Review Reports - Analysis of Spatiotemporal Changes in NDVI-Derived Vegetation Index and Its Influencing Factors in Kunming City (2000 to 2020)

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

Comments and Suggestions for Authors
Summary of Comments for Authors
Thank you for submitting your manuscript, "Analysis of Spatio-temporal Changes in Vegetation Cover and Its Influencing Factors in Kunming City (2000 to 2020)". While the topic is timely and the long-term analysis holds promise, the manuscript requires substantial revisions to meet publication standards. Below is a synthesized overview of the key issues to address:
•Introduction: Strengthen the narrative to clearly justify the study's significance, define key terms (e.g., "improvement"), and explicitly state research questions, objectives, and hypotheses. A more critical literature review is needed to contextualize Kunming as a case study.
•Methods: Enhance methodological transparency and reproducibility. Provide detailed steps for data preprocessing (e.g., MODIS cloud masking), specify statistical tests (e.g., Mann-Kendall, Sen's slope), and clarify how driver analyses (climatic/socioeconomic) were conducted, including software and versions used.
•Results: Ground findings in statistical evidence. Report quantitative metrics (e.g., slope values, p-values, confidence intervals) for trends and driver relationships. Use tables to summarize change categories and avoid causal language where only correlations exist.
•Discussion: Move beyond result repetition to interpret mechanisms behind observed patterns (e.g., reasons for regional improvements/degradation). Compare findings with existing studies, acknowledge limitations (e.g., data resolution), and discuss practical implications for policy and land-use management.
•Conclusions: Concisely synthesize key insights and their broader relevance, avoiding mere repetition of results. Include specific recommendations for future research.
•Figures and Tables: Improve cartographic standards by adding scale bars, coordinate systems, and numerical legends. Use tables to present quantitative data systematically.
Addressing these points, particularly in methods rigor and interpretive depth, will significantly strengthen the manuscript. We encourage a thorough revision and look forward to reviewing your resubmission.

Comments for author File: Comments.pdf

Author Response

Editor

Thank you for submitting your manuscript, "Analysis of Spatio-temporal Changes in Vegetation Cover and Its Influencing Factors in Kunming City (2000 to 2020)". While the topic is timely and the long-term analysis holds promise, the manuscript requires substantial revisions to meet publication standards. Below is a synthesized overview of the key issues to address:

Re: We sincerely thank the Editor for recognizing the value of our work and providing us with the opportunity to revise and improve our manuscript. We also appreciate anonymous reviewers for their insightful and constructive comments. We have carefully considered all the feedback and made point-by-point revisions throughout the manuscript. These modifications have substantially improved the clarity, rigor, and overall quality of our work. We believe that the revised version now meets the standards of the Journal. We truly appreciate the Editor’s and Reviewers’ time and efforts in evaluating our paper and look forward to receiving favorable feedback on this revised submission.

Reviewer
1. Introduction: Strengthen the narrative to clearly justify the study's significance, define key terms (e.g., "improvement"), and explicitly state research questions, objectives, and hypotheses. A more critical literature review is needed to contextualize Kunming as a case study.

Re: We sincerely thank the reviewer for this valuable and insightful suggestion. In accordance with the comment, we have substantially revised the Introduction to enhance the logical flow and clarify the study’s significance and novelty. The revised version now provides a more focused and critical literature review that situates Kunming City within the broader context of vegetation change studies, highlighting its unique subtropical plateau setting, rapid urbanization, and ecological importance. Furthermore, we have explicitly stated the research questions and objectives at the end of the Introduction to clearly guide the reader through the study’s framework. The new paragraph reads as follows:

The objectives of this study are to (1) quantitatively identify the temporal trends and spatial patterns of vegetation coverage over the past two decades, (2) reveal the relationships between vegetation dynamics and both climatic and socioeconomic factors, and (3) evaluate the relative contributions of natural and socioeconomic factors to vegetation changes during the rapid urban development of Kunming City, thereby providing an integrated perspective on the drivers of vegetation dynamics in this rapidly developing region.” These revisions improve the clarity, coherence, and academic rigor of the Introduction, ensuring that the study’s rationale, novelty, and objectives are now clearly articulated and better contextualized.

Methods: Enhance methodological transparency and reproducibility. Provide detailed steps for data preprocessing (e.g., MODIS cloud masking), specify statistical tests (e.g., Mann-Kendall, Sen's slope), and clarify how driver analyses (climatic/socioeconomic) were conducted, including software and versions used.

