Analysing the dynamic relationship of land surface temperature and landuse pattern: A city level analysis of two climatic regions in India

Das, Dipendra Nath; Chakraborti, Suman; Saha, Gourab K.; Banerjee, Anushna; Singh, Dharmaveer

doi:10.1016/j.cacint.2020.100046

Cited by 32 publications

(9 citation statements)

References 60 publications

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“…The presence of vegetation impacts the balance between latent and sensible heat fluxes, contributing to cooling effect on LST through transpiration [ 52 ]. However, our linear regression analysis reveals a weak correlation between NDVI and LST ( Table 4 ), contrary to the expected strong association of higher vegetation cover with lower temperatures [ 9 , 35 , 54 ], as linear regression may not effectively capture the complex non-linear interactions present in the data. In contrast, the more flexible and adaptive GB model, reveals a negative relationship between NDVI and LST in this case-study.…”

Section: Discussioncontrasting

confidence: 72%

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Exploring the land-use urban heat island nexus under climate change conditions using machine learning approach: A spatio-temporal analysis of remotely sensed data

Rao

Tassinari

Torreggiani

2023

Heliyon

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Section: Discussioncontrasting

confidence: 72%

“…The importance of LST computation and its relation with UHI has been observed in recent research works [ 12 , 35 , 36 ]. In the process of computation of LST, conversion of digital number (DN) of TIR band to spectral radiance is the first step.…”

Section: Methodsmentioning

confidence: 99%

Exploring the land-use urban heat island nexus under climate change conditions using machine learning approach: A spatio-temporal analysis of remotely sensed data

Rao

Tassinari

Torreggiani

2023

Heliyon

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“…For LST measurement and analysis, various methods and approaches have been applied by different authors [50][51][52]. Satellite sensor data of Landsat-5 (Thematic Mapper), Landsat-7 (Enhanced Thematic Mapper Plus), and Landsat-8 (Operational Land Imager and Thermal Infrared Scanner) imagery are utilized for measurement of surface temperature and analyzing biophysical parameters [53].…”

Section: Data Collectionmentioning

confidence: 99%

Land Surface Temperature Regulation Ecosystem Service: A Case Study of Jaipur, India, and the Urban Island of Jhalana Reserve Forest

Yosef

Rakholia

Mehta

et al. 2022

Forests

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Although Land Surface Temperatures (LSTs) are on the rise globally, the distribution of LSTs varies depending on the land cover type. Urban Heat Island and Urban Cool Island effects act differently, especially in semi-arid regions. Therefore, we identify demi-decadal, seasonal, and zonal differences in LSTs in a semi-arid region in the city of Jaipur, where zones include rural and urban areas that encircle the Jhalana Reserve Forest (JRF). After deriving LSTs from remotely sensed thermal bands of Landsat satellites’ Multi-spectral datasets, we found that there is a significant difference in LST (p < 0.01) among the zones. In addition, LSTs were found to be significantly lower in JRF compared to Urban and Rural areas in all seasons and all study years, which indicates the urban cooling effect due to the presence of the forest. Nevertheless, summer LSTs have warmed with a mean difference of 4.8 °C between 2000 and 2020. Therefore, our study supports the promotion of Urban Forests, especially in semi-arid zones, for inculcating LST regulation ecosystem services to enrich and enhance the standard of living of the human population.

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“…An object emits thermal energy when its temperature is more than absolute zero. Thermal sensors capture this energy and store them as digital number (DN) (Das et al, 2020). These DN are converted to TOA spectral radiance (𝐿𝜆) with pertinent image constants using equation 2.…”

Section: Computation Of Land Surface Temperaturementioning

confidence: 99%

Urban sprawl impact assessment on the land surface temperature over the green capital of Gujarat using a geo-spatial approach

Kalyan,

Pathak

2024

Preprint

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In developing countries, examining land use land cover (LULC) change pattern is crucial to understand the land surface temperature (LST) effect as urban development lacks coherent policy planning. The variability in LST is often determined by continuously changing LULC pattern. In this study LULC change effect analysis on LST has been carried out using geometric and radiometric corrected thermal bands of multi-spectral Landsat 7 ETM + and 8 TIRS/OLI satellite imagery over Gandhinagar, Gujarat in the years 2001 and 2022 respectively. Maximum likelihood classification (MLC) was applied to assess LULC change while NDVI-based single channel algorithm was used to retrieve LST using google earth engine (GEE). Results showed a substantial change in built-up (+ 347.08%), barren land (-50.74%) and vegetation (-31.66%). With change in LULC and impervious surfaces, the mean LST has increased by 5.47 ℃. The impact of sparse built-up was seen on vegetation and agriculture as maximum temperature of > 47℃ was noticed in all LULC classes except agriculture where the temperature reached as high as > 49℃ in 2022. Since Gandhinagar is developing on a twin-city (Gandhinagar-Ahmedabad) plan this study could be used as scientific basis for sustainable urban planning to overcome dynamic LULC change impacts.

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Analysing the dynamic relationship of land surface temperature and landuse pattern: A city level analysis of two climatic regions in India

Cited by 32 publications

References 60 publications

Exploring the land-use urban heat island nexus under climate change conditions using machine learning approach: A spatio-temporal analysis of remotely sensed data

Exploring the land-use urban heat island nexus under climate change conditions using machine learning approach: A spatio-temporal analysis of remotely sensed data

Land Surface Temperature Regulation Ecosystem Service: A Case Study of Jaipur, India, and the Urban Island of Jhalana Reserve Forest

Urban sprawl impact assessment on the land surface temperature over the green capital of Gujarat using a geo-spatial approach

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