This study assessed slum growth and its impact on climatic elements in Kuje urban area of in Nigeria. Multi-criteria and quantitative research design were adopted in this study. Moderate satellite images of the study area were obtained from Google Earth Pro for period of 2002 and 2012, and high-resolution satellite image was acquired from SAS Planet for the period of 2022. Earth surface temperature, relative humidity and surface soil moisture data for the different land uses/land covers were obtained from National Centers for Environmental Prediction Climate Forecast System Reanalysis (CFSR) and NASA Climatic for 20 years (2002 to 2022). This study adopted the supervised classification method that uses the spectral signatures obtained from training samples to classify an image. With the aid of ground truth, a signature file was created by training samples cells, which was used for the multivariate classification tools to classify the image. The climatic data were subjected to time series analysis, coefficient of determination (R2) was carried from the trend line equations. One Way Analysis of Variance (ANOVA) Test was used to test variation of climatic elements from the land uses/land cover overs the study period in Kuje urban slum.

Between the years 2002 to 2012, there was an increase in agricultural land use with 17.74 hectares, bare surface increased with 147.89 hectares, urban slum area increased with 115.67 hectares, vegetation had decrease of -282.196 hectares. Between 2012-2022 there was a loss in agricultural land use area with -107.84 hectares, bare surface declined with -154.81 hectares, vegetation land cover declined with -8.28 hectares and there was an increase in urban slum area with 271.82 hectares. Between 2012-2022 agricultural land use, bare surfaces and vegetal land cover were loss to urban slums. Bare surface and built up area had the highest temperature values compared to that from vegetal cover and agricultural land uses. Relative humidity is higher on vegetal covered areas and agricultural land used areas and slum growth negatively affects relative humidity in the Kuje slum area. There is less difference in the mean values of surface soil moisture over the land uses/land covers but the soil moisture is higher in vegetated area and agricultural land use area but lower at built up slum areas and bare surfaces with a significance rise over study period of 2002 to 2022. ANOVA result shows that earth’s surface temperature is not significantly different across the four land uses, with an F-value of 0.689 and a p-value of 0.562. The F-value for surface soil moisture is 2.173 and the p-value is 0.098, indicating that there is only a little variation in the amount of surface soil moisture among the four land cover/land uses. It was observed that the relative humidity across the four land covers/land uses has F-value of 0.080 and p-value of 0.971, showing that there is no discernible difference. It was recommended that slum need to be incorporated into Kuje urban area planning through gentrification of buildings since demolition is not an options due to large concentration of indigenous people within the slum.

• Remote Sensing; GIS; Land Use; Land Cover; Surface; Slums; Moisture; Temperature; Relative Humidity

Ishaya S, ShakiratY, Ikediashi T (2024) Assessment of Slum Growth Impact on Climatic Elements in Kuje Urban Area of the Federal Capital Territory of Nigeria. JSM Environ Sci Ecol 12(2): 1095.

A slum is usually a highly populated urban residential area consisting mostly of closely packed, decrepit housing units in a situation of deteriorated or incomplete infrastructure, inhabited primarily by impoverished persons [1]. UNPD-DESA (2011) observed that the absolute number of people living in slums or informal settlements grew to over 1 billion, with 80% attributed to three regions; Eastern and South-Eastern Asia (370 million), Sub-Saharan Africa (238 million) and Central and Southern Asia (227 million). In the sub-Saharan Africa’s projection, it was estimated that in the next thirty years, the global number of slum dwellers would have increased to about two billion. In the developing, informal or slum settlements are absorbing an increasing number of the expanding urban population and are home to the vast majority of the urban poor [2]. In some cities in developing countries, slums are so pervasive that it is the rich who segregate themselves behind small gated enclaves. In 2020, in the developing countries the number of slum dwellers was estimated to be 1.43 billion by 2020. Slum formation in Nigeria and in Africa is on the increase as the world rapidly urbanizes [3].

The urbanisation rate in Nigeria is 5.5%, almost twice the population growth rate of the country and this comes with the growth of slums [3]. The development out of Abuja as the Federal Capital Territory of Nigeria 40 years ago which led to the development of the city, is now having emerging slum problem. Most of the slums are not far away from the city centres with a substantial number of people residing. As a result of housing deficit in the Nigeria capital city, many people live in slums but work in the Abuja city.

