The Effects of Land Indices on Thermal State in Surface Urban Heat Island Formation: A Case Study on Agra City in India Using Remote Sensing Data (1992–2019)

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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Urban Heat Island Simulations in Guangzhou, China, Using the Coupled WRF/UCM Model with a Land Use Map Extracted from Remote Sensing Data

Sustainability ◽

10.3390/su8070628 ◽

2016 ◽

Vol 8 (7) ◽

pp. 628 ◽

Cited By ~ 9

Author(s):

Guang Chen ◽

Lihua Zhao ◽

Akashi Mochida

Keyword(s):

Remote Sensing ◽

Land Use ◽

Urban Heat Island ◽

Remote Sensing Data ◽

Heat Island ◽

Sensing Data ◽

Urban Heat

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The urban heat island in Rio de Janeiro, Brazil, in the last 30 years using remote sensing data

International Journal of Applied Earth Observation and Geoinformation ◽

10.1016/j.jag.2017.08.012 ◽

2018 ◽

Vol 64 ◽

pp. 104-116 ◽

Cited By ~ 27

Author(s):

Leonardo de Faria Peres ◽

Andrews José de Lucena ◽

Otto Corrêa Rotunno Filho ◽

José Ricardo de Almeida França

Keyword(s):

Remote Sensing ◽

Urban Heat Island ◽

Rio De Janeiro ◽

Remote Sensing Data ◽

Heat Island ◽

Sensing Data ◽

Urban Heat

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THE EFFECT OF ENVIRONMENTAL CONDITION CHANGES ON DISTRIBUTION OF URBAN HEAT ISLAND IN JAKARTA BASED ON REMOTE SENSING DATA

International Journal of Remote Sensing and Earth Sciences (IJReSES) ◽

10.30536/j.ijreses.2015.v12.a2670 ◽

2017 ◽

Vol 12 (1) ◽

pp. 27

Author(s):

Indah Prasasti ◽

. Suwarsono ◽

Nurwita Mustika Sari

Keyword(s):

Remote Sensing ◽

Land Use ◽

Urban Heat Island ◽

Environmental Conditions ◽

Remote Sensing Data ◽

Parameter Extraction ◽

Landsat 8 ◽

Heat Island ◽

Sensing Data ◽

Urban Heat

Anthropogenic activities of urban growth and development in the area of Jakarta has caused increasingly uncomfortable climatic conditions and tended to be warmer and potentially cause the urban heat island (UHI). This phenomenon can be monitored by observing the air temperature measured by climatological station, but the scope is relatively limited. Therefore, the utilization of remote sensing data is very important in monitoring the UHI with wider coverage and effective. In addition, the remote sensing data can also be used to map the pattern of changes in environmental conditions (microclimate). This study aimed to analyze the effect of changes in environmental conditions (land use/cover, Normalized Difference Vegetation Index (NDVI) and Normalized Difference Build-up Index (NDBI)) toward the spread of the urban heat island (UHI). In this case, the UHI was identified from pattern changes of Land Surface Temperature (LST) in Jakarta based on data from remote sensing. The data used was Landsat 7 in 2007 and Landsat 8 in 2013 for parameter extraction environmental conditions, namely: land use cover, NDVI, NDBI, and LST. The analysis showed that during the period 2007 to 2013, there has been a change in the condition of the land use/cover, impairment NDVI, and expansion NDBI that trigger an increase in LST and the formation of heat islands in Jakarta, especially in the area of business centers, main street and surrounding area, as well as in residential areas.

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