Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Variations in urban land surface temperature intensity over four cities in different ecological zones

Abstract

This study aims at assessing variations and changes in the intensity of urban land surface temperature (LST) over four major cities in different ecological zone. The study intends to examine the contributions of different land cover types and variation in ecological locations on the intensity of urban LST. Remote Sensing and GIS techniques were used to measure the extent of the LST intensity over different cities and implications of land use/land cover (LULC) changes, using the Landsat TM/ ETM from 1984 to 2012, and Landsat OLI/TIRS from 2015 to 2019. The contributions of different landscape types to urban LST intensity were examined, using contribution index (CI) and Landscape index (LI) methods while the relationship between urban LST, and changes in LULC was examined using zonal statistics. The results revealed that the spatial and temporal changes in the LULC have greatly influenced the LST in the cities, though this varies from identified LULC. Changes in estimated LST vary from 0.12 to 1 °C yearly, while the changes are much intensified in the core section of the cities. The contribution of each landscapes varies, − 0.25 < CI > − 1.17 for sink landscape and 0.24 < CI > 1.05 for source landscape. The results further reveal that as LI ≥ 1, the contribution of source landscape to intensity of LST is lesser than that of sink landscape, but LI ≤ 1 shows that source landscapes contribute more to intensity of LST than sink landscapes. This might be as a result of changes in the vegetation cover between 1984 and 2019 as revealed in LULC change. Loss in the vegetal cover is anthropogenically induced leading to an increase in built-up and impervious surfaces resulted in mean monthly and yearly temperature changes. It is observed that the core and densities areas of cities witnessed higher LST compared with the rural area. The study concludes that different types of land cover within an urban area can affect the spatial pattern of urban LST, though this varies from one ecological zone to another and distribution of LST intensity in the urban area depends on its changes LULC. Thus, as cities’ population is expected to keep expanding there is a need to establish more viable linkages between the ever-growing population and land use patterns. The major findings from this study are useful in informing policymakers of the need to promote more sustainable urban development in the cities.

Introduction

Change in urban land use is becoming noticeable in many cities, not only in developed countries but also in African cities, and this is most responsible for changes in urban land surface temperatures witnessed in recent years1,2,3,4,5. A combination of factors has been reported in the literature as the drivers of changes in LST. Such factors include the removal of vegetation within urban areas6,7, change in urban thermal and physical properties of construction materials, building, morphology, surface roughness8,9, and anthropogenic heat sources modify the local energy and urbanization, leading to increases in atmospheric temperature in urban areas compared to their surroundings10,11,12,13. Many of these studies concluded that the main causes of the recent intensification of LST in urban can be related to structural and land cover differences of urban and rural areas. Urban areas are rough with buildings extending above ground level and are dry and impervious with construction materials extending across natural soils and vegetation.

These urban characteristics alter the natural surface energy and radiation balances such that urban areas are relatively warm places14,15. Variations in the canopy-layer of urban heat occur close to the surface in cities and extend to approximately the mean building height. These changes have as well affected thermal, radiative, moisture and aerodynamic characteristics of the environment leading to the accumulation of heat in urban areas. Thus, a huge amount of vegetation cover in the cities is replaced by built-up surfaces, that absorbs incoming solar radiation and re-radiate it at night16,17. The urban canopy-layer is governed by local or micro-scale processes and refers to that portion of the planetary boundary layer whose characteristics are affected by an urban area18,19. Other studies have revealed that the LST difference is usually higher during the day than at the night5,6,20.

Different LST retrieval methods and platform data have been used in recent years though, the study of urban LST changes over the years have taken very varied dimensions, using both ground and satellite datasets. Satellite observations are used to study the changes in urban land surface temperature21,22. The remote sensing methods which have been used include the split-window method14,23,24, the single-channel algorithm25,26, temperature/emissivity separation method27,28,29, and mono-window algorithm30,31,32. Studies have assessed the performance of the mono-window algorithm (MWA), single-channel method (SCM), split-window algorithm (SWA-Q) and split-window algorithm (SWA-S). Although all of these methods can provide good results, the radioactive transfer equation works in conjunction with in situ measurements collected simultaneously with the satellite data, while the mono-window algorithm with radio sounding data can get a better result than the single-channel algorithm with a root mean square deviation of 0.9 K33, while Tsou et al.34 further reported that LST retrieval by SWA-Q had better accuracy than the other algorithms. Generally, what is obvious in literature is that the cities’ population is expected to keep expanding thus there is a need to establish more viable linkages between the ever-growing population and land use patterns, essentially because these are the factors directly related to the cities’ land surface temperature regime. On the other hand, raised temperatures from urban heat islands, especially throughout the midyear, can influence a community’s environment and the quality of life of people. Meanwhile, a steady and perceptible detailed study to assess the changes and effects of change in land cover on land surface temperature in cities across different ecological zones especially is rare. The present study, therefore, seeks to contribute to this by looking at past issues related to urban heat islands in the cities in different ecological zone in the tropics. The examination of variations in the cities’ land surface temperature, the establishment of the causes and effects of these variations, and the examination of the relationship between land surface temperature and land use land cover in the cities in the specified study period are thus essential. Hence, the present study aims at assessing land surface temperature in four selected cities, using satellite data. The specific objectives of the study are to examine variations in land surface temperature in the cities; and examine the relationship between land use/land cover change and land surface temperature.

