Spatial Analysis of Surface Urban Heat Islands in Four Rapidly Growing African Cities

Abstract

Africa’s unprecedented, uncontrolled and unplanned urbanization has put many African cities under constant ecological and environmental threat. One of the critical ecological impacts of urbanization likely to adversely affect Africa’s urban dwellers is the urban heat island (UHI) effect. However, UHI studies in African cities remain uncommon. Therefore, this study attempts to examine the relationship between land surface temperature (LST) and the spatial patterns, composition and configuration of impervious surfaces/green spaces in four African cities, Lagos (Nigeria), Nairobi (Kenya), Addis Ababa (Ethiopia) and Lusaka (Zambia). Landsat OLI/TIRS data and various geospatial approaches, including urban–rural gradient, urban heat island intensity, statistics and urban landscape metrics-based techniques, were used to facilitate the analysis. The results show significantly strong correlation between mean LST and the density of impervious surface (positive) and green space (negative) along the urban–rural gradients of the four African cities. The study also found high urban heat island intensities in the urban zones close (0 to 10 km) to the city center for all cities. Generally, cities with a higher percentage of the impervious surface were warmer by 3–4 °C and vice visa. This highlights the crucial mitigating effect of green spaces. We also found significant correlations between the mean LST and urban landscape metrics (patch density, size, shape, complexity and aggregation) of impervious surfaces (positive) and green spaces (negative). The study revealed that, although most African cities have relatively larger green space to impervious surface ratio with most green spaces located beyond the urban footprint, the UHI effect is still evident. We recommend that urban planners and policy makers should consider mitigating the UHI effect by restoring the urban ecosystems in the remaining open spaces in the urban area and further incorporate strategic combinations of impervious surfaces and green spaces in future urban and landscape planning.

1. Introduction

Despite Africa being the least urbanized continent, its urbanization is arguably one of the fastest in the world [1]. Africa’s urban population has been growing at a very high rate, i.e., from an estimated 28% in 1980 [2] to 43% in 2018 and projected to be about 60% by 2050 [3]. Much of the urbanization in Africa has been unplanned and unregulated, exacerbated by the legacy of colonialism, structural adjustment and neo-liberalism that has continuously spawned weak urban planning institutions [4]. Most of the African cities have thus emerged as unplanned cities dominated by overcrowded informal settlements haphazardly located close to urban growth centers such as the central business district and other industrial and commercial areas [5]. Consequently, ecological and environmental conditions in African cities are under constant threat.

One of the ecological consequences of urbanization is the urban heat island (UHI) effect, a phenomenon that refers to the occurrence of higher temperatures in urban areas than the surrounding rural areas [6,7,8,9,10]. UHI occurs as a result of land cover transformations, mainly the replacement of natural vegetation and agricultural lands by impervious surfaces (concrete, asphalt, rooftops and building walls) associated with urban land use [11]. Some of the negative impacts of the UHI include increased energy consumption, elevated emissions of air pollutants and greenhouse gases, impaired water quality as well as causing compromised environmental conditions that affect human health and comfortability [12,13]. It is for this reason that the UHI phenomenon has become a key research focus in various disciplines such as urban geography, urban planning, urban ecology and urban climatology.

Generally, there are two types of UHIs: Atmospheric UHI (AUHI) and surface UHI (SUHI) [13]. AUHIs are measured using air temperature while SUHIs are measured using surface temperature [8,10,13]. The high temporal resolution of air temperature makes AUHIs effective in describing the temporal variation of UHIs. However, AUHIs have a drawback of failing to depict the spatial variation of UHIs [14]. Conversely, surface temperature patterns can exhibit both the spatial and temporal variation of SUHIs of entire cities [14,15]. The use of land surface temperature (LST) retrieved from remotely sensed thermal infrared data has since become widely recognized as an effective tool for examining spatial patterns of UHIs in relation to urban landscape patterns [14,15,16,17,18,19,20]. This study focuses on SUHIs based on LST retrieved from Landsat data.

Many studies have shown that LST can be related to land cover, mainly impervious surfaces [6,8,21,22] and green spaces [14,23,24,25], to comprehend the SUHI effect in urbanized landscapes. Researchers have consistently demonstrated that increasing green space or vegetation cover in urban areas has a mitigating effect on UHIs, while the growth of impervious surfaces increases urban heating [17,24,26,27]. Recently, techniques such as urban–rural gradient and statistical analysis [8,28,29] as well as UHI intensity analysis [23,30,31] have been familiar in understanding the effect of landscape patterns on LST (i.e., the UHI effect). There has also been increasing interest in the spatial composition and configuration of impervious surface and green spaces owing to the different mix or complexity of different urban environments. A proliferation of studies has applied urban landscape metrics-based techniques to show that the spatial composition and configuration of impervious surfaces and green spaces (e.g., size, patch density and complexity) affect the magnitude of LST [7,8,14,24,26].

It is evident from the vast literature that the UHI phenomenon has been extensively studied in cities worldwide irrespective of their sizes and locations. Several studies have examined the relationship between LST and the composition and configuration of impervious surfaces and green spaces. While some recent studies (e.g., [32] in Durban (South Africa), [33] in Lagos (Nigeria) and [34] in Addis Ababa (Ethiopia)) have been conducted, UHI studies are still very uncommon in Africa. Moreover, previous studies have been conducted on individual cities based on the specific conditions of their urban environments. The uncontrolled and unplanned urbanization that has been experienced in African cities in recent decades makes them interesting case studies for a comparative study of UHIs.

