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CHAPTER ONE

1.0 INTRODUCTION

The rapid growth of population and the process of urbanization have resulted in an

increasing demand for land in urban settlements. A city grows not only by population but also by

changes in spatial dimensions. The prime factors of increasing spatial dimension of the city are

also the population growth and related requirements of urban life, such as development of

transport and communication and others infrastructure facilities. The mismatch between the

supply and demand of land leads to the degradation of environmentally fragile land, occupation of

hazard prone areas, and loss of cultural resources, open space and prime agricultural land. Within

the existing built-up areas of cities uncontrolled growth of population and inadequate

infrastructure may cause irreversible losses of cultural resources and open space. Poorly managed

development may also cause excessive urban sprawl and negative impact on air quality, energy

consumption and aesthetic quality. The conversion of prime agricultural land to urban use may

increase costs for locating, storing and purchasing food (Bernstein, 1994).

Moreover, the development of an urban centre as a result of gradual evolution and

metamorphosis of the city, with the market place and profit motive being the principal criteria on

which land use and land value were established is a common phenomenon. This can also lead to

change in land use pattern and urban growth in our growing centres. Similarly, by 2015, the 10

largest cities in the world will be in Asia, Latin America and Africa among which are Tokyo

(Japan) 28.7 million, Bombay (India) 27.4 million, Lagos (Nigeria) 24.4 million, Shanghai

(China) 23.4 million, Jakarta (Indonesia) 21.2 million, Sao Paulo (Brazil) 20.8 million, Karachi

(Pakistan) 20.6 million, Beijing (China) 19.4 million, Dhaka (Bangladesh) 19 million, and

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Mexico city (Mexico) 18.8 million people. Overall, 80 percent of the world’s urban residents will

live in the developing countries.

About 500 million urban dwellers are homeless or live in inadequate housing. This

problem is getting worse because housing cannot keep up with an exploding urban population,

which will double from 2.4 billion in 1995 to 5 billion in 2025. Housing shortage and poor

housing conditions were the results of massive urbanization, which are life-threatening.

Substandard housing, unsafe water and poor sanitation in densely populated cities are responsible

for 10 million deaths worldwide every year.

In 1952, only six towns in Nigeria had a population greater than 100,000, the number

increased to twelve by 1960 while in 1970, there were twenty-five of such centres (Ayeni, 1978).

In the last decade, more settlements have experienced rapid expansion following the recognition

of local government’s structure as the third level of government, each with administrative

headquarters. These new centres had transformed, in most cases, into nuclei of urban growth,

population concentration and development (Akinmuwagun, 2008). In all these centres, more lands

for housing and other facilities were required. The result is the lateral expansion of urban centres.

The Land Use Act of 1978 vested control of states land in the hands of the state governors.

This had made all the available urban spaces to be earmarked for one use type or the other by the

state government. Urban growth in Ondo state (Akure in particular) as well as other part of

Nigeria dates back to the pre-colonial period. However, extensive urban development is a feature

of the recent past. The old western Nigeria, which comprises of the present Ekiti, Ondo, Ogun

Oyo and Lagos states, had been recognized as the most urbanized part of Nigeria before and after

the colonial period (Mabogunje, 1968). The effect of uncontrolled land use pattern had started to

emerge in our cities. This is because of the early neglect of Town planners and also because of

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structures that predate planning which resulted into indiscriminate uses of land by city residents

of our cities out of ignorance.

Satellite Imagery, which is acquired through Remote Sensing (RS), and Geographic

Information System (GIS) are now providing new tools for advanced ecosystem management.

The collection of remotely sensed data facilitates the synoptic analyses of earth-system function,

patterning, and change at local, regional and global scales over time (Rajeshwari, 2006).

Therefore, this study employs the technique to examine changes in land use pattern of Akure over

a 16-year period through satellite remote sensing data.

1.1 RESEARCH PROBLEM

The pattern of city growth and its spatial structure are determined by various historical,

economic, social and ecological forces that influence urban land-use. The improper use of urban

land poses serious problems in all countries simply because the supply of surplus land is limited

and subject to many competing claims. Therefore, proper planning of urban land-use is most

essential for an orderly and efficient growth of urban areas.

Urban growth and sprawl are almost synonymous and edge cities have become the

dominant urban form. Urbanization is simply defined as the shift from a rural to an urban society,

and involves an increase in the number of people in urban areas during a particular year. The

percentage increase in population is accompanied by more than proportional percentage increase

of an urbanized area. This is an indication that the two growth rates differ and urban area grows in

a more rapid pace. At first, people continued to work in cities but lived in sprawling suburbs

which is characterized by an unplanned and uneven pattern of growth. Thus, urban centres begin

to sprawl or expand physically into the surrounding agricultural suburbs.

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High urban population place enormous stress on natural resources and imposes ‘ecological

footprints’ on the peri-urban areas (Rees, 1992; Rees and Wackernagel, 1994). For example,

urbanization leads to the outward expansion of cities and results in changes in land use whereby

urban residents buy up prime agricultural land for residential or commercial purposes. The

conversion of farm lands and watersheds for residential purposes have negative consequences on

food security, water supply as well as the health of the people, both in the cities and in the peri-

urban areas.

Although urbanization process often means accelerated economic performance for a

country, the accompanying rise in prices of urban land and its conversion from one form to

another affects the natural and cultural resources of the city. The influx of people into the cities

complicated the urban condition through structural growth. The population increase naturally

creates adjustment and readjustment of human and land use activities in space within urban

systems thus causing lateral and structural changes. Lateral changes occur when the city expands

in geographic boundaries leading to sprawl and peripheral developments while structural growth

relates to increase in land use density within urban centres. As demand for land uses increases due

to increase in urban population, lateral growth or urban expansion result. Also, as more and more

land are being converted, due to rise in population and land prices, low density areas gradually

become subjected to intensive use and thus become high density or medium density use.

The existence of crises in the Nigerian land market could be traced to the following factors

according to Bernstein (1994); rapid and uncontrolled migration, natural increase and

urbanization, existing socio-cultural cleavages, lack of secure tenure, inadequate information,

inappropriate and inefficient land policies and instruments, weak institutional structures and lack

of land management capacity, among other factors.

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According to Olotuah (2005), rapid increase in the population of the urban centres has

resulted in an increase in the cost of living, because of higher demand on urban commodities that

are getting shorter in supply by the day. Thus there is a dearth and high cost of urban land, and

high cost of housing, which is often in short supply and out of the economic reach of the majority

of the urban households who incidentally fall into the low-income category. The slow process of

urban planning and zoning, in the face of rapid urbanization in most urban centres, has resulted in

poor layout of buildings with inadequate roads between them and inadequate drainage and

provision for refuse evacuation. Thus there is a high incidence of pollution (water, solid waste, air

and noise) and inadequacy of open spaces for other land uses. These constitute urban poverty

consequential of the rapid urbanization in the country.

The expansion of urban population in most developing countries has resulted into a rapid

rise in the demand for housing, land for industry and commerce, and public buildings and

infrastructure. Due to many competing claims for urban land and the consequent rise in land

prices, supplying land which can be developed at the pace and scale required is a challenge for

authorities in most of the cities in the developing countries Nigeria inclusive.

1.2 JUSTIFICATION FOR THE STUDY

Settlements represent the most profound human alteration of the natural environment

through a spectrum of urban land use activities which include, but are not restricted to,

transportation, commercial, industrial, residential, institutional, and recreational land uses

(Ifatimehin and Ufuah, 2006). The urban expansion that ensues as a result of increase in the

demand for these land uses explains the underlying and fundamental cause of urban expansion

which is population increase. Urban built-up area increased significantly, whereas bare,

undeveloped land decreased. This increasing demand and expansion leads to sprawling which

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promotes the spread of urban land use into the rural-urban fringe and draws a larger number of

people into the rural-urban interface. Therefore, agricultural lands are bought up for residential

and commercial purposes which has negative effects on food security, water supply as well as the

health of the people of the study area.

The forest belt used to provide both a limit to urban expansion and to commercial and

recreational resource for the people of the city. This vegetation and areas close to small rivers or

streams within and immediately surrounding the city are now giving way for residential and other

land uses. Nevertheless, the importance of vegetation in the environment is underscored by the

role it plays as a major carbon sink. Therefore, there is a need for up-to-date and accurate land

use/land cover information that can be utilized in the production of sustainable land use policies.

A variety of factors affect the supply and demand of land and its uses in a city. The supply

of urban land is affected by such factors as location of city, laws related to land-use, structure of

land markets and urban management. Nevertheless, increasing population size of cities and

physical expansion of the built-up area beyond the city limits as well as rising demand for more

land for various purposes induce changes in urban land-use. This undocumented change has

driven unprecedented changes in ecosystems and environmental processes at local and regional

scales. Monitoring and mediating the negative consequences of land use changes while sustaining

the production of essential resources has therefore become a major priority of researchers and

policymakers around the world.

Increasing population size of Akure and physical expansion of its built-up area, or urban

extension, beyond the city limits as well as rising demand for more land for various purposes

induce changes in urban land-use. This increasing population is indicated by the population of

Akure which increased from 239,124 in 1991 (1991 National Population Census) to 420, 594

(NPC, 2011), almost doubled in a short span of 20 years. Akure grew from 38, 852 in 1952 to

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106,000 in 1963. Its population was estimated to be 112,850 in 1980 (DHV, 1985); 144,544 in

1987, 148,880 in 1988, 153,347 in 1989 and 157,947 in 1990 (Ondo State of Nigeria, 1990). The

1991 national population census however, put the population of Akure at 239,124 and its

estimated population in 1996 was 269,207 (NPC, 1996). At present the city is estimated to have

over 420,594 people and it is projected that by 2015, Akure will be inhabited by about 1.8 million

people. This land use change, which is attributed to urban growth alters the pattern of land use

and land values within an urban area.