Methodology

The study area

This study involves different cities from ecological zones in Nigeria: Akure, Ibadan, Lagos, and Saki (Fig. 1). The study cities were purposively selected to cover different ecological zones: Guinean Savannah, derived Savannah, tropical rainforest and mangrove zones (Table 1). The four cities being considered in this study have experienced some level of urban growth over the years. In recent years, there has been a major increase in the built-up of the area in Akure but the thick forest vegetation has been massively depleted to give room for farming and other agricultural activities, a similar change in the land use from thick vegetal cover to agricultural farmlands could also be observed in Saki. Lagos is a large port city in southwestern Nigeria and one of the most rapidly developing cities in Africa with a landmass of about 2341.72 km2. Lagos is also a key cultural centre of the whole region, with very interesting and old local traditions, cuisine, music, and lifestyle. Tourism plays an important role in the city's economy, and most of the guests come to the city when it is time for numerous music festivals and cultural events. Ibadan city, the administrative capital of Oyo state, lies completely within the boundary between the tropical forest zone and derived savannah. According to the Koppen climate classification, it has a tropical wet and dry climate. Its mean total rainfall is 1420.06 mm. Its mean maximum temperature is 26.46 °C while its mean minimum temperature is 21.42 °C.

Figure 1
figure1

Map of the study area showing the location of four cities examined in the study. The figure was generated using ArcGIS 10.5 software (http://www.esri.com/).

Table 1 Ecological characteristics of the cities examined in the study.

Multi-year dataset acquisition

A multi-year Landsat dataset, from 1984 to 2019 were used in the study, to examine long period LST. The dataset cover both dry and wet seasons, at least a Landsat scene for a month, depending on low cloud coverage. The Landsat Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+) and an Operational Land Imager-Thermal Infrared Sensor (OLI-TIRS) dataset from 1984 to 2019 were used in this study to assess change in urban LST. The complete multi-year Landsat dataset was used to develop long term LST database for the four cities, results of 1984, 1994, 2004 and 2019 were presented to show the decadal variation. To examine the changes in Land use/Land cover (LULC) within and around the cities, as it varies over years, Landsat datasets for years 1984, 2004 and 2019 were used for classification.

Land surface temperature retrieve and assessment

The LST over the cities were retrieved from the thermal bands of multi-year Landsat datasets. Thermal bands 6 (10.40–12.50 μm), 6H (10.4–12.5 μm) and 10 (10.60–11.19 μm) of TM, ETM + and OLI-TIRS respectively, with spatial resolutions of 120 m, 60 m and 100 m respectively were used in the study to distinguish nature urban surface temperature in different cities. Also, multi-year Landsat datasets as it relates to changes in land use and land cover (LULC) and NDVI, over four cities in southwestern Nigeria (Fig. 1) were acquired from the United States Geological Survey (USGS), using a path of 191 and a row of 54 for Lagos, path of 191and a row of 55 for Ibadan, the path of 190 and a row of 55 for Akure and path of 191 and a row of 55 for Saki (Table 2).

Table 2 Characteristics of the satellite dataset used in the study.

Subsequently resampling the spatial resolutions to a uniform resolution of 30 m, these thermal bands were used to retrieve LST over the study area for three different periods were done following five major steps: (1) converting Digital Numbers (DNs) to spectral radiance, (2) obtaining effective at-sensor brightness temperature also known as black body temperature from the spectral radiance using Plank’s inverse function, (3) estimating land surface emissivity, (4) extracting urban land surface temperature, and (5) converting the final LST values to degrees Celsius5,22,35. Conversion of Digital Numbers (DNs) to spectral radiance was performed using the equation:

$${\text{L}}_{\lambda } = \left( {{\text{M}}_{{\text{L}}} \times {\text{Q}}_{{{\text{Cal}}}} } \right) + {\text{A}}_{{\text{L}}}$$
(1)

where Lλ is the spectral radiance at the sensor’s aperture in Wm−2 sr−1 μm−1; Q Cal is quantized calibrated pixel values in DNs, and ML and AL are band-specific rescaling factors given in the metadata file as radiance multi-band x and radiance add band x respectively.

The effective at-sensor brightness temperature also known as black body temperature (TB) was obtained from the spectral radiance using Plank’s inverse function:

$$T= \frac{{K}_{2}}{In \left(\frac{{\in K}_{1}}{{L}_{\lambda}}\right)+ 1}$$
(2)

where Tk is at-satellite brightness temperature (in Kelvin); K2 is a calibration constant in Wm−2 sr−1 μm−1; K1 is calibration constant 1 in Wm−2 sr−1 μm−1; and Lλ is the spectral radiance at the sensor in Wm−2 sr−1 μm−1. However, the land surface emissivity estimation was calculated using the temperature values obtained using Eq. (2), which referenced to a blackbody. Therefore, corrections for spectral emissivity (ε) are necessary according to the nature of the land cover.

Valuation of contributions of different landscapes to urban LST intensity

The contributions of different landscape types to urban LST intensity were examined, using the contribution index (CI) and Landscape index (LI) approach. The CI was used to examine both the spatial and temporal distinctive characteristics of both vegetation (sink) and non-vegetation (source) landscapes and how they contribute to the development/changes of urban LST. The urban landscapes were grouped into two: sink and source landscapes. The source landscape types are characterized by built-up surfaces part of the cities which are distinguished with the concrete surface while sink landscapes are mainly vegetation and water surfaces. The contribution of both sink and source landscapes to urban LST intensity were calculated CI as in Eq. (3):

$$CI= {D}_{t} \times {S}$$
(3)

Dt represents the difference in the temperature between the sink or source landscape and the entire region while the proportion of the area that is source landscape or sinks landscape in the entire area are present as S. Thus for sink landscape, the contribution to LST intensity is the fraction of the area (S) that is part of the sink landscape multiplied by the difference in temperature (Dt) between the sink landscape and the entire region. For a sink landscape, the Dt values are negative but the contribution index is also positive for the source landscape. The landscape index approach was used to determine the contribution of both landscapes to the urban LST intensity as in Eq. (4):

$$LI=\left|{CI}_{sink}/{Ci}_{source}\right|$$
(4)

where landscape index (LI) is the function of the contribution index (CI) of both source and sink landscapes. This evaluated how the changes in land use/land cover within and around the cities affect the intensity of urban LST. Also, zonal statistics were used to calculate the relationship of the statistics and to show if a landscape zone feature contains overlapping the LST zones. This was also used to compare the intensity of the LST in a different zone.