Therefore, this study conducts a comparative analysis to examine the relationship between LST and the spatial patterns, composition and configuration of impervious surfaces and green spaces in four African cities, Lagos (Nigeria), Nairobi (Kenya), Addis Ababa (Ethiopia) and Lusaka (Zambia). Landsat OLI/TIRS data and various geospatial approaches, including urban–rural gradient, UHI intensity, statistics and urban landscape metrics-based techniques, were used to facilitate the analysis. The four cities were selected to get a good representation of African cities based on the following criteria: (i) Being the largest or capital city; (ii) being the main economic and commercial center of their country; and (iii) experiencing rapid urbanization with the highest population in their respective countries. In 2016, the population of Lagos was estimated at 13.7 million, Nairobi at 4.2 million, Addis Ababa at 3.3 million, while Lusaka was at 2.3 million [35].

2. Data and Methods

2.1. Study Areas

The study areas include the city cores of Lagos (Nigeria) located in West Africa, Nairobi (Kenya) and Addis Ababa (Ethiopia) located in East Africa and Lusaka (Zambia) located in central-southern Africa (Figure 1). For comparison, we used a 40 km × 40 km subset with a 20 km radius from the city center of each city as a common unit of analysis (Figure 1). All the study areas are located in the tropical climate zones of sub-Saharan Africa.

According to [36], the climate in Lagos is tropical with two distinct seasons, i.e., a pronounced dry season in the low-sun months and a wet season is in the high-sun months. The annual mean temperature in Lagos is approximately 26.5 °C. The climate in Addis Ababa, Nairobi and Lusaka is generally sub-tropical with moderate seasonality, although there are variations across the cities. Addis Ababa and Lusaka have a climate characterized by dry winters and mild rainy and hot humid summers with annual mean temperatures of 15.9 °C and 19.9 °C, respectively. Nairobi has a marine west-coast climate that is mild with no dry season, warm summers and an annual mean temperature of 17.7 °C.

The land cover features in the four cities are typical of those in rapidly urbanizing African cities with built-up lands (impervious surfaces) characterized by various land uses including commercial, industrial, public institutions and residential areas dominated by informal settlements located close to urban growth centers, especially the central business district [5,37]. Other land cover features include forests, woodlands, grasslands, croplands and water surfaces such as the sea, lakes, rivers and dams [38].

2.2. Satellite Data and Pre-Processing

The satellite data used in this study were six cloud-free (<10%) Landsat-8 OLI/TIRS images obtained from the US Geological Survey website for each of the study areas. All the Landsat-8 OLI/TIRS data obtained were acquired during the dry season (

Table 1. Details of the Landsat-8 imagery used.

City	Sensor	Scene ID	Acquisition Date	Time (GMT)	Season
Addis Ababa	Landsat-8 OLI/TIRS	LC81680542017026LGN00	26-January 2017	7:40:29	Dry
Lagos	Landsat-8 OLI/TIRS	LC81910552015342LGN00	8-December 2015	10:03:03	Dry
Nairobi	Landsat-8 OLI/TIRS	LC81680612016216LGN00	3-August 2016	7:43:12	Dry
Lusaka	Landsat-8 OLI/TIRS	LC81720712016212LGN00	20-July 2016	8:11:54	Dry

). LST in tropical cities is better derived for the dry season due to cloud-coverage problems [39]. Other studies have also shown that the SUHI phenomenon is more prominent during the dry season [8,33,34]. The dry season was also chosen to eliminate non-permanent green spaces that only exist during the wet season. The pre-processing for each image included radiometric calibration and atmospheric correction (dark-object subtraction) carried out using the TerrSet Geospatial Monitoring and Modeling Software. The purpose of this pre-processing was to convert the digital number (DN) values of the multispectral bands (bands 1–7 and 9) into surface reflectance values and convert the DN values of the thermal bands (bands 10 and 11) into at-satellite brightness temperature (T_B) expressed in degrees Kelvin [8]. The pre-processed data were then used to extract the green spaces, impervious surfaces and LST.

2.3. Retrieval of LST

The methods for retrieving LST from Landsat data have been extensively documented in the literature. The process involves, first, converting DNs of the thermal bands (i.e., bands 10 and 11 in Landsat-8) to absolute units of at-sensor spectral radiance [6,15,40]. Second, under the assumption that the Earth’s surface is a black body (i.e., spectral emissivity = 1), the thermal band data is converted from at-sensor spectral radiance to effective at-sensor brightness temperature using Equation (1) [40,41].

$$T_B=\frac{K_2}{\mathrm{In}\left(\frac{K_2}{{\mathrm{L}}_{\mathsf{\lambda }}}+1\right)}$$

(1)

where T_B is the effective at-sensor brightness temperature in degrees Kelvin, L_λ is the spectral radiance at the sensor’s aperture in W/(m2 sr μm) and K₁ and K₂ are pre-launch calibration constants (i.e., thermal conversion constants for the bands 10 or 11 provided in the Landsat-8 metadata [41] in this study). Finally, the at-sensor brightness temperatures are corrected for varied spectral emissivity depending on the nature of the land cover and LST is retrieved [6,8,17].

