Spatiotemporal Evolution and Driving Factors of Desertification Sensitivity During Urbanization: A Case Study of the Beijing–Tianjin–Hebei Core Region
Round 1
Reviewer 1 Report
Comments and Suggestions for AuthorsTitle
The phrase "in the Urbanization Process" could be reworded for brevity, e.g., "during Urbanization" or "under Urban Expansion."
Abstract
Some sentences are long and could be broken down for better readability. Consider simplifying phrases like:
“A GIS-based model integrating vegetation degradation, topography, soil erodibility, and human activity intensity revealed an overall sensitivity decline...”
Instead, try: “Using a GIS-based model, we found a decline in overall desertification sensitivity, with vegetation degradation emerging as a key factor.”
Introduction
Page 2 - Line 57. Ensure all abbreviations are defined at first mention and consistently used throughout. Normalized Difference Vegetation Index (NDVI)”—should only use "NDVI" after the first definition.
Introduce the Beijing-Tianjin-Hebei (BTH) region by highlighting its ecological importance and vulnerability due to rapid urbanization.
Justify why this study is needed.
Clearly state why examining urban expansion, vegetation degradation, and human activity intensity in the BTH region is necessary.
Materials and Methods section
To improve the methods section for better replication by other researchers, consider the following suggestions:
- Enhance Data Collection Details:
Specify the exact satellite products and versions used (e.g., MOD13Q1 for NDVI, NPP-VIIRS for nighttime lights).
Include acquisition dates and preprocessing steps (e.g., atmospheric corrections, resampling methods).
Provide details on coordinate projection transformations and standardization techniques applied to the data.
- Clarify GIS and Remote Sensing Methods:
Describe the specific tools and software used (e.g., ArcGIS version)
Detail the weight assignment process in the weighted overlay analysis, including references for weight coefficients.
- Improve Model Reproducibility:
Elaborate on the classification criteria for desertification sensitivity levels (e.g., Natural Breaks method parameters).
Explain the rationale behind sensitivity classification thresholds.
Include a step-by-step workflow diagram summarizing data processing and analysis.
- Provide Statistical Analysis Details:
Specify the software and exact methods used for correlation analysis (e.g., Pearson correlation coefficient).
Clarify the statistical significance thresholds and confidence intervals.
Describe how the Getis-Ord Gi* hotspot analysis was implemented and validated.
- Data Availability & Code Sharing:
Mention whether raw data and scripts will be available for replication.
Consider providing a GitHub or supplementary material link with the exact preprocessing and analysis steps.
Page 3 – Figure 1. A georeferenced figure in a scientific paper should include the following key elements to ensure clarity:
Latitude and Longitude Grid: Include coordinates at the edges of the map to facilitate georeferencing.
Units: Clarify what the elevation values ​​(DEM) mean, indicating units (meters above sea level).
Scale bar: Modify the scale bar to have greater segmentation (e.g., every 10 km instead of a single 40 km segment).
Avoid colour saturation in the DEM and use a perceptually uniform palette.
Add more information in the figure caption, for example: "Location map of the study area, showing the terrain elevation (DEM in meters) and the surrounding regions (Beijing, Tianjin, and Langfang)."
Page 3 – Line 102. Mistake with the symbol of full stop.
Page 3 – Line 94. Data sources
Clarify Resampling Method:
The table does not specify whether the resampling is done using bilinear interpolation, cubic convolution, or nearest neighbour methods. The choice affects accuracy.
More Details on Void Filling:
What method is used? Interpolation? Neighbouring pixel estimation? These choices impact DEM accuracy.
Potential Loss of Information:
Resampling data (especially NDVI and soil texture) to a finer resolution (30m from 250m or coarser) does not add new detail—it only interpolates existing data. If done improperly, it could introduce artificial precision without real accuracy gains.
References
The authors have a problem with the references, the guidelines of the journal show the following description:
References must be numbered in order of appearance in the text (including table captions and figure legends) and listed individually at the end of the manuscript. In the text, reference numbers should be placed in square brackets [ ], and placed before the punctuation; for example [1], [1–3] or [1,3].
