Browsing by Author "Macharia, N. A."

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    Dry-Season Variability in Near-Surface Temperature Measurements and Landsat-Based Land Surface Temperature in Kenyatta University, Kenya
    (Computational Urban Science, 2022) Macharia, N. A.; Mbuthia, S. W.; Musau, M. J.; Obando, J. A.; Ebole, S. O.
    Understanding thermal gradients is essential for sustainability of built-up ecosystems, biodiversity conservation, and human health. Urbanized environments in the tropics have received little attention on underlying factors and processes governing thermal variability as compared to temperate environments, despite the worsening heat stress exposure from global warming. This study characterized near surface air temperature (NST) and land surface tempera ture (LST) profles across Kenyatta University, main campus, located in the peri-urban using in situ traverse tempera ture measurements and satellite remote sensing methods respectively. The study sought to; (i) fnd out if the use of fxed and mobile temperature sensors in time-synchronized in situ traverses can yield statistically signifcant tempera ture gradients (ΔT) attributable to landscape features, (ii) fnd out how time of the day infuences NST gradients, (iii) determine how NST clusters compare to LST values derived from analysis of ‘cloud-free’ Landsat 8 OLI (Operational Land Imager) satellite image, and (iv) determine how NST and LST values are related to biophysical properties of land cover features.. The Getis–Ord Gi* statistics of ΔT values indicate statistically signifcant clustering hot and cold spots, especially in the afternoon (3–5 PM). NST ‘hot spots’ and ‘cold spots’ coincide with hot and cold regions of Landsat based LST map. Ordinary Least Square Regression (OLS) indicate statistically signifcant (p<0.01) coefcients of MNDWI and NDBI explaining 15% of ΔT variation, and albedo, MNDWI, and NDBI explaining 46% of the variations in LST patterns. These fndings demonstrate that under clear sky, late afternoon walking traverses records spatial variabil ity in NST within tropical peri-urban environments during dry season. This study approach may be enhanced through collecting biophysical attributes and NST records simultaneously to improve reliability of regression models for urban thermal ecology