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Peer-Review Record

Influence of Impervious Surface Area and Fractional Vegetation Cover on Seasonal Urban Surface Heating/Cooling Rates

Remote Sens. 2021, 13(7), 1263; https://doi.org/10.3390/rs13071263
by 1,2,*, 3,4 and 1
Reviewer 1: Anonymous
Reviewer 2: Anonymous
Reviewer 3: Anonymous
Remote Sens. 2021, 13(7), 1263; https://doi.org/10.3390/rs13071263
Received: 19 February 2021 / Revised: 20 March 2021 / Accepted: 22 March 2021 / Published: 26 March 2021
(This article belongs to the Section Environmental Remote Sensing)

Round 1

Reviewer 1 Report

1) As far as can be understood, 3 thematic classes (ISA, FVC, Soil) are used, making up a total of 100%. Thus, as one class increases, the share of the rest decreases. At the same time, the regression is constructed only for single component percentage, ISA or FVC, without taking into account the influence of the rest. Why wasn't a two-variable regression used? A similar question for determining SHR, SCR

2) It is not indicated how the estimate of the percentage of ISA, FVC, Soil changes (at the same pixel) between seasons, and how this affects the result. For example, for non-evergreen vegetation (if such vegetation is present), what does the fraction of FVC go into after falling leaves

Author Response

1) As far as can be understood, 3 thematic classes (ISA, FVC, Soil) are used, making up a total of 100%. Thus, as one class increases, the share of the rest decreases. At the same time, the regression is constructed only for single component percentage, ISA or FVC, without taking into account the influence of the rest. Why wasn't a two-variable regression used? A similar question for determining SHR, SCR

RE: Thanks for this comment. We just analysed percent ISA and FVC because of

‘Urban impervious areas can increase the urban heat stress by increasing LST through the absorption of sensible heat [20]. In contrast, urban vegetation reduces LST by redistributing sensible heat to latent heat, reduces downward solar radiation reaching the ground via shading effects, and mitigates air pollution and the UHI effect [21,22].’ (in the third paragraph of section 1 Introduction).

About without taking into account the influence of soil, the most important is the focus of our research is about ISA and vegetation, not soil. In addition, because the distribution of soil is very small in urban area (especially from the spatial resolution imagery of Landsat), and the focus of our manuscript is to analyse the heating/cooling rates influenced by percent ISA and FVC. We think it is no need to taking into account of the soil. Even though soil impact the thermal environment, the impact of soil on LST is very little because of the distribution of soil is very small in urban area.

About why wasn't a two-variable regression used: Different from previous studies that analyzed the relationship between LST with ISA and vegetation (this analysis may use a two-variable regression), our focus is to calculate surface heating rate (SHR) and surface cooling rate (SCR). SHR is calculated based on LST and percent ISA, and SCR is calculated based on LST and FVC. Finally, in order to analyse the heating effect of ISA and cooling effect of vegetation, the variations of LST with SHR and SCR were compared between different percent ISA and FVC categories in four seasons. Our methodology integrates SHR, SCR, percent ISA, FVC and LST for a quantification of the urban thermal environment and can provide information for urban planning and climate adaptation.

Therefore, we don’t use a two-variable regression for SHR and SCR. We hope the reviewer can be satisfied with our reply.

2) It is not indicated how the estimate of the percentage of ISA, FVC, Soil changes (at the same pixel) between seasons, and how this affects the result. For example, for non-evergreen vegetation (if such vegetation is present), what does the fraction of FVC go into after falling leaves

RE: We think the reviewer’s comment is about the impact of seasonal variations on the percentage of ISA and FVC. In addition, seasonal variations also impact on LST. In reality, Figure 3 (fractional cover images of ISA and vegetation) can indicate the changes of the percentage of ISA and FVC. If one fractional cover image is subtracted by another fractional cover image, we can get the difference images that show the percentage changes of ISA and vegetation.

We don’t directly calculate and analyse the differences of fractional cover images of ISA and vegetation between different seasons in our manuscript. The reasons are as below:

Firstly, in reality, when we calculated the surface heating rate of ISA based on LST and percent ISA and surface cooling rate of vegetation based on LST and FVC (Eqs. (3) and (4)) and compared them in different seasons (this is our research focus), we analysed the impact of seasonal variations on the LST, percent ISA and FVC. Therefore, we think we can show Figure 3 in our manuscript and no need to directly analyse the changes of percent ISA and FVC between different seasons.

Secondly, considering our research focus, we think the direct analysis of the differences of fractional cover images of ISA and vegetation between different seasons is not significant in our manuscript.

We hope the reviewer can agree with our opinion.

Reviewer 2 Report

The study aims to present the research on “Seasonal urban surface heating/cooling rates influenced by percent impervious surface area and fractional vegetation cover.” The manuscript is presented clearly and nicely. The Paper is interesting. However, there are some essential suggestions to improve the paper before acceptance. Thus, I would like to suggest major revisions. 

 

  1. The introduction is well organized with considerable related information to the study.
  2. It is better to add “metadata” such as path, row, date, time of the Landsat data used for this study (line 111-120)
  3. No need to include table 1. It is very general information. Could you remove it?
  4. Figure 2: it is very difficult to capture the LST values. At least you have to show the minimum and maximum LST value.
  5. Fig should be Figure
  6. The extraction of ISA is not precise. You have to show the method clearly.
  7. Discussion and conclusion are fair.
  8. Better to explain how the proposed methodology can be adapted to similar study areas.

Author Response

The study aims to present the research on “Seasonal urban surface heating/cooling rates influenced by percent impervious surface area and fractional vegetation cover.” The manuscript is presented clearly and nicely. The Paper is interesting. However, there are some essential suggestions to improve the paper before acceptance. Thus, I would like to suggest major revisions.  

