Examining and Modelling the Determinants of the Rising Land Surface Temperatures in Arabian Desert Cities: An Example from Riyadh, Saudi Arabia

Abstract

The Saudi capital city of Riyadh has experienced rapid population growth and urban expansion over the past 4 decades. One major consequence of such growth is the rising of the city's land surface temperature (LST). This study used Landsat 7 ETM+ sensor data to map the distribution of Riyadh's LST and then examined and modelled the impacts of five contributing factors known to increase urban LST. The contributing factors are size/area and population density of each neighbourhood, along with amounts of impervious surfaces, vegetations, and soil/sand measured through remote sensing indices NDBI, NDVI, and NDBsI. The data were analyzed using Pearson's Product Moment Correlation values, Path Analysis, and Multiple Regression analysis. The result shows that neighbourhood population densities and NDBsI index have strong positive correlations (r=0.68 and r=0.60) with LST. Neighbourhood area showed significant but low positive correlation (r=0.33) and the NDBI and NDVI indices showed strong negative correlations (r=-0.55 and r=-0.64) with the LST. The multiple regression model explained about 77% of the total variation in the LST. The model can be used to predict and simulate future LST distribution for Riyadh as well as other cities in the Kingdom and the region.