# Evaluation of Development Dynamics of Awka Capital Territory, Anambra State, using Remote Sensing

Esomchukwu Chinagorom Igbokwe, Ebele Josephine Emengini, Johnbosco

Chibueze Ojiako

► To cite this version:

Esomchukwu Chinagorom Igbokwe, Ebele Josephine Emengini, Johnbosco Chibueze Ojiako. Evaluation of Development Dynamics of Awka Capital Territory, Anambra State, using Remote Sensing. International Journal of Engineering Science and Computing, 2019, 9 (4), pp.21719-21728. hal-04601991

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# Evaluation of Development Dynamics of Awka Capital Territory, Anambra State, using Remote Sensing

Esomchukwu Chinagorom Igbokwe, Ebele Josephine Emengini, Johnbosco

Chibueze Ojiako

► To cite this version:

Esomchukwu Chinagorom Igbokwe, Ebele Josephine Emengini, Johnbosco Chibueze Ojiako. Evaluation of Development Dynamics of Awka Capital Territory, Anambra State, using Remote Sensing. International Journal of Engineering Science and Computing, 2019, 9 (4), pp.21719-21728. hal-04601991

HAL Id: hal-04601991

https://hal.science/hal-04601991v1

Submitted on 5 Jun 2024

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

Public Domain
<figure><img src="image_1.png" /></figure>

# 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

Previous studies have shown that there remain only few landscapes on the Earth that is still in their natural state (Opeyemiet al, 2015; Kumar et al, 2007). Due to man's activities, the landscape is being altered and man's use of land has had a profound effect upon the environment thus resulting into an accelerated expansion (Zubair, 2008). Urban expansion is dynamic and constantly changing with man's changing social and economic needs and therefore require constant monitoring. This is especially true in most developing countries including Nigeria where proper and periodic monitoring of urban expansion is not carried out (Opeyemiet al, 2015). Urbanization is among the most significant process that has shaped landuse activities and has drawn a great deal of attention throughout the world (United Nations, 2017). It is estimated that urban population will rise from 3.57 billion in 2010 to 6.34 billion in 2050 where almost 70 percent of the world's population is expected to live in the cities (United Nations, 2017). This immense figure is mainly due to migration from rural to city in search of better quality of life generated by urban activities and services (Deng et al, 2018). However, an increase of urban population has forced cities to expand vertically or horizontally, encroaching into agricultural land and natural boundaries, and changing landuse and landcover without us realizing it (Suet al, 2017). Awka Capital Territory is no exception; Awka Capital Territory is rapidly developing into a mass of urban areas growing to merge with each other which has caused spatial

landuse and landcover changes. There has been a migration from rural to urban areas, resulting in the territory becoming a highly urbanized with 62% of its population living in urban areas (UN-HABITAT, 2009). The shift in urban migration has posed problems for Awka Capital Territory, infrastructure improvements, both physical and social, has lagged behind the growth in population. There are problems in erosion, flooding due to unregulated building patterns, poor sanitation, noise and air pollution, and overcrowding. The available information on the growth dynamics in the Awka Capital City is insufficient. This makes decision making process complex and less transparent, deeper understanding of the mechanisms underlying the urban dynamics can assist toward formulating appropriate policies of urban growth management, and thus, lessening the negative impacts of urbanization while maximizing the positive impacts. Thus, in order to understand the dynamic process of urban spatial growth, researchers throughout the world have implemented diverse approaches, where spatial models have been developed to study, predict and simulate future urban growth (Batty, 1994). To understand the dynamics of growth in Awka Capital Territory, a broad analysis of the spatial trends of urban growth would help in addressing the needs of the present and future needs of the region. This plays a key role in planning for infrastructure and becomes crucial in planning especially when resources are scarce. Remote Sensing is an effective tool for analyzing urban development in Awka Capital Territory. The collection of remotely sensed data facilitates the synoptic analyses of Awka Capital Territory over
<figure><img src="image_2.png" /></figure>

# 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

Previous studies have shown that there remain only few landscapes on the Earth that is still in their natural state (Opeyemiet al, 2015; Kumar et al, 2007). Due to man's activities, the landscape is being altered and man's use of land has had a profound effect upon the environment thus resulting into an accelerated expansion (Zubair, 2008). Urban expansion is dynamic and constantly changing with man's changing social and economic needs and therefore require constant monitoring. This is especially true in most developing countries including Nigeria where proper and periodic monitoring of urban expansion is not carried out (Opeyemiet al, 2015). Urbanization is among the most significant process that has shaped landuse activities and has drawn a great deal of attention throughout the world (United Nations, 2017). It is estimated that urban population will rise from 3.57 billion in 2010 to 6.34 billion in 2050 where almost 70 percent of the world's population is expected to live in the cities (United Nations, 2017). This immense figure is mainly due to migration from rural to city in search of better quality of life generated by urban activities and services (Deng et al, 2018). However, an increase of urban population has forced cities to expand vertically or horizontally, encroaching into agricultural land and natural boundaries, and changing landuse and landcover without us realizing it (Suet al, 2017). Awka Capital Territory is no exception; Awka Capital Territory is rapidly developing into a mass of urban areas growing to merge with each other which has caused spatial

landuse and landcover changes. There has been a migration from rural to urban areas, resulting in the territory becoming a highly urbanized with 62% of its population living in urban areas (UN-HABITAT, 2009). The shift in urban migration has posed problems for Awka Capital Territory, infrastructure improvements, both physical and social, has lagged behind the growth in population. There are problems in erosion, flooding due to unregulated building patterns, poor sanitation, noise and air pollution, and overcrowding. The available information on the growth dynamics in the Awka Capital City is insufficient. This makes decision making process complex and less transparent, deeper understanding of the mechanisms underlying the urban dynamics can assist toward formulating appropriate policies of urban growth management, and thus, lessening the negative impacts of urbanization while maximizing the positive impacts. Thus, in order to understand the dynamic process of urban spatial growth, researchers throughout the world have implemented diverse approaches, where spatial models have been developed to study, predict and simulate future urban growth (Batty, 1994). To understand the dynamics of growth in Awka Capital Territory, a broad analysis of the spatial trends of urban growth would help in addressing the needs of the present and future needs of the region. This plays a key role in planning for infrastructure and becomes crucial in planning especially when resources are scarce. Remote Sensing is an effective tool for analyzing urban development in Awka Capital Territory. The collection of remotely sensed data facilitates the synoptic analyses of Awka Capital Territory over
time; such data also provide an important link between intensive, localized ecological research and regional, national and international conservation and management of biological diversity (Wilkie and Finn, 1996). With the collection of these spatial and statistical data for a different time period, it is then possible to monitor, manage urban growth and also predict what future urban growth would look like. The combination of remotely sensed data and Molusce algorithm in this research provides a detailed insight into the spatial extents of landcover /landuse with emphasis on urban growth of Awka Capital Territory, in the past, present and also the future. Therefore, this research investigated and analyzed the status of development dynamics of Awka capital territory between 1990 and 2017 with a view to detecting the change dynamics that has taken place

particularly in the urban area and also to ascertain the trend and characteristics of the growth dynamics in the last 27 years so as to predict possible changes that might take place in this status in the next 30 years.

## 2.0 STUDY AREA

Awka Capital Territory is located in Anambra State, South Eastern Nigeria (See fig. 2.0). It is located between latitude 6° 5' N and 6° 15' N and longitudes 7° 0' E and 7° 5' E. Awka capital territory covers a land mass of 400 square kilometres and comprises of six local government areas namely Anaocha. Awka North, Awka South, Dunukofia, Njikoka and Orumba North, in part or full (UN-HABITAT, 2009).

<figure><img src="image_2.png" /><figcaption>Figure 2.0: Map of Study area</figcaption></figure>

## 3.0 MATERIALS AND METHOD

### 3.1 Materials

The data that was used in this research were Landsat 5 Thematic Mapper for the year 1990, Landsat 7 Enhanced Thematic Mapper for 1999 and 2008, Landsat 8 Operational Land Imager for 2017 and Sentinel-2 for 2018, all obtained from www.earthexplorer.usgs.gov. Other ancillary data used were boundary map of Awka Capital Territory, transportation data of Awka Capital Territory and population data of Awka Capital Territory.

