Driving the Evolution of Land Use Patterns: The Impact of Urban Agglomeration Construction Land in the Yangtze River Delta, China

Response to Reviewer 1 Comments

1. Summary

Dear Reviewer,

Greetings!

First, we would like to express our sincere gratitude for taking the time to thoroughly review our manuscript. It is an honor for all the authors to receive your detailed and insightful comments, which have significantly driven the improvement of this paper.

We would like to clarify that, in response to the publisher's requirements regarding similarity rate and manuscript length, we have taken several measures. These include providing prior declarations for additional technical terms and using abbreviations, as well as removing unnecessary images, tables, or text. As a result, we have generated a fully updated version of the manuscript, which now complies with the formal review requirements. Based on this new version, we carefully reviewed your comments and have provided detailed revisions and responses to each.

Please find our detailed responses below, and the corresponding revisions, corrections and track changes can be found highlighted in the resubmitted document.

2. Point-by-point response to Comments and Suggestions for Authors

Comments 1:

The article is devoted to the study of land use changes in the Yangtze Delta.

This work solves several problems at once. This is land use mapping, identifying land use changes, determining spatial directions of changes, describing and assessing the strength of the driving factors of changes. Each problem separately could be described in a separate article. We can praise the authors for not taking the popular but wrong path of splitting the results and describing everything in one article.

Response 1:

We sincerely appreciate your positive evaluation of the significance of our research. We observed that changes in urban construction land in urban agglomerations are closely related to the evolution of land use patterns in the Yangtze River Delta over an extended time series. Therefore, we aimed to test and explain these changes and correlations through a series of methods. By employing advanced analytical and mapping techniques, we were able to demonstrate land use pattern evolution across temporal and spatial dimensions, as well as describe and assess the intensity of driving factors behind these changes.

In various sections of the manuscript, we provide detailed explanations of the spatiotemporal evolution of land use, including scale, dynamics, and transition matrices. We also thoroughly describe the spatiotemporal evolution of landscape patterns, including the distribution of transition types and changes at the overall landscape level. Additionally, we applied multiple methods to analyze the driving factors and intensity of urban construction land expansion and its evolution.

It is worth noting that we did not adopt the conventional approach of conflating land use and landscape patterns to describe urban construction land changes in a generalized manner. Instead, we introduced an innovative approach by breaking down the analysis into multiple dimensions, including land use, landscape patterns, time, space, and different land-use types. We believe this approach enhances the clarity and persuasiveness of the research results.

Comments 2:

The solution to the set tasks is based on the use of ready-made land use data. The factors influencing land use change were also described based on ready-made spatial data. Thus, the use of standardized products eliminated possible problems with the collection of initial data. The authors clearly described the set of initial data in paragraph 2.2. This description allows other research teams to reproduce the author's data. The authors themselves only calculated the FRAGSTAT metrics based on the land use layers.

Response 2:

We sincerely appreciate your acknowledgment of the data sources. This study collected, cleaned, and ultimately utilized a substantial amount of historical and current land use and spatial data, all of which are standardized. This ensures that the source data do not present issues related to inaccuracies or inconsistencies. Furthermore, we have provided the sources of all initial datasets in Table 1 of Section 2.2, allowing other research teams to replicate and validate our findings, as well as inspire further studies. This is one of the primary goals we aim to achieve.

Comments 3:

To analyze the data, the authors used the standard deviation ellipse method, multiple linear regression, and the geographic detector model. These methods have long been used in the analysis of spatial data. They are well known to a wide range of researchers and have proven themselves to be the best. The nuances of using these methods are well described by the authors, which ensures the reproducibility of the study. The quality of the source data and analysis methods allows us to trust the authors’ results and conclusions.

Response 3:

We sincerely appreciate your recognition of the research methodology. This study collected extensive data to derive more precise conclusions. Specifically, we conducted a differentiated analysis of large datasets across both temporal and spatial dimensions.

From a temporal perspective, methods such as standard deviation ellipse and multiple linear regression were applied to analyze data without spatial attributes. Comparative and correlation analyses across different time periods were conducted to effectively explain how each independent variable changes over time.

On the spatial dimension, we employed methods such as the geographical detector model to thoroughly analyze the spatial heterogeneity of data with spatial attributes. This is crucial, as some independent variables may exhibit significant differences due to their geographical location (i.e., spatial attributes) at a single time point. Our analysis successfully demonstrated the existence of such spatial disparities and provided further discussion on this phenomenon.

By integrating both temporal and spatial dimensions and distinguishing the nuances and applicability of various research methods, we enhance the scientific rigor of our analysis. This approach also allows other scholars to replicate similar methods and data in future research.

Comments 4:

The article is characterized by a large volume of text. The article takes up 40 pages. This volume creates a large number of large tables and figures. Due to the need to accommodate large tables, the authors even reduced the size of many figures. All these tables are necessary. They will be interesting for the reader. However, I would remove table 3 from the text and move it to the appendix. Moving table 3 to the appendix will make it possible to increase the size of some figures. The information in Table 3 is not a description of the original data or results. It is a description of the FRAGSTAT metrics. Information about them is generally known. It would be sufficient to provide a literary reference instead of a table. But I appreciate that the authors want to make it easier for the reader to perceive the information and make sure that the reader can learn as much as possible directly from the text.

Response 4:

We sincerely appreciate your acknowledgment of the manuscript's length and content. We also agree that most tables and figures are essential for clearly elucidating the research process. We fully support your comments regarding Table 3 and have accordingly reviewed and revised all tables and figures in the manuscript, as detailed below:

First, we thank you for recognizing Table 3 in the original manuscript. Our intention was to make it easier for all readers to understand the definitions and calculation methods of the landscape pattern indices used in this study, especially for those less familiar with ecological research. As a result, Table 3 provided detailed explanations of the FRAGSTAT indices, which contributed to an unnecessary length of the article. Following your suggestion, we have revised the original presentation by removing Table 3 from the main text and relocating it to the appendix, while also adjusting the font size appropriately. These changes can be found in red text in Sections 2.3 (3) and the appendix.

Comments 5:

One of the advantages of the article is its numerous and high-quality illustrations. The most original graphical solution is the use of radar charts. Figure 14 is a concentrated expression of the results of the work. This figure should be a mandatory element in the graphic abstract of the article. Figures 4, 5, 6 and 7 turned out to be unsuccessful. The dynamics are poorly visible on them, the line seems almost even. The reason for this is that the Y axis is constructed from 0. If the authors had constructed the Y axis from higher values on each graph, the dynamics would have been better visible.

