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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.
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remote sensing
Article
Spatial Analysis of Surface Urban Heat Islands in
Four Rapidly Growing African Cities
Matamyo Simwanda 1, 2, *, Manjula Ranagalage 1,3 , Ronald C. Estoque 4and Yuji Murayama 1
1Faculty of Life and Environmental Sciences, University of Tsukuba, 1-1-1 Tennodai,
Tsukuba City 305-8572, Ibaraki, Japan
2Department of Plant and Environmental Sciences, School of Natural Resources, Copperbelt University,
P.O. Box 21692, Kitwe 10101, Zambia
3
Department of Environmental Management, Faculty of Social Sciences and Humanities, Rajarata University
of Sri Lanka, Mihintale 50300, Sri Lanka
4National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba City, Ibaraki 305-8506, Japan
*Correspondence: matamyo.simwanda@cbu.ac.zm; Tel.: +260-978-652214
Received: 23 May 2019; Accepted: 7 July 2019; Published: 10 July 2019


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 aect Africa’s urban dwellers is the urban heat island (UHI) eect.
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 eect 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 eect is still evident. We recommend that urban planners
and policy makers should consider mitigating the UHI eect 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.
Keywords:
urban heat island; land surface temperature; impervious surface; green space; African
cities; Landsat data
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
].
Remote Sens. 2019,11, 1645; doi:10.3390/rs11141645 www.mdpi.com/journal/remotesensing
Remote Sens. 2019,11, 1645 2 of 20
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) eect,
a phenomenon that refers to the occurrence of higher temperatures in urban areas than the surrounding
rural areas [
6
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 aect 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
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
25
], to comprehend the SUHI eect in urbanized landscapes. Researchers
have consistently demonstrated that increasing green space or vegetation cover in urban areas has a
mitigating eect 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 eect of landscape patterns
on LST (i.e., the UHI eect). There has also been increasing interest in the spatial composition and
configuration of impervious surface and green spaces owing to the dierent mix or complexity of
dierent 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) aect 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
Remote Sens. 2019,11, 1645 3 of 20
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.
Remote Sens. 2019, 11, x FOR PEER REVIEW 3 of 21
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.
Figure 1. Location of study areas in Africa. Study areas are displayed using a false color composite of
Landsat 8 images (band 5—red, band 4—green and band 3—blue).
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].
Figure 1.
Location of study areas in Africa. Study areas are displayed using a false color composite of
Landsat 8 images (band 5—red, band 4—green and band 3—blue).
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].
Remote Sens. 2019,11, 1645 4 of 20
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). 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.
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
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 eective at-sensor brightness temperature using Equation (1) [40,41].
TB=K2
In K2
Lλ+1(1)
where T
B
is the eective at-sensor brightness temperature in degrees Kelvin, L
λ
is the spectral radiance
at the sensor’s aperture in W/(m2 sr
µ
m) and K
1
and K
2
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 (Pv), calculated by Equations (2)–(4), respectively, to obtain the land surface emissivity(ε)
(Equation (5)) for each study area.
m=(εvεs)(1εs)Fεv4 (2)
n=εs+(1εs)Fεv(3)
PV= NDVI NDVImin
NDVImax NDVImin !2
(4)
ε=mPV+n(5)
Remote Sens. 2019,11, 1645 5 of 20
where,
εs
is the soil emissivity,
εv
is the vegetation emissivity and Fis a shape factor whose mean
value, assuming dierent geometrical distributions, is 0.55 (Sobrino et al., 1990 in [
42
]). NDVI is the
normalized dierence vegetation index derived using the surface reflectance of bands 4 (
ρred
) and 5
(ρNIR) of Landsat-8 (Equation (6)) [8]:
NDVI =(ρred ρNIR)
(ρred +ρNIR)(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]:
LST(C)=TB
1+(λ×TB/ρ)Inε(7)
where
λ
=wave- length of emitted radiance (
λ
=10.8
µ
m, 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 εis 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 eect 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 dierence 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]:
MNDWI =(ρGreen ρSWIR1)
(ρGreen +ρSWIR1)(8)
where
ρGreen and ρSWIR1
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 dierence 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:
VrNIR-BI =(ρRed ρNIR)
(ρRed +ρNIR)(9)
where
ρRed and ρNIR
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
Remote Sens. 2019,11, 1645 6 of 20
NDVI, the thresholds applied were
0.425,
0.245,
0.169 and
0.315 for Lagos, Nairobi, Addis Ababa
and Lusaka, respectively (see Figure 2for a zoomed-in sample of VrNIR-BI and Landsat 8 imagery in
each study area).