Re: We sincerely thank the reviewer for this insightful and constructive suggestion. In response, we have revised and expanded the “Data Sources and Statistical Analysis” section to improve methodological transparency and reproducibility. The revised version now provides clearer descriptions of data preprocessing procedures, including MODIS NDVI cloud masking, resampling, and time-series filtering steps. We have also explicitly specified the statistical tests used, including the Mann-Kendall trend test and Sen’s slope estimator, to ensure analytical rigor. Furthermore, detailed explanations of the driver analyses for climatic and socioeconomic factors have been added, describing how correlation and regression analyses were conducted to assess their respective contributions to vegetation changes. In addition, we have included information on the software and versions used throughout the study, such as MATLAB R2023a and ArcGIS 10.8, which were applied for data processing, statistical analysis, and spatial visualization. These additions substantially improve the clarity, transparency, and reproducibility of the methodology.

Results: Ground findings in statistical evidence. Report quantitative metrics (e.g., slope values, p-values, confidence intervals) for trends and driver relationships. Use tables to summarize change categories and avoid causal language where only correlations exist.

Re: We sincerely appreciate the reviewer’s constructive and thoughtful comments. In response, we have revised the Results section to provide stronger statistical grounding and clearer quantitative support for the findings. To further improve clarity, we also revised the figures to better illustrate statistical relationships and highlight the spatial and temporal patterns of vegetation change. In order to avoid unnecessary redundancy, we retained the combination of figures and explanatory text rather than adding new tables, as the existing visual and textual presentation effectively conveys the core results without repetition. Additionally, we carefully reviewed the language throughout the Results section to ensure that only correlational relationships are described where causality cannot be established. These revisions improve the precision, transparency, and interpretive rigor of the Results section.

Discussion: Move beyond result repetition to interpret mechanisms behind observed patterns (e.g., reasons for regional improvements/degradation). Compare findings with existing studies, acknowledge limitations (e.g., data resolution), and discuss practical implications for policy and land-use management.

Re:

Conclusions: Concisely synthesize key insights and their broader relevance, avoiding mere repetition of results. Include specific recommendations for future research.

Re: We sincerely appreciate the reviewer’s insightful and constructive comments. Following the suggestion, we have thoroughly revised and refined the Conclusion section to make it more concise, integrative, and forward-looking. The revised version avoids repetition of results, synthesizes the key findings, and highlights their broader implications for regional ecological management and sustainable development. In addition, we have included clear recommendations for future research directions to strengthen the scholarly contribution of the study. We believe these revisions have substantially improved the logical clarity, depth, and academic value of the manuscript. The revised Conclusion is provided below:

This study provides a comprehensive assessment of the spatiotemporal dynamics of vegetation coverage in Kunming City from 2000 to 2020 and its responses to climatic and socioeconomic factors. The results revealed a distinct spatial pattern of vegetation coverage, with higher values in the west and mountainous regions and lower values in the east and basin areas. Areas with high vegetation coverage were primarily distributed in Luquan, Xishan, Anning, and Jinning, whereas low-coverage zones were concentrated in the urban core and surrounding rapidly developing districts. Overall, vegetation coverage exhibited a fluctuating but upward trend over the two decades, with the mean annual NDVI increasing from 0.48 in 2000 to 0.55 in 2020. More than half of Kunming’s area (52.5%) showed stable vegetation coverage, while 37.2% experienced improvement, especially in the northern and northeastern mountainous counties. In contrast, only 10.3% of the area showed degradation, mainly concentrated in the main urban zones and new urbanization corridors such as the Konggang-Songming line. These findings indicate that urban expansion remains the principal driver of localized vegetation loss, whereas ecological restoration policies have promoted greening in peripheral regions.

Climatic analysis demonstrated that both precipitation and temperature exerted significant but spatially heterogeneous influences on vegetation dynamics. Vegetation generally responded positively to precipitation, particularly along the Jinsha River, Pudu River, and Xiaojiang River basins, underscoring the dependence of local vegetation growth on water availability in a subtropical monsoon environment. Conversely, temperature had contrasting effects, promoting vegetation growth in northern highlands while inhibiting it in the low-lying plains during warmer periods. Socioeconomic development has profoundly reshaped the vegetation landscape. Between 2000 and 2020, Kunming’s GDP increased more than tenfold, accompanied by substantial urban expansion and infrastructure construction, which caused a marked decline in vegetation coverage in urban cores and surrounding industrial zones. This spatial inverse relationship between vegetation and GDP highlights the ecological costs of rapid urbanization and the need for more balanced land-use planning.