### 3.2 Method

The thematic map per for the year 1990, enhanced thematic mapper for 1999 and 2008, operational land imager for 2017 and sentinel-2 for 2018 were radio metrically and geometrically corrected as used in (Orimoloye et al, 2018), after which level one classification scheme was developed for the study area after (Anderson et al, 1967), this resulted in the following class features: urban area, water body, vegetation and open space. Ground truthing was carried out to collect sample data for accuracy assessment, forth is purpose, the coordinates of 256 selected ground control points was collected and used to assess

the accuracy of classification of images. Random sampling technique was used to determine the location of the points. The spectral bands were then stacked to produce a color composite of the study area for each year (1990, 1999, 2008, 2017 and 2018), after which land cover maps of the study area were produced using the supervised maximum like lihood classification algorithm in ERDAS Imagine used by (Onojeghuo and Onojeghuo,2013). In order to ascertain the trend of change and transition of the landcover/landuse classes in the last 27 years, the results obtained from the landcover/landuse classification statistics table were used to compute trend analysis as adopted from long et al (2007). The comparison of the landcover/landuse statistics assisted in identifying the annual rate of change between 1990 and 2017. In achieving this, table was prepared showing the areas and percentage change for each year measured against each other. To determine the rate of change of landcover/landuse, the year period 1990-2017 was divided into three sub-periods 1990 – 1999, 1999 – 2008 and 2008 - 2017 and compared against each other. A quantitative method by Zenget al, (2010) was also used to distinguish three urban growth types: infilling, edge expansion, and spontaneous growth in the study area. The dominance of each growth types is meaningful to describe the process of landscape pattern changes
time; such data also provide an important link between intensive, localized ecological research and regional, national and international conservation and management of biological diversity (Wilkie and Finn, 1996). With the collection of these spatial and statistical data for a different time period, it is then possible to monitor, manage urban growth and also predict what future urban growth would look like. The combination of remotely sensed data and Molusce algorithm in this research provides a detailed insight into the spatial extents of landcover /landuse with emphasis on urban growth of Awka Capital Territory, in the past, present and also the future. Therefore, this research investigated and analyzed the status of development dynamics of Awka capital territory between 1990 and 2017 with a view to detecting the change dynamics that has taken place

particularly in the urban area and also to ascertain the trend and characteristics of the growth dynamics in the last 27 years so as to predict possible changes that might take place in this status in the next 30 years.

## 2.0 STUDY AREA

Awka Capital Territory is located in Anambra State, South Eastern Nigeria (See fig. 2.0). It is located between latitude 6° 5' N and 6° 15' N and longitudes 7° 0' E and 7° 5' E. Awka capital territory covers a land mass of 400 square kilometres and comprises of six local government areas namely Anaocha. Awka North, Awka South, Dunukofia, Njikoka and Orumba North, in part or full (UN-HABITAT, 2009).

<figure><img src="image_3.png" /><figcaption>Figure 2.0: Map of Study area</figcaption></figure>

## 3.0 MATERIALS AND METHOD

### 3.1 Materials

The data that was used in this research were Landsat 5 Thematic Mapper for the year 1990, Landsat 7 Enhanced Thematic Mapper for 1999 and 2008, Landsat 8 Operational Land Imager for 2017 and Sentinel-2 for 2018, all obtained from www.earthexplorer.usgs.gov. Other ancillary data used were boundary map of Awka Capital Territory, transportation data of Awka Capital Territory and population data of Awka Capital Territory.

### 3.2 Method

The thematic map per for the year 1990, enhanced thematic mapper for 1999 and 2008, operational land imager for 2017 and sentinel-2 for 2018 were radio metrically and geometrically corrected as used in (Orimoloye et al, 2018), after which level one classification scheme was developed for the study area after (Anderson et al, 1967), this resulted in the following class features: urban area, water body, vegetation and open space. Ground truthing was carried out to collect sample data for accuracy assessment, forth is purpose, the coordinates of 256 selected ground control points was collected and used to assess

the accuracy of classification of images. Random sampling technique was used to determine the location of the points. The spectral bands were then stacked to produce a color composite of the study area for each year (1990, 1999, 2008, 2017 and 2018), after which land cover maps of the study area were produced using the supervised maximum like lihood classification algorithm in ERDAS Imagine used by (Onojeghuo and Onojeghuo,2013). In order to ascertain the trend of change and transition of the landcover/landuse classes in the last 27 years, the results obtained from the landcover/landuse classification statistics table were used to compute trend analysis as adopted from long et al (2007). The comparison of the landcover/landuse statistics assisted in identifying the annual rate of change between 1990 and 2017. In achieving this, table was prepared showing the areas and percentage change for each year measured against each other. To determine the rate of change of landcover/landuse, the year period 1990-2017 was divided into three sub-periods 1990 – 1999, 1999 – 2008 and 2008 - 2017 and compared against each other. A quantitative method by Zenget al, (2010) was also used to distinguish three urban growth types: infilling, edge expansion, and spontaneous growth in the study area. The dominance of each growth types is meaningful to describe the process of landscape pattern changes
between two or more time points. The landcover maps of 1990, 1999, 2008 and 2017 were used as inputs in Molusce algorithm alongside explanatory variables such as distance to roads, population density and distance to developable lands. Transition potential was modelled and trained using artificial neural network, the training works by using classic realization of multilayer perceptron. After the ANN training, the model was then used to predict urban development in 2018. The result of the prediction was then validated by comparing it with the reference data i.e. the landcover/landuse map of 2018, using kappa statistics and image correlation. After the validation, and ascertaining that the model results were within acceptable range, future urban development prediction for the next 30years (2018 - 2048).

## 4.0 RESULTS

### 4.1 Landcover/Landuse Classification

The landcover/landuse distribution of Awka Capital Territory in 1990 indicated that vegetation, accounted for the largest land cover with 50% and an area of 23144.9 hectare. Urban area had 27.92% and a coverage area of 12922.45 hectares. Open space and water body had the lowest turnout with 12.82% and 9.26% with an area of 5936.22 and 4286.22 hectares respectively. In 1999, vegetation, decreased from 50% to 46.73% to an area of about 21629.79 hectares. Urban area increased from 27.92% to

31.19%, to an area of 14437.68 hectares. Open space decreased from 12.82% to 12.30 to an area of 5693.72 hectares while water body increased from 9.26% to 9.78% to an area of 4528.6 hectares.

In 2008, vegetation decreased further from 46.73% to 44.46%, to an area of 20583.59 hectares. Urban area increased further from 31.19% to 33.67%, to an area of 15586.73 hectares while open space decreased from 12.30% to 12.07%, to an area of 5589.67 hectares. Water body increased slightly from 9.78% to 9.78% to an area of 4529.8 hectares.

In 2017, vegetation continued its gradual decrease from 44.46% 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 hectares. Open space continued decreasing from 12.07% to 11.52%, to an area of 5334.6 hectares while water body increased from 9.78% to 9.94%, to an area coverage of 4601.35%. The land cover/land use distribution of Awka Capital Territory in 2018 also indicate gradual decrease of vegetation from 44.46% to 40.08%, to an area of 18555.33 hectares, while Urban area increased from 33.67% to 38.45%, to an area of 17798.44 hectares. Open space also decreased from 11.52% to 11.50%, to an area of 5324.67 hectares, while water body increased from 9.94% to 9.96, to an area of 4611.35 hectares. This is shown in table 4.1 and figure 4.1.

