis. By ensuring independence among the components, PCA enables these transformed variables to serve as independent inputs in the regression model, avoiding biases that could arise from multicollinear relationships. Following this, the main components are introduced into the linear regression model to capture the relationship between the principal components and PLES changes. The linear regression model thus reflects the direction and extent of influence that these key driving factors have on PLES dynamics. To ensure interpretability of the results, the principal components are then back-transformed to express the original independent variables. This allows for a direct quantification of each specific independent variable’s relationship with PLES changes. The validity of this approach lies in PCA's ability to eliminate multicollinearity and to distill the driving forces into independent comprehensive variables, thereby enhancing the stability of the regression model. The back-transformation of variables retains the completeness and interpretability of the analysis, providing both a robust quantitative foundation and clear insights into the specific driving forces. This method is therefore both rigorous and suitable for an in-depth analysis of the driving factors.
Finally, an objective definition of PLES has been included in the second paragraph of the introduction to ensure longitudinal comparability. Additionally, a detailed explanation and references of the PLES classification system have been added to section 2.3.1.
COMMENT 3: The results showcase the evolution of PLES in Henan Province over the past thirty years and the underlying causes of the complex changes in ecological space. However, the findings do not fully account for the deeper social, political, and policy factors that have influenced these changes. For instance, the size of production land in Henan Province, a major grain-producing province in China, is a crucial factor in China's food security.
Response 3: A detailed analysis of the social, political, and policy factors influencing PLES changes in Henan has been added to the first paragraph of section 4.1.
COMMENT 4: While the article includes a chart illustrating the change in mulberry area, it lacks an accompanying explanation. It would be beneficial to provide a description of the chart and a conclusion analysis. In the global impact analysis, considering other countries and regions as case studies would further elucidate the global implications of the findings.
Response 4: I believe you are referring to Figure 7, the Sankey diagram. This figure has already been explained, along with Table 3, in the last part of section 3.2, which includes a detailed explanation and conclusion analysis. The Sankey diagram visually represents land use transitions, while Table 3 outlines the corresponding area changes. Moreover, other countries and regions with similar land use evolution characteristics have been involved in paragraphs 2-4 of section 4.1 to elucidate the global implications of the findings.
COMMENT 5: This article examines the spatial and temporal evolution of PLES in Henan Province over the past three decades. It utilizes remote sensing technology to analyze the trends of change in production space, living space, and ecological space amidst rapid urbanization and industrialization. However, the article lacks an objective definition of PLES to ensure longitudinal comparability.
Response 5: An objective definition of PLES has been added in the second paragraph of the introduction to ensure longitudinal comparability. Additionally, a detailed explanation of the PLES classification system and references have been included in Section 2.3.1.
Reviewer 2 Report
Comments and Suggestions for AuthorsComments:
This paper analyzes the spatiotemporal evolution of production, living, and ecological spaces (PLES) in Henan Province from 1990 to 2020 and explores the relationship between this change and sustainable development through quantitative analysis, providing guidance for policies. Overall, the topic of this study is meaningful, but significant improvements are needed in summarizing the specific research content, presenting research methods and results. Below are my detailed comments:
Major Comments:
1.Most importantly, Lines 161-174: The research background and objectives need to be re-organized. The current presentation raises confusion. For example, "analyzing the relationship between PLES evolution and SDGs," but SDGs encompass various fields of sustainable development. Which specific goal of the SDGs is being analyzed in this paper? Currently, there is no detailed introduction. As an important content mentioned in the research objectives, SDGs need to be clearly elaborated. "Exploring strategies from Henan’s PLES evolution for sustainable development in similar regions" is summarized as a key research point, but throughout the research content, there is no indication of extending this method to similar regions or even globally. I believe this research objective is missing.
2.The abstract needs improvement. Line 15, "yet there remains a significant research gap in examining their dynamics and driving factors" should be revised. The current statement is not objective enough. For instance, references 9-11 have conducted some research in this field, so it's not completely unexplored. Additionally, Line 19 lacks a description of quantitative data in the results.
3.Line 21: The statistical data mentioned here need to include reference links. Importantly, how these statistical yearbook data were transformed into the data used in the experiment requires a more detailed introduction. For example, which indicators correspond to the factors proposed in the paper? How were missing values handled? Was standardization required? Especially for the study of GDP data series changes, was annual base period adjustment performed? These all require more specific descriptions.
4.Line 236: It is mentioned that land-use changes come from LUCC land-use data. Wasn't this data extracted from the Landsat data mentioned in Line 214? This needs to be clarified. If LUCC land-use data was used, please indicate which public dataset was used.
5.Line 255: Why was PCA used in the methodology of this paper? What was the purpose? This needs to be explained.
