Internationa l Journal of Geosciences, 2014, 5, 50-62
Published Online January 2014 (
Changing Vegetation Patterns in Yobe State Nigeria: An
Analysis of the Rates of Change, Potential Causes and the
Implications for Sustainable Resource Management
Ali I. Naibbi*, Brian Baily, Richard G. Healey, Peter Collier
Department of Geography, University of Portsmouth, Portsmouth, UK
Email: *
Received November 21, 2013; revised December 23, 2013; accepted Jan uary 15, 2014
Copyright © 2014 Ali I. Naibbi et al. This is an open access article distributed under the Creative Commons Attribution License,
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The exploitation of natural resources for timber produc tion, fue lwood use and co nversion to agricultur al land is
increasing to such a n extent that t he sustaina ble use of many areas of the w orld is in doubt. This pape r exami nes
three decades of freely available Landsat satellite images of the northeastern part of Nigeria using a supervised
classification based technique to create maps of vegetation change in Yobe State. The maps are then used to ex-
amine the temporal and spatial aspects of changes which have occurred in the context of previous evidence and
literature. The results indicate that the vegetation of the area has drastically reduced since the 1970’s. However,
as this study show s, the patte r n of these changes is complicated and cannot be e xplained by any single physical o r
anthropogenic causal factor. Similarly, e vidence fro m gro und truthing inve stiga tion indicat es the importa nce of
fuelwood collection to the deforestation process within the region. This article shows the value of an existing re-
mote sensing and image processing methodology for the assessment of vegetation change in developing countries
in relation to the sustainable management of natural resources. The study also discusses the overall change
within the study area and discusses several potential causative factors of the observed patterns of cha nge.
Savanna Vegetation; Yobe St ate; Fuelwood; Remote Sensing; Land Cover; Deforestation; Sustainable
1. Introduction
Sustainable natural resource management is imperative if
we are to maintain the natural resource base for future
generations. Around the world, many natural resources
are being depleted faster than it is possible for nature to
replenish these (see for example [1,2]). Two aspects of
crucial importance in natural resource management are
the need for an inventory of an environmental resource
and informed knowledge of the spatial and temporal pat-
terns of change to this resource [3]. With regard to large
areas of vegetation cover, remote sensing provides an
invaluable tool in the management and assessment of
natural resources (see for example [3,4]). Various tech-
niques using satellite imagery or aerial imagery have
bee n emplo yed to mon ito r change s i n vege tatio n c over in
Africa and around the world. Remote sensing is a partic-
ularly valuable tool in the study of vegetation cover
change over large areas. Platforms such as the Landsat
satellite programme offer freely available, multispectral
data which can be analyzed to examine broader vegeta-
tion change. Analysis of patterns and rates of change
may provide invaluable information for environmental
mana gers and policy maker s.
This study concentrates on land cover and vegetation
chan ge in no rthern N igeri a. The no rther n part o f Nigeria,
which is endowed with a large expanse of arable land,
and a huge potential for crop production is being threat-
ened by bo th cli mate change and man-made deforestation,
which has over the years proven to be the cause of the
major decline in vegetation cover in the region. A very
*Corresponding author.
popular, though unscientific claim, among researchers
and policy makers in Nigeria, is that the country, with a
total area of 923,768 km2, is annually losing about 3500
km2 of its arable land to desertification (encroaching
southwards from the north), leading to demographic dis-
placements in some villages across 11 states in the north
[5]. Yobe state is one of the worst affected areas, and
Odiogor ([5]) quoted the Yobe state governor, Alhaji
Ibrahim Geidam, emphasising that “sand dunes are en-
croaching at a rate of 0.3 km2 annually in the northern
part of Yobe state; taking over villages”. While climatic
change is very popular among environmentalists, as be-
ing the main cause of vegetation decrease in the northern
part of Nigeria, some also believe that the high demand
for b oth agric ultural la nd and fue lwood i n the regio n is a
major contributor to the region’s vegetation decline
([6,7]). Even though the consumption of fuelwood is
undoubtedly high in the region, the work of some earlier
researchers exaggerated the situation. A notable example
is the work of Bdliya (in [7]), who reported that the way
in which vegeta ti o n was bei ng used for fue lwood in so me
areas of Borno and Yobe states, would lead to the ab-
sence of vegetation cover in the area by 2000. This was
demonstrabl y not the case. W hile Bdli ya’s claims did not
materialise, some recent global vegetation modellings
(using Remote Sensing) of Sub-Saharan Africa (SSA)
(between 1982 and 1999—see for example [8] and be-
tween 1981 and 2003—see for example [9]), have re-
vealed that the vegetation of most areas in the region is
increasing (Northern Nigeria included). These two con-
trasting statements require further localised investigation
in order to assess the resilience of the region’s vege tatio n,
which is the aim of this paper.
