New ECOSTRESS and MODIS Land Surface Temperature Data Reveal Fine-Scale Heat Vulnerability in Cities: A Case Study for Los Angeles County, California

Round 1

Reviewer 1 Report

The research presents the application of ECOSTRESS Land Surface Temperature (LST) to the estimation of Heat Vulnerability Indices (HVI). The work is rare in the sense that it presents HVIs at the diurnal scale over a large megacity. Furthermore, the HVIs can provide a quick measure for urban planners and policy-makers The approach presented is also clear, concise, and easy to follow. Below are a few general inquiries, comments (or suggestions) to improve the manuscript:

1. ECOSTRESS LST is highlighted in the work as impressive due to its availability of diurnal measurements. However, looking at the scene acquisitions (https://ecostress.jpl.nasa.gov/gmap/index_html), there are long periods of unavailable data (e.g. Oct 2018 to Jan 2019). Shouldn't the authors fairly mention this issue or (if possible) whether this issue can be resolved in the future?

2. Kindly double check the sequence of citations. The statement in L111 seems to point to L41 rather than L38.

3. L281 (building height estimation). Citation is needed. How was University of Maryland able to estimate building heights from Landsat 8 since stereo imagery was not provided? In Table A1, the acronym, UMD is not stated. Does this mean "University of Maryland"? Also, why is it 200-m. in the table when it is 30-m. according to L281.

4. L186 The inputs are of different spatial resolutions. Which method of resampling or spatial resolution was used and during which part of the calculation? Since none of the inputs have 100-m x 100-m, what is the basis for setting it as such or why is it necessary?

5. When utilizing the historical climatologies of MODIS LST as inputs (Sect. 3.3), kindly state clearly which variables were time-invariant. Was LST the only variable which was allowed to change when estimating the HVI? If so, isn't the result of similar HVIs coming from both ECOSTRESS and MODIS mainly due to the lesser correlation of LST vs HVI than poverty vs HVI? Furthermore, if LST was left constant, it might be interesting to see the changes in exposure (E) and how it compares with ECOSTRESS.

6. The work is meant to encourage other researchers/ planners to follow similar approach. In relation to the authors' statement in L535, "The model can be applied to any city with associated sociodemographic information", based on the principal component analyses, is it possible to mention which minimum sociodemographic information are required? The reason for this is that not all cities have the same amount of available sociodemographic information as in Los Angeles.

7. In this process-oriented work, a flowchart will be very useful and can easily be cited. The authors should consider plotting one.

Author Response

Please see the attachment.

Author Response File: Author Response.docx

Reviewer 2 Report

The authors applied a new type of satellite images (ECOSTRESS) for HVI mapping. Although the method itself is not new, the promotion of applications with this new type of satellite image is important.

However, there are some critical elements that should be clarified before final acceptance of this paper.

a. The authors applied LST instead of air temperature for HVI or heat risk mapping. However, more papers have already pointed our that LST is not really correlated with air temperature in the urbanized area. The use of LST in this study should be justified. 

b. Lines 46 - 49: "Numerous past studies have attempted to quantify urban areas with high risk using a Heat Vulnerability Index (HVI) derived primarily from socio-demographic variables [11-15]. But of greater interest is whether areas of high social vulnerability coincide with areas of high heat exposure, particularly in cities more vulnerable to extreme heat." This statement clearly indicates that the authors did not work on the literature review, or at least did not work well with searching past literature. Specifically, using both heat exposure data with socioeconomic data for HVI or heat risk mapping can be traced back to Vescovi et al (2005). In addition, there are already more than 30 papers for such applications since 2005. Can the authors work on a good summary regarding all these literature in order to enhance your introduction, please? The follow list includes the missing references which are not listed in the current bibliography. 

Reference: 

Vescovi, L., Rebetez, M., & Rong, F. (2005). Assessing public health risk due to extremely high temperature events: climate and social parameters. Climate Research, 30(1), 71-78.

