Analysis of deforestation pattern in the Niger Delta region of Nigeria

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Analysis of deforestation pattern in the
Niger Delta region of Nigeria
Glory O. Enaruvbe
a
& Ozien P. Atafo
a
a
Regional Centre for Training in Aerospace Surveys (RECTAS),
Obafemi Awolowo University, Ile-Ife, Nigeria
Published online: 03 Oct 2014.
To cite this article: Glory O. Enaruvbe & Ozien P. Atafo (2014): Analysis of deforestation pattern in
the Niger Delta region of Nigeria, Journal of Land Use Science, DOI: 10.1080/1747423X.2014.965279
To link to this article: http://dx.doi.org/10.1080/1747423X.2014.965279
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Analysis of deforestation pattern in the Niger Delta region of Nigeria
Glory O. Enaruvbe* and Ozien P. Atafo
Regional Centre for Training in Aerospace Surveys (RECTAS), Obafemi Awolowo University, Ile-Ife,
Nigeria
(Received 2 November 2013; final version received 1 September 2014)
This study examines deforestation in a part of the Niger Delta region of Nigeria
between 1987 and 2013 using remote sensing data and geographic information sys-
tems. The results indicate an increasing rate of deforestation, an increasing rate of
water body loss and a decreasing rate of exposed surface/settlement increase. The
results also show that the rate at which cropland/secondary vegetation is increasing is
above average while sandbar is increasing below average. The implications of these
changes include environmental degradation resulting from loss of biodiversity and loss
of forest goods and services. These losses can also affect the social and economic
conditions of local people. The study recommends that policies and strategies targeted
at slowing down the rate of forest loss and degradation should be pursued.
Keywords: tropical forest; deforestation; remote sensing; geographic information
systems; Niger Delta; Nigeria
1. Introduction
The constant quest for economic growth and social development has resulted in an
increased demand for resources. This demand implies that man would have to resort to
measures that may have negative impacts on the natural environment such as clearing the
forest for infrastructure development or food production, mining and deep-sea probes.
Etter, McAlpine, Phinn, Pullar, and Possingham (2006) posited that the human impact on
forest resources are majorly influenced by spatial characteristics such as proximity to
roads and settlements, distance to forest edges, soil fertility, rainfall pattern, topography,
and population growth and density. Government policies, economic processes and the
level of economic development are other factors that influence deforestation (Angelsen &
Kaimowitz, 1999). The World forest resources have continued to reduce primarily because
of anthropogenic impacts driven by the need to meet the demand for industrial and social
development necessary for economic growth. Goldewijk and Ramankutty (2004) noted
that the rate of deforestation increased globally between 1700 and 1990. The impacts of
large-scale forest depletion are particularly severe in parts of South-East Asia, sub-
Saharan Africa and South America.
Natural forest depletion, especially in the tropical region, has been of major concern to
conservationist and governments because of its rapid rate of change and widespread
nature (Read & Lam, 2002). In spite of this, much of the estimates derived for tropical
forest loss have remained controversial (Agarwal, Silander, Gelfand, Dewar, &
Mickelson, 2005; Etter, McAlpine, Wilson, Phinn, & Possingham, 2006). Several studies
*Corresponding author. Email: enaruvbe@rectas.org
Journal of Land Use Science, 2014
http://dx.doi.org/10.1080/1747423X.2014.965279
© 2014 Taylor & Francis
Downloadedby[OAREConsortium]at09:5603October2014