Changes in land use/land cover as relate to urban LST intensity

Multispectral image classification was used to extract thematic information from satellite images. It aids in having a good understanding of the different land cover classes found within an area of interest. Classification in this study was performed using the Maximum likelihood classification form. The maximum likelihood method of classification algorithm is one of the common parametric classifiers used for supervised classification36,37. The algorithm is used for Computing the weighted distance or likelihood (D) of unknown measurement vector (X) belonging to one of the known classes (Mc) which is based on the Bayesian equation:

$${\text{D}} = {\text{ln}}\left( {{\text{ac}}} \right) - \left[ {0.{\text{5ln}}\left( {{\text{covc}}} \right)} \right] - \left[ {0.{5}\left( {{\text{X}} - {\text{Mc}}} \right){\text{ T}}\left( {{\text{covc}} - {1}} \right) \, \left( {{\text{X}} - {\text{Mc}}} \right)} \right]$$
(5)

A supervised classification was performed by creating a training sample, and based on a spectral signature curve, different land-use classes were created including Built-up area, Waterbody, Cultivation, Natural vegetation and Rock outcrop. While the land surface temperature was extracted using the equation:

$${\text{LST}} = {\text{T}}_{{\text{B}}} /{1} + \left( {\lambda \times {\text{T}}_{{\text{B}}} /\rho } \right) \times {\text{ln}}\varepsilon$$
(6)

Since the LST values were obtained in degree Kelvin, LST was converted into degrees Celsius. The LST is the land surface temperature is the function of TB, λ, ρ and ε, such as λ is the central wavelength of emitted radiance (11.45 μm for TM and ETM+; 10.90 μm for OLI-TIRS); TB is at-satellite brightness temperature; ρ is h × c/σ (1.438 × 10−2 mK or 14,380 µmK); h is Planck’s constant (6.26 × 10−34 Js); c is the velocity of light (2.998 × 108 m/s); σ is Stefan Boltzmann’s constant (1.38 × 10−23 J K−1); and ε is the land surface emissivity. In this study, the relationship between LST and vegetation availability was calculated using zonal statistics.

Likewise, each of the LULC categories is assigned an emissivity value by reference to the emissivity classification scheme by Snyder et al.38. A heterogeneous surface (that is, a mixture of bare soil, exposed water surface and vegetation) was considered in this study. The emissivity of a heterogeneous surface (mixed pixels) was computed using Eq. (3), taking into account the proportion of vegetation in each pixel (fv) and the cavity effect due to surface roughness (Cλ).fv was estimated using Eq. (4)33, while Cλ was calculated using Eq. (5), with the geometrical (shape) factor ‘F’ having the mean value 0.55, according to Sobrino et al.39:

$$\varepsilon = \varepsilon_{{\text{v}}} \times {\text{f}}_{{\text{v}}} + \varepsilon_{{\text{s}}} \times \left( {{1} - {\text{f}}_{{\text{v}}} } \right) + {\text{C}}_{\lambda }$$
(7)
$${\text{f}}_{{\text{v}}} = \left[ {{\text{NDVI}} - {\text{NDVI}}_{{\text{s}}} /{\text{NDVI}}_{{\text{v}}} - {\text{NDVI}}_{{\text{s}}} } \right]^{{2}}$$
(8)
$${\text{C}}_{\lambda } = \left( {{1} - \varepsilon_{{\text{s}}} } \right) \times \varepsilon_{{\text{v}}} \times {\text{F}}\prime \times \left( {{1} - {\text{f}}_{{\text{v}}} } \right)$$
(9)

NDVI is the Normalized Difference Vegetation Index as computed in Eq. (10) for the respective years considered in this study;

$${\text{NDVI}} = {\text{NIR}}{-}{\text{RED}}/{\text{NIR}} + {\text{RED}}$$
(10)

NDVIs and NDVIv are Normalized Difference Vegetation Index Threshold values for soil pixels (NDVIs = 0.2) and pixels of full vegetation (NDVIv = 0.5) respectively (Eq. 4), as proposed by Sobrino et al.39 and Sobrino et al.33; and εs and εv are the emissivity (ε) of soil pixels and full vegetation pixels with the mean values 0.97 and 0.99, respectively. The final expression for land surface emissivity is given by:

$$\varepsilon \, = 0.00{\text{4f}}_{{\text{v}}} + 0.{986}$$
(11)

Detail flow of the methodology is presented in Fig. 2.

Figure 2
figure2

Flow chart showing the detailed steps of methods and material use in the study. This figure was designed by Michael I Aigbiremolen using flowchart symbols in Microsoft Word.

Results and discussion

In this study, the results revealed three major outcomes: (a) the cities centres are much warmer and this increased considerably over the years; (b) the contribution index (CI) of source landscapes are positive while the values are negative for sink landscapes in all cities irrespective of their ecological location; (c) changes in land use/land cover within and around the cities affect the intensity of urban LST.