Missing Details:
Some references lack journal names, volume, issue numbers, or DOIs (e.g., Ref. [3], [4], [5]). Please I recommend preparing the references with a bibliography software package, such as EndNote, ReferenceManager or Zotero to avoid typing mistakes and duplicated references.
Please, dear authors, take into account the journal guidelines. Ensure all sources have complete citation details, including page numbers for journal articles.
Page 2 – Line 53. Mistake in the format of the reference.
Author Response
Comments 1: [The phrase "in the Urbanization Process" could be reworded for brevity, e.g., "during Urbanization" or "under Urban Expansion."]
Response 1: Thank you for pointing this out. We agree with the comment and have revised the title accordingly for improved clarity and conciseness. The phrase “in the Urbanization Process” has been changed to “under Urban Expansion”. This revision can be found in the title on Page 1, Lines 2–4 of the revised manuscript.
Comments 2: [Some sentences are long and could be broken down for better readability. Consider simplifying phrases like:“A GIS-based model integrating vegetation degradation, topography, soil erodibility, and human activity intensity revealed an overall sensitivity decline...”Instead, try: “Using a GIS-based model, we found a decline in overall desertification sensitivity, with vegetation degradation emerging as a key factor.”]
Response 2: Agree. We have, accordingly, revised the sentence for improved readability and clarity by shortening and simplifying the expression. The updated sentence appears on line 17 of the revised manuscript.
Comments 3: [Page 2 - Line 57. Ensure all abbreviations are defined at first mention and consistently used throughout. Normalized Difference Vegetation Index (NDVI)”—should only use "NDVI" after the first definition.]
Response 3: Thank you for pointing this out. We agree with the comment and have revised the manuscript accordingly. We now define Normalized Difference Vegetation Index (NDVI) and Digital Elevation Model (DEM) at their first mentions and consistently use the abbreviations thereafter. These changes can be found on Page 2, Lines 53 and 70 of the revised manuscript.
Comments 4: Introduce the Beijing-Tianjin-Hebei (BTH) region by highlighting its ecological importance and vulnerability due to rapid urbanization.
Response 4: Thank you for your valuable feedback. We agree with your suggestion and have revised the manuscript to explicitly highlight the ecological significance and vulnerability of the Beijing-Tianjin-Hebei (BTH) region in the context of rapid urbanization. The revised text now reads:"In northern China, semi-arid climatic conditions combined with intensive human activities have exacerbated land degradation issues. The Beijing-Tianjin-Hebei (BTH) region, encompassing core areas such as Wuqing District, Daxing District, Tongzhou District, Gu'an County, and Yongqing County, represents a critical zone for studying urbanization-driven ecological transformation. As one of China’s most rapidly urbanizing regions, the BTH region has experienced unprecedented urban expansion, resulting in fragmented landscapes and heightened pressure on its fragile ecosystems. Its ecological importance lies in its role as a key biodiversity corridor and a buffer against desertification, while its vulnerability stems from the compounding effects of climatic stressors and anthropogenic disturbances."These revisions emphasize the BTH region’s dual status as an ecologically vital yet vulnerable area, directly addressing your comment. The changes can be found on Page 2, Lines 58–64 of the revised manuscript.
Comments 5: Justify why this study is needed. Clearly state why examining urban expansion, vegetation degradation, and human activity intensity in the BTH region is necessary.
Response 5: Thank you for raising this critical point. We fully agree with the need to clarify the necessity of this study and have revised the manuscript to strengthen the justification. The updated text now states:"Existing studies on land degradation in the Beijing-Tianjin-Hebei (BTH) region predominantly focus on isolated factors such as climate variability or soil erosion. However, the synergistic effects of urban expansion, vegetation degradation, and human activity intensity—key drivers of ecosystem vulnerability in rapidly urbanizing areas—remain underexplored. This gap is particularly significant in the BTH region, where unprecedented urban growth has created complex interactions between socio-economic development and ecological fragility. Addressing this knowledge deficit is essential to formulate holistic strategies that balance urban planning with ecosystem resilience, especially in semi-arid regions facing compounded climatic and anthropogenic pressures."These revisions explicitly link the study’s focus on multi-factor synergies to the BTH region’s unique challenges as a national urbanization hub and ecologically vulnerable zone. The changes can be found on Page 2, Lines 64–68 of the revised manuscript.