<table><caption>Table 4.1: Landcover Distribution for Awka Capital Territory between 1990 and 2018</caption><thead><tr><th rowspan="2">Class Name</th><th colspan="2">1990</th><th colspan="2">1999</th><th colspan="2">2008</th><th colspan="2">2017</th><th colspan="2">2018</th></tr><tr><th>Area</th><th>%</th><th>Area</th><th>%</th><th>Area</th><th>%</th><th>Area</th><th>%</th><th>Area</th><th>%</th></tr></thead><tbody><tr><td>Urban area</td><td>12922.45</td><td>27.92</td><td>14437.68</td><td>31.19</td><td>15586.73</td><td>33.67</td><td>17237.45</td><td>37.24</td><td>17798.44</td><td>38.45</td></tr><tr><td>Vegetation</td><td>23144.9</td><td>50.00</td><td>21629.79</td><td>46.73</td><td>20583.59</td><td>44.46</td><td>19115.32</td><td>41.29</td><td>18555.33</td><td>40.08</td></tr><tr><td>Open space</td><td>5936.22</td><td>12.82</td><td>5693.72</td><td>12.30</td><td>5589.67</td><td>12.07</td><td>5334.67</td><td>11.52</td><td>5324.67</td><td>11.50</td></tr><tr><td>Water body</td><td>4286.22</td><td>9.26</td><td>4528.6</td><td>9.78</td><td>4529.8</td><td>9.78</td><td>4602.35</td><td>9.94</td><td>4611.35</td><td>9.96</td></tr><tr><td>Total</td><td>46289.79</td><td>100</td><td>46289.79</td><td>100</td><td>46289.79</td><td>100</td><td>46289.79</td><td>100</td><td>46289.79</td><td>100</td></tr></tbody></table>

<figure><img src="image_3.png" /><figcaption>Figure 4.1: Histogram of landcover/landuse distribution of Awka Capital Territory between 1990 and 2018</figcaption></figure>

**4.2 Trend Analysis:** The annual rate of change between 1990 and 2017, for urban area was given as 0.62% between 1990 and 1999, 0.43% between 1999 and 2008 and 0.56% between 2008 and 2017. For vegetation, the annual growth rate was given as -0.37% between 1990 and 1999, -0.27% between 1999 and 2008, and -0.41% between 2008 and 2017. For open space, the annual

rate of change was given as -0.23% between 1990 and 1999, 0.10% between 1990 and 2008 and -0.25% between 2008 and 2017. For water body the annual rate of change was given as 0.30% between 1990 and 1999, 0.001% between 1999 and 2008 and 0.08% between 2008 and 2017, as shown in table 4.2 and figure 4.2.
between two or more time points. The landcover maps of 1990, 1999, 2008 and 2017 were used as inputs in Molusce algorithm alongside explanatory variables such as distance to roads, population density and distance to developable lands. Transition potential was modelled and trained using artificial neural network, the training works by using classic realization of multilayer perceptron. After the ANN training, the model was then used to predict urban development in 2018. The result of the prediction was then validated by comparing it with the reference data i.e. the landcover/landuse map of 2018, using kappa statistics and image correlation. After the validation, and ascertaining that the model results were within acceptable range, future urban development prediction for the next 30years (2018 - 2048).

## 4.0 RESULTS

### 4.1 Landcover/Landuse Classification

The landcover/landuse distribution of Awka Capital Territory in 1990 indicated that vegetation, accounted for the largest land cover with 50% and an area of 23144.9 hectare. Urban area had 27.92% and a coverage area of 12922.45 hectares. Open space and water body had the lowest turnout with 12.82% and 9.26% with an area of 5936.22 and 4286.22 hectares respectively. In 1999, vegetation, decreased from 50% to 46.73% to an area of about 21629.79 hectares. Urban area increased from 27.92% to

31.19%, to an area of 14437.68 hectares. Open space decreased from 12.82% to 12.30 to an area of 5693.72 hectares while water body increased from 9.26% to 9.78% to an area of 4528.6 hectares.

In 2008, vegetation decreased further from 46.73% to 44.46%, to an area of 20583.59 hectares. Urban area increased further from 31.19% to 33.67%, to an area of 15586.73 hectares while open space decreased from 12.30% to 12.07%, to an area of 5589.67 hectares. Water body increased slightly from 9.78% to 9.78% to an area of 4529.8 hectares.

In 2017, vegetation continued its gradual decrease from 44.46% 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 hectares. Open space continued decreasing from 12.07% to 11.52%, to an area of 5334.6 hectares while water body increased from 9.78% to 9.94%, to an area coverage of 4601.35. The land cover/land use distribution of Awka Capital Territory in 2018 also indicate gradual decrease of vegetation from 44.46% to 40.08%, to an area of 18555.33 hectares, while Urban area increased from 33.67% to 38.45%, to an area of 17798.44 hectares. Open space also decreased from 11.52% to 11.50%, to an area of 5324.67 hectares, while water body increased from 9.94% to 9.96, to an area of 4611.35 hectares. This is shown in table 4.1 and figure 4.1.

<table><caption>Table 4.1: Landcover Distribution for Awka Capital Territory between 1990 and 2018</caption><thead><tr><th rowspan="2">Class Name</th><th colspan="2">1990</th><th colspan="2">1999</th><th colspan="2">2008</th><th colspan="2">2017</th><th colspan="2">2018</th></tr><tr><th>Area</th><th>%</th><th>Area</th><th>%</th><th>Area</th><th>%</th><th>Area</th><th>%</th><th>Area</th><th>%</th></tr></thead><tbody><tr><td>Urban area</td><td>12922.45</td><td>27.92</td><td>14437.68</td><td>31.19</td><td>15586.73</td><td>33.67</td><td>17237.45</td><td>37.24</td><td>17798.44</td><td>38.45</td></tr><tr><td>Vegetation</td><td>23144.9</td><td>50.00</td><td>21629.79</td><td>46.73</td><td>20583.59</td><td>44.46</td><td>19115.32</td><td>41.29</td><td>18555.33</td><td>40.08</td></tr><tr><td>Open space</td><td>5936.22</td><td>12.82</td><td>5693.72</td><td>12.30</td><td>5589.67</td><td>12.07</td><td>5334.67</td><td>11.52</td><td>5324.67</td><td>11.50</td></tr><tr><td>Water body</td><td>4286.22</td><td>9.26</td><td>4528.6</td><td>9.78</td><td>4529.8</td><td>9.78</td><td>4602.35</td><td>9.94</td><td>4611.35</td><td>9.96</td></tr><tr><td>Total</td><td>46289.79</td><td>100</td><td>46289.79</td><td>100</td><td>46289.79</td><td>100</td><td>46289.79</td><td>100</td><td>46289.79</td><td>100</td></tr></tbody></table>

<figure><img src="image_4.png" /><figcaption>Figure 4.1: Histogram of landcover/landuse distribution of Awka Capital Territory between 1990 and 2018</figcaption></figure>

**4.2 Trend Analysis:** The annual rate of change between 1990 and 2017, for urban area was given as 0.62% between 1990 and 1999, 0.43% between 1999 and 2008 and 0.56% between 2008 and 2017. For vegetation, the annual growth rate was given as -0.37% between 1990 and 1999, -0.27% between 1999 and 2008, and -0.41% between 2008 and 2017. For open space, the annual

rate of change was given as -0.23% between 1990 and 1999, 0.10% between 1990 and 2008 and -0.25% between 2008 and 2017. For water body the annual rate of change was given as 0.30% between 1990 and 1999, 0.001% between 1999 and 2008 and 0.08% between 2008 and 2017, as shown in table 4.2 and figure 4.2.
<table><caption>Table 4.2: Annual rate of change between 1990 and 2017</caption><thead><tr><th>Class Name</th><th>Annual Rate of change 1999</th><th>Annual Rate of change 1999 - 2008</th><th>Annual Rate of change 2008 - 2017</th></tr></thead><tbody><tr><td>Urban area</td><td>0.61</td><td>0.42</td><td>0.55</td></tr><tr><td>Vegetation</td><td>-0.37</td><td>-0.27</td><td>-0.41</td></tr><tr><td>Open space</td><td>-0.23</td><td>-0.10</td><td>-0.25</td></tr><tr><td>Water body</td><td>0.30</td><td>0.001</td><td>0.08</td></tr></tbody></table>

<figure><img src="image_4.png" /><figcaption>Figure 4.2: Annual rate of change between 1990 and 2017</figcaption></figure>

This indicated that both urban area and water body had a positive growth between 1990 and 2017, while vegetation and open space declined between 1990 and 2017.

### 4.3 Growth Pattern within Awka Capital Territory

In order to measure the spatial dimension of urban growth pattern in Awka Capital Territory, this study employed the use of spatial metrics to analyze urban growth patches in Awka Capital Territory. This metrics are numerical indicators that quantify the spatial pattern of urban patches within the study area. The metrics used were: size and urban expansion index.

