ISODATA Clustering Approach for Mapping and Characterization of Surface Urban Heat Island Intensities Using Time Series of Remotely Sensed Land Surface Temperature and Land-Use/Land-Cover Products

<jats:p>This study evaluated spatial patterns and trends in surface urban heat island intensities (SUHIIs) across three major cities in Alabama, USA: Huntsville, Birmingham, and Mobile. Spatial expansion of developed areas and magnitudes and trends of SUHIIs were mapped and quantified using land-use/land-cover (LULC) data from the National Land Cover Database (NLCD) and land-surface temperature (LST) products from the Moderate Resolution Imaging Spectroradiometer (MODIS), respectively. Our findings reveal urban expansion over all three study areas, but at varying rates and patterns, with the highest rates observed over the Huntsville city area. The ISODATA clustering approach using LST time series mapped surface urban heat islands (SUHIs) as distinguished clusters of significantly (p=0.01) warmer surface temperatures compared to their surrounding non-urban areas. The SUHI clusters also resembled spatial distributions of NLCD-developed LULCs, with the highest resemblance between NLCD-developed LULC clusters and SUHI clusters mapped using summer daytime time series. Our SUHIIs estimated using the derived SUHI clusters correspond well with the values reported in SUHI literature. Yearly seasonal average SUHIIs varied significantly (p = 0.05) among the three study areas and during seasonal and diurnal cycles. However, SUHIIs during summer daytime were consistently larger across all sites and throughout the study period. During both seasons (winter and summer) and over all sites, the highest SUHIIs were reported over the SUHI-3 clusters that resembled high-intensity developed areas. These findings indicate novel insights suggesting our ISODATA clustering approach using time series of satellite-derived LST products is a promising approach for local-scale mapping and characterization of SUHIs. Our findings also provide detailed insights for improved understanding of the spatial distributions and temporal variations of SUHI developments over similar, less studied, but rapidly developing mid-size cities.</jats:p>