Harlan, S. L., Brazel, A. J., Prashad, L., Stefanov, W. L., & Larsen, L. (2006). Neighborhood microclimates and vulnerability to heat stress. Social science & medicine, 63(11), 2847-2863.

Wolf, T., & McGregor, G. (2013). The development of a heat wave vulnerability index for London, United Kingdom. Weather and Climate Extremes, 1, 59-68.

Ho, H., Knudby, A., & Huang, W. (2015). A spatial framework to map heat health risks at multiple scales. International journal of environmental research and public health, 12(12), 16110-16123.

Tomlinson, C. J., Chapman, L., Thornes, J. E., & Baker, C. J. (2011). Including the urban heat island in spatial heat health risk assessment strategies: a case study for Birmingham, UK. International journal of health geographics, 10(1), 42.

Buscail, C., Upegui, E., & Viel, J. F. (2012). Mapping heatwave health risk at the community level for public health action. International journal of health geographics, 11(1), 38.

Aminipouri, M., Knudby, A., & Ho, H. C. (2016). Using multiple disparate data sources to map heat vulnerability: Vancouver case study. The Canadian Geographer/Le Géographe canadien, 60(3), 356-368.

Dong, W., Liu, Z., Liao, H., Tang, Q., & Li, X. E. (2015). New climate and socio-economic scenarios for assessing global human health challenges due to heat risk. Climatic Change, 130(4), 505-518.

Dong, W., Liu, Z., Zhang, L., Tang, Q., Liao, H., & Li, X. E. (2014). Assessing heat health risk for sustainability in Beijing’s urban heat island. Sustainability, 6(10), 7334-7357.

Bao, J., Li, X., & Yu, C. (2015). The construction and validation of the heat vulnerability index, a review. International journal of environmental research and public health, 12(7), 7220-7234.

Johnson, D. P., Stanforth, A., Lulla, V., & Luber, G. (2012). Developing an applied extreme heat vulnerability index utilizing socioeconomic and environmental data. Applied Geography, 35(1-2), 23-31.

Johnson, D. P., Wilson, J. S., & Luber, G. C. (2009). Socioeconomic indicators of heat-related health risk supplemented with remotely sensed data. International Journal of Health Geographics, 8(1), 57.

Rinner, C., Patychuk, D., Bassil, K., Nasr, S., Gower, S., & Campbell, M. (2010). The role of maps in neighborhood-level heat vulnerability assessment for the city of Toronto. Cartography and Geographic Information Science, 37(1), 31-44.

Uejio, C. K., Wilhelmi, O. V., Golden, J. S., Mills, D. M., Gulino, S. P., & Samenow, J. P. (2011). Intra-urban societal vulnerability to extreme heat: the role of heat exposure and the built environment, socioeconomics, and neighborhood stability. Health & Place, 17(2), 498-507.

Kershaw, S. E., & Millward, A. A. (2012). A spatio-temporal index for heat vulnerability assessment. Environmental monitoring and assessment, 184(12), 7329-7342.

Zhang, W., McManus, P., & Duncan, E. (2018). A Raster-Based Subdividing Indicator to Map Urban Heat Vulnerability: A Case Study in Sydney, Australia. International journal of environmental research and public health, 15(11), 2516.

Zhang, W., Zheng, C., & Chen, F. (2019). Mapping heat-related health risks of elderly citizens in mountainous area: A case study of Chongqing, China. Science of the total environment, 663, 852-866.

Räsänen, A., Heikkinen, K., Piila, N., & Juhola, S. (2019). Zoning and weighting in urban heat island vulnerability and risk mapping in Helsinki, Finland. Regional Environmental Change, 19(5), 1481-1493.

Méndez-Lázaro, P., Muller-Karger, F. E., Otis, D., McCarthy, M. J., & Rodríguez, E. (2018). A heat vulnerability index to improve urban public health management in San Juan, Puerto Rico. International journal of biometeorology, 62(5), 709-722.

Taylor, J., Wilkinson, P., Davies, M., Armstrong, B., Chalabi, Z., Mavrogianni, A., ... & Bohnenstengel, S. I. (2015). Mapping the effects of urban heat island, housing, and age on excess heat-related mortality in London. Urban Climate, 14, 517-528.