Remote Sens. 2019, 11, x FOR PEER REVIEW 6 of 21
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).
Figure 2. Zoomed-in sample of visible red and NIR-based built-up index (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.
2.4. Analysis of Spatial Patterns
2.4.1. 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
Figure 2.
Zoomed-in sample of visible red and NIR-based built-up index (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 eective 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
Remote Sens. 2019,11, 1645 7 of 20
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 eect across the urban–rural landscape.
It is generally defined as the dierence 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 dierence 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:
SUHI intensity =µLST0µLSTi(10)
where
µ
LST
0
is the kilometer 0 mean LST at the city center of each study area and
µ
LST
i
is the mean
LST in each buer zone (SZ), where i=1,2,3
. . .
.nand nis the total number of buer 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.
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.
Remote Sens. 2019,11, 1645 8 of 20
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 1
10,000×n×
n
P
i=1ai
Patch Density (PD)
The number of patches per unit area of
impervious surface or green space (unit:
number per km2).
Composition and
spatial arrangement n
A×106
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 1
n×0.25 Pi
ai
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 1
n×2 Ln 0.25 Pi
Ln ai
Aggregation Index (AI)
The tendency of impervious surface or
green space patches to be spatially
aggregated (unit: none).
Spatial arrangement AI =gi
maxgi(100)
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].
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 buer 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 Figures 3and 4. Figure 5shows 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
Remote Sens. 2019,11, 1645 9 of 20
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.
Remote Sens. 2019, 11, x FOR PEER REVIEW 9 of 21
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).
Figure 3. Land surface temperature (LST) distribution in the study areas: Lagos, Nairobi, Addis Ababa
and Lusaka.
Figure 3.
Land surface temperature (LST) distribution in the study areas: Lagos, Nairobi, Addis Ababa
and Lusaka.
Remote Sens. 2019,11, 1645 10 of 20
Remote Sens. 2019, 11, x FOR PEER REVIEW 10 of 21
Figure 4. Land cover maps for the study areas: Lagos, Nairobi, Addis Ababa and Lusaka.
Figure 5. LST distribution and the percentage of impervious surfaces (IS) and green spaces (GS) in
each study area.
Figure 4. Land cover maps for the study areas: Lagos, Nairobi, Addis Ababa and Lusaka.
Remote Sens. 2019, 11, x FOR PEER REVIEW 10 of 21
Figure 4. Land cover maps for the study areas: Lagos, Nairobi, Addis Ababa and Lusaka.
Figure 5. LST distribution and the percentage of impervious surfaces (IS) and green spaces (GS) in
each study area.
Figure 5.
LST distribution and the percentage of impervious surfaces (IS) and green spaces (GS) in
each study area.
Remote Sens. 2019,11, 1645 11 of 20
Remote Sens. 2019, 11, x FOR PEER REVIEW 11 of 21
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 (r
2
= 0.9483; slope = 0.0641) and Lusaka (r
2
= 0.5766; slope = 0.0438) was relatively high
compared to Nairobi (r
2
= 0.2783; slope = 0.0258) and Addis Ababa (r
2
= 0.1776; slope = 0.0186). In
contrast, the correlation of green spaces with mean LST was relatively very low in Nairobi (r
2
= 0.3085;
slope = –0.0355) compared to Lagos (r
2
= 0.9482; slope = –0.0676), Addis Ababa (r
2
= 0.7881; slope = –
0.0659) and Lusaka (r
2
= 0.801; slope = 0.0199).
Figure 6. Relationships between the LST and impervious surfaces and green spaces along the urban–
rural gradients of Lagos (a), Nairobi (b), Addis Ababa (c) and Lusaka (d). Note: Urban and rural were
discerned based on the physical extent of the built-up footprint for each city.
Figure 6.
Relationships between the LST and impervious surfaces and green spaces along the
urban–rural gradients of Lagos (
a
), Nairobi (
b
), Addis Ababa (
c
) and Lusaka (
d
). Note: Urban and
rural were discerned based on the physical extent of the built-up footprint for each city.
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 (r
2
=0.9483; slope =0.0641) and Lusaka (r
2
=0.5766; slope =0.0438) was relatively
high compared to Nairobi (r
2
=0.2783; slope =0.0258) and Addis Ababa (r
2
=0.1776; slope =0.0186).
In contrast, the correlation of green spaces with mean LST was relatively very low in Nairobi
(r
2
=0.3085; slope =–0.0355) compared to Lagos (r
2
=0.9482; slope =–0.0676), Addis Ababa (r
2
=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 dierent 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.
