CHAPTER 8: REMOTE SENSING BASED PREDICTION OF URBAN GROWTH AND
8.5 Discussion
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Table 8.11: Projected changes in surface temperature due to urban growth Coverage in each (km2 and % of total area)
Temperature (oC) 2015 2025 2035 2045
18 - 28 86.23 (10.1) 65.43 (7.7) 60.08 (7.0) 55.97 (6.3) 28 - 32 166.26 (19.4) 122.64 (14.3) 115.22 (13.5) 107.68 (12.7) 32 - 36 239.56 (28.0) 209.34 (26.5) 203.10 (23.7) 197.74 (23.1) 36 - 45 363.78 (42.5) 458.43 (53.6) 477.43 (55.8) 498.45 (58.0)
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and water consumption increases with urban heat intensity, hence the high correlation between UI and temperature (Rawal & Shukla, 2014; Wang, Chen, et al., 2010). The UI was also found to be high in bare areas thus enhances its potential to predict temperature since bare and built- up areas are comparatively hot during the day (Srivanit, et al., 2012; Pu, et al., 2006). Therefore, the comparative strength of the relationship between UI and land cover properties enhanced its potential to map urban growth and corresponding responses of temperature.
The SVM is a classifier of repute hence high classification accuracies obtained both in 1984 and 2015 despite the complexity of urban LULC distribution. This is consistent with previous studies such as by Adelabu et al (2013) which showed that SVM classifier results in high accuracy maps. The high quality of maps is also linked to the use of digitized regions of interest instead of points as ground-truth data for classification hence accuracies above the 80%
requirement (Omran, 2012). The derived LULC maps showed that built up areas increased while wetlands and vegetation cover decreased between 1984 and 2015 which agrees with previous studies in the same area (Wania, et al., 2014; Kamusoko, et al., 2013). The Cellular Automata Markov Chain analysis reliably predicts LULC patterns as indicated by strong agreement between the predicted 2015 map and the one derived from supervised classification.
The KIA was close to 1, implying close similarity and strong agreement between the modeled and observed LULC distribution for 2015. Due to high accuracy and strong agreement with the known LULC distribution for 2015, the Cellular Automata Markov Chain model was deemed reliable for future predictions. Based on LULC changes between 1984 and 2015, the Cellular Automata Markov Chain model projected that unless other interventions are employed and similar patterns persist, coverage of built up area will continue increasing at the expense of natural covers through to 2040. This finding is consistent with global predictions that urban population will continue to rise resulting in expansion of built-up areas at the expense of green- space (Araya & Cabral, 2010; Ahmed & Ahmed, 2012; Seto, et al., 2012; McCarthy, et al., 2010; Nayak & Mandal, 2012).
Based on Cellular Automata Markov model, temperatures may increase due to urban growth between 2015 and 2040, which agrees with already observed warming trends in Zimbabwe (Dube & Phiri, 2013; Chagutah, 2010; Unganai, 1996; Zvigadza, et al., 2010; Mushore, 2013a;
Brown., et al., 2012). In this study, area covered by 18-28, 28-32 and 32-34 oC temperature categories are projected to decrease while area covered by warmer categories such as the 40-
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46oC are expected increase. The warming patterns are in response to LULC distribution changes which will see built up areas increase in coverage at the expense of wetlands and green-spaces. The future rises in temperature due to urban growth induced LULC changes are also consistent with other previous studies (McCarthy, et al., 2010; Larsen & Gunnarsson- Östling, 2009; Omran, 2012; Sithole & Odindi, 2015; Odindi, et al., 2015; Nayak & Mandal, 2012; Zhang, Qi, et al., 2013; Hung, et al., 2006; Amiri, et al., 2009). For example, Hung et al (2006) and Senanayake, et al., (2013 observed that as urban population increases, urban growth increase, commensurate with the urban heat island effect.