Lateral physical expansion and outlying physical developments of Akure over a large area

that used to be rural and undeveloped away from the city centre forms a basis for which this study

is justified. This is as a result of high cost of urban land and housing at the city centre beyond the

reach of the majority of urban household and cheaper rates in the urban fringe. This urban

extension has its impact on the city’s road traffic congestion and high cost of agricultural

commodities. This, too, can be attributed to population growth.

1.3 AIM AND OBJECTIVES OF THE STUDY

1.3.1 AIM OF THE STUDY

This research work aims at examining the socio-economic implications of urban growth and its

trends on land use pattern of Akure between 1986 and 2002 using LandSat remote sensing

imageries.

1.3.2 OBJECTIVES OF THE STUDY

The objectives of the study are to:

i. Study the pattern of land uses in the year 1986

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ii. Examine the land use/land cover change that have occurred and the rate of these changes

between 1986 and 2002.

iii. Evaluate the relationship between population growths with these changes.

iv. Determine the socio-economic implications of this land use change on Akure.

1.5 RESEARCH LIMITATIONS

One major limitation to this study is problem of acquisition and analysis of

geographically-referenced data. Satellite imageries of Akure of different years have to be

procured for this project to carry trend analysis and determine rate of growth. These imageries are

costly and analyzing them requires a level of expertise in geographic information system (GIS)

and is expensive.

The dynamic nature of demographic characteristics of a town means that reliable and

current data has to be obtained. Therefore, up-to-date and reliable data on population distribution

and density of Akure over a period of years has to be acquired. These data are also not readily

available.

1.6 THE STUDY AREA

The study area is Akure, the administrative capital of Ondo State. The city is located within Ondo

State in the South Western part of Nigeria. It lies approximately on latitude 70 151 North of the

Equator and longitude 50 151 East of the Greenwich Meridian. Akure is a medium- sized urban

centre and became the provincial headquarter of Ondo province in 1939. It also became the

capital city of Ondo State and a Local Government headquarters in 1976. The area towards Ado-

Ekiti and Idanre are hilly and studded with large granite formation, rising to 410 metres and 496

metres above sea level respectively. These granitic formations are said to be of volcanic origin,

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underlained by basement complex rocks, which are mostly impermeable gneisses and granites. The

city’s morphology has changed over time to assume its present status with its attendant land use

problems, as experienced in similar medium sized urban centres in Nigeria. Akure is located

approximately 700 kilometers South West of Abuja, the Federal Capital of Nigeria and about 350

kilometers to Lagos the former capital of Nigeria. It is located within the tropical rain forest

region of Nigeria where rainfall is high throughout the year.

Fig 1: Map of Nigeria showing Ondo State.

Source: Diva GIS

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Fig 2: Map of Ondo state showing Akure South Local Governemnt Area

Source: Diva GIS

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Fig 3: Map of Akure South Local Government Area

Source: Ondo State Spotlight Major Towns, Akure Town Series No. 1, (Ondo State Ministry of Physical Planning and Urban Development, 2010)

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CHAPTER TWO

LITERATURE REVIEW AND CONCEPTUAL FRAMEWORK

2.0 LITERATURE REVIEW

In his study of the changing pattern of land uses and rate of changes in Calabar river

catchment between 1967 and 2008, Efiong (2011) used the GIS database and visual image

interpretation method to examine the various land uses in the area. The result showed that

increasing human and agricultural activities (fallow land and low forest land use classes) brought

about high rate of reduction of high forest (0.730 per cent year-1). Within that period, high forest

had reduced by 29.92%. Additional land use class (quarry) was also detected in the land use map

of 2008 all encroaching on the high forest area making it the most affected land use type thereby

threatening its existence and causing greater harm to the environment.

Mapping the land use/land cover status of the of the Federal Capital Territory, Abuja over

a 20-year period using remote sensing and GIS techniques shows that rapid urban expansion

resulted to high rate of vegetation cover loss. Fanan et al (2010) measured the extent of change in

the various land uses from the Landsat imageries of 1987 and 2001, and Nigeriasat-1 imagery of

2006 in the Federal Capital Territory (FCT). The total built-up area gained within 1987 to 2001

was 180.8 km2 shifting from the least-occurring land cover type in 1987 to the most gained land

use/land cover (LULC) of 245.65% in 2001. They also found that the transformation of LULC

between 2001 and 2006 shows that built-up area gained 122.08 km2 from cultivated land, while

vegetation and wetland vegetation lost 73.49 and 2.39 km2, respectively to cultivated land

showing that as pressure is exerted on the surrounding cultivated land for construction purposes,

vegetation and wetland vegetation suffers subsequently. They recommend that policy-makers

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should strictly enforce the existing laws on afforestation and parks establishment and other

measures.

Abimbola (2008) studies the implication of urban sprawl in Akure and observes that it

leads to land-use patterns which are unfavourable to the development of sustainable transport

modes and hence, increase the use of private car that in turn result in increased trip lengths,

congestion, increase in fuel consumption and air pollution. He explains that the growth of the

built-up area of Akure during the second half of the 20th century (from 16 km2 in the 1970s to 68

km2 in the 1990s) shows clearly that Akure is experiencing rapid population growth. He affirmed

that in the 1980s, the Ijoka – Oluwatuyi road generated the greatest urban sprawl (east and north

of the city), followed by the Idanre – Akure road (west of the city) and since then Akure has

spread further into the neighbouring local government areas of Akure North and Owena. He

identified population growth, rise in household income, subsidization of infrastructure

investments like roads, ineffective land-use, excessive growth, social problems in central cities

and poor land policies as the main causes of sprawl.

A study on the effect of population growth on land-use changes of Mumbai Metropolitan

Area, India between 1971 and 2011 shows that increase in built-up area is at the cost of

agricultural land, forest land and wetland (Kumar et al, 2004). The areas in different categories of

land-use were calculated by superimposing map of 2011 on the map of 1991 and 1971. They

found that with the saturation of land in the city followed by suburbs, other parts of the

metropolitan region experienced fast growth. Due to growing population pressure the total built-

up and industrial area in Mumbai Metropolitan Region has increased from 4.9 percent in 1971 to

12 percent in 1991 and is projected to constitute 31 percent of the total area in 2011. On the other

hand, area under forest cover has declined from 30 percent in 1971 to 27 percent in 1991 and it is

expected to declines further to 22 percent by 2011. To see the relationship between population

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growth and land-use change, isopleth maps was prepared for 1971, 1991 and 2011 and was

superimposed. The 1971-91 isopleth map showed a strong relationship between population

growth and land-use change in Island city, Suburbs, and other parts.

To examine the relation between population growths and land use change, Ningal et al

(2007) presented a case study from Papua New Guinea where the population has increased from

2.3 million in 1975 to 5.2 million in 2000. The land use change in the Morobe province

(33,933km2) was assessed using topographic maps of 1975 and Landsat TM images of 1990 and

2000. They found that between 1975 and 2000, agricultural land use increased by 58% and

population grew by 99%. Most new agricultural land was taken from primary forest and the forest

area decreased from 9.8 ha person-1 in 1975 to 4.4 ha person-1 in 2000. They observed that

agricultural land use changes in logged-over areas were in the vicinity of populated places

(villages), and in close proximity to road access and there was considerable variation between the

districts but districts with higher population growth also had larger increases in agricultural areas.

They concluded that in the absence of improved farming systems the current trend of increased

agriculture with rapid population growth is likely to continue.

In their study on participatory land use planning for community based forest management

in south-eastern Nigeria, Bisong, et al (2007) presented an empirical classification of current land

uses in their study area. They include those under high forest, secondary forest,

savanna/grasslands, swamplands and farm/fallow lands. About 51 per cent of the overall land uses

was classified under tropical high forest from a total land area of 65.72km2. The values ranged

between 38 per cent in Agoi-Ekpo community to 91 per cent in Etara/Ekuri-Eyeyeng

Communities. They concluded that high forest was the dominant land use type followed by

secondary forest and farm/fallow lands in all the three communities studied.

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2.0 SATELLITE REMOTE SENSING IN URBAN GROWTH MODELI NG

In recent times, viewing the earth from space has become crucial to the understanding of

the influence of man’s activities on his natural resource base over time Fanan et al (2010). In

situations of rapid and often unrecorded landuse change, observations of the earth from space

provide objective information of human utilization of the landscape. Over the past years, data

from earth remote sensing satellites have become vital in mapping the earth’s features and

infrastructures, managing natural resources and studying environmental change (Arimoro et al.,

2003). Satellite Imagery, which is acquired through Remote Sensing (RS), and Geographic

Information System (GIS) are now providing new tools for advanced ecosystem management.

The collection of remotely sensed data facilitates the synoptic analyses of earth-system function,

patterning, and change at local, regional and global scales over time (Rajeshwari, 2006).

According to Fabiyi (2006), remote sensing image (platforms) generally do not record

activity or the way land is being used (i.e., forest, agricultural, residential or industrial) directly

but acquires response based on characteristics of land surface. Weng (2002) states that satellite

remote sensing is effective in providing multi-temporal and multispectral data along with the

required information for understanding and monitoring land development patterns and processes

for building land use and land cover datasets.