have highlighted the importance of the tropical forest and its rapid rate of depletion
(Kaimowitz, Mendez, Puntodewo, & Vanclay, 2002; Linkie, Smith, & Leader-Williams,
2004; Read & Lam, 2002; Zipperer, 1993). Skole and Tucker (1993) asserted that the
tropical forest is home to more than half of all plants and animal species despite the fact
that it occupies less than 7% of the terrestrial surface. Deforestation therefore poses a
serious threat to biological diversity and is a primary cause of the present extinction crisis
(Wilcove, McLellan, & Dobson, 1986; Wilcox & Murphy, 1985).
Studies have shown that the loss of forest resources has negative consequences on the
quality of the environment (Angelsen & Kaimowitz, 1999; Goldewijk & Ramankutty,
2004; Ludeke, Maggio, & Reid, 1990). In order to increase our understanding of the
impact of rapid deforestation on environmental systems, information is needed on the
pattern of change as well as the social and physical factors that lead to the observed
changes (Lambin, 1997).
The need for environmental protection and forest conservation is even more critical in
the Niger Delta region of Nigeria, which is characterised by sensitive ecosystems
(Akegbejo-Samsons & Omoniyi, 2009; Fabiyi, 2011). However, most studies on environ-
mental change in Nigeria has focused on urban sprawl (Balogun, Adeyewa, Balogun, &
Morakinyo, 2011; Mahmud & Achide, 2012; Njike, Igbokwe, & Orisakwe, 2011; Obiefuna,
Idris, & Uduma-Olugu, 2011), while others analysed specific plant species and human
impacts on forest resources (Aweto, 1990; Azeez, Ikponmwonba, Popoola, & Amusa, 2010;
Mmon & Arokoyu, 2010; Onuche, 2010). Also, apart from Mengistu and Salami (2007),
who investigated land use/land cover change and detection in a part of southwestern
Nigeria, and Fabiyi (2011), who examined the impact of change actors on vegetation
loss, research on the pattern of deforestation in the Niger Delta region of Nigeria seems
to be lacking. The objectives of this study therefore are to analyse the pattern of deforesta-
tion and determine the rate and dimension of deforestation in the study area.
2. Conceptual and theoretical framework
2.1. The concepts of deforestation and land degradation
Lambin, Rounsevell, and Geist (2000) asserted that land cover includes the physical,
biological, and human components of the Earth’s surface and immediate subsurface. Land
use entails the exploitation of the land cover by man in satisfying his social, cultural,
spiritual, economic and physical needs.
The need for social, economic and cultural development by man may cause a stress on
environmental resources. A manifestation of this stress is deforestation, which is the term
most often used in describing the threat to the tropical forest (Phillips, 1997).
Deforestation is the conversion of forest land for uses other than its natural cover, e.g.
cultivation, mining, and human structures such as urban infrastructure (FAO, 2000). FAO
(2000) further noted that deforestation occurs when the tree canopy cover falls below a
minimum percentage threshold of 10%. Geist and Lambin (2004) observed that defor-
estation is an important contributor to global environmental change. However, the rate of
deforestation varies within and between regions in response to policy and institutional
failure and economic, technological, cultural and population factors (Geist & Lambin,
2002; Rolfe, Bennett, & Louviere, 2000). Tropical deforestation, often depicted as an
important environmental concern, is the cause of biodiversity and ecosystem loss, soil
degradation and vulnerability of people and places to climate change and socio-economic
disturbances (Lambin, Geist, & Lepers, 2003).
2 G.O. Enaruvbe and O.P. Atafo

Though deforestation is a commonly cited cause of tropical forest depletion, it is only
one of the threats confronting the tropical forest. Habitat fragmentation has also been
identified as a key factor in tropical forest depletion (Phillips, 1997; Skole & Tucker,
1993). Also closely related to the concept of deforestation is forest degradation, which is
the reduction of the quality and quantity of forest cover because of human activities (FAO,
2000). Lambin (1999) noted that forest degradation, usually caused by an increase in
forest disturbances, is a process capable of significantly altering the tropical forest density
or structure of vegetation cover leading to a lower species composition and productive
capacity of the forest. There has been an increase in forest disturbances and deforestation
in the Niger Delta because of the increasing population pressure on the natural environ-
ment, the need for fertile agricultural land and the quest for more forest products and
services required for industrial raw materials (Enaruvbe & Ige-Olumide, 2014; Fabiyi,
2011), resulting in the degradation of forest and land resources in the region. Land
degradation entails soil erosion and fertility decline. This implies a decline in the usability
of the land for productive purposes such as agriculture (Lambin, 1997), which may also
necessitate the clearing of more forest land for agricultural purpose.
Studies (Darwin, Tsigas, Lewandrowski, & Raneses, 1996; Kaimowitz et al., 2002)
have discussed the importance of the tropical forest, while the impacts of environmental
change and conversion were highlighted by Lambin et al. (2000). These impacts include
quality of water, land and air resources, ecosystem processes and services, and climate
conditions. Tropical forest conservation is therefore an important way of mitigating the
increasing impacts of climate change on man and his natural environment. Changes in
land use/land cover and the need for biodiversity conservation are two critical issues that
have gained attention in tropical forest research in recent years. There has, however, been
intense debate on the most appropriate approach to adopt in tropical forest conservation
(Bawa & Seidler, 1998; Terborgh, 2000).
2.2. Land use and land cover change models
The increasing awareness of the role of the tropical forest on the status of climate
condition, biodiversity and net primary production, local-to-global land status and eco-
system, and other valuable functions have prompted research interests in land use/land
cover change (Turner II et al. 2001). Because of this, a number of models have been
formulated to improve our understanding of land use/land cover change processes. These
models serve as tools for supporting the analysis of the causes and consequences of land
use dynamics and provide a basis for planning and policy formulation (Verburg, Schot,
Dijst, & Veldkamp, 2004).
Land use models have been categorised based on the scale and methods of analysis
(Angelsen & Kaimowitz, 1999; Verburg et al., 2004). Angelsen and Kaimowitz (1999),
for instance, categorised deforestation models into three classes: analytical, simulation
and empirical models. They noted that the models exist at three distinct levels: micro-
economic models, regional models and macroeconomic models. Lambin et al. (2003)
also identified three categories of land use/land cover models. However, in spite of the
similarities in the characteristics of these models and those of Angelsen and Kaimowitz
(1999), Lambin et al. (2003) used different terms in describing their models. They
referred to the categories as agent-based models, the systems models and the narrative
models. They noted that each of the approaches evaluates the impact of the interactions
between macro-structure and micro-agency in different ways. It has been noted that
Journal of Land Use Science 3