Contribution of different landscapes to LST intensity

Different landscapes contribute differently to the intensity of LST. The contribution indexes (CI) of source landscapes are positive while the values are negative for sink landscapes (Tables 3, 4, 5, 6). The size (S) of both landscapes changes (Tables 3, 4, 5, 6) over the years, as source landscapes increases, the sink landscapes decreases from 1984 to 2019. The S values for sink and source landscapes, in Akure for example, changes from were 67% and 33% in 1984 to 49% and 51% in 2019, respectively. Similar increases were observed in all other cities but much more high in Ibadan and Lagos. In Ibadan, the S value for sink landscape was 40% in 1984 but changed to 29% in 2019 while source landscape change changed from 60% in 1984 to 71% in 2019 (Table 5). Much of these changes are observed in Lagos where S value for sink landscape was 47% in 1984 but changed to 25% in 2019 while the source landscape change changed from 53% in 1984 to 75% in 2019. Both spatial and temporal changes in the landscapes types affect the CI of each landscape and the intensity of the LST in all the cities (Tables 3, 4, 5, 6). In terms of the individual contribution of each landscape type in the cities of study, source landscape contributes positively to the intensity of LST while sink landscape has a negative contribution as presented in CI in Tables 3, 4, 5, and 6. The range of contribution varies, − 0.25 < CI > − 1.17 for sink landscape and 0.24 < CI > 1.05 for source landscape. The contributions of source landscapes are much more intense in recent years, especially in Lagos with CI = 1.05 in 2004 and CI = 0.82 in 2019. These results imply that the source landscape contributes mostly to the intensity of the urban LST in the study cities.

Table 3 Variations in LST of the
Table 4 Variations in LST of the
Table 5 Variations in LST of the
Table 6 Variations in LST of the

The result of LI further revealed that the contribution of the landscape types to the intensity of LST in the cities varies. In all the cities, it is obvious that LI decreases from LI = 1.84 in 1984 to LI = 0.57 in 2019 for Akure (Table 3); from LI = 1.58 in 1984 to LI = 0.41 in 2019 for Ibadan(Table 4); from LI = 1.13 in 1984 to LI = 0.36 in 2019 for Lagos (Table 5); and from LI = 1.90 in 1984 to LI = 0.677 in 2019 for Saki (Table 6). The results imply that as LI ≥ 1, the contribution of source landscape to intensity of LST is lesser than that of sink landscape, but LI ≤ 1 shows that source landscapes contribute more to intensity of LST than sink landscapes (Tables 3, 4, 5, 6). In all the cities examined in this study, the core part of each city was much warmer compared to the neighbouring areas (Figs. 3, 4, 5, 6). The intensity of the LST increased over the years but much more in the centre of the cities.

Figure 3
figure3

Land surface temperature intensity over Akure from 1984 to 2019. The figure was generated using Quantum GIS (QGIS 3.4) software (https://qgis.org/en/site/).

Figure 4
figure4

Land surface temperature intensity over Ibadan from 1984 to 2019. The figure was generated using Quantum GIS (QGIS 3.4) software (https://qgis.org/en/site/).

Figure 5
figure5

Land surface temperature intensity over Lagos from 1984 to 2018. The figure was generated using Quantum GIS (QGIS 3.4) software (https://qgis.org/en/site/).

Figure 6
figure6

Land surface temperature intensity over Saki from 1984 to 2019. The figure was generated using Quantum GIS (QGIS 3.4) software (https://qgis.org/en/site/).

The general observation in Akure is that only the central part of the city was much warmer in 1984 compared to other past of the city but a significant increase in LST was observed towards the northeastern part of Akure as of 2004 and this scenario appears much more towards the northwestern and southern part of the city in 2019 (Fig. 4).

The intensity of LST clusters in high-density built-up areas in the different cities. It implies thus that the expansion of built-up areas further influences the intensity of LST in the urban area. The intensity of LST increases along the region of the cities with the high rate of high-density and expansion of built-up areas. These results further indicate more of a distinction between the built-up and the vegetal cover in the area as they contribute to the intensity of LST in the study cities. However, the observation of temperature that show a higher mean temperature in Saki compared to other cities. The ecological location of Saki, in Guinean Savannah, may be responsible for this. The intensity of LST increases from year to year as the density of built-up areas expanding. In Lagos for example, it could be visually seen (Fig. 5) high cluster of LST in the southern part of the city in 1984 but there appears a significant spread with much more intensity of LST across every part of the city as observed in 2019. In other cities, the high cluster of LST is obvious in the later years from 2004 to 2019. The reasons for these outcomes are very obvious as the majority of source landscapes in the cities centres are usually built-up surfaces distinguished with the concrete surface while sink landscapes are mainly vegetation and water surfaces. This is much more apparent in Lagos with a strong heterogeneity of land surface characteristics, increasing built-up and soil between the coast and mainland, but the vegetation within the city is exceedingly degraded, leading to high intensity of LST40,41. Thus, source landscapes are noted for large exposed concrete surfaces with high heat absorption which eventually increases the temperature in the cities while much evapotranspiration and reduction of temperature takes place in the sink landscapes.

What are the drivers of differences in the intensity of LST?