In this study, prior to LST retrieval, we used the method from [42] that takes into account standard deviation (m), a combined mean value of the soil and vegetation emissivities cd (n) and the vegetation proportion (P_v), calculated by Equations (2)–(4), respectively, to obtain the land surface emissivity($\mathsf{\epsilon })$ (Equation (5)) for each study area.

$$m=\left({\mathsf{\epsilon }}_v-{\mathsf{\epsilon }}_s\right)-\left(1-{\mathsf{\epsilon }}_s\right)F{\mathsf{\epsilon }}_{v}4$$

(2)

$$n{=\mathsf{\epsilon }}_s+\left(1-{\mathsf{\epsilon }}_s\right)F{\mathsf{\epsilon }}_v$$

(3)

$$P_V={\left(\frac{\mathrm{NDVI}-{\mathrm{NDVI}}_{\min }}{{\mathrm{NDVI}}_{\max }-{\mathrm{NDVI}}_{\min }}\right)}^2$$

(4)

$$\mathsf{\epsilon }=m{P}_V+n$$

(5)

where, ε_s is the soil emissivity, ε_v is the vegetation emissivity and F is a shape factor whose mean value, assuming different geometrical distributions, is 0.55 (Sobrino et al., 1990 in [42]). NDVI is the normalized difference vegetation index derived using the surface reflectance of bands 4 (${\mathsf{\rho }}_{\mathrm{red}}$) and 5 (${\mathsf{\rho }}_{\mathrm{NIR}}$) of Landsat-8 (Equation (6)) [8]:

$$\mathrm{NDVI}=\frac{\left({\mathsf{\rho }}_{\mathrm{red}}-{\mathsf{\rho }}_{\mathrm{NIR}}\right)}{\left({\mathsf{\rho }}_{\mathrm{red}}+{\mathsf{\rho }}_{\mathrm{NIR}}\right)}$$

(6)

We applied the values 0.004 for m and 0.986 for n based on the findings of Sobrino et al. (2004) to calculate ε. Finally, we converted the brightness temperatures (T_B) obtained through pre-processing band 10 of Landsat-8 (see Section 2.2) to degrees Celsius (°C) [8] and calculated the emissivity-corrected LST using Equation (7) [6,17,43]:

$$\mathrm{LST}\left(°\mathrm{C}\right)=\frac{T_B}{1+(\mathsf{\lambda }\times T_B/\rho )\mathrm{In}\mathsf{\epsilon }}$$

(7)

where λ = wave- length of emitted radiance (λ = 10.8 μm, for Landsat-8 band 10 [8]); ρ = h × c/σ (1.438 × 10−2 m K), σ = Boltzmann constant (1.38 × 10−23 J/K), h = Planck’s constant (6.626 × 10−34 Js) and c = velocity of light (2.998 × 108 m/s); and ε is the land surface emissivity.

2.4. Extraction of Land Cover

Many studies have demonstrated that LST can be related to land cover, mainly impervious surfaces [6,8,21] and green spaces [14,17,24], to comprehend the SUHI effect in urbanized landscapes. In this study, we used the pre-processed Landsat-8 images to extract impervious surfaces and green spaces using spectral indices. Several studies have shown the aptness of the spectral-based approach in land cover extraction [8,9,28]. Our land cover extraction process was as follows. First, we used the modified normalized difference water index (MNDWI) to extract water bodies and exclude them from the images. The MNDWI has been proven to accurately discriminate water from non-water features [44]. Equation (6) was used to compute the MNDWI for each study area [44]:

$$\mathrm{MNDWI}=\frac{\left({\mathsf{\rho }}_{\mathrm{Green}}-{\mathsf{\rho }}_{\mathrm{SWIR}1}\right)}{\left({\mathsf{\rho }}_{\mathrm{Green}}+{\mathsf{\rho }}_{\mathrm{SWIR}1}\right)}$$

(8)

where ${\mathsf{\rho }}_{\mathrm{Green}}\mathrm{and}{\mathsf{\rho }}_{\mathrm{SWIR}1}$ are the surface reflectance values of bands 3 and 6 of the Landsat-8 images, respectively.

Afterwards, we used the visible red and NIR-based built-up index (VrNIR-BI) to extract impervious surfaces. One of the most noted spectral confusions in the land cover classification of African landscapes is between the impervious surface (IS) and bare lands usually characterized by dry grasslands and abandoned croplands. The VrNIR-BI can accurately separate impervious surfaces from bare lands [8]. The VrNIR-BI was recommended by [45] after comparing the index to six other spectral built-up indices, including the commonly applied normalized difference built-up index (NDBI) [46] based on Landsat ETM+ and Landsat OLI/TIRS images. Equation (9) was used to compute the VrNIR-BI for each study area:

$$\mathrm{VrNIR}\text{-}\mathrm{BI}=\frac{\left({\mathsf{\rho }}_{\mathrm{Red}}-{\mathsf{\rho }}_{\mathrm{NIR}}\right)}{\left({\mathsf{\rho }}_{\mathrm{Red}}+{\mathsf{\rho }}_{\mathrm{NIR}}\right)}$$

(9)

where ${\mathsf{\rho }}_{\mathrm{Red}}{\mathrm{and}\mathsf{\rho }}_{\mathrm{NIR}}$ are the surface reflectance values of bands 4 and 5 of the Landsat-8 images, respectively. To extract the green spaces for each study area, we used the NDVI expressed in Equation (6) above. NDVI is one of the extensively applied indices when relating LST to green spaces in SUHI studies [17]. Manual thresholding was applied to extract VrNIR-BI and NDVI after several tests through visual assessments of the index maps with close reference to the Landsat-8 images and high-resolution Google earth imagery in each study area. The thresholds applied to extract VrNIR-BI for Lagos, Nairobi, Addis Ababa and Lusaka were 0.45, 0.565, 0.352 and 0.485, respectively. To extract NDVI, the thresholds applied were −0.425, −0.245, −0.169 and −0.315 for Lagos, Nairobi, Addis Ababa and Lusaka, respectively (see Figure 2 for a zoomed-in sample of VrNIR-BI and Landsat 8 imagery in each study area).

