Land cover transition and fragmentation of River Ogba catchment in Benin City, Nigeria

See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/329139952
Land cover transition and fragmentation of River Ogba catchment in Benin City,
Nigeria
Article  in  Sustainable Cities and Society · February 2019
DOI: 10.1016/j.scs.2018.11.022
CITATIONS
0
READS
14
2 authors:
Glory Enaruvbe
African Regional Institute for Geospatial Information Science and Technology (forme…
8 PUBLICATIONS   47 CITATIONS
SEE PROFILE
Ozien Atafo
Regional Centre for Training in Aerospace Surveys (RECTAS)
2 PUBLICATIONS   6 CITATIONS
SEE PROFILE
All content following this page was uploaded by Glory Enaruvbe on 04 December 2018.
The user has requested enhancement of the downloaded file.

Accepted Manuscript
Title: Land cover transition and fragmentation of River Ogba
catchment in Benin City, Nigeria
Authors: G.O. Enaruvbe, O.P. Atafo
PII: S2210-6707(18)30621-8
DOI: https://doi.org/10.1016/j.scs.2018.11.022
Reference: SCS 1347
To appear in:
Received date: 11 April 2018
Revised date: 14 November 2018
Accepted date: 15 November 2018
Please cite this article as: Enaruvbe GO, Atafo OP, Land cover transition and
fragmentation of River Ogba catchment in Benin City, Nigeria, Sustainable Cities and
Society (2018), https://doi.org/10.1016/j.scs.2018.11.022
This is a PDF ﬁle of an unedited manuscript that has been accepted for publication.
As a service to our customers we are providing this early version of the manuscript.
The manuscript will undergo copyediting, typesetting, and review of the resulting proof
before it is published in its ﬁnal form. Please note that during the production process
errors may be discovered which could affect the content, and all legal disclaimers that
apply to the journal pertain.

Land cover transition and fragmentation of River Ogba catchment in Benin City, Nigeria
G. O. Enaruvbe* and O. P. Atafo
African Regional Institute for Geospatial Information Science and Technology (formerly RECTAS),
Obafemi Awolowo University, Ile-Ife, Nigeria.
*Correspondence author’s email/Mobile: enaruvbe@gmail.com/+234-705-5579-003
Highlights
1. The study compares the intensity of land cover change at intervals of 14 years from 1988 and
2016
2. Accelerating urban growth is leading to unsustainable and undesirable social, economic and
environmental consequences in cities
3. Increasing landscape complexity and fragmentation is a threat to habitat conservation and
sustainable ecosystem management in protected areas near cities
Abstract
This study examined variation in the intensity of land cover transition and the pattern of habitat
fragmentation of River Ogba catchment. Landsat images of 1988, 2002 and 2016 were classified into
five categories: low density urban, high density urban, mixed vegetation, agriculture and dense forest
using maximum likelihood classifier. Intensity analysis approach and landscape metrics were used to
analyze the changes and fragmentation of the land cover. Number of patches, largest patch Index, area-
weighted shape index and Euclidean nearest neighbour were computed using FRAGSTATS v 4.2. The
results show that although mixed vegetation accounted for the largest land cover category in 1988 and
2002, low density urban dominated the study area in 2016. Intensity analysis of land cover change in
the study area indicates a rising trend. The urban fringe is observed to be highly dynamic zone and this
is primarily driven by changes in agriculture, low density urban and mixed vegetation. The implications
of rapid land cover transition and fragmentation in River Ogba catchment, and especially in the urban
fringe, include threat to biodiversity, food supply and deteriorating environmental conditions. This
study provides necessary insights for developing sustainable strategies for urban landscape planning,
administration and governance.
1
A
CCEPTED
M
A
N
U
SCRIPT

Keywords: Urban sprawl; tropical rainforest deforestation; Intensity analysis; fragmentation; Benin
city, Nigeria.
1. Introduction
Scientists have been occupied by the challenges posed by rapid land cover change in the rainforest
ecosystem for decades. Deforestation, habitat degradation and fragmentation are common trends in
many tropical countries in sub-Sahara Africa, southeast Asia and Latin America (Gaveau et al., 2016;
Sarathchandra et al., 2018; Tutu Benefoh et al., 2018). Population growth rate is high and cities are
projected to expand rapidly in these countries. Much of these changes are caused by a desire to satisfy
economic, social, cultural and political needs of societies (Lambin et al., 2018; Mertens et al., 2000;
Rudel, 2007; van Soest, 1998). In fact, United Nations (2016) estimate indicates that approximately 60%
of the population in many African countries are expected to reside in cities by 2050. This is expected to
cause rapid urban expansion and increase the pressure on nearby ecosystems (Buffa et al., 2018;
Enaruvbe and Ige-Olumide, 2015; Ordway et al., 2017; Satterthwaite, 2009).
The proximity of human settlements such as large cities to natural landscapes aggravates rainforest
deforestation and alters the status of biodiversity (Nagendra et al., 2015; Tomaselli et al., 2012;
Wadduwage et al., 2017). Landscape structure is a product of complex interactions between physical,
biological, social, economic and political processes (Apan et al., 2002). Studies suggest that urban
development affects the diversity and ecological functions of surrounding landscapes. For instance,
landscape changes affect wildlife and vegetation (Buffa et al., 2018; Chamberlain et al., 2016;
Melland et al., 2018; Munroe et al., 2007), air pollution, water quality and climate conditions (Buffa et
al., 2018; Çakir et al., 2007; 2009). Although the rainforest provides essential ecosystem goods and
services and accounts for more than half of all biological diversity on earth, the impacts of
deforestation and fragmentation on the tropical rainforest ecosystem remain high (Skole and Tucker,
1993; Turner, 2004).
2
A
CCEPTED
M
A
N
U
SCRIPT

