Materials and methods

CHAPTER 6: RESPONSES OF URBAN LAND SURFACE TEMPERATURES TO LONG

6.2 Materials and methods

6.2.1 Description of the study area

This study was conducted in Harare, the capital city of Zimbabwe (Figure 6.1). The city is experiencing growth as evidenced by increase in population and built up area (Kamusoko et al., 2013; ZIMSTAT, 2012). The urban core and industries are found at the centre while major roads radiate from the city centre. Settlements are more spacious in the north where mostly low and medium density residential suburbs are found (Wania, et al., 2014). The month of October is the hottest and driest while the summer season is noted to be warming and experiencing prolonged hot spells (Manatsa et al., 2013), hence the selection of the period.

Figure 6.1: Location of the study area and general variations in spectral properties of land- cover regimes.

103 6.2.2 Pre-processing of remotely sensed data

Landsat Thematic Mapper TM 5, Landsat ETM+7 and Landsat 8 OLI and TIRS images with Path/Row of 170/72 were acquired from the United States Global Survey Earth Resources Observation System (USGS-EROS) website. Landsat data were selected due to adequate archival data, ease of access and previous performance in land cover classification and temperature analysis (Odindi et al., 2015). The 30 year time-span was selected in line with World Meteorological Organization’s recommended length for climate change analysis (World Meteorological Organization, 2000, 2007). The image reflective bands were corrected for atmospheric effects using the Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) module in the ENVI software (Dube & Mutanga, 2015a; Mushore, et al., 2016).

The images were geometrically corrected using a 1:50 000 topo-sheet and 30 ground control points collected at intersection of major roads and invariant features recognisable on satellite images. The Landsat imagery used, as well as the meteorological condition at Harare Airport Meteorological Office during the time of Landsat acquisition are shown in Table 6.1.

Table 6.1: Landsat path/row 170/72 images used for temperature analysis in this study.

Meteorological conditions at Harare Airport Meteorological Station are also presented.

Image Date Temperature (^oC) Humidity (%)

Landsat 5 17 October 1984 28.4 37.0

Landsat 5 21 October 1993 28.7 33.0

Landsat 7 19 October 2001 28.6 36.3

Landsat 8 18 October 2015 29.0 42.0

6.2.3 Land use and cover classification, accuracy assessment and change detection Land use land cover maps for the year 1984, 1993, 2001 and 2015 were derived using the 30m reflective bands of Landsat 5, 7 and 8 images. Ground truth data per LULC type for classification and accuracy assessment were obtained during a field survey as already described in Chapter 3. In order to improve accuracy classes with spectral similarities were merged following a separability test before classification using the Transformed Divergence Separability Index (TDSI) (Chemura & Mutanga, 2017; Matongera et al., 2017). According to Matongera, et al. (2017), the closer the TDSI to 2 the higher the separability of two LULC classes from each other using a specific remote sensing dataset. Value less than 1 implies that two LULC types are difficult to separate such that trying to do so will reduce classification accuracy. Separability test was thus done because high accurate classification is obtained when LULC classes are adequately separable for a given spatial resolution of remote sensing data.

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This also enabled merging of classes that have similar spectral properties. As a result, contrary to Chapter 3 where seven general classes were used, in this Chapter the LULC were reduced to six major classes described in Table 6.2 after separability test. Supervised classification using the Support Vector Machine (SVM) algorithm was implemented to generate LULC maps for each of the years under investigation. The SVM algorithm places no assumption to the probability distribution of the data and has low training data requirements. The classifier was found in previous studies to be better than commonly used algorithms like Maximum Likelihood Classifier (MLC), Parallelepiped, Minimum Distance, Mahalanobis Distance and the Artificial Neural Network classifiers (Omran, 2012; Adelabu, et al., 2013).

Table 6.2: Description of LULC classes observed in Harare during field survey LULC class Description

CBD/Industrial (CBDI)

Areas with very high density of buildings and a very high proportion of impervious surface that include central business district and industrial areas.

High density residential (HDR)

High density residential areas and areas under residential development (bare or impervious) with low vegetation fraction.

Low-medium density residential (LMR)

Established low and medium density residential areas with high vegetation fraction.

Croplands (Cr) Areas where intra-urban agriculture is practised including research sites which could be bare in the dry season

Green spaces (Gr) Areas covered by grasslands and clusters of tree characterised by high vegetation fraction even during the dry season.

Water (Wt) Areas covered by water bodies or wetlands.

Supervised classification requires field observation for training and accuracy assessment, therefore, 120 points per class were obtained from a field survey using a GPS between the 1^st and 30^th of April 2015. The points were split into training (80%) and validation (20%) based on recommendation by Adelabu et al. (2013). Regions of interest (polygons created around ground truth points) were used instead of points to increase the number of sample points upon which to base classification and validation. Acharya et al., (2015) showed that higher accuracy is achieved using regions of interest than points. Accuracy was assessed using the kappa coefficient by comparing mapped LULC classes with field observations, expert knowledge and auxiliary LULC data from topo-sheets and aerial photographs. LULC changes were analysed using visual inspection and calculation of changes in spatial coverage.