A number of researchers had used multispectral remote sensing to evaluate land use land

cover changes at the micro scales including Bradon & Bottomley (2000) and Mass (1998). All

these works examined changes in land use pattern over a given period. It is often not sufficient to

analyse the changed process in land use land cover but also to understand the forces at play in the

changed process.

Rao (1995) moved close to this when he used Indian remote sensing to examine the

changed pattern and used auxiliary data to project the land area of Tuni region into the year 2000.

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The approach was validated by two other methods including Causal modelling, cross-

classification model and rating methods. He observes that human needs transform the land to

different activities and that the quantifiable information of these dominant activities is necessary

to develop future planning. Urban changes can be modelled through the observation of the past

and projection to the future. Rao used a causal modelling approach where the causative factors

were modeled to estimate the future changes in land use. In his work, correlation analysis were

uses to examine three factors that are judged responsible for urban land use change while other

methods causal modeling techniques, cross-classification and rating methods, were used to

validate the results of spatial analysis.

Ekpenyong (2008) used the GIS database to model the land use/cover change between

1984 and 2003 for Akwa Ibom State. The result showed that some urban centres had expanded

into farmlands/fallow lands and the surrounding secondary forest. Within that period, mangrove

forest had reduced by 50 per cent. Other forest covers in the area also changed, threatening food

security and climate among others.

While examining the process of land use conversion and rate in common and public lands

in South-Eastern Nigeria between 1972 and 2001, Bisong (2007) showed that deforestation was

higher in publicly controlled lands than in communal lands. Agricultural land use characteristics,

such as farm types and the nature of croplands/fallows, correlated strongly and significantly with

deforestation rates.

Similarly, Idoko, et al (2008) analyzed satellite imageries of 1987 and 2004 for the

Federal Capital Territory, Abuja for land use change. The imageries were characterized into five

classes using maximum likelihood algorithm. The classes were vegetation, built-up, rock outcrop,

water body and farm land. The two classified imageries were compared to identify any changes.

Also, a spatio-temporal change in land use type was obtained in quantitative terms. The analysis

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revealed that vegetation cover reduced by 85.22 per cent between 1987 and 2004. Again, the

built-up area increased by 21.99 per cent and farm land increased by 0.14 per cent.

Bisong and Okon (2008) analyzed satellite imageries of 1987 and 2004 for the Federal

Capital Territory, Abuja for land use change. The imageries were characterized into five classes

using maximum likelihood algorithm. The classes were vegetation, built-up, rock outcrop, water

body and farm land. The two classified imageries were compared to identify any changes. Also, a

spatio-temporal change in land use type was obtained in quantitative terms. The analysis revealed

that vegetation cover reduced by 85.22 per cent between 1987 and 2004. Again, the built-up area

increased by 21.99 per cent and farm land increased by 0.14 per cent.

2.1 CONCEPTUAL FRAMEWORK/ THEORETICAL FRAMEWORK

2.1.1 THE CONCEPT OF LAND USE

According to Clawson and Stewart (1965), land use refers to, “mans activities on land

which are directly related to the land.” He describes how a portion of the earth’s surface is used

by man (such as for industry, habitation, agriculture, etc). But Efiong-Fuller (2008) defines land

use in terms of syndromes of human activities such as agriculture, forestry and building

construction that alter land surface processes including biogeochemistry, hydrology and

biodiversity.

However, there is no ideal classification system of land use, and it is unlikely that one

could ever be developed (Anderson et al, 1976; Efiong-Fuller, 2008). This is because there are

many perspectives in the process of classification. Again, each classification is made to suit

specific user needs which thereby impose a number of criteria in deciding the land use classes.

According to Joel (2011), land use is dynamic and their patterns change over time in response to

human demands for natural resources. A particular land use classification scheme may therefore

not be adequate.

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2.1.2 CONCEPT OF URBAN LANDUSE

A number of approaches emerged in the form of models, attempting to describe the

internal structure of contemporary cities consequent upon series of studies conducted on the

nature of urban landuses structure by pioneer urban analysts (Bashorun, 2003). The rationale

behind each concept may make sense to some extent, especially when the substantive

developmental stages are keenly followed. The three best known form of urban land use structure

theories are the concentric zone theory, the sector theory and the multi-nuclei theory.

2.1.2.1 Concentric zones model

Burgess developed the concentric zone theory, which states that cities tend to expand

radially from the centre to form a series of concentric zones. The expansion comes about as a

result of centripetal forces from an original core and as the growth occurs, each inner zone tends

to extend its area by invading the next outer zone following a sequence know as invasion -

succession. The rate of this process depends on the rate of the city’s economic growth and on

population expansion. In his study of American cities, Burgess postulates that urban land use

development and pattern are represented in 5 concentric zones and a sixth lying beyond the

immediate confine of the urban area.

i. The central business district (CBD), which is the focus of commercial social, civil

life and of transportation.

ii. The fringe of the CBD which is an area of wholesale, truck and rail road depot.

iii. The zone of transition characterized by property in poor condition and run-down

area being invaded by business and light manufacturing.

iv. A worker’ housing and factory zone with residence and plants in close proximity

v. A high class residential zone.

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vi. A commuter zone of residential suburbs and satellites commercial and shopping

areas within accessible time distance.

Though, this theory was based on certain assumptions such as; cultural and social homogeneity,

economy based on commerce and industry, private ownership of property and economic

competition for space, easy rapid and cheap transportation in every direction the city centre is the

main centre for employment. It provides a simplistic view and serves pedagogical purposes with

regards to dynamics or urban growth. Hoyt, when he developed the sector theory amplified the

limitations of these theories.

2.1.2.2 Sector theory

Homer Hoyt developed radial sector theory; he concentrated on aerial pattern of shifts in

residential location. He argued that the different income groups or classes in a city tend to live in

district areas describable in terms of sectors of a circle around a city centre. Hoyt argued that the

location and extension of high quality zones tend to proceed along the fastest existing

transportation lines and either toward another existing nucleus of building of trading centres or

toward the section of the city with open country. The competition for the centre increases rent

thereby making it affordable only to the business outfits and the growth along any particular

transport route consists of land use of similar characteristics.

According to Fabiyi (2006), there are certain factors that often influence the concentration

of certain economic class or land uses in a given place in the Nigerian context and particularly

against the contention that the business outfits always occupy the centre. These include social,

economic, ethnic and fear of crime. Mabogunje further contested that Nigerian urban centres are

rather multi-centric rather than monocentric.

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2.1.2.3 Multiple-nuclei theory

Harris and Ulman contended that cities often develop around several distinct nuclei rather than

one centre of origin. These other centres may be district centre established in an earlier

urbanization phase and which persist as centres as city growth fills in the space between them.

According to the theory, the emergence of separate nuclei reveals four main factors, which

include;

i. Interdependence of some types of activity that have to be close to each other

because of dependence on specialized needs.

ii. The tendency or complimentary activities to agglomerate such as retail shops and

office buildings.

iii. The locational antagonism between dissimilar activities such as heavy industry and

high-class residential development.

iv. High rent and high land costs may attract or repel certain kinds of land use.

The number of nuclei and their functions will vary from city to city. Generally, the larger the city,

the more nuclei it will contain. The major flaw of this theory is the clarification of the

characteristic of the nuclei. This integrates the hierarchical core periphery model to the pattern of

urban structures. There are traditional centres, modern centres, commercial centres,

political/administrative centres, industrial centres and business centres. These also have ranges of

goods and services from their distinct hubs. The concern of these papers is the analysis of the

change process and projections of future changes in urban land use. In Nigeria, all cities apart

from Abuja are organic in their origin and developed over a long period of time as build- on for

existing pre-colonial urban centres.

These theories, however, cannot singly serve as a tool in understanding contemporary

urban land use development and structure but provide us with the useful starting point in the

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search for the understanding of urban land use in developing countries. The multi-nuclei and the

concentric urban landuse concepts best fit the case of Akure city as it comprises of many

segments all grouping around a separate nucleus or centre (Bashorun, 2003). Some of these

centres include Alagbaka, Nepa, Oja Oba area, Isikan, Road block/Orita-obele area among others.

In addition, Akure also exhibit the concentric structure of urban development with four zones

(Olujimi, 2010). These are the core area, the transition area, the peripheral area and the public

housing districts.

2.1.3 THE CONCEPT OF CHANGE DETECTION

The environment is progressively dynamic which is in response to the use man puts it and

management practices. Therefore, when the environment reacts, it is simply readjusting to the

changes which either sustains or degrades it. Change detection according to Singh (1989) and

Mejabi (2008) is the concept of identifying contrasts or discrepancies in the state of an object or

phenomenon by observing it at different times. This involves the ability to quantify temporal

alteration and transformation using multi-temporal data sets. In general, change detection involves

the application of multi-temporal data sets to quantitatively analyse the temporal effects of the

phenomenon. According to Green, et al (1994), the objective of change detection is to compare

spatial representation of two points in time by controlling all variances that are not of interest and

to measure changes caused by differences in the variables of interest.

Mejabi (2008) noted that change detection studies recognise the biotic and abiotic

components of multi-spectral and multi-temporal variations that are occurring within an

ecosystem. These studies are based on two types of techniques:

1) Map-to-map comparison: This relies on the identification of the differences between two or

more maps of different data set of the same location

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2) Image-to-image comparison: This relies on the identification of two or more imageries of

different data sets of the same location. Many change detection techniques have been

developed.

For the sake of convenience, Lu, et al (2004) grouped change detection methods into

seven categories: (1) algebra, (2) transformation, (3) classification, (4) advanced models, (5)

Geographical Information System (GIS) approaches, (6) visual analysis, and (7) other approaches.