#### a. Size

The size of an urban area has often been used as a simple index of growth. The idea of using the urban area size for growth, causes more land consumption than compact development. Because growth is characterized by an increase in the built-up area, this attribute gives considerable information for understanding the behavior of such growth. Larger urban area size values indicate a greater degree of growth. This study was able to identify the sizes of urban area patches within the study area. The urban area patches and sizes occupied between 1990 and 2017 area is displayed in table 4.3.

<table><caption>Table 4.3: Urban patches and area occupied between 2008 and 2017</caption><thead><tr><th rowspan="2">Urban Patch</th><th colspan="3">Area (Hectares)</th><th colspan="3">Area (Hectares)</th><th colspan="2">Area (Hectares)</th></tr><tr><th>1990</th><th>1999</th><th>% Change 1990-1999</th><th>Annual Rate 1990-1999</th><th>% Change 1999-2008</th><th>Annual Rate 1999-2008</th><th>2008</th><th>2017</th><th>% Change 2008-2017</th><th>Annual Rate 2008-2017</th></tr></thead><tbody><tr><td>Abagana</td><td>1235.30</td><td>1380.15</td><td>0.53</td><td>0.06</td><td>0.37</td><td>0.04</td><td>1489.99</td><td>1647.79</td><td>0.48</td><td>0.05</td></tr><tr><td>Agulu</td><td>1167.22</td><td>1304.08</td><td>0.50</td><td>0.06</td><td>0.35</td><td>0.04</td><td>1407.87</td><td>1556.97</td><td>0.45</td><td>0.05</td></tr><tr><td>Amawbia</td><td>1788.17</td><td>1997.84</td><td>0.77</td><td>0.09</td><td>0.53</td><td>0.06</td><td>2156.85</td><td>2385.27</td><td>0.70</td><td>0.08</td></tr><tr><td>Awka</td><td>2693.04</td><td>3008.81</td><td>1.15</td><td>0.13</td><td>0.80</td><td>0.09</td><td>3248.28</td><td>3592.29</td><td>1.05</td><td>0.12</td></tr><tr><td>Enugwu-Ukwu</td><td>1198.12</td><td>1338.61</td><td>0.51</td><td>0.06</td><td>0.35</td><td>0.04</td><td>1445.14</td><td>1598.19</td><td>0.47</td><td>0.05</td></tr><tr><td>Ifite</td><td>900.21</td><td>1005.76</td><td>0.39</td><td>0.04</td><td>0.27</td><td>0.03</td><td>1085.81</td><td>1200.80</td><td>0.35</td><td>0.04</td></tr><tr><td>Okpuno</td><td>700.10</td><td>782.19</td><td>0.30</td><td>0.03</td><td>0.21</td><td>0.02</td><td>844.44</td><td>933.87</td><td>0.27</td><td>0.03</td></tr><tr><td>Nibo</td><td>979.08</td><td>1093.88</td><td>0.42</td><td>0.05</td><td>0.29</td><td>0.03</td><td>1180.94</td><td>1306.01</td><td>0.38</td><td>0.04</td></tr><tr><td>Nise</td><td>936.20</td><td>1045.97</td><td>0.40</td><td>0.04</td><td>0.28</td><td>0.03</td><td>1129.22</td><td>1248.81</td><td>0.36</td><td>0.04</td></tr><tr><td>Nwafia</td><td>1325.01</td><td>1480.37</td><td>0.57</td><td>0.06</td><td>0.39</td><td>0.04</td><td>1598.19</td><td>1767.45</td><td>0.52</td><td>0.06</td></tr><tr><td>Total</td><td>12922.45</td><td>14437.66</td><td>5.54</td><td>0.62</td><td>3.83</td><td>0.43</td><td>15586.73</td><td>17237.45</td><td>5.03</td><td>0.56</td></tr></tbody></table>
<table><caption>Table 4.2: Annual rate of change between 1990 and 2017</caption><thead><tr><th>Class Name</th><th>Annual Rate of change 1999</th><th>Annual Rate of change 1999 - 2008</th><th>Annual Rate of change 2008 - 2017</th></tr></thead><tbody><tr><td>Urban area</td><td>0.61</td><td>0.42</td><td>0.55</td></tr><tr><td>Vegetation</td><td>-0.37</td><td>-0.27</td><td>-0.41</td></tr><tr><td>Open space</td><td>-0.23</td><td>-0.10</td><td>-0.25</td></tr><tr><td>Water body</td><td>0.30</td><td>0.001</td><td>0.08</td></tr></tbody></table>

<figure><img src="image_5.png" /><figcaption>Figure 4.2: Annual rate of change between 1990 and 2017</figcaption></figure>

This indicated that both urban area and water body had a positive growth between 1990 and 2017, while vegetation and open space declined between 1990 and 2017.

### 4.3 Growth Pattern within Awka Capital Territory

In order to measure the spatial dimension of urban growth pattern in Awka Capital Territory, this study employed the use of spatial metrics to analyze urban growth patches in Awka Capital Territory. This metrics are numerical indicators that quantify the spatial pattern of urban patches within the study area. The metrics used were: size and urban expansion index.

#### a. Size

The size of an urban area has often been used as a simple index of growth. The idea of using the urban area size for growth, causes more land consumption than compact development. Because growth is characterized by an increase in the built-up area, this attribute gives considerable information for understanding the behavior of such growth. Larger urban area size values indicate a greater degree of growth. This study was able to identify the sizes of urban area patches within the study area. The urban area patches and sizes occupied between 1990 and 2017 area is displayed in table 4.3.

<table><caption>Table 4.3: Urban patches and area occupied between 2008 and 2017</caption><thead><tr><th rowspan="2">Urban Patch</th><th colspan="3">Area (Hectares)</th><th colspan="3">Area (Hectares)</th><th colspan="2">Area (Hectares)</th></tr><tr><th>1990</th><th>1999</th><th>% Change 1990-1999</th><th>Annual Rate 1990-1999</th><th>% Change 1999-2008</th><th>Annual Rate 1999-2008</th><th>2008</th><th>2017</th><th>% Change 2008-2017</th><th>Annual Rate 2008-2017</th></tr></thead><tbody><tr><td>Abagana</td><td>1235.30</td><td>1380.15</td><td>0.53</td><td>0.06</td><td>0.37</td><td>0.04</td><td>1489.99</td><td>1647.79</td><td>0.48</td><td>0.05</td></tr><tr><td>Agulu</td><td>1167.22</td><td>1304.08</td><td>0.50</td><td>0.06</td><td>0.35</td><td>0.04</td><td>1407.87</td><td>1556.97</td><td>0.45</td><td>0.05</td></tr><tr><td>Amawbia</td><td>1788.17</td><td>1997.84</td><td>0.77</td><td>0.09</td><td>0.53</td><td>0.06</td><td>2156.85</td><td>2385.27</td><td>0.70</td><td>0.08</td></tr><tr><td>Awka</td><td>2693.04</td><td>3008.81</td><td>1.15</td><td>0.13</td><td>0.80</td><td>0.09</td><td>3248.28</td><td>3592.29</td><td>1.05</td><td>0.12</td></tr><tr><td>Enugwu-Ukwu</td><td>1198.12</td><td>1338.61</td><td>0.51</td><td>0.06</td><td>0.35</td><td>0.04</td><td>1445.14</td><td>1598.19</td><td>0.47</td><td>0.05</td></tr><tr><td>Ifite</td><td>900.21</td><td>1005.76</td><td>0.39</td><td>0.04</td><td>0.27</td><td>0.03</td><td>1085.81</td><td>1200.80</td><td>0.35</td><td>0.04</td></tr><tr><td>Okpuno</td><td>700.10</td><td>782.19</td><td>0.30</td><td>0.03</td><td>0.21</td><td>0.02</td><td>844.44</td><td>933.87</td><td>0.27</td><td>0.03</td></tr><tr><td>Nibo</td><td>979.08</td><td>1093.88</td><td>0.42</td><td>0.05</td><td>0.29</td><td>0.03</td><td>1180.94</td><td>1306.01</td><td>0.38</td><td>0.04</td></tr><tr><td>Nise</td><td>936.20</td><td>1045.97</td><td>0.40</td><td>0.04</td><td>0.28</td><td>0.03</td><td>1129.22</td><td>1248.81</td><td>0.36</td><td>0.04</td></tr><tr><td>Nwafia</td><td>1325.01</td><td>1480.37</td><td>0.57</td><td>0.06</td><td>0.39</td><td>0.04</td><td>1598.19</td><td>1767.45</td><td>0.52</td><td>0.06</td></tr><tr><td>Total</td><td>12922.45</td><td>14437.66</td><td>5.54</td><td>0.62</td><td>3.83</td><td>0.43</td><td>15586.73</td><td>17237.45</td><td>5.03</td><td>0.56</td></tr></tbody></table>
From table 4.3, Abagana occupied an area of 1235.30 hectares in 1990, 1380.15 hectares in 1999, 1489.99 hectares in 2008 and 1647.79 hectares in 2017, with an annual growth of 0.06%, 0.04% and 0.05% between 1990-1999, 1999-2008 and 2008-2017 respectively. Similarly, Agulu occupied an area of 1167.22 hectares in 1990, 1304.08 hectares in 1999, 1407.87 hectares in 2008 and 1556.97 hectares in 2017, with an annual growth of 0.06%, 0.04% and 0.05% between 1990-1999, 1999-2008 and 2008-2017 respectively. Enugwu-Ukwu occupied an area of 1198.12 hectares in 1990, 1338.81 hectares in 1999, 1445.14hectares in 2008 and 1598.19 hectares in 2017, with an annual growth of 0.06%, 0.04% and 0.05% between 1990-1999, 1999-2008 and 2008-2017 respectively. Thus, indicating a similar growth rate between Abagana, Agulu and Enugu-Ukwu. Amawbia occupied an area of 1788.17 hectares in 1990, 1997.84 hectares in 1999, 2156.85 hectares in 2008 and 2385.27 hectares in 2017, with an annual growth of 0.09%, 0.06% and 0.08% between 1990-1999, 1999-2008 and 2008-2017 respectively. Awka occupied an area of 2693.04 hectares in 1990, 3008.81 hectares in 1999, 3248.28 hectares in 2008 and 3592.29 hectares in 2017, with an annual growth of 0.13%, 0.09% and 0.12% between 1990-1999, 1999-2008 and 2008-2017 respectively. This indicated that Awka and Amawbia had the highest growth

