Change Detection Approach in Determining the Rate of Urban

Full Length Research Paper

Change detection approach in determining the rate of urban expansion dynamics and changes in land use/land cover: A case study of Owerri municipal, Imo State, Nigeria

PETER, Chibuike C.1*, ALOZIE, Michael C.2, AZUBUINE, Chika E.3, and OTI, U. Christian3

1Abia State University, Department of Environmental Resource Management. 2Abia State University, Department of Geography and Planning. 3Abia State University, Department of Architecture.

Accepted 8 May, 2017

Land use/Land cover dynamics over time is a phenomenon occurring globally due to varied interests of the inhabitants in a particular area. GIS and remote sensing is now the most evolved method of determining the rate, nature and extent of this phenomenon. Change detention is one of the accurate approaches in determining the rate of change in terms of space and time. This approach has the ability to measure the rate of change as regards to space and time as well as the nature of change with high kappa accuracy. The aim of this study was to map the LULC dynamics and estimate the rate of urban expansion dynamics using Owerri Imo State Nigeria as a case study. Landsat satellite imageries of 1994, 2004 and 2014 of the study area were brought into GIS environment and processed using ENVI 4.5 and ArcGIS to determine the rate, extent and nature of the urban dynamics. The study revealed significant changes in LU types over the study periods. It was observed that built up area increased, farmland decreased in the first period. In 2014, farmland decreased, forest and vegetation also decreased. Open spaces decreased, this was because of the increase in built up area. There were fluctuations in water body over the periods; this was due to change in seasons of the year. The change detection analysis integrated with spatial metrics performed in this research allowed for the monitoring of land use/land cover changes overtime and space in the area.

Key words: LULC, GIS, dynamics, satellite, Kappa accuracy Owerri, ARCGIS, remote sensing.

INTRODUCTION

Urbanization is one of the several anthropogenic are in developing countries. This has resulted in a high activities that impact on land use/land cover dynamics. rate of urbanization. Ifatimehin and Musa (2008) revealed Rate of urbanization describes the projected average rate that, “with increase in urban population comes a whole of change of the size of urban area over a given period. spectrum of activities such as commercial, agricultural, Urban population has been growing more rapidly than transportation, industrial, recreational, residential, rural population worldwide, particularly in developing institutional, water, etc. These land uses exert pressure countries (Nnaji et al., 2016). According to the United on the seemingly finite land resources in urban centers, Nations Population Division (2002), as at the year 2000, thus, land is fast becoming a critical resource”. Its towns and cities sheltered nearly half of the world’s demand is likely to continue growing, but maintaining the population (over 2.9 billion people), the majority of which capacity to sustain that demand remains a fundamental issue of both academic and policy discourse. Over the past years, data from earth sensing satellites has become vital in mapping the earth’s features and *Corresponding author. E-mail: [email protected]. infrastructures, managing natural resources and studying 42 Sky. J. Soil. Sci. Environ. Manage.

