Assessing the Spatial Mapping of Heat Vulnerability under Urban Heat Island (UHI) Effect in the Dhaka Metropolitan Area
Abstract
:1. Introduction
2. Materials and Methods
2.1. Description of the Study Area
2.2. Materials
2.3. Preparation of Parameters for HVI Modeling and Their Rationale
2.3.1. Satellite Image Processing and Extraction of Parameters
2.3.2. Method for LULC Classification and Validation
2.4. Heat Vulnerability Index Modeling
Modeling Process
2.5. Spatial Analysis Using the Statistical Method
3. Results
3.1. Analysis of LULC Classification and Accuracy Assessment
3.2. Modeling the Urban Heat Island
3.3. Principle Component Analysis
3.3.1. Statistical Analysis of Variables of Exposure
3.3.2. Statistical Analysis of Variables of Sensitivity
3.3.3. Statistical Analysis of Variables of Adaptive Capacity
3.4. Modeling Spatial Distribution Pattern of HVI in Dhaka Metropolitan Area
3.5. Thana-Based Land Use Land Cover Variation of Urban Heat Island Vulnerability Index in Dhaka Metropolitan Area
3.6. Identifying Correlation of NDVI, NDBI, and NDWI with Urban Heat Island Vulnerability Index in Dhaka Metropolitan Area
3.7. Identifying Spatial Distribution Pattern of Cluster-Outlier Analysis and Hotspot Analysis in Dhaka Metropolitan Area
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Indicators | Rationale | Unit/Resolution/Data Format | Source | |
---|---|---|---|---|
Exposure | LST | Land Surface Temperature indicates the intensity of heatwaves in Iceland that are exposed by the situation [59]. | °C, 30 m, and Raster | USGS, 2021 |
Population | Increasing population causes urbanization, water shortage, climate change, etc., and gets exposed to natural disaster [60]. | Number, Vector | BBS, 2011 | |
Population concentration | Population concentration causes congestion, poverty, and a low standard of living, which contributes to vulnerability, as in exposure [61]. | People/km2, Vector | ||
Sensitivity | Elderly population | Elderly people aged >65 are extremely vulnerable to urban heat sensitivity because of chronic medical condition and health issue [62]. | ||
Very young population | Young people <9 years are even more vulnerable to heatwaves than old people, caused by their lower sweating rate and body mass ratio [63]. | |||
Female | Women’s responses to heat wave sensitivity differs from men due to their higher percentage of body fat (BF%) and physical strength [64]. | |||
Illiterate people | Being unaware of the potential danger of heatwaves, illiterate people are highly sensitive to heat wave vulnerability [65]. | |||
Disabled person | Disabled people are sensitive to heat exposure due to their dependency on others as well as physical health and fragility [66]. | |||
Working age population | Employed people are a huge population working altogether in a city and respond to heat wave sensitivity; they are mostly exposed due to their criteria of work [67]. | |||
Household | Because of congested and increased population, the number of households are also increasing to a great extent and are exposed to heat wave sensitivity [68]. | Household/km2, Vector | ||
Poverty | People below the poverty line lead a miserable life with poor nutrition, poor housing conditions, and obstructive socioeconomic situations that leads to heat wave sensitivity exposure [69]. | People/km2, Vector | ||
Water accessibility | Usage of water for different purposes, especially for the tempering process, as well as treatment, refers to heat wave sensitivity [70]. | |||
Floating people | Floating people are vulnerable to heat wave sensitivity because they are homeless, they lack water access, acute illness, and electricity access; their very poor living conditions make them sensitive to heatwaves [71]. | |||
Kaccha Structure | Assessing Kacha structure of a household can indicate the sensitivity when exposed to heatwaves. Kacha structures are not properly stable, and because of the tin roof, heat affects the people miserably [72]. | Number/km2, Vector | ||
NDBI | Normalized Difference Buildup Index is an important indicator for heat wave sensitivity because the increasing build-up area, spatial growth of the buildings, and congested urban lands are simultaneously exposed to sensitivity [73]. | Zonal Pixel ratio, 30 m, Raster | USGS, 2021 | |
Built-up Area | Built-up area contributes to urban heat sensitivity because of its characterization of the overcrowding nature of buildings, apartments, settlements, and so on [74]. | km2, 30 m, Raster | ||
Adaptive Capacity | Literate | Literate people are aware of the facts and knowledge regarding heatwaves, and they prepare themselves to adapt to the situation smartly [75]. | People/km2, Vector | BBS, 2011 |