rate amongst other urban patches between 1990 and 2017. Ifite occupied an area of 900.21 hectares in 1990, 1005.76 hectares in 1999, 1085.81 hectares in 2008 and 1200.80 hectares in 2017, with an annual growth of 0.04%, 0.03% and 0.04% between 1990-1999, 1999-2008 and 2008-2017 respectively. Similarly, Nise with the same growth rate as Ifite, occupied an area of 936.20 hectares in 1990, 1045.97 hectares in 1999, 1129.22 hectares in 2008 and 1248.81 hectares in 2017, with an annual growth of 0.04%, 0.03% and 0.04% between 1990-1999, 1999-2008 and 2008-2017 respectively. Okpuno occupied an area of 700.10 hectares in 1990, 782.19 hectares in 1999, 844.44 hectares in 2008 and 933.87 hectares in 2017, with an annual growth of 0.03%, 0.02% and 0.03% between 1990-1999, 1999-2008 and 2008-2017 respectively. Nibo occupied an area of 979.08 hectares in 1990, 1093.88 hectares in 1999, 1180.94 hectares in 2008 and 1306.01 hectares in 2017, with an annual growth of 0.05%, 0.03% and 0.04% between 1990-1999, 1999-2008 and 2008-2017 respectively. Lastly, Nawfia occupied an area of 1325.01 hectares in 1990, 1480.37 hectares in 1999, 1598.19 hectares in 2008 and 1767.45 hectares in 2017, with an annual growth of 0.06%, 0.04% and 0.06% between 1990-1999, 1999-2008 and 2008-2017 respectively. The urban patches growth rate is summarized in Figure 4.3.

<figure><img src="image_5.png" /><figcaption>Figure 4.3: Urban patch growth rate</figcaption></figure>

b. Landscape Expansion Index

A quantitative method by zheng et al. (2010) was used to distinguish three urban growth types: infilling, edge expansion, and spontaneous growth. The dominance of each growth types is meaningful to describe the process of landscape pattern changes between two or more time points. Whether a growth patch is called infilling, edge expansion, or spontaneous growth is determined by LEI value which is calculated as follows:

$$LEI = \frac{L_c}{P} \quad (4.1)$$

where LEI is Landscape Expansion Index, L_c is the length of the common boundary of a newly grown urban patch and the pre-growth urban patches, and P is the perimeter of this newly grown patch. Urban growth type is identified as

(a) infilling when LEI > 0.5,

(b) edge-expansion when 0 < LEI ≤ 0.5,

(c) spontaneous growth when LEI = 0, which indicates no shared-boundary

The results show two types of growth within the study area. Between 1990 and 1999, 68.2% of overall growth in the study area was attributed to edge-expansion, while 31.8% was 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 edge-expansion, while 44.3% was attributed to infilling growth. In the last epoch between 2008 and 2017, edge-expansion was the dominant growth pattern in the study area with 75.61% of growth while 24.39% was attributed to infilling. There was no indication of spontaneous growth in the study area. The summary of the results is displayed in figures 4.4–4.6.
From table 4.3, Abagana occupied an area of 1235.30 hectares in 1990, 1380.15 hectares in 1999, 1489.99 hectares in 2008 and 1647.79 hectares in 2017, with an annual growth of 0.06%, 0.04% and 0.05% between 1990-1999, 1999-2008 and 2008-2017 respectively. Similarly, Agulu occupied an area of 1167.22 hectares in 1990, 1304.08 hectares in 1999, 1407.87 hectares in 2008 and 1556.97 hectares in 2017, with an annual growth of 0.06%, 0.04% and 0.05% between 1990-1999, 1999-2008 and 2008-2017 respectively. Enugwu-Ukwu occupied an area of 1198.12 hectares in 1990, 1338.81 hectares in 1999, 1445.14hectares in 2008 and 1598.19 hectares in 2017, with an annual growth of 0.06%, 0.04% and 0.05% between 1990-1999, 1999-2008 and 2008-2017 respectively. Thus, indicating a similar growth rate between Abagana, Agulu and Enugu-Ukwu. Amawbia occupied an area of 1788.17 hectares in 1990, 1997.84 hectares in 1999, 2156.85 hectares in 2008 and 2385.27 hectares in 2017, with an annual growth of 0.09%, 0.06% and 0.08% between 1990-1999, 1999-2008 and 2008-2017 respectively. Awka occupied an area of 2693.04 hectares in 1990, 3008.81 hectares in 1999, 3248.28 hectares in 2008 and 3592.29 hectares in 2017, with an annual growth of 0.13%, 0.09% and 0.12% between 1990-1999, 1999-2008 and 2008-2017 respectively. This indicated that Awka and Amawbia had the highest growth

rate amongst other urban patches between 1990 and 2017. Ifite occupied an area of 900.21 hectares in 1990, 1005.76 hectares in 1999, 1085.81 hectares in 2008 and 1200.80 hectares in 2017, with an annual growth of 0.04%, 0.03% and 0.04% between 1990-1999, 1999-2008 and 2008-2017 respectively. Similarly, Nise with the same growth rate as Ifite, occupied an area of 936.20 hectares in 1990, 1045.97 hectares in 1999, 1129.22 hectares in 2008 and 1248.81 hectares in 2017, with an annual growth of 0.04%, 0.03% and 0.04% between 1990-1999, 1999-2008 and 2008-2017 respectively. Okpuno occupied an area of 700.10 hectares in 1990, 782.19 hectares in 1999, 844.44 hectares in 2008 and 933.87 hectares in 2017, with an annual growth of 0.03%, 0.02% and 0.03% between 1990-1999, 1999-2008 and 2008-2017 respectively. Nibo occupied an area of 979.08 hectares in 1990, 1093.88 hectares in 1999, 1180.94 hectares in 2008 and 1306.01 hectares in 2017, with an annual growth of 0.05%, 0.03% and 0.04% between 1990-1999, 1999-2008 and 2008-2017 respectively. Lastly, Nawfia occupied an area of 1325.01 hectares in 1990, 1480.37 hectares in 1999, 1598.19 hectares in 2008 and 1767.45 hectares in 2017, with an annual growth of 0.06%, 0.04% and 0.06% between 1990-1999, 1999-2008 and 2008-2017 respectively. The urban patches growth rate is summarized in Figure 4.3.

