Exploring the Spatio-temporal pattern of regional heat island (RHI) in an urban agglomeration of secondary cities in Eastern India

Dutta, Ipsita

doi:10.1016/j.uclim.2020.100679

Cited by 38 publications

(19 citation statements)

References 36 publications

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“…The CI measures the thermal contribution of various LULC categories on the LST by multiplying the proportion of a given LULC type to the entire study area by the difference between the mean LST of the LULC type in the question and that of the whole study area. Positive CI values of an LULC type indicate a direct influence on enhancing SUHIs, whereas negative values indicate a negative effect on SUHIs [138]. The index was first employed by Chen et al to assess the impact of 10-year LUCL spatiotemporal changes on SUHIs at a regionalscale level in the Pearl River Delta located in Guangdong Province and a city-scale level in Shenzhen city [87].…”

Section: Relationship Assessment Of Lulc and Suhismentioning

confidence: 99%

“…The index was first employed by Chen et al to assess the impact of 10-year LUCL spatiotemporal changes on SUHIs at a regionalscale level in the Pearl River Delta located in Guangdong Province and a city-scale level in Shenzhen city [87]. It was then used in multiple studies in different research areas, including Shenyang, China [94]; Ethekwini, South Africa [77]; Wuhan, China [139]; Malda, India [138]; and Delhi, India [55]. Based on the obtained CI values of different LULC types, Pramanik and Punia further calculated another index called the landscape index (LI), defined as the quotient of the CI of SUHI sink (i.e., croplands, vegetation, and water bodies) and source (i.e., built-up, fallow, and bare lands) landscapes [55].…”

Section: Relationship Assessment Of Lulc and Suhismentioning

confidence: 99%

“…In an investigation of the Chinese city of Wuhan, in which water accounts for over 25% of the total area of the central district-including two rivers (the Yangtze River and Han River), East Lake (the largest urban lake in China), and dozens of other lakes-Wu et al found that both the area and the spatial distribution of water bodies contributed to significant distributions in the effects caused by SUHIs [70]. Moreover, the findings reported in multiple studies have affirmed that water bodies exhibit the lowest LSTs [11,20,41,45,47,60,73,78,79,88,90,93,97,98,111,112,115,138,139,[151][152][153], along with vegetation cover. Furthermore, water has generally been found to have a negative correlation with LST [20,111,154], except for a few circumstances due to various reasons primarily related to the climate characteristics of cities.…”

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confidence: 99%

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Understanding the Links between LULC Changes and SUHI in Cities: Insights from Two-Decadal Studies (2001–2020)

et al. 2021

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An urban heat island (UHI) is a serious phenomenon associated with built environments and presents threats to human health. It is projected that UHI intensity will rise to record levels in the following decades due to rapid urban expansion, as two-thirds of the world population is expected to live in urban areas by 2050. Nevertheless, the last two decades have seen a considerable increase in the number of studies on surface UHI (SUHI)—a form of UHI quantified based on land surface temperature (LST) derived from satellite imagery—and its relationship with the land use/cover (LULC) changes. This surge has been facilitated by the availability of freely accessible five-decade archived remotely sensed data, the use of state-of-art analysis methods, and advancements in computing capabilities. The authors of this systematic review aimed to summarize, compare, and critically analyze multiple case studies—carried out from 2001 to 2020—in terms of various aspects: study area characteristics, data sources, methods for LULC classification and SUHI quantification, mechanisms of interaction coupled with linking techniques between SUHI intensity with LULC spatial and temporal changes, and proposed alleviation actions. The review could support decision-makers and pave the way for scholars to conduct future research, especially in vulnerable cities that have not been well studied.

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Section: Relationship Assessment Of Lulc and Suhismentioning

confidence: 99%

Section: Relationship Assessment Of Lulc and Suhismentioning

confidence: 99%

mentioning

confidence: 99%

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Understanding the Links between LULC Changes and SUHI in Cities: Insights from Two-Decadal Studies (2001–2020)

et al. 2021

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“…Many studies have illustrated that high temperatures occur in urban areas (e.g., Dutta and Das 2020;Mohan et al 2020;Zhang et al 2017b). A UHI study of Wuhan examined and found that high and sub-high temperature regions were concentrated from rural to urban regions (Shen et al 2016).…”

Section: Introductionmentioning

confidence: 99%

Capturing urban heat island formation in a subtropical city of China based on Landsat images: implications for sustainable urban development

Zhang

Estoque

Murayama

et al. 2021

Environ Monit Assess

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Land use/cover change is the main driving force of urban expansion which influences humanenvironment interactions. Generally, the formation of urban heat islands (UHIs) can be referred to as a negative "by-product" of urbanization. In the context of rapid urbanization, the present paper aims to capture the landscape changes and three patterns of urban expansion (i.e., infill, extension, and leapfrog), and provide a better understanding of the formation of the surface urban heat island (SUHI) in Dongguan, China, during the past 20+ years.

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“…Relative normalization methods have been adopted to correct the "unexpected" signals in the LST images. The two most commonly used normalization methods are the centralization method (subtracting the pixel values by the average of the study area, called MEAN hereafter) [18,20,21] and the minimum-maximum normalization method [9,22]. Similar to the histogram matching method, the MEAN method subtracts the average of each LST image from its original values.…”

Section: Introductionmentioning

confidence: 99%

Pseudo-Invariant Feature-Based Linear Regression Model (PIF-LRM): An Effective Normalization Method to Evaluate Urbanization Impacts on Land Surface Temperature Changes

et al. 2021

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The Landsat land surface temperature (LST) product is widely used to understand the impact of urbanization on surface temperature changes. However, directly comparing multi-temporal Landsat LST is challenging, as the observed LST might be strongly affected by climatic factors. This study validated the utility of the pseudo-invariant feature-based linear regression model (PIF-LRM) in normalizing multi-temporal Landsat LST to highlight the urbanization impact on temperature changes, based on five Landsat LST images during 2000–2018 in Changsha, China. Results showed that LST of PIFs between the reference and the target images was highly correlated, indicating high applicability of the PIF-LRM to relatively normalize LST. The PIF-LRM effectively removed the temporal variation of LST caused by climate factors and highlighted the impacts of urbanization caused land use and land cover changes. The PIF-LRM normalized LST showed stronger correlations with the time series of normalized difference of vegetation index (NDVI) than the observed LST and the LST normalized by the commonly used mean method (subtracting LST by the average, respectively for each image). The PIF-LRM uncovered the spatially heterogeneous responses of LST to urban expansion. For example, LST decreased in the urban center (the already developed regions) and increased in the urbanizing regions. PIF-LRM is highly recommended to normalize multi-temporal Landsat LST to understand the impact of urbanization on surface temperature changes from a temporal point of view.

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Exploring the Spatio-temporal pattern of regional heat island (RHI) in an urban agglomeration of secondary cities in Eastern India

Cited by 38 publications

References 36 publications

Understanding the Links between LULC Changes and SUHI in Cities: Insights from Two-Decadal Studies (2001–2020)

Understanding the Links between LULC Changes and SUHI in Cities: Insights from Two-Decadal Studies (2001–2020)

Capturing urban heat island formation in a subtropical city of China based on Landsat images: implications for sustainable urban development

Pseudo-Invariant Feature-Based Linear Regression Model (PIF-LRM): An Effective Normalization Method to Evaluate Urbanization Impacts on Land Surface Temperature Changes

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