Urban land surface temperature variations with LULC, NDVI and NDBI in semi-arid urban environments: case study of Gaborone City, Botswana (1989–2019)

Yashon O. Ouma; Tuazako Tjitemisa; Mooketsi Segobye; Kealeboga Moreri; Boipuso Nkwae; Lopang Maphale; Bagadzi Manisa

doi:10.1117/12.2595031

12 September 2021 Urban land surface temperature variations with LULC, NDVI and NDBI in semi-arid urban environments: case study of Gaborone City, Botswana (1989–2019)

Yashon O. Ouma, Tuazako Tjitemisa, Mooketsi Segobye, Kealeboga Moreri, Boipuso Nkwae, Lopang Maphale, Bagadzi Manisa

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Proceedings Volume 11864, Remote Sensing Technologies and Applications in Urban Environments VI; 1186409 (2021) https://doi.org/10.1117/12.2595031
Event: SPIE Remote Sensing, 2021, Online Only

Abstract

This study assessed 30 years (1989–2019) of urban heat phenomena variations for the case study of Gaborone City (Botswana) through the analysis of the land surface temperatures (LST). The LST variability was determined using the Landsat Thermal Infrared Sensor (TIRS) band and the land surface emissivity (ε_λ) factor. The study investigated the influence of land-use and land-cover change (%LULC), normalized difference vegetation index (NDVI) and the normalized difference building index (NDBI) on the variability LST. For the city boundary which occupies 190.96 km², the vegetation cover decreased by nearly 40%, built-up area increased by 38.9%, water bodies decreased by 3% and bare-land increased by approximately 4.1%, while the 30-year mean near-air temperature was observed to have increased by +2.6 ºC. The urban LST variations were observed to increase exponentially with LSTmin of -2.5 ºC – 14.4 ºC and LSTmax of 24.4 ºC – 30.2 ºC respectively from 1989-2019. Using multiple linear regression, the mean LST was observed to be inversely proportional to NDVI (-0.934) and directly proportional to NDBI (+0.949). In correlation with %LULC, the land surface temperature increased with increase in density of the built-up area and bare-land but decreased with increase in vegetation cover and water bodies. Regression of NDVI, NDBI and %LUCC indices for the prediction of LST showed their suitability in the estimation of LST in the arid urban environment with R² of 0.996

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Yashon O. Ouma, Tuazako Tjitemisa, Mooketsi Segobye, Kealeboga Moreri, Boipuso Nkwae, Lopang Maphale, and Bagadzi Manisa "Urban land surface temperature variations with LULC, NDVI and NDBI in semi-arid urban environments: case study of Gaborone City, Botswana (1989–2019)", Proc. SPIE 11864, Remote Sensing Technologies and Applications in Urban Environments VI, 1186409 (12 September 2021); https://doi.org/10.1117/12.2595031

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