environmental change. Remote Sensing (RS) and River to the south. It is bounded on the Northwest by Geographic Information System (GIS) have been Amakohia on the North East by Uratta, on the East by recognized as powerful and effective tools and widely Egbu, on the South East by Naze. The Owerri Slogan is applied in detecting the rate, nature and extent of Heartland. It is currently referred to as the entertainment urbanization caused by anthropogenic activities. They capital of Nigeria because of its high density of spacious have helped in presenting new tools for advanced hotels, high street casinos, production studios and high ecosystem management, agriculture and environment as quality centers of relaxation. It is the home to an annual a whole (Eludoyin et al., 2010). beauty pageant called "Miss Heartland". It has Change detection is a process that measures how the experienced enormous growth and urban spurt of the last attributes of a particular area have changed between two 20years.Owerri has a tropical wet climate according to or more time periods. Change detection involves the the Köppen-Geiger system. Rain falls for most months of comparison of two or more aerial or satellite imageries of the year with a brief dry season. The Harmattan affects an area at different pointing time. In other words, change the city in the early periods of the dry season and it is detection is a GIS tool that deals with the spatio-temporal noticeably less pronounced than in other cities in Nigeria. analysis of an area using aerial or satellite imageries The average temperature is 26.4°C (Table 1). (Nnaji et al., 2016). Change detection has been widely used to assess shifting cultivation, deforestation, and urban growth, impact of natural disasters like tsunamis, MATERIALS AND METHODS earthquakes, and land use land cover changes. This technique involves acquisition of landsat imageries, Knowledge of remote sensing “ENVI” and Geographic processing those imageries in GIS and remote sensing Information System “ArcGIS” were used in the production environment and the output will be in form of of land use land cover maps. The landsat images ETM+ geographical referenced information. There are many at band 2, 3, 4 for the years in consideration were made change detection techniques and analytical methods that to pass through the processes of image composition, can be employed to achieve LULCC. Change detection enhancement, geo-referencing and region of interest methods are as follows; Image differencing, image selection, digitizing and image classification. A rationing, image regression, change vector analysis, supervised classification was performed on color vegetation index differencing, manual on-screen composites of band 2, 3 and 4 into the following Land- digitization of change, and multi- date principal Use and Land-Cover classes; Built-up areas, vegetation, component analysis. Change detection approaches can forest, open spaces, farmlands and water bodies. be broadly divided into either post-classification or pre- Thus, the landsat images were composited, designed, classification (spectral) methods. Post-classification uses classified and post classified using supervised minimum two or more images from different dates and classifies distance method. The post classification analyses carried them independently (Omran, 2012). However, pre- out includes: classification or spectral techniques rely on the principle that LULC changes result in persistent changes in the Confusion matrixes: For accuracy assessment in kappa spectral signature of the land surface (Omran, 2009). coefficient. The aim of this study was to detect, analyze and monitor the Land Use Land Cover dynamics of Owerri Class statistics: To observe the mass class municipal using change detection approach. It has the conformation per year calculated in square kilometer. objectives of assessing the pattern and magnitude of changes in the Land Use Land Cover through various Change detection: To detect changes of mass class multi-temporal satellite data through Image Processing, between 1994-2004 and 2004-2014. Subsequently, the Post classification and change vector detection. classified imageries of 1994, 2004 & 2014 were Vectorized and exported to ArcGIS and their respective mass class maps were produced. Study area The Land-Use and Land-Cover classification class statistics for each year in (km2) were copied to MS Owerri is the capital of Imo State in Nigeria, set in the Access where table and charts were created. Also, the heart of Igbo land. It is also the state's largest city, vectorized images of each year was exported to ArcGIS followed by Orlu and Okigwe as second and third and clipped (clip analysis) with the study area boundary. respectively. Owerri consists of three local government The resultants were categorized in the ArcGIS areas including owerri municipal, Owerri North and environment using the symbiological property (Figure 2). Owerri West, it has an estimated population of about The classification scheme, which was developed, gave 401,873 as of 2010 and is approximately 100 square a broad classification and six different classes were used kilometers (40 sq mi) in area (Figure 1). Owerri is (Tables 2 and 3 and 4). bordered by the Otamiri River to the east and the Nworie Peter et al. 43

Figure 1. Location map.

44 Sky. J. Soil. Sci. Environ. Manage.

Table 1. Climate data for Owerri.