Human activities such as urban development and farm cultivation significantly altered and impacted the natural surface conditions and atmospheric properties of the urban areas. This is observed to result in different heat patterns within the cities. Changes in the LST observed in the study cities are attributed to the changes in Land use/Land cover (LULC) within and around the cities (Fig. 7, Table 7). The results reveal that as the built-up areas increases in the cities (Fig. 8), the intensity of LST is much more upsurged (Figs. 3, 4, 5, 6). Change in LULC is noticeable in all the cities examined in this study. For example, the total land area of Akure, used in this study, is approximately 1638 km2. In 1984, the built-up was about 33 km2 which increase to nearly 100 km2 in 2019. Cultivation also increased from 217.6 to 837.95 km2 in 2019. However, the vegetation decreases significantly from 1357 km2 in 1984 to 1067 km2 in 2004 and many reductions in 2019 to about 701 km2 (Table 7). What is obvious from these results is that as both built-up within the city and the cultivation land around the city expanding, vegetation decreases (Fig. 7a), leading to much of the LST intensification in the city (Fig. 3). A similar scenario is obvious in Ibadan in 1984 where for instance, the built-up and cultivation increase from 101 km2 and 242 km2 to 343 km2 and 269 km2 in 2019, respectively. These expansions decrease in vegetation land from 702 km2 in 1986 to 434 km2 in 2019 (Table 7).

Figure 7
figure7

Land use/land cover classification (a) Akure; (b) Ibadan; (c) Lagos; and (d) Saki; 1984, 2004 and 2019. The Figure was generated using ArcGIS 10.5 software (http://www.esri.com/).

Table 7 Land use/land cover change in different cities between 1984 and 2019.
Figure 8
figure8

Changes in built-up area (A) Akure; (B) Ibadan; (C) Lagos; and (D) Saki. The Figure was generated using ArcGIS 10.5 software (http://www.esri.com/).

In Lagos in 1984, the built-up was about 296 km2 but increased each year to 1053 km2 in 2019 Cultivation, however, decreased from 193 km2 in 1984 to nearly 165 km2 in 2019. The reason for the reduction in cultivation land in Lagos is obvious, since the city is a major industrial location in the country, only a few lands are used for farming. As the built-up increases in the city, the natural vegetation in Lagos decreased from 1371.19 km2 in 1984 to about 553 km2 in 2004 and 686 km2 in 2019. Even the waterbody is shrinking as it reduces from 481.56 km2 in 1984 to about 439 km2 in 2019 (Table 7). Reduction in waterbody may be due to sand filling of some canals to accommodate the expansion of built-up. The total land area of Saki, used in this study, is about 122 km2. In 1984, the built-up was 5.98 km2 which increased to 8.91 km2 in the year 2004 and over 15 km2 in 2019. Natural vegetation in Saki was on constant reductions, it decreased from 42.79 km2 in 1984 to 34.12 km2 in 2019 (Table 7).

Much of the changes in the LST in the cities (Figs. 3, 4, 5, 6) are the results of changes in the built-up area (Fig. 8). The relationship between LST and LULC was further examined by calculating the zonal statistics of the images for the different years across all four cities (Fig. 6). The result reveals that the mean surface temperature variations over different land cover types. For Akure, the mean temperature over built-up ranges between 24 and 29 °C from 1984 and 2019, this is a high temperature. For the Ibadan area, the mean surface temperature variation over different land cover types revealed that the majority of the bare surface and impervious surface of Ibadan metropolis had temperatures between 22 and 38 °C, while the mean temperature for built-up ranges between 26 and 29 °C from 1984 and 2019 in which by implication of the interpretation of the zonal statistic corresponding to high temperature. For Lagos metropolis, the mean surface temperature variation over different land cover types revealed that the majority of the bare surface and impervious (built-up) surface of Lagos metropolis had temperatures between 20 and 39 °C, while the mean temperature for built-up ranges between 25 and 30 °C from 1984 and 2019. For the Saki area, the mean surface temperature variation over different land cover types revealed that the mean temperature for built-up ranges between 29 and 36 °C from 1984 and 2019 in which by implication of the interpretation of the zonal statistics corresponding to high temperature. The highest mean temperature was observed in Saki and this again is as a result of the ecological location of Saki in the Guinean Savannah.

The results are indicated that the LST is strongly correlated with change in LULC. Hence areas with the least vegetation are experiencing higher land surface temperatures. Again in this study, LST and LULC were found to be closely correlated in all land cover categories, especially in urban and vegetated/rural areas across the four cities that are being considered. Putting the side of the image by side one another could help to visually observe the relationship amongst these variables, seeing the correlation between LST and LULC in a specific location as well as their disparities as they are located in different ecological zones. Thus the results from this study reveal that anthropogenic factors such as urban encroachment into agricultural or rural land due to urban expansion and farming activities have contributed immensely to the removal of vegetation cover. As a result, this alteration in land cover is most responsible for the variation in land surface temperatures over time and space. Besides, the impervious surface is one of the most important land cover types and a feature of urban/suburban environments which affects the LST in the urban area. It is known to affect urban surface temperatures by altering the sensible and latent fluxes. Impervious surfaces represent materials that do not absorb water or dampness40,41,42. Most materials used in urban constructions, for example, housetops, roads, interstates, parking areas and walkways are impervious.

Conclusion

The study attempted to compare spatiotemporal variations and intensity of land surface temperature (LST) in four cities located in different ecological zones. Based on the satellite dataset, remote sensing and Geographic Information System (GIS) techniques, the study assesses variations in urban LST in the cities over a period of 30 years. The drivers of LST and consequently urban heat islands, including changes in vegetation cover, bare surface (considering soil moisture content), water bodies and population (hence built density and impervious surfaces) are examined in the study. Compared to the traditional meteorological observation method, remote sensing technology has the advantage of wide coverage, which makes the large-scale assessment of LST possible43.