Finally, we produced a land cover map for each study area containing four categories, impervious surfaces, green spaces, other and water. Impervious surfaces included buildings, transport utilities and all other impervious areas. Green spaces comprised forests, grass and all healthy green vegetation cover, while other comprised all land cover features excluding impervious surface, green space and water. Water included the sea, lakes, rivers, streams, dams, swamps, reservoirs and ponds. The other and water categories were excluded in all further analyses.

2.5. Analysis of Spatial Patterns

2.5.1. Urban–Rural Gradient Analysis

The aptness of the gradient analysis approach in revealing the distribution and spatial variations of LST along the urban–rural areas has been shown in recent studies [8,28,29]. There are two main urban–rural gradient analysis methods that have been developed and applied in the literature. The first one is the use of directional transects running across the city center with their ends both extending to the rural areas [47,48]. The second one applies concentric rings or zones around the city center with standard distance intervals extending to the rural areas [8,48]. The concentric ring gradient analysis method is effective in cities exhibiting single-core urban growth patterns around the city center such as the four African cities in this study [8,28,49].

Therefore, we used the concentric ring gradient analysis method to study the spatial patterns and influences of impervious surfaces, and green spaces on LST along the urban–rural landscape of each city. Considering that the urban development patterns of all the African cites in this study are based on the single-core concept, we selected the city center by identifying the oldest building around the city center area in each city. We then created multiple concentric rings around the city center of each study area with distance intervals of 200 m. Subsequently, the densities of impervious surfaces and green spaces were determined in each zone and plotted across the urban–rural gradient for each study area.

2.5.2. SUHI Intensity Analysis

The SUHI intensity is a well-known measure of the SUHI effect across the urban–rural landscape. It is generally defined as the difference in temperature between an urban and a rural area [50]. The SUHI intensity is calculated using either air temperature from meteorological data (e.g., [51,52]) or mean surface temperatures using satellite images [23]. Analyzing SUHI intensity patterns and their urban and rural area variations has remained an imperative part of SUHI studies [30,53].

To analyze the SUHI intensity patterns, we divided the study areas into two major areas, urban and rural. To delineate the urban and rural areas, we estimated the urban area (also referred to as the ‘built-up footprint’), based on the physical extent of the impervious surface in each city. By way of justification, a wide range of social, economic, demographic, administrative or political indicators have been used to define urban areas, but there is no consensus on how to construct a consistent definition based on any single set of attributes [54]. For example, an administrative boundary of a city cannot be relied on as a means of defining an urban area as boundaries frequently change over time, are not comparable across cities and are usually over- or under-estimated [55]. The terms ‘urban area’ or ‘urban footprint’ are widely used to basically refer to the spatial extent of urbanized areas on a regional scale; a definition which is both fuzzy and inconsistent [56,57].

As such, defining the urban area based on the physical extent of the built-up land (impervious surface), as adopted in most remote sensing urban studies (e.g., [58,59]), is the best potion. We used the concentric zones defined in Section 2.5.1 to determine the urban area, i.e., all concentric zones that contained impervious surfaces in each city. Accordingly, all concentric zones beyond the maximum radius of the urban or built-up footprint were considered as rural. We calculated the SUHI intensity by calculating the difference between the mean LST at the city center of each study area (i.e., the kilometer 0) and the mean LST in each of the 200 m concentric zones created as outlined in Section 2.5.1 across the urban–rural landscape. Equation (7) was used to calculate the SUHI intensity for each study area:

$$\mathrm{SUHI}\mathrm{intensity}=\mu LS{T}_0-\mu LS{T}_i$$

(10)

where µLST₀ is the kilometer 0 mean LST at the city center of each study area and µLST_i is the mean LST in each buffer zone (SZ), where i = 1,2,3…. n and n is the total number of buffer zones in each study area.

2.5.3. Urban Landscape Metrics Analysis

One of our other interests in this study was to comprehend how the composition, shape, complexity and spatial arrangement of impervious surfaces and green spaces could have influenced the spatial distribution of LST across the landscape of each study area. The use of urban landscape metrics has been widely proven to enhance the understanding of LST spatial variability in relation to the configuration of landscape features (e.g., impervious surface and green spaces) [7,14,24,26]. In this study, we selected five class level spatial metrics, patch density (PD), mean patch area (AREA_MN), mean shape index (SHAPE_MN), mean fractal dimension index (FRAC_MN) and aggregation index (AI). The descriptions and equations for calculating each selected spatial metric are presented in

Table 2. Selected class level spatial metrics.