Overall, the article is interesting. I believe that the article can be published in its current form.

Response 5:

We sincerely appreciate your recognition of the research's graphical results. In response to your constructive feedback, we have prominently included Figure 14 in the graphical abstract of the manuscript. Additionally, we have rigorously reviewed Figures 4, 5, 6, and 7. We found that these figures, with their Y-axis starting at zero, failed to effectively illustrate the dynamics of changes. Moreover, we observed that the content presented in these figures overlaps significantly with the information in "Table 10: Landscape Pattern Indices and Calculation Details (Original manuscript)." These figures merely visualized some of the numerical data from the table, yet occupied considerable space, which seemed inappropriate.

Therefore, following thorough discussion among the authors, we have decided to remove Figures 4, 5, 6, and 7, as the information they conveyed is comprehensively expressed in the existing Table 8: LSP Index and Calculation Details. The landscape index data for five different years from 2000 to 2020 in the table sufficiently captures the annual changes in the Landscape Fragmentation Index, Landscape Heterogeneity Index, Landscape Aggregation Index, and Landscape Diversity Index, making additional visualization unnecessary.

Furthermore, the aforementioned figures and tables focused on the overall landscape changes in the region, without analyzing the changes in landscape levels for different land use types. To address this, we conducted a supplementary experiment, further subdividing the five time points from 2000 to 2020 into six land use types. We analyzed eight landscape pattern indices for these types. This approach better explains the changes in landscape levels for different land use types and more effectively supports the conclusions of our study. You can find these changes highlighted in red in Table 9: LSP Index in Different Land Use Types and Calculation Details in Section 3.2.2.

3. Response to Comments on the Quality of English Language

Response 1:

Although you did not raise any concerns regarding the quality of the manuscript’s English, we have nonetheless conducted a thorough internal review to further improve the quality. Since none of the authors are native English speakers, the previous version contained several errors. We have now performed a comprehensive and meticulous check of the manuscript, ensuring professional editing and refinement of the English language. A key part of this revision was to standardize the majority of the tense usage to the simple present tense. We also removed unnecessary instances of the simple past and present perfect tenses, using them only where necessary.

We have carefully corrected all tense-related errors throughout the manuscript, including those on the following lines:

25, 28, 97, 100, 115, 146-147, 248-249, 251-252, 278-279, 294, 296, 298, 315, 329, 353-354, 365, 369, 375, 394, 400, 402-403, 412, 414, 432, 434, 443, 461, 478, 505, 513-514, 524-525, 575-576, 579, 581, 633-634, 637-638, 648-649, 651, 656, 658, 664, 745-746, 749-750, 756, 760, 763, 767, 777, 782, 785, 795, 889, 895, 923, 925, 928-930, 935.

Numerous corrections related to proper tense and grammatical expression can be found highlighted in red within these sections.

Response to Reviewer 2 Comments

1. Summary

Dear Reviewer,

Greetings!

First, we would like to express our sincere gratitude for taking the time to thoroughly review our manuscript. It is an honor for all the authors to receive your detailed and insightful comments, which have significantly driven the improvement of this paper.

We would like to clarify that, in response to the publisher's requirements regarding similarity rate and manuscript length, we have taken several measures. These include providing prior declarations for additional technical terms and using abbreviations, as well as removing unnecessary images, tables, or text. As a result, we have generated a fully updated version of the manuscript, which now complies with the formal review requirements. Based on this new version, we carefully reviewed your comments and have provided detailed revisions and responses to each.

Please find our detailed responses below, and the corresponding revisions, corrections and track changes can be found highlighted in the resubmitted document.

2. Point-by-point response to Comments and Suggestions for Authors

Comments 1:

The paper presents a clear and simple, well-executed research, but some general remarks must be proposed and discussed.

The spatiotemporal evolution of land use and landscape patterns is a usual topic; similarly patches and deeper land fragmentation are often discussed in literature.  Even though the paper says it uses a novel approach, it is not clear how the paper proposes some new points of view and new results.  The sentences

- “The findings reveal that over the two decades, the expansion of urban construction areas in the YRD has become more concentrated in patches, exhibiting significant expansion effects. Relatively unspoiled farmland and FL are progressively encroached upon, resulting in an increased number of patches and heightened land fragmentation..”

-This underscores that urban land expansion is characterized by complexity and variability, with diverse factors exerting uneven influences, thus demonstrating its dynamic spatiotemporal nature. .

Claim something evident.  How are concentration and expansion combined?

Please explain, especially in relation to Zhou Zhechen.

Response 1:

We sincerely appreciate your positive evaluation of the overall logic and execution of our research. The spatiotemporal evolution of land use and landscape patterns is indeed a common study area globally. Our focus has been on the driving role of urban construction land, and we have used a range of methods to examine and explain these changes and their correlations, as well as to describe and assess the intensity of the driving factors.

In contrast to existing studies, our novel approach and perspective lie in avoiding the common practice of conflating land use and landscape patterns to describe urban construction land changes in a general manner. Instead, we have disaggregated the analysis into multiple dimensions, including land use, landscape patterns, time, space, and different land-use types. We believe this approach provides clearer and more persuasive research results.

We are also very grateful for and fully acknowledge your concerns regarding certain statements in the article. To ensure clarity in our response, we will further elaborate and address your comments on "concentration and expansion" in the following sections, providing detailed evidence and responses to each of your points.

Comments 1-1:

- “The findings reveal that over the two decades, the expansion of urban construction areas in the YRD has become more concentrated in patches, exhibiting significant expansion effects. Relatively unspoiled farmland and FL are progressively encroached upon, resulting in an increased number of patches and heightened land fragmentation..”

Response 1-1:

Thank you very much for your insightful comments regarding the expansion effects and specific landscape patches. We sincerely acknowledge that our previous research focused solely on overall landscape pattern indices, and we neglected to differentiate among specific land use types for a more detailed analysis of landscape patch indices. We apologize for this oversight and appreciate your thorough feedback.

In response to this issue, we have promptly conducted additional experiments. We further divided the five time points from 2000 to 2020 into six land use types and analyzed eight landscape pattern indices for each. These changes can be found in red text in Table 9 of Section 3.2.2.