105 6.2.4 Derivation of thermal characteristics

We adopted stages of retrieving temperature from Landsat data series which include the conversion of digital numbers of thermal bands to thermal radiances, calculation of brightness temperature and emissivity correction (Sobrino, et al., 2004). We used thermal Band 6 of Landsat 5 for 1984 and 1993, Band 6 of Landsat 7 for 2001 and Band 10 of Landsat 8 for 2015 for analysis. The surface emissivity maps used to compute surface temperature from brightness temperature were derived using Normalized Difference Vegetation Index (NDVI) for each period (Jiang & Tian, 2010). The land surface temperatures were used to derive the relative radiative temperature with respect to the average of 1984 using equation 1 (Zhang, et al., 2012;

Xu, et al., 2013).

𝑻_𝑹,𝒏 =^{𝑻𝒏−𝑻𝒎𝒆𝒂𝒏,𝟏𝟗𝟖𝟒}

𝑻𝒎𝒆𝒂𝒏,𝟏𝟗𝟖𝟒 Equation 6.1

Where Tn is the temperature at a point year n, TR,n is the relative temperature in year n and n is the year for example 2015. The average temperature of 1984 was used as a reference when LULC distribution had not been significantly modified by urbanization. In order to compare heat island distribution of a year with that of 1984, we computed the spatial distribution of relative radiative temperatures for four periods in different decades. For ease of comparison, the relative temperatures were further classified into categories described in Table 6.3 as recommended by Zhang, et al., (2012).

Table 6.3: Description of relative temperature level

UHI level Description

Less than 0 Green Island

0 – 0.005 Weak heat island

0.005 – 0.010 Strong heat island

0.010 – 0.015 Stronger heat island

0.015 – 0.020 Strongest heat island

Greater than 0.020 Violent heat island

6.2.5 Responses of temperature to LULC changes

We calculated average temperature of each class for each year collected from points evenly distributed across the study area to capture all possible inter- and intra-class variations. We further calculated the difference between the average temperature in 1984 and 2015 for each land cover. The differences were attributed to other anthropogenic factors than land cover changes. In order to determine the change in average temperature, due to change from LULC changes, we used the normalized difference in temperature to correct for influence of other

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anthropogenic factors. This was computed using the equations adapted from Zhou & Wang (2010) and expressed as;

𝒅𝑻_𝒊𝒋= 𝑻_{𝒋𝟐𝟎𝟏𝟓}− 𝑻_{𝒊𝟏𝟗𝟖𝟒} Equation 6.2

∆𝑻_𝒊= 𝑻_{𝒊𝟐𝟎𝟏𝟓}− 𝑻_{𝒊𝟏𝟗𝟖𝟒} Equation 6.3

𝒅𝑻_𝒏 = 𝒅𝑻_𝒊𝒋− ∆𝑻_𝒊 Equation 6.4

Where dTn is the change in temperature cause by replacement of i by land cover j, ∆𝑇_𝑖 is the change due to other anthropogenic factors than LULC change and 𝑑𝑇_𝑖𝑗 is the change in temperature before normalization.

6.2.6 Changes in the contribution of land cover to the thermal environment in the city The proportional contribution of land covers to the thermal characteristics was expressed using the contribution index (CI) based on Equation 6.5 (Odindi, et al., 2015; Chen, et al., 2006).

𝑪𝑰 = 𝑫_𝒕× 𝑺 Equation 6.5

Where 𝐷_𝑡 is the difference between the average temperature of the entire study area and the average of the LULC class type. Variable 𝑆 is the proportional area of the LULC type, which is the ratio of the area covered by the class to the total area of the study. Positive values of CI indicate how much the LULC type contributes to raising the surface temperatures of an area while negative values indicate heat mitigation value. The CI was computed from the year 1984 to 2015 using the same value of 𝐷_𝑡 but varying for each land cover. The assumption was that the changes in the contribution of LULC were not due to changes in average temperatures, but as a result of changes in proportional areas covered. The assumption was made in order to eliminate the contribution of other external factors, such as ozone depletion and greenhouse gas concentrations.

6.2.7 Normalized change in average temperature due to land cover changes

We proposed a technique to derive changes in average temperature solely due to LULC changes that excludes changes in other contributing factors to surface temperature rises. In this technique, we assumed that contribution of LULC changes was due to changes in proportional area covered between the year 1986 and 2015. The proposed normalized average temperature of the study area was computed using the Equation 6.6 (Feng, et al., 2014).

𝑳𝑺𝑻_{𝒂𝒗𝒆𝒓𝒂𝒈𝒆,𝒌} =^{∑ 𝑺𝒊}_{∑ 𝑺}^{𝒊,𝒌×𝑻𝒊,𝒋}

𝒊 𝒊,𝒌 Equation 6.6

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Where 𝐿𝑆𝑇_{𝑎𝑣𝑒𝑟𝑎𝑔𝑒,𝑘} is the average temperature of the study area in year k. 𝑇_𝑖,𝑗 is the average temperature of the land cover type i in year j before land cover changes took place and 𝑆_𝑖,𝑘 is the proportional area of land cover type i in year k. We compared the change in temperature of the study area from the year 1984 to 2015 with and without normalization. This provided both a measure of how LULC alone and how a composite of contributing factors changed the average temperature of the study area.