Largely local climate is determined by location and exposure of the area and surface conditions such as heat capacity, moisture content, vegetation cover, albedo and roughness of the ground surface [4,5]. A major cooling factor in a natural environment is the evapotranspiration process, which occurs in areas with vegetation and access to water and acts cooling due to the energy consumed in the process. Reduction of natural vegetation in an area lessen the possibilities for the evapotranspiration process and thereby possibly reduces the natural cooling effect where vegetation is scarce [4]. Due the pave ground and growing structures urban slum areas have a much faster run-off of excessive water, which may further reduce the natural cooling due to the shorter time the water is available for evaporation [5].

Over the years, the link between urban sprawl and climate change has gained global attention in recent decades due to high rise in urban population [6,7]. Urban slum growth leads to distortion in surface temperature, wind speed, humidity, rainfall and surface soil moisture. Urban thermal environment dynamics were associated with Land Use and Land Cover (LULC) changes as well as seasonal variations [8]. Urban slums are particularly more vulnerable to changing climatic elements due to their high poverty level [9]. Remote sensing provides various data for investigating urban structures, land cover, and biophysical characteristics [10]. These urban characteristics are extremely important in understanding the urban thermal environment, especially the relationships between LULC and vegetation indices [11]. Existing remote sensing-based data enable the investigation of temporal and spatial patterns, as well as thermal characteristics, although observations in specific areas can remain challenging.

In recent years, studies on urban land use/land covers changes abound but studies that characterize and assesses climatic variables under different land use/land cover are lacking except LST studies. Studies with geospatial techniques in assessing urban expansion, sprawls and slums in Nigeria includes the studies of [12-17] and other studies evaluate slums development and their effects on inhabitant’s environment and living conditions [11-18] attempted mapping and m Middle monitoring of slum development in Abuja using remotely sensed imageries and geographical information system but the temporal scope was between 2005 to 2012 in the FCT. Other researchers, Toby and Traci, [4-24] applied remote sensing and GIS first in classifying land use/land cover and subsequently assessed climatic variables under the classified land use/land cover. Despite wide coverage of such researches no study was identified that assessed urban slum growth impact on climatic elements under different land use/land cover in Kuje urban area of the Federal Capital Territory of Nigeria hence the stimulant of this study.

The study area (Kuje slum) is located within Kuje town which is the headquarters of Kuje Area Council that lies between latitude 80 27’43’’ to 80 56’32’’’ North of the equator and longitude 60 58’13’’ to 70 33’11’’ East (Figure 1) while the urban slum in cover 609.066 hectares (Figure 2).

Figure 1: Area Councils in the FCT.

Figure 2: Image of Kuje Town Showing Slum Area.

The Kuje slum area is void of vegetation with distinct wet and dry season climatic condition. The wet season falls between the months of April to October while dry season is between November and March with average rainfall of 1555 mm and mean temperature during the dry season ranges between 30.4 and 35.10 C while the temperature ranges between 25.8 and 30.20 C in the raining season [25].

At the fringes of the town, the suitability of the soil for agriculture influenced the major economic activity in the study area which is farming. The major ethnic groups are the Gbagyi and Gwandara who are mostly farmers engaged in farming of yams, cassava, cocoyam, sweet potatoes, groundnuts, rice, maize, guinea corn, rice, melon. Cat fish farming, cattle are reared by the nomadic Fulanis, sheep, goats and poultry are kept mostly on free range system mostly by other tribes other than the Gbagyi and Gwandaras who have come to settle there. Due to its proximity to the city, Kuje town has been experiencing rapid expansion buildings and spontaneous increase in human activities over the years as the population continued to increase in the area, the urban renewal exercise witnessed between 2003 and 2005 has forced many people to settle in the satellite towns of Kuje, Gwagwalada, Kubwa, Zuba and Bwari which is one of the major causes of slums growth in the satellites towns in the FCT [26].

Multi-Criteria Remote Sensing, GIS techniques and quantitative research design were adopted in this study. The output of the multi-criteria Remote Sensing, GIS techniques design was for the land use/land cover and change detection of the Kuje slum. The quantitative design allowed the manipulation of temporal temperature, surface soil wetness and relative humidity data to ascertain trend and anomalies under different land use/land cover in the Kuje slum.

Data Used

Moderate satellite images of the study area were obtained from Google Earth Pro for period of 2002 and 2012, and the high-resolution satellite image was acquired from SAS Planet for the period of 2022 (Table 1).

Table 1: Satellite Images Utilized.