Wetlands and green-spaces have high thermal capacity enabling them to serve as heat sinks, hence replacing them with impervious surface will raise temperature of an area (Sithole &
Odindi, 2015; Zemba, 2010). This is why northern areas with low density residential areas and characterized by high vegetation fraction are projected to potentially remain cooler than high density residential areas. Buildings absorb heat, furthermore high building density areas like the CBD impede heat removal by wind, further elevating temperatures (Sertel, et al., 2011;
Pielke, et al., 2011; Zhou & Wang, 2011a; Blake, Grimm, et al., 2011). The removal of vegetation and emission of waste heat also leads to accumulation of heat energy (Owen, et al., 1998; Senanayake, et al., 2013; Blake, Grimm, et al., 2011). According to Owen et al (1998), this increases sensible heat flux at the expense latent heat flux, hence high urban surface temperatures.
The projected rises in temperature between 2015 and 2040 is consistent with predictions from regional and global models (Ahmed, et al., 2013; McCarthy, et al., 2010; Unganai, 1996;
Simone, et al., 2011; Brown., et al., 2012; Newland, 2011; Blake, Grimm, et al., 2011). Blake et al (2011) projected that urban population will grow to 70% by 2050, causing surface alterations and anthropogenic heat emissions that will increase temperatures while Newland (2011) estimated that by 2050, 200 million people will be displaced by warming. According to Brown et al (2012) such increase will result in degradation of air quality and increased energy demand for cooling. Using an urban land surface model in the HadAM3 Global Circulation Model, high population growth was found to coincide with high heat island areas. These findings are also consistent with predictions for cities in other countries for example Bahrain, Tokyo in Japan and Dhaka in Bangladesh (Hassan Radhi et al., 2013; Saitoh, et al., 1996;
Ahmed, et al., 2013).
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Findings in this study show that temperature may increase even in areas where LULC types may remain unchanged. The rise in temperature is due to a combination of factors including change in surface characteristics and background of already warming temperatures global temperatures as a result of greenhouse effect and global warming. The increase can be attributed to background global warming, which will affect all areas even in the absence of increased urban growth (Argueso et al., 2014; Terando et al., 2014; Kahn, 2009; Dereczynski et al., 2013; Grimmond, 2007). This concurs with Terando et al (2014) who note that temperature rises due to urban growth are superimposed on rising global temperatures. Lauwet at al. (2015) also observed that due to increases in greenhouse gas concentrations, there is an increase in incoming long wave radiation towards the lower atmosphere. However, although low-medium density residential areas and other vegetated areas are expected to warm, their extent is smaller than in high density residential areas in the southwest. This is because the vegetation cover around the buildings mitigates the impact of global warming (Weng, et al., 2004; Hu & Jia, 2010; Zhou & Wang, 2011b; Amiri, et al., 2009; Dousset & Gourmelon, 2003;
Smith & Roebber, 2011; Odindi, et al., 2015; Senanayake, et al., 2013). Hence, maintenance of urban greenery remains a significant mitigation measure against extreme temperature elevation, even when greenhouse gas emissions and ozone depletion continue uncontrolled.
The predicted possible increases in temperature due to increased continued urban growth could concur with a variety of global and regional models which predict continuation of ‘business as usual’ approach to urban growth (Dholakia et al., 2015; Pilli-Sihvola et al., 2010; Isaac & van Vuuren, 2009; Flanner, 2009; Satterthwaite, 2008). This is primarily a consequence of high energy demands caused by increase in the middle income class as cities grow, as well as a result of economic and political reasons which make it difficult to implement policies to reduce emissions or manage urban growth. For example Kahn (2008) and Flanner (2009) observe that anthropogenic heat emissions and greenhouse gas concentration will increase in response to urban growth as the population of the middle class will increase. Middle level income earners raise the consumption of electricity from use of gadgets such as heaters, air conditioners, cooking stoves and fridges (Kahn, 2009). Satterthwaite (2008) observed that urban areas are important political and economic hubs as they also provide over 50% of national GDPs even in mostly rural nations thus difficult to implement strict emission reduction policies. Although
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difficult to implement effectively, McMichael et al (2006) stressed that forecasts will strengthen policies and guide priorities for planned adaptation strategies.