6.For the software used in the research methodology (e.g., Line 260: SPSS), please provide the specific software version number. This is a requirement of MDPI journals, please refer to the submission instructions.
Furthermore, the presentation of figures and formulas in this paper needs further modification to enhance visibility. Although these modifications are minor, they are crucial for readers to clearly understand the paper:
1.Figure 2: Please move this figure to Section 2.3. Methodology.
2.Figure 3: The figure lacks (a), (b), (c), (d) labels, making it difficult to correspond to the descriptions in the captions. Please add them.
3.In formulas (1) and (2), both use A. In formula (1), Ann represents the area of a certain land-use type, while in formula (2), A represents the dynamic degree of land use. It is recommended to replace A in formula (2) with another variable to avoid confusion among readers who might misunderstand it as an area.
4.Figure 4: The font of the vertical coordinates overlaps. In (b) Secondary classification of PLES, it is difficult to distinguish the separations between the four groups on the horizontal axis. The presentation of this figure needs to be redesigned.
5.Figure 5: Please add a scale bar, consistent with the styles of Figure 1 and Figure 3.
6.Figure 6: Please confirm whether the scale of the color bar is accurate. The maximum value of the scale is 5, but there are numbers up to 64.71 in the figure.
7.Figure 7: The colors of the lines representing flows need to be darkened to better present the transfer characteristics between different land-use types.
8.Lines 569 and 570: What is the difference between the two y's? It is recommended to use different variable representations.
Finally, there are still the following minor suggestions:
1.Both "km²" and "square kilometer" (e.g., Line 183) appear in the text. Please uniformly use "km²."
2.Line 274: It is suggested to change the title to "Results and Analysis."
3.Line 302: Change "4a" to "4(a)."
4.Thoughout the paper, please follow the rule of three-section counting for numbers, including in the text and figures.
Comments on the Quality of English Languageno
Author Response
REVIEWER #2: This paper analyzes the spatiotemporal evolution of production, living, and ecological spaces (PLES) in Henan Province from 1990 to 2020 and explores the relationship between this change and sustainable development through quantitative analysis, providing guidance for policies. Overall, the topic of this study is meaningful, but significant improvements are needed in summarizing the specific research content, presenting research methods and results. Below are my detailed comments:
We would like to thank Reviewer#2 for your invaluable and constructive suggestions, which have been applied to the revised manuscript. Responses to specific comments are provided below. All the changes made in the revised manuscript are highlighted in colour to facilitate their identification.
Major Comments:
Comment 1: Most importantly, Lines 161-174: The research background and objectives need to be re-organized. The current presentation raises confusion. For example, "analyzing the relationship between PLES evolution and SDGs," but SDGs encompass various fields of sustainable development. Which specific goal of the SDGs is being analyzed in this paper? Currently, there is no detailed introduction. As an important content mentioned in the research objectives, SDGs need to be clearly elaborated. "Exploring strategies from Henan’s PLES evolution for sustainable development in similar regions" is summarized as a key research point, but throughout the research content, there is no indication of extending this method to similar regions or even globally. I believe this research objective is missing.
Response 1: The specific SDGs have been clearly identified within the research questions and objectives. Additionally, lines 183-202 have been revised to provide a more detailed explanation of these SDGs. Furthermore, Section 4.5 has been updated to clarify how the strategies for sustainable development derived from Henan’s PLES evolution can be extended to similar regions on a global scale.
Comment 2: The abstract needs improvement. Line 15, "yet there remains a significant research gap in examining their dynamics and driving factors" should be revised. The current statement is not objective enough. For instance, references 9-11 have conducted some research in this field, so it's not completely unexplored. Additionally, Line 19 lacks a description of quantitative data in the results.
Response 2: The sentence has been rephrased for clarity. Additionally, a description of quantitative data has been added to the results.
Comment 3: Line 21: The statistical data mentioned here need to include reference links. Importantly, how these statistical yearbook data were transformed into the data used in the experiment requires a more detailed introduction. For example, which indicators correspond to the factors proposed in the paper? How were missing values handled? Was standardization required? Especially for the study of GDP data series changes, was annual base period adjustment performed? These all require more specific descriptions.
Response 3: Reference links have been added to Section 2.2. Additionally, this section has been expanded to provide a detailed explanation of how statistical yearbook data were collected and processed for this study. The revisions address the correspondence between indicators and factors, the handling of missing values, data standardization, and the annual base period adjustment applied to the GDP data series.
Comment 4: Line 236: It is mentioned that land-use changes come from LUCC land-use data. Wasn't this data extracted from the Landsat data mentioned in Line 214? This needs to be clarified. If LUCC land-use data was used, please indicate which public dataset was used.
