Next Article in Journal
Utilization of Image, LiDAR and Gamma-Ray Information to Improve Environmental Sustainability of Cut-to-Length Wood Harvesting Operations in Peatlands: A Management Systems Perspective
Next Article in Special Issue
Characterizing the Up-To-Date Land-Use and Land-Cover Change in Xiong’an New Area from 2017 to 2020 Using the Multi-Temporal Sentinel-2 Images on Google Earth Engine
Previous Article in Journal
Understanding the Relationship between Dominant Geo-Environmental Factors and Rural Poverty in Guizhou, China
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Spatiotemporal Influence of Land Use/Land Cover Change Dynamics on Surface Urban Heat Island: A Case Study of Abuja Metropolis, Nigeria

1
College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China
2
International Center for Architecture and Urban Development Studies, Zhejiang University, Hangzhou 310058, China
3
Institute of Applied Remote Sensing and Information Technology, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2021, 10(5), 272; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10050272
Submission received: 24 February 2021 / Revised: 11 April 2021 / Accepted: 22 April 2021 / Published: 23 April 2021

Abstract

:
Rapid urbanization in cities and urban centers has recently contributed to notable land use/land cover (LULC) changes, affecting both the climate and environment. Therefore, this study seeks to analyze changes in LULC and its spatiotemporal influence on the surface urban heat islands (UHI) in Abuja metropolis, Nigeria. To achieve this, we employed Multi-temporal Landsat data to monitor the study area’s LULC pattern and land surface temperature (LST) over the last 29 years. The study then analyzed the relationship between LULC, LST, and other vital spectral indices comprising NDVI and NDBI using correlation analysis. The results revealed a significant urban expansion with the transformation of 358.3 sq. km of natural surface into built-up areas. It further showed a considerable increase in the mean LST of Abuja metropolis from 30.65 °C in 1990 to 32.69 °C in 2019, with a notable increase of 2.53 °C between 2009 and 2019. The results also indicated an inverse relationship between LST and NDVI and a positive connection between LST and NDBI. This implies that urban expansion and vegetation decrease influences the development of surface UHI through increased LST. Therefore, the study’s findings will significantly help urban-planners and decision-makers implement sustainable land-use strategies and management for the city.

1. Introduction

The world has recently witnessed an increased urban population due to perceived socio-economic opportunities in cities, contributing to rapid urbanization [1]. The global population in urban centers and cities has grown from 1.731 billion inhabitants (39.35%) in 1980 to 3.968 billion (53.91%) in 2015, and is further predicted to over 9.725 billion (68%) by 2050 [2]. The projection indicates that 35% of this growth is expected to occur in Africa and Asia in the next three decades. The consequence of this growth is the tremendous changes in land use/land cover (LULC) pattern and the alteration of various biophysical climatic conditions, particularly the Surface Urban Heat Island (UHI) that is measured using land surface temperature (LST) [3,4,5]. The transformation of land-use such as wetlands, vegetation, and agricultural areas into built-up and impervious surfaces can considerably influence LST [6]. Therefore, land use/land cover change dynamics are crucial factors influencing surface UHI due to the unique qualities (i.e., surface reflectance and roughness) attributed to each LULC category regarding its radiation and absorption energy [7]. Studies of rapidly growing cities globally indicate an increased LST, which usually forms an urban heat island due to the dramatic changes in land-use associated with urbanization [8,9,10]. This growth has also contributed to high-energy demand that affects human health and wellbeing due to air pollution and greenhouse gas (GHG) emissions. Therefore, the study of LULC changes and their influence on surface UHI using land surface temperature as a key indicator is crucial in implementing policies and strategies aimed at mitigating the negative impacts of urban growth due to rapid urbanization.
The use of remotely sensed data and Geographical Information Systems has been widely considered as a responsive tool in urban climatic studies for achieving sustainable cities [11,12,13,14]. It provides an accurate, timely, and reliable method of measuring several spatio-temporal variations and indices in a cost-effective approach [15]. The use of satellite datasets provides a medium to high-resolution satellite imager capable of continually monitoring the earth’s surface and atmosphere. Satellite-derived images are often utilized for the inventory and mapping of LULC changes [16,17,18]. The continuous availability of various satellite sensors such as Landsat 4 and 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), and Landsat 8 Operational Land Imager (OLI) has frequently been utilized to provide the necessary data for monitoring the recent changes in LULC and its influence on surface UHI [19,20,21]. The process involves using GIS techniques to quantitatively analyze previous LULC conditions in detecting changes related to the various satellite-derived indices [22,23,24,25].
Spectral indices from remotely sensed data usually give a comprehensive understanding of the relationship between LST, which is crucial in measuring surface UHI, and LULC conditions [26,27,28]. The most common satellite-derived indexes for estimating spatio-temporal variations of land surface temperature (LST) are the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Built-up Index (NDBI) [29,30]. These indices are indicators of LULC changes in the relationship with LST [31,32]. The correlation can be achieved using scatter plots and regression analysis. Previous studies have analyzed the different relationships between LULC, LST, NDVI, and NDBI. In Shenzhen city, located in China’s Pearl River Delta, a negative correlation was established between NDVI and LST, while the connection between LST and NDBI was positive [30]. A study in Tehran’s Metropolitan city indicated a negative correlation between vegetation index and land surface temperature [27]. Similar studies in Sivas city, Turkey [15], Egypt’s greater Cairo region [19], and some megacities of southern Asia such as Bangkok (Thailand), Manila (Philippines), and Jakarta (Indonesia) [28] revealed significant interactions, more precisely negative correlation, between land surface temperature and NDVI and a positive correlation between LST and built-up surfaces. These results were predominantly attributed to the cities’ continuous growth and expansion due to urbanization and socio-economic developments, which influenced land-use and regional climate changes. Studies on changes in LULC and surface UHIs help mitigate the adverse effects of climate change by analyzing the implications of various human activities and providing adaptive strategies aimed at sustainable management of land-use. [33], hence, helping significantly improvements in the liveability of cities.
Although several studies exist on LULC scenarios of selected cities in developing countries such as Nigeria [34,35,36,37,38,39], comprehensive studies on the spatiotemporal analysis of LULC changes and their influence on the surface UHI of Nigeria’s rapidly growing cities are still relatively limited to non-existent. Abuja, Nigeria’s capital and one of Nigeria’s largest cities, has been under tremendous pressure over the last few decades due to rapid urbanization and population growth. Like many other developing megacities, the city has rapidly experienced various LULC changes, mainly an increasing built-up area and decreasing vegetation. The continuous alteration of land-uses for residential, commercial, and industrial activities often contributes to climate change, particularly global warming, through increased UHI. Therefore, to effectively reduce the surface UHI in Abuja Metropolis, it is of utmost importance to study the LULC change scenario and its relationship with LST. The present study aims to monitor and analyze the spatio-temporal trends of LULC changes and establish their relationship with the LST changes of Abuja Metropolis, Nigeria, using high-resolution satellite datasets and GIS techniques. More specifically, the study seeks to (i) map and analyze the various changes in the LULC pattern of Abuja metropolis over the last 29 years (i.e., 1990–2019); (ii) study the city’s distribution of LST, NDVI, and NDBI; (iii) correlate and analyze LST with satellite-derived indices comprising NDVI and NDBI.
The study will help in advocating urban planning policies and adaptive strategies aimed at developing and improving the city’s liveability. The study area overview alongside the materials and methods utilized for this study are discussed in Section 2 and Section 3. Section 4 and Section 5 present the results and discuss the study’s findings. Finally, Section 6 highlights the concluding remarks and suggests pathways for future research.

2. The Study Area

Abuja, popularly called Federal Capital Territory (FCT), is Nigeria’s capital city, situated in Nigeria’s North-central region at about 840 m above mean sea level. It lies between Latitude 8°24′ N and 9°28′ N and Longitude 6°40′ E and 7°45′ E covering an area of approximately 7760 square kilometers (Figure 1). It has a tropical wet and dry climatic condition, i.e., non-arid, according to the Koppen-Geiger’s classification, with an annual temperature ranging between 30–37 °C and a mean annual total precipitation of approximately 1650 mm per annum [40]. The metropolis experiences a warm, humid rainy season between April and October and a blistering dry season between November and March. The dry season’s main features include dust-laden wind, harmattan haze, and intensified cold and dryness. The study area has a high altitude and undulating terrain that moderates the city’s climatic conditions [41]. The Guinea-Savannah vegetation characterizes the city due to its abundant rainfall and strategic position between Nigeria’s northern and southern ecological transitional zone type and has fertile agricultural land with maize, millet, guinea corn, and tubers as the dominant crops [42,43,44]. Abuja’s metropolis has recently witnessed a continuous influx of populace due to its centrality and the deliberate establishment of government and private institutions, contributing to the development of satellite towns, and thereby expanding the urban area. The population has grown from within the city’s metropolis to the fringes of the four (4) other area councils that comprise Kuje, Gwagwalada, Bwari, and Kwali. Studies have shown that Nigeria’s high rural–urban migration and the relocation of the country’s capital from Lagos to Abuja have contributed to the city’s population increasing from 364,086 in 1991 to 759,547 in 1999 to 1,429,801 in 2006 [45]. The population is presently estimated to be over 3.2 million [46]. The United Nations Population Prospects estimates, that with the city’s steady growth rate, Abuja is expected to have approximately 5.1 million inhabitants by 2030 [47]. This growth’s consequences are land-use changes and microclimate modification due to urban heat island (UHI).