The study of land cover change in developing coun-
tries has been prioritised by researchers and policy mak-
ers in recent times [10,11]. Land cover studies are essen-
tial for the assessment and management of natural re-
sources and the implementation of management ap-
proaches and policies. There has been little research in
the area of land use and land cover in Nigeria [12] and
the majority of the African countries as a whole. This
was originally highlighted by the Food and Agricultural
Organisation (FAO) in the late 1970’s during its forest
assessment project [13]. The FAO claimed at that time
that there was a lack of sufficient data for the assessment
of the state of the forest areas that could assist in the
identification or prediction of forest areas under threat or
where reserves and shelter belts needed to be located [13].
The vegetation change pattern in Nigeria represents a
unique situation, as highlighted in the 2010 forest as-
sessme nt repor t [4]. Nigeria recorded t he highes t perce n-
tage of forest loss among the ten top countries with the
largest net loss of forest area since 1990 ([4] p. 21). The
report further attributed this loss partly to the high de-
mand and consumption of fuelwood in the country. This
is an i mportant concern in the northern part of the coun-
try where the savanna vegetation prevails [14]. This
study primarily looks at mapping land cover change in
some parts of Yobe state, northeastern Nigeria (Figure 1).
Withi n t his r egio n, the e xplo it atio n of the sava nna regi o n
of Nigeria (see Figure 2) has been declared as unsus-
tainable in terms of meeting its fuelwood demand, on
which the majority of its population depend [14]. As a
consequence, if vegetation studies in this part of Nigeria
are to be implemented, both the past and recent patterns
of vegetation change require close monitoring through
empirical studie s. T his appro ach was earlier supp or ted by
Forsyth ([3] p. 33-36) and Adams ([15], pp. 242-243),
who argued that localised empirical studies are needed in
the study of deforestatio n. At the same time, it is of criti-
cal importance that the underlying local cause(s) and
reason(s) for deforestation are fully understood, in par-
ticular because of the alleged misuse of the concept
among environmental researchers.
In order to achieve sustainable resource management,
comprehensive land cover change information for the
entire area is required. One potential tool to establish
land cover change is the use of remote sensing tech-
niques. For example, part of the possible reasons given
for the apparently slower decline of FAO’s figure of the
total global net change in forest area from 1990 to 2000
(8.3 million ha per year) ([4], p. 17), was argued by
Mather ([16]) as due to more studies using remote sens-
ing techniques, which have been us ed to cons t ruct a more
accurate global forest cover map.
This study therefore investigates Yobe state (an area
that falls within those examined by Bdliya (in [7]); An-
yamb aa and Tucker ([9]) and Olsso n et al. ([8]) and one
of the country’s known hotspotsfor vegetation dege-
neration [ 5 ] ), using Re mote S ensi ng and fie ldwor k-based
findings. Initially, this study uses remote sensing tech-
nique s to mea sur e t he ve geta ti on cha nge in Yobe state. It
uses Landsat satellite imagery (freely available from the
Global Land Cover Facility, University of Maryland) to
classify the changes in vegetated areas over time. Erdas
Imagine 11 software is employed for the classification
and extraction of land cover statistics. Vegetatio n change
in this study refers to any type of woody cover, which
includes both close and open forests, plantations and
shrubs. Potential causes of the changes are discussed in
the final section of this paper .