<figure><img src="image_6.png" /><figcaption>Figure 4.3: Urban patch growth rate</figcaption></figure>

b. Landscape Expansion Index

A quantitative method by zheng et al. (2010) was used to distinguish three urban growth types: infilling, edge expansion, and spontaneous growth. The dominance of each growth types is meaningful to describe the process of landscape pattern changes between two or more time points. Whether a growth patch is called infilling, edge expansion, or spontaneous growth is determined by LEI value which is calculated as follows:

$$LEI = \frac{L_c}{P} \quad (4.1)$$

where LEI is Landscape Expansion Index, L_c is the length of the common boundary of a newly grown urban patch and the pre-growth urban patches, and P is the perimeter of this newly grown patch. Urban growth type is identified as

(a) infilling when LEI > 0.5,

(b) edge-expansion when 0 < LEI ≤ 0.5,

(c) spontaneous growth when LEI = 0, which indicates no shared-boundary

The results show two types of growth within the study area. Between 1990 and 1999, 68.2% of overall growth in the study area was attributed to edge-expansion, while 31.8% was 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 edge-expansion, while 44.3% was attributed to infilling growth. In the last epoch between 2008 and 2017, edge-expansion was the dominant growth pattern in the study area with 75.61% of growth while 24.39% was attributed to infilling. There was no indication of spontaneous growth in the study area. The summary of the results is displayed in figures 4.4–4.6.
GROWTH PATTERN 1990-1999

<figure><img src="image_6.png" /><figcaption>Figure 4.4: Growth percentage between 1990 and 1999</figcaption></figure>

GROWTH PATTERN 1999-2008

<figure><img src="image_7.png" /><figcaption>Figure 4.5: Growth percentage between 1999 and 2008</figcaption></figure>

GROWTH PATTERN 2008-2017

<figure><img src="image_8.png" /><figcaption>Figure 4.6: Growth percentage between 2008 and 2017</figcaption></figure>

**4.5 Model Validation:** Traditionally, model validation refers to comparing the simulated and reference maps (Vliet *et al*, 2011). Sometimes the simulated maps can give misleading results. In

that case, it is necessary to validate the predicted/simulated map with the base/reference map. The main objective of model validation is to find out whether the simulation is giving any
GROWTH PATTERN 1990-1999

<figure><img src="image_7.png" /><figcaption>Figure 4.4: Growth percentage between 1990 and 1999</figcaption></figure>

GROWTH PATTERN 1999-2008

<figure><img src="image_8.png" /><figcaption>Figure 4.5: Growth percentage between 1999 and 2008</figcaption></figure>

GROWTH PATTERN 2008-2017

<figure><img src="image_9.png" /><figcaption>Figure 4.6: Growth percentage between 2008 and 2017</figcaption></figure>

**4.5 Model Validation:** Traditionally, model validation refers to comparing the simulated and reference maps (Vliet *et al*, 2011). Sometimes the simulated maps can give misleading results. In

that case, it is necessary to validate the predicted/simulated map with the base/reference map. The main objective of model validation is to find out whether the simulation is giving any
abrupt result or not. This justifies the modeling output in terms of reality. In order to validate the model's prediction of landcover/landuse of 2018, the predicted landcover/landuse of 2018 was compared to the actual landcover/landuse of 2018 using kappa statistics, and image correlation. The predicted landcover/landuse of Awka Capital Territory in 2018 as shown in table 4.4 indicate that vegetation had 40.04 %, with area of about 18534.38 hectares. Urban area had 38.67% with an area of 17901.32 hectares while water body and open space had 9.95% and 11.33% with an area of 4609.22 and 5244.87 hectares

respectively. In comparison with the actual landcover/landuse of Awka Capital Territory in 2018, it read that vegetation had 40.08 %, with area of about 18555.33 hectares. Urban area had 38.45% with an area of 17798.44 hectares while water body and open space had 9.96% and 11.50% with an area of 4611.35 and 5324.67hectares respectively. This gives a difference of 102.88 hectares between actual and predicted urban area, 20.99 hectares between actual and predicted vegetation, 79.8 hectares between actual and predicted open space and 2.13 hectares between actual and predicted water body.

<table><caption>Table 4.4: Comparison between 2018 predicted landcover/landuse and actual 2018 landcover/landuse</caption><thead><tr><th rowspan="2">Class Name</th><th colspan="2">2018 Landcover/landuse</th><th colspan="2">2018 Predicted Landcover/landuse</th><th colspan="2">Difference</th></tr><tr><th></th><th></th><th></th><th></th><th></th><th></th></tr></thead><tbody><tr><td>Urban area</td><td>17798.44</td><td>38.45%</td><td>17901.32</td><td>38.67%</td><td>102.88</td><td>0.22</td></tr><tr><td>Vegetation</td><td>18555.33</td><td>40.08%</td><td>18534.38</td><td>40.04%</td><td>20.99</td><td>0.04</td></tr><tr><td>Open space</td><td>5324.67</td><td>11.50%</td><td>5244.87</td><td>11.33%</td><td>79.8</td><td>0.17</td></tr><tr><td>Water body</td><td>4611.35</td><td>9.96%</td><td>4609.22</td><td>9.95%</td><td>2.13</td><td>0.01</td></tr><tr><td>Totals</td><td>46289.79</td><td>100%</td><td>46289.79</td><td>100%</td><td></td><td></td></tr></tbody></table>

<figure><img src="image_9.png" /><figcaption>Figure: 4.7: (a) 2018 landcover/landuse map, (b) predicted 2018 landcover/landuse map</figcaption></figure>

The validation tab in Molusce was used to validate the predicted map against the reference map and the overall accuracy gotten was 93.36%; overall kappa was given as 0.9083 and the model error was given as 6.64%. Hence the model result is adjudged to be acceptable. Image correlation coefficient (r) between two images was also calculated to determine the similarities between the two images. The r-value is a measure of the linear association in the variation of the input variables

(images, in this case). The coefficient ranges from -1, indicated a perfect negative linear association, to 1, indicated a perfect positive linear association. An r-value of 0 indicates no correlation between the test variables. The results of the correlation coefficient gave a value of 0.9585, which indicates a good positive relationship between the two images as shown in table 4.4 and figure 4.8

<table><caption>Table 4.4. Image correlation matrix</caption><thead><tr><th></th><th>2018 LULC</th><th>Reference</th><th>2018 Predicted LULC</th></tr></thead><tbody><tr><td>2018 Reference LULC</td><td>1.000</td><td></td><td>0.9585</td></tr><tr><td>2018 Predicted LULC</td><td>0.9585</td><td></td><td>1.000</td></tr></tbody></table>
abrupt result or not. This justifies the modeling output in terms of reality. In order to validate the model's prediction of landcover/landuse of 2018, the predicted landcover/landuse of 2018 was compared to the actual landcover/landuse of 2018 using kappa statistics, and image correlation. The predicted landcover/landuse of Awka Capital Territory in 2018 as shown in table 4.4 indicate that vegetation had 40.04%, with an area of about 18534.38 hectares. Urban area had 38.67% with an area of 17901.32 hectares while water body and open space had 9.95% and 11.33% with an area of 4609.22 and 5244.87 hectares

respectively. In comparison with the actual landcover/landuse of Awka Capital Territory in 2018, it read that vegetation had 40.08%, with an area of about 18555.33 hectares. Urban area had 38.45% with an area of 17798.44 hectares while water body and open space had 9.96% and 11.50% with an area of 4611.35 and 5324.67 hectares respectively. This gives a difference of 102.88 hectares between actual and predicted urban area, 20.99 hectares between actual and predicted vegetation, 79.8 hectares between actual and predicted open space and 2.13 hectares between actual and predicted water body.

