Mapping Heat-Health Vulnerability Based on Remote Sensing: A Case Study in Karachi

Round 1

Reviewer 1 Report

The results of this paper can help local government formulate more targeted control measures to reduce morbidity and mortality during heat waves. Therefore, this paper has academic value and practical value. However, in order to make this paper more complete, the authors refer to the following suggestions.

  1. Some of the presentation of the content of this paper is too colloquial, therefore, I suggest that the author adjust some of the content in the academic paper writing format. 2. It is recommended to add a text description of "data sources". 3. Please check again whether the regression line and the R-square coefficient of "Figure 3" are correct. 4. The comprehensively calculated "HHV" in this study still has limitations in its application, and the authors are advised to state it clearly in the conclusion.

Author Response

Dear Reviewer 1:

Thank you for your comments and suggestions on our manuscript ID: remotesensing-1622284. These comments were all valuable and very helpful for revising and improving our paper. The revised portions are modified in the paper. The main responses to the comments are given in the attached file.

Best wishes, Xilin Wu    _______ Xilin Wu 

PhD Student

Email: [email protected]

 

State Key Laboratory of Resources and Environmental Information System, 

Institute of Geographic Sciences and Natural Resources Research,

Chinese Academy of Sciences, Beijing, China

 

 

Author Response File: Author Response.docx

Reviewer 2 Report

This is a well written paper with clear descriptions and logical structure. It describes a study which evaluates heat related vulnerability in a densely populated, hot tropical city, Karachi, Pakistan.  The study results are of value to sociologists, health experts and city planners. 

I have 3 main criticisms of the study: (i) the objectives of study appear more related to the urban/planning discipline than to remote sensing, because although remote sensing datasets are used, these are already processed image products which are simply downloaded, for incorporation into models. The methodology lacks description of how these image datasets were used eg. how were the DMSP data used to represent degree of urbanisation?  How many LST images were used in the study in total, and how many images per day? What time of day?  (ii)  Authors appear to have little understanding of climate and urban heat islands. Karachi is an arid zone city surounded by farmland, so daytime heat islands would not exist. Instead you would see a cool island, as city buildings create shadow whereas rural areas are directly exposed to the sun. This is not to say that the city does not heat up, but the urban heat island is a nighttime phenomenon.  MODIS images are in the day.   Authors need to recognise that the urban heat island concept is not relevant in this study, and remove it from the discussion - because you are simply measuring heat and heat exposure during daytime (iii) Heat related mortality is known to be more severe when night-time temperatures remain high and the body cannot cool over the 24-hour period -there is no mention of this in the paper

I have some specific points, mainly relating to omissions in the methodology:-

As MMT is not available globally, the study uses the MFT index. When MFT is mentioned in the Abstract and on line 66 the wording suggests that this is a new index created by the authors. eg. ' --we introduce the concept of MFT to redefine a heat wave'  Please rephrase here, to clarify that MFT is from Yin et al.

Figure 1 shows around 20 stations used in the linear regression of LST to air temperature, and the correlation shown in the scatterplot is not very high. Were all the points  during the study period?- please explain. However, it is good that on lines 456-8 you recognise that the relationship between air temp. and LST  is complicated

Table 2. In the 'Description' column, why are both Green Coverage  and Urbanisation Level both 'Negative' - they have opposite effects on LST, Please explain in the text.

Figure 2. Source of data is missing from the caption

line 81  'accurately

 

Author Response

Dear Reviewer 2:

 

Thank you for your comments and suggestions on our manuscript ID: remotesensing-1622284. These comments were all valuable and very helpful for revising and improving our paper. The revised portions are modified in the paper. The main responses to the comments are given in the attached file.

