CHAPTER 4: SPATIAL DISTRIBUTION OF EXTREME HEAT VULNERABILITY AND
4.1 Introduction
Urbanization changes the distribution of surface land covers and alters landscape energy and water balance, which in turn alters surface thermal characteristics (Zhang, et al., 2009; Chen, et al., 2006; Sobrino, et al., 2012; Amiri, et al., 2009). Due to urban growth, natural surfaces such as forests are replaced with impervious surfaces that absorb and emit thermal energy, resulting in creation of Urban Heat Islands (UHI) (Johnson, et al., 2014; Steeneveld, et al., 2014; Tomlinson, et al., 2011; Hua, et al., 2013; Song & Wu, 2015; Sobrino, et al., 2012). Such thermal elevation exposes residents to heat related health risks, especially residents without air conditioning systems. Studies have shown that extreme temperatures result in reduced indoor and outdoor comfort and performance at work and increase morbidity and mortality (Tanabe et al., 2015; Humphreys, 2015; Lin, et al., 2016). Within cities in developing countries, vulnerability to elevated temperatures varies due to heterogeneity in surface bio-physical properties and socio-demographic factors (Johnson, et al., 2014). According to Wilhelmi and Hayden (2010), contextualizing vulnerability to local settings can influence formulation of successful approaches that are targeted locally using resources allocated at national level.
Therefore, to design effective adaptation and mitigation measures for vulnerable areas, heat vulnerability maps are valuable in identifying high risk areas.
Urban geophysical (e.g. heat islands, vegetation health and abundance and building density) economic and socio-demographic factors constitute exposure to hazard, sensitivity and adaptive capacity, which determine differences in heat vulnerability between places (Johnson, et al., 2014; Aubrecht & Özceylan, 2013; Uejio, et al., 2011). Whereas earlier thermal vulnerability studies solely stressed the role of socio-demographic factors e.g. age, race, gender, education, health and economic status (Cutter, 2009; Cutter, et al., 2003; Vescovi, et al., 2005; Reid, et al., 2009), recent studies have sought to incorporate quantitative and qualitative socio-demographic and biophysical variables in risks associated with elevated urban temperatures (van-Westen; Johnson, et al., 2014; Buscail, et al., 2012). Recent studies have also sought to incorporate remote sensing derived heat exposure factors such as land surface temperature, land use and land cover maps, and land cover indices (Johnson, et al., 2014;
Johnson, et al., 2012; Johnson, et al., 2009; Aubrecht & Özceylan, 2013; Uejio, et al., 2011;
Wolf & McGregor, 2013; Depietri, et al., 2013; Hansen, et al., 2013; Buscail, et al., 2012; Reid, et al., 2012). Hence, space-borne remote sensing has the potential to yield a variety of spatial information valuable for reliable heat vulnerability mapping.
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Remote sensing has several advantages in urban thermal studies. These include synoptic view of large areas, availability of archival data, and effectiveness in mapping land surface characteristics. In addition, medium resolution space-borne remote sensing detects localized variations in land surface characteristics even in complex urban areas where changes are observed within small distances. However, despite the reliability of space-based sensors like Landsat in mapping heterogeneous urban landscapes, heat vulnerability maps have remained largely coarse and generalized. For instance, previous studies have mapped vulnerability at the low spatial resolution of demographic variables, such as census block and district level (Johnson, et al., 2014; Heaton, et al., 2014; Buscail, et al., 2012). This has a major disadvantage of assuming uniform heat exposure over large regions thus ignoring variability within each block/district. For example, Dewan and Corner (2012) noted that use of census blocks weakened the correlation between population density and land surface temperature because of variability of land cover within each census tract. However, mapping risk using medium resolution remotely sensed data has the potential to improve area specific assessment interventions required to curb heat related stress in cities. Therefore, there is a need to improve the spatial resolution of heat vulnerability maps using spatial details of variations in heat exposure obtained from reputable medium resolution sensors such as Landsat missions.