Month Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Year Average high °C (°F) 31.6 32.5 32.5 32.2 31.2 30.1 28.6 28.1 29.3 29.9 31.1 31.4 30.7 (88.9) (90.5) (90.5) (90) (88.2) (86.2) (83.5) (82.6) (84.7) (85.8) (88) (88.5) (87.3) Daily mean °C (°F) 26.7 27.5 27.9 27.6 26.8 26.0 25.1 25.0 25.5 25.9 26.7 26.6 26.4 (80.1) (81.5) (82.2) (81.7) (80.2) (78.8) (77.2) (77) (77.9) (78.6) (80.1) (79.9) (79.5) Average low °C (°F) 21.8 22.6 23.4 23.1 22.5 22.0 21.6 22.0 21.7 21.9 22.3 21.9 22.2

(71.2) (72.7) (74.1) (73.6) (72.5) (71.6) (70.9) (71.6) (71.1) (71.4) (72.1) (71.4) (72) Average precipitation mm 17 37 98 166 225 363 313 339 322 269 52 18 2,219 (inches) (0.67) (1.46) (3.86) (6.54) (8.86) (14.29) (12.32) (13.35) (12.68) (10.59) (2.05) (0.71) (87.36)

Data

ArcGISArcGi ENVIENVI

Geo-referenced Owerri Landsat imagery Municipal 1994, 2004 &2014

Image subset ENVI to ArcGIS subset Color composite creation Clip Management

Classification ArcGIS to ENVI (maximum likelihood)

Post Classification (Class statistics, confusion Matrix, and Change detection)

Vectorization

Clip analysis

Database

- Results

- LULC Maps (1994, 2004 & 2014)

Figure 2. Cartographic flow chart of the methodology. Peter et al. 45

Table 2. Primary data.

S/N Data Year Resolution Source 1 Landsat Image (ETM+) 1994 30meters Global Land Use and Land Cover Facility GLCF 2 Landsat Image (ETM+) 2004 30meters GLCF 3 Landsat Image (ETM+) 2014 30meters GLCF 4 Nigerian Copy of State/LGA and Ministry of Land and Survey. town map 5 Latitudes and Longitude readings Field work around the study area.

Table 3. Software used for the work.

S/N Name Type Purpose 1 Arc Catalog GIS application Software It was used to manage spatially referenced data and for creating shape files for the analysis. 2 Arc Map GIS Application software It was used for map based analysis. 3 ENVI 4.5 Remote Sensing Software It was used for classification, post classification and vectorization of the raw satellite Imageries. 4 MS Access Microsoft office It was used to prepare the aspatial attributes.

Table 4. Land-Use and Land-Cover classification scheme and their general description.

Classes Description Built-Up Area Residential, Commercial, Industrial, Government facilities and settlement. Open Space Open land and non-vegetated land. Farmland All types of agricultural crops. Forest Evergreen forest and mixed forests with higher density of trees Vegetation Including mangrove, sparse vegetation etc Water bodies Areas cover by open water such as river, ponds, Lagoons, dam and water logged area.

Table 4 shows LULC classes and their respective are vegetation and areas with yellow color are open descriptions. spaces. From the maps there was rapid increase in built-up areas which meant that there was evident increment in RESULTS AND DISCUSSION urbanization. This increment was evident from the first period (1994-2004) to the second period (2004 - 2014). The major Land-Use Land-Cover classes for 1994, 2004 As built up area increased, other forms of land use and 2014 were analyzed in the GIS and Remote Sensing decreased to accommodate the increment. Software, the result show the Land Use Land Cover Further GIS analysis was performed to reveal the Maps of the years in consideration. nature and pattern of the changes.

Discussions Change detection analysis Figures 3, 4 and 5 show the Land Use land Cover Maps of the study area. The results were quantitatively analyzed for the area The study area was characterized and mapped into six covered by each Land-Use and Land-Cover categories. major Land Use/ Land Cover classes and shown to The result of the land-use type observed in the 1994, reveal the spatio-temporal patterns of these Land Use/ 2004 and 2014 Mass Classes are shown in Tables 5 and Land Cover dynamics as shown in Figures 3, 4 and 5 6. representing 1994, 2004 and 2014 respectively. As In 1994 mass class, vegetation and built-up area were illustrated by the legend, areas colored red are built up dominant at 33.2 and 27.3 % respectively. Water areas, areas colored blue are water bodies, areas with occupied the least space at 3.4% while Forest, Farmland dark green color are forest areas, areas with yellowish and Open Space had relatively close possessions at green color are farmlands, areas with light green color 10.7%, 12.8% and 12.6% respectively. 46 Sky. J. Soil. Sci. Environ. Manage.

