Multi-Temporal Analysis of the Impact of Summer Forest Dynamics on Urban Heat Island Effect in Yan’an City

Round 1

Reviewer 1 Report (New Reviewer)

Please see below for my comments. Overall, the paper has been well written; however, some improvements are still recommended. 

The introduction provides a broad overview of UHI and its impacts on human health and urban environments. However, it could benefit from more specific examples or case studies to illustrate the points made. Providing specific instances of UHI mitigation efforts and their outcomes would strengthen the argument.

The introduction briefly mentions the research gap concerning UHI mitigation in less developed areas but does not elaborate on why this is significant or how the current study addresses this gap uniquely. Providing more context on the specific gaps in literature and how the study aims to fill them would strengthen the rationale for the research.

Some sentences are lengthy and complex, which may hinder readability. Please consider simplifying the language and breaking down complex ideas into more digestible chunks – e.g., lines 31-31 “Records of UHI in London, dating …”

In the Materials and Methods section, there are several areas that could be improved for clarity and coherence. For instance, please provide clearer explanations of the data processing steps, including any transformations or adjustments made to the raw data. It may help if you break down complex technical terms and procedures into simpler language aiming to enhance understanding by readers who may not be familiar with remote sensing techniques. For example, explain in simpler terms how the MODIS data was processed using the Google Earth Engine platform, including the rationale for selecting specific time periods and processing techniques.

In the discussion section, please provide more context for the significance of the study's findings within the broader field of urban planning and environmental management. Discuss how the findings contribute to existing knowledge on UHI mitigation strategies and highlight their potential implications for sustainable urban development in Yan'an City and beyond. Here, authors can also consider comparing the effectiveness of green belts with other UHI mitigation measures mentioned in the literature review, such as green roofs, alterations to building surface materials, and urban pattern rationalization. This comparative analysis can help readers understand the relative advantages and disadvantages of different approaches and inform decision-making in urban planning.

Acknowledge the limitations of the study and propose avenues for future research to address these limitations. For example, discuss the need for further analysis of factors influencing the UHI, such as humidity and economic factors, and the potential use of more precise temperature data sources to improve the accuracy of temperature assessments.

Please elaborate on situating the study's findings within the broader context of previous research on UHI mitigation strategies and highlight any novel contributions or insights generated & offered by the current study.

Moderate proofreading is recommended prior to resubmission. 

Author Response

Please see the attachment.

Author Response File: Author Response.docx

Reviewer 2 Report (New Reviewer)

This manuscript analyzed the relationship between vegetation types, NDVI and LST during the summers of 2017-2022, in particular the impact of forest NDVI on LST for each year. The current title's emphasis on the green belt does not accurately reflect the manuscript's core focus. It is suggested to revise the title to highlight themes such as forest change and multi-temporal sequences.

Some specific changes are as follows:

1. Lines 22-26 “In this study, the regulation effect of …for future urban planning and ecological construction”

There is no need for the abstract to be presented in segments; it is recommended that authors consolidate it into one paragraph

2. Line87 “2.1 Study Area”

It is suggested to include the rationale for selecting Yan'an as a case site for mitigating the urban heat island effect in section 2.1, e.g. to show that the UHI benefits have risen significantly in recent years, etc.

It's important to clarify the definition and specific attributes of the Green Belt, as well as its distinct features as an object of study. The reviewer only saw forests and other types of land cover types, not objects such as green belts with significant morphological characteristics. Authors may consider revising the title to accurately reflect the content or provide a clearer definition and delineation of the Green Belt within the manuscript.

3.Lines431-432 “The data analysis within the 500-meter buffer zone (Figure 9a) shows that in 2017, 2019, and 2021…”

The reviewer did not find Figure 9a.

4.Lines434-435 “The data analysis within the 1000-meter buffer zone (Figure 9b) shows…”

The reviewer did not find Figure 9b.