Remote Sens. 2019,11, 1645 12 of 20
Remote Sens. 2019, 11, x FOR PEER REVIEW 12 of 21
Figure 7. Correlation between mean LST and density of impervious surfaces (a)–(d) and green
spaces (e)–(h).
Figure 7.
Correlation between mean LST and density of impervious surfaces (
a
d
) and green spaces (
e
h
).
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.
Remote Sens. 2019,11, 1645 13 of 20
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).
Remote Sens. 2019, 11, x FOR PEER REVIEW 13 of 21
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.
Figure 8. Urban heat island intensity ( mean LST) patterns along the urban–rural gradients of Lagos
(a), Nairobi (b), Addis Ababa (c) and Lusaka (d). Note: Urban and rural were discerned based on the
physical extent of the built-up footprint for each city.
3.3. 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
Figure 8.
Urban heat island intensity (
mean LST) patterns along the urban–rural gradients of Lagos
(
a
), Nairobi (
b
), Addis Ababa (
c
) and Lusaka (
d
). Note: Urban and rural were discerned based on the
physical extent of the built-up footprint for each city.
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 eect is still evident. This could be because impervious surfaces have a greater impact on
Remote Sens. 2019,11, 1645 14 of 20
surface temperature than green spaces [
8
,
28
,
29
,
34
]. Still, this means that, without the mitigating eect
of green spaces that provide the cool island eect, 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 dierence 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 dierences 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 dierences 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 dierence (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 dierence 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 dierence 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.
Remote Sens. 2019, 11, x FOR PEER REVIEW 15 of 21
(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.
Figure 9. Overall mean LST and differences between the mean LST of impervious surfaces (IS) and
green spaces (GS) in African and other cities [8,17,19,26,62].
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 Figures 6 and 8 and Section
2.4.2). 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
Figure 9.
Overall mean LST and dierences between the mean LST of impervious surfaces (IS) and
green spaces (GS) in African and other cities [8,17,19,26,62].
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,
Remote Sens. 2019,11, 1645 15 of 20
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 Figures 6and 8and 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) 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 eects [
6
,
64
]. The spatial arrangement of green spaces is also important
in providing the cool island eect [
65
]. Larger and contiguous green spaces produce stronger cool
island eects than those of several smaller patches of green space whose total area equals the large,
contiguous patches [14,65].
Remote Sens. 2019,11, 1645 16 of 20
Table 3. Correlations between mean LST and selected urban landscape metrics.
Study Area PD AREA_MN SHAPE_MN FRAC_MN AI
rSig. rSig. rSig. rSig. rSig.
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
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 eect.
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 eect 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 eects. 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 eect. 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
Remote Sens. 2019,11, 1645 17 of 20
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 eects 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 eect 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
eects. Accordingly, the important mitigating eect 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 eects. 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 eects 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.
Author Contributions:
All the authors (M.S., M.R., R.C.E. and Y.M.) participated in the research concept design
and implementation, data processing and analysis, and writing of the manuscript.
Funding:
This research was supported by the Japan Society for the Promotion of Science (JSPS) through
Grant-in-Aid for Scientific Research (B) 18H00763 (2018-20, representative: Yuji Murayama).
Acknowledgments:
The authors are grateful to the editor and the anonymous reviewers for their helpful comments
and suggestions to improve the quality of this paper.
Conflicts of Interest: The authors declare no conflicts of interest.
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... The effects of LST on various subjects have been investigated by several researchers, including surface heat island (SHI), geological and geothermal studies (Coolbaugh et al., 2007;Eskandari et al., 2015;Mia et al., 2018;Sekertekin and Arslan, 2019), evapotranspiration (Elnashar et al., 2021), forest fire monitoring (Maffei et al., 2018), and urban climate studies (Sekertekin et al., 2016;Naughton and McDonald, 2019;Simwanda et al., 2019). LST has also been acknowledged as one of the essential criteria for the International Geosphere and Biosphere Program (IGBP) (Townshend et al., 1994;Li et al., 2013). ...
