CHAPTER 9: REMOTE SENSING OF THE RESPONSES OF INDOOR AND OUTDOOR
9.1 Introduction
According to the IPCC (2007), mean temperatures are rising while the frequency and intensity of heat waves are also increasing. Recently, there has been an improved understanding of the nexus between the rising temperatures and climate change. However, existing global models stress on the impact of greenhouse gases and largely ignore the contribution of land covers, clouds, water vapor and ocean circulations (Loehle, 2011; IPCC, 2007). Specifically, the irreversible nature of urbanization implies that related changes in temperature could be a major contributor to long term changes in temperature at local, regional and global scales (Owen, et al., 1998). According to Cinar (2015) land cover changes may result in a 4oC temperature increase by 2100. In urban areas, this can be attributed to among others replacement of natural with impervious surfaces, changes in radiative transfer due to complex buildings and streets geometry and increase anthropogenic heating (Rasul, et al., 2015; Dousset & Gourmelon, 2003;
Xiao, et al., 2007). Studies have indicated that this may cause extreme temperatures which significantly reduce indoor and outdoor comfort while at the same time compromising productivity by reducing performance at work and increasing morbidity and mortality (Tanabe, et al., 2015; Humphreys, 2015; Lin, et al., 2016). Therefore, sustainable development requires in-depth understanding of impact of urban growth patterns on temperature, hence the need to review previous studies to identify strength and gaps in the subject area.
Urbanization is characterized by surface alterations which mostly entail increase in area covered by surfaces which absorb heat (Zhang, et al., 2009; Sobrino, et al., 2012; Amiri, et al., 2009). For example, vegetated areas are replaced with impervious surfaces and buildings, resulting in elevated surface temperatures, much higher than the surrounding rural and undisturbed areas (Johnson, et al., 2014; Steeneveld, et al., 2014; Tomlinson, et al., 2011; Hua, et al., 2013; Song & Wu, 2015; Sobrino, et al., 2012). Furthermore, surface temperatures are closely linked to near surface air temperatures, which affects human comfort inside and outside buildings (Guan, 2011). According to Xian and Crane (2005), urbanization alters air temperature of the atmospheric boundary and is a key component of the surface energy balance.
Generally, the impact of elevated temperatures within cities differs from place to place due to differences in physical exposure, landscape characteristics and socio-demographic factors
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(Johnson, et al., 2014). Moreover, literature has revealed that increase in surface temperatures have the potential to expose residents to heat related stress, especially the urban poor without air conditioning facilities (Parsons, 2014; Hsiang, 2010; Dokladny, et al., 2006). Additionally previous studies have also indicated that extreme temperatures significantly reduce indoor and outdoor comfort while at the same time compromising productivity by reducing performance at work and increasing morbidity and mortality (Tanabe, et al., 2015; Humphreys, 2015; Lin, et al., 2016).
Thermal analysis and forecasting enables assessment and prediction of impacts of heat island on temperature sensitive organisms, processes and activities useful for planning, policy formulation as well as for identifying adaptation and mitigation priorities. For example, Brune (2016) used temperature predictions and projected that a variety of urban tree species within built up areas may not tolerate the urban heat projected in 2050. In Japan heat island projection was used to project energy demand for air conditioning (Hirano, et al., 2009). In Australia thermal forecasts are used to influence heat adaptation and mitigation strategies such as use of green infrastructure to reduce greenhouse gas emissions (Block et al., 2012). In Kunming China understanding of the influence of urban growth on thermal characteristics of a city led to a decade of grass recovery to mitigate the heat (Zhou & Wang, 2011a). In Chicago Illinois thermal forecasts were used to model the impact of green roof on urban heating by simulating under variable roof types important for climate change adaptation (Smith & Roebber, 2011).
Using future projections of temperature enabled assessment of value of external shading which was found to be significantly valuable in mitigating future overheating risks (McLeod et al., 2013). The benefits also include reduction in energy wastage and pollution, thermal comfort in buildings and outdoor as well as sustainable growth of urban areas. This is important especially for growing cities in developing countries where vulnerability to climate change is already high while resources for adaptation and mitigation to further changes in climate may not be adequate in future.
In situ meteorological data is useful in quantifying the responses of near surface temperature to changes in land use and land cover. Studies have used in-situ air temperature data to monitor seasonal and long term climatic changes, to assess human thermal discomfort as well as to quantify the effect of temperature changes on household air conditioning energy consumption.
However, in-situ observations are limited in spatial coverage thus inadequate to for monitoring
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spatial variations in temperature such as in heterogeneous and complex urban landscapes. This inadequacy is pronounced in developing countries where financial constraints make it difficult to establish a high weather station density. Further, even in well-resourced developed nations, it is economically impractical to establish a desired network of meteorological stations. On the other hand, space-borne remote sensing has capability to simultaneously monitor both land use land cover changes and responses of the thermal environment. Using medium resolution space- borne multi-spectral data allow mapping of complex landscapes such as urban areas as well as the complex spatial structure of surface temperature. Due to exchange of heat between the land surface and the lower atmosphere, there is a strong link between land surface temperatures and near-surface air temperatures. This linkage enables spatial up scaling of thermal analysis by combining or replacing in-situ observations of air temperature with land surface temperature maps retrieved from remote sensing dataset. Therefore, combining in situ meteorological data with multi-spectral medium resolution remote sensing data has potential to improve understanding of the implications of urban growth on the thermal environment especially in data scarce countries.
In view of urban growth and potential implications on the thermal environment, inadequacy of situ data, availability and ease of access of medium resolution space-borne multispectral data and paucity of literature especially in the study area, the objectives of the study were;
1. To assess the potential of merging thermal data and vegetation indices with multi- spectral medium resolution remote sensing data in improving urban land use/cover mapping
2. To determine extreme heat vulnerability of Harare metropolitan city using multi- spectral remote sensing and socio-economic data
3. To assess seasonal and spatial daytime outdoor thermal comfort variations using recently launched and improved Landsat 8 data
4. To link major dynamics in urban near-surface temperatures to long term changes in land use/cover
5. To understand the link between built-up density and indoor air-conditioning energy demand in Harare using degree days derived from remote sensing and in-situ data 6. To predict future land use/cover distribution and implications on near-surface
temperatures in Harare using land cover indices retrieved from remote sensing data in CA-Markov modelling
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9.2 Chapter 3: Potential of merging thermal data and vegetation indices with multi-