5.Line445 “Figure 10. Correlation Analysis of LST and NDVI in Yan'an City (2017-2022)”

Authors are advised to enhance the clarity of the diagrams, particularly by ensuring that the formulas are clearly labeled and easily visible. Consistency in the dimensions of all formulas and the scales of the axes should be maintained across the diagrams to improve readability and understanding.

6. Line446 “4. Discussion”

The reviewers recommend centering the discussion section around the findings of the current study rather than solely citing findings from other studies to illustrate the mitigation of UHI. It's crucial for the authors to understand that the discussion should focus on analyzing and interpreting the results obtained from this study, rather than generalizing the study objectives.

7.Line496 “5. Conclusions”

The current conclusions are too lengthy and it is recommended that the authors sharpen their focus and reduce the number of enumerative descriptions. For example: “In the analysis of the spatial variation of Yan'an city's green belts, the NDVI calculation can objectively reflect changes in vegetation coverage and growth conditions. Areas with NDVI≥0.2 are classified as vegetation zones, and areas with NDVI≥0.4 are classified as well-growing zones.”

8.Lines534-535 “Among all temperature fields, the transition from sub-high-temperature to UHI zones accounts for 20.69%, which is the main driver for the increase in UHI area”

Avoid using terms like "driver" casually. The increase in UHI area is typically attributed to changes in land cover or human activity rather than solely understanding the expansion of high-temperature areas.

The authors are encouraged to polish the expression of English in the manuscript.

Author Response

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Author Response File: Author Response.docx

Reviewer 3 Report (New Reviewer)

The manuscript under review is devoted to the effect of green belt on urban heat island. The authors use the data for Yan’an City, China. The sources of data are Moderate Resolution Imaging Spectroradiometer, China land cover dataset and Shaanxi Provincial Statistical Yearbook, other datasets for 2017-2022 Summer. The authors showed negative correlation between Land Surface Temperature and Normalized Difference Vegetation Index for Yan’an City and the production of the best cooling effect by forest type of vegetation. The study is interesting and useful, richly illustrated by maps and schemes, well rooted by the data and is quite reasonable.

The manuscript itself, even the abstract and keywords list, are overloaded with abbreviations. They make reading and understanding of the text harder and it is better to avoid, or, at least, to minimize the use of abbreviations. The reviewer advices to exclude completely the abbreviations from the abstract and keywords, from figure and table captions, to add abbreviation list somewhere between keywords and introduction. There are some mistakes/misprints), e.g. “data from 2017 to 2017” (p.6, l.180) in the text.

Author Response

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Author Response File: Author Response.docx

Round 2

Reviewer 1 Report (New Reviewer)

The authors managed to address my comments, hence the paper can be considered for publication - as far as my comments are concerned. 

Moderate proofreading is recommended before publication. 

Reviewer 2 Report (New Reviewer)

No further comments.

This manuscript is a resubmission of an earlier submission. The following is a list of the peer review reports and author responses from that submission.

Round 1

Reviewer 1 Report

Dear authors,

I read the paper titled “Exploring the mitigating effect of green belts on urban heat is-2 land in Yan'an City based on Geographic Information System” finding the addressed topic of great interest.

However, I do feel that certain noteworthy matters need addressing. While the paper is rich in numerical data and graphical representations, I perceive the outcomes and discussions to lean more towards qualitative rather than quantitative analysis as reported at lines 74-77 “By quantitatively determining the effect of green belts in mitigating UHI, and exploring the magnitude of changes in the cooling effect of green belts with distance, we study the mitigation effect of green belt on UHI effect in Yan'an City. Other urban planning measures to mitigate UHI will be addressed in the discussion section”. In particular, I didn't quite understand how the authors “quantitatively determined the effect of green belts in mitigating UHI”.

The objectives outlined in lines 122-127 (Why in this section?), i.e.,  “1) The heat island effect exists in the city surrounded by a protective forest belt in summer, and the changes in the spatial distribution of the UHI are studied over a five-year period;2) to quantitatively prove that shelterbelts have a mitigating effect on the UHI on a statistical basis; and 3) to show that the mitigating effect is not useful at an infinite distance, but loses its effect when the distance from the forest exceeds a certain threshold” appear to resemble findings rather than objectives.