2. Study Area
Figure 1 shows the location of the study area in Yobe
state. Like most Northern Nigerian states, Yobe state is
primarily an agricultural area with large expanses of sa-
vanna vegetation. Several areas in the northern part of
the state have experienced desertification, which is
mostly connected to both climate change and man-made
Figure 1 . A map sh o wing the locati on of the study sites of Nangere and Potisku m in north-eastern Ni geria (inset).
Figure 2 . N igerian ecolog ical zones (S ourc e : map adapted and digitised from FOR MECU, (n.d)).
deforestation ([7]). The southern part of Yobe state in-
cluding the Potiskum, Nangere and Fika local govern-
ment areas (referred here to Potiskum and its environs)
was selected for in vestiga tion here ( Latitude s 11˚30'33"N
& 12˚00'00"N & Longitudes 10˚50'10"E & 11˚14'11"E).
The choice of these areas was primarily because they
have seldom been examined in the previous literature on
vegetation degeneration in Yobe state, despite being one
of the most densely populated areas in the region. This
may be because researchers felt that the area, despite its
large population, was less prone to desertification than
the northern parts of Yobe state (see Figure 2, Yobe state
is covered by two vegetation zones; Sahel Savanna to the
north and Sudan Savanna to the south). The study area
falls within the Sudan Savannah vegetation zone, and is
characterised by a hot and dry climate for most of the
year [17]. The dry season starts from early November to
late May and the hottest months are March, April and
May with temperatures ranging between 39˚C and 42˚C.
The period of the rainy season in the area varies, but
generally lasts for about 120 to 140 days from June to
early October and ranges between 500 mm to 1000 mm
in total [17]. T he vege tatio n z o ne to whic h the s tud y area
belongs extends across about 11 of the 19 northern states
(see Figure 2). Thus the findi ngs potentially have impli-
cations for northern regions well beyond the study area
The study area covers approximately 3000 km2, wi th a
population of about 300,000 (National Population Com-
mission of Nigeria [18]. The topography of this area is
relatively flat and the elevat ion is appro ximately between
450 to 480 meters above sea level [17]. The first author
has extensive local knowledge of the study area having
lived there for many years.
This is very important because the area (at the moment)
is a difficult part of the world in which to undertake
fieldwork (due to the Boko H ara m crisis- see for example
([19]). Therefore, the choice of the study area facilitated
the successful execution of the overall research, because
of a pre-existing network of local contacts.
3. Materials and Methods
In an approach first suggested by Olsson [20], this study
uses the Landsat satellite (MSS and ETM+) and eva-
luates the extent of the changes in the Sudan savanna
vegetation cover around Potiskum and its environs, Yobe
state Nigeria. It should be noted the concept of deforesta-
tion is still u nder deb ate [3] with rega rd s to the “usa ge o f
the FAO’s (whic h publishes the most widely cited statis-
tics) definition of the term forest, which is based on a
commercial definition that includes both natural forests
and forestry plantations as long as they satisfy the quan-
titative criteria of areas with over 10 percent tree cover
and no agricultural activity” ([21] p. 1607). Although
Landsat image analysis can detect tree cover as low as
10% ([22] p. 92), researchers still suggest that remote
sensing should be complemented by in-depth studies at
the local level to ensure that deforestation for the purpose
of other land use (e.g. agricultural land and fuelwood
collection) c an ac tually be ide ntified.
For the purpose of this research, a remote sensing me-
thodology was adopted (see for example Sader and
Winne ([23]) and field survey information collected for
ground truthing purposes. Landsat imagery was used as it
has been proven to be very useful in conservation and in
natural resource management at the local level (see for
example [22-26]).
Landsat images covering the period from 1975 to 2005
were selected from those available based on quality and
temporal spacing. In total eight different epochs were
selected (Multispectral Scanner (MSS) images for 1975,
1978, 1984, 1986 and 1987; Enhanced Thematic Mapper
Plus (ETM+) for 1999, 2002 and 2005). These images
were also selected to provide image capture dates which
were the same as far as was possible to remove seasonal
effects (see Table 1).