Comments 6: Enhance Data Collection Details: Specify the exact satellite products and versions used (e.g., MOD13Q1 for NDVI, NPP-VIIRS for nighttime lights). Include acquisition dates and preprocessing steps (e.g., atmospheric corrections, resampling methods). Provide details on coordinate projection transformations and standardization techniques applied to the data.
Response 6: Thank you for your valuable comment. We have revised the manuscript to include more specific details regarding data collection. The exact satellite products and versions used, as well as acquisition dates and preprocessing steps, are now specified in the text. The resampling method and coordinate projection transformations (WGS_1984_UTM_Zone_51N) are clarified in the revised manuscript. These modifications can be found in Lines 96–117 and Table 1 of the revised manuscript.
Comments 7: Clarify GIS and Remote Sensing Methods: Describe the specific tools and software used (e.g., ArcGIS version) Detail the weight assignment process in the weighted overlay analysis, including references for weight coefficients.
Response 7: Thank you for your comment. We have clarified the specific GIS tools and software used in the study, including the version of ArcGIS (10.8). Additionally, we have provided more details on the weight assignment process in the weighted overlay analysis, including the reference for the weight coefficients based on the "Technical Specification for Ecological Quality Assessment in Desertification Areas" (LY/T 3242-2020). These revisions can be found in Lines 120–143 of the revised manuscript.
Comments 8: Improve Model Reproducibility: Elaborate on the classification criteria for desertification sensitivity levels (e.g., Natural Breaks method pa-rameters). Explain the rationale behind sensitivity classification thresholds. Include a step-by-step workflow diagram summarizing data processing and analysis.
Response 8: Thank you for your comment. We have provided more detailed information regarding the classification criteria for desertification sensitivity levels using the Natural Breaks method. The specific thresholds for sensitivity classification are 1.39, 1.65, 1.90, 2.15, and 2.68, which are discussed in Lines 183–196 on Page 6. Additionally, we have included a step-by-step workflow diagram summarizing the data processing and analysis, which can be found in Figure 2 on Page 5.
Comments 9: Provide Statistical Analysis Details: Specify the software and exact methods used for correlation analysis (e.g., Pearson correlation coefficient). Clarify the statistical significance thresholds and confidence intervals. Describe how the Getis-Ord Gi* hotspot analysis was implemented and validated.
Response 9: Thank you for your comment. We have specified that Pearson correlation coefficients were calculated using IBM SPSS Statistics 26 for the analysis, and the details can be found in Lines 343–346 on Page 11. The statistical significance thresholds and confidence intervals are also clarified in the same section. Regarding the Getis-Ord Gi hotspot analysis*, we implemented validation by testing multiple neighborhood radii. This validation process is described in Lines 328–335 on Page 10.
Comments 10: Data Availability & Code Sharing: Mention whether raw data and scripts will be available for replication. Consider providing a GitHub or supplementary material link with the exact preprocessing and analysis steps.
Response 10: Thank you for your comment. We agree with your suggestion regarding data availability and code sharing. The raw data used in this study is available for replication and can be downloaded from the relevant websites, with specific download links provided in Table 1 of the manuscript. The preprocessing and analysis steps are detailed in Sections 2.2 and 2.3 of the manuscript, specifically on pages 95-148.
Comments 11:Page 3 – Figure 1. A georeferenced figure in a scientific paper should include the following key elements to ensure clarity: Latitude and Longitude Grid: Include coordinates at the edges of the map to facilitate georeferencing. Units: Clarify what the elevation values (DEM) mean, indicating units (meters above sea level). Scale bar: Modify the scale bar to have greater segmentation (e.g., every 10 km instead of a single 40 km segment). Avoid color saturation in the DEM and use a perceptually uniform palette. Add more information in the figure caption, for example: "Location map of the study area, showing the terrain elevation (DEM in meters) and the surrounding regions (Beijing, Tianjin, and Langfang)."