This additional analysis provides a clearer explanation of the landscape level changes for different land use types and more effectively supports the conclusions of our study, including the specific point you raised in your comments.

Specifically:

On the one hand, for the theoretical aspects, we have included definitions of the indices to clarify how landscape fragmentation and aggregation are measured. These changes are highlighted in red in the appendix tables and Sections 3.2.2 (1) and (3):

-    Landscape Fragmentation refers to the process in which a landscape transitions from a single, homogeneous, and continuous whole to a complex, heterogeneous, and discontinuous mosaic of patches due to interference from natural or human factors. Higher values of NP, PD, and ED, along with lower MPS values, indicate increased landscape fragmentation. Higher values of NP, PD, and ED, along with lower MPS values, indicate increased landscape fragmentation.

-    Higher AI values indicate a greater level of patch aggregation, while higher CONTAG values indicate that a leading patch type in the landscape shows significant connectivity and clustering.

On the other hand, when analyzing the "concentration and expansion" of specific landscape patches, we use the Mean Patch Size (MPS) and Aggregation Index (AI) as examples.

Regarding the "expansion effects" of urban construction land mentioned in the original text, Table 9 in Section 3.2.2 shows that the MPS for urban construction land increased from 21.19 ha in 2000 to 43.15 ha in 2020, reflecting more than a twofold increase over the past 20 years. Additionally, the AI for urban construction land rose from 51.89% in 2000 to 69.37% in 2020. These figures indicate both the expansion of patch area and increased aggregation, thereby confirming the "expansion effect" of urban construction land.

For the "encroachment of cultivated land and forest land" mentioned in the original text, Table 9 in Section 3.2.2 shows that the MPS for cultivated land and forest land decreased from 525.67 ha and 652.12 ha in 2000 to 424.66 ha and 650.74 ha in 2020, respectively. This supports the observation of "encroachment."

The supplementary experiments and quantified data provide clearer support for the research conclusions. We have also revised inappropriate expressions in the original text, which can be found in red text in lines 5-10 of the first paragraph of Section 5: Conclusion. However, it must be acknowledged that these landscape pattern indices only partially measure changes, and future research will need additional indicators and studies to more comprehensively consider the spatiotemporal evolution of landscape patches.

Thank you once again for your precise identification of the inconsistencies in our manuscript.

Comments 1-2:

-This underscores that urban land expansion is characterized by complexity and variability, with diverse factors exerting uneven influences, thus demonstrating its dynamic spatiotemporal nature. .

Response 1-2:

Thank you for your thorough identification of the imprecision in some conclusions. We have revisited these conclusions and revised the statements accordingly.

Specifically, the term "complexity and variability" in the sentence refers to the complexity and variability of urban land expansion in terms of both "location (direction)" and "intensity." The standard deviation ellipse method is used to analyze the complexity and variability of urban land expansion in these aspects. This is detailed in Section 3.3, with discussions on direction in Section 3.3.1 and intensity in Section 3.3.2. The red text in Section 3.3 reflects our additions and revisions to the relevant statements.

Furthermore, the phrase “with diverse factors exerting uneven influences” refers to the fact that urban land expansion is driven by multiple factors, each exerting an uneven level of influence. Section 3.4 employs multiple linear regression and geographic detector methods to analyze the uneven impacts of these driving factors. The red text in Section 3.4 includes our revisions and clarifications regarding these statements.

Once again, we appreciate your precise identification of the issues with the manuscript's expressions. We have reorganized and corrected the ambiguous statements, and you can find these changes in the red text in lines 16-19 of the first paragraph of 5 Conclusion.

Comments 1-3:

Claim something evident.  How are concentration and expansion combined?

Please explain, especially in relation to Zhou Zhechen.

Response 1-3:

Thank you for your insightful questions regarding "concentration and expansion." We have given further consideration to these issues and made additional refinements to our analysis.

In response to your comments, we have conducted more detailed experiments, breaking down the data from 2000 to 2020 into six land use types and analyzing eight landscape pattern indices. These updates can be found in Table 9 of Section 3.2.2. Based on the original Table 8 and the newly added Table 9, the changes in overall and detailed indicators such as Mean Patch Size (MPS) and Aggregation Index (AI) quantitatively describe the dynamics of concentration and expansion in urban construction land.

Specifically, as addressed in Responses 1-1 and 1-2, the manuscript explains the "concentration and expansion" through quantitative data analysis. Here, I will provide a comprehensive explanation and further summary.

It is important to clarify that "concentration and expansion" refers to the main research focus of this study—urban construction land.

For "concentration," the gradual increase in the Mean Patch Size (MPS) and Aggregation Index (AI) indicators of urban construction land, as detailed in Table 9, suggests a growing level of aggregation for urban construction land from 2000 to 2020. This effectively demonstrates the "concentration" of urban construction land. Detailed explanations can be found in Table 9 of the manuscript and in Sections 3.2.2(1) to (3), highlighted in red.

Regarding "expansion," the "expansion intensity" of urban construction land is quantitatively explained. From 2000 to 2020, the standard deviation ellipse for urban construction land in the Yangtze River Delta Urban Region expanded and shifted, primarily from northwest to southeast. The most significant expansion occurred in the EN direction, covering an area of 1641.24 km². This expansion was influenced by different temporal and spatial drivers. Further quantitative explanations of the expansion's area, direction, and intensity are provided in Table 10, and in Sections 3.3.1 and 3.3.2 of the latest manuscript, along with relevant figures.

We have also added explanations to clarify these changes. You can find these updates in Sections 3.2.2(1) and (3) of the manuscript. Additional explanations regarding the conclusions are reflected in the aforementioned content and in our responses to specific sentences you mentioned.

Regarding Zhou Zhechen's research, his team used landscape dynamics and landscape pattern indices to identify relevant changes in the Yangtze River Delta. They observed a gradual weakening of natural landscapes and an increase in landscape fragmentation with increasingly complex distribution. Zhou’s findings are highly consistent with our results, such as the increase in urban construction land and the reduction of natural landscapes like forests and water bodies. The observed changes in landscape fragmentation corroborate the accuracy of our study.

In addition to these similarities, Zhou's research focuses more on the ecological perspective of the overall landscape patterns and their connections with climate and topography. In other words, Zhou's study emphasizes the "integrity" of regional landscape patterns and seeks potential correlations from a broader perspective. In contrast, our study goes beyond analyzing the "integrity" of landscape patterns by connecting these factors with "specific" aspects of urbanization. We delve deeper into the role of urban construction land in the spatiotemporal evolution of landscape patterns, particularly focusing on the differences in various driving factors. This approach allows us to better address the complexity of urban changes and propose more targeted policy recommendations.