The region around Benin city is rich in a variety of plant and animal species. This prompted the British
colonial administration to document forest resources and subsequently, created many forest reserves
around the city in the early 1900s (Thompson, 1911). Gelle Gelle, Okomu, Iguobazuwa and Ogba
forest reserves were created to conserve the biological diversity of the region (Jones, 1955; Kalu et al.,
2010; Li et al., 2015; Morgan and Moss, 1965; von Hellermann and Usuanlele, 2009). Ogba forest
reserve was established in 1945 (Iyawe, 1989) as a management strategy to reduce deforestation and
loss of biodiversity in Ogba district. However, population pressure on forest resources, land
management policies (Enaruvbe and Atafo, 2016; von Hellermann, 2011), urban and agricultural land
expansion and infrastructure development in the region have resulted in rapid forest degradation,
fragmentation and loss in recent decades (Odjugo et al., 2015). Although studies have shown that
population increase and rapid urban growth influence the pattern of land cover around cities
(Akintunde et al., 2016; Li et al., 2001), little attention has been paid to the impact of urban growth on
forest degradation and fragmentation in the urban fringe and especially in protected landscapes close to
large cities in rainforest ecosystems.
Quantifying landscape patterns is critical for evaluating habitat dynamics in an ecosystem (Li et al.,
2001). Landscape evaluation provides critical indicators for ecological health in an area. A number of
tools and methods exist for analyzing the patterns and processes of land cover change. These tools are
important because of the impacts of land cover change on environmental variables such as climate
change and biodiversity. The methods of Intensity Analysis (Aldwaik and Pontius, 2012) and
FRAGSTATS (Mcgarigal and Mark, 1995) have gained wide acceptance among scientists (Enaruvbe
and Pontius Jr., 2015; Li et al., 2001; Ordway et al., 2017) for land change and ecological analyses.
River Ogba catchment occupies about 60% of Benin city; covering the Benin Airport, National
Museum, Government Reservation Area (GRA) and hosts Ogba Zoo and Botanical Garden. Even
though Ogba Zoo and Botanical garden were intended to provide a safe haven for plant and animal
3
A
CCEPTED
M
A
N
U
SCRIPT

species, nevertheless, urban expansion, logging and agricultural practices continue to encroach into the
reserve. This encroachment is leading to habitat loss and degradation which threatens biodiversity in
the reserve. This study therefore seeks to quantify variation in the intensity of land cover transition, and
determine the pattern of habitat fragmentation in River Ogba catchment to gain insight into the pattern
and processes of landscape change and fragmentation around rapidly expanding cities. This will
provide information for scientists, planners and decision-makers to develop sustainable strategies for
urban and regional planning, administration and governance
2. Materials and methods
2.1. The study area
The study area, River Ogba catchment covering an area of 737.85km2
, is one of two River catchments
that drain Benin City. River Osiomo catchment, in the northeastern part of the city, is a larger
catchment but drains about 30% of Benin City. It extends to the northern part of Edo State and as far as
Delta State at some point. River Ogba catchment drains more than 60% of Benin City (Figure 1). The
catchment drains a large portion of Ogba forest reserve which has been largely degraded by human
activities such as agriculture or replaced by plantation forestry (Iyawe, 1989). Some common tree
species in the area include Terminalia ivonrensis, Strombosia grandiflora, and Newbouldia leavis.
The United Nations (2016), estimate shows that the population of Benin City was 1.543 million
inhabitants in 2016 and this is expected to increase to 2.667 million by 2030. The climate of the area is
moist tropical rainforest with two distinct seasons: dry and rainy season. The rainy season starts in
March and terminates in November with a short dry season in August. The dry season is between
December and March. Mean annual rainfall of about 2000 mm is common and mean maximum and
minimum temperature is 28°C and 23°C respectively. Relative humidity of up to 90% may be observed
in the area around September (Iyawe, 1989; Odjugo et al., 2015).
4
A
CCEPTED
M
A
N
U
SCRIPT

Figure 1: The study area and location of River Ogba catchment in Edo State, Nigeria
2.2. Image preprocessing and land cover classification
Cloud-free Landsat images of the study area that coincided with the dry season were obtained from the
archives of the United State Geological Surveys website at an interval of 14years from 1988 to 2016.
The images are Landsat TM image of January 6, 1988, ETM+
of January 28, 2002 and OLI_TIRS
image of December 28, 2016. Radiometric calibration, atmospheric corrections and dark object
subtraction were performed on all the images. These procedures are important to ensure that all
changes are at the earth surface and not due to atmospheric conditions, sensor variation or solar
illumination differences (Munroe et al., 2007).
On the basis of data generated from field survey conducted between December 2016 and January 2017,
supervised image classification using maximum likelihood classification algorithm was applied in
ENVI 5.1 to classify the study area into five land cover categories. The categories are high-density
5
A
CCEPTED
M
A
N
U
SCRIPT