3. Materials and Methods

3.1. Data Acquisition and Pre-Processing

To identify the changes in LULC, LST, NDVI, and NDBI. We acquired an image for each year under study, i.e., 1990, 1999, 2009, and 2019, using various remotely sensed satellite images presented in Table 1. These images were downloaded without any cost from path 189, row 54 of the Earth Observing System of the United States Geological Survey (USGS) (https://earthexplorer.usgs.gov, accessed on 22 April 2021). We deliberately obtained datasets at an interval of 10 years to ensure uniformity between the time-nodes. However, the unavailability of the 1989 satellite data led to the utilization of the subsequent year’s image. The datasets were acquired during the dry season, more precisely in January and February, to obtain cloud-free images, minimize seasonal effects, and ensure accurate image comparison. Studies indicate that spectral images acquired by satellite sensors are often affected by numerous factors such as sensor calibration, atmospheric absorption, scattering, and illumination geometry [48]. As a result, all the acquired images were subjected to radiometric calibration and geometric corrections to rectify the various surface reflectance variations due to the acquiring systems. This pre-processing operation improves atmospheric absorption/scattering, sensor sensitivity, topography and sun angle, scene illumination, and visible near-infrared wavelengths [49,50]. The pre-processed images were then employed to map LULC using visible light bands and LST using the thermal infrared band. Auxiliary data in the form of reference maps (Abuja Master Plan and land-use maps) were obtained from Abuja Geographic Information Systems (AGIS) and Federal Capital Development Authority (FCDA), which are government agencies responsible for the city’s planning and development. However, the city’s ground truth condition was analysed using Google earth imagery of 12th February 1990, 28th January 1999, 15th January 2009, and 4th February 2019 due to poor adherence to the master plan of the city [51].

3.2. Methods

3.2.1. LULC Classification

The classification of satellite images in urban centers and cities is considered a complex process due to its spectral heterogeneity [52,53,54]. Several classification methods have been employed in previous studies using remote sensing data and geospatial techniques to map satellite image pixels into various land use/land cover [55]. In this study, we employed Maximum Likelihood (ML) using the supervised classification method to classify LULC for the different study periods. ML is one of the most widely used methods for classifying LULC due to its high classification accuracy with appropriate selection of training data [56,57,58,59]. The study deliberately developed the city’s LULC classification scheme after a careful study of relevant literature, reference maps, and field observations. The study area’s land-use was then categorized into four (4) classes encompassing the built-up area, vegetation, barren land, and water bodies. The built-up areas represent all residential, commercial, industrial, and related urban infrastructural facilities. The vegetation class signifies agricultural lands and other grass-cover areas, while the barren land represents the city’s non-inhabited areas, as described in Table 2.
The main procedures for mapping the LULC classification include: (i) creating training samples, i.e., using polygons that represent the four LULC classes to be classified, (ii) using the satellite images to achieve supervised classification with the aid of maximum likelihood classification (MLC) and, (iii) evaluating the accuracy of the classified images using the Kappa coefficient [59,60,61].

3.2.2. Accuracy Assessment

A quantitative assessment was utilized to evaluate the study’s land cover classification. For the Accuracy Assessment, the study employed a stratified random sampling approach to generate sample points of the study area. These samples were used to compare classified image pixels with reference data for each year. The validation/testing points used for the accuracy assessment were independent of the training points used for image classification (i.e., different locations were selected for the training and validation). About 450 samples were created for each year to ensure the reliability of the results. Seventy percent of samples were used as training samples while 30% were used for validation. For each year, a minimum of 100 samples was created for each LULC class. The study then used the validation samples of the different years to assess the classified image accuracy. The results were statistically presented and analyzed using the confusion (error) matrix approach [61,62,63,64]. The confusion matrix is widely used for deriving analytical and descriptive data in classification accuracy. It comprises numbers displayed in columns and rows that present the various sample points (i.e., polygons, pixels, or pixel clusters) allocated to a specific land cover class relative to the class’s actual ground condition [65]. The matrix has an overall accuracy comprising Producer and User Accuracy and Kappa coefficient (KC) as its assessment indices [64,65,66,67]. Producer Accuracy is the ratio of the total classified pixels in the error matrix diagonals to the total classified pixels in that category of the error matrix column. User Accuracy is the ratio of the total correctly classified pixels in the error matrix diagonals to the total classified pixels in that category of the error matrix row. Overall accuracy is the ratio of correctly classified pixels to the classified reference pixels. Finally, the Kappa index ‘KC’ was calculated as adopted by [68] using Equation (1).
Kappa   Coefficient   ( KC ) = N Σ i = 1 r x i i Σ i = 1 r x i + × x + i N 2 i = 1 r x i + × x + i ,
where N is the sum of pixels in the error matrix; r is the sum of columns/rows; x i i is the value correctly classified pixels in the ith column and row; x + i is the sum of pixels in the ith column; x i + is the sum of pixels in the ith row, and N2 is the square of the total number of pixels.
The Kappa Coefficient (KC) is a non-parametric index used in evaluating the level of agreement between pre-defined values and user-assigned value [69]. It has values between 0 and 1 with the result below 0.40, i.e., 40% demonstrating a weak agreement. A result ranging between 0.40 to 0.80 signifies a moderate agreement, while values above 0.80, i.e., 80%, signifies a good agreement [65]. Previous studies recommended the adoption of 80% as the minimum accuracy level for land use/land cover classification assessment [66,70].

3.2.3. Land Surface Temperature (LST) Retrieval

The study employed thermal infrared (TIR) bands to retrieve and map the study area’s LST. This process uses a radiometric calibration technique that relies on an image header file, gain offset, solar radiation angle, and various calibration parameters. The procedure involves converting digital numbers (DNs) of thermal bands into spectral radiance values [71,72]. These values are then used in deriving the at-satellite (sensor) brightness temperature quantified in degrees Kelvin (°K), which were computed using thermal Conversion Constants [73,74,75]. The at-sensor brightness temperature values were further converted into degrees Celsius (°C) to derive the LST. The procedures used for the retrieval of LST are discussed below.
1.
Conversion of DN to spectral radiance conversion
The DN of thermal infrared (TIR) bands were converted into spectral radiance with the aid of ArcGIS 10.7.1 image processing software using the Radiative Transfer Equation (RTE) presented in Equations (2) and (3) [76,77].
  • For Landsat TM and ETM+
    L λ = L MAX λ   L MIN λ Q CAL MAX Q CAL MIN × Q CAL Q CAL MIN + L MIN λ ,
    where L λ is the value of spectral radiance; Q CAL represents the DN value of the quantized calibrated pixel; L MAX λ represents the value of spectral radiance in ( Wm 2 sr 1 μ m 1 ) scaled to Q CAL MAX ; L MIN λ represents the value of spectral radiance in ( Wm 2 sr 1 μ m 1 ) scaled to Q CAL MIN ; Q CAL MIN   and   Q CAL MAX are the min. and max. DN values of the quantized calibrated pixels that correspond to L MIN λ   and   L MAX λ , respectively.
  • For Landsat OLI/TIRS
    L λ = M L ×   Q CAL + A L ,
    where L λ represents the top of the atmosphere spectral radiance in ( W m 2 s r 1 μ m 1 ) ; M L is the rescaling factor for radiance multiplicative band obtained from metadata (i.e., Radiance_Mult_Band 10); Q CAL is the DN value of the calibrated and quantized product pixel; and A L is the rescaling factor for the radiance additive band obtained from metadata (i.e., Radiance_Add_Band 10).
2.
Conversion of spectral radiance to TOA brightness temperature (BT)
For this, spectral radiance values of the converted pixels digital numbers were used to extract the top of atmosphere (TOA) brightness temperature (BT), also known as satellite-derived temperature, and expressed in Kelvin. Using uniform emissivity assumption, the brightness (sensor) temperature values were computed using Equation (4) [3,27,78].
B T = K 2 ln K 1 L λ + 1 ,
where B T is brightness temperature at the top of atmosphere (TOA) expressed in °K; L λ is the spectral radiance at TOA expressed in ( Wm 2 sr 1 μ m 1 ) ; K 1 and K 2 are the retrived metadata’s thermal conversion constants (presented in Table 1).
3.
Derivation of Land Surface Temperature from brightness temperature (BT)
The study then derived the emissivity values of the corrected LST (in Kelvin) with the aid of at-satellite brightness temperature (TB) using Equation (5) [3,27,79,80].
LST   ° K = B T 1 +   λ T B E ln ε ,
where B T is the brightness temperature at-satellite (sensor); λ is the wavelength of emitted radiance (i.e., 11.5 μ m in Band 6 for Landsat 4/5/7 and 10.8 μ m in Band 10 for Landsat 8); E is   h   × v / s ( 1.4388 × 10 2   mK ) ; h represents the Planck’s constant ( 6.626 × 10 34   mK ) ; v represents the velocity of light ( 2.998 × 10 8   m / s ) ; s represents the Boltzmann constant ( 1.38 × 10 23   JK ) , and ε represents emissivity of the land surface.
We calculated the emissivity of the land surface   ε in the study using Equation (6) [81]
ε = N P v   + n ,
where N is 0.004; n is 0.986; and P v is the vegetation proportion expressed in Equation (7) [82].
P v   = NDVI NDVI min NDVI max     NDVI min 2 ,
where NDVI are the values of DN obtained from the NDVI image; NDVI max and NDVI min are the highest and lowest DN values obtained from the NDVI image.
Lastly, the study converted the Land Surface Temperature value (in Kelvin) into degree Celsius (°C) using Equation (8) [27,79,81].
LST   ° C = LST   ° K 273.15 ,

3.2.4. Normal Difference Vegetation Index (NDVI) Estimation

One of the most commonly used urban climate indicators in environmental studies is the Normalized Difference Vegetation Index (NDVI) [3,83], which serves as a reliable index for extracting vegetation conditions of remotely sensed data [11]. Therefore, we employed the NDVI to examine the study area’s vegetation distribution and extract emissivity values. The index is often associated with various other indices such as biomass, leaf area, and vegetation cover percentage and, as such, is closely related to the vegetation proportion ( P v ) that is needed in calculating land surface emissivity ε . The NDVI has values ranging between 1 and + 1, where negative values indicate non-vegetated areas and positive values represent vegetated areas [84]. It is often calculated based on image pixels using the normalized difference between the near-infrared band (i.e., band 4 in Landsat TM and band 5 in Landsat OLI) and red band (i.e., band 3 in Landsat TM and band 4 in Landsat OLI) [28,85]. The NDVI of the study area was extracted using Equation (9) [79,80].
NDVI = NIR Band   4 , 5       RED   Band   3 ,   4     NIR Band   4 , 5   +   RED   Band   3 ,   4   ,
where NIR Band   4 is 0.76 0.90   μ m (For Landsat 4–5 TM) and NIR Band   5 is 0.85 0.88   μ m (For Landsat 8 OLI). RED Band   3 is 0.63 0.69   μ m (For Landsat 4–5 TM and Landsat 7 ETM+) and RED Band   4 is 0.64 0.67   μ m (For Landsat 8 OLI).

