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dc.contributor.authorMacharia, N. A.
dc.contributor.authorMbuthia, S. W.
dc.contributor.authorMusau, M. J.
dc.contributor.authorObando, J. A.
dc.contributor.authorEbole, S. O.
dc.date.accessioned2023-03-17T08:59:13Z
dc.date.available2023-03-17T08:59:13Z
dc.date.issued2022
dc.identifier.citationMacharia, 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.en_US
dc.identifier.urihttp://ir-library.ku.ac.ke/handle/123456789/24975
dc.descriptionArticleen_US
dc.description.abstractUnderstanding 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.en_US
dc.language.isoenen_US
dc.publisherComputational Urban Scienceen_US
dc.subjectLSTen_US
dc.subjectNSTen_US
dc.subjectMicro climateen_US
dc.subjectClimate changeen_US
dc.subjectPeri-Urbanen_US
dc.titleDry‑Season Variability in Near‑Surface Temperature Measurements and Landsat‑Based Land Surface Temperature in Kenyatta University, Kenyaen_US
dc.typeArticleen_US


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