The main characteristics, advantages and disadvantages, key factors affecting change detection

results and some application examples have been provided in their work for the first six

categories. The seventh category includes those change detection methods that are not suitable to

be grouped into any one of the six categories and are not yet used extensively in practice. For this

study, the GIS and visual analysis methods based on the map-to- map comparison techniques was

adopted. Han, Li and Gu (2008) has noted that conventional map-to-map comparison method is

used frequently for land use change detection.

The visual analysis category includes visual interpretation of multi-temporal image

composite and on-screen digitizing of changed areas (Lu et. al., 2004). Practically, it involves the

visual interpretation of the colour composite to identify changed areas or implementing an on-

screen digitizing of changed areas using visual interpretation based on overlaid images of

different dates. This method makes use of the analyst experience and knowledge. Loveland et. al.

(2002) used visual interpretation of fine resolution data combined with sampling design to detect

United States land cover changes and estimate change rates. This method has also been adopted

by Sunar (1998), Ulbricht and Heckendorff (1998), Sadar and Winne (1992), Stone and Lefebvre

(1998), Asner, Keller, Pereira and Zweede (2002) and Slater and Brown (2000), among others.

23

CHAPTER THREE

3.0 RESEARCH METHODOLOGY

Methodology refers to the various steps through which research data and information are

collected, stored, processed, analyzed and presented manually or electronically. This chapter

therefore gives description of the method of data collection used in the course of this study.

In order to achieve the objectives of the study, data were collected on socio-economic

characteristics, physical and human factors of growth in the study area.

3.1 RESEARCH DESIGN

This refers to all the preliminary steps taken to ensure that appropriate data is obtained for

analysis. In this study, 2 set of questionnaires were used- one for Akure population and the other

for saw-millers associations in Akure.

3.2 RESEARCH POPULATION

The population of Akure as at 1991 was put at 239,124 based on the census carried out by

National Population Commission (NPC, 1991). A population projection formula Pt = P0 (1 + r)n

was adopted in order to determine the present population of the area.

Where Pt = Future population

P0 = Base year population

r = Annual rate of growth (2.5%) = (0.025)

n = Time interval = 10 years

Pt = 239,124 (1 + 0.025)10 = 306,099

The present total population of Akure as projected to 2011 is put to 306,099 as calculated

above.

24

3.3 SAMPLING FRAME

This is the summation of all the elements or units in the sample. It is a complete list of all

units from which the sample is drawn. The sampling frame in this study refers to the population

of Akure. Using cluster sampling technique, Akure was sub-divided into 20 residential

neighborhoods namely Ilesha Road/Alaba Layout Residential Area, Okuta-Elerinla Residential

Area, Akure High School/Kajola Residential Area, Ijapo Residential Area, Alagbaka Residential

Area, Ala River Residential Area, Federal Housing Estate (Shagari Village) Residential Area,

Oba-ile Residential Area, Fanibi Layout/Lafe Residential Area, Oke-igan/Eruoba Residential

Area, Isinkan/Ondo Road Residential Area, Oshinle Residential Area, Ijoka/Sijuwade Residential

Area, Araromi/Isolo Residential Area, Oke-jebu Residential Area, Idi-agba Ijanikan Residential

Area, Erekesan/Erekefa Residential Area, Ijomu Ilisa Residential Area (Okoko, 2002). Four (4)

residential areas were then selected randomly from the 20 and each was further sampled on street

basis. These four residential areas are Erekesan/Erekefa, Ijoka/Sijuwade, Isinkan/Ondo Road, and

Federal Housing Estate (Shagari Village). The questionnaires were allotted based on the number

of buildings in the selected streets in each area and these were further shared equally between the

streets. Table 5 on page 41 shows how the questionnaires were distributed based on a total of 604

buildings in the selected streets of the four residential areas.

In addition, a separate questionnaire was designed for saw-millers and truck-owners in the

study area. Two branches of Saw-millers association of Akure were identified located at

Aijegboro, Ondo road, and Custom Saw-mill also along Ondo road. The Aijegboro branch has 21

members while the Custom branch has 26 members making a total of 47 members. All members

in both branches were administered with questionnaires. The questions borders on the number and

capacity of truck, number of load per day, number of trees fell per day among others.

25

Fig 4: Map showing the 20 residential areas of Akure

Source: Adapted from Okoko (2000)

26

Fig 5: Map showing the sampled residential areas of Akure

Source: Adapted from Okoko (2000) with modification

27

3.4 SAMPLE SIZE

Sample size depends on the variability of the population one is sampling (Kitchin and

Tate, 2000). This means that if the variability is known then the size of sample needed could be

estimated. In view of this, a total of 348 questionnaires representing 0.11% of the research

population were administered in the study area based on number of buildings in sampled areas.

3.5 SAMPLING TECHNIQUE

Stratified sampling and systematic sampling techniques were used for this research. Using

stratified sampling technique, residential areas were used to separate Akure population into

groups. Systematic random sampling technique was also adopted for this study. The choice of

these techniques was based on time constraints and size of the study area. In this case, the

questionnaires administered in each street were distributed to every 5th house after a count of four

houses.

3.6 DATA COLLECTION INSTRUMENT

Data used for this research work are:

Primary Data Survey

In the course of this research work, a total of 348 questionnaires were used to collect

information directly from the Akure city dwellers. Physical evaluation and assessment of people’s

perception of the growth and lateral expansion of the area was carried out.

Secondary Data Survey

In addition to data collected through journals, thesis dissertation, monograph series,

textbooks, proceedings of seminars and conferences, LandSat imagery of 1986 and 2002 for

Akure were acquired from the United State Geological Survey archive data through the Global

Earth Geo-System Consult, Ile-Ife, Osun State. A high-resolution IKONOS satellite image of

28

2009 for Akure was downloaded from the internet to provide recent and additional information as

to the changes that have taken place in the study area.

3.7 PROCEDURE FOR DATA COLLECTION

This research used both primary and secondary data. Questionnaires were administered in the

study area to collect primary data on socio-economic, physical and human factors responsible for

the growth of the area.

LandSat satellite images of the study area were acquired for two years (1986 and 2002) from the

United State Geological Survey through the Global Earth Geo-System Consult, Ile-Ife, Osun State

while IKONOS satellite image of Akure of 2009 was downloaded from the internet using the

Google Earth Pro software. This study were intended to be on regular interval of 10 years, but the

inability to access the satellite image data for the period interval in terms of cost and time

constraint resulted to the present interval used for the research.

3.8 METHODS OF DATA ANALYSIS

Data collected through questionnaires were analyzed manually and with the aid of computer

which is then presented in form of tables, percentages and ratios and charts, information obtained

through interview and discussions were synthesized and applied in the write up.

The data for the topographic map was produced from aerial photograph of April 1963 and

January 1965 and was published as first edition by the Federal Surveys, Nigeria in 1966. The

topographic map was obtained through the GIS firm and transferred onto the GIS environment.

Akure falls within four standard topographic map sheets which are Akure North West, Akure

North East, Akure South West and Akure South East. Each sheet was geo-referenced, geo-coded

and glued together to form a composite topographic map sheet covering the study area as shown.

The necessary land use features of the glued map such as roads, built-up areas (clusters of

29

developments), rock outcrops (indicated by the contours), rivers including the Akure Forest

Reserve on the map were digitized and ploygonised to form the base map of Akure in 1965 as

shown in the figure below.

Fig 7: Geo-referenced Topographic sheets of Akure with their Sigma values

(a) Akure264NE (b) Akure264NW

(c) Akure264SE (d) Akure264SW

30

Fig 8: Glued Topographic Map of Akure

It should be noted that the most part of the area has been generalized as dense forest based on the

fact that Akure has not started developing into a big city and the proportion of built-up area is

small. Therefore, there is apparently less exploitation of the natural resources and most of the

vegetation remains rain forest. In addition, the rock extracted from the 1965 topographic map

cannot categorically be classified as bare rock because some are expectedly covered by

vegetation.

31

Fig 9: Base Map produced for Akure from topographic map

Source: Topographic map of 1965 for Akure

Table 1 shows the land use land cover distribution of Akure in 1965

Table 1: Landuse Landcover distribution of Akure in 1965

Source: Classified topographic map of the study area 1965.

Land Use class Percentage Area (m2)

Builtup Area 0.78 1344.94 Rock outcrop 2.29 3953.66

Bareland 0.02 27.0 Dense forest 96.23 166307.79

Forest reserve 0.69 1189.29 Total 100.00 172822.66

32

Different land use classes were obtained from the satellite imagery of 1986, 2002 and

2009 and their different thematic maps produced using the Integrated Land and Water

Information System (ILWIS) version 3.2 software developed by the International Institutes for

Aerospace Survey and Earth Sciences, Netherlands (1997). For the image classification, a sample

set of the training sites was created and a sample class was then generated for each feature class.

In collaboration with my supervisor and expert in remote sensing couple with some basic training

in photo interpretation during this project, the spectral signatures of the different land use classes

that exist in the study area was identified and demarcated using the Remote Sensing application.

The degree of darkness or brightness is a function of the amount of light reflected from the

scene within the specific wavelength interval or band (Jenson, 2007). Satellite images of bands

within the infra-red region (bands 4, 5, and 7) which gives a higher contrast of different land

cover within the study area was used for the study. Very dark-red colours were interpreted as

forest with no observed human impact. These were regarded as the dense forests. Lighter red

colours were interpreted as areas with little human activities and were considered as light forest

which is characterized by fresher trees as a result of natural reforestation. Brighter areas on the

map were interpreted as built-up areas while black colours were regarded as water body. The

forest along the river course was classified as gallery forest. These look like the light forest but

are of lighter red colours, younger and fresher in appearance due to the constant supply of water

in the wetland.