Response 4: Apologies for the confusion. The sentence on lines 275-277 (original line 236) has been rephrased to clarify that the CNLUCC dataset is used in this study. Additionally, a detailed introduction to this dataset has been provided in Section 2.2.
Comment 5: Line 255: Why was PCA used in the methodology of this paper? What was the purpose? This needs to be explained.
Response 5: The reason and purpose of using PCA have been explained in Section 2.3.4.
Comment 6: For the software used in the research methodology (e.g., Line 260: SPSS), please provide the specific software version number. This is a requirement of MDPI journals, please refer to the submission instructions.
Response 6: The version numbers of the software used in this study have been added in accordance with MDPI submission instructions.
Furthermore, the presentation of figures and formulas in this paper needs further modification to enhance visibility. Although these modifications are minor, they are crucial for readers to clearly understand the paper:
Comment 1: Figure 2: Please move this figure to Section 2.3. Methodology.
Response 1: Figure 2 has been moved to Section 2.3, Methodology, as suggested.
Comment 2: Figure 3: The figure lacks (a), (b), (c), (d) labels, making it difficult to correspond to the descriptions in the captions. Please add them.
Response 2: Apologies for the oversight. Labels (a), (b), (c), and (d) have been added to Figure 3 to improve clarity.
Comment 3: In formulas (1) and (2), both use A. In formula (1), Ann represents the area of a certain land-use type, while in formula (2), A represents the dynamic degree of land use. It is recommended to replace A in formula (2) with another variable to avoid confusion among readers who might misunderstand it as an area.
Response 3: Apologies for the confusion. The dynamic degree of land use is now represented by "D" instead of "A" to avoid any misunderstanding.
Comment 4: Figure 4: The font of the vertical coordinates overlaps. In (b) Secondary classification of PLES, it is difficult to distinguish the separations between the four groups on the horizontal axis. The presentation of this figure needs to be redesigned.
Response 4: The figure size was adjusted by the editors to meet the journal's print standards. To address the overlapping labels and enhance group distinction, numbers now represent the PLES land uses. A note has been added to the figure title to clarify the correspondence between these numbers and the land use categories.
Comment 5: Figure 5: Please add a scale bar, consistent with the styles of Figure 1 and Figure 3.
Response 5: A scale bar has been added to Figure 5 to ensure consistency with Figures 1 and 3.
Comment 6: Figure 6: Please confirm whether the scale of the color bar is accurate. The maximum value of the scale is 5, but there are numbers up to 64.71 in the figure.
Response 6: Apologies for the confusion. The color bar in Figure 6 is specifically applicable to changed land use categories and does not represent unchanged land uses. A note has been added to the figure to clarify this distinction and improve clarity.
Comment 7: Figure 7: The colors of the lines representing flows need to be darkened to better present the transfer characteristics between different land-use types.
Response 7: The line colors have been darkened to enhance the visualization of PLES transfers.
Comment 8: Lines 569 and 570: What is the difference between the two y's? It is recommended to use different variable representations.
Response 8: Apologies for the confusion. Both instances of "Y" represent the changed areas of PLES, reflecting PLES dynamic changes. The paragraph has been revised to clarify the meanings of variables X, Y, and Z, as well as their relationships.
Finally, there are still the following minor suggestions:
Comment 1: Both "km²" and "square kilometer" (e.g., Line 183) appear in the text. Please uniformly use "km²."
Response 1: "Square kilometer" has been standardized to "km²" throughout the entire manuscript.
Comment 2: Line 274: It is suggested to change the title to "Results and Analysis."
Response 2: The title has been updated to "Results and Analysis" as suggested.
Comment 3: Line 302: Change "4a" to "4(a)."
Response 3: The phrase has been revised to "4(a)" as suggested.
Comment 4: Thoughout the paper, please follow the rule of three-section counting for numbers, including in the text and figures.
Response 4: All numbers have been standardized to follow the three-section counting rule.
Round 2
Reviewer 2 Report
Comments and Suggestions for AuthorsThis manuscript has undergone significant revisions in the writing of the abstract and introduction and in the visualization of the results. The current version has resolved my doubts, but I still have the following suggestions.
1. The explanation of Figure 6 is to dispel my doubts I think there is no relationship between the color of land use categories and the presentation of heat maps, which can also lead to misunderstandings for readers
2. More details can be added to present the methodology section. For example, as an important part of calculating indicators in this article, PCA only introduces the principle in the method section. However, has the multicollinearity test been performed on each indicator to demonstrate the rationality of using PCA? What is included in the input data when using PCA in this article? Is there standardized processing? These specific contents should be introduced in the methods section.
I will agree to recommend the publication of the improved version of the paper based on the above issues.
