Abstract
Rapid urban land use and land cover changes have become a major environmental issue because of their ecological effects, including loss of green space and urban heat islands. Effective monitoring and management techniques are required. The Saudi Arabian twin city of Abha-Khamis Mushyet was selected as a case study for this research. As a result, the current study aimed to statistically and spatially investigate the relationship between land surface temperature (LST) and land use land cover based urban biophysical parameters such as normalized difference built-up index (NDBI), normalized difference vegetation index (NDVI), and normalized difference water index (NDWI). This study used random forest (RF) to classify LULC in 1990, 2000, and 2018. We also validated the LULC maps in a novel way. Using mono window algorithm techniques, we extracted LST for three periods. The dynamics of LULC, LST, and biophysical parameters were investigated using standard statistical graphs such as the heat map and the Sankey diagram. The correlation coefficient and the global bivariate Moran' I approach were used to determine the association between LST and urban biophysical parameters. The relationship was then established in greater detail by categorizing the entire pixel into percentile classes and employing parallel coordinate plots. Finally, the association was built using GeoDA software and a conditional map. The LULC maps revealed a 334.4 percent increase in urban areas between 1990 and 2018. The built-up region is the largest stable LULC, with an 83.6 percent transitional probability matrix between 1990 and 2018. While 17.9%, 21.8%, 12.4%, and 10.5% of agricultural land, scrubland, exposed rocks, and water bodies were converted to built-up areas, respectively. The LST has increased rapidly over time because of LULC changes. The link between LST and urban biophysical parameters revealed that NDBI had a positive relationship, whereas NDWI and NDVI had a negative relationship. As a result, this study could be very important because it could help decision makers figure out how to lessen the effects of urban heat islands because of changes in LULC.