Land use classes such as built-up area, light forest, dense forest, bareland/cultivation, bare

rock, water body, gallery forest were noted on both the 1986 and 2002 land use maps. A white

patch on the 1986 image was interpreted as area with intensified human activities and was

classified as exposed soil. During ground-truthing, this area was later identified as construction

33

site. Areas with very light red on the 2009 image were classified as grassland. Computed areas of

the various land use classes were retrieved from the ILWIS software used for the satellite data

analysis. Areas (in percentages) of each of the land use classes with respect to the total of the

study area were determined for the four land use maps. The change in land use was obtained as

the difference in areal calculation between 1986 and 2002 landuse landcover maps for each of the

land use classes. Similarly, the percentage change in land use was calculated as the difference

between the percentage of the land use types in 1986 and 2002 land use maps.

Finally, the average rates of change for the different classes of land uses were obtained by

dividing the percentage change in areal coverage of the land use by the number of years taken for

the change to occur. In this particular case, the number of years taken for the change to occur is

2002-1986 which is equal to 16 years.

3.8.1 Standard Satellite Image Classification Procedure

Classification entails looking at the radio-metric values of irradiation of the objects on ground to

the satellite sensors where they are registered. Radio-metric simply refers to the characteristics of

objects on the earth surface making them appearing differently to the sensors. These differences

in the appearance of the various objects on the earth surface are referred to as spectral signature of

objects. In order for us to have a good perception of reality or our real world as required by the

study, the combination of imagery within the infra-red bands does satisfy this requirement. In this

project, therefore, LandSat Thematic Mapper (LandSat TM) bands 4, 5, and 7 were combined

during the digital image processing as colour composite. The individual bands forming this colour

composite of the study were enhanced by removing noise and atmospheric perturbations from the

image through the use of linear filtering so that we can see the features as they are in reality.

34

Fig 10: LandSat TM image and colour composite of Akure captured in 17th December, 1986

5 5

(a) Akure 1986 Band 4 (b) Akure 1986 Band5

(c) Akure 1986 Band7

(d) Colour Composite of Akure 1986

35

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Fig 11: Graph of the filtered images of the study area

Akure 1986 Band 4 Akure 1986 Band 5 Akure 1986 Band 5

Fig 12: Filtered bands of the study area

(a) Akure 1986 Band 4 (b) Akure 1986 Band 5

(c) Akure 1986 Band 7

36

The three filtered images were again combined to form a composite enhanced image on

which sample set were created. Sample set refers to land use classes selected on the enhanced

image as training site during the image classification process. The training sites selected was then

used for the image classification procedure using the Maximum Likelihood Algorithm. The

Maximum Likelihood classification assumes that spectral values of training pixels are statistically

distributed according to a 'multi-variate normal (Gaussian) probability density function' and is

suitable for distinguishing features present in our tropical environment for best result in order to

achieve the aim of the project. Therefore, Maximum Likelihood Algorithm was used to classify

the images and identify changes that have taken place in the study area. Without this

classification, these changes can neither be detected, nor statistically analyzed and processed.

Fig 13: LandUse LandCover Map of Akure in 1986

37

The map shows the result of the image classification of 1986 LandSat TM image of Akure. There is a

significant increase in the size of the built-up area from 8839.09m2 in 1986 to 27972003.98m2 in

2002. The built-up area is still surrounded by the bare land which is exposed land under cultivation.

Table 2 presents the Land Use Land Cover distribution of Akure in 1986

Table 2: LandUse LandCover distribution of Akure in 1986

Land Use class Percentage of total Area (ha)

Builtup Area 1.62 2797.20 Bare rock 2.64 4568.19 Bareland 13.53 23397.79

Dense forest 7.43 12850.17 Gallery forest 0.87 1502.11 Light forest 73.87 127737.37 Water body 0.05 80.61

Total 100 172933.43 Source: Derived from Classified image of Akure 1986

Thesame procedure was also used for the LandSat Extended Thematic Mapper Plus (LandSat

ETM+) images captured over the study area in January 3rd, 2002 while the high-resolution

IKONOS image of 2009 was equally enhanced and classified using the colour composite of bands

1, 2, and 3. The focus of 2009 image classification was on built-up areas while deeper study in

other classes was left for further study. This is because the available high-resolution were neated

together to cover much of the built-up area.

Figure 10 shows the landuse landcover map of the study area in 2002. It could be observed that

the settlements which are referred to as built-up are still surrounded by the bare land or cultivation

while the rocks remain in their original location. The dense forest is no longer concentrated as it

were in 1986 while built-up areas are expanding in size.

38

Fig 14: LandUse LandCover Map of 2002 study area

Table 3: Land use Land cover distribution of Akure in 2002

Land Use class Percentage of total Area (ha) Builtup Area 4.08 7,058.58

Bare rock 2.33 4,029.47 Bareland 23.36 40,396.22

Dense forest 5.77 9,977.33 Gallery forest 0.45 783.99 Light forest 63.96 110,592.73 Water body 0.05 80.03

Total 100 172918.35 Source: Classified satellite image of the study area for 2002

39

Fig 15: LandUse LandCover Map of 2009 for the study area

Table 4: Land use Land cover distribution of Akure in 2009

Land Use class Percentage of total Area (ha) Builtup Area 16.63 8839.09

Bare rock 7.24 3,846.92 Bareland 13.08 6953.56 Grassland 29.25 15545.43

Gallery forest 20.92 11119.07 Light forest 12.70 6753.15 Water body 0.18 96.80

Total 100 53154.02

40

Source: Classified satellite image of the study area for 2009

It can be seen from the 2009 land use map that those areas that were bare land/cultivation in the

2002 land use map are now occupied with buildings in the 2009 land use map. It was also noticed

that areas that were formerly light forest became grassland and dense forest has changed to light

forest due to encroachment effects of development activities. There is restriction of growth of

development towards the southwest of the area as a result of bare rock existing around the area

while development could be seen growing along transportation route in the northern and eastern

parts of the study area. The image was affected by hamattan haze as it was captured shortly after

raining season in November and area under bare land has not been fully exposed. Therefore, level

of bare land/cultivation cannot be fully ascertained.

To evaluate the relationship between population growth and land use pattern particularly

in the built-up area of Akure, the land area data derived from the classified imagery of the years

being studied were used to determine population density of the study area. The population of

Akure in 1963 and 1991 was put at 106,000 and 239,124 respectively based on the census

conducted by the National Population Commission in 1963 and 1991. Population projection

formula Pt = P0 (1 + r)n was adopted to project the population of Akure from 1963 to 1965 and

1986 using population growth rate (r) of 1.3% per year while the population census result of 1991

was used to determine the population of Akure in 2002 and 2009 using population growth rate of

2.77% per year. These rates gives the population census result figures of Akure for 1987

(144,544) and 2006 (360,268, (NPC, 2006)) when used to project from 1963 and 1986

respectively using the projection formula.

41

CHAPTER FOUR

4.0 DISCUSSIONS AND FINDINGS

This chapter involves the analysis of field raw data obtained via questionnaires and interviews. It

also discusses the results of satellite image analysis data as part of secondary means of data

collection. In the course of getting data for this research, 47 questionnaires were administered to

members in the association of saw-millers and truck-owners in two branches and 301

questionnaires were administered on household heads in the study area making a total of 348

questionnaires administered in the study area. All were properly filled as respondents were guided

on how to fill the questionnaires which were collected immediately. This was possible with the

help of some field survey assistants used on the site. The table below shows questionnaire

distribution analysis.

Table 5: Questionnaire distribution analysis

Residential Areas Sampled streets No. of Buildings in the streets

No. of Questionnaires Administered

% Retrieved

Erekesan/Erekefa

Odo-ikoyi and Car streets

291 145 100

Isinkan/Ondo Road Ajebamidele and Gbogi

104 52 100

Ijoka/Sijuwade Canaanland and Sunday

167 83 100

Federal Housing Shagari Village)

Presidential Avenue and Quarterguard

42 21 100

Sub-total 604 301 100

42

Saw-millers and truck owners association

Custom and Aijegboro branch

47 100

Total 604 348 100 Source: Author’s survey (2011)

4.1 RESPONDENTS CHARACTERISTICS (UNIVARIATE ANALYSI S)

Sex distribution of Respondents

From the sex distribution table, 52.16% of the respondents are male while 47.84% are female.

This shows that respondents are more of men than women as the general questionnaire is

primarily designed for household heads.

Table 6: Sex distribution of respondents

Sex Frequency % Male 157 52.16

Female 144 47.84 Total 301 100

Source: Author’s survey (2011)

It should be noted that all members in the saw-millers association were male and there were no

female member as shown in table 7.

Table 7: Sex distribution of saw-millers

Source: Author’s survey (2011)

4.1.2 Age distribution

Sex Frequency % Male 47 100

Female 0 0 Total 47 100

43

Table 8 shows the age of saw-millers. All the members in the association are above 25 years of

age while most of the members corresponding to about 94% on the cumulative are within the

range of active population as demanded by the labour-intensive nature of their occupation.

Table 8: Age distribution of saw-millers

Source: Author’s survey (2011)

Educational status of respondents

The research shows that about 45% of the total respondents in the study area had tertiary

education. 26.58% had secondary education while 17.61 could not further their primary

education. It should be noted that less than 11% of the total population had no formal education.