<table><caption>Table 4.4: Comparison between 2018 predicted landcover/landuse and actual 2018 landcover/landuse</caption><thead><tr><th rowspan="2">Class Name</th><th colspan="2">2018 Landcover/landuse</th><th colspan="2">2018 Predicted Landcover/landuse</th><th colspan="2">Difference</th></tr><tr><th></th><th></th><th></th><th></th><th></th><th></th></tr></thead><tbody><tr><td>Urban area</td><td>17798.44</td><td>38.45%</td><td>17901.32</td><td>38.67%</td><td>102.88</td><td>0.22</td></tr><tr><td>Vegetation</td><td>18555.33</td><td>40.08%</td><td>18534.38</td><td>40.04%</td><td>20.99</td><td>0.04</td></tr><tr><td>Open space</td><td>5324.67</td><td>11.50%</td><td>5244.87</td><td>11.33%</td><td>79.8</td><td>0.17</td></tr><tr><td>Water body</td><td>4611.35</td><td>9.96%</td><td>4609.22</td><td>9.95%</td><td>2.13</td><td>0.01</td></tr><tr><td>Totals</td><td>46289.79</td><td>100%</td><td>46289.79</td><td>100%</td><td></td><td></td></tr></tbody></table>

<figure><img src="image_10.png" /><figcaption>Figure: 4.7: (a) 2018 landcover/landuse map, (b) predicted 2018 landcover/landuse map</figcaption></figure>

The validation tab in Molusce was used to validate the predicted map against the reference map and the overall accuracy gotten was 93.36%; overall kappa was given as 0.9083 and the model error was given as 6.64%. Hence the model result is adjudged to be acceptable. Image correlation coefficient (r) between two images was also calculated to determine the similarities between the two images. The r-value is a measure of the linear association in the variation of the input variables

(images, in this case). The coefficient ranges from -1, indicated a perfect negative linear association, to 1, indicated a perfect positive linear association. An r-value of 0 indicates no correlation between the test variables. The results of the correlation coefficient gave a value of 0.9585, which indicates a good positive relationship between the two images as shown in table 4.4 and figure 4.8

<table><caption>Table 4.4. Image correlation matrix</caption><thead><tr><th></th><th>2018 LULC</th><th>Reference</th><th>2018 Predicted LULC</th></tr></thead><tbody><tr><td>2018 Reference LULC</td><td>1.000</td><td></td><td>0.9585</td></tr><tr><td>2018 Predicted LULC</td><td>0.9585</td><td></td><td>1.000</td></tr></tbody></table>
<figure><img src="image_10.png" /><figcaption>Figure 4.8. Correlation graph between 2018 reference LULC and 2018 predicted LULC</figcaption></figure>

## 4.6 Future Urban Development Prediction

Urban growth prediction in Molusce is an empirically given process that moves in a step wise fashion from change analysis; to transition Potential Modeling; to change prediction. It is based on the historical change from 1990 to 2018. The change assessed

between 1990 and 2017 are identified and modeled as transitions from one landcover/landuse state to another. Molusce was used to predict the change for (30 years) from the 2018 to 2048 and the result is shown in fig 4.9 and table 4.5.

<figure><img src="image_11.png" /><figcaption>Figure 4.9. 2048 predicted future urban development</figcaption></figure>

<table><caption>Table: 4.5: Landcover/landuse distribution of Awka capital territory 2048</caption><thead><tr><th rowspan="2">Class Name</th><th colspan="2">2048 Landcover/landuse Prediction</th></tr><tr><th>Hectares</th><th>Percentage (%)</th></tr></thead><tbody><tr><td>Urban area</td><td>22871.51</td><td>49.41%</td></tr><tr><td>Vegetation</td><td>13853.39</td><td>29.93%</td></tr><tr><td>Open space</td><td>4852.99</td><td>10.48%</td></tr><tr><td>Water body</td><td>4711.9</td><td>10.18%</td></tr><tr><td>Totals</td><td>46289.79</td><td>100%</td></tr></tbody></table>

The prediction results tabulated in table 4.5, indicated that by 2048 urban area is expected to grow to 49.41% covering an area of 22871.51 hectares (figure 4.9), vegetation is expected to decrease to 29.93% covering an area of 13853.39 hectares, open

space is expected to decrease to 10.48% covering an area of 4852.99 hectares while water body is expected to increase to 10.18% covering an area of 4711.9 hectares.
<figure><img src="image_11.png" /><figcaption>Figure.4.8. Correlation graph between 2018 reference LULC and 2018 predicted LULC</figcaption></figure>

## 4.6 Future Urban Development Prediction

Urban growth prediction in Molusce is an empirically given process that moves in a step wise fashion from change analysis; to transition Potential Modeling; to change prediction. It is based on the historical change from 1990 to 2018. The change assessed

between 1990 and 2017 are identified and modeled as transitions from one landcover/landuse state to another. Molusce was used to predict the change for (30 years) from the 2018 to 2048 and the result is shown in fig 4.9 and table 4.5.

<figure><img src="image_12.png" /><figcaption>Figure 4.9. 2048 predicted future urban development</figcaption></figure>

<table><caption>Table: 4.5: Landcover/landuse distribution of Awka capital territory 2048</caption><thead><tr><th rowspan="2">Class Name</th><th colspan="2">2048 Landcover/landuse Prediction</th></tr><tr><th>Hectares</th><th>Percentage (%)</th></tr></thead><tbody><tr><td>Urban area</td><td>22871.51</td><td>49.41%</td></tr><tr><td>Vegetation</td><td>13853.39</td><td>29.93%</td></tr><tr><td>Open space</td><td>4852.99</td><td>10.48%</td></tr><tr><td>Water body</td><td>4711.9</td><td>10.18%</td></tr><tr><td>Totals</td><td>46289.79</td><td>100%</td></tr></tbody></table>

The prediction results tabulated in table 4.5, indicated that by 2048 urban area is expected to grow to 49.41% covering an area of 22871.51 hectares (figure 4.9), vegetation is expected to decrease to 29.93% covering an area of 13853.39 hectares, open

space is expected to decrease to 10.48% covering an area of 4852.99 hectares while water body is expected to increase to 10.18% covering an area of 4711.9 hectares.
<figure><img src="image_12.png" /><figcaption>Figure.4.10. Graph of urban development from 1990 to 2048</figcaption></figure>

Thirty years from 2018, the vegetation and open space classes are expected to lose part of its area to urban area based on their distance to roads, distance to developable land and as population density increases. Water body is expected to increase from 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 area coverage of 17798.44 hectares to 22871.51 hectares while open space and vegetation is expected to decrease from 11.50% to 10.48% i.e. from 5324.67 hectares to 4852.99 hectares and 40.08% to 29.93% i.e. from 18555.33 hectares to 13853.39 hectares respectively.

## 5.0 CONCLUSION

This study has demonstrated the ability of evaluating development dynamics of Awka Capital Territory between 1990 and 2018 and predicting future urban development in Awka Capital Territory using remote sensing and molusce algorithm plugin qgis. The study work captured as accurate as possible five landcover/landuse classes as they change through time. The five classes were distinctly produced in contrast to urban development for 1990, 1999, 2008, 2017 and 2018. The study also generated the trend of change and annual rate of change between 1990 and 2017. A modeled prediction was made into 2018 and validated using 2018 reference landcover/landuse by determining their kappa and image correlation. A final prediction was then made into 2048 for future urban development dynamics. This prediction can be used a decision support tool and will guide the relevant authorities on how to manage and monitor development to ensure a habitable environment in the near future.

## 6.0. REFERENCES

[1]. Anderson, E. (1976). A Landuse and Landcover Classification System for Use with Remote Sensor Data. Geological Survey Professional Paper No. 964, U.S. Government Printing Office, Washington, D.C. p. 28.

[2]. Batty, M. (1994). A chronicle of scientific planning: The Anglo-American modeling experience, in Journal of the American Planning Association, 60, 1, pp. 7-12.

[3]. Deng, J.S., Wang, K., Hong, Y. and Qi, J.G. (2018). Spatio-temporal dynamics and evolution of land use change and landscape pattern in response to rapid urbanization. *Landscape and Urban Planning*, 92, 187-198.

[4]. Kumar, J. A. V., Pathan, S. K. and Bhanderi, R. J. (2007). Spatio-temporal Analysis for monitoring urban growth: A case study of Indore city. *Journal of the Indian Society of Remote Sensing*, 35, 11-20.