Therefore, while previous research has provided relatively complete findings, our study offers further updates. These updates include a focus not only on the "integrity" of regional landscape patterns and risks but also on the "specific" evolution of landscape patterns and risk impacts for different land use types, particularly urban construction land. We investigate the variations in driving factors for specific landscape patches from an urbanization perspective, which facilitates the development of more nuanced and targeted policy recommendations for urban development.

Comments 2:

The paper says “fragmentation of ecological and agricultural spaces is worsening”: please explain what definition of fragmentation is adopted, both quantitative and qualitative.  Does the research find more fragmentation than expected?

Response 2:

Thank you for your careful attention to the issue of fragmentation. We acknowledge that the definition of fragmentation was not clearly articulated in the previous manuscript. We have updated both the quantitative and qualitative descriptions of this definition, which can be found in red text in Section 3.2.2(1) of the revised manuscript:

-    Landscape Fragmentation refers to the process in which a landscape transitions from a single, homogeneous, and continuous whole to a complex, heterogeneous, and discontinuous mosaic of patches due to interference from natural or human factors. Higher values of NP, PD, and ED, along with lower MPS values, indicate increased landscape fragmentation. Higher values of NP, PD, and ED, along with lower MPS values, indicate increased landscape fragmentation.

Theoretically, our study identified an increase in fragmentation for some landscape types over time, exceeding initial expectations. Both at the overall landscape level and within specific land use types related to ecological and agricultural spaces, the data from the past 20 years indicate a deepening of fragmentation for certain landscape types. For instance, the number of patches (NP) for cultivated land increased from 21,482 to 23,656, while the mean patch size (MPS) decreased from 525.67 ha to 424.66 ha, indicating an increase in fragmentation.

The indicators representing fragmentation—number of patches (NP), patch density (PD), edge density (ED), and mean patch size (MPS)—generally support our conclusions. However, in some cases, these indicators do not show a clear trend of fragmentation, and in a few instances, the trend is even contrary. Consequently, we have revised the statement "fragmentation of ecological and agricultural spaces is worsening," as we believe it was inappropriate. The revised statement can be found in red text in the second paragraph of Section 2.1 of the manuscript.

Comments 3:

I would also suggest not to make very general statements such as “As global population growth and economic activities accelerate, land use and spatial 44 evolution have emerged as major international concerns” because they state the obvious.  Other elements should be defined such as expansion: “three types of construction land expansion areas: high expansion, moderate expansion, and low expansion.”  What is the quantitative definition of expansions?  Does it include the density of settlement?  % or green land included in the built area?

Response 3:

Thank you very much for pointing out some very general statements in the article. We have carefully reviewed the entire manuscript and removed all clearly unnecessary statements, including those you identified. These changes are highlighted in red in the Introduction section of the revised manuscript. Additionally, we have revised vague and unclear expressions throughout the manuscript to enhance the overall clarity of the text.

We appreciate your insightful questions regarding the definitions and classifications of "expansion" and its intensity, which were not adequately addressed in the previous manuscript. We have now included the relevant explanations in the manuscript. These updates can be found in red text in the first paragraph of Section 3.3.2:

-    Through introducing indicators such as expansion area, annual average expansion area, and annual average expansion intensity, the CTL area in 16 subdivided expansion zones were assessed across different directions for each period to compare the strength, speed, and trend. This assessment revealed the scale and intensity of land expansion in these regions from 2000 to 2020. Using the natural fracture method and referring to relevant literature and planning policies, the classification of expansion intensity is represented by the annual average expansion intensity index over a span of 20 years. The classifications are defined as follows: 0-0.15% for low intensity expansion, 0.15%-0.36% for medium intensity expansion, and 0.36%-0.87% for high intensity expansion, with specific calculation details provided in Table 10.

As for “three types of construction land expansion areas: high expansion, moderate expansion, and low expansion,” we would also like to clarify the definitions of the three types of construction land expansion areas: high expansion, moderate expansion, and low expansion.

The key to distinguishing between these levels of expansion lies in the thresholds of 0.15% and 0.36% for expansion intensity indices. Initially, we defined the threshold for low expansion and moderate expansion at 0.15% by using the natural breaks method combined with existing literature (as referenced in [1-4]). Subsequently, we determined the threshold for high expansion and moderate expansion at 0.36%, taking into account relevant literature and the regional characteristics of the Yangtze River Delta. These regional characteristics were outlined in official policy documents published by the Chinese government. For instance, documents such as the "Yangtze River Delta Integration Development Action Plan" and the "Land Spatial Planning of the Yangtze River Delta (2021-2035)" suggest that a reasonable area for urban construction land expansion is around 70 square kilometers. Based on this figure, we calculated the threshold for the expansion intensity index to be 0.36%. Therefore, the expansion intensity indices are classified as follows: [0-0.15%] for low intensity expansion, (0.15%, 0.36%] for moderate intensity expansion, and (0.36%-0.87%] for high intensity expansion.

Additionally, we wish to address the scope of "expansions" as you mentioned. First, our discussion on expansions does not include population density (density of settlement). This is because the base data we used is derived from satellite remote sensing images, which identify built environments but do not measure urban population density. Secondly, whether our expansions include green space within built-up areas depends on the size of the green space. Due to the resolution of our remote sensing data, with a base grid size of 30 meters by 30 meters, green spaces larger than 900 square meters can be identified as part of the expansion. Conversely, green spaces smaller than 900 square meters cannot be detected.

We hope that the above supplementary explanations can effectively answer your questions. The references mentioned are as follows.

1. Liu, Y.; Song, W.; Deng, X. Understanding the spatiotemporal variation of urban land expansion in oasis cities by integrating remote sensing and multi-dimensional DPSIR-based indicators. Ecol. Indic. 2019, 96, 23-37.

2. Jiang, L.; Deng, X.; Seto, K.C. The impact of urban expansion on agricultural land use intensity in China. Land Use Policy. 2013, 35, 33-39.

3. Jiang, Y.; Jin, X.; Qin, L.; Xue, Q.; Cheng, Y.; Long, Y.; Yang, X.; Zhou, Y. Process and Characteristics of Urban Built-Up Area Expansion in Jiangsu-Shanghai Region in The Past 600 Years. City Planning Review. 2019, 43, 55-68.