  1. The introduction is well organized with considerable related information to the study.

RE: Thanks for this comment.

  1. It is better to add “metadata” such as path, row, date, time of the Landsat data used for this study (line 111-120)

RE: We have listed the DATE_ACQUIRED in the second paragraph of the Section 2 (Study area and the data). The metadata of four Landsat data list as below:

TARGET_WRS_PATH = 120

TARGET_WRS_ROW = 38

DATE_ACQUIRED = 2017-03-15

SCENE_CENTER_TIME = "02:37:05.5987870Z"

 

TARGET_WRS_PATH = 120

TARGET_WRS_ROW = 38

DATE_ACQUIRED = 2017-07-21

SCENE_CENTER_TIME = "02:37:14.6891600Z"

TARGET_WRS_PATH = 120

TARGET_WRS_ROW = 38

DATE_ACQUIRED = 2017-10-09

SCENE_CENTER_TIME = "02:37:38.4500190Z"

 

TARGET_WRS_PATH = 120

TARGET_WRS_ROW = 38

DATE_ACQUIRED = 2017-12-12

SCENE_CENTER_TIME = "02:37:31.4440240Z"

 

When we download Landsat data from the website, we also need to download the metadata files of all the Landsat imagery. We list the metadata of four Landsat imagery here, and we advise not to add these metadata in our manuscript (as many other scholars usually did) because this information is not important in our manuscript. If the reviewer insists on adding this information, we will add later.

  1. No need to include table 1. It is very general information. Could you remove it?

RE: Thanks for this comment. Yes, Table 1 is not important, and we have removed Table 1.

  1. Figure 2: it is very difficult to capture the LST values. At least you have to show the minimum and maximum LST value.

RE: In Figure 2, the legend shows the minimum (4.1 ℃) and maximum (49.4 ℃) LST value of four seasons. In other words, we used one legend for four seasonal images. One advantage of one legend for four images is that we can generally see the variations of LST between different seasons as many other scholars did. If we give a legend for each image, we can not directly see the variations of LST between different seasons because the colour of legend are same for four images and the minimum and maximum LST values are different between each image. Therefore, we advise not to show the minimum and maximum LST value for each image here as many other scholars did. We hope the reviewer can agree with our opinion.

In addition, the focus of our manuscript is not the analysis of the LST variations. Our methodology integrates SHR, SCR, percent ISA, FVC and LST for a quantification of the urban thermal environment and can provide information for urban planning and climate adaptation.

  1. Fig should be Figure

RE: We have checked the whole manuscript and replaced all the ‘Fig.’ with Figure.

  1. The extraction of ISA is not precise. You have to show the method clearly.

RE: We have modified further the first paragraph of Section 3.2 (Linear spectral unmixing by fully constrained least squares and accuracy assessment) and supplemented some sentences as follows:

(1) ‘All linear spectral unmixing procedures were undertaken in ENVI 4.5.’

(2) ‘The urban environment can be assumed to consist of four fundamental components: water, vegetation, impervious surfaces and soil.’

(3) ‘because the spectral features of water are similar to those of low-albedo impervious areas’

(4) ‘bright ISA such as concrete’, ’ dark ISA such as asphalt’

(5) ‘and four fractional images were derived. The high- and low-albedo percent ISA images were aggregated to a total percent ISA image.’

We think we have shown the method clearly now. Because the linear spectral unmixing by fully constrained least squares (FCLS) is a normal method as other scholars did and spectral unmixing is only a procedure in our manuscript (not our focus). All spectral unmixing procedures were undertaken in ENVI 4.5 (a remote sensing image processing software), we don’t need to introduce too much about it. We hope the reviewer can be satisfied with our modifications and can agree with our opinion.

  1. Discussion and conclusion are fair.

RE: Thanks for this comment.

  1. Better to explain how the proposed methodology can be adapted to similar study areas.

RE: Our manuscript is helpful for other scholars’ research. The relationships and change trends between SHR, SCR, percent ISA, FVC and LST in different cities may be area-dependent (the change trends may be different between different cities). Our methodology can be used for similar study areas and future research can further compare the results for different cities under different climatic conditions. Through comparing the SHR, SCR and mean LST based on the different ranges of ISA and FVC in different seasons and different cities, it can provide a new method to explore the impact of the urban landscape pattern on the thermal environment.

We have indicated these in the Section 5 Conclusions, especially in the last paragraph of our manuscript (Section 5). We hope the reviewer can agree with our opinion.

Author Response File: Author Response.docx

Reviewer 3 Report

The research focus on seasonal urban surface heating effects. As parameter, the percent impervious surface area and the fractional vegetation cover is used.

The research methodology is well described. The extraction and calculation of the parameters based on Landsat remote sensed data is well documented. The results are good visualized by different graphs and a comprehensible textual description is added.

The results are new from a scientific point of view and are of great interest for urban planners.

The is ready for publication without any changes

Remark:
in figure 5 the legend should be moved to a location as in figure 7, so it will become clear that it is valid for all 4 graphs

 

Author Response

The research focus on seasonal urban surface heating effects. As parameter, the percent impervious surface area and the fractional vegetation cover is used. The research methodology is well described. The extraction and calculation of the parameters based on Landsat remote sensed data is well documented. The results are good visualized by different graphs and a comprehensible textual description is added. The results are new from a scientific point of view and are of great interest for urban planners. The is ready for publication without any changes.

Remark:
in figure 5 the legend should be moved to a location as in figure 7, so it will become clear that it is valid for all 4 graphs

RE: Thanks for this comment. We have moved the location of the legend in figure 5 to the location same as that in figure 7.

 

Author Response File: Author Response.docx

Round 2

Reviewer 2 Report

Thank you very much for incorporating my comments. Now the paper is strong enough to accept. 

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