[5]. Long, H.G., Tang, G., Li, X. and Heilig, G.K. (2007). Socio-economics driving forces of landuse change in Kunshan, the Yangtze river delta economic area of the China. *Journal of Environmental Management*, 83 (3): 351-364

[6]. Onojeghuo A. and A. Onojeghuo (2013). Mapping and Predicting Urban Sprawl Using Remote Sensing and Geographic Information System Techniques: A Case Study of Eti-Osa Local Government Area, Lagos, Nigeria. FIG Working Week 2013 Environment for Sustainability, Abuja, Nigeria, 6 – 10 May 2013

[7]. Opeyemi, A. Z., Lazarus, M. O., Richard A. M., (2015), Urbanization: A Catalyst For The Emergence Of Squatter Settlements And Squalor In The Vicinities Of The Federal Capital City Of Nigeria, Journal of Sustainable Development; Vol. 8, No. 2; ISSN 1913-9063 E-ISSN 1913-9071

[8]. Orimoloye, I.R., Mazinyo, S.P., Nel, W., and Kalumba, A.M. (2018) Spatiotemporal monitoring of land surface temperature and estimation radiation using remote sensing: human health implications for East London, South Africa Environ. *Earth Sci.* 77, 77 (10.1007/s12665-018-7252-6)

[9]. Su, S., Jiang, Z., Zhang, Q. and Zhang, Y. (2017). Transformation of agricultural landscapes under rapid urbanization: a threat to sustainability in Hang-Jia-Hu region, China. *Applied Geography*, 31, 439-449

[10]. UN-HABITAT, (2009), *Executive summary plans for Awka, Onitsha and Nnewi* Accessed at https://unhabitat.org/books/ executive-summary-of-structure-plans-for-awka-onitsha-and-
<figure><img src="image_13.png" /><figcaption>Figure.4.10. Graph of urban development from 1990 to 2048</figcaption></figure>

Thirty years from 2018, the vegetation and open space classes are expected to lose part of its area to urban area based on their distance to roads, distance to developable land and as population density increases. Water body is expected to increase from 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 area coverage of 17798.44 hectares to 22871.51 hectares while open space and vegetation is expected to decrease from 11.50% to 10.48% i.e. from 5324.67 hectares to 4852.99 hectares and 40.08% to 29.93% i.e. from 18555.33 hectares to 13853.39 hectares respectively.

## 5.0 CONCLUSION

This study has demonstrated the ability of evaluating development dynamics of Awka Capital Territory between 1990 and 2018 and predicting future urban development in Awka Capital Territory using remote sensing and molusce algorithm plugin qgis. The study work captured as accurate as possible five landcover/landuse classes as they change through time. The five classes were distinctly produced in contrast to urban development for 1990, 1999, 2008, 2017 and 2018. The study also generated the trend of change and annual rate of change between 1990 and 2017. A modeled prediction was made into 2018 and validated using 2018 reference landcover/landuse by determining their kappa and image correlation. A final prediction was then made into 2048 for future urban development dynamics. This prediction can be used a decision support tool and will guide the relevant authorities on how to manage and monitor development to ensure a habitable environment in the near future.

## 6.0. REFERENCES

[1]. Anderson, E. (1976). A Landuse and Landcover Classification System for Use with Remote Sensor Data. Geological Survey Professional Paper No. 964, U.S. Government Printing Office, Washington, D.C. p. 28.

[2]. Batty, M. (1994). A chronicle of scientific planning: The Anglo-American modeling experience, in Journal of the American Planning Association, 60, 1, pp. 7-12.

[3]. Deng, J.S., Wang, K., Hong, Y. and Qi, J.G. (2018). Spatio-temporal dynamics and evolution of land use change and landscape pattern in response to rapid urbanization. *Landscape and Urban Planning*, 92, 187-198.

[4]. Kumar, J. A. V., Pathan, S. K. and Bhanderi, R. J. (2007). Spatio-temporal Analysis for monitoring urban growth: A case study of Indore city. *Journal of the Indian Society of Remote Sensing*, 35, 11-20.

[5]. Long, H.G., Tang, G., Li, X. and Heilig, G.K. (2007). Socio-economics driving forces of landuse change in Kunshan, the Yangtze river delta economic area of the China. *Journal of Environmental Management*, 83 (3): 351-364

[6]. Onojeghuo A. and A. Onojeghuo (2013). Mapping and Predicting Urban Sprawl Using Remote Sensing and Geographic Information System Techniques: A Case Study of Eti-Osa Local Government Area, Lagos, Nigeria. FIG Working Week 2013 Environment for Sustainability, Abuja, Nigeria, 6 – 10 May 2013

[7]. Opeyemi, A. Z., Lazarus, M. O., Richard A. M., (2015), Urbanization: A Catalyst For The Emergence Of Squatter Settlements And Squalor In The Vicinities Of The Federal Capital City Of Nigeria, Journal of Sustainable Development; Vol. 8, No. 2; ISSN 1913-9063 E-ISSN 1913-9071

[8]. Orimoloye, I.R., Mazinyo, S.P., Nel, W., and Kalumba, A.M. (2018) Spatiotemporal monitoring of land surface temperature and estimation radiation using remote sensing: human health implications for East London, South Africa Environ. *Earth Sci.* 77, 77 (10.1007/s12665-018-7252-6)

[9]. Su, S., Jiang, Z., Zhang, Q. and Zhang, Y. (2017). Transformation of agricultural landscapes under rapid urbanization: a threat to sustainability in Hang-Jia-Hu region, China. *Applied Geography*, 31, 439-449

[10]. UN-HABITAT, (2009), *Executive summary plans for Awka, Onitsha and Nnewi* Accessed at https://unhabitat.org/books/ executive-summary-of-structure-plans-for-awka-onitsha-and-
nnewi-and-environ-2009-2027/executive-summary-of-structure
-plans-for-awka-onitsha-and-nnewi-and-environ-2009-2027/

[11]. United Nations (2017). *World Urbanization Prospects (CD-ROM Edition)*. Population Division of the Department of Economic and Social Affairs, United Nations.

[12]. Vliet, J, Bregt, A, and Hagen Z. (2011). Revisiting Kappa to Account for Change in the Accuracy Assessment of Land-Use Change Models. *Ecological Modeling*. 222. 1367-1375. 10. 10.1016/j.ecolmodel.2011.01.017.

[13]. Wilkie, D.S. and Finn, J.T. (1996). Remote Sensing Imagery for Natural Resources Monitoring. *Columbia University Press*, New York. p. 295.

[14]. Zeng, Yongnian& Xu, Y & Li, Songnian& He, L & Yu, F & Zhen, Z & Cai, Chenglian. (2012). Quantitative analysis of urban expansion in central China. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. XXXIX-B7. 363-366. 10.5194/isprs.archives-XXXIX-B7-363-2012.

[15]. Zubair, A. O. (2008), Monitoring the Growth of Settlements in Ilorin, Nigeria (A GIS and Remote Sensing Approach), The International Archives of The Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B6b. Beijing 2008, Pp 225-232
nnewi-and-environ-2009-2027/executive-summary-of-structure
-plans-for-awka-onitsha-and-nnewi-and-environ-2009-2027/

[11]. United Nations (2017). *World Urbanization Prospects (CD-ROM Edition)*. Population Division of the Department of Economic and Social Affairs, United Nations.

[12]. Vliet, J, Bregt, A, and Hagen Z. (2011). Revisiting Kappa to Account for Change in the Accuracy Assessment of Land-Use Change Models. *Ecological Modeling*. 222. 1367-1375. 10. 10.1016/j.ecolmodel.2011.01.017.

[13]. Wilkie, D.S. and Finn, J.T. (1996). Remote Sensing Imagery for Natural Resources Monitoring. *Columbia University Press*, New York. p. 295.

[14]. Zeng, Yongnian& Xu, Y & Li, Songnian& He, L & Yu, F & Zhen, Z & Cai, Chenglian. (2012). Quantitative analysis of urban expansion in central China. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. XXXIX-B7. 363-366. 10.5194/isprs.archives-XXXIX-B7-363-2012.

[15]. Zubair, A. O. (2008), Monitoring the Growth of Settlements in Ilorin, Nigeria (A GIS and Remote Sensing Approach), The International Archives of The Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B6b. Beijing 2008, Pp 225-232