3.2.5. Normalized Difference Built-Up Index (NDBI) Estimation

The Normalized Difference Built-up Index (NDBI) is another vital urban climate indicator for environmental monitoring [3,68]. This serves as an effective method of mapping and analyzing land-uses by providing information on the spatial extent of built-up areas and impervious surfaces. The NDBI designates built-up area’s density in unit pixel, with values ranging from positive 1 to negative 1. The negative values often signify vegetation, while the positive denotes built-up urban/impervious surfaces [3,28]. The NDBI was estimated using the mid and near-infrared bands presented in Equation (10).
NDBI = MIR Band   5 ,   6   NIR   Band   4 ,   5   MIR Band   5 ,   6 + NIR   Band   4 ,   5   ,
where MIR Band   5 . is 1.55 1.75   μ m (For Landsat 4–5 TM and Landsat 7 ETM+) and MIR Band   6 is 1.57 1.65   μ m (For Landsat 8 OLI). NIR Band   4 is 0.76 0.90   μ m (For Landsat 4–5 TM and Landsat 7 ETM+) and NIR Band   5 is 0.85 0.88   μ m (For Landsat 8 OLI).
The methodological flow chart illustrated in Figure 2 summarizes the several procedures used in this study.

3.2.6. Correlation Analysis

The study employed a correlation analysis to analyze LULC changes on surface UHI using the LST of Abuja Metropolis. We performed linear regression analysis using scatter plots of all four time nodes (i.e., 1990, 1999, 2009, and 2019) to examine the relationship between the different study variables. This was achieved by converting the study area’s pixels into point data. These points’ parametric values were then retrieved from the derived maps of the different periods using 1371 sample points for each period under consideration. Pearson’s correlation coefficient ‘ r ’ was further employed to effectively quantify and analyze the study’s variables using Equation (11).
r = i = 1 n x i x ¯ × y i y ¯ i = 1 n x i x ¯ 2 × i = 1 n y i y ¯ 2 ,
where r represents the Person’s correlation coefficient; x represents the independent variables measuring the value of x i ; y represents the dependent variable measuring value of y i ; x i   and   y i represents the individual sample points indexed i ; while x ¯   and   y ¯ represents the mean of the samples.

4. Results

This section presents and discusses the study’s results. It analyzes the historical trend of LULC patterns, and distribution of LST, NDVI, and NDBI. The section also studies LULC changes and their influence on surface UHI by analyzing the city’s LST variations with LULC classes, NDVI and NDBI.

4.1. Land Use/Land Cover Classification

The classified land cover maps of Abuja metropolis for the different periods (i.e., 1990, 1999, 2009, and 2019) are presented in Figure 3 and quantified in Table 3. The LULC were classified into four broad classes. These classes comprising built-up areas, vegetation, barren land, and water bodies, are the earlier defined land cover categories of the study area in Section 3.2.1. The metropolis covers approximately 1722.99 sq. km.
The result reveals the built-up areas to have expanded the most among the four LULC classes in the metropolis. However, the city’s vegetation cover decreased continuously throughout the study period. The gradual decrease in vegetation cover can be attributed to urban growth and human interference to the natural environment, which led to the continuous cutting down of forest areas to accommodate the populace’s influx. The results indicate vegetation loss of about 252.33 sq. km (14.65%) during the study period due to various human activities. Barren land witnessed a slight increase from 1990 to 2019, which can mainly be attributed to the massive construction and urban development in the metropolis. The water bodies in the metropolis declined by approximately 162.22 sq. km (9.41%) between 1990 and 2019. The distribution of the individual LULC classes extracted from the four years’ LULC classified maps are graphically presented in Figure 4a,b. The results show the study area to have undergone four epochs of notable change that might negatively affect the environment by influencing surface urban heat islands due to the various LULC changes.

4.2. Accuracy Assessment of Land Use/Land Cover Classification

As earlier stated, the land use/land cover pattern of Abuja metropolis was defined in four LULC classes that comprise built-up areas, vegetation, barren land, and water bodies. In this study, the Maximum Likelihood Algorithm (MLA) was employed for the LULC classification. The accuracy assessments of each year were evaluated using the error matrix that shows the correctly and incorrectly classified pixels as presented in Table 4. The producer accuracy and user accuracy of each LULC class in the different period is also shown in Table 5. The Kappa coefficient was further employed for the assessment of LULC classification accuracy. The overall accuracies of the four periods were above 90%, signifying a reliable land cover classification and a good agreement between classified maps and referenced maps [66,70]. Kappa coefficients ranging between 0.87 and 0.93 were observed during the study period.

4.3. Change Detection Analysis

Remotely sensed data are useful in detecting and analyzing spatiotemporal changes in LULC. The analysis of land cover changes due to urban growth and rapid urbanization often helps monitor the negate e effects of various human activities on the environment. The present study analyzed the LULC changes of the Abuja metropolis between 1990 and 2019 in five (5) different periods. These periods include: period 1 (1990–1999), period 2 (1999–2009), period 3 (2009–2019), period 4 (1990–2009), and period 5 (1990–2019). The study utilized the four identified (4) LULC classes to analyze the study area’s mapping. The results are quantitatively presented in Table 6, showing each period’s LULC change in sq. km and percentage.
The study revealed notable spatiotemporal LULC changes during the period, showing both negative and positive changes in the various LULC classes, which may influence the ecosystem and are likely to contribute to varying climatic conditions.
During period 1, the land use/land cover change was characterized by an expansion in the magnitude of built-up areas and barren land while vegetation cover and water bodies decreased significantly. These positive and negative changes may be attributed to the city’s growth and development to an urban settlement due to the relocation of Nigeria’s capital city to Abuja in 1991. During period 2, the study area witnessed a slight increase in built area by approximately 20.83 sq. km while barren land increased by about 59.47 sq. km. Vegetation and waterbodies continued along this decreasing trend in this period, declining by 45.53 sq. km and 34.77 sq. km. During period 3, the metropolis witnessed an abrupt increase in built areas and a rapid decrease in barren land. Likewise, vegetation declined substantially during this period. The results suggest that water bodies experienced little or no significant change between 2009 and 2019 compared to other LULC classes due to human-induced activities.
The city’s change detection result at an interval of 9 years (1990–1999), 19 years (1990–2009), and 29 years (1990–2019) revealed remarkable LULC changes. The study indicated an average annual change in built-up areas by approximately 8.94 sq. km, 5.33 sq. km, and 13.46 sq. km during the span of 9 years, 19 years, and 29 years. The city’s barren land increased annually by 18.84 sq. km during the period between 1990–1999. However, this rate declined to 12.05 sq. km between 1990 and 2009. A decreasing trend of approximately 9.13 sq. km and 8.25 sq. km were observed annually in vegetation and water bodies between 1990 and 2009. Likewise, between 1990 and 2019, the city’s vegetation and waterbodies declined annually by 8.70 sq. km and 5.59 sq. km, respectively.
Therefore, the LULC change scenarios of the study area suggest the development and expansion of built-up areas, depicting the rapid urban growth of the metropolis. This increase in built areas may have contributed to the negative changes in some LULC classes, as illustrated in Figure 5.
Figure 6 shows the LULC transition map of the Abuja metropolis from 1990 to 2019 and the result presented in Figure 7. This indicates approximately 969.99 sq. km changes in the study area’s different LULC classes over 29 years. The results show 301.24 sq. km (17.48%) of barren land converted into built-up areas as the highest land cover transition between 1990 and 2019. It was seconded by the transformation of 289.41 sq. km (16.80%) of vegetation into the barren land and subsequently followed by the conversion of 158.27 sq. km (9.19%) of water bodies into barren land. A moderate transition was observed in the conversion of barren land into vegetation with 84.75 sq. km (4.92%), while 45.06 sq. km (2.62%) of vegetation was transformed to built-up areas. On the other hand, 23.64 sq. km (1.37%) of vegetation was converted into water bodies, and 22.62 sq. km (1.31%) of water bodies were converted to vegetation. A minuscule transition of 0.82 sq. km (0.05%) was seen in the transformation of built-up areas into water bodies.

4.4. LST Distribution and Its Relationship with LULC

The spatial distribution of LST in Abuja Metropolis for the years 1990, 1999, 2009, and 2019 were extracted as described in Section 3.2.3 and illustrated in Figure 8. The statistical data are presented in Table 7. The results indicate that the LST of Abuja metropolis ranged between approximately 20.30–37.11 °C, 21.50–44.46 °C, 20.55–46.34 °C, and 20.58–40.13 °C during the four distinct periods (i.e., 1990, 1999, 2009, and 2019, respectively). The result revealed a substantial increase in the mean LST of the metropolis from approximately 30.65 °C in 1990 to 32.69 °C in 2019. The LST analysis indicates that between 1990 and 1999, the mean LST of the metropolis has decreased by 0.25 °C. A similar decrease of 0.24 °C was observed between 1999 and 2009. However, the metropolis witnessed an increase in the mean LST with roughly 2.50 °C between 2009 and 2019. This result indicates a mean LST increase of about 2.04 °C over the last 29 years.
Therefore, to effectively analyze LST and LULC relationship, it is essential to study the thermal signature of individual land use/land cover classes [73]. In this study, the LST and LULC comparison was carried out using numerous sampling points. These points were selected to compare the four LULC classes with the LST values of the study area in 1990, 1999, 2009, and 2019. The mean LST values of each LULC class were computed by averaging the specific land cover category’s image pixels. The results of the four distinct periods under consideration are presented statistically in Table 8.
The mean LST value of the built-up areas was established to be 31.09 °C in 1990 and by 1999 this had reduced to 30.27 °C. However, it rose slightly to 30.50 °C in 2009 and further increased to 32.98 °C in 2019. The result clearly shows that the Abuja metropolis’ built areas witnessed a higher mean LST of 1.89 °C in 2019 than in 1990. Analysis of the different periods indicates the mean LST in built-up areas has experienced the highest increase of 2.48 °C between 2009 and 2019. The mean LST for vegetation was 28.18 °C in 1990, and subsequently, in 1999, it reduced to 27.44 °C. However, the mean LST increased to 28.05 °C in 2009 and increased further to 29.67 °C in 2019. Therefore, it is evident that vegetation witnessed a rise of 1.49°C in mean LST from 1990 to 2019. The result revealed that the mean LST of vegetation observed the most significant rise of 1.62 °C between 2009 and 2019. The mean LST value of barren land was 31.83 °C in 1990, which decreased slightly to 31.12 °C in 1999. The result showed a further decline to 30.73 °C in 2009 and a rapid increase to 33.23 °C in 2019. Therefore, it is apparent that barren land experienced various changes with a higher LST value of 1.40 °C in 2019 than in 1990. Additional analysis reveals that barren land has experienced the highest increase in mean LST of 2.50 °C from 2009 to 2019 and the lowest decrease of 0.39 °C from 1999 to 2009. The study also revealed the mean LST of water bodies in 1990 to be 30.15 °C, which decreased slightly to 30.09 °C in 1999. The mean LST further declined to 29.21 °C in 2009, but significantly increased to 30.65 °C in 2019. The result indicates an increase of 0.50 °C in the mean LST of water bodies from 1990 to 2019, signifying the lowest mean LST change during the study period.