S.No	Data	Type		Year	Format	Resolution	Source	Relevance
1	Delineated slum boundary	Secondary			Shape file		Field observation and Google Earth	To define the spatial boundary
2	Moderate resolution aerial imagery	Secondary		2002 & 2012	TIFF	30m	Google Earth Pro	For object-based image analysis
3	High resolution aerial imagery	Secondary		2022	TIFF	0.5m	SAS Planet	For image classification analysis

Location data for verification of Land use/Land cover characteristics were sourced using hand held GPS 60cx. Climatic data (earth surface temperature, relative humidity and surface soil wetness) from different land use/land cover (vegetation, slum built up area and bare surface) in Kuje slum was obtained from the National Centers for Environmental Prediction (NCEP) Climate Forecast System Reanalysis (CFSR) and NASA Climatic data from https://power.larc.nasa.gov/dataaccess-viewer/ was compiled for 20 years (2002 to 2022).

Method of Data Analysis

Pre-Processing of Satellite Image: Object based image analysis was used in obtaining a remotely sensed data via satellite feeds, the process involves launching of the Google Earth Pro software, navigated to the area of interest, the north arrow toggle was aligned to fitting the satellite imagery to be captured. The time slider tool was used to back date the time for historical imageries, afterward the four point were indicated on the four cardinal point of the imageries using point tool and their ground coordinate points were obtained to enable geo-referencing. The satellite imageries were saved and map option was adjusted and maximum resolution was selected, and save image to download to the satellite imagery (Table 2).

Table 2: Coordinates Points for Geo-referencing.

S. No	Point ID	Easting	Northing
1	P1	304337.166	982670.115
2	P2	306266.366	982483.616
3	P3	305648.751	981172.886
4	P4	304955.773	981327.334

Ten geographic coordinates were obtained in the study area for ground truthing so as to aid precise mapping of the Kuje slum. Image pre-processing involved image geometric correction to normalize the imageries as expected for classification from sensors and image enhancement was for ratification during preprocessing which includes the resolution of the spectral bands of the imagery, year of acquisition, the features shown, data evaluation and geo-referencing and the projection of the imagery itself for the imageries were used in this study.

Land use/Land cover Analysis: Three bands combinations of 4, 3, 2 (Red, Green and Blue bands) were used for all the imageries. This study adopted the supervised classification method that uses the spectral signatures obtained from training samples to classify an image. With the aid of ground truth, a signature file was created by training samples cells, which was used for the multivariate classification tools to classify the image (Table 3).

Table 3: Land Use/Land Cover Classes considered.

Classes	Description
Bare Surface	Fallow fields/Lands
Slum Built-up areas	Paved surfaces, settlements, developed infrastructures and tarred roads
Agricultural Land Use	This include cultivated fields
Vegetation cover	Vegetation, in form of trees, shrubs of different height and density

The results of the land use/land cover types were produced in hectares to enable evaluation of land use/land cover changes. The statistical results were generated using version 15 of ERDAS Imagine software. The land use/land cover statistics were extracted using ArcGIS 10.1 while assessment of changes over the four satellite epochs were carried out from one class to the other. Using the land use/land cover maps, the change magnitude, change trend and annual rate change were generated using the formulas thus;

Magnitude = Magnitude of the new year - Magnitude of the previous year……… Equation 1

The percentage change for each land use/land cover classes were calculated by dividing magnitude change by sum of the observed changes between the years concerned and multiplied by 100 as presented in the equation thus:

Trend = Magnitudeof Change x 100¼Equation 2

Sumof Change

In ascertaining the annual rate of land use/land cover classes, the trend multiply by number of study years divided by 100o periods, for example 1988 – 1998, 1998-2008 as shown in equation:

Annual Rate of Change = Trend ´ number of study years in between ¼¼Equation 3

                                                                                  100

Graphs representing spectral signatures for each of the land use/land cover characteristics were generated and plotted using the statistical results of classified imageries of the three studied years.

Method of Climatic Data Analysis: Several statistical techniques were used in analyzing the climatic data (earth surface temperature, relative humidity and surface soil wetness). The preparatory level of the secondary data analysis involved simple statistical computation of totals, means, and standard deviation of earth surface temperature, relative humidity and surface soil wetness from the land use/land cover from Kuje urban slum. The earth surface temperature, relative humidity and surface soil wetness from the land use/land cover data from Kuje slum were subjected to time series analysis. Time series analysis is the arrangement of statistical data in respect to the time of occurrence. This was shown in line graph as it does remain the most popular method of time series presentation. This study adopted the line graph because it helps explain the nature of fluctuation of rainfall characteristics in the study area over years. The data on rainfall were plotted on the y-axis and the time (years 2002-2022) were presented on the x-axis.

A simple linear regression analysis technique was used for determining trend analysis for rainfall. The formula for trend analysis is presented below:

Y=a+bx

b = ∑(x – x) (y -y).................... Equation 4.