The images were also visually checked to ensure
weather conditions at the time of image capture were not
restrictive (i.e. no excessive cloud or atmospheric inter-
ference). The main temporal period selected for exami-
nation was between November and January when the
study area experiences a dry season. The Landsat im-
agery from 1978 was chosen for the baseline analysis
data set (i.e. the base map from which all subsequent
change would be measured - see for example [26]). Pre-
vious research suggested that the study area had suffered
a severe drought in the early 1970’s [27] and that this
dro ught perio d would have af fected the vegeta tion of t he
area. It is assumed here that the area had recovered suf-
ficiently by 1978 for meaningful analysis. The MSS
Landsat images were enhanced geometrically and re-
sampled to 30 metres in order to enable direct compari-
son with the Landsat ETM+ sensor images (1999 and
2005) which are also of a 30 metre resolution. The 2005
ETM+ image was used for the registration of the other
Landsat imagery.
Table 1. Information about the images used.
Image Type Satellite Type Acquisition
Date Resolution
MSS 1978 Landsat-2 1978-11-18 60 × 60
MSS 1984 Landsat-5 1984-11-20 60 × 60
MSS 1987 Landsat-5 1987-01-13 60 × 60
ETM+ 1999 Landsat-7 1999-12-08 30 × 30
ETM+ 2005 Landsat-7 2005-11-03 30 × 30
3.1. RGB-NDVI Classification
One of the most effective methods for measuring
changes in vegetation cover is to carry out a Normalised
Difference Vegetation Index (NDVI) classification. The
NDVI is a model that makes use of the differential in-
formation arising from the distinctive spectral reflectance
properties of healthy vegetation in the red (R) and near
infra-red (NIR) portion of the electromagnetic (EM)
spectrum. The result of the NDVI analysis is a panchro-
matic, single layer image where the white areas represent
dense and healthy vegetation. In contrast, the darker
areas in the image represent land cover with little or no
vegetation cover. One of the key advantages of NDVI is
its tendency to eliminate errors that can affect the spec-
tral properties of vegetation. This is because green vege-
tation surfaces absorb proportionally more red light and
less infrared light than other surfaces. Therefore, as ve-
getation increases, high NDVI values are obtained. In
contrast, as vegetation decreases, lower NDVI values are
found [23].
Note that since the study area experiences only two
main distinctive seasons in a year (rainy season and dry
season), the choice of the dry season images make it
possible for the NDVI results to depict visually the dis-
tinction between unvegetated areas (including farmland,
because it is devoid of crop plants during the dry season
following the harvest) and other vegetation types in the
study area.
Once the NDVI image had been created, the images
from the different epochs were combined to analyse the
change in vegetation cover. Sader and Winne ([23]) de-
veloped a technique to visual ise vegetation change using
three dates of NDVI imagery simultaneously by adopting
the simple additive colour method. Additive colour
theory simply suggests that RGB are the p ri mary ” co-
lours of white light and all the three colours combined
together will result in white, while the absence of all the
three colours will produce black in an image [28].
Therefore, by stacking the three years of NDVI images
together, a new RGB image is produced (RGB-NDVI)
where the colours represent vegetation loss, vegetation
gain o r no change. U si ng t hi s a p p ro a ch, majo r cha n ges in
the vegetation cover between years appear as the additive
colour combination. The final result of all the image
processing stages was a coloured (RGB) i mage where the
non-vegetated areas had been excluded and the coloured
areas represented changes in vegetation cover over three
separate epochs. From these images it was then possible
to automatically calculate areas of cover for each colour,
thereby creating maps and areas of change statistics for
vegetation cove r in the study area.
Note tha t t he change s i n ve ge t a tio n c ove r for P o ti sk u m
and its environs were obtained from the original results
of the RGB-NDVI images (1978-2005). However, for a
better cartographic visualisation of the pattern of vegeta-
tion c hange (d ue to the n umerous tiny pixels observed in
the images), a 7 × 7 low-pass window median filter was
applied to the images in order to reduce their pixel noise.
3.2. Accuracy Assessment and Image Results
Campbell ([29]) argued that the evaluation of image ac-
curacy seldom explicitly considers precision, but rather
the accuracy should be appropriate for the purpose at
hand. Two types of accuracy assessments are used in this
study [29]. They are as follows:
1) “Producers Accuracy (PA)” that corresponds to er-
ror of omission (excl usion), which is the percentage of a
given class that is correctl y ide ntified on map.
