Abstract
Abstract. The rapid urban expansion in Abuja, Nigeria, has resulted in the replacement of land surface previously occupied by natural vegetation with various impermeable materials. This study examines the impact of the spatial distribution of impervious surfaces (IS) on land surface temperature (LST) in the study area using both graphical and quantitative approach. A Normalized Difference Impervious Surface Index (NDISI) was adopted to estimate IS and LST from Landsat ETM+ and OLI/TIRS satellite images (path: 189, row: 54) of Abuja for 4 distinct epochs of 2004, 2008, 2014 and 2018. In order to analyze the effect of IS on LST, the relationship between the normalized difference indices and LST, for each epoch, were determined using regression and correlation analyses. Results show the spatial patterns of impervious surfaces as distributed over Abuja, Nigeria and its impact on LST dynamics. It was observed that mean surface temperature increased by at least 2 °C every 4 years. Furthermore, results of the correlation analysis between NDISI and LST reveal that there exist varying positive correlations between the two variables in with correlation coefficients; R = 0.511, 0.166, 0.505, 0.785 in 2004,2008, 2014 and 2018 respectively, suggesting that impervious surfaces areas accelerate LST rise and Urban Heat Island (UHI) formation. This study gives great insight on the concept of impervious surfaces and its spatial pattern in Abuja city, Nigeria. The study recommends the widespread use of highly reflective or natural surfaces for rooftops, pavements and roads and that afforestation should be encouraged to increase green areas.
Highlights
An important parameter employed in the assessment of the environment is the land surface temperature (LST), which is usually heavily influenced by surface structures
This study examines the impact of the spatial distributions of Impervious Surfaces (IS) on LST in the study area for a 14-year period from 2004 to 2018, using both graphical and quantitative approach; with a view to investigate the impact of IS on the urban heat environment
Results show the spatial patterns of IS as distributed over Abuja, Nigeria, incorporating urban IS such as rooftops, roads, parking lots and natural impervious surface such as wetlands, and waterbodies
Summary
An important parameter employed in the assessment of the environment is the land surface temperature (LST), which is usually heavily influenced by surface structures. The machine learning methods include artificial neural networks (ANNs) (Voorde, Roeck & Canters, 2009), support vector machines (SVMs) (Okujeni, Linden & Hostert, 2015), decision tree classification (DTC), classification and regression tree (CART) analysis (Xian and Crane, 2006), and regression modeling (Mohapatra, Wu, 2010) Amongst these techniques, spectral indices have been found, to measure the biophysical properties of the earth’s surface. The city of Abuja is currently experiencing rapid urbanization, there is a rise in anthropogenic activities such as construction of roads, pavements, residential areas etc., all comprising of highly impervious surfaces This increases the urban mean surface temperature over time (Adeyeri et al 2015; Isioye, Ikwueze, Akomolafe, 2020).
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