∑ (x - x)

a = y –bx

Where; a = the base intercept

b = the regression coefficient or slope

x = rainfall/temperature values y = months/years

Coefficient of determination (R2) was carried for the

regression result of earth surface temperature, relative humidity and surface soil wetness trend line equation from the land use/ land cover from Kuje urban slum. One-Way Analysis of Variance (ANOVA) Test was used to test variation in earth surface temperature, relative humidity and surface soil wetness for the land use/land cover over the study period in Kuje Urban slum. The formula for Analysis of Variance is thus:

ANOVA coefficient, F= Mean sum of squares between the

groups (MSB)/ Mean squares of errors (MSE). Degrees of freedom of errors, N – k = df2

Where, N is the total number of observations throughout k groups

.

Where F is the variance ratio for the overall test, MST is the mean square due to treatments/groups (between groups), MSE is the mean square due to error (within groups, residual mean square), Yij is an observation, Ti is a group total, G is the grand total of all observations, ni is the number in group i and n is the total number of observations.

Hypotheses

Two hypotheses were tasted in this study at 0.05 Alpha level.

These hypotheses are;

1. Ho: There is no significant changes/variation in land use/land cover classes over study period.
2. Ho: There is no significant variation in the characteristics of climatic elements (earth surface temperature, relative humidity and surface soil moisture) over the land use/ land cover (vegetation, agricultural land, bare surface and slum built up area in Kuje area between 2002-2020.

In line with the objectives of this study, the results are presented into two sections. The first presentation and discussion of results is on Kuje slum land use/land cover categorization and change detection from 2002 to 2022. The second objective is on Kuje slum land use/land cover climatic elements characterization from 2002 to 2022.

Kuje Urban Slum Area Land use/Land Cover Categorization between 2002-2022

The categorization of slum land cover/land uses (agricultural land, bare surface, vegetation and residential buildings) in the Kuje urban slum area (Kayarda Kuje and Central Pasali Kuje) of Federal Capital Territory in the year 2002 depicts that vegetation area coverage was the highest with 292.186 hectares (47.97%), agricultural land covered 220.28 hectares (36.17%), urban slum covered 78.21 hectares (12.84%) and the least was bare surface with an area coverage of 18.39 hectares (3.01%) (Table 4 and Figure 3).

Table 4: Land use Characterization of Kuje Slum from 2002 and 2022

	Year	2002		2012		2022
S. No	Class	Area (Ha)	%	Area (Ha)	%	Area (Ha)	%
1	Agricultural land	220.28	36.17	238.02	39.08	130.18	21.37
2	Bare surface	18.39	3.01	166.28	27.3	11.47	1.88
3	Urban slum	78.21	12.84	193.88	31.83	465.7	76.46
4	Vegetation	292.186	47.97	9.99	1.64	1.71	0.28
Total		609.066	100	609.066	100	609.066	100
Overall accuracy		79%		71%,		82%
Kappa accuracy		1.12		1.07		1.2

Figure 3: Classified Kuje Urban Area Slum in 2002.

In 2012, Kuje urban slum area shows that land use/land cover with the highest area coverage was agricultural land with 238.02 hectares (39.08%), built up area with 193.88 hectares (31.83%), bare surface having 166.28 hectares (27.3%) while had an area coverage of 9.99 hectares (1.64%) in the years 2012. In 2012, the dominated land use/land cover were agricultural land cover against the vegetated covered area in 2002 (Table 4 and Figure 4). From the classified image (Figure 5 and Table 4) it was revealed that in 2022 vegetation covered an area of 1.71 hectares (0.28%) in 2022, bare surface covered an area of 11.47 hectares (1.88%), agricultural land occupied 130.18 hectares (21.37%), while built up area covered 465.7 hectares (76.46%) being the highest in 2022.

Figure 4: Classified Kuje Urban Area Slum in 2012.

Figure 5: Classified Kuje Urban Area Slum in 2022.

Slum Land Use/Land Cover Changes in Kuje Urban Area from 2002 to 2022.

Findings shows that between the year 2002 to 2012, there was an increase in agricultural land use with 17.74 hectares, bare surface increased with 147.89 hectares, urban slum area increased with 115.67 hectares while vegetation had the largest change in area with a decrease of area coverage with -282.196 hectares. Between 2002 to 2012 vegetation lands cover changed to agricultural land use, bare surface and built up land use. The large increase of bare surface area between 2002-2012 was highly related to the clearing of vegetated land for fuelwood and for slum buildings (Table 5 and Figure 6).

