CHAPTER 8: REMOTE SENSING BASED PREDICTION OF URBAN GROWTH AND
8.1 Introduction
Urban growth, which is characterised by replacement of natural surfaces with heat absorbing impervious surfaces (artificial structures such as pavements covered by asphalt, concrete, brick, stone and roof tops) and buildings, results in elevated surface temperatures in cities compared to surrounding rural areas (Rao, 1972). Typically, high thermal values are obtained where density of buildings is high, proportion of impervious surfaces is high, as well as in areas where heat removal by advection and radiation loss is retarded, such as at city core with tall buildings (Hu & Jia, 2010; Zhang, Schaaf, et al., 2013; Amiri, et al., 2009). Such increase in temperature may have adverse socio-economic and environmental impacts on urban residents that include increased water use, energy cost for air conditioning and health risk, due to pollution (McCarthy, et al., 2010; Tonnang et al., 2010; McDonald, et al., 2011a; Hung, et al., 2006;
Guhathakurta & Gober, 2007). Furthermore, the influence of urban growth on urban micro- climate is projected to continue increasing as urban population continue to rise globally (McCarthy, et al., 2010; Seto, et al., 2012; Zhang, Schaaf, et al., 2013; Valsson & Bharat, 2009). Natural landscapes, particularly vegetation and wetlands favour latent heat transfer and play a significant role in mitigating against urban heat (Odindi, et al., 2015). However, their coverage and mitigation is reduced, due to replacement by impervious surfaces and buildings as cities grow. For the purpose of sustainable urban growth and planning, the link between Land Use and Land Cover (LULC) transitions and future climate projections need to be understood. Specifically, there is need to predict the implication of long term localized LULC transformation on surface temperatures in order to enhance area specific adaptation, mitigation, as well as policy formulation and implementation.
A number of studies have analysed the relationship between urban LULC patterns and land surface temperatures, using remotely sensed imagery without making future projections (Larsen & Gunnarsson-Östling, 2009; Yuan & Bauer, 2007; Xu, et al., 2013; Wilson &
Brandes, 1979; Hu & Jia, 2010). These studies have shown that impervious surfaces within urban areas are characterised by high temperatures, due to a combination of high heat absorption rate, low thermal emissivity and low latent heat transfer. Conversely, natural landscapes like wetlands and vegetated areas have also been characterised by low temperatures (Jiang & Tian, 2010; Sung, 2013; Mushore, et al., 2016). Several studies also explored seasonal and long term historical changes in temperature with urban growth (Yuan & Bauer, 2007; Hu
& Jia, 2010; Valsson & Bharat, 2009; Odindi, et al., 2015). Other studies have used urban and vegetation indices to show the quantitative relationship between LULC and temperatures but
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placing little focus on future temperature patterns (Weng, et al., 2004; Chen, et al., 2006; Yuan
& Bauer, 2007; Tran, et al., 2006; Xiao, et al., 2007; Wilson & Brandes, 1979; Yang Zhang et al., 2012; Senanayake, et al., 2013; Hung, et al., 2006). For example, Chen et al. (2006) showed that temperatures decrease with normalized difference vegetation index, normalized difference bareness index and normalized difference wetness index while increasing with normalized difference built up index. The relationship between land surface temperature and a variety of land cover indices are known to be strong. Therefore, trends in land cover indices such as vegetation fraction (FVG) and normalized difference built-up index (NDBI) have potential to accurately project future temperature. However, there is paucity of literature on the use of land cover indices to project localized future distribution of urban LULC and temperature patterns.
Despite their strength to forecast urban growth patterns, only a single study used land cover indices to predict future distribution of land surface temperature (Ahmed, et al., 2013). Whereas Ahmed et al. (2013) used Normalised Difference Vegetation Index (NDVI) to project remnant urban natural landscape and future land surface temperature values, NDVI is known to saturate at high vegetation fraction, thus offering a limited temperature range. Studies have also shown that NDVI is a weaker predictor of land surface temperature than other indices like the Normalised Difference Built Index (NDBI), vegetation fraction and the percentage Impervious Surface Area (ISA) (Li & Liu, 2008; Chen, et al., 2006; Yuan & Bauer, 2007; Deng & Wu, 2013); Chen et al 2006). Furthermore, Ahmed et al. (2013) used single date images to compute NDVI to represent entire season; a method which is subject to randomness given that land cover may vary significantly with a season. There is thus need to improve the approach such as by using seasonal averages of land cover indices. In another study, Hasanlou & Mostofi, (2015) estimated LST based on a linear function of a combination of indices which included NDVI, NDBI, Normalized Difference Bareness Index (NDBaI), Normalized Difference Water Index (NDWI), Soil Adjusted Vegetation Index (SAVI), Enhanced Built-up and Bareness Index (EBBI), Urban Index (UI), and Built Up Index (BUI) (Hasanlou & Mostofi, 2015).