Table 9: Educational status of respondent

Source: Author’s survey (2011)

Table 10 shows the educational status of the saw-millers. Nearly half of the respondents could

boast of primary education while approximately 16% had no formal education. About 28% had

Age of saw-millers % Percent 18-25 - - 26-35 31 65.96 36-55 13 27.66

56 and above 3 6.38

Total 47 100

Educational status Frequency %

No formal education 33 10.96 Primary 53 17.61

Secondary 80 26.58 Tertiary 135 44.85 Total 301 100

44

secondary education while only 4% of the saw-millers had tertiary education. The result shows

that the level of education of the saw-millers is generally low.

Table 10: Educational status of saw-millers

Source: Author’s survey (2011)

Occupational status of respondents

Table 11 shows the occupational status of the research population. Nearly one-tenth of the

respondents are artisans such as bricklaying, welding, carpentry among others. The data indicate

that 42.53% of the respondents are civil servants and more than one-fourth engage in trading

activities. It is pertinent to note that only approximately 15% are into farming while less than 6%

are either unemployed or engage in other forms of occupation.

Table 11: Occupational status of respondents

Occupational status % Frequency Farming 14.95 45 Trading 27.57 83 Artisan 9.3 28

Civil service 42.53 128 Others 5.65 17 Total 100 301

Educational status Frequency %

No formal education 8 16.3265 Primary 23 46.9388

Secondary 14 28.5714 Tertiary 2 4.08163 Total 47 100

45

Source: Author’s survey (2011)

Average number of trees fell per day

Table 12 shows the average number of trees fell per day by the saw-millers. Analysis of the data

show data shows that more than half (53.32 per cent) of the respondents cut down a tree per day

on the average while about 32 percent fell 2 trees per day. Only about one-tenth fell 3 trees per

day on the average and none of the members fell more than 3 trees per day. This shows a great

decline in the number of forest resources in our environment as most of the members interviewed

were already complaining of unavailability of trees in the forest.

Table 12: Average numbers of tree fell day

Number of tree % Frequency 1 55.32 26 2 31.91 15 3 12.77 6 4 - -

5 and above - - Total 100 47

Source: Author’s survey (2011)

Research data reveals that 33.55 percent of the households in the study area comprise of 1 to 4

persons and more than half of the respondents live together with 4 to 6 other persons under the

same roof. The data also indicate that less than 9 percent of the respondents have 8 or more

persons in their household. This shows that household size in the Akure is high which has positive

effect on the rate of population growth of the study area.

Table 13: Household size of respondent Persons per household % Frequency

1-4 33.55 101 5-7 57.81 174

8 and above 8.64 26 Total 100 301

Source: Author’s survey (2011)

46

Table 14 shows the household size of the saw-millers. 27.66 percent of the members come from

households comprising of 1 to 4 persons and 61.7 percent live with 4 to 6 other persons. About 10

percent have large family size of 8 and above. The analysis of the data shows that majority of the

members comes from large families and this correlate strongly with low level of education of the

members.

Table 14: Household size of saw-millers

Persons per household % Frequency 1-4 27.66 13 5-7 61.70 29

8 and above 10.64 5 Total 100 47

Source: Author’s survey (2011)

Location of farm

Table 15 present the location of farms in the study area. It was discovered that existing farms are

either within the compound of buildings or at the outskirt far away from Akure town where

sufficient land are available. The research data indicate that there were no neighborhood farms

where food crops can be cheaply grown as it were in the past. This shows high rate of

urbanization in the study area.

Table 15: Location of farm

Location of farm % Frequency Within compound 5.98 18

within neighborhood - - outskirt Akure 8.97 27

None of the above 85.05 256 Total 100 301

Source: Author’s survey (2011)

Physical factors

Level of water supply in respondents’ area

47

The research on assessment of water supply in respondents’ locality shows that the sources of

water supply available are abundant. A high cumulative of 77.77 percent believes that water

supply is very abundant or abundant in their area while less than 13 percent said that water supply

is fairly abundant. Only 13.29 percent affirms that they do not have access to abundant water

supply.

Table 16: Level of water supply in respondents’ area

Level of water supply % Frequency Very abundant 40.20 121

Abundant 33.55 101 Fairly abundant 12.96 39 Not abundant 13.29 40

Total 100 301 Source: Author’s survey (2011)

Infrastructure

Respondent’s rating of road accessibility in their area

Research data shows that approximately 18 percent of the respondents agrees that the area is

highly accessible while more than half of the respondents (66.11%) enjoys road accessibility.

13.62 percent agree that accessibility is fair in their locality and only 2.33 percent of the

respondent affirmed that their locality is not accessible. It can be said therefore that the study area

is well-connected and accessible.

Table 17: Respondent’s rating of road accessibility in their area

Road accessibility % Frequency Highly accessible 17.94 54

Accessible 66.11 199 Fairly accessible 13.62 41 Not accessible 2.33 7

Total 100 301 Source: Author’s survey (2011)

48

4.2 ANALYSIS OF SATELLITE IMAGERY OF THE STUDY AREA

Mosaics of land uses in 1986 and 2002 are presented in figures 16 and 17 respectively. Major

land use land cover in figure 16 include built-up areas, light forest, dense forest, bare

land/cultivation, water body, gallery forest and bare rocks. Figure 17 which present the land use

map of the study area in 2002 shows that a lot of changes have taken place.

Table 18 shows the land use mosaics of Akure as they were in 1965 and 1986 while Table 19

presents that of 1986 and 2002. Table 20 presents the rate of land use change.

4.2.1 DISCUSSION OF FINDINGS

Table 18 present the changes in land use pattern in Akure during 1965 to 1986. In 1965 more than

three-quarter of the land (cumulative 96.92 per cent) in the study area was under forest and less

than 3 per cent was covered by bare rock. Only a small part of the area was under built-up.

Looking at the data derived from the topographic map based on the 1965 aerial photograph, it will

be discovered that vegetation cover was very prominent represented by 166,307.79 ha of dense

forest. There was less environmental degradation because the population of Akure by that time

was low and economic demand particularly on the utilization of fuel was less compared to what it

is today. In addition, agricultural production during the period was majorly on tree crop such as

cocoa, kola nut, oil palm among others and there is need to preserve these trees for maximum

yielding. Although timbers were used for decking houses at the time, fewer buildings require

decking and plants are used for roofing.

In 1986, the proportion of area under forest declined by 14.75 per cent from 96.96 percent to

82.17 percent. On the other hand, built-up area increased to 1.62 per cent and bare land rose to

13.54 percent showing increase in agricultural activities. There was hardly any change in water

body and bare rock land-use categories. The forest reserve in 1965 had become light forest in

1986 as a result of deforestation.

49

Table 18: Land use sizes for 1965 and 1986 (Area in ha).

Land use class 1965 % 1986 % Built-up 1,344.94 0.78 2797.20 1.62

Bare rock 3,953.66 2.29 4568.19 2.64 Bare land 27.0 0.02 23397.79 13.53

Dense forest 166,307.79 96.23 12850.17 7.43 Gallery forest - - 1502.11 0.87 Light forest - - 127737.37 73.87 Water body - - 80.61 0.05

Forest reserve 1,189.29 0.69 - - Total 172822.66 100 172861.44 100 Source: Topographic map of 1965 and classified satellite image of Akure for 1986.

From the change detection analysis of the 1986 and 2002 land use maps of Akure, a

drastic change in land use has been observed (Chart 1). The most affected of all the land uses was

the light forest which reduced from 73.87 per cent of the total study area in 1986 to 63.98 per cent

in 2002. The rate of reduction here has been noted to be higher than any other land use type

within the study area (Table 20). The rate of this negative change has been determined as 0.62 per

cent per year. If current rate of reduction in the light forest area is allowed to continue, there may

be no light forest again in the area in the next 50 years, jeopardizing the need of the future

generation and causing greater harm to the environment.

Forest removal has been observed as one of the key factors enhancing global warming that

is threatening the environment. Plants make use of carbon dioxide (a greenhouse gas) during

photosynthesis thereby reducing its concentration in the atmosphere. Hence, forest removal

should be discouraged as much as it is possible. Whereas the light forest experienced a reduction

50

in size between 1986 and 2002, the reverse was the case for bare land/cultivation. This is the case

as most of the conversions of light forest to other land uses are done in order to provide arable

land for cultivation. Hence, bare land increased in size from 23397.79ha in 1986 to 40396.22ha in

2002. The data indicate that built-up area gained 4261.38ha from the bare land which in turn

increased by 16,998.43 into the light forest. This shows that as the built-up is increasing into the

bare land, light forest is being encroached by the bare land.

Table 19: Land use sizes for 1986 and 2002 (Area in ha).