explanatory variables appear to follow a consistent pattern with scale (Veldkamp &
Lambin, 2001).
The aim of land use/land cover models is to provide answers to the questions of
where, when and why land use/land cover changes take place (Angelsen & Kaimowitz,
1999; Lambin et al., 2000; Veldkamp & Lambin, 2001). Studies, however, show that
quantitative data on where, when and why land use/land cover changes take place on a
global scale are still incomplete (Lambin, 1997). Angelsen and Kaimowitz (1999) asserted
that the factors affecting deforestation or other land use/land cover change, the interaction
between them and the extent of their impacts vary significantly from one location to
another. Also, a high geographic variation in land cover and variation in biophysical and
socio-economic drivers of land use/land cover changes as well as institutional structure
exist (Etter, McAlpine, Phinn, et al., 2006; Serneels & Lambin, 2001; Veldkamp &
Lambin, 2001). This variation in geographic situations reduces the predictive ability of
current land use/land cover model (Angelsen & Kaimowitz, 1999; Verburg, de Koning,
Kok, Veldkamp, & Bouma, 1999). This has, therefore, resulted in the need to adopt
different approaches in the modelling of land use and environmental changes in order to
gain a better understanding of the processes at work in shaping the observed changes. The
approaches adopted include agent-based approach, the system approach and the narrative
approach.
2.2.1. The agent-based approach
The focus of the agent-based approach is on individual land use decision-making process.
Lambin et al. (2003) noted that the approach represents the motivation behind decisions
and the external factors that influence land use decisions. The approach applies the rule of
economic decision-making processes in developing land use/land cover change models.
These models are broadly divided into two groups: spatially explicit and non-spatially
explicit models (Irwin & Geoghegan, 2001). Spatially explicit land use/land cover models
focus on disaggregate patterns of land use/land cover change and can identify critical
areas of land use/land cover change. The models provide insight into the pattern and
factors driving the observed changes (Verburg et al., 1999). Irwin and Geoghegan (2001)
categorised spatially explicit models of land use/land cover change into simulation
models, estimation models and hybrid models. Verburg et al. (1999), however, referred
to the categories as explorative and predictive models. Explorative (simulation) models
aim at designing alternative land use pattern using optimisation techniques based on
biophysical potentials and socio-economic variables. Projective (estimation) models, on
the other hand, provide an understanding of the processes of possible changes in land use
in the near future as a function of driving forces (Agarwal et al., 2005). These predictions
provide insights into the extent and location of land use/land cover changes (Agarwal
et al., 2005; Serneels & Lambin, 2001).
Lambin et al. (2000) categorise land use/land cover models into four groups: empiri-
cal-statistical, stochastic, optimisation and dynamic (process-based) simulation models.
There is, however, consensus among researchers on the need for integrated land use/land
cover models in which the elements of the various modelling approaches are combined in
ways that most adequately provide answers to specific research questions.
Sun, Forsythe, and Waters (2007) simulated land use pattern in Calgary, Canada, using
the Markov chain and cellular automata analysis based on land use and transportation
interactions. They observed that the model reveals the manner in which land use is likely

