CHAPTER 5: IMPLICATIONS OF LAND USE AND LAND COVER DISTRIBUTION
5.2 Methodology
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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.
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5.2.2 Meteorological data collection and processing
Meteorological field data were obtained from Kutsaga Research Station located near Harare International Airport in the southeast of the city (Figure 5.1). These were relative humidity and dry bulb temperature collected at hourly resolution using an automatic weather station for the period from 1 January 2013 to 31 December 2015. The data period was selected in order to capture the variations between and within sub-seasons while at the same enabling obtaining of data coinciding with cloud free Landsat images. In the computation of thermal discomfort using Discomfort Index (DI) relative humidity data are required (Yilmaz, 2007; de-Azevedo, et al., 2015; Abdel-Ghany, et al., 2014; Polydoros & Cartalis, 2014; Tulandi, et al., 2012). Humidity varies in space within a city such that Meteorological field measurements from a single site are not sufficient. There was need to upscale relative humidity measurements from point observation in order to obtain representative and accurate spatially variable relative humidity measurements. Regression analysis was, therefore, used to model the relationship between air temperature and relative humidity for each sub-season. The regression models were validated by comparing observed with modelled relative humidity for each of the four sub-seasons.
5.2.3 Remote sensing data collection and pre-processing
The properties and functions of the 11 bands of Landsat 8 have been extensively described in several recent studies (Dube & Mutanga, 2015a; Jia, et al., 2014; Banskota, et al., 2014; Oumar, 2015; Kharat & Musande, 2015; Blackett, 2014; Han & Nelson, 2015). Cloud free daytime Landsat data acquired on dates corresponding to the four sub-seasons (Table 5.1) and covering the entire study area were freely downloaded from the USGS earth explorer website. It is difficult to obtain cloud-free images during the December to February period as this coincides with the peak of rainfall in Zimbabwe
Table 5.1: Landsat data (Path/row 170/72) used in this study
Image date Season Image date Season
24 March 2015 Rainy season 6 June 2013 Cool season
19 April 2013 Post rainy season 25 August 2013 Cool season 25 April 2015 Post rainy season 25 June 2014 Cool season 11 May 2015 Post rainy season 11 July 2014 Cool season 26 September 2013 Hot season 27 July 2014 Cool season 28 October 2013 Hot season 12 August 2014 Cool season 13 November 2013 Hot season 28 August 2014 Cool season 31 October 2014 Hot season 13 September 2014 Cool season
Coordinates on an image must agree with those on the ground in order to accurately relate remote sensing retrievals with ground reality. The process of geo-referencing makes use of
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ground control points whose coordinates to correct the coordinates on an image. The images were geo-referenced using 30 ground control points obtained at intersection of major roads.
The points were collected from 30 different and far-spaced locations in Harare where major roads were meeting. Intersections of major roads were sampled because they are easy to identify on an image when locating ground control points during geo-referencing. Atmospheric correction was done using the FLAASH module in the ENVI Version 4.7 software (Dube &
Mutanga, 2015a). Emissivity correction is necessary in the conversion from at-satellite brightness temperature to surface temperature (Wu, et al., 2014). For each season, the reflectance of near infrared and Red bands were used to retrieve the normalized difference vegetation index, vegetation fraction and surface emissivity as describe by Wu et al., (2014).
The thermals band 10 for each date was used to compute the brightness temperature which was then converted to surface temperature through emissivity correction. Since each sub-season spans for about 3 months, there was need to cater for intra-season variability in surface property. Therefore, instead of using a single date as representative for each season, average temperature was derived from the available data for further analysis.
5.2.4 Relative humidity retrieval from satellite and field observation
Relative humidity was determined using a linear regression model relating relative humidity with air temperature obtained using data from Kutsaga Research Station. Another linear regression model was developed for obtaining air temperature from land surface temperature derived from Landsat 8 for each sub-season. In order to obtain relative humidity map for each sub-season, we applied the relationship between air temperature and relative humidity obtained from field observations to the air temperatures retrieved from thermal data. A two-step approach was thus taken involving i) estimation of air temperature from land surface temperature and ii) further estimating spatial distribution of relative humidity using linear regression models aforementioned.