Table 5: Slum Land Use/Land Cover Changes in Kuje Urban Area from 2002 to 2022

LU/LC	2002 Area (Ha)	2012 Area (Ha)	change Area (Ha)	2012 Area (Ha)	2022 Area (Ha)	change Area (Ha)
Agricultural land	220.28	238.02	17.74	238.02	130.18	-107.84
Bare surface	18.39	166.28	147.89	166.28	11.47	-154.81
Urban slum	78.21	193.88	115.67	193.88	465.7	271.82
Vegetation	292.186	9.99	-282.196	9.99	1.71	-8.28
Total	609.066	609.066		609.066	609.066

Figure 6: Slum Land Use/Land Cover Changes in Kuje Urban Area from 2002 to 2022.

It was observed that between 2012-2022 there was a loss in agricultural land use area coverage with -107.84 hectares, bare surface declined with -154.81 hectares, vegetation land cover also declined with -8.28 hectares while there was a massive increase in urban slum area with 271.82 hectares. It was obvious that between 2012-2022 agricultural land use, bare surfaces and vegetal land cover were loss to urban slums (Table 5 and Figure 6). The null hypothesis that state there is no significant changes in land use/land cover classes over study period clearly is in dispute as findings from this study shows a reduction in agricultural land use area, bare surface and vegetal cover but with consistent increase in the urban slum built up area as shown in (figure 6). This result coincides with findings by [27-30].

Had similar findings in Greater Karu Urban Area where a large influx of population leading to significant dynamic changes in the growth of the settlements in the area over the past two decades which is as a result of risen cost of accommodation in the Federal Capital City of Abuja. Just as observed in this study a discontinuous spread of urban leap-frogging into other land uses and land covers, fast spreading and converting viable agricultural lands, vegetal land and bare surfaces into slums are evidence of rapid sprawl. This also is in agreement with findings on urban slums having resultant impacts on loss of productive agricultural lands and open green spaces [31].

Kuje Slum Land Use/Land Cover Climatic Characterization from 2002 to 2022

The interaction between LULC and climate is complex and it can be viewed from three dimensions. First, the interaction of LULC (more specifically vegetation) on climate LULC induce changes in climate of a region as it is a factor of climate. The interaction of climate on LULC or vegetation change in climate induced LULC change due to settlement expansion and other LULC of a place stimulates effects of local climate.

Trend of Kuje Slum Land Use/Land Cover Earth Surface Temperature from 2002 to 2022: The earth surface temperature from 2002 to 2022 in Kuje slum depict that area with vegetation cover had a mean temperature value of 25.920C, maximum value of 27.410C in 2006 and minimum value of 24.590C in 2002. Agricultural land use depicts mean earth surface temperature value of 26.0180C, highest mean temperature value (27.660C) was recorded in 2006 and the minimum mean temperature value recorded in 2002. Bare surface had a mean earth surface temperature value of 26.0680C, the maximum value of 27.690C was recorded in the year 2006 and the minimum value (24.170C) was recorded in 2009. Observation shows that mean earth surface temperature of the built up area within the slum is 26.2650C having a maximum temperature (27.720C) recorded in 2006 and the minimum earth surface temperature (24.780C) recorded in 2009 (Table 6 and Figure 7).

Table 6: Land Use/Land Cover Climatic Data Statistics from 2002 to 2022.

Temperature	Vegetation Cover	Agricultural Land	Bare Surface	Urban slum
Mean	25.920	26.018	26.068	26.265
Maximum	27.41	27.66	27.69	27.72
Minimum	24.59	24.64	24.71	24.78
Relative Humidity
Mean	69.14	69.22	69.19	68.82
Maximum	73.68	73.62	73.58	73.5
Minimum	61.75	64.19	64.1	61.75
Surface Soil Moisture
Mean	0.586	0.575	0.552	0.548
Maximum	0.67	0.65	0.63	0.63
Minimum	0.48	0.46	0.44	0.44

Figure 7: Kuje Slum Land Use/Land Cover Earth Surface Temperature from 2002 to 2022.

From 2002-2022 within Kuje slum area, the highest mean earth surface temperature among land use/land cover was recorded at the built up slum area with a temperature value of 26.2650C followed by 26.0680C with the lowest earth surface temperature recorded at vegetal covered area with 25.920C follow by 26.0180C recorded within the agricultural land use area.