4. Geng, T.; Mao, Y.; Li, J.; Chen, H. Spatio-Temporal Characteristics and Driving Mechanism of Xi’an Urban Expansion. Economic Geography. 2019, 39, 62-70.

Comments 4:

The paper makes some preliminary simplifications of the complex urban form, which is not fully convincing, such as “the spatial expansion direction of urban construction land”: expansion direction?  Even though Chinese master planning is precise and usually is precisely implemented, what a direction is -  EN orientation being the 32 most intense expansion area - should be explained.  Why is this concept useful or fertile for future research?

Response 4:

Thank you for your suggestion regarding the further discussion on the "expansion direction." We have indeed found some ambiguities in the original manuscript and have made revisions accordingly. For instance, we have clarified the methods for simplifying urban morphology, and you can find these changes in the highlighted sections.

According to the formula presented in section 2.3(5) and the results obtained in section 3.3.2, indicators such as expansion area, annual average expansion area, and annual average expansion intensity show that the EN direction is one of the 32 most densely expanded areas. This direction is associated with the rapidly developing Shanghai metropolitan area over the past 20 years, which aligns with mainstream understanding. However, some inappropriate statements were present in the previous manuscript. We appreciate your identification of these issues and have revised them accordingly. You can see the changes in the highlighted sections of lines 12-14 in the first paragraph of section 5 Conclusion.

We would also like to clarify the rationale behind the simplification of complex urban forms in our study. While some researchers categorize urban morphology based on "administrative boundaries," this approach may not be future-proof.

In our study, urban forms were classified directly by "direction" to examine their expansion. This approach is due to the Yangtze River Delta being one of China's most dynamic urban agglomerations, with an important task of "integrated development." The overall plan for the Yangtze River Delta is based on the development of several "urban clusters." The region is recognized by the Chinese government as a typical case of "urban integration" and "metropolitan area" development. This implies that development cannot be guided by single cities alone. By classifying urban forms by direction—such as aligning with the geographic locations of metropolitan areas like Hangzhou and Shanghai—we emphasize the concept of "regional integration" rather than administrative boundaries. This approach supports the research perspective on "urban agglomeration development."

Therefore, we believe that classifying urban forms by "direction" rather than administrative boundaries is not a mere simplification. This method is based on the planning policy direction provided by the Chinese government and aligns with the overall vision for future urban agglomeration development in China. We consider the "direction" concept to be valuable for future planning research and regard it as one of the unique innovations of this study. We hope this explanation clarifies our rationale for this approach.

Comments 5:

Indeed, the results find urbanization “has expanded in concentrated patches”, which could be easily expected because the master plans usually plan CBD, centers, areas of higher densities.  But how large are these patches?  Are they some valuable resources for agricultural production and ecological services?

Response 5:

Thank you very much for your attention to and reminder about the phenomenon of "expansion in concentrated patches" in the urbanization process. It is indeed acknowledged that urban planning often focuses on CBDs, centers, and high-density areas, making "expansion in concentrated patches" a predictable phenomenon. Many studies have confirmed similar findings. Clustered development of construction land is a real aspect of urban development, and we fully appreciate your evaluation of this matter.

1) Regarding the size of these patches, we apologize for not providing a thorough discussion in the previous manuscript. Your concerns are quite reasonable. We have now conducted an additional experiment to detail the specific sizes of different types of patches. The results of this experiment are outlined in Response 1-3, and you can find these updates in the red font of Table 9 in section 3.2.2.

Through this supplementary experiment, we can quantitatively address the sizes of these patches. By analyzing the Mean Patch Size (MPS) and the Aggregation Index (AI), we found that the MPS of construction land increased from 21.19 hectares in 2000 to 43.15 hectares in 2020, more than doubling over the past 20 years. Additionally, the AI of construction land rose from 51.89% in 2000 to 69.37% in 2020, indicating an increase in both the size of concentrated construction patches and their degree of clustering. Therefore, we can conclude that the average patch size of these concentrated construction areas was 43.15 hectares in 2020.

However, it should be noted that MPS and AI are just two of the indicators we consider reasonably suited for measuring patch size, and they may still be incomplete. In the future, more appropriate indicators should be introduced in landscape pattern index analysis to more precisely describe the size of these patches.

2) Regarding whether these patches are valuable resources for agricultural production and ecological services, our study results indicate that the expansion of construction land indeed impacts agricultural production and ecological services spatially, particularly encroaching upon ecological service resources. These expanding patches are, to some extent, valuable resources for both agricultural production and ecological services.

On one hand, the expansion of urban space disrupts the connectivity of existing ecological and agricultural spaces, hindering the flow of energy and information among biological species. This disruption directly affects the ecosystem's service functions, leading to a strong negative impact on ecological service resources.

On the other hand, while the expansion of urban space impedes the contiguous development of agricultural areas, increasing their fragmentation, it also provides certain benefits to agricultural development through technology and funding. Urban agglomeration supports the advancement of efficient technological agriculture. Thus, the expansion of urban space has both negative and positive effects on agricultural production resources and should be considered from a dialectical perspective.

In summary, from these two perspectives, our study results show that the patches of expansion in urbanization indeed occupy, encroach upon, and impact valuable resources for agricultural production and ecological services. We hope this explanation clarifies our viewpoint more clearly.

In the previous manuscript, we only provided a rough summary of this conclusion. We appreciate your scrutiny of this matter and fully agree with your observations. We have revised the relevant sections to better illustrate the impact of construction land expansion on agricultural and ecological spaces. The updated and more detailed explanations can be found in the following sections of the revised manuscript:

Red text in sections 3.2.2(1) and (3)

-    In summary, over the last 20 years, the landscape fragmentation degree in the YRDUR has progressively decreased. Nevertheless, the ongoing transformation of agricultural land into various other uses, particularly for construction, has exacerbated fragmentation. These changes indicate that the agricultural space and related ecological space dominated by CVL are being affected and squeezed by urban construction space, largely driven by human development activities.

-    The relevant patches are mainly concentrated and developed in large towns in the YRD. This This reduction in connectivity and aggregation of patches are mainly linked to urban sprawl, which disrupts the flow of energy and information among species in ecological and agricultural spaces.