Overall, our results shown that vegetation dynamics in rapidly urbanizing subtropical regions are jointly regulated by climate variability and human activities, with land-use change and water availability playing dominant roles. Future research should focus on integrating high-resolution remote sensing data with landscape-scale process models to quantify the relative contributions of policy-driven ecological restoration and urban expansion. Long-term monitoring of ecosystem services is also essential to guide sustainable urban development and maintain ecological resilience in plateau cities such as Kunming.

Figures and Tables: Improve cartographic standards by adding scale bars, coordinate systems, and numerical legends. Use tables to present quantitative data systematically.Addressing these points, particularly in methods rigor and interpretive depth, will significantly strengthen the manuscript. We encourage a thorough revision and look forward to reviewing your resubmission.

Re: We sincerely thank the reviewer for this constructive and detailed suggestion. In accordance with the comments, we have revised all figures to meet higher cartographic standards by adding scale bars, coordinate grids, and numerical legends, thereby improving the readability and precision of spatial information (Figs 1, 2, 3, 4, 5, 6, 7). These enhancements ensure that the figures now clearly convey both spatial extent and quantitative variation, aligning with professional mapping standards.

In addition, we carefully reviewed the presentation of quantitative data. Since the study primarily focuses on spatiotemporal patterns and the key results are already clearly illustrated and described in the figures and text, creating additional tables would lead to redundancy rather than improve clarity. Therefore, we have chosen to maintain the current figure-based presentation, which we believe most effectively communicates our findings.

We are grateful for this valuable suggestion, as these improvements have not only enhanced the visual quality and methodological rigor of the manuscript but also improved its overall interpretive depth and academic presentation.

Reviewer 2 Report

Comments and Suggestions for Authors

In the summary, add the implications.
In the introduction, start with the importance of vegetation in cities. Overall, the introduction should be concise and seek to answer the following questions: What is known about the subject? What are the gaps? How and in what ways (approaches) will this study fill these gaps? Why is the case of Kumming so relevant to this topic? A clear hypothesis is missing.
Study area: provide geographical coordinates and present the socio-economic context.
In section 2.2 – Methods, there is no justification for the choice of NDVIsoil and NDVIveg thresholds (5% and 95% percentiles) and no reference or field validation is provided. The pre-processing of MODIS data is insufficiently described: no mention of atmospheric correction, cloud pixel interpolation, or temporal smoothing. ERA5 climate data are used without local validation; the spatial resolution (~30 km) is unsuitable for the fine scale of the study (250 m for NDVI). No sensitivity or uncertainty analysis is presented for the FVC model or climate data.

Sections 3 and 4 should be merged into a single section entitled ‘Results’, with several subsections. In the former section 3, spatial distribution is described without advanced spatial statistical analysis (e.g. autocorrelation, hot spots). Simple linear regression (R² = 0.404) is insufficient to capture interannual variability; no breakpoint or seasonality analysis is performed. The FVC categorisation seems arbitrary: the five classes of equal intervals do not necessarily reflect the actual ecological distribution. In the former section 4, the correlation analysis is simplistic: no multiple regression or control of covariates (e.g. altitude, soil type) is performed. Precipitation lag is mentioned but not quantified (e.g. by cross-correlation analysis). Finally, the GDP-vegetation relationship is presented in a correlative rather than causal manner; no spatialised indicators of urbanisation or land use are included.

The discussion should be divided into three sections: methodological limitations (lack of multivariate modelling or causality detection (e.g. panel regression models, time series analysis); potential spatial scale bias between climate data (ERA5) and NDVI (MODIS); no field validation or comparison with independent data (e.g. Landsat)), the main results (an in-depth analysis of the results based on cause and effect, supported by contextualised references) and the implications and avenues for future research.
The conclusion is unusually long and focuses solely on the results: it does not reiterate the objective and methodology; it does not contrast the results with the hypotheses; it does not present the limitations, conclusion, implications and avenues for future research.

Author Response

Please find the attachment.

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

Dear authors,

The topic is relevant to vegetation monitoring based on remote-sensing data. This manuscript investigates spatiotemporal variations in vegetation coverage across Kunming City from 2000 to 2020 using MODIS NDVI data combined with temperature, precipitation, population, and GDP records. The study reports an overall increase in vegetation coverage, identifies spatial patterns of improvement and degradation, and explores relationships between vegetation, climatic, and socioeconomic variables.