Land use class 1986 % 2002 % Built-up 2,797.20 1.62 7,058.58 4.08

Bare rock 4,568.19 2.64 4,029.47 2.33 Bare land 23,397.79 13.53 40,396.22 23.36

Dense forest 12,850.17 7.43 9,977.33 5.77 Gallery forest 1,502.11 0.87 783.99 0.45 Light forest 127,737.37 73.87 110,592.73 63.96 Water body 80.61 0.05 80.03 0.05

Total 1728.61 100 172918.35 100 Source: Classified satellite imagery of 1986 and 2002

Table 20: Rate of land use change of Akure between 1986 and 2002

Land use class

Percentages according to year

% change

Areal change (ha)

Rate of change

1986 2002 Percent per year

Area per year (ha)

Built-up 1.62 4.08 +2.46 +4,261.38 0.15 266.34 Bare rock 2.64 2.33 -0.31 -538.72 0.02 33.67 Bare land 13.53 23.36 +9.84 +16,998.43 1.46 1062.40

Dense forest 7.43 5.77 -1.66 -2,872.84 0.10 179.55 Gallery forest 0.87 0.45 -0.42 -718.12 0.03 44.88 Light forest 73.87 63.96 -9.89 -17,144.64 0.62 1071.54 Water body 0.05 0.05 0.00 -0.58 0.00 0.036

Source: Author’s research (2011)

51

Fig 17: LandUse LandCover Map of Akure in 2002

52

Chart 1: Akure LandUse LandCover Change (1986-2002)

Table 21: Land-use changes in Akure between 2002 and 2009 (Area in ha)

Land use class 2002 2009 Areal change

Built-up 7,058.58 8,839.09 +1,780.5 Bare rock 4,029.47 3,846.92 -182.5 Bare land 40,396.22 6,953.56 -33,442.7

Dense forest 9,977.33 - -9,977.3 Gallery forest 783.99 11,119.07 +10,335.1 Light forest 110,592.73 6,753.15 -103,839.6 Water body 80.03 96.80 +16.77 Grassland - 15,545.43 +15,545.43

Total 172918.35 53,154.02 Source: Classified imagery for Akure 2002 and 2009

53

Table 21 shows the changes in the actual area under different uses during the period

between 2002 and 2009. Nearly 1,780 hectares of land under different land uses was converted to

built-up area during the period while 10,335 hectares of land was added to gallery forest along the

water courses or wetland as a result of natural growth. This is confirmed by the area covered by

the water body which increased by 16.77 hectares in 2009 and serves as sources of water supply

to the forest in the wetland. Area under cultivation or bare land was noticed to have reduced

significantly from 40,396.22 hectares in 2002 to 6,953.56 in 2009 with serious adverse

consequences on food supply. It is pertinent to note that bare rock lost 182.5 hectares of land

majorly to built-up area while light forest declined greatly by 103,839.6 hectares.

Other land uses were also discovered to have changed at very high rates. The rate of population

explosion has also become a dynamic factor of land use change in the area. This has direct effect

on the rate of positive change of the built-up area which has been determined to be 0.15 percent

per year (Table 20). This is clearly illustrated in the map of built-up area of Akure from 1965 to

2002 land use map (Figure 18) which shows significant increase in proportion of built-up area of

Akure during the period from 1344.94 hectares in 1986 to 7058.58 hectares in 2002.

Fig 18: Map of Akure Built-up area from 1965 to 2002

54

The overlaid built-up map shows that Akure is growing radially and along the

transportation route. Nevertheless, it point out some major land use conflicts in the utilization of

available land resources. For example, most arable land in 2002 land use map have been taken

over for residential development by 2009 thereby depriving urban dwellers spaces for urban

agricultural development. This phenomenon could greatly affect the well-being of the people as a

healthy living is a potential tool for a rapid economic development.

Also, it was discovered that less than 15 per cent of the people in the study area were

involved in farming (Table 11) while a higher percentage of the people were discovered to be

involved in activities other than farming. This is as a result of unavailability of arable land for

agricultural production as built up area is encroaching on area under cultivation.

Table 22 shows the trends of population growth of Akure during the period of study using

the built-up area derived from the classified satellite imagery and the population data of Akure

from the National Population Commission. The data shows that within the space of 16 years

population density in Akure reduced from to 3.43 persons per m2 per year in 1986 to 1.81 density

per year. This shows that built-up area increased more rapidly than population growth in the study

area leading to the decrease in density per year during the period while increase in density from

1.81 to 290.71 shows that population increased at a higher rate than the rate of urban expansion

from 2002 and 2009.

Table 22: Trends of Population Growth in Akure, 1965-2009

55

Year Population Built-up area (m2)

Population density (Persons per m2)

Density per year

1965 108,774 1,3449353.25 124 - 1986 142,991 27972003.98 196 3.43 2002 313,751 70585829.28 225 1.81 2009 391,037 883908908.00 2260 290.71 Source: Classified satellite imagery and NPC census results, 2011

4.2.2 SOCIO-ECONOMIC IMPLICATIONS OF URBAN GROWTH I N AKURE

From the findings discussed above, the following socio-economic implications were identified:

TRANSPORTATION

Akure is growing along the transportation routes. From figure 18, Akure-Owo express way,

Akure-Ijare road, Akure-Ado road to the east, north-west and north-east of the map respectively

all witnessed physical developments. This implied that the development of transportation

infrastructure as a factor of growth in the study area will further facilitate the growth of the area.

This will also increase economic activities in the study area. On the other hand, the concentration

of residential developments along these roads has led to the increase in traffic volume experienced

on the roads. This has implication on the efficiency of workers and waste valuable working time.

LOSS OF ARABLE LAND

Findings shows that area under built-up experienced continuous growth while the bare

land/cultivation declined greatly from 2002 and 2009 highlighting the loss of suitable land for

growing crops to residential developments. The natural vegetation has been destroyed to give way

to the expanding residential and commercial houses in the areas. This has negative effect on food

supply and leads to decrease in the share of the population engaging in urban agriculture thereby

compounding the problem of unemployment in the study area. Thus, less than 15% of the

population engages in farming while research data also showed that none of the existing farms are

56

within the Akure neighborhoods. This implied that urban expansion contribute to the problem of

insufficiency of land for farming in the study area with the resultant hike in prices of food items.

The indiscriminate logging of trees in the light forest area of Akure also brings about loss in soil

quality and degradation which are the factors of low agricultural productivity. As the top layer of

soil are removed and exposed to heat and weather conditions through deforestation, the soil loses

its fertility gradually and becomes unproductive.

OVER-UTILSATION OF INFRASTRUTURAL FACILITIES

The settlements that have experienced this growth in the urban fringe have generally suffered the

lack of urban services and lack access roads. The expansion have completely overwhelmed the

area and urban infrastructures have not kept pace with the expansion leading to lack of vital

services as water supply, drainage, roads among others. Where they area available, they are over-

utilized. This is attributed to rapid population increase of the study area.

NOISE POLLUTION AND RISE IN TEMPERATURE

Higher traffic volume along the roads as well as loss of green areas contributes to higher level of

air and noise pollution as well as the rise in temperatures of Akure. These could have costly

implications on the economy of the area.

HOUSING

The expansion of Akure has most likely increased the cost of more accessible lands near the city

centre. This led to high cost of house rent in the study area as confirmed by the field survey which

indicated that about 80 percent of the tenant agrees that urbanization has been the cause of

increase in rent paid especially in the centre. Thus, low income earners are forced to live on less

expensive land at the farthest distances to work which are found in the centre. This has

implications for the affordability of transport for the low income earners with meagre options for

mobility in Akure.

57

LOSS OF TIMBER AND TREE CROPS

The extensive deforestation that occurred in the study area is associated with the shortage of tree

crops of economic value such as cocoa, kolanut, oil palm among others. This is as a result of the

forest land and plantations being cleared to give way for the developmental activities of man. In

addition, forests constitute resources and sources of revenue to government thereby contributing

to the economy of an area. The removal of forest, therefore, means economic loss to Akure.

CHAPTER FIVE

SUMMARY, RECOMMENDATIONS AND CONCLUSION

5.0 Summary of Findings

This study revealed a high rate of land use change in Akure. A major change was noted with

respect to the change from light forest to other land uses between 1986 and 2002 and from other

land uses to built-up between 2002 and 2009. Most of the conversions of light forest to other land

uses between 1986 and 2002 were done in the process of preparing land for planting.

There was an high rate of change of light forest to other land uses. If such rate continues, there

may be no light forest in the catchment in the next 50 years. This is against the concept of

sustainable development and may trigger off other environmental problems like erosion, flooding

and increase carbon dioxide in the atmosphere which could contribute to global warming.

Findings also showed that the conversion of land under different uses to built-up from 2002 to

2009 resulted to loss of arable land under cultivation thereby broken the food supply chain of the

study area with adverse consequences on food supply. This is confirmed by the share of research

population engaging in farming which indicates less than 15 percent.

There is hardly any change in use of bare rock and water between 1965 and 1986 but it was also

discovered that bare rock reduced in size between 2002 and 2009 when the rate of urbanization

increased and rock are used greatly for construction.

58

The information derived from the image analysis gives the pattern and direction growth of Akure

town. This information is valuable in evolving a master plan for the city as it reveals the

constraint areas which could be easily avoided.

It also shows where the rocks are concentrated thereby serving as pointer to potential resources to

be harnessed such as in the location of tourist site for socio-economic development.

It was discovered that there was increase in agricultural activities between 1965 and1986 when

rate of urban growth is at the minimum further buttressing the negative impact of urbanization on

agricultural production.

5.1 Recommendations

Based on the findings of this study, it is recommended that reforestation should commence in

earnest, particularly in forest reserve area and the current ban on indiscriminate logging of wood

from the existing forest should be enforced by the government. This would reduce indiscriminate

felling of trees to encourage sustainable development.

As land use planners, landscape improvement proposals should be included in our land use

proposals to cope with the adverse effects of desertification and man-made destructions on the

environment.

Shelter belt should be introduced and created around the city boundary where trees are

consciously planted and designated as green-belt to prevent further expansion in form of

encroachment on adjoining zones thereby checking the spilling of residential developments to our

farmlands. This will prevent unguided expansion of urban boundary for sustainable development.