urban area, low-density urban area, agriculture, mixed vegetation and dense forest. High-density urban
area includes the highly developed and often congested city center. This largely unplanned part of the
city is composed of the central business district (CBD) where major businesses, low-quality housing
and administrative activities are concentrated. The low-density urban area has some of the better-
planned parts of the city with good quality housing particularly in the government reservation area
(GRA). This area gradually merges into the chaotic sprawl (Fathizad et al., 2017) in the outskirt of the
city. Agriculture category is composed of areas dominated by cultivation, oil palm plantations and open
spaces outside the city characterized by sparse buildings. Mixed vegetation is composed of secondary
forest and abandoned agricultural land with pockets of degraded forest. The dense forest areas include
rainforest with layers of dense tree canopies in most places. A subset of the data collected from field
survey were combined with existing maps and google earth image data to assess the accuracy of maps
derived from image analysis.
2.3 Intensity and Fragmentation Analyses
Intensity Analysis and landscape fragmentation analysis depend on post-classification categorical
image maps at two or more time points. Consequently, successive image pairs (1988-2002 and 2002-
2016) were used to generate transition matrix of each interval in Idrisi Selva software. The transition
matrix is the only data required for the computation of Intensity Analysis. It was computed using the
Intensity Analysis program available at https://sites.google.com/site/intensityanalysis/free-computer-
programs. Intensity analysis approach was computed at three levels: interval, category and transition
levels following the method of Aldwaik and Pontius (2012). Landscape fragmentation of River Ogba
catchment was computed using a selection of FRAGSTATS’ class level metrics using FRAGSTATS v
4.2 software (McGarigal, 2015). Although landscape fragmentation analysis proposed by Mcgarigal
and Mark (1995) is at three levels - patch, class and landscape - studies have shown that the level of
analysis depends on the specific environment and the purpose of the study (Cushman et al., 2008;
6
A
CCEPTED
M
A
N
U
SCRIPT

Munroe et al., 2007). Therefore, in this study, fragmentation analysis was restricted to four commonly
evaluated metrics at the category level for quantification of different meaningful aspects of
fragmentation. Landscape metrics that were measured are number of patches (NP), largest patch Index
(LPI), area-weighted shape index (SHAPE_AM) and Euclidean nearest neighbor (ENN) (Table 1)
Table 1: Description of landscape metrics
Metrics Formula Description
Number of patches (NP) Number of patches of the corresponding
patch type
(Class)
Largest patch index (LPI)
The area (m2
) of the largest patch of the
corresponding patch type divided by the
total landscape area (m2
) multiplied by
100 to convert to percentage
Area-weighted shape index
(SHAPE_AM)
Patch perimeter (m) divided by the
square-root of patch area (m2
) adjusted by
a constant to adjust for a square standard
Euclidean nearest neighbor
(ENN)
ENN equals the distance to the nearest
neighbor patch of the same type, based on
shortest edge-to-edge distance,
computed from cell center to cell
center.
Source: (McGarigal, 2015)
3. Results
3.1. Accuracy of land cover maps
Remote sensing and GIS tools are increasingly being integrated into the planning and management of
the urban landscape and ecological systems in many countries (Feng et al., 2018; Kamusoko, 2017).
However, the accuracy of the maps derived from remote sensing data limits their usefulness and
influences the quality of planning and management decisions based on these maps. Shao and Wu
7
A
CCEPTED
M
A
N
U
SCRIPT

(2008), argued that the accuracy of maps derived from remote sensing influence the outcome of
landscape quantification and our understanding of landscape processes. Many studies have proposed
that accuracy of 85% is adequate for maps derived from remote sensing data (Kamusoko and Aniya,
2007). In this study, we used 30% of the data collected during fieldwork for the assessment of map
accuracy. Enaruvbe and Pontius Jr. (2015), highlighted the difficulty of obtaining field information
from the distance past and suggested that accuracy assessment should be based on information derived
from fieldwork in the current year. This necessitated the use of the same set of field data for accuracy
assessment of all the maps (1988, 2002 and 2016) in this study. Table 2 shows that the producer’s
accuracy of agriculture and user’s accuracy of mixed vegetation is about 77%. This slightly lower
accuracy levels underscore the challenge of accurately separating land cover classes with similar
spectral signatures, such as mixed vegetation and agriculture, especially in the distance past. For
instance, Çakir et al. (2007), reported user’s accuracy of 73.3% and 56.6% for degraded forest and
forest openings categories respectively in 1975 maps of Maçka State forest in Turkey derived from
Landsat data. However, in this study, the overall accuracy is 96.02% in 2016, 93.64% in 2002 and
88.51% in 1988.
Table 2: Accuracy of land cover maps of River Ogba catchment, Benin City
Class
Producer's
accuracy
(%)
User's
accuracy
(%)
Producer's
accuracy
(%)
User's
accuracy
(%)
Producer's
accuracy
(%)
User's
accuracy
(%)
2016 2002 1988
Dense forest 99.39 99.76 96.34 97.95 97.48 99.38
Agriculture 87.67 96.13 94.87 88.49 76.64 89.21
Mixed vegetation 96.98 92.54 87.40 92.60 88.07 76.51
Low density urban 99.81 91.82 98.67 93.33 96.68 87.62
High density urban 99.65 100.00 98.76 99.69 99.03 99.03
Overall classification accuracy: 2016 = 96.02%; 2002 = 93.64%; 1988 = 88.51%
3.2. Land cover change in River Ogba catchment
8
A
CCEPTED
M
A
N
U
SCRIPT