to evolve in relation to transportation system and that this interaction may have encour-
aged the increasing rate of urban sprawl in Calgary between 1985 and 2001.
Serneels, Said, and Lambin (2001) observed that land use decisions, which may be the
outcome of individual farmer’s decisions, influence the pattern of land use change in the
Mara wildlife reserve in Kenya. They described the resulting spatial pattern as an outer
ring, characterised by centripetal expansion of cultivation that is causing a compression of
the spatial systems, an inner ring, which acts as a buffer zone and is subjected to
rangeland modification, and the reserve, which is relatively stable.
The goal of non-spatially explicit models is to explain the equilibrium land use pattern
resulting from the aggregate decision-making processes of individual actors. Irwin and
Geoghegan (2001) grouped the models as microeconomic models and other economic
factors across regions. The non-spatial explicit model tend to ignore the spatial component
in predicting the pattern of land use/land cover change as it assumes that space is a
‘featureless plane’ where the only consideration is distance to the central business district.
Ogunleye, Adeola, Ojo, and Aduradola (2004), for example, used statistical analysis in
evaluating the pattern of forest depletion in Olokomeji forest reserve in southwestern
Nigeria. They observed that farming activities in Olokomeji forest reserve have resulted in
changes in the vegetation composition in the reserve.
2.2.2. The system approach
The system approach examines the influence of institutional and organisational arrange-
ment of society on land use/land cover change pattern. These institutions, including
cultural, local, regional and national agencies, operate interactively at different spatial
and temporal scales and therefore link local conditions to global processes (Lambin et al.,
2003). The system perspective evaluates the impact of changes in economic systems on
environmental conditions.
Geist and Lambin (2002) showed that at the underlying level, public and individual
decisions are largely a result of changes in the national, regional or global economic
opportunities and/or policies mediated by local-scale institutional factors. However, prox-
imate factors including regional modes of agricultural expansion, wood extraction and
infrastructure expansion are important determinants of deforestation in an area. They
noted the difficulty in modelling tropical deforestation at the global scale, but suggested
that a detailed understanding of the complex relationship between proximate and under-
lying factors is required prior to any form of policy intervention. They identified eco-
nomic factors, institutional/policy factors, technological factors, cultural/sociopolitical
factors and demographic factors as determinants of tropical deforestation which are
beyond individual control.
Angelsen and Kaimowitz (1999), however, question the role of population growth,
poverty reduction, national income, economic growth and foreign intervention on tropical
deforestation, noting that claims by scholars that these factors are the cause of deforesta-
tion is ambiguous because the processes involved are complex macroeconomic processes
and isolating the nature of their effects on deforestation is a difficult task. They posit that
agricultural prices, prices of agricultural inputs and credit, wages and off-farm employ-
ment, technological progress in agriculture, accessibility and road, property regime and
tenure security as well as timber prices are the immediate causes of deforestation while
population pressures, income level and economic growth, external debt, trade and struc-
tural adjustment as well as indirect effects of technological changes are the underlying
causes. Armenteras, Rudas, Rodriguez, Sua, and Romero (2006), for instance, observed

that changes in the Colombian Amazon forest are related to land use history as well as
environmental and historical socio-economic factors. They stated that these factors may
be responsible for the higher deforestation rate in the area than in the rest of the Amazon.
Mertens, Sunderlin, Ndoye, and Lambin (2000) examined the impact of macroeco-
nomic changes on deforestation pattern in south Cameroon. They showed that macro-
economic changes in Cameroon played a fundamental role in the way land use practices
influence the forest cover. They noted that a strong relationship exists between increased
deforestation and population growth, increased marketing of food crops, modification of
farming systems and colonisation of new agricultural areas in remote forest zones. Yiridoe
and Nanang (2001) analysed the impact of the interlink between various sectors of the
Ghanian economy on the rate of deforestation. They observed that export of forest
products, fuelwood consumption and food crop production directly aggravates deforesta-
tion in the country.
2.2.3. Narrative models
The narrative approach involves the use of historical data interpretation in providing an
understanding of current processes. This approach, which is also referred to as analytical,
is an abstract theoretical construct and does not present empirical evidence to show the
outcome of the analysis (Angelsen & Kaimowitz, 1999). Turner II et al. (2001), for
example, provided a historical account of the land use of the Yucatan peninsular region of
Mexico from the 1960s. They, however, integrated the approach with modern land use
change approach in analysing deforestation pattern of the southern Yucatan peninsular
region.
Though Ickowitz (2006) argues that there is inadequate evidence to show that shifting
cultivation practiced in tropical Africa leads to forest loss, empirical evidence (Akinyemi,
2013; Gobin, Campling, & Feyen, 2002) suggests that agricultural land use plays an
important role in tropical deforestation in Africa. For instance, Serneels and Lambin
(2001) developed a descriptive spatial model in evaluating the impact of changes in the
driving forces of land use/land cover change in the Norak district of Kenya. They noted
that the explanatory variable of the model suggests a von Thunen-like model of agricul-
tural land use. Serneels and Lambin (2001) observed, however, that conversion of land to
agricultural use is controlled by distance to market and agro-climatic variables, while the
expansion of agriculture and settlement were also influenced by land rent.
Economic factors appear to be the primary drivers of land use change. This fact is
evident from the models developed for analysing land use change. The focus of the agent-
based and systems approaches of land use change model is economic consideration that
influences the use of land in a locality, while narrative models depend on historical data in
analysing changes in land use pattern. The economic models are therefore quantitative in
nature while the narrative could be qualitative.
3. Pattern of tropical forest loss in Nigeria
Several studies have examined the scale and pattern of deforestation in various parts of
Nigeria in recent decades with the aim of providing information necessary for environ-
mental and socio-economic planning and management. Salami (1999), Ogunleye et al.
(2004) and Akinyemi (2013), noted that the increasing intensity of agricultural activity
was the main driver of deforestation in southwestern Nigeria. They stated that most of the
native forest was being converted to agricultural land. Salami (1999) particularly observed

