Analysis of class surface temperature
We computed the mean, min, max and standard deviation of surface
temperature pixels that fell within each cell of the STURLA grid using
zonal statistics (Table 1) and joined these results with the STURLA
class variable. Averaging was necessary because Landsat 7 thermal bands
are resampled to 30 meters for distribution (Roy et al., 2010) while the
STURLA grid is 120 m. Thus, we averaged sixteen 30 m pixels that fell
within each 120 m cell. Similar to Hamstead et al. (2016) and Larondelle
et al. (2014) we focused the class temperature analysis on the most
frequently occurring classes, which cumulatively comprise 90% of the
city’s land area. As done with comparison of STURLA classes between
cities, permutational t-tests with Bonferroni correction were employed
to test significance. Likewise, the null hypothesis of the permutational
t-test is that ST does not differ between the STURLA classes.