Metric (Abbreviation)	Description	Measure	Equation
Mean Patch Area (AREA_MN)	Average patch area—total impervious surface or green space area divided by number of their respective patches - (unit: km2)	Composition	$\frac{1}{\mathrm{10,000}\times \mathrm{n}}\times {\displaystyle \sum _{\mathrm{i}=1}^{\mathrm{n}}}{\mathrm{a}}_{\mathrm{i}}$
Patch Density (PD)	The number of patches per unit area of impervious surface or green space (unit: number per km2).	Composition and spatial arrangement	$\frac{\mathrm{n}}{\mathrm{A}}\times {10}^6$
Mean Shape Index (SHAPE_MN)	Mean value of shape index—it is the simplest and most straightforward measure of shape complexity. MSI is greater than one; MSI = 1 would result if all impervious surface or green space patches were circular or square grids (unit: none).	Shape and complexity	$\frac{1}{\mathrm{n}}\times \frac{0.25{\mathrm{P}}_{\mathrm{i}}}{\sqrt{{\mathrm{a}}_{\mathrm{i}}}}$
Mean Fractal Dimension Index (FRAC_MN)	FRAC_MN also measures shape complexity. FRAC_MN approaches one for shapes with simple perimeters and approaches two when shapes are more complex (unit: none).	Shape and Complexity	$\frac{1}{\mathrm{n}}\times \frac{2\mathrm{Ln}0.25{\mathrm{P}}_{\mathrm{i}}}{\mathrm{Ln}\sqrt{{\mathrm{a}}_{\mathrm{i}}}}$
Aggregation Index (AI)	The tendency of impervious surface or green space patches to be spatially aggregated (unit: none).	Spatial arrangement	$\mathrm{AI}=\left[\frac{{\mathrm{g}}_{\mathrm{i}}}{\max -{\mathrm{g}}_{\mathrm{i}}}\right]\left(100\right)$

Note: a_i= area of patch i; n = number of patches; A = total class area; p_i = perimeter of patch i; g_i = number of like adjacencies (joins) between pixels of patch type (class) i based on the single-count method; max−g_i = maximum number of like adjacencies (joins) between pixels of patch type (class) i based on the single-count method (details in [60]). A patch is defined as a relatively homogeneous area (i.e., impervious surface or green space in this study) that differs from its surroundings [61].

. To relate the spatial metrics to LST distribution, each study area was first divided into 100 polygon grids (4 km x 4 km). Then, impervious surfaces and green spaces in each polygon grid were extracted and used in the computation of spatial metrics in each study area. We computed the class level spatial metrics using Fragstats software (version 4.2v) [60]. We defined the patch neighbor using the 8-cell rule.

2.5.4. Statistical Analysis

Statistical analysis was conducted using the Pearson correlation analysis and scatter plots to examine the relationship of mean LST and the density of impervious surfaces and green spaces in each of the 200 m buffer zones created as outlined in Section 2.5.1. We further conducted Pearson correlation analysis to investigate the relationship between mean LST and spatial metrics based on the 100 grid polygons created as outlined in Section 2.5.3 for each study area.

3. Results

3.1. LST Relationship with Impervious Surfaces and Green Spaces

The LST and land cover maps for the study areas, Lagos, Nairobi, Addis Ababa and Lusaka, are shown in Figure 3 and Figure 4. Figure 5 shows the minimum, maximum and mean LST of impervious surfaces and green spaces, and the percentage of impervious surfaces and green spaces relative to the total landscape (40 km x 40 km) considered for each study area. The results revealed that Lagos had the highest percentage of impervious surface (40%). Compared to Lagos, the other three cities had very low percentages of impervious surface, i.e., Addis Ababa 12%, Lusaka 11% and the lowest being Nairobi with 8%. However, Nairobi had the highest percentage of green spaces (32%) followed by Lagos (25%), Addis Ababa (23%) and the lowest in Lusaka (20%) (Figure 5c).

In terms of the relationship between mean LST and the impervious surfaces and green spaces, the results revealed that cities with a higher percentage of impervious surface were warmer and vice versa. The results showed that Lagos was the warmest city and Nairobi was the coolest city, while Addis Ababa and Lusaka were slightly warmer than Nairobi but cooler than Lagos. Lagos recorded the highest maximum and minimum LST values of impervious surfaces (i.e., 42.0 °C and 25.1 °C, respectively) while Nairobi recorded the lowest maximum and minimum LST values of impervious surfaces (i.e., 33.5 °C and 15.4 °C, respectively). The mean LST of impervious surfaces in Lagos was 32.4 °C and 27.8 °C in Nairobi. Addis Ababa and Lusaka had a mean LST of impervious surfaces of 29.5 °C (Figure 5a). With regard to the LST of green spaces, Lagos still had the highest, with a maximum of 41.2 °C and a minimum of 24.6 °C, and Nairobi still had the lowest, with maximum and minimum LST values of 31.5 °C and 16.5 °C, respectively. For the mean LST of green spaces, Lagos had 28.4 °C, Lusaka had 27.7 °C and Addis Ababa had 25.4 °C, with the lowest being in Nairobi, 23 °C (Figure 5b).

3.2. LST Relationship with Impervious Surfaces and Green Spaces along the Urban–Rural Gradient

According to the results, the relationships between mean LST and impervious surface and green space density along the urban–rural gradient of Nairobi, Addis Ababa and Lusaka had similar spatial patterns (Figure 6). The impervious surface and green space density decreased and increased gradually along the urban–rural gradient, respectively. However, the mean LST had a similar pattern with impervious surfaces within the urban footprint, decreasing from the city center to the maximum spatial extent of the urban area (i.e., around the cross-point of impervious and green space density in Figure 6). Beyond the spatial extent of the urban area, the mean LST increased gradually, similar to the pattern of green space density. Unlike the other three cities, the mean LST and impervious surface density in Lagos decreased while the green space density increased through the urban–rural gradient (Figure 6). This could be because of urban area spatial extent in Lagos, which dominates the landscape with almost no rural areas as defined in this study.