However, the manuscript relies almost entirely on NDVI as a proxy for vegetation coverage, without sufficiently justifying the assumptions, validation, or statistical rigor of the analysis. The linkage between NDVI, vegetation coverage, and influencing factors (particularly GDP) needs clarification and stronger quantitative support. It requires stronger conceptual justification and statistical analysis before it can be considered for publication.

Major Comments

Conceptual Framework and Title

The study makes a strong assumption that NDVI directly represents vegetation coverage. Please justify this assumption. Two regions with identical NDVI values can differ substantially in vegetation type, canopy density, and soil background.
Clarify how pixel-level NDVI variations were aggregated to infer area-wide vegetation coverage changes. Small average NDVI increases may not reflect spatial expansion of vegetation cover, how do you treat these cases? Did you consider the conceptual difference between NDVI variation (greenness) and areal extent of vegetation coverage? NDVI can increase within existing vegetation without an expansion of vegetated area.
Acknowledge uncertainty sources (sensor noise, soil reflectance, atmosphere, vegetation type) and nonlinearity in NDVI–FVC in interpreting long-term trends.

Abstract

Line 8. Clarify “Vegetation…cornerstone”—of what?
Lines 11–13. The abstract jumps from listing data sources to presenting results. Summarizing the methods (e.g., NDVI-to- fractional vegetation cover (FVC) derivation, trend analysis, Pearson correlation mapping).
Line 14. Quantify the reported correlations between vegetation and temperature/precipitation by specifying the correlation coefficients or significance thresholds.
Line 19. Define what constitutes “moderate” or “significant” vegetation improvement (e.g., NDVI slope values or statistical criteria).
Line 26. Provide a brief ecological interpretation of the negative correlation between temperature and vegetation (e.g., drought stress, evapotranspiration effects).

Introduction

Line 55. Correction: “Human activities are…” not “ere”
Justify the choice of MODIS NDVI (250 m resolution) for urban–mountain areas.

2.2 Research Methods

Line 120. Clarify whether FVC was calculated using fixed NDVIsoil/NDVIveg values across all years or re-computed annually. Annual recalibration may reduce comparability. Ensure that the FVC index is comparable across the 20-years.
Lines 125–128. The use of 5% and 95% cumulative NDVI values as NDVIsoil and NDVIveg needs proper justification and citation. Provide literature references supporting this percentile-based scaling and explain why it is suitable for the Kunming region.
Additional comment. Describe preprocessing of MODIS data (e.g., atmospheric correction, cloud masking, compositing strategy). Data quality control is essential for credible trend analysis.
Additional comment. Discuss mixed-pixel effects in built-up and peri-urban zones and their potential impact on NDVI/FVC reliability.
Additional comment. Specify whether any statistical test (e.g., Mann–Kendall trend test, linear regression with p-values, or Sen’s slope) was used to determine significance of NDVI/FVC trends. Classification of “improvement” or “degradation” requires statistical justification.

3.2 (Results section)

Line 205. Clarify what the reported “gain of 0.065” represents, discuss its ecological significance.
Figure 2. The caption “vegetation coverage change” may be misleading if NDVI values are plotted. Adjust the caption or explicitly state that NDVI (or FVC) is used as a proxy for vegetation coverage.
Line 230 / Figure 4. NDVI increases after 2014, while temperature shows a steadier rise. Discuss possible causes. Explain the 2016 discrepancy when NDVI increases despite stable temperature.
Line 264. Avoid stating a “degree of correlation” without numerical correlation coefficients. Report Pearson’s r values or statistical significance instead of relying on visual trends.
Lines 341–343. These appear to belong to Figure 7 −ensure correct placement.
Confirm that spatial and temporal NDVI trends were statistically significant. Otherwise, visually identified “improvements” or “degradations” may result from noise or seasonal anomalies.

4.3 (GDP and Vegetation Changes)

The relationship between GDP and vegetation is not clearly established. Specify:

the spatial and temporal resolution of GDP data,
the method used to relate GDP to vegetation, and
whether correlations were tested statistically.

Line 353. Figure 9.2 is missing while it is described in the text.
Do you imply that GDP growth related to vegetation loss. What you are doing is “association” without to be sure about the “influence” .