that agricultural activity increased the likelihood of savannah incursion in the fringes of
the lowland humid tropical forest of southwestern Nigeria. He showed that forest receded
in the transition zone at 1.90% per annum between 1963 and 1991, while Ogunleye et al.
(2004) reported a low diversity in the Olokemeji forest reserve as a result of farming
activity which has brought about large hectares of impoverished secondary forest, bare
and degraded land, grasslands and plantation of exotic species.
Fanah, Dlama, and Oluseyi (2011), Fabiyi (2006) and Adepoju, Millington, and
Tansey (2006) examined the impact of urban growth on tropical deforestation. Fanah
et al. (2011) observed that increasing population and urban expansion is largely respon-
sible for land degradation including forest loss. They opined that in view of the important
contribution of the forest to environmental health, steps need to be taken to check
indiscriminate forest clearing. Adepoju et al. (2006) noted that urban expansion is a
major threat to the natural forest around the metropolitan Lagos area.
Fabiyi (2011) noted that anthropogenic activities were more important determinants of
vegetation change in the Niger Delta region than the activities of oil exploration and
exploitation companies in the area. Mengistu and Salami (2007) observed that disturbed/
degraded forest constituted the largest land cover type in a part of the southwest of Nigeria
and attributed the condition to increasing population and economic activities.
Effiong (2011) observed that high forest was the most affected land cover category in
the Calabar River catchment in southeastern Nigeria. He noted that forest lost 29.29% at
0.73% per annum in the area. He argued that the current rate of forest loss in the area
needs to be urgently controlled through reforestation efforts. Ogbuene (2010) investigated
the consequences of rainfall variability and deforestation in southeastern Nigeria using
archival rainfall data. He observed that soil erosion, flood disaster, decline in volume of
streams, seasonal drought and increasing carbon emission are some of the consequences
associated with rainfall variability and deforestation in the area. Ogbuene (2010) noted
that afforestation, development of forest and game reserves and application of necessary
adaptation measures may mitigate these environmental challenges.
4. Material and methods
4.1. Study area
The Niger Delta region of Nigeria is situated along the Gulf of Guinea. It is the world’s third
largest and Africa’s largest delta. It is also West and Central Africa’s most extensive wetland
(Akegbejo-Samsons & Omoniyi, 2009). The region extends from Aboh (5°33′49″ N and
6°31′38″ E) in the north to palm point (4°16′22″ N and 6°05′27″ E) in the south. The east-west
limit is between Benin River estuary (5°44′11″ N and 5°3′49″ E) in the west and Imo River
estuary (4°27′16″ N and 7°35′27″ E) to the east (Niger Delta Environmental Survey (NDES),
1997) (see location in Figure 1a). The region is endowed with a rich mosaic of fragile,
sensitive and diverse ecosystems (Akegbejo-Samsons & Omoniyi, 2009; Fabiyi, 2011).
NDDC (2005) observed that the major ecological zones of the area include mangrove forest
and coastal vegetation zone, freshwater swamp forest zone, lowland rainforest zone and the
derived savannah zone found in the northern parts of the region.
Aspects of the geology and hydrology of the study area are reported by Short and
Stauble (1967) and Akpoborie (2011), respectively. The rainfall pattern of the area is
seasonal, characterised by wet season, which lasts between March and October in most
parts of the region except in the coastal area where it rains all year round. The dry season
begins in November and ends in February. A short dry season usually interrupts the wet

season in late July or August. Mean annual rainfall ranges from about 4000 mm in the
coastal areas to 1500 mm in the northern part of the region. Temperatures are generally
high and relatively constant all year round. Average monthly maximum and minimum
temperatures vary from 28°C to 38°C and from 21°C to 23°C, respectively (NDDC,
2005).
The primary economic activities in most of the rural communities in the study area
include peasant farming, petty trading and fishing. Shifting cultivation (slash and burn),
which involves cultivating a piece of land for a number of years and then abandoning it
for a more fertile land, is traditionally practiced in the area. Some cash crops grown in the
area include oil palm (Elaeis guineensis), cacao (Theobroma cacao), cassava (Manihot
esculenta) and rubber (Hevea brasiliensis).
Figure 1. (a) Location map of study area. (b) Land use categories, 1987. (c) Land use categories,
2002. (d) Land use categories, 2013.