The trend of temperature over the different land use/land cover within Kuje slum from 2002 to 2022 shows that the vegetal land cover had temperature trend line equation of y = 0.0204x+25.697 which clearly depicts a positive trend in temperature with R² = 0.0267 (2.67%). This mean a 2.67% certainty in the predictions of rise in future skin temperature in vegetal covered area in Kuje Urban slum. Between 2002-2022, the bare surface trend of temperature over the years within Kuje slum depicts a temperature trend line equation of y = 0.0184x +25.866 which clearly depicts a positive trend in temperature with R² = 0.0207 (2.07%). This means a 2.07% certainty in the predictions of rise in future skin temperature in bare surface covered area in Kuje urban slum (Figure 7). It was observed that between 2002-2020, the built up slum land use trend of temperature over the years depicts a temperature trend line equation of y = 0.0089x +26.116 which clearly shows an insignificant positive trend in temperature with R² = 0.0045 (0.45%). This means a 0.45% certainty in the predictions of rise in future skin temperature in built-up slum area in Kuje urban slum. From 2002-2020, the agricultural land use cover trend of temperature over the years within Kuje urban slum depicts a temperature trend line equation of y = 0.0191x +25.808 which clearly shows an insignificant positive trend in temperature with R² = 0.0216 (2.16%). This means a 2.16% certainty in the predictions of rise in future skin temperature in agricultural land use area in Kuje urban slum (Figure 7).

As observed in table 6, bare surface and built up area had the highest temperature values compared to temperature from vegetal cover and agricultural land use. This findings is not far- fetched from the findings of with results indicating that rapid urban sprawl in recent times has significantly undermined the local climate through land use and land cover changes [22]. Observed that there was strong statistical relationships between temperature-up areas (p <0.05), grass/shrub cover (p < 0.04) and all vegetation cover (p < 0.03) [22]. There was also strong statistical relationship between rainfall and built-up areas (p < 0.03), grass/shrub cover (p < 0.04) and all vegetation (p <0.02). They observed also that expansion in built up areas and reduced grass/shrub cover led to increases in temperature, rainfall and surface soil moisture.

The findings in this study is also in affirmation with that of where he observed that urban growth was found to have a large impact on local temperatures, in some cases by up to 50C in North-east USA [21-32]. Also affirm temperature rise over all land use/land cover [5-33].

Trend of Kuje Urban Slum Land Use/Land Cover Relative Humidity from 2002 to 2022: The relative humidity from 2002 to 2022 in Kuje urban slum shows that area with vegetation cover had a mean relative humidity value of 69.14%, maximum value of 73.68% in 2010 and minimum value of 61.75% in 2002. Agricultural land use depicts mean relative humidity value of 69.22%, maximum relative humidity value of 73.62% recorded in 2010 and the minimum relative humidity value (64.19%) was recorded in 2015. Bare surface had a mean relative humidity of 69.19%, maximum relative humidity value of 73.58% in 2010 and the minimum value of relative humidity (64.1%) was recorded in 2005. In the built-up slum area, between 2002-2022 had mean relative humidity of 68.82%, maximum relative humidity of 73.5% was observed in 2010 and the minimum relative humidity value of 61.75% recorded in 2016. Between 2002-2022 within Kuje urban slum area, the highest mean relative humidity among land use/land cover was recorded at the agricultural land use cover with a value of 69.22% followed by 69.19% the value recorded on bare surface. The lowest relative humidity (68.82%) was recorded in built up slum area (Table 6 and Figure 8).

Figure 8: Kuje Slum Land Use/Land Cover Relative Humidity from 2002 to 2022.

The trend of relative humidity over the different land use/ land cover within Kuje slum from 2002 to 2022 shows vegetal land cover relative humidity trend line equation of y = -0.0306x +69.474 which clearly depicts an insignificant negative trend in relative humidity with R² = 0.0036 (0.36%). This mean a 0.36% certainty in the predictions of decrease in relative humidity in vegetal covered area in Kuje urban slum (Table 6 and Figure 8). Between 2002-2020, the agricultural land cover trend of relative humidity over the years within Kuje slum depicts a trend line equation of y = -0.0161x +69.396 which clearly shows an insignificant negative trend in relative with R² = 0.0012 (0.12%).

This mean a 0.12% certainty in the predictions of decrease in future relative humidity in agricultural land covered area in Kuje Urban slum (Figure 8).

It was observed that between 2002-2020, the bare surface area trend of relative humidity over the years within Kuje slum depicts a trend line equation of y = -0.0099x +69.303 which clearly portrays an insignificant negative trend in temperature with R² = 0.0005 (0.05%). This mean a 0.05% certainty in the predictions of decrease in future relative humidity in bare surface area in Kuje Urban slum. From 2002-2020, the built up slum area trend of relative humidity over the years within Kuje slum depicts a trend line equation of y = -0.0432x +69.296 which clearly portrays an insignificant negative trend in relative humidity with R² = 0.0073 (0.073%). This mean a 0.073% certainty in the predictions of decrease in future relative humidity in built up slum area in Kuje urban slum (Figure 8).