Urbanization is a major environmental issues in many cities around the world. The problem of rapid
urban growth is particularly severe in the urban fringe of developing countries where urban
development is largely unplanned, informal and uncoordinated. The drivers of rapid urban growth may
therefore be characterized by spatial and temporal variation. For instance, Braimoh and Onishi (2007)
used binary logistic regression to model the probability that a given location was converted to
residential or commercial/industrial land use in Lagos between 1984 and 2000. They noted that access
to land may be a more important driver of residential land use change in Lagos, Nigeria than population
increase. This access is much easier in the fringe than in the city center in most cases. Similarly,
Simwanda and Murayama (2017), reported rapid urban development in Lusaka, Zambia noting that
urban expansion was more intense in the years after year 2000 than in the 1990s. They also observed
the spatial dependency of informal settlements on commercial and industrial, and planned high density
residential land uses. It is clear that social, economic, cultural and political considerations are important
drivers of land use trends in many countries (Lambin et al., 2018; Mertens et al., 2000).
In this study, land cover change was computed by examining the differences in each land cover
category between two successive years. Figure 2 shows the spatial pattern of land cover in the
catchment over the period of study. Figure 3 shows that although mixed vegetation accounted for the
largest land cover category in 1988 and 2002, low density urban dominated the study area in 2016. On
the other hand, dense forest decreased at moderate proportion and agriculture declined slightly. Odjugo
et al. (2015), reported rapid growth in the size of Benin city between 1987 and 2013.
9
A
CCEPTED
M
A
N
U
SCRIPT

Figure 2: Spatial pattern and proportion of land cover in River Ogba catchment, Benin City:
(a) 1988; (b) 2002 and (c) 2016
10
A
CCEPTED
M
A
N
U
SCRIPT

Figure 3: Land cover pattern in River Ogba catchment, 1988-2016
3.3 Intensity of land use transition in River Ogba catchment
In this study, we adopted the approach of intensity analysis in investigating variation in the intensity of
land cover change in River Ogba catchment. The approach provides a method for detailed analysis of
land use change in an area. Aldwaik and Pontius (2012), described intensity analysis as an accounting
methods for the analysis of categorical maps in two or three time points. The method uses the transition
matrix as input to analyze changes in land cover in progressive details at interval, category and
transition levels. The row totals in a traditional transition matrix indicate the categorical land cover in
the initial time and the column totals indicate the land cover by categories in the subsequent time. The
transition matrix is used to compute the net change of each category. However, Manandhar et al.
(2010), noted that the traditional transition matrix approach is inadequate for measuring changes in a
landscape. The intensity analysis approach extends the traditional transition matrices beyond the size of
each category of landscape transition. It reveals systematic landscape changes by highlighting deviation
of observed patterns of land cover change from random patterns of change. In this study, we compared
the intensity of land cover change at intervals of 14 years from 1988 and 2016. To quantify variation in
the intensity of land cover transition, we analyzed intensity at three levels: interval, category and
11
A
CCEPTED
M
A
N
U
SCRIPT

transition intensity (Pontius Jr et al., 2004), using image pairs of 1988 - 2002 and 2002 - 2016. In
general, the intensity of land cover change in the study area is on the rise. Figure 4 shows that intensity
of land cover change was less than the expected uniform annual change (2.45%) in 1988 - 2002 interval
but it rose above the uniform annual change in the interval between 2002 – 2016 which suggests a
rapidly changing landscape.
Figure 4: Interval Intensity (%) of land cover transition in River Ogba catchment
Figure 5 shows that low density urban category has a consistently high intensity of gain and lose in the
first (1988 – 2002) and second (2002- 2016) intervals. This suggests that the low density urban area is a
highly dynamic zone and may determine the pattern and direction of urban growth in the catchment.
This is in line with Omuta (1985), who asserted that the urban fringe in Benin city is an unsustainable
environment because of its uncoordinated growth pattern and it attracts various incompatible land uses.
Figure 6 and 7 present the transition of land cover categories during the first and second intervals of the
study respectively. In general, high density urban and low density urban growth is consistently derived
from mixed vegetation and changes in mixed vegetation and agriculture is driven by low density urban.
In addition, agriculture is a major contributor to changes in dense forest. However, with the exception
12
A
CCEPTED
M
A
N
U
SCRIPT

of dense forest which show a similar level of transition, and mixed vegetation that decreased in
transition intensity, there has been a general increase in the intensity of land cover change in the study
area. For instance, uniform intensity of low density urban increased from 0.59% in 1988 – 2002
interval to 1.14% in 2002 - 2016 interval. The results of this study provide evidence of a highly
dynamic urban fringe zone in River Ogba catchment.
Figure 5: Category Intensity (%) of land cover change in River Ogba catchment
13
A
CCEPTED
M
A
N
U
SCRIPT

Figure 6: Transition Intensity (%) at Ogba River catchment, Benin City, during 1988 – 2002
14
A
CCEPTED
M
A
N
U
SCRIPT

Figure 7: Transition Intensity (%) at Ogba River catchment, Benin City, during 2002-2016
15
A
CCEPTED
M
A
N
U
SCRIPT