4.5. NDVI and Its Relationship with LST

The derived maps of the Normalized Difference Vegetation Index of Abuja Metropolis are presented in Figure 9 which portrays the four different study periods, i.e., 1990, 1999, 2009, and 2019. The statistical results are quantified and presented in Table 9. The result revealed the highest NDVI values ranging between approximately 0.29 and 0.54, with such areas having mostly shrubs, grasslands, cultivated lands, and undeveloped natural surfaces, while the lowest NDVI values ranged between −0.09 to −0.39, with such areas covering mainly built-up areas, barren land, and water bodies.
The results demonstrated the highest NDVI in the southern part and north-eastern fringes of the metropolis, mainly covered by forest areas and vegetation. To examine the relationship between LST and NDVI, we generated 1371 random sample points for each period’s scattered plots (i.e., 1990, 1999, 2009, and 2019). The results are shown in Figure 10, indicating a negative relationship between the values of LST and NDVI in all four periods. The scatter plots analysis results show a considerable decline in the determination coefficient during each period. This had a value (R2) of approximately 0.42 in 1990, 0.40 in 1999, 0.38 in 2009, and 0.20 in 2019.

4.6. NDBI and Its Relationship with LST

The spatiotemporal maps of the Normalized Difference Built-up Index of Abuja Metropolis are presented in Figure 11 and quantified in Table 10. The results of the different periods (i.e., 1990, 1999, 2009, and 2019) indicates that the metropolis’ NDBI values ranges between approximately 0.65 to −0.25 in 1990, 0.77 to −0.96 in 1999, 0.66 to −0.54 in 2009, and 0.58 to −0.25 in 2019. These values represent the maximum and minimum NDBI for the different periods, respectively. Previous studies suggest that NDBI values greater than −0.22 represent land mainly occupied by built-up areas [3].
The graphical relationship between LST and NDBI is demonstrated in Figure 12. It shows a positive association between LST and built-up areas. The results indicate that lower LST values corresponded to lower NDBI, while higher LST values corresponded to built-up areas of high density.

5. Discussion

From the change detection results obtained, it is evident that urbanization coupled with socio-economic activities in Abuja metropolis may have contributed remarkably to the transition of natural surfaces into built areas. The results conform with previous studies, which suggest an increasing trend in the spatial extent of built-up/urban areas in developing countries such as Nigeria, Bangladesh, Egypt, and many others [6,19,37,39,68]. These are consequences of rapid urban growth and the quest for better living conditions. The development of urban areas has negatively affected the natural and built environment, contributing significantly to the increase in the land surface temperatures of cities [8,39,68].
The present study revealed the built-up area of Abuja Metropolis to have exhibited the most significant increase in the mean LST, followed by barren land, vegetation, and water bodies over the last 29 years. During the study periods between 1990 and 2019, the western, northwestern, eastern, and central parts of the study area exhibited the highest LST, with such areas corresponding to built-up areas and barren land. The southern parts of the metropolis exhibited the lowest LST, with such areas corresponding to vegetation and waterbodies. The lower LST values can be ascribed to the high evapotranspiration in vegetation that reduces land surface temperatures [86,87]. In contrast, the study attributes the higher values of LST in most areas of the metropolis to urban development and the replacement of natural vegetation with non-evaporative and non-transpiring surfaces that comprise construction sites for residential, commercial, and industrial development. The consequences of these land use/land cover changes play a significant role in the increased LST of the metropolis and contribute to Urban Heat Island development, as observed in similar studies [11,80,86,88,89].
Findings from the study area’s NDVI indicate that the vegetation cover of Abuja metropolis tends to decrease with an increase in the alteration of the natural environment into other land uses, as found in some rapidly growing cities [11,29,32]. However, it is often challenging to use the NDVI to differentiate between LULC categories such as barren land and built-up areas due to their relative similarities [90,91]. Therefore, our study established the city’s vegetation cover as areas with higher NDVI and lower LST. The results of NDVI shows a significant decrease over the last 29 years, which can be ascribed to the transformation of natural surfaces to built-up areas [15,27,92]. Due to the negative correlation between LST and NDVI during the different study periods, it is also apparent that the decrease in the vegetation of Abuja Metropolis has contributed substantially to the increase in land surface temperature of the city. This result aligns with similar studies in Anshun City, China [93], Colombo Metropolitan Area and Kandy City, Sri Lanka [8,9], Seoul Metropolis, Korea [94], Bahir Dar city, Ethiopia [86], and many others [15,27,83,85,88]. Their findings found that vegetation cover comprising forest areas, shrublands, green belts, and surfaces usually have lower LST within cities and urban centers due to the cool-island effect. Therefore, an increase in NDVI leads to a decrease in LST.
The study also observed a gradual increase in the city’s NDBI, i.e., built-up areas, which can be mainly ascribed to urban growth, which has contributed to the reduction of the city’s vegetation cover. This aligns with previous studies that reported positive NDBI representing built-up areas and negative NDBI signifying vegetation cover [3,30,83,85]. The positive correlation between LST and built-up areas conforms to earlier studies that revealed higher variation in the LST of impervious surfaces, i.e., mostly built-up areas and barren land/soil, compared to vegetated areas [31,88]. This implies that urban growth and land-use alterations have contributed substantially to the decline of vegetation, thereby increasing surface UHI through higher LST [8,15,87]. The development of surface UHI affects the environment and its inhabitants through increased demand for energy that adversely affects life quality and human health [32,95].
Therefore, it is of paramount importance for the city’s authorities to implement the following land-use strategies to mitigate the increasing surface Urban Heat Island. These strategies include:
  • Increasing the city’s vegetation and tree cover: the increase in trees, shrubs, grasses, vines, and other smaller plants can significantly lower the city’s land surface temperature by providing shading and cooling the urban environment through evapotranspiration. Other potential benefits of utilizing this strategy include reducing energy demand, reducing greenhouse gas emissions and air pollution.
  • Encouraging the use of green and cool roofs: the use of vegetative layers such as trees, plants, grasses, and shrubs on rooftops provides shading and removes heat through evapotranspiration. Cool roofs also help in reflecting heat and sunlight. Therefore, this strategy will mitigate the city’s urban heat island by reducing roofs’ surface temperature. It will also contribute significantly towards improving the thermal condition of the urban environment through reduced energy demand.
  • Adopting cool pavements as an alternative to the conventional impermeable surfaces: the use of cool pavements on parking lots, sidewalks, and streetways has the potential not only to store less heat than conventional paving materials but also to lower the city’s surface temperature by reflecting more solar energy and enhancing water evaporation.
  • Implementing smart growth practices: the implementation of smart growth strategies can reduce the effect of urban heat through the design of urban spaces. This strategy covers wide-ranging conservative and developmental measures that seek to protect the natural environment and make the city more livable. It includes the creation of walkable, bike-friendly, transit-oriented, and mixed-use neighborhoods.
The recommended strategies align with the UHI cooling strategies of the U.S. Environmental Protection Agency [96]. Therefore, the city’s planning authorities can effectively implement these initiatives through the deliberate enactment of zoning and other planning regulations.

6. Conclusions

The present study analyzed the spatiotemporal influence of LULC changes on the surface UHI of Abuja metropolis over the last 29 years (1990–2019) with the aid of multi-temporal satellite data. The change dynamics were mapped and quantified for four periods (1990, 1999, 2009, and 2019) using four different LULC classes comprising built-up areas, vegetation cover, barren land, and water bodies. To achieve the study’s objectives, we examined the spatial distribution of LST, NDVI, and NDBI. We also studied the relationship between LST and the different LULC classes and the correlation between LST and land-use indices such as NDVI and NDBI. The LULC change analysis indicates a rapid urban growth in Abuja Metropolis with a considerable built-up area increase from 77.26 sq. km in 1990 to 467.68 sq. km in 2019. On the other hand, vegetation and water bodies decreased significantly during the study period by 252.33 sq. km and 162.22 sq. km, respectively. The most remarkable land cover transition in the metropolis was the conversion of barren land to built-up areas with an area of 301.24 sq. km. The LST analysis result revealed barren land and vegetation as the LULC classes with the highest and lowest LST during the study period. The mean LST also increased from 30.65 °C in 1990 to 32.69 °C in 2019. This suggests that the LST of the metropolis transformed along with changes in LULC. The most significant change in mean LST was observed in built-up areas with a 1.89 °C increase between 1990 and 2019. Similarly, the mean LST of vegetation cover, barren land, and water bodies increased by 1.49 °C, 1.40 °C, and 0.50 °C, respectively. Therefore, the result indicates a substantial LST increase in all the LULC classes. The study further revealed a negative relationship between LST and NDVI while establishing a positive relationship between LST and NDBI during the different periods. This implies that higher LST is experienced along with a decline in vegetation and an increase in built-areas. This study’s findings suggest that LULC changes in Abuja metropolis have substantially influenced the city’s increase in LST, therefore contributing to the development of surface UHI. The present study only examined the historical period between 1990 and 2019. Therefore, further research is needed to investigate the city’s future LULC change dynamics and its potential LST variations using various geospatial-modeling techniques. The study concluded by recommending various strategies to mitigate the adverse influence of LULC changes by ensuring sustainable land-use practices.