5.2.5 Retrieval of seasonal thermal discomfort patterns
Discomfort indices are commonly used due to parsimony while derivation of comfort indices such as by PMV using the Rayman, ENVI-MET or other models requires parameterization (Mohan, et al., 2014; Roelofsen, 2015; Shastry, et al., 2016). Furthermore, thermal indices are simple to compute; for example the ET only requires outdoor temperature to compute indoor thermal comfort and the Discomfort Index (DI [oC]) requires temperature (oC) and humidity (%) data only. For this reason the Discomfort Index was used in this study for analyzing
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outdoor thermal discomfort and was computed using mean air temperature (Ta [oC]) and mean relative humidity (RH [%]) derived from land surface temperature as described above. The equation for computing DI from air temperature and relative humidity (Equation 5.1) and the criteria for categorizing discomfort were obtained from Polydoros and Catalis (2014).
π«π°(β) = π»πβ π. ππ(π β π. πππΉπ―)(π»πβ ππ. π) Equation 5.1 In this study equation one was further adjusted using regression models so that discomfort was computed as a function of air temperature only for each of the four sub-seasons. This thus further reduced the data requirements for the computation of thermal discomfort.
5.2.6 Linkage between land cover fraction and thermal discomfort patterns
The link between spatial distribution of thermal discomfort and land cover types as well as land cover fractions per sub season was investigated. Land cover fraction provides a quantitative analysis of distribution of surface covers while land cover classification provides qualitative classes. The vegetation fraction was used to represent land cover fraction with high values (close to 1) representing abundant vegetation cover while low values (close to zero) representing impervious, bare and built-up areas. Vegetation fraction (Fc) was retrieved from NDVI map for each sub-season according to dimidiate pixel model using Equation 5.2 (Cao, et al., 2008).
ππ = π΅π«π½π°βπ΅π«π½π°ππππ
π΅π«π½π°πππβπ΅π«π½π°ππππ Equation 5.2
Where NDVIsoil is NDVI for a pure soil pixel and NDVIveg is for a pure vegetation pixel. In this study NDVIsoil of 0.05 and NDVIveg of 0.7 (Hu & Jia, 2010) were used.
For qualitative land cover classification, the Support Vector Machine (SVM) algorithm (Petropoulos, et al., 2012; Adelabu, et al., 2013; Jia, et al., 2014; Yang, Lin, et al., 2014;
Forkuor & Cofie, 2011) was used in supervised classification to map Harare into seven classes described in Table 5.2. The advantages of the Support Vector Algorithm and high performance in land cover mapping are explained by Yu et al., (2014) and Forkuor and Cofie (2011). A cloud free image obtained on 13 September 2013 was used together with 100 ground control points per class obtained from locations evenly distributed across cover type and study area to capture variability within and between classes. The points were split into 70% for training/classification and 30% for accuracy assessment as recommended by Adelabu, et al.
(2013). The 30m resolution visible/infrared bands, except for the cirrus clouds band (band 9) and sea coastal water monitoring band (band 1) and the 15m resolution panchromatic band
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(band 8) were used for land cover classification. Thermal infra-red bands (Band 10 and 11) were not included for image classification due to their relative low resolution (100m) which may not effectively map heterogeneous urban landscapes. The accuracy of the classification was assessed using independent ground control points for each land cover type obtained in field survey described already in Chapter 3. In order to increase number of control points per LULC type, the points were superimposed on an RGB composite image of the study area and polygons were digitized around them creating regions of interest (ROI) in ENVI software. ROI instead of points were then used for both classification and accuracy assessment. This was following recommendation that use of ROI instead of direct (Global Position System) GPS based points from field survey increases classification accuracy (Acharya, et al., 2015). A confusion matrix was obtained by cross validating the classified map with field observations. Further, accuracy was quantified using Producerβs accuracy (PA), Userβs accuracy (UA), Overall accuracy (OA) and kappa. Several studies on image classification explain the extraction of classification accuracy indicators from the confusion matrix (Southworth, 2004; Panah, et al., 2001; Witt, et al., 2007; Sun & Schulz, 2015; Liu et al., 2003).
Table 5.2: Description of the major land cover classes considered for this study
Class Description
Densely built (DB) Very high built density (CBD and industrial areas) Low-medium density residential Low and medium density residential areas with higher (LMR) vegetation fraction than high density residential High density residential (HDR) Built-up with higher density of building and lower
vegetation cover than low-medium residential Forested Areas (Fr) moderate to dense forest cover
Development (Dv) High density residential under development; mixture of bare and building with very low vegetation cover Grasslands (Gr) Grass covered areas with little or no trees
Water (Wt) Water bodies
We further investigated the link between thermal discomfort and land cover quantitatively and qualitatively. Quantitatively, we analysed the responses of average temperature and discomfort to average land cover fraction (vegetation fraction) across the four sub-seasons. Qualitatively, we analysed the link between the spatial distribution of thermal discomfort and land cover themes in Harare per sub-season.
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