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The Bakun-Murum (BM) catchment region of the Rajang River Basin (RRB), Sarawak, Malaysia, has been under severe threat for the last few years due to urbanization, global warming, and climate change. The present study aimed to evaluate the time series analysis and impact assessment of the temperature changes on the vegetation/agricultural lands and the water availability within the BM region. For this purpose, the Landsat data for the past thirty years (1990–2020) were used. Remote sensing techniques for estimating the surface temperatures and variation within the vegetation and water bodies were utilized, and validation was done using on-ground weather stations. Google Earth Engine (GEE) and other RS & GIS tools were used for analyzing the time series trends of land surface temperature (LST), normalized difference vegetation index (NDVI), and normalized difference water index (NDWI). The results exposed an overall rise of 1.06 °C in the annual mean temperatures over the last thirty years. A maximum annual mean NDVI of 0.48 was recorded for 2018 and 2019. The lowest annual mean NDVI (0.27) was observed in 2005. The annual mean NDWI increased to 0.48 in 2018 and 2019, respectively. The statistical correlation results revealed the coefficient of determination (R2) of 0.09 and 0.13 for the annual mean LST and annual mean NDVI and the annual mean LST and annual mean NDWI, respectively. Moreover, the Mann-Kendall trend test for the annual mean temperature series indicates a slightly increasing trend with Sen's slope of 0.03 °C/year. It is found that there is a positive trend in the annual mean rainfall patterns, as Sen's slope indicates a yearly increase of 50.58 mm/year. This study found significant changes in the LST, NDVI, and NDWI of the BM catchment region during the last thirty years, demanding the concerned authorities' instant attention to alleviate the adverse effects of such changes to protect the ecosystem.
... Other strategies, such as designing the size, orientation, and shape of buildings, could improve the wind flow in the city landscape [72]. Moreover, distinctive mitigation approaches, such as preserving wetlands, small to large water bodies, and greenery plantations on barren spaces, can significantly improve the local climate and ecosystem of the city landscape [38,73,74]. For this strategy and subsequent planning and creation, remote sensing and GIS-based information using a multitemporal spatial database can be applied to recognize the ground reality. ...
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LST has been fluctuating more quickly, resulting in the degradation of the climate and human life on a local-global scale. The main aim of this study is to examine SUHI formation and hotspot identification over Prayagraj city of India using seasonal Landsat imageries of 1987-2018. The interrelationship between six land indices (NDBI, EBBI, NDMI, NDVI, NDWI, and SAVI) and LST (using a mono-window algorithm) was investigated by analyzing correlation coefficients and directional profiling. NDVI dynamics showed that the forested area observed lower LST by 2.25-4.8 °C than the rest of the city landscape. NDBI dynamics showed that the built-up area kept higher LST by 1.8-3.9 °C than the rest of the city landscape (except sand/bare soils). SUHI was intensified in the city center to rural/suburban sites by 0.398-4.016 °C in summer and 0.45-2.24 °C in winter. Getis-Ord Gi* statistics indicated a remarkable loss of areal coverage of very cold, cold, and cool classes in summer and winter. MODIS night-time LST data showed strong SUHI formation at night in summer and winter. This study is expected to assist in unfolding the composition of the landscape for mitigating thermal anomalies and restoring environmental viability.
... More recently, the landscape-LST relationship was investigated for concentric rings with a fixed pitch drawn from the city centre towards the outskirts. Within each ring indicators of complexity and form, such as patch density and mean area, fractal dimension, aggregation of impervious surfaces, and green spaces were related to mean LST [8][9]. ...
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Over the next 30 years, temperatures are expected to increase in hot semi-arid zones. Despite increasing studies on urban heat, cooling measures suitable for this climate zone remain poorly investigated. The proposed method is innovative because it focuses on significant landscape metrics for determining the land surface temperature (LST) and evaluating cooling measures. Recurrence of warm spells was identified analysing the daily air temperatures. Daytime and night-time LST data acquired from space were correlated with landscape metrics extracted from very high-resolution satellite imagery. Stepwise linear regression was used to identify the significant metrics that affected it. Cooling measures were selected considering implementation leeway; performance of existing measures; strengths, weaknesses, opportunities, and threats, equity analyses. Although the method was tested in Niamey, Niger, it can be applied to any city or town in hot semi-arid Global South, requiring decision-making support on cooling policies. • Landscape metrics are consistent with development standard and general requirements • Evaluation of measures to reduce land surface temperature includes experts’ advice • Equity of measures to reduce land surface temperature is considered
... LST is an important metric frequently used to investigate ecological processes and climate change [61][62][63], as well as to study drought, evapotranspiration, vegetation density and surface energy balance [54,64,65]. The LST of Landsat 5 TM is evaluated as [66]: ...
