9 Comparative analysis of NDVI and LST to identify Urban Heat Island effect using remote sensing and GIS

2021 ◽  
pp. 99-110
Author(s):  
Shubham Chandrakar ◽  
Manish Kumar Sinha
Keyword(s):  
Remote Sensing ◽  
Comparative Analysis ◽  
Urban Heat Island ◽  
Remote Sensing And Gis ◽  
Urban Heat Island Effect ◽  
Heat Island ◽  
Island Effect ◽  
Urban Heat

Evaluation of the surface urban heat island effect in the city of Madrid by thermal remote sensing

2012 ◽  
Vol 34 (9-10) ◽  
pp. 3177-3192 ◽  
Author(s):  
José A. Sobrino ◽  
Rosa Oltra-Carrió ◽  
Guillem Sòria ◽  
Juan Carlos Jiménez-Muñoz ◽  
Belén Franch ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Urban Heat Island ◽  
Urban Heat Island Effect ◽  
Heat Island ◽  
Thermal Remote Sensing ◽  
Island Effect ◽  
Urban Heat ◽  
Surface Urban Heat Island ◽  
The City

scholarly journals Remote sensing of the urban heat island effect across biomes in the continental USA

2010 ◽  
Vol 114 (3) ◽  
pp. 504-513 ◽  
Author(s):  
Marc L. Imhoff ◽  
Ping Zhang ◽  
Robert E. Wolfe ◽  
Lahouari Bounoua
Keyword(s):  
Remote Sensing ◽  
Urban Heat Island ◽  
Urban Heat Island Effect ◽  
Heat Island ◽  
Island Effect ◽  
Urban Heat

scholarly journals Data and techniques for studying the urban heat island effect in Johannesburg

2015 ◽  
Vol XL-7/W3 ◽  
pp. 203-206 ◽  
Author(s):  
C. H. Hardy ◽  
A. L. Nel
Keyword(s):  
Remote Sensing ◽  
Urban Heat Island ◽  
Urban Areas ◽  
Remote Sensing Data ◽  
Urban Heat Island Effect ◽  
Heat Island ◽  
Sensing Data ◽  
Island Effect ◽  
Urban Heat ◽  
The City

The city of Johannesburg contains over 10 million trees and is often referred to as an urban forest. The intra-urban spatial variability of the levels of vegetation across Johannesburg’s residential regions has an influence on the urban heat island effect within the city. Residential areas with high levels of vegetation benefit from cooling due to evapo-transpirative processes and thus exhibit weaker heat island effects; while their impoverished counterparts are not so fortunate. The urban heat island effect describes a phenomenon where some urban areas exhibit temperatures that are warmer than that of surrounding areas. The factors influencing the urban heat island effect include the high density of people and buildings and low levels of vegetative cover within populated urban areas. This paper describes the remote sensing data sets and the processing techniques employed to study the heat island effect within Johannesburg. In particular we consider the use of multi-sensorial multi-temporal remote sensing data towards a predictive model, based on the analysis of influencing factors.

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