 

Best wishes,

Xilin Wu

_______

Xilin Wu 

PhD Student

Email: [email protected]

State Key Laboratory of Resources and Environmental Information System, 

Institute of Geographic Sciences and Natural Resources Research,

Chinese Academy of Sciences, Beijing, China

Author Response File: Author Response.docx

Reviewer 3 Report

I think it is a good perspective to objectively estimate the determination of the thermal
health vulnerability (HHV) of an area. A spatial resolution of 1 km is also practical. The
use of publicly available data is also commendable. The results of other studies that can
be directly compared with the results shown in Fig. 4 would be helpful in conveying the
merits of this study. It would be better if the weighted sensitivity results of the
indicators were also shown.
Minor comments:
The following minor comments are intended to make the paper easier to read. Referee
hopes that co-authors will take a close look at the paper and ensure that it is free of
careless errors.
L17 Is “MFT” daily average?
L75-L76 Is “MMT” daily average?
L131 It would be better to move L483-L486 here.
Table 1
”Format” is not necessary.
Add the column for Index layer (C1 to C8).
“MODIS/Terra Land Surface Temperature data.” is “MODIS/Terra,Aqua Land Surface
Temperature data.”
“L152-L153” is not consistent with “L433-L434”.
Fig.1
The enlarged map in the Karachi section is too complicated. The location of the
meteorological station is not clear. Shaded lines are unnecessary. The names of regions,
such as "Karachi East" in the south, should be simplified. The region names could be
numbered consecutively, with the numbers in the map and the number-region name
correspondence in the legend. What is “Bulidings”?
L178 Please write the full name of the abbreviation "CI".
L206 It would be better to move L456-L459 here.
L207 Is Tair C1?
L220-223
It would be better to add C2, C3 and C4 as follows.
On this basis, the poverty rate (C2) and the vulnerable population (elderly individuals
over 65 years old) (C4) were also selected as the main index factors in the sensitivity
assessment layer. Here, we chose the normalized difference built-up index (NDBI) (C3)
[77] to represent the distribution of impervious areas.
L227-L234 Same as above but for C5, C6, C7 and C8.
Operational Line Scanner (DMSP/OLS) (C8) night-time light data have become an
effective means for estimating the level of urbanization, population density, electric
energy consumption and other factors [78-79]. A region’s adaptability to disaster mainly
depends on the local medical excellence and financial status. In this article, the GDP
(C7) was selected to indicate the national economic development and government
expenditure. The distance from a medical institution (C5) was selected to indicate the
timeliness of obtaining professional medical rescue services. Furthermore, green
coverage (C6) was selected to indicate the cooling effect for the surrounding area [80].
It would be better that Table 2 and Table 3 are combined into one Table.
Please explain “Description”.
Table 3 Unify the effective digit.
L338 Is HWRI HHV?
Fig. 2
X axis: draw line every fifth day. Date format is month and day.
Y axis: It would be better that temperature is even number.
Fig. 3
(a) What is DEM?
(b) Same comments as Fig. 1. It would be better that lower temperature scale is not 0
but 30 degree-C or so.
L376 This sentence is not complete.
Fig. 4
The location of the district name (Karachi Center, Karachi East and Karachi South) is
not overlaid on the map.
“Heat health (a) exposure, B1 (b) sensitivity, B2 (c) adaptability, B3 (d) vulnerability, A
in Karachi, Pakistan” is better.
What are the areas with colors other than HHV level (from Low to High) colors found in
Karachi Central, Karachi South and Karachi East? Please explain.
Since you have obtained the results at 1km resolution, why don't you show, for example,
an enlarged map of Karachi Central, East and South (similar to Fig. 4 (d))?
L431-L432 This sentence alone is not clear. A little more explanation is needed.
L437 “ThinkHzard! [93]” is “ThinkHzard [93]”
Table 5
“Hulley et al, 2019”, “Oh et al, 2017” and “Phung et al, 2016” are not listed in the
References.
L481 Please write the full name of the abbreviation "GIS".
There is no Table 4.
 

Author Response

Dear Reviewer 3:

 

Thank you for your comments and suggestions on our manuscript ID: remotesensing-1622284. These comments were all valuable and very helpful for revising and improving our paper. The revised portions are modified in the paper. The main responses to the comments are given in the attached file.

 

Best wishes,

Xilin Wu

_______

Xilin Wu 

PhD Student

Email: [email protected]

State Key Laboratory of Resources and Environmental Information System, 

Institute of Geographic Sciences and Natural Resources Research,

Chinese Academy of Sciences, Beijing, China

Author Response File: Author Response.docx

Round 2

Reviewer 1 Report

After revisions, this paper has met the criteria for acceptance for publication. Appropriate final checking and editing may be required by the authors.

Reviewer 2 Report

Authors have responded constructively to all of my comments, and made suitable amendments to the text