Assessing the effect of elevation on land surface temperature in the Kwahu East and West Enclaves in Ghana

Abstract

The study assessed the effect of elevation on Land Surface Temperature (LST) in the Kwahu East and West enclaves in Ghana. It also investigated the proximate factors influencing the LST in the area. Quantitative and qualitative methods were employed. The quantitative phase involves using GIS/Remote Sensing analytical tools and software. Geographically Weighted Regression (GWR), was employed to examine the changes, relationships, variability, and influence among the variables across the spatial locations. The qualitative phase conveniently sampled ten participants’ views and analysed thematically. The findings suggest a significant positive trend in the LST from 2003 to 2022 with high LST recorded in the lower elevations. The result also suggests that any projections of the LST for the area may continue to show an upward trend with relatively stable variability. The GWR indicated that elevation has greater effect on the LST as it explained about 89% in the LST variability. It also influences or moderates the explanatory power of the proximate variable when combined in a model. The proximate factors, (forest, elevation, geology, distance from water bodies, Normalised Difference Built Index (NDBI), and slope) account for about 82% of the changes in the LST across the spatial location of the area with geology having the greater impact on the LST of the area. The result indicated that the residents are aware of the changes in the LST and pointed out bushfires, surface pavement, overgrazing by cattle, rock quarrying, rise in population, excessive sunshine, and reduction in rainfall as the factors causing the change in the LST of the area. Based on the findings, the study proposed that, the Kwahu East and West, in consultation with the chiefs must collaborate with technical experts and researchers to develop spatial models and heat maps that visualise LST variations and inform decision-making processes at the local levels by utilising remote sensing data, satellite imagery, and geospatial tools.