Conclusions

Conclusions are too long.

Major Methodological and Interpretive Issues

Quantitative rigor: Provide correlation coefficients, p-values, and clearly defined thresholds for improvement/degradation.
Spatial resolution limitations: Acknowledge MODIS pixel size limitations in urban and heterogeneous landscapes.
Significance testing: Don’t just show trends visually; test them statistically to prove the changes are real. Apply and report a formal statistical test (e.g., Mann–Kendall or linear regression) for NDVI/FVC trends to confirm whether the changes labeled as ‘improvement’ or ‘degradation’ are statistically significant.
Validation: Without validation, it’s unclear whether NDVI/FVC changes represent. Verify the reliability of NDVI/FVC-derived vegetation coverage by comparing it with higher-resolution satellite data (e.g., Landsat, Sentinel-2) or field observations where available. This validation would confirm that NDVI changes correspond to actual vegetation dynamics rather than remote-sensing artifacts.
Modeling: While exploratory correlation analysis is useful, a multivariate regression or time-series model could better quantify the joint influence of temperature, precipitation, and socioeconomic factors.
Terminology: Use “NDVI-derived vegetation index” or “fractional vegetation cover” rather than directly calling it “vegetation coverage” unless explicitly validated.

Author Response

Please find the attachment.

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

Dear Authors,

Thank you for your thorough and thoughtful revisions in response to the reviewers' comments. You have done an excellent job in addressing the points raised, and the manuscript has been significantly strengthened as a result.

The introduction now provides a much more critical and contextualized literature review, clearly justifying the importance and originality of your study in Kunming. The explicit statement of research objectives at the end of the section effectively guides the reader.

The methodology section is now substantially improved. The added details on data preprocessing (e.g., cloud masking), the specification of the Mann-Kendall trend test and Sen's slope estimator, and the mention of software versions greatly enhance the transparency and reproducibility of your work.

The results are presented clearly, and the revised figures with scale bars, coordinate grids, and numerical legends are a major improvement. The discussion now successfully moves beyond a mere restatement of results to provide a meaningful interpretation of the mechanisms behind the observed patterns (e.g., the lagged effect of precipitation, the contrasting role of temperature, and the clear impact of socioeconomic development). The conclusions are concise, synthetic, and forward-looking, with valuable recommendations for future research.

I have only a few minor suggestions for final polishing:

Results Section: While the figures are excellent, consider adding a few key statistical values (e.g., specific p-values or confidence intervals for the main trends and correlations) directly into the results text to provide immediate quantitative support for readers.
Clarity of Terms: In the results and discussion, ensure it is always clear what is meant by "urban core" or "main urban area" versus the broader administrative districts, to avoid any potential ambiguity for readers unfamiliar with Kunming's geography.

Overall, this is a robust and valuable contribution to the field. I congratulate you on a well-executed study and a successful revision process.

Author Response

Results Section:While the figures are excellent, consider adding a few key statistical values (e.g., specific p-values or confidence intervals for the main trends and correlations) directly into the results text to provide immediate quantitative support for readers.

Re: We sincerely appreciate the reviewer’s valuable suggestion. In accordance with the reviewer’s comments, key statistical values have been incorporated into the Results section to reinforce the quantitative support for our findings. We believe these revisions improve the clarity, readability, and scientific rigor of the results presentation.

Clarity of Terms:In the results and discussion, ensure it is always clear what is meant by "urban core" or "main urban area" versus the broader administrative districts, to avoid any potential ambiguity for readers unfamiliar with Kunming's geography.

Re: We sincerely thank the reviewer for this helpful comment. We apologize for the confusion caused by the mixed use of “urban core” and “main urban area” in the previous version. According to the reviewer’s suggestion, we have now standardized the terminology throughout the manuscript. In the revised version, we consistently use “main urban area” to refer to the continuous built-up districts of Wuhua, Panlong, Guandu, Xishan, and Chenggong. We believe this adjustment improves clarity for readers who may not be familiar with the administrative and spatial structure of Kunming.

Overall, this is a robust and valuable contribution to the field. I congratulate you on a well-executed study and a successful revision process.

Re: We sincerely appreciate the reviewer’s positive and encouraging comments, as well as the recognition of our work and the revision process. We hope that the revised manuscript meets the expectations of the editor and the reviewers and fully complies with the journal’s publication standards.