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The rapid growth of China’s economy has greatly accelerated the process of urbanization during China’s reform periods. Urbanization has significantly caused land use and land cover (LULC) changes and thus has impacts on the local climate and ecosystem. This study chooses Quanzhou, a fast-developing city of southeast China, as an example to detect and quantify the LULC and ecological changes from 1989 to 2018 by using the remotely sensed technique. The LULC of Quanzhou was derived from the four Landsat images taken in 1989, 1999, 2007 and 2018, and the land-use-degree ratio index and land-use–change method were used to estimate the change of land use. The remote sensing based ecological index (RSEI) was used to detect the ecological changes of the city. The built-up land expansion intensity and annual built-up land expansion rate were carried out for seven districts of Quanzhou. The results show that the urban area of Quanzhou has drastically grown by 192.99 km ² at the expense of forest, water, and cropland land during the 1989~2018 period. Moreover, the built-up land of seven districts had expanded at the average rate of 0.027~0.154 per year and the built-up expansion intensity was higher than 0.59. The average RSEI value of Quanzhou city dropped from 0.78 in 1989 to 0.34 in 2018, which suggested an overall decline in ecological quality. The proportion of areas with an RSEI rating good decreased from 30.84% to 11.52% while the proportion of areas with rating bad increased from 4.73% to 19.11% during the past 29 years. This study has shown the built-up land expansion intensity is negatively correlated with the ecological quality change, and the increase in built-up land can greatly accelerate the decline of the ecological quality. Government policies play a profound impact on land use changes, urbanization and eco-environment changes. Therefore, the policy decision-makers should take enough action and consider integrating the concept of ecology to enable the healthy and sustainable development of the city.
... Urban environments are typical hotspots for human activity, leading to the alteration of several habitats. Cities experience changes in abiotic factors, such as warming (Mentaschi et al., 2022;Simwanda et al., 2019;Tran et al., 2017), increased noise (Swaddle et al., 2015), light (Duque et al., 2019;Gaston et al., 2013), and chemical pollution (Castells-Quintana et al., 2021;Han et al., 2014), as well and biotic factors, such as habitat fragmentation and shrinking, due to the development of artificial structures (Liu et al., 2016). ...
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Cities experience changes in abiotic factors, such as warming, increases in noise and light. These changes can lead to phenotypic changes. Several studies have revealed that altered environments change phenotypes in plants and animals in cities. However, limited studies have isolated evolutionary from nongenetic changes. Here, we analyzed the evolution of thermal tolerance and diurnal activity patterns in the urban population of the fruit pest, Drosophila suzukii. Urban and rural isofemale lines were reared under constant conditions. We compared the lower and upper thermal limits (CTmin and CTmax, respectively), and effects of temperature exposure on the thermal limits of urban and rural populations. Common garden experiments showed that urban populations exhibit a lower CTmin than rural populations, suggesting genetic difference in CTmin among populations. On the other hand, the difference in CTmax between urban and rural populations was not significant. Exposure to cold temperature did not affect CTmin in both urban and rural populations. In contrast, exposure to hot temperature increased CTmax especially in urban population, suggesting that urban populations evolved in response to urban heat. We also investigated the daily activity patterns of urban and rural populations and the effect of lifelong artificial light at night on daily activity. We found that night-time light (dim light) reduced the total amount of activity compared to dark night condition. In addition, dim light at night altered the daily rhythm of activity and increased the activity rate at night. The effect of night light on total activity was less in urban than that in rural populations, suggesting that populations in cities evolved to mitigate decreased activity under night light. Our results showed that environmental temperature and artificial light at night evolutionarily and plastically influence ecologically important traits, such as temperature tolerance and diurnal activity.
... On the website of the National Meteorological Observatory of China, the start of summer in the study area is around May 21 and the start of autumn is around August 10, so this period was defined as summer in the study area. In addition, considering the low temporal resolution of the Landsat satellite in acquiring two periods of LST data, the images of the same season in adjacent years were taken as the maximum and average values in this study to eliminate the effects of extreme weather (Table 1) [48]. Radiation calibration and atmospheric correction were performed using ENVI 5.3 software during pre-processing, and the surface temperature was calculated afterwards. ...
