Spatio-Temporal Modeling of the Urban Heat Island in the Phoenix Metropolitan Area: Land Use Change Implications

Abstract

This study examines the spatial and temporal patterns of the surface urban heat island (SUHI) intensity in the Phoenix metropolitan area and the relationship with land use land cover (LULC) change between 2000 and 2014. The objective is to identify specific regions in Phoenix that have been increasingly heated and cooled to further understand how LULC change influences the SUHI intensity. The data employed include MODerate-resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) 8-day composite June imagery, and classified LULC maps generated using 2000 and 2014 Landsat imagery. Results show that the regions that experienced the most significant LST changes during the study period are primarily on the outskirts of the Phoenix metropolitan area for both daytime and nighttime. The conversion to urban, residential, and impervious surfaces from all other LULC types has been identified as the primary cause of the UHI effect in Phoenix. Vegetation cover has been shown to significantly lower LST for both daytime and nighttime due to its strong cooling effect by producing more latent heat flux and less sensible heat flux. We suggest that urban planners, decision-makers, and city managers formulate new policies and regulations that encourage residential, commercial, and industrial developers to include more vegetation when planning new construction.

The Relationship between Urban Land Surface Material Fractions and Brightness Temperature Based on MESMA

“…The correlation analysis results (Table 2) also show the same results. This finding is consistent with some other studies [1,51,54].…”

“…Many researches have been concentrated on the relationship between land-use/cover and surface temperature [1,[51][52][53][54], but they have ignored the mixed pixel in the image. In our research, we focus on the relationship between ULSMFs fractions and LSBT, which means that we classified one pixel into three components and analysed the relationship between the fractions and LSBT, achieving an accuracy of 98.6% in multivariate regression analysis.…”

Urbanization Effects on Vegetation and Surface Urban Heat Islands in China’s Yangtze River Basin

“…We further compared the urban area extracted using nighttime data with that from Landsat data (maximum likelihood classification method). The results showed that the urban area extracted using the method in this study was larger than that from Landsat data ( Table 2); however, it is appropriate for this study due to these reasons: (a) the footprint of SUHI was larger than the actual urban size according to previous studies [40,48,49]; (b) the urbanization primarily occurred in the suburban areas according to previous studies [26,36]; (c) the study period in this study was 2001-2016, the land cover maps used were during 2001-2013, and the urban area may expand during 2014-2016. Finally, we generated the buffer zone between 20 and 25 km from the urban areas, and we excluded the pixel with DN > 10 (SNLD) in the 20-25 km buffer and defined it as rural area [5,41].…”

“…These results were similar to previous studies [3,10,32] and can be attributed to vegetation activities. Vegetation can release more latent heat fluxes but less sensible heat fluxes (than the artificial impervious surfaces: roads and buildings) by evapotranspiration and then decrease the temperature [3,26,35]. However, rapid urbanization transformed the cropland and forest to impervious surfaces, decreasing the vegetation coverage and increasing the SUHII.…”

“…Therefore, the higher SUHII was generally observed in summer. Moreover, vegetation evapotranspiration mostly occurred during daytime, which may lead to the higher SUHII in daytime than in nighttime [3,10,26].…”