Results Section:While the figures are excellent, consider adding a few key statistical values (e.g., specific p-values or confidence intervals for the main trends and correlations) directly into the results text to provide immediate quantitative support for readers.

Clarity of Terms:In the results and discussion, ensure it is always clear what is meant by "urban core" or "main urban area" versus the broader administrative districts, to avoid any potential ambiguity for readers unfamiliar with Kunming's geography.

Overall, this is a robust and valuable contribution to the field. I congratulate you on a well-executed study and a successful revision process.

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

The authors have generally responded well to the comments and incorporated most of the requested changes into the revised manuscript. The structure, clarity and methodological rigour have been significantly improved. However, the justification for the NDVI thresholds and the mention of the limitations related to field validation and ERA5 data resolution still need to be incorporated into the manuscript. In addition, minor typographical errors (e.g. ‘lentold’ → ‘tenfold’, duplicate commas, figure captions) still need to be corrected.

Author Response

However, the justification for the NDVI thresholds and the mention of the limitations related to field validation and ERA5 data resolution still need to be incorporated into the manuscript.

Re: We sincerely thank the reviewer for this important reminder. In the revised manuscript, we have now incorporated a justification for the NDVI thresholds used in the slope classification and provided the relevant literature support (Lines 194-202). In addition, the limitations related to field validation and the spatial resolution of ERA5 climate data have been added to the Discussion to clearly acknowledge potential uncertainties associated with the analysis (Lines 531-539). We appreciate the reviewer’s guidance, which has helped further improve the completeness and transparency of the manuscript.

In addition, minor typographical errors (e.g. ‘lentold’ → ‘tenfold’, duplicate commas, figure captions) still need to be corrected.

Re: We sincerely thank the reviewer for the careful reading and helpful reminder. In the revised manuscript, we have thoroughly checked and corrected the typographical issues, including the removal of duplicate commas and the refinement of figure captions. All such minor errors have now been addressed to ensure accuracy and consistency throughout the manuscript.

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

Dear auhtors,
Thank you for considering my previous comments. Based on the revisions, the following:

1 → The title now reflects a different aim compared to the original version.

2 → Why the authors removed from the manuscript? Are the authors the same as in the previous submission?

3 → How do you define moderate and significant NDVI slopes? Please provide references for the thresholds that indicate significant positive or negative slope values. Additionally, provide the overall average trend of the NDVI-derived vegetation index for the entire study area, similar to Figure 1 (showing slope vs. years for the NDVI-derived vegetation index).
- Abstract: During the study period, 26.86% 19 of the area showed moderate (NDVI slope: 0.005-0.016, p < 0.05) improvement and 10.35% showed significant (NDVI slope: 0.016-0.063, p < 0.01) improvement, while 10.28% exhibited degradation.
- Lines 185-187. Specifically, −0.072 to −0.021 indicates significant degradation, −0.021 to −0.007 moderate degradation, −0.007 to 0.005 stable conditions, 0.005 to 0.016 moderate improvement, and 0.016 to 0.063 significant improvement.

4 → Lines 183–184: Please insert references for the statement: “The resulting slope values were classified using the Jenks natural breaks method to identify improvement, stability, and degradation patterns.”

5 → The numbering in the manuscript is incorrect and should be revised.

6 → Please number all indices/equations, particularly (Lines 161 and 180).

7 → Line 161: Explain how NDVIsoil and NDVIveg were derived/calculated.

8 → Figure 7 does not adequately represent Section 3.5. Impact of GDP Changes on the NDVI-derived Vegetation Index in Kunming.
What was your initial hypothesis regarding the influence or association of GDP change on vegetation? Please state this clearly in the Methods section and provide a more informative figure in the resutls.

9 → Conclusions:
- Line 505: Why does the following statement appear in the Conclusions section when it is not part of your analysis?: “Night-time light records to assess the spatiotemporal changes…”
- Do you conclude that the oveal vegetation increased based on the FVC index?
- Do temperature, rainfall, and GDP trends correspond to improved vegetation conditions? The figures seem to show positive relationships between these factors and the NDVI-derived index.

Author Response

The title now reflects a different aim compared to the original version.

Re: We sincerely thank the reviewer for identifying the inconsistency between the title and the stated research objectives. To ensure full coherence, we have revised the third objective as follows:

“(3) evaluate the relative contributions of natural and socioeconomic factors to vegetation changes under the context of Kunming’s rapid urban development.” (lines 106-108)

We hope that this adjustment allows the research objectives to more accurately reflect the scope and focus of the study. We greatly appreciate the reviewer’s constructive comments, which have helped improve the consistency and clarity of the manuscript.