Author Contributions

Conceptualization, Auwalu Faisal Koko; methodology, Auwalu Faisal Koko, and Ghali Abdullahi Abubakar; software, Auwalu Faisal Koko; validation, Ghali Abdullahi Abubakar, Wu Yue and Akram Ahmed Noman Alabsi; formal analysis, Auwalu Faisal Koko; writing—original draft preparation, Auwalu Faisal Koko; writing—review and editing, Ghali Abdullahi Abubakar, Wu Yue, Akram Ahmed Noman Alabsi and Roknisadeh Hamed; visualization, Auwalu Faisal Koko and Ghali Abdullahi Abubakar; supervision, Wu Yue; funding acquisition, Wu Yue. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the National Natural Science Foundation of China, grant number 51778559 (2018/01–2021/12).

Data Availability Statement

The data presented in this study are available from the corresponding author (W.Y) on request.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Sarif, M.O.; Rimal, B.; Stork, N. Assessment of Changes in Land Use/Land Cover and Land Surface Temperatures and Their Impact on Surface Urban Heat Island Phenomena in the Kathmandu Valley (1988–2018). Int. J. Geo-Inf. 2020, 9, 726. [Google Scholar] [CrossRef]
  2. United Nations Department of Economic and Social Affairs. World Population Prospects 2019 Volume I: Comprehensive Tables; United Nations Department of Economic and Social Affairs: New York, NY, USA, 2019. [Google Scholar]
  3. Ranagalage, M.; Estoque, R.; Handayani, H.; Zhang, X.; Morimoto, T.; Tadono, T.; Murayama, Y. Relation between Urban Volume and Land Surface Temperature: A Comparative Study of Planned and Traditional Cities in Japan. Sustainability 2018, 10, 2366. [Google Scholar] [CrossRef] [Green Version]
  4. Wang, R.; Derdouri, A.; Murayama, Y. Spatiotemporal Simulation of Future Land Use/Cover Change Scenarios in the Tokyo Metropolitan Area. Sustainability 2018, 10, 2056. [Google Scholar] [CrossRef] [Green Version]
  5. Fu, P.; Weng, Q. A time series analysis of urbanization induced land use and land cover change and its impact on land surface temperature with Landsat imagery. Remote Sens. Environ. 2016, 175, 205–214. [Google Scholar] [CrossRef]
  6. Kafy, A.A.; Rahman, M.S.; Faisal, A.-A.; Hasan, M.M.; Islam, M. Modelling future land use land cover changes and their impacts on land surface temperatures in Rajshahi, Bangladesh. Remote Sens. Appl. Soc. Environ. 2020, 18, 100314. [Google Scholar] [CrossRef]
  7. Tao, Y.; de Leeuw, G.; Zhao, L.; Fan, C.; Elnashar, A.; Bashir, B.; Wang, G.; Li, L.; Naeem, S.; Arshad, A. Modeling Spatio-temporal Land Transformation and Its Associated Impacts on land Surface Temperature (LST). Remote Sens. 2020, 12, 2987. [Google Scholar] [CrossRef]
  8. Dissanayake, D.; Morimoto, T.; Ranagalage, M.; Murayama, Y. Land-Use/Land-Cover Changes and Their Impact on Surface Urban Heat Islands: Case Study of Kandy City, Sri Lanka. J. Clim. 2019, 7, 99. [Google Scholar] [CrossRef] [Green Version]
  9. Ranagalage, M.; Estoque, R.; Murayama, Y. An Urban Heat Island Study of the Colombo Metropolitan Area, Sri Lanka, Based on Landsat Data (1997–2017). Int. J. Geo-Inf. 2017, 6, 189. [Google Scholar] [CrossRef] [Green Version]
  10. Akbaria, H.; Kolokotsab, D. Three decades of urban heat islands and mitigation technologies research. Energy Build. 2016, 133, 834–842. [Google Scholar] [CrossRef]
  11. Pal, S.; Ziaul, S. Detection of land use and land cover change and land surface temperature in English Bazar urban centre. Egypt. J. Remote Sens. Space Sci. 2017, 20, 125–145. [Google Scholar] [CrossRef] [Green Version]
  12. Ameen, R.F.M.; Mourshed, M. Urban environmental challenges in developing countries—A stakeholder perspective. Habitat Int. 2017, 64, 1–10. [Google Scholar] [CrossRef]
  13. AbdelRahman, M.A.E.; Natarajan, A.; Hegde, R. Assessment of land suitability and capability by integrating remote sensing and GIS for agriculture in Chamarajanagar district, Karnataka, India. Egypt. J. Remote Sens. Space Sci. 2016, 19, 125–141. [Google Scholar] [CrossRef] [Green Version]
  14. Liu, S.; Zang, Z.; Wang, W.; Wu, Y. Spatial-temporal evolution of urban heat Island in Xi’an from 2006 to 2016. Phys. Chem. Earth Parts A/B/C 2019, 110, 185–194. [Google Scholar] [CrossRef]
  15. Karakus, C.B. The Impact of Land Use/Land Cover (LULC) Changes on Land Surface Temperature in Sivas City Center and Its Surroundings and Assessment of Urban Heat Island. Asia-Pac. J. Atmos. Sci. 2019, 55, 669–684. [Google Scholar] [CrossRef]
  16. Ayele, G.T.; Tebeje, A.K.; Demissie, S.S.; Belete, M.A.; Jemberrie, M.A.; Teshome, W.M.; Mengistu, D.T.; Teshale, E.Z. Time Series Land Cover Mapping and Change Detection Analysis Using Geographic Information System and Remote Sens., Northern Ethiopia. Air Soil Water Res. 2018, 11, 1–18. [Google Scholar] [CrossRef] [Green Version]
  17. Zhao, Y.; Feng, D.; Yu, L.; Cheng, Y.; Zhang, M.; Liu, X.; Xu, Y.; Fang, L.; Zhu, Z.; Gong, P. Long-Term Land Cover Dynamics (1986–2016) of Northeast China Derived from a Multi-Temporal Landsat Archive. Remote Sens. 2019, 11, 599. [Google Scholar] [CrossRef] [Green Version]
  18. Raza, A.; Raja, I.; Raza, S. Land-Use Change Analysis of District Abbottabad, Pakistan: Taking Advantage of GIS and Remote Sens. Analysis. Sci. Vis. 2012, 18, 43–50. [Google Scholar]
  19. Aboelnour, M.; Engel, B. Application of Remote Sens. Techniques and Geographic Information Systems to Analyze Land Surface Temperature in Response to Land Use/Land Cover Change in Greater Cairo Region, Egypt. J. Geogr. Inf. Syst. 2018, 10, 57–88. [Google Scholar] [CrossRef] [Green Version]
  20. Tan, J.; Yu, D.; Li, Q.; Tan, X.; Zhou, W. Spatial relationship between land-use/land-cover change and land surface temperature in the Dongting Lake area, China. Sci. Rep. 2020, 10, 9245. [Google Scholar] [CrossRef]
  21. Ahmad, F. A review of remote sensing data change detection: Comparison of Faisalabad and Multan Districts, Punjab Province, Pakistan. J. Geogr. Reg. Plan. 2012, 5, 236–251. [Google Scholar] [CrossRef]
  22. Arora, G.; Wolter, P.T. Tracking land cover change along the western edge of the U.S. Corn Belt from 1984 through 2016 using satellite sensor data: Observed trends and contributing factors. J. Land Use Sci. 2018, 13, 59–80. [Google Scholar] [CrossRef]
  23. Chen, L.; Jin, Z.; Michishita, R.; Cai, J.; Yue, T.; Chen, B.; Xu, B. Dynamic monitoring of wetland cover changes using time-series remote sensing imagery. Ecol. Inf. 2014, 24, 17–26. [Google Scholar] [CrossRef]
  24. Tan, Y.; Bai, B.; Mohammad, M.S. Time series remote sensing based dynamic monitoring of land use and land cover change. In Proceedings of the 4th International Workshop on Earth Observation and Remote Sens. Applications (EORSA), Guangzhou, China, 4–6 July 2016; pp. 202–206. [Google Scholar]
  25. Roy, B.; Kanga, S.; Singh, S. Assessment of Land use/Land Cover Changes Using Geospatial technique at Osian-Mandore, Jodhpur (Rajasthan). Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol. 2017, 2, 73–81. [Google Scholar]
  26. Fu, P.; Weng, Q. Responses of urban heat island in Atlanta to different land-use scenarios. Theor. Appl. Clim. 2018, 133, 123–135. [Google Scholar] [CrossRef]
  27. Bokaie, M.; Zarkesh, M.K.; Arasteh, P.D.; Hosseini, A. Assessment of Urban Heat Island based on the relationship between land surface temperature and Land Use/ Land Cover in Tehran. Sustain. Cities Soc. 2016, 23, 94–104. [Google Scholar] [CrossRef]
  28. Estoque, R.C.; Murayama, Y.; Myint, S.W. Effects of landscape composition and pattern on land surface temperature: An urban heat island study in the megacities of Southeast Asia. Sci. Total Environ. 2017, 577, 349–359. [Google Scholar] [CrossRef]
  29. Kumar, D.; Shekhar, S. Statistical analysis of land surface temperature–vegetation indexes relationship through thermal remote sensing. Ecotoxicol. Environ. Saf. 2015, 121, 39–44. [Google Scholar] [CrossRef] [PubMed]
  30. Chen, X.; Zhao, H.; Li, P.; Yin, Z. Remote sensing image-based analysis of the relationship between urban heat island and land use/cover changes. Remote Sens. Environ. 2006, 104, 133–146. [Google Scholar] [CrossRef]