3.4 Landscape fragmentation in River Ogba catchment
Landscape fragmentation involves changes in the composition and configuration of the landscape over
time. In examining landscape composition and configuration of River Ogba catchment, we computed
four commonly used class-level metrics (Number of patches, largest patch index, area-weighted shape
index and mean Euclidean nearest neighbour) using FRAGSTATS v 4.2 (McGarigal, 2015). The
overall accuracy of the input maps is above 85% in all the cases. Shao and Wu (2008), posited that the
outcome of fragmentation analysis depends on the accuracy of the input maps. In general, 85% overall
map accuracy is accepted as adequate for landscape analysis (Ancog and Ruzol, 2015). The results of
our analysis show that the landscape of River Ogba catchment is generally more fragmented,
characterized by more complex dense forest and more dispersed fragments in 2016 than was the case in
1988. The largest patch index of agriculture and dense forest increased appreciably during the study
period. In contrast, however, largest patch index of high density urban, low density urban and mixed
vegetation reduced sharply (Table 3). Although the dense forest patches are relatively larger in 2016
than in 1988, the dense forest patches are more complex and also more isolated in 2016.
Table 3: Class metrics of land cover in River Ogba catchment
Metrics NP LPI
(%)
SHAPE_AM ENN MN (m)
Year 1988 2002 2016 1988 2002 2016 1988 2002 2016 1988 2002 2016
Agriclture 1734.00 1730.00 3449.00 3.40 5.55 11.84 26.31 25.67 15.46 114.41 118.93 106.63
Dense forest 889.00 1471.00 1284.00 0.95 1.39 2.19 13.97 17.02 21.90 97.89 92.84 99.12
High density
urban
3074.00 1329.00 1084.00 8.94 8.56 6.17 17.63 18.83 16.80 118.46 120.72 93.13
Low density
urban
12140.00 5332.00 6309.00 5.74 1.45 3.30 63.73 11.82 18.89 85.19 95.03 79.93
Mixed
vegetation
7081.00 4735.00 2480.00 10.63 6.68 2.15 58.42 25.48 26.60 83.73 97.83 81.60
NP = Number of patches; LPI = Largest patch index; SHAPE_AM = Area-weighted shape index; ENN = Euclidean nearest neighbour
4. Discussion
16
A
CCEPTED
M
A
N
U
SCRIPT

We examined variation in the intensity of land cover transition, and determine the pattern of habitat
fragmentation of River Ogba catchment between 1988 and 2016 using Intensity analysis approach and
landscape metrics. Our results showed that although mixed vegetation accounted for the largest land
cover category in 1988 and 2002, low density urban dominated the study area in 2016 (Figure 3). This
suggests urban sprawl is dominant in River Ogba catchment. Omuta (1985), highlighted the
environmental consequences of the urban-rural fringe of Benin city. Apart from environmental
consequences such as deforestation, waste management and pollution, urban sprawl pose social and
economic threats in many cities. These include access to farmland, availability, and in many cases, lack
of basic social infrastructure and services, increasing social inequality and poverty. The urban poor are
deprived access to farmland which invariably worsen their social status. Agricultural land is usually
targeted for urban development. This is because urban land use tends to have higher return per unit of
land than agricultural produce (Livanis et al., 2006). Ancog and Ruzol (2015), observed that urban
expansion threatened the means of livelihood in the fringe of urban areas in the Philippines. Feng et al.
(2018), also reported that urban expansion usually leads to the conversion of agricultural land to urban
use. In addition, the growth of urban sprawl results in urban slums, solid waste management and lack
basic social infrastructure which results in increasing inequality in the urban area. Also, the proximity
of the city threatens biodiversity in protected areas. For instance, Buffa et al. (2018), in a recent study
showed that the pattern of local species richness was sensitive to the structure and composition of the
surrounding landscape. Similarly, Chamberlain et al. (2016), suggested that urbanization had a
negative impact on birds species richness leading to a reduction in species richness from the rural to
urban sites in Kampala, Uganda. There are also indications that human-dominated landscapes may
interact with fragmentation to threaten, agriculture, vegetation and wildlife (Fazal, 2000; Laurance et
al., 2000).
In this study, we compared the intensity of land cover change at intervals of 14 years from 1988 to
2016. In general, the results of intensity analysis of land cover change in the study area indicate a rising
17
A
CCEPTED
M
A
N
U
SCRIPT

pattern of change. Although the intensity of land cover change was slow in the first interval, this is not
the case in the 2002-2016 interval where the intensity of land cover change is faster than expected
(Figure 4). Figure 5 suggests that urban expansion appears to be the major contributor to land cover
change in River Ogba catchment. Low density urban area is observed to be highly dynamic zone and
this is primarily driven by changes in agriculture, low density urban and mixed vegetation. This rapid
change in land cover in the urban fringe is a threat to biodiversity and sustainable urban planning and
management.
The results of our analysis show a generally more fragmented landscape. The shape index of dense
forest is more complex and more dispersed in 2016 than it was in 1988. The largest patch index of
agriculture and dense forest increased appreciably during the study period. In contrast, however, largest
patch index of high density urban and mixed vegetation reduced sharply (Table 3). This result agrees
with findings reported by Yu and Ng (2007), who found higher fragmentation in the urban fringe than
in the city center. The impact of urban sprawl on food supply is also highlighted in China by He et al.
(2017). The findings of this study suggests that the increasing landscape fragmentation, which appears
to be driven principally by urban sprawl, will most likely lead to further loss of dense forest, agriculture
and mixed vegetation in the face of increasing urbanization. This will also increase the pressure on
Ogba forest reserve. The implications of rapid fragmentation in the catchment, and especially in the
urban fringe, include threat to biodiversity, food supply and deteriorating environmental conditions. In
line with previous studies, urban expansion targets agriculture and mixed vegetation while agriculture
expansion is pushed to dense forest areas (Enaruvbe and Pontius Jr., 2015). This study however,
extends Enaruvbe and Pontius Jr. (2015) and Odjugo et al. (2015) by examining the pattern of
fragmentation resulting from rapid land cover transition of River Ogba catchment.
Although this study has determined the intensity of land cover transition and fragmentation of River
Ogba catchment between 1988 and 2016, land cover change and fragmentation affects several others
aspects of the social and economic environment. Land cover change could affect air and water quality,
18
A
CCEPTED
M
A
N
U
SCRIPT