However, Ahmed et al. (2013) notes that when several factors are used in a linear regression model, accuracy of retrieved dependent variable may be compromised, due to noise caused by collinearity between the factors. Climate forecasts are as useful as they are accurate thus there is need to identify indices that best predict LST accurately without errors due to collinearity.
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The Markov Chain Model has been widely used among others to predict LULC changes and urban expansion (Ahmed & Ahmed, 2012; Fan, et al., 2008; Hashem & Balakrishnan, 2015;
Araya & Cabral, 2010). For example, Hashem and Balakrishnan (2015) used Markov Chain analysis and predicted a 20% increase of built-up areas for Doha, Qatar in 2020. Fan, et al.
(2008) predicted farmland loss due to urban expansion between 2003 and 2013 in Pearl river delta using Cellular Automata Markov Chain analysis. However, there is paucity in literature on extending the adoption of Markov Chain analysis to further determine effect of LULC transformation on urban surface temperature change. Temperature predictions have widely been done using global and regional models which usually exclude urban trends and consider their impact as negligible (McCarthy, et al., 2010; Saitoh, et al., 1996; Unganai, 1996). Such models are often at coarse resolution, require further downscaling and therefore not very suitable for understanding localized phenomena (Hoffmann, et al., 2012; Smith & Roebber, 2011). Furthermore, global and regional models commonly emphasize on greenhouse induced temperature changes, disregarding the implication of LULC transformation on temperature change, particularly in urban areas. Analysis based on Markov Chain offer an opportunity for projecting landscape transformation, providing insight into future surface thermal characteristics, due to landscape change (Ahmed et al 2013). The analysis is suitable for predicting temperature changes at the same spatial and temporal resolution with LULC changes, thus capable of mapping localized phenomena such as urban surface dynamics. Due to previous successes in mapping LULC changes related impacts, accessibility, simplicity and parsimony, the Markov Chain model offers great potential to predict future temperature, hence needs to be further explored. The analysis is important for providing guidance and impression about how future urban thermal environment may be affected if historical urban growth patterns persist.
Despite the growing evidence from other parts of the world that urban growth leads to surface temperature changes, there is still a paucity of literature on the subject in Zimbabwe. Climate studies in the country have largely used in situ meteorological data and large scale climate models, concentrated on rainfall and mostly focused on impacts on agriculture (Manatsa et al., 2017; Mushore, Manatsa, et al., 2017; Mazvimavi, 2010; Moyo et al., 2012; Charles et al., 2014). Remote sensing based analysis of climate, especially at much localized scale such as the urban microclimate has remained scarce in the country. On the other hand, remote sensing based assessments of urban growth have only focussed on quantifying long term historical
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LULC changes (Wania, et al., 2014; Kamusoko, et al., 2013). For example, Wania, et al. (2014) used high resolution SPOT data to map expansion of built-up areas in Harare between 2004 and 2010 without providing further insight into the expected future patterns and impacts.
Similarly, using medium resolution Landsat multi-spectral data, Kamusoko, et al. (2013) delimited expansion of built-up areas in Harare between 1984 and 2013 but did not extend focus to implications on observed and future land surface temperature patterns. Recently, Mushore, Mutanga, et al. (2017b) linked urban growth to historical land surface temperature trends using multi-spectral Landsat datasets but did not predict future trends as well as their implication on micro-climate of Harare. Attempts to predict future urban growth patterns and their implications on surface temperature using remote sensing in Zimbabwe have thus not yet been made, to the best of our knowledge. Therefore, there is need to predict future urban growth and implications on the thermal environment of Zimbabwean cities with high level of detail using medium resolution remote sensing datasets. This has potential to enhance local level adaptation practices, improve temperature related decision making and encourage sustainable urban growth which incorporates future implications of LULC conversions on micro-climates.
This study sought to identify optimal land cover indices derived from medium resolution Landsat data that best represents a correlation between urban surface temperature and LULC changes in Harare, Zimbabwe. The study further sought to adopt the selected indices to predict future distribution of LULC and surface temperatures using the coupled Cellular Automata and Markov Chain analysis. The study also aims at quantitatively using seasonally averaged land cover indices rather than single date states used in previous studies to represent land cover patterns of a season as input in the Cellular Automata Markov model.