Response 11: Thank you for your detailed suggestions. We have made the necessary modifications to Figure 1 on Page 3 to address these comments. The following changes have been implemented: Latitude and Longitude Grid has been added along the edges of the map for georeferencing. Elevation units are now specified as "meters above sea level" in the figure. The scale bar has been adjusted to show greater segmentation (every 10 km). The color saturation in the DEM has been minimized, and a perceptually uniform color palette has been applied. The figure caption has been expanded to include more information, as suggested: "Location map of the study area, showing the terrain elevation (DEM in meters) and the surrounding regions (Beijing, Tianjin, and Langfang)."
Comments 12: Page 3 – Line 102. Mistake with the symbol of full stop.
Response 12: Thank you for pointing this out. We have corrected the mistake with the full stop symbol. The revised text can be found on Page 3, Line 113 of the manuscript.
Comments 13: Clarify Resampling Method: The table does not specify whether the resampling is done using bilinear interpolation, cubic convolution, or nearest neighbour methods. The choice affects accuracy. More Details on Void Filling: What method is used? Interpolation? Neighbouring pixel estimation? These choices impact DEM accuracy. Potential Loss of Information: Resampling data (especially NDVI and soil texture) to a finer resolution (30m from 250m or coarser) does not add new detail—it only interpolates existing data. If done improperly, it could introduce artificial precision without real accuracy gains.
Response 13: Thank you for your comment. Specifically, NDVI data and soil texture data were resampled from their original coarser resolutions to 30 m resolution using bilinear interpolation, which minimizes accuracy degradation compared to nearest neighbor methods. The terrain data (DEM) underwent void-filling preprocessing by interpolating neighboring pixel values using the inverse distance weighted (IDW) interpolation method. Nighttime light data were corrected for consistency and continuity across the study period and resampled to 30 m using bilinear interpolation to match other datasets. Although resampling to a finer resolution does not increase actual detail or accuracy, employing the bilinear interpolation method reduces artificial precision and minimizes information loss. This can be found in Page 3, Lines 104-113 of the revised manuscript.
Comments 14: The authors have a problem with the references, the guidelines of the journal show the following description: References must be numbered in order of appearance in the text (including table captions and figure legends) and listed individually at the end of the manuscript. In the text, reference numbers should be placed in square brackets [ ], and placed before the punctuation; for example [1], [1–3] or [1,3].
Missing Details: Some references lack journal names, volume, issue numbers, or DOIs (e.g., Ref. [3], [4], [5]). Please I recommend preparing the references with a bibliography software package, such as EndNote, ReferenceManager or Zotero to avoid typing mistakes and duplicated references. Please, dear authors, take into account the journal guidelines. Ensure all sources have complete citation details, including page numbers for journal articles.
Response 14: Thank you for your comment. We have addressed the reference formatting issue by re-inserting the references using EndNote, following the journal’s guidelines. All references have been checked and corrected for any errors, and the missing details (such as journal names, volume, issue numbers, and DOIs) have been added. The references are now correctly formatted with square brackets [ ] and listed individually at the end of the manuscript. We have ensured that all sources have complete citation details, including page numbers for journal articles.
Comments 15: Page 2 – Line 53. Mistake in the format of the reference.
Response 15: Thank you for pointing this out. We have revised the reference format accordingly. The issue has been corrected, and the reference is now properly formatted. This change can be found in Line 50 on Page 2 of the revised manuscript.
Reviewer 2 Report
Comments and Suggestions for AuthorsThe paper presents a study on spatiotemporal evolution and driving factors of desertification sensitivity in the urbanization process. The study focuses on the Beijing-Tianjin-Hebei Core Region. The authors based their study on NDVI, soil texture, DEM, and nighttime light data.
The authors clearly presented the data used and the period covered by the study. The study results are clearly presented and well supported by the figures. The results are compelling and the conclusions are based on findings.
The Targeted Recommendations to Mitigate Desertification Sensitivity Risks should be listed more clearly.
Does the topic original or relevant to the field? Does it address a specific gap in the field? Please also explain why this is/ is not the case. There is a need for analysing the urbanisation process in relation to environmental problems.
Author Response
Comments 1: The Targeted Recommendations to Mitigate Desertification Sensitivity Risks should be listed more clearly.