2) “Users Accuracy (UA)” that corresponds to error of
commission (inclusion), which is the probability that a
given pixel will appear on the ground as i t is classed.
The majority of vegetation map classifications were as-
sessed using topographic maps as the reference map. It is
important at this stage to point out that updating such to-
pographical maps to include all vegetation coverage in
most developing countries (including Nigeria) is not very
reli able and is the refo re no t useful whe n ass essi ng ac cur a-
cy. For example, the first author’s personal observation at
the time of writing revealed that the most up-to-date map
of the study area was produced in 1985 (East View Carto-
graphichttp ://www.c a ex.asp). As
twent y seven yea r s ha ve now ela p sed , thi s map ca nno t b e
considered to contain information that reflects current
realities on ground. This lack of regular update of the
topographical maps of Nigeria was also highlighted in a
Nigerian local newspaper (Vanguard of 7/2/2012, p. 30),
which quoted the Nigerian Surveyor General (Mr. Peter
Nwilo) saying “the last time Nigeria was comprehen-
sively mapped was more than 30 years ago”. Therefore,
the lack of conviction and failure from both commercial
and government agencies to regularly update the maps
reveals some of the dif ficultie s in obta ining a reliab le and
up to date topographical map of the study area that can
be used reliably as a reference map. For this reason as
well as the choice of technique used in this study
(RGB-NDVI), the accuracy assessment was achieved
using the original images for each year as the reference
map. Cohen et al. ([30]); Sader et al. ([24]) and Sader
and Legaard ([26]) employed similar methods using the
original Landsat TM image as the reference image in
their respective classification accuracy assessments.
Using the accuracy assessment tool in the ERDAS
I magin e Software, the accuracy of the classified RGB-
NDVI images was assessed (see Table 2 for results). In
addition to using the original Landsat images as the ref-
erence maps, the resulting images were further examined
using Google images in conjunction with the personal
Table 2. RGB-ND VI nine classes comparative accuracy assess ment results.
RGB-NDVI 1978, 1984 and 1987 R GB -NDVI 1 987, 1999 and 2005
Producer’s Accuracy Users Accuracy Producer’s Accuracy User’s Accuracy
Overall Classification Accuracy 84.1%
Overall High Vegetation Classification Accuracy 75.8%
Overall Low Vegetation Classification Accuracy 94.4%
knowledge of the study area by the first author (see for
example [26] ). This method obviates the need for using
topographic maps due to the possibilities of error and
misrepresentation in determining the correct classes.
From Table 2, all the classification accuracy results
(the overall classification accuracy, overall high vegeta-
tion classification accuracy and overall low vegetation
classification accuracy) show similar patterns of agree-
ment with the NDVI image reference points for the
change detection maps (1978-1987 & 1987-2005). The
range of the agreement is between 70% and 95.6% re-
spec tivel y for b oth U A and P A. This hig h range of accu-
racy results revealed a satisfactory agreement between
the sample control points and the RGB-NDVI classified
The visual inte r pr e ta tio n of the i ma ges u si ng the RGB-
NDVI techniques explained earlier gave a general idea of
the cha nges t hat have occurred in the forest area over the
per iod of inve stigatio n. Howe ver, the need fo r field ver i-
fication was emphasised by Tole ([22]) for accuracy as-
sessment. Therefore, the accuracy of the interpreted im-
ages was also verified by ground truthing of some se-
lected areas on the analysed images in the field. As part
of this process, two afforestation sites and three other
areas were visited (in October, 2010). In addition, other
places visited during the field investigation were loca-
tions identified to the first author b y the local commercial
fuelwood vendors as fuelwood collection centres in the
study area. Through this process, an opportunity for dis-
cussion with the fuelwood vendors and a direct compari-
son of their activities in the forest area with the remote
sensing image results was also achieved. The methods
used i n the field i nclud ed the use of a Global Positioning
S ystem device (GPS) for recording locations and a digi-
tal camera for taking photographs of the activities in the
areas visited.
4. Results of the Vegetation Cover Analysis
The overall pattern of vegetation change for each year is
illustrated in Figure 3. The results reveal that about
44.4% of the study area was covered with vegetation in
1978, but this had reduced to about 27.5% in 2005.
