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In urban areas, spectral mixture analysis (SMA) is a common technique for deriving the fractions of land covers within a pixel and information on the distribution of impervious surfaces. This study examined how the selection of endmembers affected the quantification of impervious surfaces using TM and ASTER imagery. Multiple subsets of endmembers derived using (1) extreme pixels from a minimum noise fraction (MNF) transformation, and (2) a manual approach usinga prioriknowledge of the study area were analysed. Two data sets were used to assess accuracy: (1) simulated image data comprising unmixed and mixed pixels of 10 typical and spectrally different urban land covers, and (2) detailed data derived from high-resolution aerial photography. The dimensionality of the imagery limited the number of endmembers, and as a result, unmixed land covers were modelled using multiple endmembers and some cells had abundance values that summed to more than one or were negative. The land covers of red roofs and concrete were the largest contributors to the error in impervious surfaces. The Sequential Maximum Angle Convex Cone (SMACC) endmember model was also used to unmix the images; however, the larger number of endmembers did not resolve the use of multiple endmembers to model the unmixed land covers and the accuracy was similar to that using SMA. The relationship between the pervious fraction estimated using the vegetation endmember and the ground reference data was stronger than that for the impervious fraction, although the fraction was underestimated. The problems in modelling highly variable impervious surfaces with a limited number of endmembers suggest that in urban environments with substantial vegetation, modelling the vegetation component as the inverse of the impervious fraction may lead to improved results. [ABSTRACT FROM PUBLISHER]

Abstract: This study analyzed the geographical patterns of heat-related mortality among the population aged 65 and over within the metropolitan area of Sydney, Australia between 1993 and 2004, and evaluated the role of some physical and socio-demographic risk factors associated with it. The effect of temperature on all-cause mortality during unusually hot days was investigated using spatial analytic techniques, such as cluster analysis and spatial regression analysis. Generalized Linear Models (GLMs) were used to investigate the role of daily average temperature, ozone (O3) and particulate matter of diameter less than 10μm (PM10) at the regions that showed a significant increase in mortality on unusually hot days. Spatial variation in mortality on unusually hot days was observed among the population 65 and over. Elderly people living within 5–20km south-west and west of the Sydney Central Business District (CBD) were found to be more vulnerable. However, analysis using GLMs showed temperature to be a significant modifier of daily mortality in the region to the south-west of the CBD only. O3 and PM10 were found to be non-significant factors in the regions where air pollutants were studied. Socio-economic status and the proportion of vegetation or developed land in each Statistical Local Area (SLA) were also not a significant factor explaining the increased mortality. A combination of social and environmental factors may be at play. Our results suggest an effect of temperature on mortality of the elderly population in Sydney Statistical Division at the SLA level. More spatially-based research would be beneficial once climate datasets with improved spatial coverage become available. [Copyright &y& Elsevier]