Response 1: Thank you for your comment. Here's a more concise version of your targeted recommendations: Targeted Recommendations to Mitigate Desertification Sensitivity Risks:
Urban expansion should be carefully managed to minimize land sensitivity, especially in high-sensitivity areas. Strict control over construction land expansion is crucial to prevent vegetation loss and soil degradation. Complementary strategies, such as establishing green infrastructure in urban fringe zones and implementing low-impact development practices, can help stabilize ecosystems and mitigate hydrological stress.
Vegetation protection and restoration are key to improving land resilience. Areas with significant NDVI decline should prioritize interventions like enclosure management, ecological replanting, and bio-engineering techniques for slope recovery. In regions impacted by wind and water erosion, soil stabilization measures like windbreak forests and grassland restoration can enhance soil stability and prevent further degradation.
The above recommendations can be found in lines 451-470 on page 14 of the revised manuscript.
Comments 2: Does the topic original or relevant to the field? Does it address a specific gap in the field? Please also explain why this is/ is not the case. There is a need for analysing the urbanisation process in relation to environmental problems.
Response 2: Yes, the topic is highly relevant to the field, as it explores the complex interactions between urbanization and desertification sensitivity, which is a critical environmental challenge in semi-arid regions. The study specifically addresses a gap in understanding the spatial and temporal dynamics of desertification sensitivity within the context of urban expansion. While there has been significant research on desertification, fewer studies have focused on how urbanization directly influences desertification processes, especially in rapidly urbanizing areas like the Beijing-Tianjin-Hebei region.
This study provides valuable insights into the driving forces behind desertification, such as urban expansion, vegetation degradation, and human activity intensity, and their relationship to land sensitivity. By integrating remote sensing data, GIS modeling, and statistical analyses, the research fills a crucial gap by assessing desertification sensitivity under the influence of urbanization processes. It emphasizes the need for sustainable urban planning and ecological restoration to mitigate the environmental impact of urban expansion.
Therefore, this study significantly contributes to the growing body of research that seeks to link urbanization with environmental challenges, addressing a clear gap in understanding the role of urban development in land degradation and desertification sensitivity.
Reviewer 3 Report
Comments and Suggestions for AuthorsDear authors
This manuscript explores how desertification sensitivity has changed over time in a rapidly urbanizing part of northern China, focusing on the Beijing-Tianjin-Hebei core area between 2018 and 2022. Using data from satellite imagery and GIS tools, the authors examine vegetation cover, topography, soil characteristics, and human activity to understand which areas are most vulnerable to desertification and how that vulnerability has shifted.
One of the key findings is that while the overall desertification sensitivity in the region has decreased in recent years, certain areas continue to deteriorate, particularly in regions experiencing rapid urban expansion. The paper offers practical suggestions to balance urban development with environmental protection, which gives it relevance for both researchers and planners working in semi-arid landscapes.
The manuscript’s structure is clear, and the topic is relevant to managing land degradation under urban pressure. The research question is well-posed, and the approach is sound. The authors effectively use available datasets and GIS techniques to track desertification sensitivity. NDVI and nighttime light data are appropriate, and the temporal coverage allows for meaningful trends. The references are mostly recent and well-chosen, aligning with current desertification and land-use change research. While self-citation is minimal, more international references would enhance the study’s broader context.
The paper could benefit from improvements. Firstly, the authors should explain why certain weights were chosen and test alternatives. Secondly, while correlation analysis between NDVI, nighttime lights, and desertification sensitivity is presented, it could be more nuanced. Other important variables like rainfall or farming practices may be missing. A multiple regression analysis or comparison with climate data could strengthen conclusions and separate human impacts from natural variability.
Some parts of the text tend to be repetitive, especially when describing year-to-year changes in sensitivity. Rather than repeating similar figures for each year, it would be more helpful to summarize the overall patterns and focus on the areas where the most notable changes occurred. Also, the maps and figures are generally good, but the paper would benefit from clearer captions that explain what is being shown without relying too heavily on the main text. The NDVI trend map, in particular, is a key part of the argument but could be explained more thoroughly. The same applies to the hotspot analysis, which is an important part of the study but is not fully explored in terms of implications.
The conclusion section is well organized and includes realistic recommendations, especially the call for a monitoring system that could provide early warnings for areas at risk of desertification. However, the authors could go a bit further in discussing the challenges of implementing such systems, especially in areas with limited resources or conflicting land-use priorities. It would also help if the paper discussed how local governments might respond to the findings and whether similar approaches could be used in other urbanizing semi-arid regions.