4.2. Data acquisition and analysis
Landsat images of 21 December 1987, 28 January 2002 and 10 January 2013, covering
the study area, were obtained from the United States Geological Survey website on Earth
Resources Observation and Science Center. At this period of the year, cloud cover effect is
usually at its lowest. Similarly, errors resulting from seasonal differences are minimised
since the images are acquired within the same season of the year and within a 1-month
interval. The study area is covered by Landsat scene with world reference system Path/
Row 189/056. A window containing 5747 × 4910 pixels was extracted from the scene
covering the study area. The areal extent of the coverage is 22,920 km2
and the
coordinates of the first corner of the window is defined by X = 1,18,185 and
Y = 7,19,775. The area covers parts of Edo, Delta, Anambra, Bayelsa, Imo and Rivers
states (Figure 1). Image composite was obtained using the short wave channel 5, mid-
infrared channel 4 and visible channel 2. Cohen and Goward (2004) showed the impor-
tance of the shortwave infrared channels in characterising vegetation. Salami (2000) noted
the relevance of a combination of channel 4 (red), channel 5 (green) and channel 1 (blue)
for vegetation studies. However, it was observed that a combination of channel 5 (red),
channel 4 (green) and channel 2 (blue) was more effective in discriminating different
vegetal cover types in the study area.
4.3. Image classification and accuracy assessment
The classification scheme developed by Adeniyi (1985) is adopted in this study. However,
there are settlements and exposed surfaces in the study area which were not identified in
Adeniyi (1985) classification scheme due to the study area used in that study. Similarly,
cropland and secondary vegetation are merged into a single class. The exposed surface/
settlement cover type consists of settlements, barren land and cleared forest. Visual image
interpretation techniques of tone, shape, size, texture and pattern of features were used in
identifying training sites for the purpose of supervised image classification. A minimum
of 10 training sites was captured for each of the land cover types so as to improve
classification accuracy. The minimum distance classification algorithm was used for
supervised classification. The classes are settlement/exposed surface, cropland/secondary
vegetation, wetland, water body, sandbar and natural forest. The natural forest includes
matured forest with a dense canopy of 60% or more. The settlement/exposed surface
category refers to cleared land, open space and bare surfaces, and urban or rural settle-
ment. Cropland/secondary vegetation is cultivated or fallow farmland, while wetland
includes swamps and waterlogged areas. Waterbody includes creeks, rivers, and streams,
and sandbar is the sand deposit long rivers and streams. Field visits were conducted to
validate the land use classes and Google Earth image was used in areas that were
inaccessible or could not be visited during the field campaign. The confusion matrix
(Table 1), which is described as the starting point for a variety of descriptive and
analytical statistical techniques for accuracy assessment (Congalton, 1991), was generated
using Idrisi software. The matrix presents a tabulated view of the accuracy level of the
map and was also used in calculating the overall accuracy of the different land use classes
individually and collectively.
Post-classification accuracy assessment of the data shows an overall accuracy of
93.4%, 92.8% and 96.4% in 1987, 2002 and 2013, respectively (Table 2). Congalton
(1991) and Liu, Frazier, and Kumar (2007) noted that the overall accuracy assessment is
perhaps the simplest and most commonly used descriptive statistical technique for

Table1.Errormatrix.
ClassifieddataWetlandWaterbodyForestSandbar
Settlement/exposed
surface
Cropland/secondary
vegetationRowtotal
1987
Wetland33607000343
Waterbody16010000602
Forest11019,9460464426224,683
Sandbar000845390884
Settlement/exposedsurface000021,64016121,801
Cropland/secondaryvegetation0073046334,06834,604
Columntotal34860120,02684522,60638,49182,917
2002
Wetland8900200140924
Forest14038,7140841732746,896
Sandbar00014775901536
Settlement/exposedsurface00471,26,34850351,31,394
Cropland/secondaryvegetation004000145222,55024,402
Columntotal904595439,13814841,28,71434,9122,11,106
2013image
Wetland29,64200025029,667
Forest001,17,940015912,8641,30,963
Sandbar0003627153505162
Settlement/exposedsurface00001,47,81034341,51,244
Cropland/secondaryvegetation001875037133,28,3933,33,981
Columntotal29,64228121,19,81536271,53,2423,44,6916,53,829