Despite their valuable contributions to the economy of a city, forests have been neglected

in planning and agencies working to manage them are poorly funded. Along with this neglect,

forests are subjected to overexploitation, encroachments, illegal felling, and so on, leading to their

degradation. To check forest degradation and fulfill the needs of forest-dependent people,

59

rehabilitating of degraded forest areas and afforestation of wastelands, improving forest

management through involvement of local communities are the strategies that need to be taken

up. As a way of encouraging reforestation, trees should also be planted around our residential

developments to reduce adverse effects of weather and desertification.

It is also obvious that the city is growing at such an alarming rate using up all the green areas and

other forms of soft landscape in the city. There is need therefore to enact development control

policies for sustainable development of the city and the region.

5.2 Conclusion

The analysis above shows that urban expansion was rapid within Akure, at the expense of

vegetal cover. The expansion was observed to be radial. The changes in land use obviously reflect

the pressure on land resources due to rising population. The distressing features are in the form of

considerable increase in land put to non-agricultural uses, rise in built-up area, steep decrease in

area under tree crops and cultivation. Area under non-agricultural use has grown very fast but not

at the cost of cultivable area.

The monitoring of rapid urban growth as evidenced by changes in land use in this study is

a major concern that were frustrated by lack of up to date information on changes in urban land

use. Such information is necessary to provide a basis for a more effective understanding and

management of the urban environment. The approach adopted in this study clearly demonstrated

the potential of GIS and remote sensing techniques in measuring change pattern of urban land

use.

5.3 Contributions to Knowledge

This research has provided necessary information as to the extent of developments from 1965 to

2009 which could form the basis for determination of future pattern and direction of growth and

development in the study area. As earlier stated, in pressured, environmentally sensitive and

60

ecologically important regions such as the study area, there is a continuing need for up-to-date

and accurate land cover information that can be utilized in the production of sustainable land use

policies. This study is one of such efforts aimed at providing information that would aid in

developing sustainable urban land use policies in Akure.

5.4 Area for further study

There is need to carry out future projection of land use change in the study area whereby

the land use changes identified between 1986 and 2002 will be utilize to project into the future

using the appropriate change detection method. This area has been left further research.

61

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APPENDIX

FEDERAL UNIVERSITY OF TECHNOLOGY, AKURE, ONDO STATE SCHOOL OF ENVIRONMENTAL TECHNOLOGY

URBAN AND REGIONAL PLANNING DEPARTMENT

QUESTIONNAIRE

PROJECT TOPIC: SOCIO-ECONOMIC IMPLICATIONS OF URBAN GROWTH ON LANDUSE PATTERN: A CASE STUDY OF AKURE

Dear sir/ma,

This research is strictly for academic purposes and any information provided shall be confidential. Your assistance is highly needed in providing relevant information. Please tick as appropriate, thanks for your anticipated cooperation. SOCIAL-ECONOMIC FACTORS Name of town/Ward number..............

1. Sex (a) Male (b) Female 2. Level of education (a) no formal education (b) primary (c) secondary (d) tertiary 3. Specify your present location/address ....................................................... 4. Monthly income (a) below N20,000 (b) N20,001- 50,000 (c) N50,001- 80,000 (d) 80,001-

100,000 (e) 100,001and above 5. What is your household size? (a) 1-3 (b) 5-7 (c) 8 and above 6. How many house(s) do you have? (a) none (b) 1 (c) 2 (d) 3 (e) more than 3 7. What do you use for cooking? (a) firewood (b) kerosene (c) gas (d) electric power 8. Ownership of house (a) tenant apartment (b) owner-occupier (c) inherited (d)

others............

65

9. How long have you been staying here? (a) less than 3 years (b) 3- 5 years (c) 6- 8 years (d) 3-10 years (e) 10 years and above

10. Rent of property per annum (a) #10,000-40,000 (b) #40,001-70,000 (c) #70,001-10,000 (d) Above #100,000

11. Occupation? (a) Farming (b) Trading (c) Artisan (d) Civil service (e) others (specify)..................................

12. Where is your workplace? (a) industrial district (b) farm/forest reserves (c) offshore (d) market (e) ministry/offices (f) others specify....................................

13. Cost of land acquisition (a) below N50,000 (b)N50,000- N100,000 (c) N100,000- N 150,000 (d) above N150,000

14. Specify cost of construction (a) below N200,000 (b) N200,000- N500,000 (c) N500,000- N1million (d) above N1million

LAND USE Change of use

15. Identify the type of building you live in (a) Courtyard (b) Brazilian type (c) Flat (d) Duplex

16. Specify the present use of building (a) residential (b) commercial (c) industrial (d) recreational (e) mix use (f) educational (g) others specify.....................

17. Original use of building (a) residential (b) commercial (c) industrial (d) recreational (e) educational (f) others specify.......................

18. Specify factors within this area influencing land use change (a) land value (b) easy accessibility (c) available infrastructure (d) aesthetic value (e) others specify.....................

19. Which one of the landuses has increased significantly in area over time? (a) institutional (b) residential (c) commercial (d) recreational (e) industrial (f) others specify

20. Effects of this land use change on surrounding environment (a) improves housing condition (b) improve economic activities (c) constitute nuisance and pollution (d) aesthetic value (e) others specify……………..

Agricultural land use 21. How long have you been farming? (a) Less than 5 years (b) 6-10 years (c) 11-15 (d) 16-20

years (e) Above 20 years 22. Where is your farm located? (a) within compound (b) within neighbourhood (c) outskirt

Akure (d) none of the above 23. Specify agricultural produce? (a) Cash crops (b) Food Crops (c) Livestock Feed (d) Others

(specify)………………………………. 24. Specify size of farmland? (a) below 1ha (b) 1-5ha (c) 6-10ha (d) 11ha & above 25. Where do you sell the farm produce? (a) market (b) offices (c) industry/factory (d) along

the road (e) in the house (f) others specify……………… PHYSICAL FACTORS Water

66

26. What is your source of water supply? (a) Well (b) Stream (c) Pipe borne water (d) Borehole

27. What is the level of water supply? (a) very abundant (b) abundant (c) fairly abundant (d) not abundant

HUMAN FACTORS Infrastructures

28. How will you rate road accessibility of your area (a) highly accessible (b) accessible (c) fairly accessible (d) not accessible

29. Which of the following establishments present in your area? (a) ministries/agencies (b) companies (c) industries (d) others specify……………

30. Which of the following describe services present in your area? (a) transportation/park (b) communication (c) educational institutions (d) health services (e) financial (f) others specify……………

31. How far is the facility mentioned above to your area (a) below 1km (b) 1-2km (c) 3-4km (d) 5km and above

SOCIO-ECONOMIC IMPLICATIONS

32. In your own opinion, what problems can you associate with the growth of the area (a) housing problem (b) environmental problem (c) crime rate (d) transportation problem (e) others specify……………..

33. How does the urban expansion of Akure affected you in terms of rent paid/collected (a) increased (b) decreased (c) unchanged (d) none of the above

34. What is your mode of disposal? (a) dumping into nearby stream (b) Burning (c) Incinerator (d) Refuse collection point

35. Which of the following urban growth implications is more pronounced in your area? (a) air and water quality (b) deforestation (c) food security loss (d) others specify...................

36. How has the urban growth/expansion of Akure affected you positively .............................................................................................................

67

FEDERAL UNIVERSITY OF TECHNOLOGY, AKURE, ONDO STATE SCHOOL OF ENVIRONMENTAL TECHNOLOGY

URBAN AND REGIONAL PLANNING DEPARTMENT

QUESTIONNAIRE

PROJECT TOPIC: SOCIO-ECONOMIC IMPLICATIONS OF URBAN GROWTH ON LANDUSE PATTERN: A CASE STUDY OF AKURE

QUESTIONNAIRE FOR SAW-MILLERS AND TRUCK-OWNERS Social-Economic Factors

1 Sex (a) Male (b) Female 2 Age (a) 18-25 (b) 26-35 (c) 36-55 (d) 56 and above 3 Level of education (a) no formal education (b) Primary (c) Secondary (e) Tertiary 4 Monthly income (a) below N20,000 (b) N20,001- 50,000 (c) N50,001- 80,000 (d) 80,001-

100,000 (e) 100,001and above 5 What is your household size? (a) 1-4 (b) 5-6 (c) 7 and above 6 How many house(s) do you have? (a) none (b) 1 (c) 2 (d) 3 (e) above 3 7 What do you use for cooking? (a) firewood (b) kerosene (c) gas (d) electric power 8 Ownership of house (a) tenant apartment (b) owner-occupier (c) inherited (d)

others............ Forestry

9 How many timber truck(s) do you have? (a) 1-5 (b) 6-10 (c) 11-15 (d) 16 and above 10 Specify the capacity of your truck (a) 5-10 tones (b) 11-20 tones (c) 21-30 tones (d) 31-40

tones (e) above 40 tones 11 How many loads of timber do you carry per day? (a) 1 (b) 2 (c) 3 (d) 4 (e) 5 and above 12 How much is a tree stand (a) #1000-3000 (b) #3001-5000 (c) #5001-7000 (d) 7001-9000

(e) above #9000

68

13 How much do you realize per load? (a) below #5000 (b) #5001-10,000 (c) #10,001-15,000 (d) above #15,000

14 How many tree(s) do you fell per day on the average? (a) 1 (b) 2 (c) 3 (d) 4 (e) 5 and above

15 From where do you source your timber? (a) Construction sites (b) Akure farmland (c) Akure forest reserves (d) Akure suburbs (e) outside Akure

16 How often do you fell trees (a) daily (b) weekly (c) fortnightly (d) monthly (e) seasonally 17 Specify the impact of forest/tree products on your livelihood? (a) raw materials (b) income

(c) food supply (d) others specify………………….