Red text in section 4 Discussion

-    On one hand, the expansion of urban space disrupts the connectivity of existing ecological and agricultural spaces, hindering the flow of energy and information among biological species. This disruption directly affects the ecosystem's service functions, leading to a strong negative impact on ecological service resources.

-    On the other hand, while the expansion of urban space impedes the contiguous development of agricultural areas, increasing their fragmentation, it also provides certain benefits to agricultural development through technology and funding. Urban agglomeration supports the advancement of efficient technological agriculture. Thus, the expansion of urban space has both negative and positive effects on agricultural production resources and should be considered from a dialectical perspective.

3. Response to Comments on the Quality of English Language

Response 1:

Although you did not raise any concerns regarding the quality of the manuscript’s English, we have nonetheless conducted a thorough internal review to further improve the quality. Since none of the authors are native English speakers, the previous version contained several errors. We have now performed a comprehensive and meticulous check of the manuscript, ensuring professional editing and refinement of the English language. A key part of this revision was to standardize the majority of the tense usage to the simple present tense. We also removed unnecessary instances of the simple past and present perfect tenses, using them only where necessary.

We have carefully corrected all tense-related errors throughout the manuscript, including those on the following lines:

25, 28, 97, 100, 115, 146-147, 248-249, 251-252, 278-279, 294, 296, 298, 315, 329, 353-354, 365, 369, 375, 394, 400, 402-403, 412, 414, 432, 434, 443, 461, 478, 505, 513-514, 524-525, 575-576, 579, 581, 633-634, 637-638, 648-649, 651, 656, 658, 664, 745-746, 749-750, 756, 760, 763, 767, 777, 782, 785, 795, 889, 895, 923, 925, 928-930, 935.

Numerous corrections related to proper tense and grammatical expression can be found highlighted in red within these sections.

Response to Reviewer 3 Comments

1. Summary

Dear Reviewer,

Greetings!

First, we would like to express our sincere gratitude for taking the time to thoroughly review our manuscript. It is an honor for all the authors to receive your detailed and insightful comments, which have significantly driven the improvement of this paper.

We would like to clarify that, in response to the publisher's requirements regarding similarity rate and manuscript length, we have taken several measures. These include providing prior declarations for additional technical terms and using abbreviations, as well as removing unnecessary images, tables, or text. As a result, we have generated a fully updated version of the manuscript, which now complies with the formal review requirements. Based on this new version, we carefully reviewed your comments and have provided detailed revisions and responses to each.

Please find our detailed responses below, and the corresponding revisions, corrections and track changes can be found highlighted in the resubmitted document.

2. Point-by-point response to Comments and Suggestions for Authors

Comments 1:

The study employs multiple quantitative methods, such as standard deviation ellipse, multiple linear regression, and the geographical detector model, to provide a thorough analysis of land use changes and their driving factors.  Focusing on the Yangtze River Delta, a region of significant economic and ecological importance, the study's findings are highly relevant for urban planning and policy-making in rapidly urbanizing areas.  The use of high-resolution remote sensing data over a 20-year period offers robust support for the study's conclusions.

Response 1:

We sincerely appreciate your positive evaluation of the significance and methodology of our study!

This research collected, processed, and utilized nearly 20 years of high-resolution remote sensing data, employing a variety of quantitative methods to generate clearer and more convincing results.

From a temporal perspective, we primarily applied standard deviation ellipse and multiple linear regression methods. For data without spatial attributes, we conducted comparative analysis across different time sections, as well as correlation analysis, to effectively explain how each independent variable changes over time.

From a spatial perspective, we focused on methods such as the geographic detector model to analyze the spatial heterogeneity of data with spatial attributes. This approach allows us to capture the significant variability of some independent variables across different geographical locations within the same timeframe. Our analysis effectively demonstrates the existence of such spatial differences and provides further discussion on their implications.

Regarding the significance of the study, the Yangtze River Delta’s crucial economic and ecological importance is widely acknowledged in academia. We aim to test and explain the changes in land use and their correlations through a series of methods, while describing and assessing the strength of the driving factors behind these changes. The results of this study provide valuable insights to support urban planning and policymaking in rapidly urbanizing areas. We also believe the findings carry substantial practical relevance.

Once again, we deeply appreciate your recognition of the study’s methodology, data, and significance!

Comments 2:

Minor comments:

Discussion: While the paper presents detailed findings, it could benefit from a more in-depth discussion of how these findings could directly inform urban planning and policy decisions.

Response 2:

Thank you very much for your recognition of the results presented in this study. We must acknowledge that the depth of the "Discussion" section, as you pointed out, was indeed insufficient. Many of the research findings were not effectively translated into practical insights for urban planning and policy decision-making.

In response to your feedback, we believe that this issue may have arisen due to the insufficient depth of analysis. For example, the analysis of landscape patterns remained at a general level and did not differentiate between specific land use types. To address this, we have supplemented our experiments by further subdividing the five temporal stages from 2000 to 2020 into six land use types, and we analyzed eight landscape pattern indices for each type. You can find these revisions highlighted in red in Table 9 of Section 3.2.2.

Additionally, based on both the original results and the outcomes of the supplemental experiments, we have thoroughly revised and expanded the discussion to provide a more in-depth analysis. Our aim is to ensure that these research findings can better inform urban planning and policy decisions. These updates can be found in the 2nd to 4th paragraph of the "4 Discussion" section, with the relevant changes highlighted in red.

We hope that these revisions address your concerns and enhance the clarity and applicability of the research outcomes.

-    At present, as economic development and international cooperation continue to progress in China's eastern region, the YRD faces challenges related to ecological environmental pressure and resource limitations.

-    On one hand, the expansion of urban space disrupts the connectivity of existing ecological and agricultural spaces, hindering the flow of energy and information among biological species. This disruption directly affects the ecosystem's service functions, leading to a strong negative impact on ecological service resources. On the other hand, while the expansion of urban space impedes the contiguous development of agricultural areas, increasing their fragmentation, it also provides certain benefits to agricultural development through technology and funding. Urban agglomeration supports the advancement of efficient technological agriculture. Thus, the expansion of urban space has both negative and positive effects on agricultural production resources and should be considered from a dialectical perspective.