  31. Yuan, F.; Bauer, M.E. Comparison of impervious surface area and normalized difference vegetation index as indicators of surface urban heat island effects in Landsat imagery. Remote Sens. Environ. 2007, 106, 375–386. [Google Scholar] [CrossRef]
  32. Yue, W.; Xu, J.; Tan, W.; Xu, L. The relationship between land surface temperature and NDVI with remote sensing: Application to Shanghai Landsat 7 ETM+ data. Int. J. Remote Sens. 2007, 28, 3205–3226. [Google Scholar] [CrossRef]
  33. John, J.; Bindu, G.; Srimuruganandam, B.; Wadhwa, A.; Rajan, P. Land use/land cover and land surface temperature analysis in Wayanad district, India, using satellite imagery. Ann. GIS 2020, 26, 343–360. [Google Scholar] [CrossRef] [Green Version]
  34. Grace, U.M.; Sawa, B.A.; Jaiyeoba, I.A. Multi-temporal remote sensing of land-use dynamics in Zaria, Nigeria. J. Environ. Earth Sci. 2015, 5, 121–138. [Google Scholar]
  35. Wang, J.; Maduako, I.N. Spatio-temporal urban growth dynamics of Lagos Metropolitan Region of Nigeria based on Hybrid methods for LULC modeling and prediction. Eur. J. Remote Sens. 2018, 51, 251–265. [Google Scholar] [CrossRef] [Green Version]
  36. Mahmoud, M.I.; Duker, A.; Conrad, C.; Thiel, M.; Ahmad, H.S. Analysis of Settlement Expansion and Urban Growth Modelling Using Geoinformation for Assessing Potential Impacts of Urbanization on Climate in Abuja City, Nigeria. Remote Sens. 2016, 8, 220. [Google Scholar] [CrossRef] [Green Version]
  37. Koko, A.F.; Yue, W.; Abubakar, G.A.; Hamed, R.; Alabsi, A.A.N. Monitoring and Predicting Spatio-Temporal Land Use/Land Cover Changes in Zaria City, Nigeria, through an Integrated Cellular Automata and Markov Chain Model (CA-Markov). Sustainability 2020, 12, 10452. [Google Scholar] [CrossRef]
  38. Essien, E.; Samimi, C. Detection of Urban Development in Uyo (Nigeria) Using Remote Sens. Land 2019, 8, 102. [Google Scholar] [CrossRef] [Green Version]
  39. Ogunjobi, K.O.; Adamu, Y.; Akinsanola, A.A.; Orimoloye, I.R. Spatio-temporal analysis of land use dynamics and its potential indications on land surface temperature in Sokoto Metropolis, Nigeria. R. Soc. Open Sci. 2018, 5, 180661. [Google Scholar] [CrossRef] [Green Version]
  40. Segun, O.E.; Shohaimi, S.; Nallapan, M.; Lamidi-Sarumoh, A.A.; Salari, N. Statistical Modelling of the Effects of Weather Factors on Malaria Occurrence in Abuja, Nigeria. Int. J. Environ. Res. Public Health 2020, 17, 3474. [Google Scholar] [CrossRef]
  41. Agbelade, A.D.; Onyekwelu, J.C.; Oyun, M.B. Tree Species Richness, Diversity, and Vegetation Index for Federal Capital Territory, Abuja, Nigeria. Inte. J. For. Res. 2017, 2017, 4549756. [Google Scholar] [CrossRef] [Green Version]
  42. Idoko, M.A.; Bisong, F.E. Application of Geo-Information for Evaluation of Land Use Change: A Case Study of Federal Capital Territory—Abuja. Environ. Res. J. 2010, 4, 140–144. [Google Scholar] [CrossRef]
  43. Abubakar, I.R. Abuja city profile. Cities 2014, 41, 81–91. [Google Scholar] [CrossRef]
  44. Ishaya, S.; Hassan, S.M.; James, S.E. Post-Adaptation Vulnerability of Cereals to Rainfall and Temperature Variability in the Federal Capital Territory of Nigeria. Ethiop. J. Environ. Stud. Manag. 2014, 7, 532–547. [Google Scholar] [CrossRef] [Green Version]
  45. National Population Commission (NPC). 2006 Population and Housing Census: Population Distribution by Age and Sex; Federal Republic of Nigeria: Abuja, Nigeria, 2010. [Google Scholar]
  46. United Nations Department of Economic and Social Affairs. World Urbanization Prospects: The 2018 Revision. Available online: https://population.un.org/wup/Country-Profiles/ (accessed on 15 June 2020).
  47. United Nations. World Population Prospects 2019 Volume II: Demographic Profiles; Department of Economic and Social Affairs, Population Division, United Nations: New York, NY, USA, 2019. [Google Scholar]
  48. Abubakar, G.; Wang, K.; Shahtahamssebi, A.; Xue, X.; Belete, M.; Abdallah, A.; Shuka, K.; Gan, M. Mapping Maize Fields by Using Multi-Temporal Sentinel-1A and Sentinel-2A Images in Makarfi, Northern Nigeria, Africa. Sustainability 2020, 12, 2539. [Google Scholar] [CrossRef] [Green Version]
  49. El-Hamid, H.; Wenlong, W.; Li, Q. Environmental sensitivity of flash flood hazard using geospatial techniques. Glob. J. Environ. Sci. Manag. 2019, 6, 31–46. [Google Scholar] [CrossRef]
  50. El-Zeiny, A.; El-Kafrawy, S. Assessment of water pollution induced by human activities in Burullus Lake using Landsat 8 operational land imager and GIS. Egypt. J. Remote Sens. Space Sci. 2017, 20, S49–S56. [Google Scholar] [CrossRef] [Green Version]
  51. Obiadi, B.; Osita, O. Abuja, Nigeria Urban Actors, Master Plan, Development Laws and their Roles in the Design and Shaping of Abuja Federal Territory and their Urban Environments. IIARD Int. J. Geogr. Environ. Manag. 2018, 4, 23–43. [Google Scholar]
  52. Wang, J.; Kuffer, M.; Pfeffer, K. The role of spatial heterogeneity in detecting urban slums. Comput. Environ. Urban Syst. 2019, 73, 95–107. [Google Scholar] [CrossRef]
  53. Momeni, R.; Aplin, P.; Boyd, D. Mapping Complex Urban Land Cover from Spaceborne Imagery: The Influence of Spatial Resolution, Spectral Band Set and Classification Approach. Remote Sens. 2016, 8, 88. [Google Scholar] [CrossRef] [Green Version]
  54. Taubenböck, H.; Esch, T.; Felbier, A.; Wiesner, M.; Roth, A.; Dech, S. Monitoring urbanization in mega cities from space. Remote Sens. Environ. 2012, 117, 162–176. [Google Scholar] [CrossRef]
  55. Rimal, B.; Zhang, L.; Keshtkar, H.; Wang, N.; Lin, Y. Monitoring and Modeling of Spatiotemporal Urban Expansion and Land-Use/Land-Cover Change Using Integrated Markov Chain Cellular Automata Model. ISPRS Int. J. Geo-Inf. 2017, 6, 288. [Google Scholar] [CrossRef] [Green Version]
  56. Shen, X.; Anagnostou, E. Chapter Twelve—Inundation mapping by remote sensing techniques. In Extreme Hydroclimatic Events and Multivariate Hazards in a Changing Environment; Maggioni, V., Massari, C., Eds.; Elsevier: Amsterdam, The Netherlands, 2019; pp. 289–315. [Google Scholar] [CrossRef]
  57. Srivastava, P.; Han, D.; Rico-Ramirez, M.; Bray, M.; Islam, T. Selection of classification techniques for land use/land cover change investigation. Adv. Space Res. 2012, 50, 1250–1265. [Google Scholar] [CrossRef]
  58. Shang, M.; Wang, S.-X.; Zhou, Y.; Du, C. Effects of Training Samples and Classifiers on Classification of Landsat-8 Imagery. J. Indian Soc. Remote Sens. 2018, 46, 1333–1340. [Google Scholar] [CrossRef]
  59. Li, C.; Wang, J.; Wang, L.; Hu, L.; Gong, P. Comparison of Classification Algorithms and Training Sample Sizes in Urban Land Classification with Landsat Thematic Mapper Imagery. Remote Sens. 2014, 6, 964–983. [Google Scholar] [CrossRef] [Green Version]
  60. Khamchiangta, D.; Dhakal, S. Physical and non-physical factors driving urban heat island: Case of Bangkok Metropolitan Administration, Thailand. J. Environ. Manag. 2019, 248, 109285. [Google Scholar] [CrossRef] [PubMed]
  61. Rawat, J.S.; Kumar, M. Monitoring land use/cover change using remote sensing and GIS techniques: A case study of Hawalbagh block, district Almora, Uttarakhand, India. Egypt. J. Remote Sens. Space Sci. 2015, 18, 77–84. [Google Scholar] [CrossRef] [Green Version]
  62. Mohajane, M.; Essahlaoui, A.L.I.; Oudija, F.; el Hafyani, M.; El Hmaidi, A.; Ouali, A.; Randazzo, G.; Teodoro, A. Land Use/Land Cover (LULC) Using Landsat Data Series (MSS, TM, ETM+ and OLI) in Azrou Forest, in the Central Middle Atlas of Morocco. Environments 2018, 5, 131. [Google Scholar] [CrossRef] [Green Version]
  63. Liu, Y.; Wang, Y.; Peng, J.; Du, Y.; Liu, X.; Li, S.; Zhang, D. Correlations between Urbanization and Vegetation Degradation across the World’s Metropolises Using DMSP/OLS Nighttime Light Data. Remote Sens. 2015, 7, 2067–2088. [Google Scholar] [CrossRef] [Green Version]
  64. Lu, D.; Weng, Q. A survey of image classification methods and techniques for improving classification performance. Int. J. Remote Sens. 2007, 28, 823–870. [Google Scholar] [CrossRef]
  65. Congalton, R.G. A review of assessing the accuracy of classifications of remotely sensed data. Remote Sens. Environ. 1991, 37, 35–46. [Google Scholar] [CrossRef]
