CHAPTER 5: IMPLICATIONS OF LAND USE AND LAND COVER DISTRIBUTION
5.5 Discussion
The potential of recently launched Landsat 8 and improved relative humidity observations in improving thermal discomfort mapping was tested. Thermal discomfort was computed for four sub-seasons using air temperature retrieved from Landsat 8. In addition, regression analysis resulted in strong correlation between relative humidity and air temperature. The correlation was stronger when seasons were considered separately (R2 greater 0.79) than when a single model was used for all sub-seasons (R2=0.33). The regression models were also verified using independent observations and their accuracy was high (relative errors below 20%) for all sub- seasons. The correlation between air temperature and relative humidity is known to be strongly negative (de-Azevedo et al., 2015). A strong correlation between air temperature and land surface temperatures (R2=0.69) was also observed. The relationship between temperature and humidity was used to retrieve relative humidity such that discomfort index was computed as a function of air temperature only, thus reducing the data requirement.
The hot and the rainy sub-seasons were observed to be more thermally uncomfortable (mean discomfort index was 31oC) than the post rainy and the cool seasons (mean DI was less than 24oC). This was because the hot and post rainy sub-seasons comprise the summer season when generally a lot of insolation is received compared to winter season (post rainy and cool sub- seasons). Further, vegetation abundance and surface wetness had a significant cooling effect on the rainy and post rainy sub-seasons. Similarly, the cool sub-season was on average more thermally uncomfortable (DI of 21.4oC) than the post rainy sub-season (mean DI of 19.9oC), although the latter receives more insolation (not quantified in this study) due to low vegetation fraction and low surface wetness. Vegetation is mostly dry in the cool sub-season while some trees even shed their leaves during the period in Harare. Vegetation cover reduces radiant heat transfer by increasing latent heat transfer thereby increased cooling effect as vegetation fraction increases between hot and the rainy sub-seasons (Odindi, et al., 2015; Cao, et al., 2008; Zhang, et al., 2012). Plants convert a lot of energy from the sun to potential energy weakening heating effect of solar energy (Klok, et al., 2012)
In order to link the distribution of land cover types to the seasonality of thermal discomfort, a supervised classification of Landsat 8’s visible/infrared bands was performed. Seven classes were observed at an overall accuracy of 84.5% and kappa of 0.81. The user’s and producer’s
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accuracy were higher than 73% for all the land use/cover types implying strong agreement between the mapped classes and field observations. The accuracy was higher than previously achieved, when Harare was classified into built and non-built using high resolution SPOT image (Wania, et al., 2014). Wania et al., (2014) obtained an overall accuracy of 83.5 and kappa of 0.64. The high accuracy of LULC mapping was attributed to the support vector machine algorithm as well as quality of Landsat 8 data, which improve land surface property retrievals;
the quality attributes include improvements in radiometric resolution, spectral range and noise to signal ratio which has been found to improve land use/cover mapping (Jia, et al., 2014;
Mwaniki, et al., 2015; Ke, et al., 2015). Landsat 8 has outperformed earlier Landsat versions as well as moderate resolution datasets such as MODIS in mapping land surface characteristics, especially in heterogeneous urban landscapes (Mwaniki, et al., 2015; Ke, et al., 2015; Yu, et al., 2013; Jia, et al., 2014).
During the post rainy sub-season, most of the city including all residential areas had no discomfort (DI less than 21oC) except for the densely built-up areas (CBD and industrial areas) where less than 50% of subjects would feel uncomfortable (DI between 21oC and 24oC). The slight thermal discomfort (DI between 21oC and 24oC) observed in the CBD in the post rainy and cool sub-seasons when other areas were more comfortable can be supported by findings that inner cities are exposed to increased health risk and intense temperatures (Tomlinson, et al., 2011). The high density of buildings here impedes wind movement hence removal of heat (Qiao, et al., 2013). The large size of the thermally comfortable proportion (59% of the city) was because during the post rainy sub-season, the ground will be relatively wet while vegetation fraction be will high (including intra-urban farming) hence high evapo- transpiration. Further, compared to the rainy sub-season, the sun will be on its northward transition making way for the cool sub-season, thereby reducing intensity of radiation received during this period. However, the densely built-up areas, although not very uncomfortable thermally, were slightly more uncomfortable (DI between 21oC and 24oC) than the bulk of the city during the same period. This was due to high absorption of radiation as well as presence of surfaces which store and release heat, thus increasing temperatures during the day (Srivanit, et al., 2012). This agrees with the observation that built-up areas have large heat storage fraction due to changes in characteristics of the ground by lowering vegetation cover and surface reflectance (Setaih, et al., 2014).
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In the hot, rainy and cool sub-seasons the southern and western areas are thermally more uncomfortable than the northern and eastern areas. For example in the southern and western areas, during the hot season, DI ranged from 27oC to 32oC while it was mostly less than 27oC in the rest of the city. The southern and south eastern areas are mostly occupied by high density residential areas and areas under residential development. Increased density of buildings results in high absorption and storage heat resulting in high surface temperatures, especially in the hot season (Qiao, et al., 2013; Chun & Guldmann, 2014). Due to large coverage of impervious surfaces, buildings and bare areas, evidenced also by small coverage of grasslands and forests in the southern areas, heat loss by evapo-transpiration is reduced during the hot season. Densely built-up and bare areas show similar thermal characteristics during the hot sub-season hence almost equal and high discomfort in high-density residential, densely built-up area and areas under development.
Even in the hot sub-season, fewer people would feel uncomfortable in the low-medium density residential areas (DI mostly below 27oC) than in the CBD and high density residential areas (DI mostly greater than 27oC). In these areas, vegetation fraction is high while density of buildings is low due to spacious settlement hence space for greenery. In all sub-seasons, greenery is generally healthy in the low-medium residential because even during the dry seasons, the citizens here afford to manage and irrigate the green spaces due to high income.
Furthermore large park and vast grasslands are also found in the north of the city thus contributing to low thermal discomfort across all sub-seasons in Harare. Even in areas where there are buildings, vegetation reduces temperatures and hence increases thermal comfort of an area. Residential areas with high vegetation cover have low irradiance during the day (Lo, et al., 1997; Lo & Choi, 2004; Weng, et al., 2004).
In all seasons there was no thermal discomfort at daytime in water covered areas (DI less than 21oC). The large water body in the extreme southeast is characterized by low discomfort index values in all seasons. Water has high heat capacity thus takes long to heat up during the day resulting in low skin temperatures (Wang & Zhu, 2011; Amiri, et al., 2009). Spraying surfaces with water mist was also found to significantly reduce temperatures and discomfort during daytime in summer (Farnham, et al., 2015). Covering a surface with water also reducing daytime temperature of an area by increasing heat loss by evaporation thus reduced daytime temperatures towards water bodies (Steeneveld et al., 2013). Surface irradiance was found, in another study, to be least in water, followed by vegetation while higher than this in residential
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areas where there was mixture of vegetation and buildings (Lo, et al., 1997; Steeneveld, et al., 2013).