Dry‑Season Variability in Near‑Surface Temperature Measurements and Landsat‑Based Land Surface Temperature in Kenyatta University, Kenya
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Date
2022
Authors
Macharia, N. A.
Mbuthia, S. W.
Musau, M. J.
Obando, J. A.
Ebole, S. O.
Journal Title
Journal ISSN
Volume Title
Publisher
Computational Urban Science
Abstract
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 temperature
(LST) profiles across Kenyatta University, main campus, located in the peri-urban using in situ traverse temperature
measurements and satellite remote sensing methods respectively. The study sought to; (i) find out if the use of
fixed and mobile temperature sensors in time-synchronized in situ traverses can yield statistically significant temperature
gradients (ΔT) attributable to landscape features, (ii) find out how time of the day influences 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 significant 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 Landsatbased
LST map. Ordinary Least Square Regression (OLS) indicate statistically significant (p < 0.01) coefficients of
MNDWI and NDBI explaining 15% of ΔT variation, and albedo, MNDWI, and NDBI explaining 46% of the variations in
LST patterns. These findings demonstrate that under clear sky, late afternoon walking traverses records spatial variability
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.
Description
Article
Keywords
LST, NST, Micro climate, Climate change, Peri-Urban
Citation
Macharia, N. A., Mbuthia, S. W., Musau, M. J., Obando, J. A., & Ebole, S. O. (2022). Dry-season variability in near-surface temperature measurements and landsat-based land surface temperature in Kenyatta University, Kenya. Computational Urban Science, 2(1), 33.