Land cover indices such as Normalized Difference Vegetation Index (NDVI) provide quantitative and reliable information of surface physical characteristics. Compared to land use and land cover classification and retrieval of land surface temperature from thermal infra-red data, indices simplify heat vulnerability mapping as they are easy to compute (Sharma, et al., 2012; Chen, et al., 2006). According to Byomkesh et al. (2012), indices help to surmount the mixed pixel problem affecting accuracy of land cover identification using moderate resolution data in heterogeneous urban environments. Indices also match the criteria by Dewan and Yamaguchi (2009) that each vulnerability indicator should simplify a number of properties and be quantifiable using existing data. For instance, Chen et al (2006) used the Normalized Difference Vegetation Index (NDVI), Normalized Difference Bareness Index (NDBaI), Normalized Difference Water Index (NDWI) and Normalized Difference Built-up Index (NDBI) to map land use and land cover types with high accuracy. Besides land cover mapping, these indices are deemed capable of determining a variety of heat exposure factors as they are strongly correlated with land surface temperature (Chen, et al., 2006; Song & Wu, 2015;
Kerchove et al., 2013; Essa et al., 2013; Xu, et al., 2013). As such, Johnson et al. (2014)
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included surface temperature, NDVI, NDBI and socio-demographic variables among others, to determine urban heat vulnerability. However, the use of remote sensing derived land cover indices as exposure factors in heat vulnerability assessments is still limited to a few studies and indices hence the need to explore the utility of other indices (Johnson, et al., 2014; Johnson, et al., 2012; Johnson, et al., 2009; Buscail, et al., 2012; Chow, et al., 2012; Harlan, et al., 2006;
Uejio, et al., 2011).
Although previous studies for instance combined NDBI, NDVI and Soil Adjusted Vegetation Index (SAVI) with socio-demographic urban thermal vulnerability mapping, the value of other indices in vulnerability assessment, such as the NDWI, remain unexplored. In addition, while inclusion of a variety of indices in a single assessment should enhance land surface characterization, the studies have been commonly confined to at most two indices per heat vulnerability analysis (Johnson, et al., 2012; Johnson, et al., 2014). Commonly, only NDVI is combined with socio-demographic factors in urban thermal vulnerability analysis (Uejio, et al., 2011; Buscail, et al., 2012; Chow, et al., 2012). However, whereas NDVI has been useful in mapping vegetation abundance and health, it saturates at high values of vegetation fraction.
Therefore, this study proposes inclusion of NDWI which gives a measure of surface water content and is critical in heat vulnerability mapping. This index is best in quantifying water depth in plants which strongly relates to turgidity of cells and thus combines effectively with NDVI to quantify vegetation health (Jackson, et al., 2004). For example, combining NDVI and NDWI provides a more robust measure of vegetation abundance and health, which are key factors in heat exposure mapping, compared to use of NDVI alone (Chen, et al., 2006; Jackson, et al., 2004; Stathakis, et al., 2012). Furthermore, the index provides a measure of surface moisture (Cao et al., 2008; Xu, et al., 2013) required for evaporative cooling, hence is valuable for mitigation against extreme surface temperatures. We therefore hypothesize that combining NDWI with NDBI and NDVI should improve delineation of spatial variations in heat exposure in heterogeneous and complex urban environments.
In previous heat vulnerability studies researchers have mainly adopted heat exposure factors derived from earlier Landsat missions; Landsat 5 and Landsat 7 (Johnson, et al., 2014;
Aubrecht & Özceylan, 2013; Harlan et al., 2013). For example, Johnson et al. (2013) derived NDBI and NDVI from optical information of Landsat 7 Earth Thematic Mapper Plus (ETM+).
Unlike earlier Landsat satellite missions, Landsat 8 satellite data has several strengths, which
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include improved radiometric and spectral resolution, signal to noise ratio, refined bandwidth and two thermal infra-red bands (Karlson et al., 2015; Almutairi, 2015; Dube & Mutanga, 2015a). Furthermore, land cover classes generated from Landsat 8 have been shown to be more accurate than the previous Landsat series and MODIS data (Mwaniki et al., 2015; Yu, et al., 2013; Jia, et al., 2014; Ke, et al., 2015). Due to these improvements, studies have shown that Landsat 8 data enhances the retrieval of surface features such as biomass estimation, land cover mapping, discrimination of crops, and active fire and volcano detection (Dube & Mutanga, 2015a; Jia, et al., 2014; Banskota et al., 2014; Oumar, 2015; Han & Nelson, 2015; Kharat &
Musande, 2015; Blackett, 2014). Therefore, in this study, we hypothesize that the indices retrieved from Landsat 8 contain valuable information for characterization of landscapes useful for reliable urban heat vulnerability mapping.
The objective of this study was therefore to (i) include NDWI among the physical factors used for determining heat exposure, (ii) to produce a heat vulnerability map with spatial resolution greater than the resolution of socio-demographic vulnerability factors and (iii) use remote sensing physical variables obtained from the improved Landsat 8 optical and thermal data to map heat vulnerability of the highly heterogeneous Harare Metropolitan City during the hot season.