  66. Thomlinson, J.R.; Bolstad, P.V.; Cohen, W.B. Coordinating Methodologies for Scaling Landcover Classifications from Site-Specific to Global: Steps toward Validating Global Map Products. Remote Sens. Environ. 1999, 70, 16–28. [Google Scholar] [CrossRef]
  67. Anderson, J.R.; Hardy, E.E.; Roach, J.T.; Witmer, R.E. A Land Use and land Cover Classification System for Use with Remote Sensor Data; US Government Printing Office: Washington, DC, USA, 1976; Volume 964. [Google Scholar]
  68. Rousta, I.; Sarif, M.O.; Gupta, R.D.; Olafsson, H.; Ranagalage, M.; Murayama, Y.; Zhang, H.; Mushore, T.D. Spatiotemporal Analysis of Land Use/Land Cover and Its Effects on Surface Urban Heat Island Using Landsat Data: A Case Study of Metropolitan City Tehran (1988–2018). Sustainability 2018, 10, 4433. [Google Scholar] [CrossRef] [Green Version]
  69. Ishtiaque, A.; Shrestha, M.; Chhetri, N. Rapid Urban Growth in the Kathmandu Valley, Nepal: Monitoring Land Use Land Cover Dynamics of a Himalayan City with Landsat Imageries. Environments 2017, 4, 72. [Google Scholar] [CrossRef]
  70. Foody, G.M. Status of land cover classification accuracy assessment. Remote Sens. Environ. 2002, 80, 185–201. [Google Scholar] [CrossRef]
  71. Li, Z.; Tang, B.-H.; Wu, H.; Ren, H.; Yan, G.; Wan, Z.; Trigo, I.F.; Sobrino, J.A. Satellite-derived land surface temperature: Current status and perspectives. Remote Sens. Environ. 2013, 131, 14–37. [Google Scholar] [CrossRef] [Green Version]
  72. Weng, Q. Thermal infrared remote sensing for urban climate and environmental studies: Methods, applications, and trends. ISPRS J. Photogramm. Remote Sens. 2009, 64, 335–344. [Google Scholar] [CrossRef]
  73. Weng, Q.; Lu, D.; Schubring, J. Estimation of land surface temperature–vegetation abundance relationship for urban heat island studies. Remote Sens. Environ. 2004, 89, 467–483. [Google Scholar] [CrossRef]
  74. USGS (United States Geological Survey). Product Guide: Landsat 8 Surface Reflectance Code (LASRC) Product; Department of the Interior, U.S. Geological Survey: Sioux Falls, SD, USA, 2018. [Google Scholar]
  75. USGS (United States Geological Survey). Landsat 4–7 Collection 1 (C1) Surface Reflectance (LEDAPS) Product Guide; Department of the Interior, U.S. Geological Survey: Sioux Falls, SD, USA, 2020. [Google Scholar]
  76. United States Geological Survey (USGS). Landsat 7 (L7) Data Users Handbook. Available online: https://www.usgs.gov/core-science-systems/nli/landsat/landsat-7-data-users-handbook (accessed on 11 November 2020).
  77. United States Geological Survey (USGS). Landsat 8 (L8) Data Users Handbook. Available online: https://www.usgs.gov/core-science-systems/nli/landsat/landsat-8-data-users-handbook (accessed on 11 November 2020).
  78. Sekertekin, A.; Bonafoni, S. Land Surface Temperature Retrieval from Landsat 5, 7, and 8 over Rural Areas: Assessment of Different Retrieval Algorithms and Emissivity Models and Toolbox Implementation. Remote Sens. 2020, 12, 294. [Google Scholar] [CrossRef] [Green Version]
  79. Avdan, U.; Jovanovska Kaplan, G. Algorithm for Automated Mapping of Land Surface Temperature Using LANDSAT 8 Satellite Data. J. Sens. 2016, 2016, 1–8. [Google Scholar] [CrossRef] [Green Version]
  80. Rosa dos Santos, A.; Santos de Oliveira, F.; Gomes da Silva, A.; Gleriani, J.M.; Gonçalves, W.; Moreira, G.L.; Silva, F.G.; Branco, E.R.F.; Moura, M.M.; Gomes da Silva, R.; et al. Spatial and temporal distribution of urban heat islands. Sci. Total Environ. 2017, 605–606, 946–956. [Google Scholar] [CrossRef]
  81. Sobrino, J.A.; Jiménez-Muñoz, J.C.; Paolini, L. Land surface temperature retrieval from LANDSAT TM 5. Remote Sens. Environ. 2004, 90, 434–440. [Google Scholar] [CrossRef]
  82. Carlson, T.N.; Ripley, D.A. On the relation between NDVI, fractional vegetation cover, and leaf area index. Remote Sens. Environ. 1997, 62, 241–252. [Google Scholar] [CrossRef]
  83. Zhang, Y.; Odeh, I.O.A.; Han, C. Bi-temporal characterization of land surface temperature in relation to impervious surface area, NDVI and NDBI, using a sub-pixel image analysis. Int. J. Appl. Earth Obs. Geoinf. 2009, 11, 256–264. [Google Scholar] [CrossRef]
  84. Barbieri, T.; Despini, F.; Teggi, S. A multi-temporal analyses of Land Surface Temperature using Landsat-8 data and open source software: The case study of Modena, Italy. Sustainability 2018, 10, 1678. [Google Scholar] [CrossRef] [Green Version]
  85. Kikon, N.; Singh, P.; Singh, S.K.; Vyas, A. Assessment of urban heat islands (UHI) of Noida City, India using multi-temporal satellite data. Sustain. Cities Soc. 2016, 22, 19–28. [Google Scholar] [CrossRef]
  86. Balew, A.; Korme, T. Monitoring land surface temperature in Bahir Dar city and its surrounding using Landsat images. Egypt. J. Remote Sens. Space Sci. 2020, 23, 371–386. [Google Scholar] [CrossRef]
  87. Simwanda, M.; Ranagalage, M.; Estoque, R.; Murayama, Y. Spatial Analysis of Surface Urban Heat Islands in Four Rapidly Growing African Cities. Remote Sens. 2019, 11, 1645. [Google Scholar] [CrossRef] [Green Version]
  88. Nse, O.U.; Okolie, C.J.; Nse, V.O. Dynamics of land cover, land surface temperature and NDVI in Uyo City, Nigeria. Sci. Afr. 2020, 10, e00599. [Google Scholar] [CrossRef]
  89. Huang, Q.; Li, L.; Lu, Y.; Yang, Y.; Li, M. The roles of meteorological parameters in Shanghai’s nocturnal urban heat island from 1979 to 2013. Theor. Appl. Climatol. 2020, 141, 285–297. [Google Scholar] [CrossRef]
  90. Khanal, N.; Uddin, K.; Matin, M.; Tenneson, K. Automatic Detection of Spatiotemporal Urban Expansion Patterns by Fusing OSM and Landsat Data in Kathmandu. Remote Sens. 2019, 11, 2296. [Google Scholar] [CrossRef] [Green Version]
  91. Ma, X.; Li, C.; Tong, X.; Liu, S. A New Fusion Approach for Extracting Urban Built-up Areas from Multisource Remotely Sensed Data. Remote Sens. 2019, 11, 2516. [Google Scholar] [CrossRef] [Green Version]
  92. Sultana, S.; Satyanarayana, A.N.V. Assessment of urbanisation and urban heat island intensities using landsat imageries during 2000–2018 over a sub-tropical Indian City. Sustain. Cities Soc. 2020, 52, 101846. [Google Scholar] [CrossRef]
  93. Deng, Y.; Wang, S.; Bai, X.; Tian, Y.; Wu, L.; Xiao, J.; Chen, F.; Qian, Q. Relationship among land surface temperature and LUCC, NDVI in typical karst area. Sci. Rep. 2018, 8, 641. [Google Scholar] [CrossRef]
  94. Priyankara, P.; Ranagalage, M.; Dissanayake, D.; Morimoto, T.; Murayama, Y. Spatial Process of Surface Urban Heat Island in Rapidly Growing Seoul Metropolitan Area for Sustainable Urban Planning Using Landsat Data (1996–2017). Climate 2019, 7, 110. [Google Scholar] [CrossRef] [Green Version]
  95. Claus, R.; Mushtaq, H. Toronto’s Urban Heat Island: Exploring the Relationship between Land Use and Surface Temperature. Remote Sens. 2011, 3, 1251–1265. [Google Scholar] [CrossRef] [Green Version]
  96. U.S. Environmental Protection Agency. Reducing Urban Heat Islands: Compendium of Strategies. In Heat Island Compendium; U.S. Environmental Protection Agency: Washington, DC, USA, 2008. [Google Scholar]
Figure 1. Location Map of Abuja Metropolis, Nigeria.
Figure 1. Location Map of Abuja Metropolis, Nigeria.
Ijgi 10 00272 g001
Figure 2. Methodological Flow chart of the study.
Figure 2. Methodological Flow chart of the study.
Ijgi 10 00272 g002
Figure 3. Classified land use/land cover maps of Abuja Metropolis in; (a) 1990, (b) 1999, (c) 2009, and (d) 2019.
Figure 3. Classified land use/land cover maps of Abuja Metropolis in; (a) 1990, (b) 1999, (c) 2009, and (d) 2019.
Ijgi 10 00272 g003
Figure 4. Distribution of LULC in Abuja Metropolis from 1990–2020 in; (a) sq. km and (b) percentage.
Figure 4. Distribution of LULC in Abuja Metropolis from 1990–2020 in; (a) sq. km and (b) percentage.
Ijgi 10 00272 g004
Figure 5. Net Changes in LULC types of Abuja Metropolis during three study periods.
Figure 5. Net Changes in LULC types of Abuja Metropolis during three study periods.
Ijgi 10 00272 g005
Figure 6. Land use/land cover transitions of Abuja Metropolis from 1990–2019.
Figure 6. Land use/land cover transitions of Abuja Metropolis from 1990–2019.
Ijgi 10 00272 g006
Figure 7. The LULC change transition of Abuja Metropolis in sq. km from 1990 to 2019.
Figure 7. The LULC change transition of Abuja Metropolis in sq. km from 1990 to 2019.
Ijgi 10 00272 g007