The paper is a great addition to the field and tackles a relevant topic. The analysis is solid, and the conclusions mostly hold up. But there are a few areas that could be improved for better clarity and depth. If we fix these, the manuscript will be even stronger and more useful for everyone.
Specific Comments
Lines 124–125: The rationale for assigning weights to vegetation, topography, soil, and human activity is not fully explained. Consider adding a sentence to justify these choices and explain whether alternative weighting schemes were tested.
Line 131: The formula for the vegetation degradation index is included, but its meaning and limitations are not discussed. A short paragraph explaining how this index reflects actual land conditions would be useful here.
Line 167: The claim that declining sensitivity is due to “optimized land use practices” should be supported with a specific example from the study area, or else rephrased more cautiously.
Line 232: The explanation about the transition from moderate to low or high sensitivity is vague. Clarify what specific types of land use changes or vegetation interventions were most important.
Line 299: The sentence mentioning a stronger role of vegetation degradation could benefit from a clearer explanation of why this shift occurs in the later period and what it implies for land management.
Line 403: When discussing thresholds and early warning systems, explain briefly what kind of indicators or thresholds would be most useful, based on your data.
Comments on the Quality of English Language
While the manuscript is understandable and the structure is solid, several sentences are awkward or overly technical. Clarity could be improved with better phrasing, especially in the methods and discussion sections.
Author Response
Comments 1: Lines 124–125: The rationale for assigning weights to vegetation, topography, soil, and human activity is not fully explained. Consider adding a sentence to justify these choices and explain whether alternative weighting schemes were tested.
Response 1: Thank you for pointing this out. We agree with this comment and have added a detailed explanation regarding the rationale for assigning weights to vegetation, topography, soil, and human activity. Specifically, topography and soil properties were assigned equal weight due to their combined role in influencing soil erosion potential and water retention capacity, which are crucial factors in desertification sensitivity. Human activity intensity was assigned a lower weight, recognizing its indirect but still notable impact on desertification processes. These weightings were directly derived from the LY/T 3242-2020 standard, which is based on extensive empirical research and expert consensus in the field of ecological assessment for desertification-prone areas. Alternative weighting schemes were not tested in this study, as the focus was to adhere to the standardized methodology in order to ensure consistency and comparability with other assessments conducted within the same framework. This revision has been made in the revised manuscript and can be found on Page 5, Lines 130-141.
Comments 2: Line 131: The formula for the vegetation degradation index is included, but its meaning and limitations are not discussed. A short paragraph explaining how this index reflects actual land conditions would be useful here.
Response 2: Thank you for the comment. We have added a short paragraph to explain the meaning and limitations of the vegetation degradation index. Specifically, NDVI-based vegetation degradation indices effectively reflect vegetation conditions by capturing long-term vegetation dynamics driven by climatic variations, land-use practices, and ecological management efforts. An increasing trend may signify successful ecological restoration or sustainable land management practices, while a declining trend typically indicates stress or degradation due to drought, human disturbances, or unsustainable agricultural activities. However, the index does have limitations, as it simplifies complex vegetation dynamics into linear trends, potentially overlooking short-term fluctuations and nonlinear vegetation responses to disturbances, thus necessitating cautious interpretation and complementary field-based validation. This can be found in Page 5, Lines 155-164 of the revised manuscript.
Comments 3: Line 167: The claim that declining sensitivity is due to “optimized land use practices” should be supported with a specific example from the study area, or else rephrased more cautiously.
Response 3: Thank you for your valuable comment. We have supported this claim with a specific example from the study area, referencing relevant literature and case studies. The discussion has been revised to provide further details on the impact of optimized land use practices, particularly focusing on vegetation restoration and land management interventions in the Beijing-Tianjin-Hebei region. This addition can be found in Page 6, Lines 208-213 of the revised manuscript.
Comments 4: Line 232: The explanation about the transition from moderate to low or high sensitivity is vague. Clarify what specific types of land use changes or vegetation interventions were most important.