My main concern is the adopted methodology. Why do the authors consider GIS a method? (line 116 and the main title of the paper).

The authors used satellite data of different spatial resolution. Landsat 8 (used for NDVI) has 30m of spatial resolution; MODIS (MYD11A2 product for LST) is at 1km.
How do the authors address the challenge of handling data at different spatial resolutions? Which is the spatial resolution of maps in Figures 3,5,7, and 8? How does it work “the method of extracting random points, to extract LST and calculate NDVI using MODIS data and Landsat with different spatial resolutions, respectively?

Moreover, at lines 191-193 the authors write that “The data were used to verify the accuracy of the remotely sensed temperature inversion, and the maximum and minimum temperature intervals of the two were verified to be basically the same, so that the inverted temperature was reliable.” How is this possible? Meteorological data measures air temperature, LST surface temperature. Please, clarify.

Hereafter some minor issues:

1) lines 12-13: “especially in the high-temperature zone with obvious spatial expansion”. Please, explain why “obvious”.

2) line 15: NDVI is for normalized difference vegetation index

3) section 2.3.1: which Landsat tiles have been used? How many data totally? What about cloud covers and the land cloud covered? Please, section about MODIS images needs to be restructured because the reading is not fluent and clear.

4) line 196: please, modify the years.

5) Figure 3: why did the authors decide to analyse vegetation in general by talking about vegetated and non-vegetated classes?

6)How was the extraction of the forests carried out? Applying a threshold at NDVI or by using Land Cover? Here I would have discussed based on a common NDVI legend…

7) Figure 6 is illegible and unclear.

8) Section 3.3: many expressions are not clear. The section needs to be re-read carefully and restructured.

Author Response

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Author Response File: Author Response.docx

Reviewer 2 Report

The paper entitled "Exploring the mitigating effect of green belts on urban heat in Yan'an City based on Geographic Information System" is a scientific article that demonstrates a commendable utilization of satellite imagery for studying vegetation and its development in a region of China. The article exhibits coherence, and its guidelines align well with the scope of the journal. However, some inconsistencies require revision before publication.

It is unnecessary to explain what MODIS is in the abstract. The authors should provide quantification for what they refer to as "good growth conditions" in general. Additionally, more detailed results are needed in this section.

The objective in the introduction is not clear. The problem's relevance seems too local or of regional interest to China, rather than as something that has been studied in other cities worldwide (such as Monte Hermoso, Argentina). Numerous cities around the world lack a degree of development where the effects of vegetation on UHI have been studied. I suggest that the authors revisit the introduction to generate international interest in the research.

Figure 1 lacks a coordinate system to accurately locate China, while the study region, although it has coordinates, does so with zero decimals. I suggest adjusting these details to ensure the map is geographically appropriate.

When analyzing the methods, the authors need to consider the differences in elevation within their study region, as they can lead to significant variations in temperature values and the type of vegetation that grows, directly influencing the NDVI value. Other spectral indices such as EVI, SAVI, and others could also be used to understand the spectral dynamics of the study area.

For the study of UHI, were the possible effects of building shadows on urban temperature considered? What time do the satellites pass over the study area?

Based on this analysis, the results lack sufficient validity. How can it be determined that areas are vegetated or non-vegetated solely based on the NDVI value? Is it because they are areas with greater heights and herbaceous vegetation?

Figure 2 lacks orientation and scale. Perhaps it would be helpful to accompany it with a graph identifying the covered area because it is difficult to differentiate the vegetation growth areas at first glance.

The same applies to the identification of forests. Was Landsat Landcover used along with NDVI values to identify them?

In Figure 7, is it possible to relate temperatures to different land cover types? Where are the cities located?

Perhaps a different presentation of the results would improve their interpretation.

Author Response

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Author Response File: Author Response.docx