The Pearson’s correlation results showed significant relationships (p < 0.001) between the mean LST and the density of impervious surfaces (positive) and green spaces (negative) in all the study areas along the urban–rural gradient (Figure 7). The correlation of impervious surfaces with mean LST in Lagos (r² = 0.9483; slope = 0.0641) and Lusaka (r² = 0.5766; slope = 0.0438) was relatively high compared to Nairobi (r² = 0.2783; slope = 0.0258) and Addis Ababa (r² = 0.1776; slope = 0.0186). In contrast, the correlation of green spaces with mean LST was relatively very low in Nairobi (r² = 0.3085; slope = –0.0355) compared to Lagos (r² = 0.9482; slope = –0.0676), Addis Ababa (r² = 0.7881; slope = –0.0659) and Lusaka (r² = 0.801; slope = −0.0199).

3.3. SUHI Intensity Patterns along the Urban–Rural Gradient

The SUHI intensity results also showed a different pattern in Lagos compared to Nairobi, Addis Ababa and Lusaka (Figure 8). In Lagos, the SUHI intensity increased from 0.5 °C to 4.0 °C, which indicated a high mean LST around the city center compared to other zones along the urban–rural gradient. For Nairobi and Addis Ababa, the SUHI intensity results showed a similar pattern for Lagos within the urban area but opposite in the rural areas. The SUHI intensity values for Nairobi ranged from 0.5 °C to 3.0 °C along the urban area and reduced from 3.0 °C to 1.0 °C along the rural area. The SUHI intensity values for Addis Ababa ranged from 0.5 °C to 2.5 °C along the urban area and also reduced from 3.0 °C to 1.0 °C along the rural area. While Lusaka showed a somewhat similar pattern to Nairobi and Addis Ababa, the SUHI intensity results generally showed an irregular pattern of decreasing mean LST along the urban–rural gradient. The SUHI intensity values for Lusaka varied from about −1.6 °C to 0.2 °C across the urban–rural gradient.

3.4. LST Relationship with Urban Landscape Metrics

The correlations between mean LST and urban landscape metrics varied across the study areas, with some variables having stronger positive and negative relationships for impervious surfaces and green spaces, respectively, and others having no relationship at all. The composition variables (PD and AREA_MN) had significant positive correlations with impervious surface mean LST in all the cities, except for the PD in Lagos (p = 0.807) and Lusaka (p = 0.076), and the AREA_MN in Lusaka (p = 0.827). For the complexity variables, SHAPE_MN showed no relationship with impervious surface mean LST in Lagos (p = 0.180) and Lusaka (p = 0.758), while FRAC_MN had no relationship in all four cities. The spatial arrangement variable (AI) had significant positive correlations with impervious surface density mean LST in all the cities excluding Lusaka (p = 0.280). For green space mean LST, PD showed a significant negative correlation only in Lagos, while AREA_MN had significant negative correlations in all the cities. SHAPE_MN and FRAC_MN had significant positive correlations with green space mean LST in all the cities, except for FRAC_MN in Nairobi (p = 0.272). The correlation between green space mean LST and AI was insignificant only in Lagos (p = 0.085).

4. Discussion

4.1. Influence of Impervious Surfaces and Green Spaces on LST

In this study, we conducted a comparative study of SUHIs in African cities by examining the relationship of the spatial patterns, composition and configuration of impervious surfaces and green spaces with LST using Landsat-8 OLI/TIRS. The results show that Lagos, with the highest percentage (40%) of impervious surfaces relative to the study area, was the warmest city, i.e., at least 3 °C warmer than Addis Ababa and Lusaka and 4 °C warmer than Nairobi. These results could be attributed to Lagos being a megacity with a population of over 10 million people while the other three cities still have less than 5 million people [35]. These results are dissimilar to the findings of [8] in Asian megacities, where they observed a city with the highest percentage of impervious surfaces to be the coolest and attributed it to geographical location and background climate.

On the other hand, Nairobi, with the highest and lowest percentage of green spaces and impervious surfaces, respectively, was the coolest city, i.e., at least 5 °C cooler than Lagos and Lusaka and 2 °C cooler than Addis Ababa. The ratio of green spaces to impervious surfaces was also highest in Nairobi (4.0) and Lowest in Lagos (0.63), while Lusaka and Addis Ababa had ratios of 1.92 and 1.83, respectively. Despite this, we observed that, although most African cities have a relatively larger green space to impervious surface ratio (e.g., Addis Ababa, Nairobi and Lusaka) compared to cities in other regions, the SUHI effect is still evident. This could be because impervious surfaces have a greater impact on surface temperature than green spaces [8,28,29,34]. Still, this means that, without the mitigating effect of green spaces that provide the cool island effect, surface temperatures are expected to escalate. For example, Lusaka, with the lowest percentage of green spaces, recorded the second highest overall mean LST of 28.6 °C, while Lagos, with the highest percentage of impervious surfaces, had 30.4 °C. Accordingly, Nairobi had the lowest overall mean LST of 25.4 °C, while Addis Ababa recorded 27.4 °C. Interestingly, Lusaka had the least overall difference of 1.8 °C between the mean LST of impervious surfaces and green spaces compared to Lagos (4.0 °C), Addis Ababa (4.0 °C) and Nairobi (4.9 °C).