measuring the classification accuracy of remote sensing data. It is computed by dividing
the total correctly classified pixels by the total number of pixels in the confusion matrix
(Congalton, 1991).
The processes of deforestation were evaluated using the land use maps derived from
supervised classification for each land use/cover. The result obtained from this process is
divided by the number of years between the land use/cover maps (1987–2013) to arrive at
the annual rate of change.
5. Results
The land use/cover maps of the study area for 1987, 2002 and 2013 (Figure 1b–d) show
the pattern of deforestation. The figures reveal that forest was more visible in the southern
and southeastern portions of the study area. The southern part of the study area is majorly
made up of the mangrove forest, which is common in the creeks of the Niger Delta region
of Nigeria, particularly around settlements such as Bomadi, Patani, Otuoke and Warri
areas. The southeastern part of the study area, however, is composed of the Niger forest
reserve. This area is surrounded by settlements including Aboh, Ossissa and Ndoni among
others. Forest in the western part of the study area is seen majorly along the Osiomo River
and in some forest enclaves in Sakponba area near Benin city. Areas around Igueben
reserve also show dense forest vegetation.
Figure 1b–d, however, shows that the forest continued to decline over the years. This
decline is most visible in the northeastern portion of the study area along the Niger River
where settlement and agriculture have encroached into the forest. Most of the forest in
this area has been converted to cropland and secondary vegetation. A close observation
of Figure 1b–d reveals that the major urban centres in the region such as Aba, Asaba,
Agbor, Benin city, Onitsha and Warri expanded significantly during the period under
study taking up secondary vegetation and cropland in the process. The wetland in the
southwestern part of the study area is observed to be expanding while water body is
declining. Sandbar along the Niger River is also increasing. Apart from the activities of
some oil prospecting companies, it appears that human activities in the wetland area are
limited.
The pattern of change in land use/cover type is shown in Table 3 and Figure 2. Table
3 indicates that the rate of wetland change consistently increased between 1987 and
2013. The table shows that wetland was 633 km2
in 1987, 782 km2
in 2002 and 971 km2
in 2013. This shows an increase of 23.5% (0.02% per year) between 1987 and 2002 and
24.2% (0.05% per year) between 2002 and 2013. The average rate of change for the
entire 26-year period was observed to be 0.02% per year. Similarly, exposed surface/
settlement was 2498 km2
in 1987, 6011 km2
in 2002 and 3463 km2
in 2013, while
cropland/secondary vegetation was 9796 km2
in 1987, 8740 km2
in 2002 and
13140 km2
in 2013. This shows that exposed surface/settlement is increasing at a
decreasing rate. It is, however, increasing at a rate above average. Cropland/secondary
vegetation on the other hand lost at the rate of 0.01% per year between 1987 and 2002.
It, however, gained between 2002 and 2013 at an average rate of 0.03% per year.
Similarly, sandbar showed a consistent rise in the rate of increase between 1987 and
2013. It gained an average of 0.02% per year between 1987 and 2002 and 0.14% per
year between 2002 and 2013.
Water body and forest categories declined during the period of this study. The area
occupied by water reduced from 408 km2
in 1987 to 402 km2
in 2002. The rate of loss
observed for water body was 0.07% per year between 2002 and 2013. This value is

Table2.Accuracyassessment.
LanduseclassReferencetotalClassifiedtotalNumbercorrectNumberwrong
Producer’s
accuracy(%)
User’s
accuracy(%)KIA
1987a
Wetland3433843364897.9687.50.98
Waterbody602601601099.831001.00
Forest24,68320,02619,9468080.8199.600.74
Sandbar884845845095.591000.96
Settlement/exposedsurface21,80122,60621,64096699.2695.730.99
Cropland/secondaryvegetation34,60438,49134,068442398.4588.510.97
Total82,91782,91777,4365517
2002b
Wetland9249248901496.3298.450.98
Waterbody5954595459540100.00100.001.00
Forest46,89646,8963871442482.5598.920.99
Sandbar153615361477796.1699.531.00
Settlement/exposedsurface1,31,3941,31,3941,26,348236696.1698.160.95
Cropland/secondaryvegetation24,40224,40222,55012,36292.4164.590.60
Total2,11,1062,11,1061,95,93315,173
2013c
Wetland29,66729,66729,642099.96100.001.00
Waterbody2812281228120100.00100.001.00
Forest1,30,9631,30,9631,17,940187590.0698.440.98
Sandbar516251623627070.26100.001.00
Settlement/exposedsurface1,51,2441,51,2441,47,810543297.7396.460.95
Cropland/secondaryvegetation3,33,9813,33,9813,28,39316,29898.3395.270.90
Total6,53,8296,53,8296,30,2242,36,055
Notes:a
Overallclassificationaccuracy=93.35%;OverallKappaIndexofAgreement(KIA)=0.90.
b
c

greater than the average rate for the entire period of the study, which was 0.03% per year.
Similarly, forest declined by 2621 km2
between 1987 and 2002, a rate equal to the average
for the entire period of study (0.02% per year). However, the rate of forest loss increased
to 0.04% per year between 2002 and 2013.
6. Discussion
The consequences of tropical deforestation are of major concern to scientists, conserva-
tionists and decision-makers. This concern has resulted in several initiatives and projects
aimed at providing a better understanding of the processes of deforestation (Angelsen &
Kaimowitz, 1999; Lambin et al., 2000). Though there was a decrease in the rate of gain in
exposed surface/settlement category between 2002 and 2013, the average rate of change
was higher than the average for the period of study. The reduction in the rate of change
may be because of a decrease in land preparation for cultivation because of rural urban
migration in search of alternative economic opportunities. Lambin et al. (2001) have noted
that peoples’ response to economic opportunities influence land use change. The overall
high rate of growth in the exposed surface/settlement category could, however, be because
of rapid population increase, worsening economic conditions and rapid urban expansion,
Table 3. Land use/cover gain/loss analysis.
Land use/cover
1987
(km2
)
2002
(km2
)
2013
(km2
)
Average annual rate of change (%)
1987–2002 2002–2013 1987–2013
Wetland 632.94 781.90 970.86 0.02 0.05 0.02
Waterbody 408.02 401.78 106.47 −0.00 −0.07 −0.03
Forest 9522.70 6901.65 5076.26 −0.02 −0.04 −0.02
Sandbar 61.82 83.66 157.81 0.02 0.14 0.06
Exposed
surface/settlement
2498.31 6010.85 3463.04 0.09 0.04 0.01
Cropland/secondary
vegetation
9796.09 8740.05 13140.26 −0.01 0.03 0.01
Figure 2. Land use/cover gain/loss between 1987 and 2013.

