Urban heat island effect of a polynuclear megacity Delhi – Compactness and thermal evaluation of four sub-cities

Budhiraja, Bakul; Agrawal, Girish; Pathak, Prasad

doi:10.1016/j.uclim.2020.100634

Cited by 24 publications

(4 citation statements)

References 39 publications

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“…There are several limitations to this study that are important to consider, some of which can be addressed in future research. First, the NDVI can be a poor indicator of biomass when there is little groundcover, such as in the semi-arid areas to the west of Delhi, and lose sensitivity when there is dense leaf cover [ 74 , 75 ]. This problem can be circumvented by applying image classification and high-resolution aerial imagery [ 76 ].…”

Section: Discussionmentioning

confidence: 99%

Social Inequities in Urban Heat and Greenspace: Analyzing Climate Justice in Delhi, India

Mitchell¹,

Chakraborty

Basu

2021

IJERPH

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Climate change and rapid urbanization currently pose major challenges for equitable development in megacities of the Global South, such as Delhi, India. This study considers how urban social inequities are distributed in terms of burdens and benefits by quantifying exposure through an urban heat risk index (UHRI), and proximity to greenspace through the normalized difference vegetation index (NDVI), at the ward level in Delhi. Landsat derived remote sensing imagery for May and September 2011 is used in a sensitivity analysis of varying seasonal exposure. Multivariable models based on generalized estimating equations (GEEs) reveal significant statistical associations (p < 0.05) between UHRI/NDVI and several indicators of social vulnerability. For example, the proportions of children (β = 0.922, p = 0.024) and agricultural workers (β = 0.394, p = 0.016) are positively associated with the May UHRI, while the proportions of households with assets (β = −1.978, p = 0.017) and households with electricity (β = −0.605, p = 0.010) are negatively associated with the May UHRI. In contrast, the proportions of children (β = 0.001, p = 0.633) and agricultural workers (β = 0.002, p = 0.356) are not significantly associated with the May NDVI, while the proportions of households with assets (β = 0.013, p = 0.010) and those with electricity (β = 0.008, p = 0.006) are positively associated with the May NDVI. Our findings emphasize the need for future research and policies to consider how socially vulnerable groups are inequitably exposed to the impact of climate change-related urban heat without the mitigating effects of greenspace.

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Section: Discussionmentioning

confidence: 99%

Social Inequities in Urban Heat and Greenspace: Analyzing Climate Justice in Delhi, India

Mitchell¹,

Chakraborty

Basu

2021

IJERPH

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“…As in previous studies (e.g. Budhiraja et al, 2020;Kwok et al, 2019;Skarbit et al, 2017;Yang et al, 2020b), we used a LCZ D site placed on the outskirts of the city as the reference for estimating the UHI intensity in all urban sites (Barajas AEMET Observatory).…”

Section: Methodsmentioning

confidence: 99%

Hourly evolution of intra-urban temperature variability across the local climate zones. The case of Madrid

2021

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“…The study evaluated the UHI and seasonality in different seasons and times of the day and identified that the average temperature values, calculated throughout the day, pointed out the presence of UHI in different zones, especially in the densely urbanized ones 38 . Other studies associate LCZ classifications as a tool to assess the influence of LST distribution and UHI/SUHI effect on specific surfaces and have identified that the temperature was higher in densely urbanized areas [39][40][41][42][43] .…”

Section: State Of the Artmentioning

confidence: 99%

Analysis of the urban heat island in Bragança, Portugal, using MODIS data (2003-2022)

Almeida,

Alírio,

Gonçalves

et al. 2023

Earth Resources and Environmental Remote Sensing/Gis Applications XIV

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Urban Heat Island (UHI) is an effect that corroborates to the increase of temperature in urban settlements when compared to the surrounding vegetated areas, especially after sunset. This research aimed to understand the Surface Urban Heat Island (SUHI), a sub-classification correlated to the UHI effect, in the city of Bragança (Portugal), at 23 points classified in different Local Climate Zone (LCZ), between 2003 and 2022, using Remote Sensing (RS) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. The data were obtained at four passage times: 11am, 1pm, 10pm and 2am and analysed separately for summer and winter. Qualitative and quantitative analyses were applied, using an average of 1,337 Land Surface Temperature (LST) data, processed in the Google Earth Engine (GEE) platform. The computation of the SUHI intensity (SUHIint) for each year was obtained from the differences in LST between each of the LCZ and the average values from Rural Areas (RCD), considering both summer and winter campaigns. The boxplots showed similar medians in all LCZs at 11am and 1pm. At 10pm and 2am, only slight differences were found among the median values. The similarity in results may be associated with the low spatial resolution of the sensor and the difficulty in distinguishing between LCZs. The SUHIint was positive in most of the results (about 71%). Of the 23 points analysed, ten were not located in unique pixels, which compromised the analysis of the results in the different LCZs. This condition reinforces the need for use higher spatial resolution data to allow for a differentiation among LCZs data.

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Urban heat island effect of a polynuclear megacity Delhi – Compactness and thermal evaluation of four sub-cities

Cited by 24 publications

References 39 publications

Social Inequities in Urban Heat and Greenspace: Analyzing Climate Justice in Delhi, India

Social Inequities in Urban Heat and Greenspace: Analyzing Climate Justice in Delhi, India

Hourly evolution of intra-urban temperature variability across the local climate zones. The case of Madrid

Analysis of the urban heat island in Bragança, Portugal, using MODIS data (2003-2022)

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