Figure 3. Map showing the towns and 1994 mass classification of the study area. Peter et al. 47

Figure 4. Map showing the towns and 2004 mass classification of the study area. 48 Sky. J. Soil. Sci. Environ. Manage.

Figure 5. Map showing the towns and 2014 mass classification of study area.

Table 5. Class statistics and percentages in the year of 1994, 2004 and 2014 Mass class respectively.

Area (km2) 1994 Area (km2) Area (km2) 2004 2014 Land-Use type 1994 Mass % 2004 Mass 2014 Mass % occurrence % occurrence Class occurrence Class Class Built-Up Area 15.80 27.30 19.50 33.70 37.60 65.00 Farmland 7.40 12.80 9.40 16.20 12.50 21.60 Forest 6.20 10.70 3.20 5.50 2.00 3.40 Vegetation 19.20 33.20 18.00 31.10 4.00 6.90 Open Space 7.30 12.60 7.30 10.90 1.60 2.80 Water body 2.00 3.40 1.50 2.60 0.20 0.30 Total 57.90 100 57.90 100 57.90 100 Peter et al. 49

Table 6. Change Detections within the Mass Classes of 1994 to 2004 and 2004 to 2014 and their percentage changes.

Difference Difference 2 Area Area Area (km ) 2 2 in area % change in area % Change Land-Use Type (km ) (km ) (km2) 1994- 1994-2004 (km2) 2004- 2004-2014 2014 1994 2004 2004 2014 Built-Up Area 15.80 19.50 37.60 3.70 23.40 18.10 92.80 Farmland 7.40 9.40 12.50 2.00 27.00 3.10 33.00 Forest 6.20 3.20 2.00 -3.00 48.40 -1.20 37.50 Vegetation 19.20 18.00 4.00 -1.20 6.30 -14.00 77.80 Open Space 7.30 7.30 1.60 -1.00 13.70 -4.70 74.60 Water Body 2.00 1.50 0.20 -0.50 25.00 -1.30 3.40 Total 57.90 57.90 57.90 0 0 0 0