It obvious that relative humidity is higher on vegetal covered areas and agricultural land used areas and slum growth negatively affects relative humidity in the Kuje slum area [34]. Also identified the relationship between urban sprawl components with changes in climate variables with depreciating relative humidity. Over the entire land use/land cover there have decrease in relative humidity all over the study period. The findings of this study affirms that of [5]. That increase trends of temperature and built up area indicated leads to decrease in relative humidity meaning that there is an inverse relationship between humidity earth skin temperature rise and relative humidity. Table 4.2 depicts that the mean value of relative humidity from the four land use/land cover assessed shows that relative humidity is lower in bare surface and built up slum areas but high in vegetated areas and agricultural land use areas. Though there is a general decrease in relative humidity as observed over all land use/land cover as observed in (Figure 4,9) as observed by [34]. also affirm that urbanization is one serious process that transforms natural environment into various surfaces that have long run pronounced effects on the whole atmospheric system [4]. Converting one time thick forest surface into concrete and asphalt surfaces greatly altered aerodynamic processes in the area, water balance system as well as evapotranspiration process which reduces the concentration of relative humidity in the atmosphere in this study and [4]. In the long run this will technically decrease trend of rainfall in the study area [24]. Also affirms that that large-scale conversion of vegetated land to urban use leads to a significant reduction in Evapotranspiration (ET) hence depreciating relative humidity due to the lack of vegetation, which may aggravate relative humidity loss and Urban Dry Island (UDI) effect.

Trend of Kuje Slum Land Use/Land Cover Surface Soil Moisture from 2002 to 2022: The surface soil moisture from 2002 to 2022 in Kuje slum shows that area with vegetation cover had a mean surface soil moisture value of 0.586, maximum surface soil moisture value of 0.67 recorded in 2009 and minimum value of 0.48 recorded in 2006. Agricultural land use depicts mean surface soil moisture value of 0.575, maximum surface soil moisture value of 0.65 recorded in 2009 and the minimum surface soil moisture value (0.46) recorded in 2006. Bare surface had a mean surface soil moisture of maximum surface soil moisture value of 0.552 in 2009 and the minimum value of surface soil moisture (0.44) was recorded in 2006. In the slum built-up area between 2002-2022 had mean surface soil moisture of 0.548, maximum surface soil moisture of 0.63 was observed in 2010 and the minimum surface soil moisture value of 0.44 was recorded in 2006. From 2002-2022 within Kuje slum area, the highest mean surface soil moisture among land use/land cover was recorded at the vegetal land cover with a mean surface soil moisture value of 0.586 followed by 0.575 recorded on agricultural land use cover with the lowest surface soil moisture recorded at bare surface and built up slum areas (Figure 9).

Figure 9: Kuje Slum Land Use/Land Cover Surface Soil Moisture from 2002 to 2022.

As shown in Figure 9, the trend of surface soil moisture over the different land use/land cover within Kuje slum from 2002 to 2022 depicts vegetal land cover surface soil moisture trend line equation of y = 0.0008x +0.5771 which clearly depicts a positive trend in surface soil moisture of R² = 0.0069 (0.69%). This mean a 0.69% certainty in the predictions of insignificant rise in future surface soil moisture in vegetal covered area in Kuje Urban slum. Within the study period, the agricultural land use cover trend of surface soil moisture over the years in Kuje slum depicts a surface soil moisture trend line equation of y = 0.001x +0.5633 which clearly shows an insignificant positive trend in surface with R² = 0.015 (1.50%). This mean a 1.50% certainty in the predicted rise in future surface soil moisture in agricultural land use area in Kuje Urban slum (Figure 9). It was observed that bare surface trend of surface soil moisture over the years within Kuje slum portrays a trend line equation of y = 0.0012x +0.539 which clearly depicts an insignificant positive trend in surface soil moisture with R² = 0.0188 (1.88%). This mean a 1.88% certainty in the predictions of rise in future surface soil moisture in bare surface covered area in Kuje Urban slum. It was observed that between 2002-2020, the built up slum land use trend of surface soil moisture over the years depicts a trend line equation of y = 0.0017x +0.5297 which clearly expressed an insignificant positive trend in surface soil moisture with R² = 0.0329 (3.29%). This mean a 3.29% certainty in the predictions of rise in future surface soil moisture in built- up slum area in Kuje Urban slum (Figure 9).

It was obvious that there is less difference in the mean values of surface soil moisture but the soil moisture is higher in vegetated area and agricultural land use area but lower at built up slum areas and bare surfaces with an significance rise in the trend of the land use/land cover in the Kuje urban slum area within the study period of 2002 to 2022. The findings agrees with that of who observed a relationship between urban sprawl components with soil atmospheric and soil moisture  [34]. The increase in surface soil moisture in vegetated area and agricultural land use area is against the findings of that observed that large-scale conversion of vegetated land to urban use leads to a significant reduction in surface soil moisture over the years [24]. The findings of this study is also agree with that of who observed loss of moisture due to loss of water retention surfaces and as such intercepting water balance system on bare surface and built up areas [4-35].

Test of Hypotheses of Variation of Climatic Elements from the Different Land use/Land Cover Classes over the Study Period

The hypothesis of this study which Ho: state that “There is no significant variation in the characteristics of climatic elements from the different land use/land cover classes over the study period was tasted at 0.05 Alpha level for earth surface temperature, relative humidity and surface soil moisture”.

The ANOVA findings expressed the variations in Earth Skin Temperature (EST), Relative Humidity (RH), and surface soil moiture among the four distinct land uses (vegetation, agricultural land use, bare surface, and urban slum over a period between 2002 and 2022. Findings show that the earth’s surface temperature is not significantly different across the four land uses, with an F-value of 0.689 and a p-value of 0.562. Though the mean values depicts variation in the earth surface temperature, but the ANOVA result did not depicts any significant variation in the earth surface temperature over the period 2002- 2022 over different land use/land cover (Vegetal cover, agricultural land use, bare surfaces and built up area). Though the findings went contrary with the observation of where they observed that urban land use/land cover depicts variation in the values of surface temperature [32]. It is obvious that LST analysis has the capacity to bring out the spatial variation even though it may not be significant as observed [36-40].

It was observed that the relative humidity across the four land cover/land uses has F-value of 0.080 and p-value of 0.971, showing that there is no discernible difference. Identified variation in the levels of relative humidity over different land uses/land cover components with equally temporal changes relative humidity [34]. In this study, relative humidity over the entire land use/land cover though differences exist but not significant as observed in study a study by [5]. The likely tendency for such result is due to small area coverage of the Kuje Urban Slum.

In contrast, the F-value for surface soil moisture is 2.173 and the p-value is 0.098, indicating that there is only a little variation in the amount of surface soil moisture among the four land cover/land uses. The p-value is higher than the usual significance level of 0.05, hence it can be said that there is no statistically significant variation in the amount in surface soil moisture it across the four various land cover/land uses. This is in line with the findings of that observed that large-scale conversion of vegetated land to urban use leads to a significant variation in surface soil moisture over different land use/land covers [24]. Similarly, also had similar observations [4-35]. In conclusion, the ANOVA findings show that during the time span from 2002 to 2022, there are no statistically significant variations in the earth’s surface temperature, relative humidity, and soil moisture across the four land cover/land uses (Table 7).

Table 7: Test of Hypotheses of Variation of Climatic Elements from the Different Land Use/Land Cover Classes Over the study period.

		Sum of Squares	df	Mean Square	F	Sig.
EST	Between Groups	1.320	3	0.440	0.689	0.562
	Within Groups	51.122	80	0.639
	Total	52.442	83
RH	Between Groups	2.138	3	0.713	0.080	0.971
	Within Groups	712.118	80	8.901
	Total	714.256	83
SSM	Between Groups	0.020	3	0.007	2.173	0.098
	Within Groups	0.250	80	0.003
	Total	0.270	83

**Anova is significant at the 0.05 level

Ts: Earth Skin Temperature; RH: Relative Humidity; SSM: Surface Soil Moisture

This study assessed slum growth impact on climatic elements in Kuje urban slum area of the Federal Capital Territory of Nigeria. Study conclude that there is serious decrease in agricultural land use area, bare surface and vegetal cover but with consistent increase in the, urban slum built up area. The vegetal land cover, agricultural land use, bare surface and built up area from 2002 to 2022 had a steady but insignificant rise in earth surface temperature. The vegetal land cover, agricultural land use, bare surface and built up area from 2002 to 2022 had a steady but insignificant decrease of relative humidity. The study also conclude that there is an insignificant increase in soil surface moisture from over the period of study. The study also conclude that there is no significant variation in the value of earth surface temperature, relative humidity and surface soil moisture over the different land use/land cover in Kuje urban slum are from 2002 to 2022.

Based on the outcomes of this study, it was recommended that slum need to be incorporated into Kuje urban area planning through gentrification of buildings since demolition is not an options due to large concentration of indigenous people within the slum. Though there is no significant variation in climatic elements as a result of changing land use/land cover in the Kuje urban slum yet there is need for consistent monitoring of slum growth and climatic element anomalies over different land use/ land cover.

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