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Urban heat islands (UHIs) endanger the health of urban residents. Different urban growth patterns (UGPs) have different effects on heat islands. However, the dynamic relationship between UGP landscape information and urban surface heat island intensity (SUHII) remains unclear. This study explored the dynamic relationship between SUHII and UGP landscape information through spatial regression and landscape pattern analysis using Landsat imagery and urban construction land data from five cities in the Beijing–Tianjin–Hebei urban agglomeration from 2010 to 2018. The results show that SUHII increase areas overlap with expansion patches, and the edge expansion and outlying areas show a warming effect. The influence of the edge expansion landscape area and pattern on SUHII changes is greater than the other two growth patterns. The relationship between UGPs’ landscape information and SUHII changes varies among cities. The larger the city size, the stronger the influence of landscape information. Among the landscape patterns, the influence of the landscape area and pattern on SUHII change is large and the influence of landscape fragmentation is smaller. Exploring the dynamic relationship between UGP landscape information and SUHII is conducive to optimizing the spatial layout and pattern selection of urban development and providing a scientific reference for sustainable and livable urban development planning.
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There has been a growing concern for the urbanization induced local warming, and the underlying mechanism between urban thermal environment and the driving landscape factors. However, relatively little research has simultaneously considered issues of spatial non-stationarity and seasonal variability, which are both intrinsic properties of the environmental system. In this study, the newly proposed multi-scale geographically weighted regression (MGWR) is employed to investigate the seasonal variations of the spatial non-stationary associations between land surface temperature (LST) and urban landscape indicators under different operating scales. Specifically, by taking Wuhan as a case study, Landsat-8 images were used to achieve the LSTs in summer, winter and the transitional season, respectively. Landscape composition indicators including fractional vegetation cover (FVC), albedo and water percentage (WP) and urban morphology indicators covering building density (BD), building height (BH) and building volume density (BVD) were employed as potential landscape drivers of LST. For reference, the conventional geographically weighted regression (GWR) and ordinary least squares (OLS) regression were also employed. Results revealed that MGWR outperformed GWR and OLS in terms of goodness-of-fit for all seasons. For the specific associations with LST, all six indicators exhibited evident seasonal variations, especially from the transition season to winter. FVC, albedo and BD were observed to possess great spatial non-stationarity for all seasons, while WP, BH and BD tended to influence LST globally. Overall, FVC exhibited certain positive effect in winter. The negative effect of WP was the greatest among all indicators, although it became the weakest in winter. Albedo tended to influence LST more complicatedly than simple cooling. BD, with a consistent heating effect, was testified to have a greater influence on LST than BH for all seasons. The BH-LST association tended to transfer into positive in winter, while the BVD-LST association remained negative for all seasons. The results could support the establishment of season-and site-specific mitigation strategies. Generally, this study facilitates our understanding of human-environment interaction and narrows the gap between climate research and city management.
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Urbanization has bloomed across Asia and Africa of late, while two centuries ago, it was confined to developed regions in the largest urban agglomerations. The changing urban landscape can cause irretrievable changes to the biophysical environment, including changes in the spatiotemporal pattern of the land surface temperature (LST). Understanding these variations in the LST will help us introduce appropriate mitigation techniques to overcome negative impacts. The research objective was to assess the impact of landscape structure on the variation in LST in the African region as a geospatial approach in Addis Ababa, Ethiopia from 1986-2016 with fifteen-year intervals. Land use and land cover (LULC) mapping and LST were derived by using pre-processed Landsat data (Level 2). Gradient analysis was computed for the pattern of the LST from the city center to the rural area, while intensity calculation was facilitated to analyze the magnitude of LST. Directional variation of the LST was not covered by the gradient analysis. Hence, multidirectional and multitemporal LST profiles were employed over the orthogonal and diagonal directions. The result illustrated that Addis Ababa had undergone rapid expansion. In 2016, the impervious surface (IS) had dominated 33.8% of the total lands. The IS fraction ratio of the first zone (URZ 1) has improved to 66.2%, 83.7%, and 87.5%, and the mean LST of URZ 1 has improved to 25.2 • C, 26.6 • C, and 29.6 • C in 1986, 2001, and 2016, respectively. The IS fraction has gradually been declining from the city center to the rural area. The behavior of the LST is not continually aligning with a pattern of IS similar to other cities along the URZs. After the specific URZs (zone 17, 37, and 41 in 1986, 2001, and 2016, respectively), the mean LST shows an increasing trend because of a fraction of bare land. This trend is different from those of other cities even in the tropical regions. The findings of this study are useful for decision makers to introduce sustainable landscape and urban planning to create livable urban environments in Addis Ababa, Ethiopia.