microclimate and hydrology processes, soil conditions and human health and comfort index (Cao et al.,
2007; Carlson and Arthur, 2000). Therefore, future studies should focus on the impacts of land cover
change and fragmentation on these aspects.
5. Conclusion
We determined the intensity of land cover change and the pattern of fragmentation of River Ogba
catchment in 1988, 2002 and 2016 using intensity analysis approach and landscape metrics. We
identified the variation in the intensity of land cover transition, and determined the pattern of habitat
fragmentation in the catchment. Mixed vegetation accounted for the largest land cover category in 1988
and 2002 in River Ogba catchment while low density urban dominated the landscape of the study area
in 2016. Rapid land cover transition in River Ogba catchment suggests limited access to farmland, poor
basic social infrastructure in the urban fringe, increasing social inequality and biodiversity loss in the
catchment. The rapid land cover transition in the catchment may be attributed to a rapidly growing
urban population. The rapid increase of the low density urban area of the catchment results in the
displacement of agriculture and mixed vegetation by urban land use while indirectly threatening dense
forest in Ogba forest reserve.
The integration of intensity analysis approach and fragmentation metrics in determining the pattern of
land cover change in River Ogba catchment has provided more insight into the pattern and processes of
landscape change and fragmentation around rapidly expanding cities. The study also improves our
understanding of the landscape pattern in River Ogba catchment and other protected areas around
rapidly growing cities. This insight is important for scientists, urban and regional planners and
decision-makers in developing sustainable strategies for landscape planning, administration and
governance in the rapidly changing urban fringe around protected areas.
19
A
CCEPTED
M
A
N
U
SCRIPT

Although we have addressed the impact of land cover transition and fragmentation in an urban
catchment in details, we have not evaluated all aspects of such changes on landscape pattern in River
Ogba catchment. Further work should examine the impacts of land cover change and fragmentation on
microclimate and hydrology processes, soil conditions and human health and comfort index. The
consequences of climate change on landscape patterns should also be examined.
Acknowledgement
We would like to express our gratitude to the editors and anonymous reviewers, who corrected an
initial draft of this manuscript, for their comments and suggestions.
References
Akintunde, J. A., Adzandeh, E. A., and Fabiyi, O. O. 2016. Spatio-temporal pattern of urban growth in
Jos Metropolis, Nigeria. Remote Sensing Applications: Society and Environment, 4, 44-54. doi:
10.1016/j.rsase.2016.04.003
Aldwaik, S. Z., and Pontius, R. G. 2012. Intensity analysis to unify measurements of size and
stationarity of land changes by interval, category, and transition. Landscape and Urban
Planning, 106(1), 103-114. doi: 10.1016/j.landurbplan.2012.02.010
Ancog, R., and Ruzol, C. 2015. Urbanization adjacent to a wetland of international importance: The
case of Olango Island Wildlife Sanctuary, Metro Cebu, Philippines. Habitat International, 49,
325-332. doi: 10.1016/j.habitatint.2015.06.007
Apan, A. A., Raine, S. R., and Paterson, M. S. 2002. Mapping and analysis of changes in the riparian
landscape structure of the Lockyer Valley catchment, Queensland, Australia. Landscape and
Urban Planning, 59, 43–57.
Braimoh, A. K., and Onishi, T. 2007. Spatial determinants of urban land use change in Lagos, Nigeria.
Land Use Policy, 24(2), 502-515. doi: 10.1016/j.landusepol.2006.09.001
Buffa, G., Vecchio, S. D., Fantinato, E., and Milano, V. 2018. Local versus landscape-scale effects of
anthropogenic land-use on forest species richness. Acta Oecologica, 86, 49–56. doi:
10.1016/j.actao.2017.12.002
Çakir, G., Sivrikaya, F., and Keleş, S. 2007. Forest cover change and fragmentation using Landsat data
in Maçka State Forest Enterprise in Turkey. Environ Monit Assess, 137(1-3), 51-66. doi:
10.1007/s10661-007-9728-9
Cao, W., Zhu, H., and Chen, S. 2007. Impacts of urbanization on topsoil nutrient balances—a case
study at a provincial scale from Fujian, China. Catena, 69(1), 36-43. doi:
10.1016/j.catena.2006.04.014
Carlson, T. N., and Arthur, S. T. 2000. The impact of land use — land cover changes due to
urbanization on surface microclimate and hydrology: a satellite perspective. Global and
Planetary Change, 25, 49–65.
Chamberlain, D., Kibuule, M., Skeen, R., and Pomeroy, D. 2016. Trends in bird species richness,
abundance and biomass along a tropical urbanization gradient. Urban Ecosystems, 20(3), 629-
638. doi: 10.1007/s11252-016-0621-6
20
A
CCEPTED
M
A
N
U
SCRIPT