-    The YRD is crucial in driving agricultural supply-side structural reforms and fostering the revitalization of rural industries. The findings of this study help identify and address key resource constraints in regional development. For example, we have found that in expansion areas of different intensities, driving factors such as road freight volume, the percentage of green inventions in the total number of annual applications in the region, and the total retail sales of consumer goods are more dominant. In future planning strategies, emphasis can be placed on developing multimodal transportation, creating spatial layouts that attract innovative industries and high-quality capital, etc., in order to strengthen the management of urban CTL. Furthermore, the research supports the formulation of rural industry revitalization policies in the YRD. For example, we have identified through quantitative data analysis that the cultivated land in the YRD region suffered significant damage and occupation in the past 20 years, offering a strong scientific basis for the ongoing advancement of regional agriculture and rural development. These results are not only crucial for the sustained development of the YRD but also offer valuable insights and strategies for urban agglomerations worldwide facing similar challenges.

Comments 3:

Use your detailed results to replace the vague statement "The influence of temporal and spatial driving factors on the expansion of urban construction land differed across various periods and regions..".

Response 3:

Thank you very much for your concerns regarding the vague statements in our study. We acknowledge that some of the phrasing lacked clarity and precision, such as the statement you highlighted: " The influence of temporal and spatial driving factors on the expansion of urban construction land differed across various periods and regions...".

We have since revised and supplemented the description to ensure that the study clearly identifies which temporal and spatial driving factors influenced urban construction land expansion across different periods and regions. These modifications and additional explanations can be found in the revised sections, highlighted in red, as detailed below.

From a temporal perspective, we have now distinguished three time periods: 2000, 2010, and 2020. Within each of these periods, we further categorized the driving factors corresponding to low, medium, and high-intensity expansion areas. This allows for a clearer understanding of how "different intensities" of "dominant driving factors" influenced urban construction land expansion across "different time periods." You can find these supplementary explanations and revisions in Section 3.4.2 (1), marked in red:

-    In 2000, the dominant temporal driving factor in low-intensity expansion regions was technological advancement. The corresponding driving factor was the percentage of green inventions in the total annual patent applications in the region (X1), which showed a positive correlation and was influenced by a single factor. In medium-intensity expansion regions, the dominant temporal driving factor was the industrial structure. The key driving factor was the proportion of added value (2nd Industry) to GDP (X8), which showed a negative correlation and was impacted by a singular element. In high-intensity expansion regions, the dominant temporal driving factors were socio-economic and environmental-humanistic conditions. The corresponding driving factors were the number of large-scale industrial enterprises (X6) and the forest coverage rate (X14), with the former being positively correlated and the latter negatively correlated, driven by dual factors.

-    In 2010, the dominant temporal driving factors in regions of low-intensity expansion were socio-economic, illustrated by local fiscal general budget expenditures (X4) and the land area requisitioned that year (X5), both showing positive correlations, thus forming a dual-factor drive. For medium-intensity expansion regions, the dominant temporal driving factors included technological development, socio-economic status, and industrial structure, represented by the percentage of green inventions in the total annual patent applications (X1), the land area requisitioned that year (X5), and the proportion of secondary industry employees (X10). These factors had positive, positive, and negative correlations respectively, indicating a multifactorial drive. In high-intensity expansion regions, the dominant temporal driving factors were socio-economic and environmental-humanistic, depicted by the forest coverage rate (X14). The first factor showed a positive correlation, whereas the second exhibited a negative correlation, suggesting a dual-factor influence.

-    In 2020, the dominant temporal driving factors in low-intensity expansion areas included industrial structure, environmental-humanistic factors, and transportation facilities. In medium-intensity expansion areas, the dominant temporal driving factor was technological development, represented by the percentage of green inventions in the total annual patent applications in the region (X1), with a positive correlation, indicating a single-factor drive. In high-intensity expansion areas, the dominant temporal driving factors were socio-economic and environmental-humanistic conditions, indicating a multifactorial drive.

In terms of spatial analysis, we applied spatial discretization to the amount of urban construction land in 2000, 2010, and 2020, using this as a dependent variable to link with the relevant dominant driving factors. We then conducted factor detection and interaction detection analyses. This approach allows us to clearly understand the "dominant driving factors" that influenced the expansion of urban construction land under "different spatial conditions" with "varying intensities." Additionally, we differentiated between the effects of single factors and interacting factors. You can find these supplemental explanations and revisions in Section 3.4.2 (2), highlighted in red.

For Factor Detection Results:

-    the influence of factors in 2000, 2010, and 2020 is consistently distributed across various types of areas.

-    In low-intensity expansion areas, the primary influencing factors were ruggedness (X21), elevation (X22), and slope (X23), significantly surpassing other factors.

-    In medium-intensity expansion areas, the main influencing factors were ruggedness (X21), elevation (X23), and slope (X22).

-    In high-intensity expansion areas, the primary influencing factors were distance from major railways (X18), ruggedness (X21), and elevation (X23).

For Interaction Detection Results,

-    In 2000,

-    within the YRD's low-intensity expansion areas, the leading three interaction determinative power (q) values for spatial driving factors influencing urban CTL were elevation (X23) ∩ ruggedness (X21), ruggedness (X21) ∩ distance from major rivers (X20), and ruggedness (X21) ∩ distance from major railways (X18), all exhibiting dual-factor enhancement relationships (Figure 15).

-    For medium-intensity expansion areas, the top three interaction determinative power (q) values were ruggedness (X21) ∩ distance from major rivers (X20), ruggedness (X21) ∩ distance from major highways (X19), and ruggedness (X21) ∩ distance from major railways (X18).

-    In high-intensity expansion areas, the foremost interaction determinative power (q) values were ruggedness (X21) ∩ distance from major railways (X18), distance from major rivers (X20) ∩ distance from major railways (X18), and elevation (X23) ∩ distance from major railways (X18).

-    In 2010,

-    within low-intensity expansion areas, the top three interaction determinative power (q) values for spatial driving factors of urban CTL were ruggedness (X21) intersecting with distance from major railways (X18), elevation (X23) intersecting with ruggedness (X21), and ruggedness (X21) intersecting with distance from major rivers (X20), all exhibiting dual-factor enhancement (Figure 16).

-    For medium-intensity expansion regions, the top three interaction determinative power (q) values were ruggedness (X21) intersecting with distance from major railways (X18), ruggedness (X21) intersecting again with distance from major railways (X18), and ruggedness (X21) intersecting with distance from major highways (X19), all showing dual-factor enhancement.