Response 4: Thank you for your comment. In response, we have provided specific examples of land use changes and vegetation interventions that played a crucial role in the transition from moderate to low or high sensitivity areas. For instance, the Beijing-Tianjin Sandstorm Source Control Project, which emphasizes afforestation and grassland restoration, has contributed significantly to vegetation recovery. This has led to a reduction in desertification sensitivity, with areas transitioning from moderate sensitivity (Level 3) to low sensitivity (Level 2) or even extremely low sensitivity (Level 1). Enhanced vegetation cover helps stabilize the soil, improve moisture retention, and mitigate desertification risks. These details can be found in Page 9, Lines 279-286 of the revised manuscript.
Comments 5: Line 299: The sentence mentioning a stronger role of vegetation degradation could benefit from a clearer explanation of why this shift occurs in the later period and what it implies for land management.
Response 5: Thank you for your comment. In response, we have further clarified why vegetation degradation plays a stronger role in the later period and what this shift implies for land management. The changes observed in the later period are likely due to ongoing vegetation restoration efforts and improvements in soil quality. These changes may result in the reduction of desertification and enhanced vegetation cover, which in turn improves soil quality and reduces desertification sensitivity. These details can be found in Page 12, Lines 364-372 of the revised manuscript.
Comments 6: Line 403: When discussing thresholds and early warning systems, explain briefly what kind of indicators or thresholds would be most useful, based on your data.
Response 6: Thank you for your comment. In response, we have clarified the key indicators and thresholds for the early warning system. Based on the data, NDVI trend thresholds, nighttime light intensity, and soil erodibility classes are considered the most important indicators for the early warning system. These thresholds will help in monitoring desertification sensitivity and guiding timely interventions. The relevant explanation can be found in Page 14, Lines 479-483 of the revised manuscript.
Comments 7: While the manuscript is understandable and the structure is solid, several sentences are awkward or overly technical. Clarity could be improved with better phrasing, especially in the methods and discussion sections.
Response 7: Thank you for pointing this out. We agree with your suggestion. To enhance clarity, we have revised and polished the manuscript, particularly the methods and discussion sections. These changes have been made to improve readability and ensure that the technical language is more accessible while maintaining the necessary scientific rigor. The updated phrasing can be found throughout the manuscript.
Round 2
Reviewer 1 Report
Comments and Suggestions for AuthorsPage 3, between lines 113 and 115, there are 2 parallel lines, please erase.
Reviewer 3 Report
Comments and Suggestions for AuthorsDear authors
This manuscript examines the spatial and temporal evolution of desertification sensitivity in the Beijing-Tianjin-Hebei core region from 2018 to 2022, with a particular focus on the influence of urban expansion and ecological change. Drawing on NDVI, nighttime light data, terrain, and soil variables, the study constructs a multi-factor GIS-based model to assess changes in desertification risk over time. The work offers a valuable contribution to the understanding of land degradation dynamics in rapidly urbanizing semi-arid environments. Its main strengths lie in the use of well-established remote sensing indicators, adherence to national ecological assessment standards, and the identification of patterns relevant for early warning systems and land use planning.
In this revised version, the authors have addressed the main concerns raised in the initial review. The rationale for the weighting of input variables is now clearly articulated and appropriately supported by reference to the LY/T 3242-2020 technical standard. The explanation of the vegetation degradation index has been expanded to include its interpretation and limitations, enhancing the reader’s understanding of the index’s practical use and potential constraints. The manuscript also improves in clarity, particularly in the methods and discussion sections, where several overly technical or ambiguous phrases have been reworked into more accessible explanations.
The structure of the paper is coherent and logically developed. The research question remains clearly defined, and the data sources and analytical framework are appropriate for addressing it. The added examples of land use change and ecological interventions give the results greater specificity and context. Figures are informative and well-integrated, and the NDVI trend and hotspot maps now have improved explanatory content. The reference list continues to be largely current and relevant, and while there is some reliance on domestic literature, this is appropriate given the regional focus of the study. No excessive self-citation is evident.
At this stage, all critical issues from the first round appear to have been satisfactorily resolved. The current version presents a clearer and more complete picture of the processes driving desertification sensitivity in the study area and can serve as a useful reference for applied ecological monitoring in similar contexts. I recommend that the manuscript be accepted for publication following minor editorial review.