Our results are analogous to other SUHI studies in other regions based on Landsat data as shown in Figure 9. For example, in Japan, the authors of [19] found overall mean LST values of 23.7 °C and 24.0 °C, with differences between the mean LST of impervious surfaces and green spaces of 1.7 °C and 1.8 °C in Tsukuba and Tsuchiura, respectively. The authors of [8] found overall mean LST values of 27.6 °C, 27.9 °C and 27.4 °C with differences between the mean LST of impervious surfaces and green spaces of 2.9 °C, 3.7 °C and 2.2 °C in Manila (Philippines), Jakarta (Indonesia) and Bangkok (Thailand), respectively. In the city of Tehran, Iran, the authors of [62] found both a much higher overall mean LST (43.0 °C) and difference (6 °C) between the mean LST of impervious surfaces and green spaces . In another study in Nanjing, China, the authors of [26] found an overall mean LST of 30.0 °C and a 3.1 °C difference between the mean LST of impervious surfaces and green spaces. In a much earlier study, the authors of [17] found an overall mean LST of 29.0 °C and a 5.4 °C difference between the mean LST of impervious surfaces and green spaces in Indianapolis City, IN, USA. The variations in the overall mean LST in this study and the other studies cited above could also be attributed to geographical location and the respective local climates.

4.2. Influence of Impervious Surfaces and Green Spaces on LST and SUHI Intensity Patterns along the Urban–Rural Gradient

Considering the relatively small urban/built-up areas of Nairobi (8%), Addis Ababa (12%) and Lusaka (11%) against the unit area of analysis (40 km × 40 km) in this study, for discussion purposes, we marked urban and rural ranges of the study areas based on the physical extent of the built-up footprint as defined in remote sensing urban studies (e.g., [58,59]) (see Figure 6 and Figure 8 and Section 2.5.2). While all the African cities present evidence of the SUHI phenomenon, the results show an interesting variation. Expectedly, the megacity Lagos, which is almost all urban, had the highest mean LST and UHI intensity in the zones close to the city center, which decreased gradually towards the rural zones. The pattern of mean LST, SUHI intensity and impervious surface density within the urban area of Nairobi (0–7 km) and Addis Ababa (0–10 km) along the urban–rural gradient was somewhat similar to Lagos, with the highest values at the 0 km zone and gradually decreasing to the cross-point of the urban and rural ranges. The density of green spaces in the two cities gradually increased from the 0 km zone to the cross-point of the urban and rural ranges. Lusaka, on the other hand, showed an irregular pattern of mean LST, with its peak at about 7 km within the urban area (0–10 km) range. Likewise, the SUHI intensity results generally showed an irregular pattern similar to mean LST along the urban–rural gradient. The pattern of the impervious surface and green space density in Lusaka could help explain the irregular pattern of mean LST and SUHI intensity, which can be likened to the findings of [8] in Bangkok and Manila. This is because, although the African cities in this study generally have their green spaces located outside the urban zones, Lusaka appears to have some green spaces within the urban area, especially in the eastern part of the city.

Another key observation in this study was the gradual increase in the mean LST (low SUHI intensity) in Nairobi, Addis Ababa and Lusaka within the defined rural area from the urban–rural cross-point. This could be explained based on the land cover in the study areas. Unlike Lagos, where the remaining land cover beyond the urban footprint was dominated by water, the other three cities’ remaining land cover was mainly characterized by bare lands and abandoned crop fields. This could have contributed to the observed higher LST values as bare lands can also elevate surface temperatures [18,63].

4.3. Influence of Spatial Landscape Configuration on LST

In this study, we used five spatial metrics (

Table 3. Correlations between mean LST and selected urban landscape metrics.

Study Area	PD		AREA_MN		SHAPE_MN		FRAC_MN		AI
	r	Sig.	r	Sig.	r	Sig.	r	Sig.	r	Sig.
Impervious Surface Mean LST vs. Spatial Metrics
Lagos	0.027	0.807	0.519	0.000	0.147	0.180	0.182	0.096	0.432	0.000
Nairobi	0.339	0.002	0.383	0.001	0.277	0.014	0.148	0.197	0.402	0.000
Addis Ababa	0.540	0.000	0.244	0.029	0.265	0.018	0.197	0.080	0.289	0.009
Lusaka	0.199	0.076	0.025	0.827	0.035	0.758	0.097	0.391	0.122	0.280
Green Space Mean LST vs. Spatial Metrics
Lagos	−0.362	0.001	−0.316	0.004	−0.421	0.000	−0.378	0.000	−0.190	0.085
Nairobi	−0.151	0.179	−0.281	0.011	−0.418	0.000	−0.123	0.272	−0.700	0.000
Addis Ababa	−0.135	0.229	−0.221	0.047	−0.489	0.000	−0.485	0.000	−0.321	0.004
Lusaka	−0.189	0.091	−0.313	0.004	−0.336	0.002	−0.298	0.007	−0.244	0.028

) to assess the influence of the spatial landscape configuration (i.e., composition, shape, complexity and spatial arrangement) on mean LST. Generally, the results show that the correlation between mean LST and the selected spatial metrics was statistically significant, i.e., positive for impervious surfaces and negative for green spaces. These results are consistent with several previous studies. For example, the authors of [6] found significant relationships between mean LST and the PD of patches of residential impervious surfaces (positive) and urban green spaces (negative) in Shanghai, China. In Baltimore, MD, USA, the authors of [7] correlated mean LST with the AREA_MN and SHAPE_MN and found significant positive and negative relationships from the patches of impervious surfaces and green spaces, respectively. The authors of [8] also found significant relationships between mean LST and the AI of the patches of impervious surface (positive) and green space (negative) in megacities in Asia.