-    In high-intensity expansion areas, the top three interaction determinative power (q) values were ruggedness (X21) intersecting with distance from major railways (X18), elevation (X23) intersecting with distance from major railways (X18), and slope (X22) intersecting with distance from major railways (X18), with all interactions demonstrating dual-factor enhancement.

-    In 2020,

-    within the low-intensity expansion regions of the YRD, the top three interaction determinative power (q) values for spatial driving factors for urban CTL were ruggedness (X21) intersecting with distance from major railways (X18), ruggedness (X21) intersecting with distance from major highways (X19), and elevation (X23) intersecting with distance from major railways (X18), all exhibiting dual-factor enhancement (Figure 17).

-    In medium-intensity expansion regions, the top three interaction determinative power (q) values were ruggedness (X21) intersecting with distance to major railways (X18), ruggedness (X21) intersecting with distance from major rivers (X20), and ruggedness (X21) intersecting with distance from major highways (X19), all demonstrating dual-factor enhancement.

-    In high-intensity expansion regions, the top three interaction determinative power (q) values were ruggedness (X21) intersecting with distance from major railways (X18), elevation (X23) intersecting with distance from major railways (X18), and slope (X22) intersecting with distance from major railways (X18), all showing dual-factor enhancement.

We hope that the detailed results, along with the modifications and supplemental explanations provided above, address your concerns regarding the statement, "The influence of temporal and spatial driving factors on the expansion of urban construction land differed across various periods and regions." Once again, we greatly appreciate your valuable feedback on the clarity of our expressions!

Comments 4:

The font size of figures are too small.  For example, Figure 1 2 4 5 16 17 19 20 21.

Response 4:

Thank you very much for your suggestion regarding the font sizes in the figures. We have reviewed every image in the manuscript, and the blurry or overly small fonts have been corrected, including those you mentioned:

Figure 1 (modified), 2 (modified), 4 (deleted), 5 (deleted), 16 (renamed as Figure 12), 17 (renamed as Figure 13), 19 (renamed as Figure 15), 20 (renamed as Figure 16), and 21 (renamed as Figure 17).

You can find these changes reflected in the figure captions highlighted in red in the latest version of the manuscript.

Comments 5:

Missing lon/lat in Figure 1.

Response 5:

Thank you very much for pointing out the missing latitude and longitude labels in Figure 1. We sincerely apologize for this oversight. We have reviewed the figure again and corrected the errors.

Comments 6:

Line  104 Start a new paragraph.

Response 6:

Thank you very much for your suggestion regarding paragraph segmentation. We fully agree that long paragraphs can hinder both the clarity of the author's intention and the reader's understanding.

We have carefully implemented your recommendation to divide the paragraph at Line 104, and have reorganized the content accordingly. You can find these changes in the last paragraph of the 1 Introduction section. Once again, we sincerely appreciate your valuable input!

Comments 7:

Line 138 "a resolution of 30M" --> "a spatial resolution of 30 m"

Response 7:

Thank you very much for your suggestion regarding the accuracy of data representation, which is both rigorous and crucial. We have adopted your recommendation concerning the phrase "a spatial resolution of 30 m." You can find these changes highlighted in red in section 2.2. Once again, we sincerely appreciate your insightful feedback!

Comments 8:

Line 176 n represents "the number of land use type", but in Table 3 n represents "number of patches".  Ensure each variable only has one unique meaning.

Response 8:

Thank you very much for your concern regarding ambiguous references! We sincerely apologize for such an oversight in the original manuscript. We have carefully reviewed the relevant references to avoid potential repetition.

As for Table 3, based on feedback from other reviewers and considerations regarding the length of the article, we have moved it to the appendix. Additionally, we have corrected the symbol for "number of patches" from n to m. You can find these changes in the appendix table. Thank you again for your valuable feedback!

Comments 9:

Table 5 Pay attention to the column names.

Response 9:

Thank you very much for your attention to the relevant tables in the paper. We sincerely apologize once again for the oversight of failing to properly update the column headings—an error that should not have occurred. Your thoughtful reminder has served as an important wake-up call for us. We have thoroughly reviewed the paper to prevent similar errors from appearing.

The column headings in this table (now updated as Table 3) have been corrected, and you can see the changes marked in red. Thank you once again for your valuable feedback!

Comments 10:

Line 419 Pay attention to the abbreviations.  "Aggregation Index" should show at its first appearance (Line 419) rather than (Line 421).

Response 10:

Thank you very much for your attention to the use of abbreviations for proper nouns in our research. We also agree that abbreviations should be introduced at the first mention of the full term in the text. We acknowledge the oversight in the previous manuscript regarding this issue. Following your kind reminder, we have reviewed and corrected the order in which abbreviations appear.

For the term "Aggregation Index," we have adjusted the text so that its full name and first abbreviation now appear in the methodology section. You can find these changes marked in red in section 2.3(3). Thank you again for your valuable feedback!

3. Response to Comments on the Quality of English Language

Point 1:

The tense of the manuscript is in a mess. Line 22 "analyzed" -> "analyze"; Line 112 "will uncover" -> "uncovers"

Response 1:

We sincerely appreciate your reminder regarding the English expression in our manuscript. As none of the authors are native English speakers, the previous version did indeed contain several errors, including the two tense-related mistakes you pointed out. We deeply regret this oversight, as it is not the outcome we intended.

At present, we have conducted a thorough review of the manuscript and have professionally refined the English language. This process primarily involved correcting the tense usage throughout the manuscript, with most of the text now standardized to the simple present tense. Additionally, we have removed unnecessary instances of the simple past and present perfect tenses, only retaining them where absolutely necessary.

The specific issues you raised regarding English expression have been addressed, with the changes highlighted in red in the Abstract (Line 25) and the 1 Introduction (Line 117). Furthermore, we have carefully revised all tense-related errors in the manuscript, including those on the following lines:

25, 28, 97, 100, 115, 146-147, 248-249, 251-252, 278-279, 294, 296, 298, 315, 329, 353-354, 365, 369, 375, 394, 400, 402-403, 412, 414, 432, 434, 443, 461, 478, 505, 513-514, 524-525, 575-576, 579, 581, 633-634, 637-638, 648-649, 651, 656, 658, 664, 745-746, 749-750, 756, 760, 763, 767, 777, 782, 785, 795, 889, 895, 923, 925, 928-930, 935.

Numerous corrections related to proper tense and grammatical expression can be found highlighted in red within these sections.

Once again, we are grateful for your timely feedback on the language, which has been invaluable to us.