Figure 8. LST Distribution of Abuja Metropolis in; (a) 1990, (b) 1999, (c) 2009, and (d) 2019.
Figure 8. LST Distribution of Abuja Metropolis in; (a) 1990, (b) 1999, (c) 2009, and (d) 2019.
Ijgi 10 00272 g008
Figure 9. NDVI Spatial Distribution of Abuja Metropolis in; (a) 1990, (b) 1999, (c) 2009, and (d) 2019.
Figure 9. NDVI Spatial Distribution of Abuja Metropolis in; (a) 1990, (b) 1999, (c) 2009, and (d) 2019.
Ijgi 10 00272 g009
Figure 10. Relationship between LST and NDVI of Abuja Metropolis for; (a) 1990, (b) 1999, (c) 2009, and (d) 2019 using scattered plots.
Figure 10. Relationship between LST and NDVI of Abuja Metropolis for; (a) 1990, (b) 1999, (c) 2009, and (d) 2019 using scattered plots.
Ijgi 10 00272 g010
Figure 11. NDBI Spatial Distribution of Abuja Metropolis in; (a) 1990, (b) 1999, (c) 2009, and (d) 2019.
Figure 11. NDBI Spatial Distribution of Abuja Metropolis in; (a) 1990, (b) 1999, (c) 2009, and (d) 2019.
Ijgi 10 00272 g011
Figure 12. Relationship between LST and NDBI of Abuja Metropolis for; (a) 1990, (b) 1999, (c) 2009, and (d) 2019 using scattered plots.
Figure 12. Relationship between LST and NDBI of Abuja Metropolis for; (a) 1990, (b) 1999, (c) 2009, and (d) 2019 using scattered plots.
Ijgi 10 00272 g012
Table 1. Details of Satellite Datasets used in the study.
Table 1. Details of Satellite Datasets used in the study.
Satellite Type/SensorWRS Path/RowDate AcquiredTime (GMT)Cloud CoverSun AzimuthSun ElevationThermal Conversion Constants
K1K2
Landsat 4 TM189/05412/02/199009:22:256.00121.589147.6548671.621284.30
Landsat 5 TM189/05428/01/199909:29:133.00128.816146.8722607.761260.56
Landsat 7 ETM+189/05415/01/200909:39:523.00135.083847.7213666.091282.71
Landsat 8 OLI189/05404/02/201909:49:566.19130.761251.8678774.891321.08
Table 2. Description of LULC Classes in Abuja.
Table 2. Description of LULC Classes in Abuja.
S/NoLand Use/Land Cover ClassesDescription
1.Urban/Built-Up AreaCovers residential, commercial, industrial developments, and infrastructural facilities.
2.VegetationComprises agricultural lands, natural vegetation, grassland, forest, trees, shrubs, parks, gardens, lawns, and other green areas
3.Barren LandIncludes all non-vegetated land, bare soils, landfills and construction sites, quarries, gravel pits, and exposed open spaces.
4.Water BodiesAreas that comprise rivers, streams, ponds, lakes, reservoirs, wetland areas, swamps, irrigation and drainage canals
Table 3. Land use/land cover distribution in 1990, 1999, 2009, and 2019.
Table 3. Land use/land cover distribution in 1990, 1999, 2009, and 2019.
S/
No
LULC Types1990199920092019
Area
(sq. km)
Area
(%)
Area (sq. km)Area
(%)
Area (sq. km)Area
(%)
Area
(sq. km)
Area
(%)
1.Built-up Area77.264.48157.759.15178.5810.36467.6827.14
2.Vegetation447.9426.00319.9118.57274.3815.92195.6111.35
3.Barren Land981.7156.981151.2866.821210.7570.281005.8458.38
4.Water Bodies216.0812.5494.055.4659.283.4453.863.13
5.Total1722.991001722.991001722.991001722.99100
Table 4. Confusion/Error Matrix of 1990, 1999, 2009, and 2019.
Table 4. Confusion/Error Matrix of 1990, 1999, 2009, and 2019.
(a) 1990 Confusion Matrix
S/NoLand Cover ClassesBuilt-UpVegetationBarren LandWater BodiesTotal
1.Built-up46960 230552
2.Vegetation28261214854
3.Barren Land11985310640
4.Water Bodies010332333
Total4829855663462379
Overall Accuracy = (2158/2379) = 90.71%
Kappa Coefficient = 0.8710
(b) 1999 Confusion Matrix
S/NoLand Cover ClassesBuilt-UpVegetationBarren LandWater BodiesTotal
1.Built-up1033321201077
2.Vegetation60537221620
3.Barren Land7258712608
4.Water Bodies01321231231
Total11005846422442570
Overall Accuracy = (2388/2570) = 92.92%
Kappa Coefficient = 0.8984
(c) 2009 Confusion Matrix
S/NoLand Cover ClassesBuilt-UpVegetationBarren LandWater BodiesTotal
1.Built-up125914291293
2.Vegetation678712229944
3.Barren Land3045227552355
4.Water Bodies02015600635
Total129585324166635227
Overall Accuracy = (4921/5227) = 94.15%
Kappa Coefficient = 0.9146
(d) 2019 Confusion Matrix
S/NoLand Cover ClassesBuilt-UpVegetationBarren LandWater BodiesTotal
1.Built-up121931731242
2.Vegetation6368310747
3.Barren Land16517428817
4.Water Bodies00 0528528
Total12987377605393334
Overall Accuracy = (3172/3334) = 95.14%
Kappa Coefficient = 0.9329
Table 5. Producer and User accuracies of individual LULC classes.
Table 5. Producer and User accuracies of individual LULC classes.
S/NoYearProducer’s Accuracy (%)User’s Accuracy (%)
Built-Up AreaVegetationBarren LandWater BodiesBuilt-Up AreaVegetationBarren LandWater Bodies
1.199097.3083.8693.8295.9584.9696.7282.9799.70
2.199993.9191.9591.4394.6795.9186.6196.5587.17
3.200997.2292.2694.1690.5097.3783.3796.6094.49
4.201993.9192.6797.6397.9698.1591.4390.82100.00
Table 6. LULC change dynamics (statistics) of Abuja Metropolis from 1990 to 2019.
Table 6. LULC change dynamics (statistics) of Abuja Metropolis from 1990 to 2019.
S/NoLULC Types1990–19991999–20092009–20191990–20091990–2019
Area
(sq. km)
Area (%)Area
(sq. km)
Area (%)Area
(sq. km)
Area (%)Area
(sq. km)
Area (%)Area
(sq. km)
Area (%)
1.Built-up Area80.494.6720.831.21289.1016.78101.325.88390.4222.66
2.Vegetation−128.03−7.43−45.53−2.65−78.77−4.57−173.56−10.08−252.33−14.65
3.Barren Land169.579.8459.473.46−204.91−11.90229.0413.3024.131.40
4.Water Bodies−122.03−7.08−34.77−2.02−5.42−0.31−156.80−9.10−162.22−9.41
Table 7. Statistics of LST (°C) in Abuja Metropolis for the four periods between 1990 and 2019.
Table 7. Statistics of LST (°C) in Abuja Metropolis for the four periods between 1990 and 2019.
S/NoAcquisition DateLand Surface Temperature (LST)
Minimum (°C)Maximum (°C)Mean (°C)Standard Deviation
1.12/02/199020.3037.1130.652.19
2.28/01/199921.5044.4630.402.13
3.15/01/200920.5546.3430.161.87
4.04/02/201920.5840.1332.692.02
Table 8. Mean LST values for each LULC type in Abuja Metropolis for the period between 1990 and 2019.
Table 8. Mean LST values for each LULC type in Abuja Metropolis for the period between 1990 and 2019.
S/NoLULC TypesMean LST (°C)Mean LST Difference (°C)
19901999200920191990–19991999–20092009–20191990–20091990–2019
1.Built-up area31.0930.2730.5032.98−0.820.232.48−0.591.89
2.Vegetation28.1827.4428.0529.67−0.740.611.62−0.131.49
3.Barren Land31.8331.1230.7333.23−0.71−0.392.50−1.101.40
4.Water Bodies30.1530.0929.2130.65−0.06−0.881.44−0.940.50
Table 9. Statistics of NDVI in Abuja Metropolis for the period between 1990 and 2019.
Table 9. Statistics of NDVI in Abuja Metropolis for the period between 1990 and 2019.
S/NoAcquisition DateNormalized Difference Vegetation Index (NDVI)
MinimumMaximumMeanStandard Deviation
1.12/02/1990−0.230.510.060.05
2.28/01/1999−0.390.540.090.07
3.15/01/2009−0.300.29−0.030.05
4.04/02/2019−0.090.400.150.04
Table 10. Statistics of NDBI in Abuja Metropolis for the period between 1990 and 2019.
Table 10. Statistics of NDBI in Abuja Metropolis for the period between 1990 and 2019.
S/NoAcquisition DateNormalized Difference Built-Up Index (NDBI)
Minimum Maximum Mean Standard Deviation
1.12/02/1990−0.250.650.240.07
2.28/01/1999−0.960.770.280.08
3.15/01/2009−0.540.660.150.07
4.04/02/2019−0.250.580.040.05
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Koko, A.F.; Yue, W.; Abubakar, G.A.; Alabsi, A.A.N.; Hamed, R. Spatiotemporal Influence of Land Use/Land Cover Change Dynamics on Surface Urban Heat Island: A Case Study of Abuja Metropolis, Nigeria. ISPRS Int. J. Geo-Inf. 2021, 10, 272. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10050272

AMA Style

Koko AF, Yue W, Abubakar GA, Alabsi AAN, Hamed R. Spatiotemporal Influence of Land Use/Land Cover Change Dynamics on Surface Urban Heat Island: A Case Study of Abuja Metropolis, Nigeria. ISPRS International Journal of Geo-Information. 2021; 10(5):272. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10050272

Chicago/Turabian Style

Koko, Auwalu Faisal, Wu Yue, Ghali Abdullahi Abubakar, Akram Ahmed Noman Alabsi, and Roknisadeh Hamed. 2021. "Spatiotemporal Influence of Land Use/Land Cover Change Dynamics on Surface Urban Heat Island: A Case Study of Abuja Metropolis, Nigeria" ISPRS International Journal of Geo-Information 10, no. 5: 272. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10050272

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop