CHAPTER 5: IMPLICATIONS OF LAND USE AND LAND COVER DISTRIBUTION
5.1 Introduction
Understanding thermal comfort patterns is important for solving related health, global warming and wasted energy problems (Goshayeshi, et al., 2013b). Thermal discomfort is when 80 to 90% express dissatisfaction with prevailing temperature at a given instant and location (Yilmaz, 2007). Heterogeneity in urban surface properties exposes citizens to spatially variable levels of thermal comfort as it is mainly affected by surface conditions (Zhang, et al., 2009).
Thermal discomfort causes fatigue, malaise, reduced ability to perform intellectual activities, health problems and even death (Buscail, et al., 2012; de-Azevedo et al., 2015; Roelofsen, 2015; Haruna et al., 2014). Studies have revealed that thermal discomfort affects physical and psychological performance. For example attention and performance in the classroom are compromised by thermal discomfort (Mazon, 2013). Furthermore, the usage of a location for activities is affected by thermal discomfort while urban citizens enjoy leisure in thermally comfortable outdoor locations such as parks and lakes (Setaih et al., 2014; Goshayeshi, et al., 2013b). Outdoor thermal discomfort also affects thermal conditions indoors; in developed countries people spend 10% of time outdoor during hot season and less than 5% in winter (Setaih, et al., 2014). Therefore, there is need for mapping the seasonal and spatial distribution of thermal comfort in order to assist citizen in making informed decisions in selecting places with thermal comforts within their preferred ranges for various activities across seasons.
Indices such at the Physiological Equivalent Temperature (PET) and Discomfort Index (DI) have been preferred by recent studies due to simplicity and parsimony compared to empirical methods such as Predicted Mean Vote (PMV) which involve significant parameterization (Mohan, et al., 2014; Roelofsen, 2015; Shastry, et al., 2016). PET requires temperature, humidity and wind speed only and DI requires air temperature and humidity, compared to more variables including human metabolism and insulation provided by clothing required in the computation of PMV (Goshayeshi, et al., 2013b). Also while other studies utilize point meteorological data to measure outdoor thermal discomfort (Yousif & Tahir, 2013; Cheng, et al., 2010; Abdel-Ghany, et al., 2014; Tulandi, et al., 2012), remote sensing enables synoptic measurement of intensity and spatial distribution of thermal discomfort for the whole city (Sobrino, et al., 2004). While low resolution National Oceanic and Atmospheric Administration’s Advanced Very High Resolution Radiometer (NOAA AVHRR) and Multi- functional transport Satellite (MTSAT) data have been used in thermal discomfort studies (Polydoros & Cartalis, 2014; Okamura, et al., 2014), the spatial resolution is not adequate in monitoring urban climates as vast changes are observed within short distances. Although
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resolution greater than 50m was recommended for urban thermal analysis (Sobrino, et al., 2004), data in this range is not readily available. There is thus need to utilize freely available medium resolution datasets such as from Landsat to understand thermal comfort patterns especially in urban areas in developing countries.
Landsat series offers freely available data for urban studies such as 30m resolution for VIS/IR bands and 100m thermal data from recently launched Landsat 8. Previously, Landsat TM and ETM have been used in thermal discomfort analysis (Wei-wu et al., 2004). However, the improved Landsat 8 have not yet been used in outdoor thermal comfort studies despite high sensitivity, improved signal to noise ratio and improved spectral range (Jia, et al., 2014; Dube
& Mutanga, 2015a). Landsat 8 data was found to improve land cover mapping, heat island analysis, monitoring of active volcanoes and identification of hydro-chemical rock alterations (Dube & Mutanga, 2015a; Jia, et al., 2014; Banskota, et al., 2014; Oumar, 2015; Kharat &
Musande, 2015; Blackett, 2014; Han & Nelson, 2015). This study thus hypothesizes that Landsat 8’s multi-spectral and multi-temporal data should effectively and parsimoniously detect and map seasonal variations in thermal discomfort in a complex urban environment.