However, although the results clearly show a decline in
total vegetation cover over the study period, the vegeta-
tion cover of the area witnessed some considerable fluc-
tuations (Figure 3). The years of 1984 and 1999 in par-
ticular witnessed a reduction in their vegetation cover, to
a little over 23% compared to the years 1978 and 1987.
The changing pattern reveals an increase of about 16.8%
in the vegetation cover of the study area from 1984 to
1987 and an increase of about 4.3% from 1999 to 2005
(Figure 3). However, apart from these two incidences of
vegetation cover increase, all other periods witnessed a
substantial decline in the vegetation cover. The periods
from 1978 to 1984, and 1987 to 1999 witnessed vegeta-
tion decline of about 20.7% and 17.3% respectively
(Figure 3). The vegetation increase from 1984 to1987 is
quite high compared to an increase of just about 4% from
1999 to 2005 and the large reduction of vegetation from
1978 to 1984 and 1987 to 1999. An important finding
there fore is the man ner i n whic h the ve geta tio n oscillated
repeatedly within the period of investigation. Vegetation
cover maps that visually elucidate the vegetation change
pattern of the study area were also created for each
epoch. Figure 4 shows the changes mapped for the pe-
riods 1978 and 2005.
The remote sensing analysis clearly shows large
changes in vegetation cover across the study area from
1978-2005. In order to statistically test the validity of
these changes (vegetation increase and decrease) over the
years, an Analysis of Variance (ANOVA) statistical test
was applied to the results. The ANOVA tested the fol-
lowing null hypothesis: there was no significant differ-
ence in the observed vegetation change patterns of the
study area over the years. The ANOVA result (F (1, 8)
= 33.64, P = 0.0004 or <0.05) rejected the null hypothe-
sis and confirmed that there was a significant difference
in the vegeta tion c hange p atte rns ob served bet ween 19 78
and 2 00 5 in the stud y area . W hils t the over all c ha nge has
been significant there has clearly been a complex pattern
of change both temporally and spatially across the region.
This change is likely to be the result of complex interac-
tions between natural factors and human pressures on the
5. Discussion of the Potential Causes of the
Land Cover Change
The results of the vegetation change pattern interpreted
from the RGB-NDVI images provide an initial impres-
sion of the vegetation cover changes that have occurred
Figure 3 . Percentage vegetation c over 1978-20 05 (Annual basis).
Figure 4 . Vegetation cover map of the entire study area for 1978 and 2005.
in the study area over the period of investigation. How-
ever, it is worth noting that several factors including
physical (rainfall variabilit y), political ( local and national
government policies) and socio-economic factors (popu-
lation increase and de mand for fuelwood) are believed to
be responsible for the observed change patterns. In addi-
tion, the influence of some of these factors may vary in
temporal and spatial location and variability. As a result,
a complex pattern of vegetation increase and decrease
occurs across the study area.
5.1. Population and Rainfall Factor
It is suspected that one of the drivers of vegetation
chan ge i n t he regio n is pop ulatio n increase. Nigeria is yet
to fully implement the compulsory registration of births
and deaths as legislated since 1979 [31] which means it
is difficult to ascertain the exact rate of the country’s
population increase. There is undoubtedly an upward
trend in its population size and the rate of the annual in-
crease was put at 45 per 1000 [32]. This increased rate
has resul ted in a massive populat ion expan s ion i n Nigeria
as a whole and within the current study area. Similarly,
demographic change is also a factor at the local scale.
For example, Potiskum area has remained the adminis-
trative centre of the region’s local authority since 1924
[33]. This administrative role has placed the town in a
favourable condition to attract more of the nearby popu-
lation from the surrounding villages and towns (pull fac-
tor of population migration [34]). This ha s re sulte d in t he
rapid expansion of the town to cater for the needs of the
influx of people. Figure 4 shows a detailed cartographic
analysis of the extent to which the vegetation cover
around the town of Potiskum has been affected from
1978 to 2005. The population increase can be seen to be
partially responsible for the rapid decline of the vegeta-
tion as the growing population has increased demand for
the co nstr ucti on o f soc ial a meni tie s, ne w hous in g and t he
associated increasing demand for farmland to supply the
increased population needs. A similar outcome was ear-
lier reported by Odihi ([7]) around the state capitals of
Borno (Maiduguri) and Yobe (Damaturu), which all
served as the state capitals of the respective study areas.
Meeting the increasing population’s demand for food
was also reported by the Population Reference Bureau
(PRB) [34], as a key challenge for the environment
through deforestation, particularly in the developing
countries (including Nigeria) where the majority of agri-
cultural practice still remains dominated by subsistence
farming [34].
One potential physical factor which is likely to have a
strong influence on vegetation cover is that of rainfall.
Using globa l modelling of r ainfall with NDVI during the
rainy season confirmed that rainfall is among the causa-
tive factors of vege tati on change in the e ntire region [8,9].
However, there was also a reported incidence of consis-
tent reduction in the NDVI of the area during the dry
season which cannot be explained by rainfall alone [7].
The vegetation change pattern observed here did not
consistently follow the trend of the rainfall pattern ob-
served in P otisk um. The graph of t he vege tation c hanges
and rainfall variability in Potiskum reveals that the in-
creased annual rainfall did not necessarily result in in-
creased annual vegetation and similarly decreased annual
rainfall did not reflect a decrease in vegetation in the
study area (Figure 5). For example, the difference in the
amount of the annual rainfall received for the years 1984
and 1987 is low, which contrasts markedly with the mas-
sive reduction of vegetation in 1984 compared with the
large vegetation increase in 1987. This is despite the fact
that 1984 received higher annual rainfall than 1987. Si-
milarly, the years of 1978 and 1987 received rainfall far
less than the mean, compared to 1999 which received
above the mean and yet witnessed a major vegetation
decrease compared with the vegetation increase observed
in 1978 and 1987. The contribution of rainfall (quantity
received in a particular year) in recharging the under-
ground waters and rivers of northern Nigeria was also
considered to have a potential influence on the area’s
vegetation cover ([17] p. 96, [35] p. 62). This indicates
that if there is any direct connection between the rainfall
of the study area and the vegetation d uring the dry season,
it could be attributed to the soil moisture content in the
periods of drought and excess rainfall ([36], p. 399). For
example, the adaptation characteristics of the xerophytic
plants during the dry season (the study area’s vegetation
has some similar characteristics to the neighbouring
northern vegetation belt of xerophytic plants which shed
their leaves in order to reduce the rate of evapo-transpi-
ration during the dry season) may affect the vegetation
health response captured by NDVI, which only records
the vegetation index based on high and low leaf cover
(chlorophyll). Therefore, the large decrease of vegetation
in 1984 (which is bounded by annual rainfall years 1983
and 1985) matches a below mean trend of annual rainfall
and the sudde n incre ase of t he veget atio n in 198 7 (whic h
is bounded by annual rainfall years 1986 and 1989 that
received more than the mean rainfall) would be logically
explained. A statement by the chair of the fuelwood
vendors association in Potiskum main market during the
field investigation confirmed the reduction of vegetation
cover in the study area around 1984 as illustrated in Fig-
ures 3 and 5. However, it should be noted that this ex-
planation is purely provisional and therefore remains
subject to verification or otherwise in future research.
Nevertheless, rainfall variation was not determined in
this study to be the principal cause of the vegetation
change in the study area.
5.2. Political Factors: Policies Regarding
Afforestation and Agricultural Programmes
One other potential factor which will undoubtedly have
had an effect on the vegetation cover is the local and in-
ternational effects of the government and government
policy. Nigeria has had eight different heads of state
from 1978 to 2005, with the majority of these coming to
power through military coups. The frequent and sudden
cha nge s of government are often accompanied by
changing policies and political priorities. For example,
the North East Arid Zone Development Programme
Figure 5 . A comparative g r aph showing the patt e rn of annual rainfall with vegetation change.