The major findings from this study reveal: (1) spatial and temporal variations in the LST in the cities; (2) change in the land use and land (LULC) are responsible for the change in the LST noted in the cities; (3) significant relationship between change LST, LULC and Ecological location of the cities. Generally, most locations in the cities with a relatively low temperature in 1984 were converted to hot spots in 2004 and hotter in 2019. What is obvious from this study is that maximum air temperature was in built-up areas of the city while the minimum temperature observed was in areas with dense vegetation. This is noticeable to be a result of an increase in built-up areas over the years and a corresponding decrease in vegetation5,44. This implies that the spatial and temporal changes in the land uses have greatly influenced the increase in the land surface temperature of each of the identified land uses. The study further reveals increasing variations in LST in all land uses which are results of rapid change LULC and modification of urban landscapes. Cities demonstrate greater temperature in their centre than the surrounding rural areas, which is known as Urban Heat Island effect. These forms a temperature difference between the cities and the surrounding suburbs because of the effect which causes discomfort to the city dwellers45. In recent years, the results from the present study demonstrate that a huge amount of vegetation cover is replaced by an artificial built surface that absorbs incoming solar radiation or heat and makes the cities warmer than ever before. The reasons for this outcome is very obvious as the majority of source landscapes in the cities centres are usually built-up surfaces distinguished from the concrete surface. According to the physical properties of the materials, thermal conductivity, and heat capacity, the materials with higher thermal conductivity conduct heat to their interior easier, whereas materials with high heat capacity store more heat and, as more heat is retained, the temperature of the material increases40,46,47.

It has been reported in the literature that the phenomenon exists in almost every big city. Numerous factors are held accountable for this effect, including land use and land cover changes in urban areas48, anthropogenic heat release, climatic conditions and air pollutants42,45. Lagos and Ibadan for instance are some of the fastest-growing cities in Africa today due to the rapid physical expansion of the cities. It is also reported that the population of the people living in Lagos has increased up to five times in the past 30 years which invariably means that there will be massive crowding in terms of establishing structures increased competition for amenities and increased thermal discomfort due to overcrowding of people as well as changes in land use and land cover. These findings are coherent with the results of the studies by Dewan and Corner48 and Ayanlade6 which revealed the impacts of land use and land cover changes on urban LST. Moreover, the results from the present study disclose a strong relation between LULC and urban temperature, especially regarding vegetation and built density. The major findings and innovation of this study are that different types of land cover within an urban area can affect the spatial pattern of the LST, though this varies from one ecological zone to another. Normally, the distribution of temperature in an urban area depends on its urban LULC. Based on findings from the present study, it can be established that vegetation availability plays a very low significant role in the temperature of the metropolis. The results from this study are also useful for urban heat mitigation efforts, which will aid policy maker in dealing with city challenges. Essentially as these are the factors directly related to the cities’ land surface temperature regime. The major findings from this study are useful in informing policy that will promote more sustainable urban development in the cities. What can be learned from this study is that cities’ population is expected to keep expanding and therefore there is a need to establish more viable linkages between the growing population and land use patterns, essentially because these are the factors directly related to the cities’ land surface temperature regime.

References

  1. 1.

    Masoudi, M. & Tan, P. Y. Multi-year comparison of the effects of spatial pattern of urban green spaces on urban land surface temperature. Landsc. Urban Plan. 184, 44–58 (2019).

    Article  Google Scholar 

  2. 2.

    Zhang, Y. & Sun, L. Spatial-temporal impacts of urban land use land cover on land surface temperature: Case studies of two Canadian urban areas. Int. J. Appl. Earth Obs. Geoinf. 75, 171–181 (2019).

    ADS  Article  Google Scholar 

  3. 3.

    Yang, J. et al. Investigating the diversity of land surface temperature characteristics in different scale cities based on local climate zones. Urban Clim. 34, 100700 (2020).

    Article  Google Scholar 

  4. 4.

    Oke, T. R. The energetic basis of the urban heat island. Q. J. R. Meteorol. Soc. 108, 1–24 (1982).

    ADS  Google Scholar 

  5. 5.

    Ayanlade, A. Variations in urban surface temperature: An assessment of land use change impacts over Lagos metropolis. Weather 72, 315–319 (2017).

    ADS  Article  Google Scholar 

  6. 6.

    Ayanlade, A. Variation in diurnal and seasonal urban land surface temperature: Landuse change impacts assessment over Lagos metropolitan city. Model. Earth Syst. Environ. 2, 193. https://doi.org/10.1007/s40808-016-0238-z (2016).

    Article  Google Scholar 

  7. 7.

    Senanayake, I. P., Welivitiya, W. & Nadeeka, P. M. Remote sensing based analysis of urban heat islands with vegetation cover in Colombo city, Sri Lanka using Landsat-7 ETM+ data. Urban Clim. 5, 19–35 (2013).

    Article  Google Scholar 

  8. 8.

    Yang, J. et al. Green and cool roofs’ urban heat island mitigation potential in tropical climate. Sol. Energy 173, 597–609 (2018).

    ADS  Article  Google Scholar 

  9. 9.

    Litardo, J. et al. Urban Heat Island intensity and buildings’ energy needs in Duran, Ecuador: Simulation studies and proposal of mitigation strategies. Sustain. Cities Soc. 62, 102387 (2020).

    Article  Google Scholar 

  10. 10.

    Grimmond, S. U. Urbanization and global environmental change: Local effects of urban warming. Geogr. J. 173, 83–88 (2007).

    Article  Google Scholar 

  11. 11.

    Zhou, X. & Chen, H. Impact of urbanization-related land use land cover changes and urban morphology changes on the urban heat island phenomenon. Sci. Total Environ. 635, 1467–1476 (2018).