Krstic, N., Yuchi, W., Ho, H. C., Walker, B. B., Knudby, A. J., & Henderson, S. B. (2017). The Heat Exposure Integrated Deprivation Index (HEIDI): A data-driven approach to quantifying neighborhood risk during extreme hot weather. Environment international, 109, 42-52.

Morabito, M., Crisci, A., Gioli, B., Gualtieri, G., Toscano, P., Di Stefano, V., ... & Gensini, G. F. (2015). Urban-hazard risk analysis: mapping of heat-related risks in the elderly in major Italian cities. PLoS One, 10(5), e0127277.

Macintyre, H. L., Heaviside, C., Taylor, J., Picetti, R., Symonds, P., Cai, X. M., & Vardoulakis, S. (2018). Assessing urban population vulnerability and environmental risks across an urban area during heatwaves–Implications for health protection. Science of the total environment, 610, 678-690.

Hu, K., Yang, X., Zhong, J., Fei, F., & Qi, J. (2017). Spatially explicit mapping of heat health risk utilizing environmental and socioeconomic data. Environmental science & technology, 51(3), 1498-1507.

Kim, D. W., Deo, R. C., Lee, J. S., & Yeom, J. M. (2017). Mapping heatwave vulnerability in Korea. Natural Hazards, 89(1), 35-55.

Chen, Q., Ding, M., Yang, X., Hu, K., & Qi, J. (2018). Spatially explicit assessment of heat health risk by using multi-sensor remote sensing images and socioeconomic data in Yangtze River Delta, China. International journal of health geographics, 17(1), 15.

Ho, H. C., Knudby, A., Walker, B. B., & Henderson, S. B. (2016). Delineation of spatial variability in the temperature–mortality relationship on extremely hot days in greater Vancouver, Canada. Environmental health perspectives, 125(1), 66-75.

Karimi, M., Nazari, R., Dutova, D., Khanbilvardi, R., & Ghandehari, M. (2018). A conceptual framework for environmental risk and social vulnerability assessment in complex urban settings. Urban climate, 26, 161-173.

Leal Filho, W., Icaza, L. E., Neht, A., Klavins, M., & Morgan, E. A. (2018). Coping with the impacts of urban heat islands. A literature based study on understanding urban heat vulnerability and the need for resilience in cities in a global climate change context. Journal of cleaner production, 171, 1140-1149.

c. Line 250: What do you mean by "heatwave average daily temperature"? There are lots of definitions for "heatwave" and you need to clearly state what types of "heatwave" you are referring to. Specifically, HVI or heat risk map is useless if you include any days with temperature lower than <90th percentiles / < 95th percentiles for mapping. This is because HVI is designed for a hazard scenario, and days with lower temperature would not induce a "heat hazard".

d. "2.2.2 Sensitivity variables" and "2.2.3 Adaptive capacity variables" What do you mean by "Sensitivity variables" and "Adaptive capacity" I assume that you are following the concept from Wilhelmi and Hayden (2010). However, please note that Wilhelmi and Hayden (2010) has already stated that their concept is a "new framework". You may need to explain in details regarding the definitions of "sensitivity", "vulnerability", "adaptive capacity" and etc to help the global audiences to clearly understand what you actually mean. This should also link to your description in Lines of 178 - 180 of Section 2.2: "The HVI is based on a summatory model with factor analysis of exposure (E), sensitivity (S), and adaptability (A) variables for each pixel, i, within the city represented by the input data [50-53]".

Reference: Wilhelmi, O. V., & Hayden, M. H. (2010). Connecting people and place: a new framework for reducing urban vulnerability to extreme heat. Environmental Research Letters, 5(1), 014021.

e. For each sensitivity variable and adaptive capacity variable, you should clearly state why you pick it with references, specifically, how each variable influence individual and household vulnerability during a heat wave, but not just listing the variables.  The following are some references for you to state the reason of using each variable.