Why the authors removed from the manuscript? Are the authors the same as in the previous submission?

Re: We sincerely thank the reviewer for the reminder. We have carefully checked the authorship information, and we confirm that the list of authors has not changed from the previous submission. No author has been removed or added. If any discrepancy was displayed during the review process, it may have been caused by a system or formatting issue during re-submission rather than an actual change in authorship. We apologize for any confusion this may have caused and truly appreciate the reviewer’s attention to this matter.

How do you define moderate and significant NDVI slopes? Please provide references for the thresholds that indicate significant positive or negative slope values.

Re: We sincerely thank the reviewer for this valuable comment. In the revised manuscript, we have clarified how the thresholds for moderate and significant NDVI slopes were defined. Specifically, the slope values were classified using the Jenks natural breaks method, which groups the full distribution of pixel-level NDVI slopes by minimizing within-class variance and maximizing between-class variance. Accordingly, the five slope intervals (significant degradation, moderate degradation, stable conditions, moderate improvement, and significant improvement) were derived objectively from the breakpoints identified by the Jenks classification.

To address the reviewer’s concern, we have also added relevant references supporting the use of the Jenks natural breaks method for classifying NDVI- or vegetation-trend slopes in remote sensing-based vegetation studies (Lines XXX–XXX). We hope that these additions improve the transparency and methodological justification of the slope-based classification.

Additionally, provide the overall average trend of the NDVI-derived vegetation index for the entire study area, similar to Figure 1 (showing slope vs. years for the NDVI-derived vegetation index).

Re: We sincerely thank the reviewer for this helpful suggestion. Following the comment, we have added a clear statement in the Results section describing the overall temporal trend of the NDVI-derived vegetation index for the whole study area. “As indicated in Figure 1, the NDVI-derived vegetation index increased over 2000–2020, with interannual variability but a statistically significant long-term upward trend (r = 0.404, p < 0.05).” (Lines 172-174, ). We hope that this addition improves the clarity of the long-term vegetation dynamics at the regional scale.

Abstract: During the study period, 26.86% 19 of the area showed moderate (NDVI slope: 0.005-0.016, p < 0.05) improvement and 10.35% showed significant (NDVI slope: 0.016-0.063, p < 0.01) improvement, while 10.28% exhibited degradation.

Lines 185-187. Specifically, −0.072 to −0.021 indicates significant degradation, −0.021 to −0.007 moderate degradation, −0.007 to 0.005 stable conditions, 0.005 to 0.016 moderate improvement, and 0.016 to 0.063 significant improvement.

Re: We sincerely thank the reviewer for the helpful comment. In the revised manuscript, the p-values that were shown together with the NDVI slope classes in the Abstract have been removed to avoid confusion between slope-based classifications (“moderate” and “significant” improvement) and statistical significance (Lines 197-202). Only the slope intervals are now retained so that the classifications are consistent with the criteria described in the Materials and Methods section “−0.072 to −0.021 indicates significant degradation, −0.021 to −0.007 moderate degradation, −0.007 to 0.005 stable conditions, 0.005 to 0.016 moderate improvement, and 0.016 to 0.063 significant improvement”. These revisions ensure full alignment between the Abstract and methodological description.

Lines 183–184: Please insert references for the statement: “The resulting slope values were classified using the Jenks natural breaks method to identify improvement, stability, and degradation patterns.”

Re: We sincerely thank the reviewer for the helpful suggestion. In the revised manuscript, we have added the corresponding references to support the statement regarding the classification of slope values. The citations have been inserted at the appropriate location in Lines 194-202.

The numbering in the manuscript is incorrect and should be revised.

Re: We sincerely thank the reviewer for pointing this out. In the revised manuscript, we have carefully checked and corrected all numbering, including section headings, figures, tables, equations, and in-text citations, to ensure that the numbering is now consistent and accurate throughout the manuscript.

Please number all indices/equations, particularly (Lines 161 and 180).

Re: We sincerely thank the reviewer for this helpful suggestion. In the revised manuscript, all indices and equations have been checked throughout the text and numbered accordingly (Lines 171, 191). The numbering has now been standardized to ensure consistency and clarity.

Line 161: Explain how NDVIsoil and NDVIveg were derived/calculated.