which lead to an increase in demand for food production and other ecological goods and
services (Jande & Nsofor, 2004). This therefore seems to suggest that the pattern of
deforestation in the Niger Delta region may largely be driven by urban development and
agricultural activities. Fabiyi (2011) arrived at a similar conclusion in his study of the
influence of change actors’ on land use and land cover change pattern in the Niger Delta
region of Nigeria. Armenteras et al. (2006) also noted that deforestation in the Colombian
Amazon is related to factors such as land use history, environmental and social economic
factors such as oil extraction, cattle ranching or illegal cropping. Furthermore, the result is
similar to results obtained in the northern part of Nigeria where an overall increase in bare
surface was reported (Adeniyi & Omojola, 1999; Eniolorunda & Bello, 2011). The need
for food production may also explain the increased rate of change in cropland/secondary
vegetation. The need for arable land may have resulted in the observed increase in the rate
of forest loss and degradation between 2002 and 2013.
The increasing rate of deforestation in the Niger Delta can also be attributed to
increasing demand for arable land for food production occasioned by increase in popula-
tion. Armenteras et al. (2006) and Eniolorunda and Bello (2011) also reported high
deforestation rates in the Colombian Amazon and Tangaza forest reserve in northwestern
Nigeria, respectively. The demand for food resulting from population increase and the
lack of alternative sources of income, coupled with an increase in the poverty level in the
Niger Delta region, is likely to exert more pressure on the forest and forest resources in
the region. The high rate of deforestation in the Niger Delta, particularly the rapid decline
in mangrove and natural forest, may cause a reduction in biodiversity and a high level of
carbon emission. Though FAO (2005) reported a declining rate of mangrove forest
depletion in the Niger Delta between the 1980s and the 1990s, the result of this study
shows an increasing rate of change in wetland and swamp in the study area. This increase,
however, seems to be closely related to the reduction in the areal extent of waterbody,
which has also resulted in an increase in rate of sand deposited along the Niger River.
Apart from the decline in vegetation diversity caused by forest loss, wildlife and marine
animals are also threatened by rapid environmental changes, particularly in mangrove and
wetland ecosystem. These changes also have implications for food security and supply of
forest goods and services as fertile agricultural land is lost to rapid urban expansion and
infrastructure development. Similarly, local industries such as wood industries and other
industries that depend on forest products are forced to source for raw materials in more
distant areas, which leads to an increase in the cost of production.
Lambin et al. (2003) posited that there is a low rate of forest loss in Africa and South-
East Asia. It, however, appears that this is not the case in the Niger Delta region. The
result of this study reveals an increasing rate of deforestation in the Niger Delta. The
increasing rate of deforestation appears to be because of an increase in human footprint in
the area occasioned by an increase in population and urban expansion. The rate of
deforestation in the area between 2002 and 2013 is higher than the average rate of change
for the entire period of the study.
The increase in deforestation also implies an increased in the rate of biodiversity loss
and an increase in the rate at which forest goods and services are lost in the Niger Delta
region. Forest managers, conservationist, environmentalists, decision-makers and other
stakeholders must therefore evolve strategies that will slow down the rate of deforestation
in the region. Uncontrolled timber harvesting, clearing and cultivation of forest reserves
and unplanned urban expansion must be checked. There is also the need to promote the
adoption of intensive agriculture by providing farming input that will encourage farmers
to cultivate the same piece of land for a long period without the need to abandon it for a

more fertile land. This will slow down the rate of forest conversion to other land uses.
Similarly, policies aimed at restricting the indiscriminate clearing of forest clearing and
bush burning should be formulated and enforced. In the same vein, the forest needs to be
closely monitored to identify ‘deforestation hot spots’ that need more intense and strategic
management approaches that will ensure sustainable use of the available forest resources
in the region.
7. Conclusion
The study has shown that the Niger Delta region has undergone changes in the recent past,
majorly because of human activities. Urban development and agricultural activities seem
to play an important role in land use/cover change in the region, resulting in a high rate of
forest loss and degradation. The increasing rate of forest disturbance observed in the
region requires urgent intervention from forest managers and decision-makers in order to
check the increase in forest loss, which also leads to loss of biodiversity in the Niger
Delta. In addition, farmers should be encouraged to practice intensive agriculture.
It is hoped that the result of this work will serve as guide to natural resource managers
in evaluating the pattern of deforestation. This will serve as a basis for formulating
policies aimed at mitigating forest loss, and by extension, biodiversity loss, in the Niger
Delta region. Furthermore, Geographic Information Systems, which provides a tool for
creating and evaluating scenarios as well as projecting into the future during the planning
process, should be used in natural resources assessment and monitoring.
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