    ADS  CAS  Article  Google Scholar 

  12. 12.

    Singh, P., Kikon, N. & Verma, P. Impact of land use change and urbanization on urban heat island in Lucknow city, Central India. A remote sensing based estimate. Sustain. Cities Soc. 32, 100–114 (2017).

    Article  Google Scholar 

  13. 13.

    Aghamohammadi, N., Ramakreshnan, L., Fong, C. S. & Sulaiman, N. M. Urban Heat Island, contributing factors, public responses and mitigation approaches in the tropical context of Malaysia. Urban Heat Island Mitigat. 2021, 107–121 (2021).

    Article  Google Scholar 

  14. 14.

    Kabano, P., Lindley, S. & Harris, A. Evidence of urban heat island impacts on the vegetation growing season length in a tropical city. Landsc. Urban Plan. 206, 103989 (2021).

    Article  Google Scholar 

  15. 15.

    Stewart, I. D. & Oke, T. R. Local climate zones for urban temperature studies. Bull. Am. Meteorol. Soc. 93, 1879–1900 (2012).

    ADS  Article  Google Scholar 

  16. 16.

    Rizwan, A. M., Dennis, L. Y. & Chunho, L. A review on the generation, determination and mitigation of Urban Heat Island. J. Environ. Sci. 20, 120–128 (2008).

    CAS  Article  Google Scholar 

  17. 17.

    Nuruzzaman, M. Urban heat island: Causes, effects and mitigation measures—a review. Int. J. Environ. Monit. Anal. 3, 67–73 (2015).

    Google Scholar 

  18. 18.

    Weng, Q., Lu, D. & Schubring, J. Estimation of land surface temperature–vegetation abundance relationship for urban heat island studies. Remote Sens. Environ. 89, 467–483 (2004).

    ADS  Article  Google Scholar 

  19. 19.

    Stewart, I. & Oke, T. Newly developed “thermal climate zones” for defining and measuring urban heat island magnitude in the canopy layer. In Eighth Symposium on Urban Environment, Phoenix, AZ 2009 (2009).

  20. 20.

    Shastri, H., Barik, B., Ghosh, S., Venkataraman, C. & Sadavarte, P. Flip flop of day-night and summer-winter surface urban heat island intensity in India. Sci. Rep. 7, 1–8 (2017).

    Article  Google Scholar 

  21. 21.

    Hu, Y. et al. Comparison of surface and canopy urban heat islands within megacities of eastern China. ISPRS J. Photogramm. Remote. Sens. 156, 160–168 (2019).

    ADS  Article  Google Scholar 

  22. 22.

    Ayanlade, A. & Jegede, O. Evaluation of the intensity of the daytime surface urban heat island: How can remote sensing help?. Int. J. Image Data Fusion 6, 348–365 (2015).

    ADS  Article  Google Scholar 

  23. 23.

    Sobrino, J. A., Li, Z.-L., Stoll, M. P. & Becker, F. Improvements in the split-window technique for land surface temperature determination. IEEE Trans. Geosci. Remote Sens. 32, 243–253 (1994).

    ADS  Article  Google Scholar 

  24. 24.

    Zhao, S., Qin, Q., Yang, Y., Xiong, Y. & Qiu, G. Comparison of two split-window methods for retrieving land surface temperature from MODIS data. J. Earth Syst. Sci. 118, 345 (2009).

    ADS  Article  Google Scholar 

  25. 25.

    Chatterjee, R. S., Singh, N., Thapa, S., Sharma, D. & Kumar, D. Retrieval of land surface temperature (LST) from landsat TM6 and TIRS data by single channel radiative transfer algorithm using satellite and ground-based inputs. Int. J. Appl. Earth Obs. Geoinf. 58, 264–277. https://doi.org/10.1016/j.jag.2017.02.017 (2017).

    ADS  Article  Google Scholar 

  26. 26.

    Pandya, M. R. et al. Retrieval of land surface temperature from the Kalpana-1 VHRR data using a single-channel algorithm and its validation over western India. ISPRS J. Photogramm. Remote. Sens. 94, 160–168. https://doi.org/10.1016/j.isprsjprs.2014.05.004 (2014).

    Article  Google Scholar 

  27. 27.

    Wang, N. et al. Evaluation and comparison of hyperspectral temperature and emissivity separation methods influenced by sensor spectral properties. Int. J. Remote Sens. 40, 1693–1708. https://doi.org/10.1080/01431161.2018.1484963 (2019).

    Article  Google Scholar 

  28. 28.

    Jacob, F. et al. Reassessment of the temperature-emissivity separation from multispectral thermal infrared data: Introducing the impact of vegetation canopy by simulating the cavity effect with the SAIL-Thermique model. Remote Sens. Environ. 198, 160–172. https://doi.org/10.1016/j.rse.2017.06.006 (2017).

    ADS  Article  Google Scholar 

  29. 29.

    Borel, C. C. & Tuttle, R. F. In 2011 Aerospace Conference. 1–14.

  30. 30.

    Kumari, B. et al. Longitudinal study of land surface temperature (LST) using mono-and split-window algorithms and its relationship with NDVI and NDBI over selected metro cities of India. Arab. J. Geosci. 13, 1–19 (2020).

    CAS  Article  Google Scholar 

  31. 31.

    Abdul Athick, A. S. M., Shankar, K. & Naqvi, H. R. Data on time series analysis of land surface temperature variation in response to vegetation indices in twelve Wereda of Ethiopia using mono window, split window algorithm and spectral radiance model. Data Brief 27, 104773. https://doi.org/10.1016/j.dib.2019.104773 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  32. 32.