Our results indicate that cities with large and more aggregated patches of impervious surfaces experience significant increases in LST, exacerbating the SUHI phenomena [7], than those with fragmented smaller patches of the impervious surface [8]. Lagos, for example, had the largest patches and showed significant correlation between mean LST and the AREA_MN and AI of the patches impervious surfaces, which could explain the high surface temperatures in Lagos. Similarly, Nairobi recorded significant correlation between mean LST and the AI of the patches of green spaces, despite having large patches of green spaces. Addis Ababa and Lusaka had more dispersed patches of green spaces. This explains the higher surface temperatures in Addis Ababa and Lusaka than in Nairobi. This is in agreement with other studies that have shown that the size of green spaces is an important factor in mitigating the SUHI effects [6,64]. The spatial arrangement of green spaces is also important in providing the cool island effect [65]. Larger and contiguous green spaces produce stronger cool island effects than those of several smaller patches of green space whose total area equals the large, contiguous patches [14,65].

More interestingly, the results consistently showed a low complexity of the impervious surface patches in all the African cities. While other factors may be at play, it is plausible to speculate that this could be caused by the unplanned urban developed pattern in Afrcian cities that tends to be clustered around the city center [5]. This could also be the reason for the lack of greenspaces within the urban area. Of note is that this pattern of development is dominated by highly dense informal settlements that are more susceptible to the SUHI effect.

4.4. Implications for Mitigating SUHIs in African Cities

The results of this study have shown that, although most African cities have a relatively larger green space to impervious surface ratio compared to cities in other regions, the SUHI effect is still evident. Altogether, we observed that there is a clear separation between the impervious surfaces and green spaces in the African cities. Most of the green spaces are found beyond the urban area. This could have emanated from the unplanned and uncontrolled urbanization of African cities that has been well documented. Unplanned urban development has likely worsened the SUHI effects. This means urban areas have continuously lost ecosystem services in the process. The observed high values of mean LST and impervious surfaces within the zones close to the city center in this study present a typical case of cities that follow central business district (CBD)-oriented urban development, which characterizes most African cities. The implication is that urban planners and policy makers in African cities, while attempting to control the unplanned development, should consider restoring the urban ecosystems through a diverse set of habitats by increasing the amount of vegetation in the remaining opens spaces such as parks, cemeteries, vacant lots, gardens and yards [66]. Increasing vegetation cover or surface water could significantly decrease LST, and thus help to mitigate excess heat in urban areas [7].

This study supports the findings of various other SUHI studies in Africa and other regions that recommend incorporating strategic combinations of impervious surfaces and green spaces in future urban and landscape planning to mitigate the SUHI effect. Some of the mitigation strategies in other studies proposed that African urban planners and policy makers could consider: The use of green walls that can mitigate indoor temperatures in tropical countries by about 2.4 °C [34,67]; the establishment of green belts along the main roads and residential areas to promote cool islands that can reduce heat stress and energy demand for urban dwellers [33]; as well as encouraging vertical, rather than horizontal, urban development to preserve space for urban greening [68].

5. Conclusions

Taking four cities, Lagos (Nigeria), Nairobi (Kenya), Addis Ababa (Ethiopia) and Lusaka (Zambia), a comparative study of SUHIs in African cities was conducted by examining the relationship of the spatial patterns, composition and configuration of impervious surfaces and green spaces with LST using Landsat-8 OLI/TIRS data. The study employed various techniques: Urban–rural gradient, urban heat island intensity, urban landscape metrics and statistical analysis. The results show a significantly strong correlation between mean LST and the density of impervious surface (positive) and green space (negative) along the urban–rural gradients of the four African cities. The study also found high urban heat island intensities in the urban area zones within the 0 to 10 km distance from the city center, where the density of green space is low. We also found significant correlations between the mean LST and urban landscape metrics (patch density, size, shape, complexity and aggregation) of impervious surfaces (positive) and green spaces (negative). The observed high values of mean LST and impervious surfaces within the zones close to the city center in this study present a typical case of cities that follow CBD-oriented urban development, which characterizes most African cities. We, therefore, suggest the urban planners and policy makers in African cities should consider decentralizing through setting up satellite economic zones in the periphery rural areas. The SUHI effects can then be mitigated by restoring the urban ecosystems in the remaining open areas such as parks, cemeteries, vacant lots, gardens, yards and campus areas; and blue spaces, mainly, streams, ponds and dams.

This study has further revealed that, although most African cities have a relatively larger green space to impervious surface ratio compared to cities in other regions, the SUHI effect is still evident. We found that cities with a larger percentage of urban area relative to the study area unit were warmer, i.e., they had mean LST values at least 3−4 °C higher than the coolest city, resulting in strong SUHI effects. Accordingly, the important mitigating effect of green spaces has been highlighted, with the coolest city having the largest percentage of green space. Another important observation highlighted in this study is that there is a general separation between the impervious surfaces and green spaces in the African cities. Most of the green spaces are found beyond the urban area. The results revealed a distinct variation in the relationship of mean LST with the density of impervious surfaces and green spaces within and beyond the urban footprint, especially in the cities with relatively small urban footprints. We attribute this to the unplanned and uncontrolled urbanization of African cities that have potentially worsened the SUHI effects. It is therefore recommended that urban planners and policy makers in African cities, while attempting to control the unplanned development, should consider the dispersion of built-up areas and paved surfaces (e.g., buildings, roads and parking lots) and maintaining or improving vegetation (e.g., grass, shrubs and trees) cover. The study, therefore, provides useful information that can help control the effects of the uncontrolled and unplanned urbanization in Africa to provide better urban environmental conditions for the urban dwellers and further encourage sustainable urban development in African cities.

In terms of future research, the current study did not evaluate the sensitivity to grid-spacing when examining the influence of impervious surface and green space on LST in African cities. This is an area worth investigating in future studies.