The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLII-4/W5
https://doi.org/10.5194/isprs-archives-XLII-4-W5-107-2017
https://doi.org/10.5194/isprs-archives-XLII-4-W5-107-2017
05 Oct 2017
 | 05 Oct 2017

THE EFFECTS OF BUILT-UP AND GREEN AREAS ON THE LAND SURFACE TEMPERATURE OF THE KUALA LUMPUR CITY

N. A. Isa, W. M. N. Wan Mohd, and S. A. Salleh

Keywords: Land Surface Temperature, Urban Parameters, Multiple Linear Regression, Remote Sensing and GIS

Abstract. A common consequence of rapid and uncontrollable urbanization is Urban Heat Island (UHI). It occurs due to the negligence on climate behaviour which degrades the quality of urban climate condition. Recently, addressing urban climate in urban planning through mapping has received worldwide attention. Therefore, the need to identify the significant factors is a must. This study aims to analyse the relationships between Land Surface Temperature (LST) and two urban parameters namely built-up and green areas. Geographical Information System (GIS) and remote sensing techniques were used to prepare the necessary data layers required for this study. The built-up and the green areas were extracted from Landsat 8 satellite images either using the Normalized Difference Built-Up Index (NDBI), Normalized Difference Vegetation Index (NDVI) or Modified Normalize Difference Water Index (MNDWI) algorithms, while the mono-window algorithm was used to retrieve the Land Surface Temperature (LST). Correlation analysis and Multi-Linear Regression (MLR) model were applied to quantitatively analyse the effects of the urban parameters. From the study, it was found that the two urban parameters have significant effects on the LST of Kuala Lumpur City. The built-up areas have greater influence on the LST as compared to the green areas. The built-up areas tend to increase the LST while green areas especially the densely vegetated areas help to reduce the LST within an urban areas. Future studies should focus on improving existing urban climatic model by including other urban parameters.