    Qin, Z., Karnieli, A. & Berliner, P. A mono-window algorithm for retrieving land surface temperature from Landsat TM data and its application to the Israel-Egypt border region. Int. J. Remote Sens. 22, 3719–3746. https://doi.org/10.1080/01431160010006971 (2001).

    Article  Google Scholar 

  33. 33.

    Sobrino, J. A., Jiménez-Muñoz, J. C. & Paolini, L. Land surface temperature retrieval from LANDSAT TM 5. Remote Sens. Environ. 90, 434–440 (2004).

    ADS  Article  Google Scholar 

  34. 34.

    Tsou, J., Zhuang, J., Li, Y. & Zhang, Y. Urban heat island assessment using the Landsat 8 data: A case study in Shenzhen and Hong Kong. Urban Sci. 1, 10 (2017).

    Article  Google Scholar 

  35. 35.

    Chander, G., Markham, B. L. & Helder, D. L. Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Remote Sens. Environ. 113, 893–903 (2009).

    ADS  Article  Google Scholar 

  36. 36.

    Ahmad, A. & Quegan, S. Analysis of maximum likelihood classification on multispectral data. Appl. Math. Sci. 6, 6425–6436 (2012).

    MathSciNet  Google Scholar 

  37. 37.

    Otukei, J. R. & Blaschke, T. Land cover change assessment using decision trees, support vector machines and maximum likelihood classification algorithms. Int. J. Appl. Earth Obs. Geoinf. 12, S27–S31 (2010).

    Article  Google Scholar 

  38. 38.

    Snyder, W. C., Wan, Z., Zhang, Y. & Feng, Y.-Z. Classification-based emissivity for land surface temperature measurement from space. Int. J. Remote Sens. 19, 2753–2774 (1998).

    Article  Google Scholar 

  39. 39.

    Sobrino, J., Caselles, V. & Becker, F. Significance of the remotely sensed thermal infrared measurements obtained over a citrus orchard. ISPRS J. Photogramm. Remote. Sens. 44, 343–354 (1990).

    ADS  Article  Google Scholar 

  40. 40.

    Kesikoglu, M. H., Ozkan, C. & Kaynak, T. The impact of impervious surface, vegetation, and soil areas on land surface temperatures in a semi-arid region using Landsat satellite images enriched with Ndaisi method data. Environ. Monit. Assess. 193, 1–21 (2021).

    Article  Google Scholar 

  41. 41.

    Xiao, R.-B. et al. Spatial pattern of impervious surfaces and their impacts on land surface temperature in Beijing, China. J. Environ. Sci. 19, 250–256 (2007).

    Article  Google Scholar 

  42. 42.

    Atasoy, M. Assessing the impacts of land-use/land-cover change on the development of urban heat island effects. Environ. Dev. Sustain. 22, 7547–7557 (2020).

    Article  Google Scholar 

  43. 43.

    Ayanlade, A. Seasonality in the daytime and night-time intensity of land surface temperature in a tropical city area. Sci. Total Environ. 557, 415–424 (2016).

    ADS  Article  Google Scholar 

  44. 44.

    Das, S. & Angadi, D. P. Land use-land cover (LULC) transformation and its relation with land surface temperature changes: A case study of Barrackpore Subdivision, West Bengal, India. Remote Sens. Appl. Soc. Environ. 19, 100322 (2020).

    Google Scholar 

  45. 45.

    Yamamoto, Y. Measures to mitigate urban heat islands. Report No. 1349-3663, (NISTEP Science & Technology Foresight Center, 2006).

  46. 46.

    Mpakairi, K. S. & Muvengwi, J. Night-time lights and their influence on summer night land surface temperature in two urban cities of Zimbabwe: A geospatial perspective. Urban Clim. 29, 100468 (2019).

    Article  Google Scholar 

  47. 47.

    Dutta, D., Rahman, A., Paul, S. & Kundu, A. Impervious surface growth and its inter-relationship with vegetation cover and land surface temperature in peri-urban areas of Delhi. Urban Clim. 37, 100799 (2021).

    Article  Google Scholar 

  48. 48.

    Dewan, A. M. & Corner, R. J. The impact of land use and land cover changes on land surface temperature in a rapidly urbanizing megacity. IEEE Int. Geosci. Remote Sens. Symp. 2012, 6337–6339 (2012).

    Google Scholar 

Download references

Acknowledgements

The authors thank NOAA Climate Diagnostics Center and NASA Langley Research Center for providing satellite data used in this study. Thanks to the International Research Institute for Climate and Society (IRI), New York, USA, for providing the initial training on satellite data acquisition and climate data analysis. This work was partly funded by the Tertiary Education Trust Fund, TETFund NRF 2020 Nigeria (GrantAward-TETF/DR&D-CE/NRF2020/CC/17/VOL.1).

Author information

Affiliations

Authors

Contributions

A.A., M.I.A., and O.R.O. conceived and designed the study, contributed to methods development, conducted the analysis and interpretation. A.A. and M.I.A. contributed in data/literature collection/analysis and to the manuscript preparation. Both A.A. and O.R.O. supervised the Postgraduate research thesis of M.I.A. All authors contributed to the paper writing.

Corresponding author

Correspondence to Ayansina Ayanlade.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Ayanlade, A., Aigbiremolen, M.I. & Oladosu, O.R. Variations in urban land surface temperature intensity over four cities in different ecological zones. Sci Rep 11, 20537 (2021). https://doi.org/10.1038/s41598-021-99693-z

Download citation

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing