Evaluation of Development Dynamics of Awka Capital Territory, Anambra State, Using Remote Sensing Igbokwe, E.C1, Emengini, E

Home , Anambra State, Awka South, Dunukofia, Enugwu Ukwu, Nawfia, Orumba North

Research Article Volume 9 Issue No.4 Evaluation of Development Dynamics of Awka Capital Territory, Anambra State, using Remote Sensing Igbokwe, E.C1, Emengini, E. J2, Ojiako, J.C3 Department of Surveying and Geoinformatics Nnamdi Azikiwe University Awka, Nigeria

Abstract: This study investigated the development dynamics of Awka Capital Territory in Anambra State, Nigeria using Remote Sensing. The objectives were to: examine the spatial extent of land cover and land use in Awka Capital Territory for the last 27 years (1990 – 2017); ascertain the trend of change, transition and pattern of the land cover/land use classes during this period and predict the future urban development dynamics for the next 30 years (2018 – 2048). The results revealed that urban area grew from 27.92% to 31.19%, to an of area of 14437.68 hectares between 1990 and 1999, 31.19% to 33.67%, to an area of 15586.73 hectares between 1999 and 2008, and 33.67% to 37.24%, to an area of 17237.45 hectares between 2008 and 2017. Trend of change analysis indicated that urban area had an annual rate of change of 0.62% between 1990 and 1999, 0.43% between 1999 and 2008 and 0.56% between 2008 and 2017. Transition results showed that between 1990 and 1999, urban area gained 1272.73 hectares from vegetation and 242.5 hectares from open space, also between 1999 and 2008 urban area gained 1000 hectares from vegetation and 149.05 hectares from open space, in the last epoch between 2008 and 2017 urban area gained 1068.27 hectares from vegetation, 582.45 hectares from open space. The study revealed two patterns inherent in the study area: edge-expansion and infilling, with edge-expansion being the most dominant growth pattern in the study area between 1990 and 2017, there was no evidence of spontaneous pattern of growth within the study area. Prediction of the future urban development dynamics of Awka Capital Territory in 2048 revealed that urban areas is expected to increase from 35.45% to 49.41% i.e. from area coverage of 17798.44 hectares to 22871.51 hectares. It was recommended that the approach be used as tool for planning and decision making in urban development.

Keywords: Development Dynamics, Growth Pattern, Remote Sensing, Trend Analysis, Urban Growth.

1.0. INTRODUCTION landuse and landcover changes. There has been a migration from rural to urban areas, resulting in the territory becoming a highly Previous studies have shown that there remain only few urbanized with 62% of its population living in urban areas (UN- landscapes on the Earth that is still in their natural state HABITAT, 2009). The shift in urban migration has (Opeyemiet al, 2015; Kumar et al, 2007). Due to man’s posed problems for Awka Capital Territory, infrastructure activities, the landscape is being altered and man’s use of land improvements, both physical and social, has lagged behind the has had a profound effect upon the environment thus resulting growth in population. There are problems in erosion, flooding into an accelerated expansion (Zubair, 2008). Urban expansion is due to unregulated building patterns, poor sanitation, noise and dynamic and constantly changing with man’s changing social air pollution, and overcrowding. The available information on and economic needs and therefore require constant monitoring. the growth dynamics in the Awka Capital City is This is especially true in most developing countries including insufficient. This makes decision making process complex and Nigeria where proper and periodic monitoring of urban less transparent, deeper understanding of the mechanisms expansion is not carried out (Opeyemiet al, 2015). Urbanization underlying the urban dynamics can assist toward formulating is among the most significant process that has shaped landuse appropriate policies of urban growth management, and thus, activities and has drawn a great deal of attention throughout the lessening the negative impacts of urbanization while maximizing world (United Nations, 2017). It is estimated that urban the positive impacts. Thus, in order to understand the dynamic population will rise from 3.57 billion in 2010 to 6.34 billion in process of urban spatial growth, researchers throughout the 2050 where almost 70 percent of the world’s population is world have implemented diverse approaches, where spatial expected to live in the cities (United Nations, 2017). This models have been developed to study, predict and simulate immense figure is mainly due to migration from rural to city in future urban growth (Batty, 1994). To understand the dynamics search of better quality of life generated by urban activities and of growth in Awka Capital Territory, a broad analysis of the services (Deng et al, 2018). However, an increase of urban spatial trends of urban growth would help in addressing the population has forced cities to expand vertically or horizontally, needs of the present and future needs of the region. This plays a encroaching into agricultural land and natural boundaries, and key role in planning for infrastructure and becomes crucial in changing landuse and landcover without us realizing it (Suet al, planning especially when resources are scarce. Remote Sensing 2017). Awka Capital Territory is no exception; Awka Capital is an effective tool for analyzing urban development in Awka Territory is rapidly developing into a mass of urban areas Capital Territory. The collection of remotely sensed data growing to merge with each other which has caused spatial facilitates the synoptic analyses of Awka Capital Territory over

International Journal of Engineering Science and Computing, April 2019 21719 http://ijesc.org/ time; such data also provide an important link between intensive, particularly in the urban area and also to ascertain the trend and localized ecological research and regional, national and characteristics of the growth dynamics in the last 27 years so as international conservation and management of biological to predict possible changes that might take place in this status in diversity (Wilkie and Finn, 1996). With the collection of these the next 30 years. spatial and statistical data for a different time period, it is then possible to monitor, manage urban growth and also predict what 2.0 STUDY AREA future urban growth would look like.The combination of remotely sensed data and Molusce algorithm in this research Awka Capital Territory is located in Anambra State, South provides a detailed insight into the spatial extents of landcover Eastern Nigeria (See fig. 2.0). It is located between latitude 60 5’ /landuse with emphasis on urban growth of Awka Capital N and 60 15’ N and longitudes 70 0’ E and 7o 5’ E. Awka capital Territory, in the past, present and also the future. Therefore, this territory covers a land mass of 400 square kilometres and research investigated and analyzed the status of development comprises of six local government areas namely Anaocha. Awka dynamics of Awka capital territory between 1990 and 2017 with North, Awka South, Dunukofia, Njikoka and Orumba North, in a view to detecting the change dynamics that has taken place part or full (UN-HABITAT, 2009).

Figure 2.0: Map of Study area

3.0 MATERIALS AND METHOD the accuracy of classification of images. Random sampling technique was used to determine the location of the points. The 3.1 Materials spectral bands were then stacked to produce a color composite of The data that was used in this research were Landsat 5 Thematic the study area for each year (1990, 1999, 2008, 2017 and 2018), Mapper for the year 1990, Landsat 7 Enhanced Thematic after which land cover maps of the study area were produced Mapper for 1999 and 2008, Landsat 8 Operational Land Imager using the supervised maximum like lihood classification for 2017 and Sentinel-2 for 2018, all obtained from algorithm in ERDAS Imagine used by (Onojeghuo and www.earthexplorer.usgs.gov. Other ancillary data used were Onojeghuo,2013). In order to ascertain the trend of change and boundary map of Awka Capital Territory, transportation data of transition of the landcover/landuse classes in the last 27 years, Awka Capital Territory and population data of Awka Capital the results obtained from the landcover/landuse classification Territory. statistics table were used to compute trend analysis as adopted from long et al (2007). The comparison of the landcover/landuse 3.2 Method statistics assisted in identifying the annual rate of change The thematic map per for the year 1990, enhanced thematic between 1990 and 2017. In achieving this, table was prepared mapper for 1999 and 2008, operational land imager for 2017 and showing the areas and percentage change for each year measured sentinel-2 for 2018 were radio metrically and geometrically against each other. To determine the rate of change of corrected as used in (Orimoloye et al, 2018), after which level landcover/landuse, the year period 1990-2017 was divided into one classification scheme was developed for the study area after three sub-periods 1990 – 1999, 1999 – 2008 and 2008 - 2017 (Anderson et al, 1967),this resulted in the following class and compared against each other. A quantitative method by features: urban area, water body, vegetation and open space. Zenget al, (2010) was also used to distinguish three urban Ground truthing was carried out to collect sample data for growth types: infilling, edge expansion, and spontaneous growth accuracy assessment, forth is purpose, the coordinates of 256 in the study area. The dominance of each growth types is selected ground control points was collected and used to assess meaningful to describe the process of landscape pattern changes

International Journal of Engineering Science and Computing, April 2019 21720 http://ijesc.org/ between two or more time points. The landcover maps of 1990, 31.19 %, to area of 14437.68 hectares. Open space decreased 1999, 2008 and 2017 were used as inputs in Molusce algorithm from 12.82% to 12.30 to an area of 5693.72 hectares while water alongside explanatory variables such as distance to roads, body increased from 9.26% to 9.78% to an area of 4528.6 population density and distance to developable lands. Transition hectares. potential was modelled and trained using artificial neural network, the training works by using classic realization of In 2008, vegetation decreased further from 46.73% to 44.46%, to multilayer perceptron. After the ANN training, the model was an area of 20583.59 hectares. Urban area increased further from then used to predict urban development in 2018.The result of the 31.19% to 33.67%, to an area of 15586.73 hectares while open prediction was then validated by comparing it with the reference space decreased from 12.30% to 12.07%, to an area of 5589.67 data i.e. the landcover/landuse map of 2018, using kappa hectares. Water body increased slightly from 9.78% to 9.78% to statistics and image correlation. After the validation, and an area of 4529.8 hectares. ascertaining that the model results were within acceptable range, future urban development prediction for the next 30years (2018 - In 2017, vegetation continued its gradual decrease from 44.46% 2048). to 41.29%, to an area of 19115.32 hectares, while urban area also increased from 33.67% to 37.24%, to an area of 17237.45 4.0 RESULTS hectares. Open space continued decreasing from 12.07% to 11.52%, to an area of 5334.6 hectares while water body 4.1 Landcover/Landuse Classification increased from 9.78% to 9.94%, to area coverage of 4601.35. The landcover/landuse distribution of Awka Capital Territory in The land cover/land use distribution of Awka Capital Territory 1990 indicated that vegetation, accounted for the largest land in 2018 also indicate gradual decrease of vegetation from cover with 50% and an area of 23144.9 hectare. Urban area had 44.46% to 40.08%, to an area of 18555.33 hectares, while Urban 27.92 % and a coverage area of 12922.45 hectares. Open space area increased from 33.67% to 38.45%, to an area of 17798.44 and water body had the lowest turnout with 12.82% and 9.26% hectares. Open space also decreased from 11.52% 11.50%, to an with an area of 5936.22 and 4286.22 hectares respectively. In area of 5324.67 hectares, while water body increased from 1999, vegetation, decreased from 50% to 46.73% to an area of 9.94% to 9.96, to an area of 4611.35 hectares. This is shown in about 21629.79 hectares. Urban area increased from 27.92% to table 4.1 and figure 4.1.

Table 4.1: Landcover Distribution for Awka Capital Territory between 1990 and 2018 Class Name 1990 1999 2008 2017 2018 Area % Area % Area % Area % Area %

Urban area 27.9 14437.6 12922.45 31.19 15586.73 33.67 17237.45 37.24 17798.44 38.45 2 8 Vegetation 50.0 21629.7 23144.9 46.73 20583.59 44.46 19115.32 41.29 18555.33 40.08 0 9 Open space 12.8 5936.22 5693.72 12.30 5589.67 12.07 5334.67 11.52 5324.67 11.50 2 Water body 4286.22 9.26 4528.6 9.78 4529.8 9.78 4602.35 9.94 4611.35 9.96 Total 46289.7 46289.79 100 100 46289.79 100 46289.79 100 46289.79 100 9

25000

20000 1990 15000 1999

10000 2008 2017 5000 2018 0 Urban area Vegetation Open space Water body

Figure 4.1: Histogram of landcover/landuse distribution of Awka Capital Territory between 1990 and 2018

4.2 Trend Analysis: The annual rate of change between 1990 rate of change was given as -0.23% between 1990 and 1999, - and 2017, for urban area was given as 0.62% between 1990 and 0.10% between 1990 and 2008 and -0.25% between 2008 and 1999, 0.43% between 1999 and 2008 and 0.56% between 2008 2017. For water body the annual rate of change was given as and 2017. For vegetation, the annual growth rate was given as - 0.30% between 1990 and 1999, 0.001% between 1999 and 2008 0.37% between 1990 and 1999, -0.27% between 1999 and 2008, and 0.08% between 2008 and 2017, as shown in table 4.2 and and -0.41% between 2008 and 2017. For open space, the annual figure 4.2.

International Journal of Engineering Science and Computing, April 2019 21721 http://ijesc.org/ Table 4.2: Annual rate of change between 1990 and 2017 Class Name Annual Rate of Annual Rate of Annual Rate of change 1990 – change 1999 – 2008 change 2008 – 1999 2017 Urban area 0.61 0.42 0.55 Vegetation -0.37 -0.27 -0.41 Open space -0.23 -0.10 -0.25 Water body 0.30 0.001 0.08

0.8 0.6 0.4 1990 - 1999 0.2 0 1999 - 2008

Annual Rate -0.2 2008 - 2017 -0.4 -0.6 Class Type

Figure 4.2: Annual rate of change between 1990 and 2017

This indicated that both urban area and water body had a a. Size positive growth between 1990 and 2017, while vegetation and The size of an urban area has often been used as a simple index open space declined between 1990 and 2017. of growth. The idea of using the urban area size for growth, causes more land consumption than compact development. 4.3 Growth Pattern within Awka Capital Territory Because growth is characterized by an increase in the built-up In order to measure the spatial dimension of urban growth area, this attribute gives considerable information for pattern in Awka Capital Territory, this study employed the use understanding the behavior of such growth. Larger urban area of spatial metrics to analyze urban growth patches in Awka size values indicate a greater degree of growth. This study was Capital Territory. This metrics are numerical indicators that able to identify the sizes of urban area patches within the study quantify the spatial pattern of urban patches within the study area. The urban area patches and sizes occupied between 1990 area. The metrics used were: size and urban expansion index. and 2017 area is displayed in table 4.3.

Table 4.3: Urban patches and area occupied between 2008 and 2017 Area (Hectares) Area (Hectares)

Urban % Annual % Annual % Annual Change Rate Change Rate Change Rate Patch 1990 1999 2008 2017 1990- 1990- 1999- 1999- 2008- 2008- 1999 1999 2008 2008 2017 2017 Abagana 1235.30 1380.15 0.53 0.06 0.37 0.04 1489.99 1647.79 0.48 0.05 Agulu 1167.22 1304.08 0.50 0.06 0.35 0.04 1407.87 1556.97 0.45 0.05 Amawbia 1788.17 1997.84 0.77 0.09 0.53 0.06 2156.85 2385.27 0.70 0.08 Awka 2693.04 3008.81 1.15 0.13 0.80 0.09 3248.28 3592.29 1.05 0.12 Enugwu- 0.47 0.05 1198.12 1338.61 0.51 0.06 0.35 0.04 1445.14 1598.19 Ukwu Ifite 900.21 1005.76 0.39 0.04 0.27 0.03 1085.81 1200.80 0.35 0.04 Okpuno 700.10 782.19 0.30 0.03 0.21 0.02 844.44 933.87 0.27 0.03 Nibo 979.08 1093.88 0.42 0.05 0.29 0.03 1180.94 1306.01 0.38 0.04 Nise 936.20 1045.97 0.40 0.04 0.28 0.03 1129.22 1248.81 0.36 0.04 Nwafia 1325.01 1480.37 0.57 0.06 0.39 0.04 1598.19 1767.45 0.52 0.06 Total 12922.45 14437.66 5.54 0.62 3.83 0.43 15586.73 17237.45 5.03 0.56

International Journal of Engineering Science and Computing, April 2019 21722 http://ijesc.org/ From table 4.3, Abagana occupied an area of 1235.30 hectares in rate amongst other urban patches between 1990 and 2017. Ifite 1990, 1380.15 hectares in 1999, 1489.99 hectares in 2008 and occupied an area of 900.21 hectares in 1990, 1005.76 hectares in 1647.79 hectares in 2017, with an annual growth of 0.06%, 1999, 1085.81 hectares in 2008 and 1200.80 hectares in 2017, 0.04% and 0.05% between 1990-1999, 1999-2008 and 2008- with an annual growth of 0.04%, 0.03% and 0.04% between 2017 respectively. Similarly, Agulu occupied an area of 1167.22 1990-1999, 1999-2008 and 2008-2017 respectively. Similarly, hectares in 1990, 1304.08 hectares in 1999, 1407.87 hectares in Nise with the same growth rate as Ifite, occupied an area of 2008 and 1556.97 hectares in 2017, with an annual growth of 936.20 hectares in 1990, 1045.97 hectares in 1999, 1129.22 0.06%, 0.04% and 0.05% between 1990-1999, 1999-2008 and hectares in 2008 and 1248.81 hectares in 2017, with an annual 2008-2017 respectively. Enugwu-Ukwu occupied an area of growth of 0.04%, 0.03% and 0.04% between 1990-1999, 1999- 1198.12 hectares in 1990, 1338.81 hectares in 1999, 2008 and 2008-2017 respectively. Okpuno occupied an area of 1445.14hectares in 2008 and 1598.19 hectares in 2017, with an 700.10 hectares in 1990, 782.19 hectares in 1999, 844.44 annual growth of 0.06%, 0.04% and 0.05% between 1990-1999, hectares in 2008 and 933.87 hectares in 2017, with an annual 1999-2008 and 2008-2017 respectively. Thus, indicating a growth of 0.03%, 0.02% and 0.03% between 1990-1999, 1999- similar growth rate between Abagana, Agulu and Enugu-Ukwu. 2008 and 2008-2017 respectively. Nibo occupied an area of Amawbia occupied an area of 1788.17 hectares in 1990, 1997.84 979.08 hectares in 1990, 1093.88 hectares in 1999, 1180.94 hectares in 1999, 2156.85 hectares in 2008 and 2385.27 hectares hectares in 2008 and 1306.01 hectares in 2017, with an annual in 2017, with an annual growth of 0.09%, 0.06% and 0.08% growth of 0.05%, 0.03% and 0.04% between 1990-1999, 1999- between 1990-1999, 1999-2008 and 2008-2017 respectively. 2008 and 2008-2017 respectively. Lastly, Nawfia occupied an Awka occupied an area of 2693.04 hectares in 1990, 3008.81 area of 1325.01 hectares in 1990, 1480.37 hectares in 1999, hectares in 1999, 3248.28 hectares in 2008 and 3592.29 hectares 1598.19 hectares in 2008 and 1767.45 hectares in 2017, with an in 2017, with an annual growth of 0.13%, 0.09% and 0.12% annual growth of 0.06%, 0.04% and 0.06% between 1990-1999, between 1990-1999, 1999-2008 and 2008-2017 respectively. 1999-2008 and 2008-2017 respectively. The urban patches This indicated that Awka and Amawbia had the highest growth growth rate is summarized in Figure 4.3.

0.13 0.12

0.09 0.09 0.08 0.06 0.06 0.06 0.06 0.06 0.05 0.05 0.05 0.05 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.03 0.03 0.03 0.03 0.02

1990-1999 1999-2008 2008-2017

Figure 4.3: Urban patch growth rate b. Landscape Expansion Index (b) edge-expansion when 0 < LEI ≤ 0.5, A quantitative method by zhenget al, (2010) was used to (c) spontaneous growth when LEI = 0, which indicates distinguish three urban growth types: infilling, edge expansion, no shared-boundary and spontaneous growth. The dominance of each growth types is meaningful to describe the process of landscape pattern changes The results show two types of growth within the study area. between two or more time points. Whether a growth patch is Between 1990 and 1999, 68.2% of overall growth in the study called infilling, edge expansion, or spontaneous growth is area was attributed to edge-expansion, while 31.8% was determined by LEI value which is calculated as follows: attributed to infilling growth with Awka and Amawbia having a high percentage of edge-expansion in the said period. Similarly, between 1999 and 2008, there was an almost even growth pattern in the study area, with 55.7% of the growth attributed to … (4.1) edge-expansion, while 44.3% was attributed to infilling growth.

In the last epoch between 2008 and 2017, edge-expansion was where LEI is Landscape Expansion Index, L is the length of the C the dominant growth pattern in the study area with 75.61% of common boundary of a newly grown urban patch and the pre- growth while 24.39% was attributed to infilling. There was no growth urban patches, and P is the perimeter of this newly indication of spontaneous growth in the study area. The grown patch. Urban growth type is identified as summary of the results is displayed in figures 4.4 4.6. (a) infilling when LEI > 0.5, –

International Journal of Engineering Science and Computing, April 2019 21723 http://ijesc.org/ GROWTH PATTERN 1990-1999

Infilling Edge-Expansion Spontaneous Growth

16.00 14.16 14.00 12.00 10.62 10.00 8.00 6.63 6.59 6.68 5.90 5.24 5.81 6.00 5.00 4.86 4.45 3.38 3.39 2.93 3.22 2.58 4.00 2.44 1.72 2.03 2.38 2.00 0.00

Figure 4.4: Growth percentage between 1990 and 1999

GROWTH PATTERN 1999-2008

Infilling Edge-Expansion Spontaneous Growth 11.38 12.00 9.46 10.00 7.18 8.00 6.66 6.12 6.13 5.61 5.33 6.00 2.79 4.07 4.12 3.87 3.70 3.263.70 3.51 3.303.95 4.00 3.15 2.62 2.00 0.00

Figure 4.5: Growth percentage between 1999 and 2008

GROWTH PATTERN 2008-2017

20.00 Infilling Edge-Expansion Spontaneous Growth 15.60 14.34 15.00 10.11 9.77 10.00 6.99 6.67 8.60 6.74 7.20 5.71 5.29 6.31 6.12 5.51 3.93 6.13 4.50 5.19 5.12 5.00 3.86

0.00

Figure 4.6: Growth percentage between 2008 and 2017

4.5 Model Validation: Traditionally, model validation refers to that case, it is necessary to validate the predicted/simulated map comparing the simulated and reference maps (Vliet et al, 2011). with the base/reference map. The main objective of model Sometimes the simulated maps can give misleading results. In validation is to find out whether the simulation is giving any

International Journal of Engineering Science and Computing, April 2019 21724 http://ijesc.org/ abrupt result or not. This justifies the modeling output in terms respectively. In comparison with the actual landcover/landuse of of reality. In order to validate the model’s prediction of Awka Capital Territory in 2018, it read that vegetation had 40.08 landcover/landuse of 2018, the predicted landcover/landuse of %, with area of about 18555.33 hectares. Urban area had 38.45% 2018 was compared to the actual landcover/landuse of 2018 with an area of 17798.44 hectares while water body and open using kappa statistics, and image correlation. The predicted space had 9.96% and 11.50% with an area of 4611.35 and landcover/landuse of Awka Capital Territory in 2018 as shown 5324.67hectares respectively. This gives a difference of 102.88 in table 4.4 indicate that vegetation had 40.04 %, with area of hectares between actual and predicted urban area, 20.99 hectares about 18534.38 hectares. Urban area had 38.67% with an area of between actual and predicted vegetation, 79.8 hectares between 17901.32 hectares while water body and open space had 9.95% actual and predicted open space and 2.13 hectares between and 11.33% with an area of 4609.22 and 5244.87 hectares actual and predicted water body.

Table 4.4: Comparison between 2018 predicted landcover/landuse and actual 2018 landcover/landuse Class Name 2018 Landcover/landuse 2018 Predicted Landcover/landuse Difference

17901.32 38.67% 102.88 0.22 Urban area 17798.44 38.45% 18534.38 40.04% 20.99 0.04 Vegetation 18555.33 40.08% 5244.87 11.33% 79.8 0.17 Open space 5324.67 11.50% 4609.22 9.95% 2.13 0.01 Water body 4611.35 9.96% Totals 46289.79 100% 46289.79 100%

Figure: 4.7: (a) 2018 landcover/landuse map, (b) predicted 2018 landcover/landuse map

The validation tab in Molusce was used to validate the predicted (images, in this case). The coefficient ranges from -1, indicated a map against the reference map and the overall accuracy gotten perfect negative linear association, to 1, indicated a perfect was as 93.36%; overall kappa was given as 0.9083 and the positive linear association. An r-value of 0 indicates no model error was given as 6.64%. Hence the model result is correlation between the test variables. The results of the adjudged to be acceptable. Image correlation coefficient (r) correlation coefficient gave a value of 0.9585, which indicates a between two images was also calculated to determine the good positive relationship between the two images as shown in similarities between the two images. The r-value is a measure of table 4.4and figure 4.8 the linear association in the variation of the input variables

Table .4.4. Image correlation matrix 2018 Reference 2018 Predicted LULC LULC 2018 Reference LULC 1.000 0.9585

2018 Predicted LULC 0.9585 1.000

International Journal of Engineering Science and Computing, April 2019 21725 http://ijesc.org/ Image Correlation

800.000

600.000

400.000

200.000

0.000 2018 Reference LULC2018 Reference 0.000 200.000 400.000 600.000 800.000 2018 Predicted LULC

Figure.4.8. Correlation graph between 2018 reference LULC and 2018 predicted LULC

4.6 Future Urban Development Prediction between 1990 and 2017 are identified and modeled as transitions Urban growth prediction in Molusce is an empirically given from one landcover/landuse state to another. Molusce was used process that moves in a step wise fashion from change analysis; to predict the change for (30 years) from the 2018 to 2048 and to transition Potential Modeling; to change prediction. It is based the result is shown in fig 4.9 and table 4.5. on the historical change from 1990 to 2018. The change assessed

Figure 4.9. 2048 predicted future urban development

Table: 4.5: Landcover/landuse distribution of Awka capital territory 2048 2048 Landcover/landuse Prediction

Class Name Hectares Percentage (%) Urban area 22871.51 49.41% Vegetation 13853.39 29.93% Open space 4852.99 10.48% Water body 4711.9 10.18% Totals 46289.79 100%

The prediction results tabulated in table 4.5, indicated that by space is expected to decrease to 10.48% covering an area of 2048 urban area is expected to grow to 49.41% covering an area 4852.99 hectares while water body is expected to increase to of 22871.51 hectares (figure 4.9), vegetation is expected to 10.18% covering an area of 4711.9 hectares. decrease to 29.93% covering an area of 13853.39 hectares, open

International Journal of Engineering Science and Computing, April 2019 21726 http://ijesc.org/ 25000

20000

15000 Urban Area Water Body 10000 Open space Area (Hectares) 5000 Vegetation

0 1990 1999 2008 2017 2018 2048 Year

Figure.4.10. Graph of urban development from 1990 to 2048

Thirty years from 2018, the vegetation and open space classes [3]. Deng, J.S., Wang, K., Hong, Y. and Qi, J.G. (2018). Spatio- are expected to loss part of its area to urban area based on their temporal dynamics and evolution of land use change and distance to roads, distance to developable land and as population landscape pattern in response to rapid urbanization. Landscape density increases. Water body is expected to increase from and Urban Planning, 92, 187-198. 9.96% to 10.18% with area coverage of 4711.9 hectares. Urban area is expected to increase from 35.45% to 49.41% i.e. from [4]. Kumar, J. A. V., Pathan, S. K. and Bhanderi, R. J. (2007). area coverage of 17798.44 hectares to 22871.51 hectares while Spatio-temporal Analysis for monitoring urban growth: A case open space and vegetation is expected to decrease from 11.50% study of Indore city. Journal of the Indian Society of Remote to 10.48% i.e. from 5324.67 hectares to 4852.99 hectares and Sensing, 35, 11-20. 40.08% to 29.93% i.e. from 18555.33 hectares to 13853.39 hectares respectively. [5]. Long, H.G., Tang, G., Li, X. and Heilig, G.K. (2007). Socio- economics driving forces of landuse change in Kunshan, the 5.0 CONCLUSION Yangtze river delta economic area of the China. Journal of Environmental Management, 83 (3): 351-364 This study has demonstrated the ability of evaluating development dynamics of Awka Capital Territory between 1990 [6]. Onojeghuo A. and A. Onojeghuo (2013). Mapping and and 2018 and predicting future urban development in Awka Predicting Urban Sprawl Using Remote Sensing and Geographic Capital Territory using remote sensing and molusce algorithm Information System Techniques: A Case Study of Eti-Osa Local plugin qgis. The study work captured as accurate as possible five Government Area, Lagos, Nigeria. FIG Working Week 2013 landcover/landuse classes as they change through time. The five Environment for Sustainability, Abuja, Nigeria, 6 – 10 May classes were distinctly produced in contrast to urban 2013 development for 1990, 1999, 2008, 2017 and 2018. The study also generated the trend of change and annual rate of change [7]. Opeyemi, A. Z., Lazarus, M. O., Richard A. M., (2015), between 1990 and 2017. A modeled prediction was made into Urbanization: A Catalyst For The Emergence Of Squatter 2018 and validated using 2018 reference landcover/landuse by Settlements And Squalor In The Vicinities Of The Federal determining their kappa and image correlation. A final Capital City Of Nigeria, Journal of Sustainable Development; prediction was then made into 2048 for future urban Vol. 8, No. 2; ISSN 1913-9063 E-ISSN 1913-9071 development dynamics. This prediction can be used a decision support tool and will guide the relevant authorities on how to [8]. Orimoloye, I.R., Mazinyo, S.P., Nel, W., and Kalumba, manage and monitor development to ensure a habitable A.M. (2018) Spatiotemporal monitoring of land surface environment in the near future. temperature and estimation radiation using remote sensing: human health implications for East London, South Africa 6.0. REFERENCES Environ. Earth Sci. 77, 77 (10.1007/s12665-018-7252-6)

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