In 2004 mass class, Built-up area dominated with 33.7% that there was fluctuation in forest, in land mass and and vegetation having 31.1% occurrence. Thus, percentage, this could be as a result of the agricultural Farmland Open space and Forest had 16.2%, 5.5% and practice of inhabitants of the area as some Farmlands 20.9% occurrence respectively while water maintained were left fallow sequel to this, and they were captured as least possession with 2.6%. forest. Also it was observed that this period, there was Furthermore, the analysis of 2014 LULC occurrences government Forest Act to conserve some areas as Forest showed that built-up area maintained its dominance with reserve areas. Vegetation decreased likewise open 65.0% occurrence while farmland, forest, vegetation, spaces. Water Body also fluctuated; this could be as a water body and open spaces had 21.6%, 6.4%, 3.4%, result of the season of the year which the imageries were 6.9%, 0.3% and 2.8% respectively. captured. In general, analysis of LULC changes across 1994 - The Figure 8 explained the changes that have occurred 2004 showed built-up area and farmland increased at across the LULC types through the period and years in 23.4% and 27.0% respectively while forest, open space, consideration. vegetation and water body decreased at 48%, 13.7%, 6.3% and 25.0% respectively. Similarly, the analyses of change detection across 2004-2014 showed that built-up Image classification accuracy area and farmland maintained their increase at 92.8% and 33.0% respectively while forest, open space, We need to test the accuracy of the classified imageries. vegetation and water body decreased at 37.5%, 74.6%, The image classification accuracy calculated from the 77.8% and 3.4% respectively. confusion matrix found in the ENVI 4.5 environment for Hence, across 1994 - 2014, the analyses showed that the 1994, 2004 and 2014 years are summarized in the only built-up area and farmland maintained a direct Table 7. increase at 138% and 67.6% respectively with built-up As shown in Table 6, 1994 has an accuracy of 90% area becoming dominant, however, forest, open space, with kappa co-efficient of 0.9099. 2004 has an accuracy vegetation and water body maintained a direct decrease of 95% with kappa co-efficient of 0.9557 and 2014 has an from 1994 - 2014 at 67.7%, 78.1%, 79.2% and 90% accuracy of 96% with kappa co-efficient of 0.9685. respectively. For a clear and informative comparison of the Land- Test of Accuracy Use and Land-Cover change, area value for the two According to Nnaji et al. (2006) if: a Kappa value is periods 1994 - 2004 and 2004 - 2014 summarized in between 0.80 - 1.00, there is a good agreement. Figures 6 and 7. Since the entire kappa co-efficient of the years in In the Figure 6 and 7, BA represents Built-Up Area; FL, consideration are greater than 0.80, then there is strong Farm Land; F, Forest; V, Vegetation; OS, Open Spaces; agreement between the classified maps and the ground W, Water Body. In the First Period (1994-2004), Built up referenced information. This means that the imageries Area (BA) has the highest occurrence in percentage and were accurately classified. land mass. It was followed by Farm land (FL), Open Spaces (OS), and Vegetation (V) respectively. Water Body and Farmland has the lowest occurrence in Land Conclusion Mass and percentage. In the second period (2004-2014), Built-up area also This study has shown that change detection approach of had the highest occurrence by Percentage and Land land use land cover changes remains one of the most Mass. Farmland decreased. However it was observed accurate techniques of monitoring evident environmental 50 Sky. J. Soil. Sci. Environ. Manage.

5 km 4 3 2 1 0 1994-2004 -1 BA FL F V OS W -2 -3 -4 -5

Figure 6. Time-Line graph of the First period (1994-2004). BA=Built UP Area, FL= Farm Land, F= Forest, V= Vegetation, OS= Open Space, W= Water

km 20

5 Figure 7: Time-Line Graph of The Second Period (2004-2014)

0 2004-2014

FigureBA 7: Time-LineFL graph Fof the SecondV PeriodOS (2004-2014)W

BA=Built-5 UP Area, FL= Farm Land, F= Forest, V= Vegetation, OS= Open Space, W= Water

-10

-15

-20

Figure 7. Time-Line graph of the second period (2004-2014). Peter et al. 51

km 40

30 builtup area 25 farmland

20 forest vegetation 15 open space 10 water body

0 1994 masclass 2004 masclass 2014 masclass

Figure 8. Chart showing Land-Use occurrence within the 3 Mass Classes of 1994, 2004 and 2014.

Table 7. Summary of overall classification accuracy and kappa coefficient.

Year Overall classification accuracy (%) Overall kappa co-efficient 1994 90 0.9099 2004 95 0.9557 2014 96 0.9685

change in Space and time. This was based on the ii.) Adequate continuous monitoring by making use of evidence from the study which revealed that the Land- satellite remote sensing should be encouraged. Use and Land-Cover in the study area changed over time iii.) Forest guards should be employed if they are not yet whereby some increased, while others reduced in terms on ground but if they exist, more should be employed and of spatial extent. they should be exposed to more training on protecting the This approach helped in comparing and monitoring of forest. the changes that occurred in the study area over the iv.) The people in the study area should be enlightened or period of study. The use of remote sensing and GIS educated on how to manage and protect the technology is a better way of decision making on complex environment. issues related to the earth (land suitability) and the v.) This research can serve as an entry point and people living therein, such as Agriculture, Forestry, platform for other research work on the study area and Health, Resource Management, Land Administration, subject area. Water Resource Planning, Location Analysis, etc.

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