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The urban heat island (UHI) and its consequences have become a key research focus of various disciplines because of its negative externalities on urban ecology and the total livability of cities. Identifying spatial variation of the land surface temperature (LST) provides a clear picture to understand the UHI phenomenon, and it will help to introduce appropriate mitigation technique to address the advanced impact of UHI. Hence, the aim of the research is to examine the spatial variation of LST concerning the UHI phenomenon in rapidly urbanizing Lagos City. Four variables were examined to identify the impact of urban surface characteristics and socio-economic activities on LST. The gradient analysis was employed to assess the distribution outline of LST from the city center point to rural areas over the vegetation and built-up areas. Partial least square (PLS) regression analysis was used to assess the correlation and statistically significance of the variables. Landsat data captured in 2002 and 2013 were used as primary data sources and other gridded data, such as PD and FFCOE, were employed. The results of the analyses show that the distribution pattern of the LST in 2002 and 2013 has changed over the study period as results of changing urban surface characteristics (USC) and the influence of socio-economic activities. LST has a strong positive relationship with NDBI and a strong negative relationship with NDVI. The rapid development of Lagos City has been directly affected by conversion more green areas to build up areas over the time, and it has resulted in formulating more surface urban heat island (SUHI). Further, the increasing population and their socio-economic activities including industrialization and infrastructure development have also caused a significant impact on LST changes. We recommend that the results of this research be used as a proxy tool to introduce appropriate landscape and town planning in a sustainable viewpoint to make healthier and livable urban environments in Lagos City, Nigeria
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This article summarized the spatiotemporal pattern of land use/land cover (LU/LC) and urban heat island (UHI) dynamics in the Metropolitan city of Tehran between 1988 and 2018. The study showed dynamics of each LU/LC class and their role in influencing the UHI. The impervious surface area expanded by 286.04 (48.27% of total land) and vegetated land was depleted by 42.06 km2 (7.10% of total land) during the period of 1988–2018. The mean land surface temperature (LST) has enlarged by approximately 2–3 ◦C at the city center and 5–7 ◦C at the periphery between 1988 and 2018 based on the urban–rural gradient analysis. The lower mean LST was experienced by vegetation land (VL) and water body (WB) by approximately 4–5 ◦C and 5–7 ◦C, respectively, and the higher mean LST by open land (OL) by 7–11 ◦C than other LU/LC classes at all time-points during the time period, 1988–2018. The magnitude of mean LST was calculated based on the main LU/LC categories, where impervious land (IL) recorded the higher temperature difference compared to vegetation land (VL) and water bodies (WB). However, open land (OL) recorded the highest mean LST differences with all the other LU/LC categories. In addition to that, there was an overall negative correlation between LST and the normal difference vegetation index (NDVI). By contrast, there was an overall positive correlation between LST and the normal difference built-up index (NDBI). This article, executed through three decadal change analyses from 1988 to 2018 at 10-year intervals, has made a significant contribution to delineating the long records of change dynamics and could have a great influence on policy making to foster environmental sustainability.
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The study used Landsat 5 TM acquired on August 1993 and Landsat 8 OLI/TIRS acquired on September 2017 to retrieve satellite brightness temperature and analyzing land use land cover change and its impact on land surface temperature of Dire Dawa City, Ethiopia. The land use/land cover classification was made using maximum likelihood algorithm and five major land use land cover (Built up, barren land, shrub land, sparse vegetation and Dry River) were identified. The land use land cover result reveal built up and shrub land area have been expanding rapidly and barren land has shown decreasing trend in areal extent. On the other side, result of the study shows, barren land and built up area exhibit highest mean value of land surface temperature. Shrub and sparse vegetation land use land cover class exhibit lowest mean land surface temperature, as they are vegetative cover. Areas with high vegetation cover or Normalized Difference Vegetation Index value often depict high land surface temperature and therefore, NDVI have strong negative correlation with land surface temperature. From the study, it is identified that, the change in land use land cover because of urban growth, settlement expansion and construction of new built up or dwelling units in the city, leads to increment in land surface temperature. As a result, urban growth has resulted in the change of land surface temperature.