Household with electricity | Access to electricity accelerates the capacity to resist heatwave disasters by using fans, air conditioners, refrigerators, and other necessary elements [76]. | Number/km2, Vector | ||
Pucca structure | Pucca structure contains proper flooring, ceiling, and structural embellishment along with socioeconomic factors, which are expressed as adaptions exposed to heatwaves [77]. | |||
Road | Larger and wider roads tend to decrease congestion from roads and land. Thus, this feature proves to be in adaptive capacity [70]. | km ratio, Vector | ||
Health Institution | Increasing the number of health institutions helps to cure people affected by heatwaves and also prevents heatwave disasters in a greater sense [76]. | Number, Vector | ||
Relative Humidity | Temperature and humidity are both related to each other, and humidity decreases when temperature increases [78]. | Percent, Vector | BMD, 2017 | |
Vegetation | Vegetation leaves moisture in the environment and the exchange of liberal amount of oxygen helps to adapt the heatwave situation [79]. | km2 ratio, Vector | USGS, 2021 | |
NDWI | Normalized Difference Water index is used for the analysis of water bodies and waterbody reduces the heat as well as adapts the heat capacity [73]. | Zonal Pixel ratio, 30 m, Raster | ||
NDVI | Normalized Difference Vegetation Index refers to the detailed analysis of vegetation, and increasing vegetation reduces the heat and keeps the environment ecofriendly [80]. | |||
NTL data | NTL data contributes to measuring the city lights at night and helps generate the functions of urban sprawl, which later on indicates the adaption of climate change synopsis [81]. | Zonal DN value, 30 m, Raster | NGDC, 2021 |
LULC Type | Area (km2) | Area (Percent) |
---|---|---|
Agriculture | 45.23 | 15.11 |
Bare Land | 11.97 | 4.00 |
Build-Up Area | 188.77 | 63.07 |
Vegetation | 32.31 | 10.80 |
Water Body | 21.01 | 7.02 |
Grand Total | 299.28 | 100.00 |
Variable | Components | Standard Deviation | % of Variance | Cumulative % |
---|---|---|---|---|
Exposure | 1 | 0.99 | 52.85 | 52.85 |
2 | 0.99 | 34.63 | 87.48 | |
Sensitivity | 1 | 1.00 | 70.76 | 70.76 |
2 | 1.00 | 11.43 | 82.19 | |
Adaptive Capacity | 1 | 1.00 | 51.92 | 51.92 |
2 | 1.00 | 18.07 | 69.99 | |
3 | 1.00 | 10.36 | 80.35 |
Variables | Components | |||
---|---|---|---|---|
Hypothetical | Measured | 1 | 2 | 3 |
Exposure | Population concentration | 0.892 | −0.160 | N/A |
LST | 0.889 | 0.190 | N/A | |
Population | −0.026 | 0.989 | N/A | |
Sensitivity | Household | 0.977 | 0.148 | N/A |
Water accessibility | 0.976 | 0.145 | N/A | |
Female | 0.974 | 0.125 | N/A | |
Working age population | 0.972 | −0.03 | N/A | |
Elderly population | 0.952 | −0.123 | N/A | |
Very young population | 0.948 | 0.272 | N/A | |
Disabled person | 0.936 | 0.046 | N/A | |
Built-up Area | 0.872 | −0.129 | N/A | |
Illiterate | 0.817 | −0.068 | N/A | |
NDBI | 0.758 | −0.098 | N/A | |
Poverty | 0.73 | 0.508 | N/A | |
Kaccha Structure | 0.199 | 0.749 | N/A | |
Floating people | 0.302 | −0.731 | N/A | |
Adaptive Capacity | Literate | 0.933 | 0.099 | −0.071 |
Household with electricity | 0.926 | −0.046 | −0.025 | |
NDVI | −0.911 | 0.029 | −0.197 | |
NDWI | 0.911 | −0.043 | 0.172 | |
Pucca structure | 0.79 | 0.172 | −0.224 | |
Road | 0.788 | 0.248 | 0.056 | |
Relative humidity | −0.134 | −0.798 | −0.259 | |
Vegetation | −0.525 | 0.766 | −0.138 | |
NTL data | 0.466 | 0.672 | 0.019 | |
Health institution | 0.007 | 0.135 | 0.937 |
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Abrar, R.; Sarkar, S.K.; Nishtha, K.T.; Talukdar, S.; Shahfahad; Rahman, A.; Islam, A.R.M.T.; Mosavi, A. Assessing the Spatial Mapping of Heat Vulnerability under Urban Heat Island (UHI) Effect in the Dhaka Metropolitan Area. Sustainability 2022, 14, 4945. https://doi.org/10.3390/su14094945
Abrar R, Sarkar SK, Nishtha KT, Talukdar S, Shahfahad, Rahman A, Islam ARMT, Mosavi A. Assessing the Spatial Mapping of Heat Vulnerability under Urban Heat Island (UHI) Effect in the Dhaka Metropolitan Area. Sustainability. 2022; 14(9):4945. https://doi.org/10.3390/su14094945
Chicago/Turabian StyleAbrar, Rakin, Showmitra Kumar Sarkar, Kashfia Tasnim Nishtha, Swapan Talukdar, Shahfahad, Atiqur Rahman, Abu Reza Md Towfiqul Islam, and Amir Mosavi. 2022. "Assessing the Spatial Mapping of Heat Vulnerability under Urban Heat Island (UHI) Effect in the Dhaka Metropolitan Area" Sustainability 14, no. 9: 4945. https://doi.org/10.3390/su14094945
APA StyleAbrar, R., Sarkar, S. K., Nishtha, K. T., Talukdar, S., Shahfahad, Rahman, A., Islam, A. R. M. T., & Mosavi, A. (2022). Assessing the Spatial Mapping of Heat Vulnerability under Urban Heat Island (UHI) Effect in the Dhaka Metropolitan Area. Sustainability, 14(9), 4945. https://doi.org/10.3390/su14094945