Cushman, S. A., McGarigal, K., and Neel, M. C. 2008. Parsimony in landscape metrics: Strength,
universality, and consistency. Ecological Indicators, 8(5), 691-703. doi:
10.1016/j.ecolind.2007.12.002
Enaruvbe, G. O., and Atafo, O. P. 2016. Analysis of deforestation pattern in the Niger Delta region of
Nigeria. Journal of Land use Science, 11(1), 113-130. doi: 10.1080/1747423X.2014.965279
Enaruvbe, G. O., and Ige-Olumide, O. 2015. Geospatial analysis of land-use change processes in a
densely populated coastal city: the case of Port Harcourt, south-east Nigeria. Geocarto
International, 30(4), 441-456. doi: http://dx.doi.org/10.1080/10106049.2014.883435
Enaruvbe, G. O., and Pontius Jr., R. G. 2015. Influence of classification errors on Intensity Analysis of
land changes in southern Nigeria. International Journal of Remote Sensing, 36(1), 244-261. doi:
10.1080/01431161.2014.994721
Fathizad, H., Tazeh, M., Kalantari, S., and Shojaei, S. 2017. The investigation of spatiotemporal
variations of land surface temperature based on land use changes using NDVI in southwest of
Iran. Journal of African Earth Sciences, 134, 249-256. doi: 10.1016/j.jafrearsci.2017.06.007
Fazal, S. 2000. Urban expansion and loss of agricultural land - a GIS based study of Saharanpur City,
India. Environment and Urbanization, 12(2), 133-149. doi: 10.1177/095624780001200211
Feng, Y., Liu, Y., and Tong, X. 2018. Spatiotemporal variation of landscape patterns and their spatial
determinants in Shanghai, China. Ecological Indicators, 87, 22-32. doi:
10.1016/j.ecolind.2017.12.034
Gaveau, D. L., Sheil, D., Husnayaen, Salim, M. A., Arjasakusuma, S., Ancrenaz, M., . . . Meijaard, E.
2016. Rapid conversions and avoided deforestation: examining four decades of industrial
plantation expansion in Borneo. Sci Rep, 6, 32017. doi: 10.1038/srep32017
He, C., Liu, Z., Xu, M., Ma, Q., and Dou, Y. 2017. Urban expansion brought stress to food security in
China: Evidence from decreased cropland net primary productivity. Science of The Total
Environment, 576, 660-670. doi: 10.1016/j.scitotenv.2016.10.107
Iyawe, J. G. 1989. The ecology of small mammals in Ogba Forest Reserve, Nigeria. Journal of
Tropical Ecology, 5, 51-64.
Jones, E. W. 1955. Ecological studies on the rain forest of southern Nigeria: IV. The plateau forest of
the Okomu forest reserve. Journal of Ecology, 43(2), 564-594.
Kalu, C., Chukwuedo, D. U., and Aghimien, V. 2010. Forest conservation policy in Edo State: An
assessment of the roles of forestry taskforce in realising the set objectives. African Scientist,
11(3), 207-216.
Kamusoko, C. 2017. Importance of Remote Sensing and Land Change Modeling for Urbanization
Studies. 3-10. doi: 10.1007/978-981-10-3241-7_1
Kamusoko, C., and Aniya, M. 2007. Land use/cover change and landscape fragmentation analysis in
the Bindura District, Zimbabwe. Land Degradation & Development, 18(2), 221-233. doi:
10.1002/ldr.761
Lambin, E. F., Gibbs, H. K., Heilmayr, R., Carlson, K. M., Fleck, L. C., Garrett, R. D., . . . Walker, N.
F. 2018. The role of supply-chain initiatives in reducing deforestation. Nature Climate Change,
8(2), 109-116. doi: 10.1038/s41558-017-0061-1
Laurance, W. F., Delamonica, P., Laurance, S. G., Vasconcelos, H. L., and Lovejoy, T. E. 2000.
Rainforest fragmentation kills big trees. Nature, 404, 836.
Li, L., Dong, J., Njeudeng Tenku, S., and Xiao, X. 2015. Mapping Oil Palm Plantations in Cameroon
Using PALSAR 50-m Orthorectified Mosaic Images. Remote Sensing, 7(2), 1206-1224. doi:
10.3390/rs70201206
Li, X., Lu, L., Cheng, G., and Xiao, H. 2001. Quantifying landscape structure of the Heihe River Basin,
north-west China using FRAGSTATS. Journal of Arid Environments, 48(4), 521-535. doi:
10.1006/jare.2000.0715
21
A
CCEPTED
M
A
N
U
SCRIPT

Livanis, G., Moss, C. B., Breneman, V. E., and Nehring, R. F. 2006. Urban sprawl and farmland prices.
American Journal of Economics, 88(4), 915–929.
Manandhar, R., Odeh, I. O. A., and Pontius, R. G. 2010. Analysis of twenty years of categorical land
transitions in the lower hunter of New South Wales, Australia. Agriculture, Ecosystems &
Environment, 135(4), 336-346. doi: 10.1016/j.agee.2009.10.016
McGarigal, K. 2015. FRAGSTAT 4.2 Help. University of Massachusetts, Amherst.
Mcgarigal, K., and Mark, B. J. 1995. FRAGSTATS: Spatial pattern analysis program for quantifying
landscape structure. Washinton D.C.: USDA Forestry Service Technical Report PNW-351.
Melland, A. R., Fenton, O., and Jordan, P. 2018. Effects of agricultural land management changes on
surface water quality: A review of meso-scale catchment research. Environmental Science &
Policy, 84, 19-25. doi: 10.1016/j.envsci.2018.02.011
Mertens, B., Sunderlin, W. D., Ndoye, O., and Lambin, E. F. 2000. Impact of Macroeconomic change
on deforestation in South Cameroun: Integration of Household survey and remotely-sensed data.
World Development, 28(6), 983-999.
Morgan, W. B., and Moss, R. P. 1965. Savanna and Forest in Western Nigeria. Africa: Journal of the
International African Institute, 35(3), 286-294.
Munroe, D. K., Nagendra, H., and Southworth, J. 2007. Monitoring landscape fragmentation in an
inaccessible mountain area: Celaque National Park, Western Honduras. Landscape and Urban
Planning, 83(2-3), 154-167. doi: 10.1016/j.landurbplan.2007.04.001
Nagendra, H., Mairota, P., Marangi, C., Lucas, R., Dimopoulos, P., Honrado, J. P., . . . Blonda, P. 2015.
Satellite Earth observation data to identify anthropogenic pressures in selected protected areas.
International Journal of Applied Earth Observation and Geoinformation, 37, 124-132. doi:
10.1016/j.jag.2014.10.010
Odjugo, P. A. O., Enaruvbe, G. O., and Isibor, H. O. 2015. Geospatial approach to spatio-temporal
pattern of urban growth in Benin City, Nigeria. African Journal of Environmental Science and
Technology, 9(3), 166-175. doi: 10.5897/ajest2014.1715
Omuta, G. E. D. 1985. Land use and environmental dereliction in the urban fringe: The case of Benin
City, Bendel State, Nigeria. Socio-Economic Planning Sciences, 19(5), 303-311.
Ordway, E. M., Naylor, R. L., Nkongho, R. N., and Lambina, E. F. 2017. Oil palm expansion in
Cameroon: Insights into sustainability opportunities and challenges in Africa. Global
Environmental Change, 42, 190–200. doi: 10.1016/j.gloenvcha.2017.10.009
Pontius Jr, R. G., Shusas, E., and McEachern, M. 2004. Detecting important categorical land changes
while accounting for persistence. Agriculture, Ecosystems & Environment, 101(2-3), 251-268.
doi: 10.1016/j.agee.2003.09.008
Rudel, T. K. 2007. Changing agents of deforestation: From state-initiated to enterprise driven processes,
1970–2000. Land Use Policy, 24(1), 35-41. doi: 10.1016/j.landusepol.2005.11.004
Sarathchandra, C., Dossa, G. G. O., Ranjitkar, N. B., Chen, H., Deli, Z., Ranjitkar, S., . . . Harrison, R.
D. 2018. Effectiveness of protected areas in preventing rubber expansion and deforestation in
Xishuangbanna, Southwest China. Land Degradation & Development, 29(8), 2417-2427. doi:
10.1002/ldr.2970
Satterthwaite, D. 2009. The implications of population growth and urbanization for climate change.
Environment and Urbanization, 21(2), 545-567. doi: 10.1177/0956247809344361
Shao, G., and Wu, J. 2008. On the accuracy of landscape pattern analysis using remte sensing data.
Landscape Ecology, 23, 505-511. doi: 10.1007/s10980-008-9215-x
Simwanda, M., and Murayama, Y. 2017. Integrating Geospatial Techniques for Urban Land Use
Classification in the Developing Sub-Saharan African City of Lusaka, Zambia. ISPRS
International Journal of Geo-Information, 6(4), 102. doi: 10.3390/ijgi6040102
Skole, D., and Tucker, C. 1993. Tropical deforestation and habitat fragmentation in the Amazon:
Satellite data from 1978 to 1988. Science, 260, 1905-1910.
22
A
CCEPTED
M
A
N
U
SCRIPT

Thompson, H. N. 1911. The forests of southern Nigeria. Journal of the Royal African Society, 10(38),
121-145.
Tomaselli, V., Tenerelli, P., and Sciandrello, S. 2012. Mapping and quantifying habitat fragmentation
in small coastal areas: a case study of three protected wetlands in Apulia (Italy). Environ Monit
Assess, 184(2), 693-713. doi: 10.1007/s10661-011-1995-9
Turner, I. M. 2004. The Ecology of trees in the tropical rainforest. Cambridge, UK: Cambridge
University Press.
Tutu Benefoh, D., Villamor, G. B., van Noordwijk, M., Borgemeister, C., Asante, W. A., and
Asubonteng, K. O. 2018. Assessing land-use typologies and change intensities in a structurally
complex Ghanaian cocoa landscape. Applied Geography, 99, 109-119. doi:
10.1016/j.apgeog.2018.07.027
United Nations. 2016. The World’s Cities in 2016: Data Booklet (ST/ESA/ SER.A/392) (Department
of Economic and Social Affairs Population Division, Trans.) (pp. 29). Italy, Rome.
van Soest, D. 1998. Tropical deforestation: An economic perspective. The Netherlands: Labyrint
Publications.
von Hellermann, P. 2011. Things fall apart? Management, environment and Taungya farming in Edo
State, southern Nigeria. Africa, 77(03), 371-392. doi: 10.3366/afr.2007.0052
von Hellermann, P., and Usuanlele, U. 2009. The owner of the land: The Benin Obas and colonial
forest reservation in the Benin division, southern Nigeria. The Journal of African History,
50(02), 223. doi: 10.1017/s002185370999003x
Wadduwage, S., Millington, A., Crossman, N. D., and Sandhu, H. 2017. Agricultural land
fragmentation at urban fringes: An application of urban-to-rural gradient analysis in Adelaide.
Land, 6(2), 1-18. doi: 10.3390/land6020028
Yu, X. J., and Ng, C. N. 2007. Spatial and temporal dynamics of urban sprawl along two urban–rural
transects: A case study of Guangzhou, China. Landscape and Urban Planning, 79(1), 96-109.
doi: 10.1016/j.landurbplan.2006.03.008
23
A
CCEPTED
M
A
N
U
SCRIPT
View publication statsView publication stats

Land cover transition and fragmentation of River Ogba catchment in Benin City, Nigeria

Recommended

Recommended

More Related Content

What's hot

What's hot (20)

Similar to Land cover transition and fragmentation of River Ogba catchment in Benin City, Nigeria

Similar to Land cover transition and fragmentation of River Ogba catchment in Benin City, Nigeria (20)

Recently uploaded

Recently uploaded (20)

Land cover transition and fragmentation of River Ogba catchment in Benin City, Nigeria