Re: We thank the reviewer for the insightful comment. In the revised manuscript, we have added a clear explanation of how NDVIsoil and NDVIveg were determined. Specifically, NDVIsoil and NDVIveg were derived from the 5% and 95% percentiles of the NDVI frequency distribution within the study area for each year, representing the reflectance characteristics of bare soil and full vegetation cover, respectively. This percentile-based scaling method has been widely applied in vegetation cover estimation and is suitable for heterogeneous landscapes. Corresponding references have been added to support this method (Lines 175–182).

Figure 7 does not adequately represent Section 3.5. Impact of GDP Changes on the NDVI-derived Vegetation Index in Kunming. What was your initial hypothesis regarding the influence or association of GDP change on vegetation? Please state this clearly in the Methods section and provide a more informative figure in the results.

Re: We sincerely thank the reviewer for this insightful comment. We recognize that the original section title might have led readers to interpret this part of the study as an analysis of the causal influence of GDP on the NDVI-derived vegetation index. However, the purpose of this section was not to investigate a driving mechanism, but rather to present the temporal and spatial characteristics of GDP during Kunming’s rapid urbanization from 2000 to 2020, thereby providing socioeconomic context for interpreting vegetation changes.

Accordingly, the GDP trend remains in the Results section as an independent socioeconomic indicator. The revised text describes the long-term increase and spatial distribution of GDP and, in combination with the NDVI maps, highlights a spatial mismatch between areas of highest economic concentration and areas of high vegetation cover. This represents a pattern of spatial co-occurrence rather than causal inference, and we do not attempt to quantify the direct impact of GDP on vegetation.

To avoid potential misunderstanding, we have revised the section title to “Trend of GDP change in Kunming from 2000 to 2020” (Line 433). In addition, we have added clarification in the Methods section stating that GDP was introduced to characterize the socioeconomic background of urbanization rather than to construct a causal mechanism between GDP and vegetation (Lines 215-217). We have also modified the Discussion to emphasize the spatial mismatch between GDP and NDVI rather than a cause-and-effect interpretation (Lines 495-510).

We appreciate the reviewer’s comment, which helped us to improve the clarity and consistency of the manuscript.

Conclusions:
11. Line 505: Why does the following statement appear in the Conclusions section when it is not part of your analysis?: “Night-time light records to assess the spatiotemporal changes…”

Re: We sincerely thank the reviewer for pointing this out. The mention of “night-time light records” in the Conclusions section was a leftover from an earlier draft and does not correspond to the final analyses presented in the manuscript. We have now removed this statement to ensure full consistency between the content of the Conclusions and the conducted analyses.

Do you conclude that the overall vegetation increased based on the FVC index?

Re: We sincerely thank the reviewer for this helpful comment. Yes, the conclusion regarding the overall increase in vegetation is based on the long-term trend of the FVC (NDVI-derived vegetation index) across the study area. In the revised version of the manuscript, we have clarified this explicitly in the Conclusions section to avoid ambiguity. We now state clearly that the vegetation improvement refers to the statistically significant upward trend of the FVC index over 2000-2020, rather than an increase in absolute vegetation biomass (Lines 543-548, 550-557). We appreciate the reviewer’s suggestion, which has helped us improve the precision and clarity of the conclusion.

Do temperature, rainfall, and GDP trends correspond to improved vegetation conditions? The figures seem to show positive relationships between these factors and the NDVI-derived index.

Re: We sincerely appreciate the reviewer’s thoughtful comment. Following the reviewer’s suggestion, we have clarified in both the Results and Discussion sections how temperature, precipitation, and socioeconomic development relate to vegetation dynamics (Lines 346-350, 376-378, 490-494).

In the Results section, we now describe only the observed spatial correspondence without implying causality. Specifically, we report that regions with rapid socioeconomic development consistently correspond to areas with low NDVI-derived vegetation index, rather than suggesting that GDP directly drives vegetation change.

In the Discussion section, we further interpret these patterns by indicating that precipitation generally promotes vegetation greening at the regional scale, whereas temperature effects are spatially heterogeneous. Conversely, in densely urbanized districts, rapid economic development coincides with suppressed vegetation conditions, reflecting anthropogenic pressure on ecosystems. We emphasize that these findings represent spatial associations rather than causal relationships.

We believe these revisions improve clarity and prevent over-interpretation of the relationships between climate, GDP, and vegetation patterns.

Author Response File: Author Response.pdf