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Presently, the urban heat island (UHI) phenomenon, and its adverse impacts, are becoming major research foci in various interrelated fields due to rapid changes in urban ecological environments. Various cities have been investigated in previous studies, and most of the findings have facilitated the introduction of proper mitigation measures to overcome the negative impact of UHI. At present, most of the mountain cities of the world have undergone rapid urban development, and this has resulted in the increasing surface UHI (SUHI) phenomenon. Hence, this study focuses on quantifying SUHI in Kandy City, the world heritage tropical mountain city of Sri Lanka, using Landsat data (1996 and 2017) based on the mean land surface temperature (LST), the difference between the fraction of impervious surfaces (IS), and the fraction of green space (GS). Additionally, we examined the relationship of LST to the green space/impervious surface fraction ratio (GS/IS fraction ratio) and the magnitude of the GS/IS fraction ratio. The SUHI intensity (SUHII) was calculated based on the temperature difference between main land use/cover categories and the temperature difference between urban-rural zones. We demarcated the rural zone based on the fraction of IS recorded, <10%, along with the urban-rural gradient zone. The result shows a SUHII increase from 3.9 °C in 1996 to 6.2 °C in 2017 along the urban-rural gradient between the urban and rural zones (10 < IS). These results relate to the rapid urban expansion of the study areas from 1996 to 2017. Most of the natural surfaces have changed to impervious surfaces, causing an increase of SUHI in Kandy City. The mean LST has a positive relationship with the fraction of IS and a negative relationship with the fraction of GS. Additionally, the GS/IS fraction ratio shows a rapid decline. Thus, the findings of this study can be considered as a proxy indicator for introducing proper landscape and urban planning for the World Heritage tropical mountain city of Kandy in Sri Lanka.
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The horizontal two-dimensional (2D) urban land use approach is not sufficient to trace rapid changes in urban environment. Hence, a three-dimensional (3D) approach that is different from the traditional geographical method is necessary to understand the mechanism of compound urban diversity. Using remote sensing data captured in 2010/2011 and geospatial tools and techniques, we quantified the urban volume (UV, consisting of urban built volume (UBV) and urban green volume (UGV)) and retrieved and mapped the land surface temperature (LST) of two cities in Japan (Tsukuba, a planned city, and Tsuchiura, a traditional city). We compared these two cities in terms of (1) UBV and UGV and their relationships with mean LST; and (2) the relationship of the UGV-UBV ratio with mean LST. Tsukuba had a total UBV of 74 million m 3 , while Tsuchiura had a total of 89 million m 3. In terms of UGV, Tsukuba had a total of 52 million m 3 , while Tsuchiura had a total of 29 million m 3. In both cities, UBV had a positive relationship with mean LST (Tsukuba: R 2 = 0.31, p < 0.001; Tsuchiura: R 2 = 0.42, p < 0.001), and UGV had a negative relationship with mean LST (Tsukuba: R 2 = 0.53, p < 0.001; Tsuchiura: R 2 = 0.19, p < 0.001). Tsukuba also had a higher UGV-UBV ratio of 54.9% in comparison with Tsuchiura, with 28.7%. Overall, the results indicate that mean LST was more intense in the traditional city (Tsuchiura). This could have been due to the difference in urban spatial structure. As a planned city, Tsukuba is still a relatively young city that has more dispersed green spaces and a well-spread (so far) built-up area.
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Cambridge Core - Ecology and Conservation - Land Mosaics - by Richard T. T. Forman
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Green infrastructures such as living walls are technological solutions to replace the declined greenery at urbanized environment and also reliable applications for thermal regulation in buildings through insulation effect and escalates the energy use efficiency. Thermal comfort and local climate are spatiotemporally variable. The existing research gap should be addressed by evaluating the performance of vertical green walls in tropical condition. In this study, thermal performance, relative humidity (RH) and CO2 concentration were quantified for basic three types of green infrastructures; such as (T1) living walls, (T2) indirect green façades and (T3) direct green façades located in Colombo metropolitan in Sri Lanka. An in-situ experimental study was conducted considering temperatures at 1 m and 0.1 m distance in front of the green walls, inside the foliage, air gap and external wall surface comparatively to adjacent bare wall control. Three case studies per green infrastructure within Colombo metropolitan area were purposively selected. Simultaneously, RH and CO2 concentration at 0.1 m in front of the green and bare walls were measured for the performance quantification. The internal thermal comfort simulation and occupants’ satisfaction questionnaire survey was executed to assess the green infrastructure performances. The study revealed that vertical greenery systems were highly effective on external wall surface temperature reductions at 1100 h–1500 h time zones. T1 and T2 accounted for superior temperature reduction in the range of 1.61 °C–1.72 °C through the façade relative to the distance than T3. Maximum temperature reduction compared to the bare wall control was obtained for the T1 (0.28 °C–8.0 °C) followed by T2 (1.34 °C–7.86 °C) and T3 (1.34 °C–6.64 °C). Averaged RH increment (1.6%–1.81%) and CO2 reduction (0.63%) occurred near green walls at day time compared to control. An average 28 °C simulated indoor temperature circumstantiate the indoor thermal comfort. 58% and 89.5% occupants’ were satisfied with thermal and visual comfort respectively, thus emphasizing façade greening as a sustainable approach on micro climatic changes and human thermal comfort.