Reference: 

Nayak, S. G., Shrestha, S., Kinney, P. L., Ross, Z., Sheridan, S. C., Pantea, C. I., ... & Hwang, S. A. (2018). Development of a heat vulnerability index for New York State. Public health, 161, 127-137.

Yardley, J., Sigal, R. J., & Kenny, G. P. (2011). Heat health planning: The importance of social and community factors. Global Environmental Change, 21(2), 670-679.

f. Section 2.2.4 Principal Component Analysis. In line 178 - 180, the authors wrote "factor analysis". However, this section stated "principal component analysis". Which one are you actually using? They are similar but different.

g. Lines 336 - 339 is just repeating the statement in lines 183 - 185.  

h. In this study, heat vulnerability is considered as a "temporal static" component. However, this is actually changing through years. The authors may need to address this problem in the section of limitation. The follow list includes some references for the authors.

References:

Binita, K. C., Shepherd, J. M., & Gaither, C. J. (2015). Climate change vulnerability assessment in Georgia. Applied Geography, 62, 62-74.

Hess, J. J., & Ebi, K. L. (2016). Iterative management of heat early warning systems in a changing climate. Annals of the New York Academy of Sciences, 1382(1), 21-30.

Ebi, K. L., Hess, J. J., & Isaksen, T. B. (2016). Using uncertain climate and development information in health adaptation planning. Current environmental health reports, 3(1), 99-105.

Haines, A., & Ebi, K. (2019). The imperative for climate action to protect health. New England Journal of Medicine, 380(3), 263-273.

Ho, H. C., Knudby, A., Chi, G., Aminipouri, M., & Lai, D. Y. F. (2018). Spatiotemporal analysis of regional socio-economic vulnerability change associated with heat risks in Canada. Applied geography, 95, 61-70.

Sheridan, S. C., & Allen, M. J. (2018). Temporal trends in human vulnerability to excessive heat. Environmental research letters, 13(4), 043001.

Sheridan, S. C., & Dixon, P. G. (2017). Spatiotemporal trends in human vulnerability and adaptation to heat across the United States. Anthropocene, 20, 61-73.

Habeeb, D., Vargo, J., & Stone, B. (2015). Rising heat wave trends in large US cities. Natural Hazards, 76(3), 1651-1665.

Author Response

Please see the attachment.

Author Response File: Author Response.docx

Reviewer 3 Report

General comments:

The manuscript titled "New ECOSTRESS and MODIS land surface temperature data reveal fine-scale heat vulnerability in cities: a case study for Los Angeles county California" presents a model to HVI maps driven by surface temperature from this new infrared sensor, also a climatology using MODIS data.

However, the manuscript needs some corrections before this work is considered suitable for publication. Besides, it is necessary to answer some doubts as to the methodology used. In general, an English grammar review is highly required.

Introduction:

The introduction does not present a logical sequence, stating what the problem to be solved and missing state of the art. Also, it already describes the methodology of the work on the second page, on line 60. From lines 62 to 102, the authors present a more detailed description of the method.

Reference 2 is not appropriate. Probably, this caused a problem when citing other works. The manuscript does not present a sequence between citations and mentioned articles in the reference.

Materials and Methods:

Although this session has content relevant to the work, it also needs a logical sequence, following the line presented in the results. It is necessary to explain how the principal components coefficients were obtained (Table 1). This explanation is relevant data, and the reference was not detailed.

Results:

This section presents the results with much discussion but again needs to follow the methodology sequence. Another confusion is the discussion of figures, as there are comparisons with figures not yet shown and which will be discussed only throughout the text. Also, the figures could display the latitude and longitude labels more often.

Summary and conclusions:

Although defined as summary and conclusions, this section looks like an expanded summary of the work. The valid conclusion is only in the last paragraph, in which a more in-depth discussion is necessary.

Author Response

Please see the attachment.

Author Response File: Author Response.docx

Round 2

Reviewer 2 Report

The authors have put a great effort to improve the manuscript. 

Reviewer 3 Report

The authors have improved the manuscript after the review, following the reviewers' recommendation.