Outdoor thermal discomfort should vary with seasons as well as between locations due to spatial and temporal variations of land cover in urban areas. Land surface temperature retrieved using remote sensing is highly correlated with air temperature, enabling estimation of air temperature from space-borne remote sensing observations of surface temperature (Widyasamratri et al., 2013; Cheng & Ng, 2006; Polydoros & Cartalis, 2014). This relationship could be useful in retrieving seasonal urban outdoor thermal discomfort using medium resolution Landsat data for the first time. Previous studies by Okamura, et al. (2014) and (Polydoros & Cartalis, 2014) used coarse spatial resolution which is not sufficient, given the heterogeneity of urban landscapes. Medium resolution data have been successfully used to link land surface temperature with land use and land cover despite their complex configuration in urban areas (Amiri, et al., 2009; Connors, et al., 2012; Chen, et al., 2006). However, adoption of medium resolution data such as Landsat data for outdoor thermal discomfort analysis has remained limited only to a study by Wei-wu, et al. (2004). Given the success of medium resolution dataset in mapping urban thermal variations, there is need to further test their potential in mapping spatial and seasonal patterns in outdoor thermal discomfort.
As aforementioned, a detailed understanding of spatial and seasonal thermal discomfort patterns is important for identifying spatial variations in thermal risk levels within an urban
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area. Wei-wu, et al. (2004) measured outdoor thermal discomfort using air temperature derived from land surface temperature and relative humidity derived from Normalized Difference Vegetation Index (NDVI). Their procedure was tedious as it required land surface temperature and NDVI obtained using Landsat 5 multi-spectral data. In this study, it is hypothesized that data requirements for retrieval of DI can be reduced by developing models for obtaining both relative humidity and air temperature from land surface temperature. Fortunately, there is also a strong inverse correlation between air temperature and relative humidity (de-Azevedo, et al., 2015) and this may also be useful in reducing data requirements for discomfort analysis using the Discomfort Index (DI). Therefore, there is need to retrieved relative humidity as a function of land surface temperature derived from medium resolution such as the recently launched and improved Landsat 8 multi-spectral data. As a result, outdoor thermal discomfort can be modelled as a function of air temperature and relative humidity both derived from surface temperatures retrieved from thermal bands of Landsat 8. This will reduce data requirement by making land surface temperature the only input in DI computation, important in data scarce cities such as Harare, Zimbabwe. This has potential to effectively map spatial variations of DI and promote thermal discomfort assessments in urban areas of developing countries where scarcity of in-situ observations may hinder such analysis. This is important for deriving area and season specific heat mitigation policies and strategies especially in cities of developing countries such as Harare city where the poorest are usually the most vulnerable (Mushore, Mutanga, et al., 2017a). Therefore, this study will develop analysis techniques which aid urban areas plan and develop sustainably.
The objective of this study was thus to use air temperature retrieved from Landsat 8’s thermal data for mapping seasonal variations in thermal discomfort in Harare, Zimbabwe as well as to investigate how the relationship between relative humidity and air temperature can be useful in reducing data requirements for thermal discomfort mapping using DI. The link between outdoor thermal discomfort and land cover types across four sub-seasons in Harare, Zimbabwe was also investigated. The aim was mainly to understand extent to which distribution of buildings and vegetation influences thermal discomfort across sub-seasons in Harare. This was important for the identification of potentially uncomfortable places for temperature related disaster management purposes as no similar study has been previously done. This was also necessary for identification of comfortable places in different sub-seasons to inform temperature sensitive outdoor activities as well as city planning and management. The hypothesis was that seasonal changes in land cover patterns could trigger significant
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differences in intensity and spatial distribution of outdoor thermal discomfort between seasons in Harare. The study was mainly driven by the paucity in literature on use of medium resolution satellite data such as from Landsat series for outdoor thermal discomfort analysis and the need for such assessment in Zimbabwe in view of the observed and expected climatic changes. The study also aimed at expressing the potential of freely available Landsat datasets for use in thermal discomfort analysis in cities of resource constrained developing countries in view of the common in-situ observation network inadequacy.