(NEAZDP) had its head office in Yobe State. NEAZDP
was a joint rural development programme between the
government, people and private enterprise which had the
objective of assisting rural populations by improving
their living standards through the sustainable use and
management of the existing local environmental re-
sources [ 7 ]. T his scheme only lasted for five years due to
political uncertainty (1990-1995). The collapse of the
programme was a direct result of the withdrawal of aid
by the foreign collaborators when sanctions were im-
posed on Nigeria during the late General Sani Abacha’s
regime. The collapse of the NEAZDP resulted in the
gradual abandonment of its initial initiatives towards
afforestation in the arid zones of Nigeria. Some changes,
however, may be directly explained by political factors.
For example, the Borno state government’s decree during
the military regime in 1986. This decree encouraged af-
forestation and discouraged deforestation by ensuring
that trees were not indiscri minatel y exploited (these laws
were contained in the Borno state of Nigeria Gazette [37]
under the Borno state Notice No. 62, captioned“Bos.
Edict No. 8 of 1987The Felling of Trees (Control)
Edict, 1986” and Bos. Edict No. 7 of 1987The Burning
of Bush Control (Amendment) Edict, 1987”). Conceiva-
bly this might have had some effects on the stud y area’s
vegetation cover in the 1990’s. Both laws emphasised
tough measures against any person caught violating
Other policies that may have contributed to the de-
crease of vegetation in Nigeria may include the govern-
ment policy of banning the importation of wheat, which
is mainly produced in the dry belt regions o f Nige ria ( in-
cluding the study area) [7]. This policy paved the way for
the clearance of more natural vegetation in order to open
up more agricultural land for the production of the com-
modity. In addition, the establishment of the Directorate
for Food, Roads and Rural Infrastructure (DFRRI) in
1985 which was circulated under Decree number four of
1987 [38] , for the purpose of providing rural infrastruc-
ture in the count ry, was a mon g the Fe deral Government’s
policies that may have aided the large reduction in vege-
tation in the 1990’s. For example, the provision of local
feeder roads, promoting of rural housing development,
provision of rural health care facilities and the boosting
of agricultural land all provided by the DFRRI project
have directly contributed to the loss of vegetation cover.
Other Federal Government policies have also favoured
the clearance of more vegetation for agricultural land by
the National Agricultural Land Development Authority
(NALDA), established under Decree no. 92 [39]. NAL-
DA was specifically initiated by the government in order
to help farmers to produce more crops. Although the in-
tention of the Nigeria n Government was to help the lo cal
population, this research supports the findings of Odihi
that both DFRRI and NALDA have contributed signifi-
cantly to the decrease in vegetation through active de-
forestation [7 ]. For example, “during the 8 years of
NALDA operation in Nigeria, more than 54,000 hectares
of land were acquired within the first year of operation
alone (1992 to 1993) of which 28,000 hectares were
cleared and utilized. Out of the cleared land, only about
15,000 hectares were cultivated, while the remaining
were either left idle or completely abandoned. As of
January 2000 when NALDA was scrapped, only a total
of 17,820 hectares have been cultivated” ([39] p.
250-251). In Yobe state alone, about 500 hectares of ve-
getated land were cleared within the first few months of
the NALDA project [39].
The government’s efforts towards afforestation in the
study area are indications that despite establishing poli-
cies that encouraged active deforestation, there were
countervailing programmes that encouraged forest rege-
neration. For example, since the 1970s drought that af-
fected the northern part of Nigeria, policies (in the name
of the Arid Zone Afforestation Project (AZAP)) were put
in place regarding afforestation programmes. Through
such programmes, an introduced species of tree called
“azadirachta indica” (Neem tree—see example in Figure
6) thrives very well in the study area and has now be-
come one of the most important tree species in terms of
use. This is because aside from the main purpose of in-
troducing the Neem tree in the area (environmental pro-
tection—to serve as a wind break and sun shade), the
local people are becoming more conscious of the tree
(through tr ee o wnership ) parti cularl y in the ir compo unds
(households) and farmlands ([7]). Discussions con-
ducted with the fuelwood vendors during field investiga-
tions revealed that unlike the practice in the past when
ample vegetation cover existed within short distances
from the households, the scarcity of vegetation (espe-
Figure 6. An Example of Neem Trees Afforestation Site
(This is close to Potiskum town, Latitude 11.66209, & Lon-
gitude 11.09745—The trees w ere pl ant ed i n the e arly 198 0s )
(Source: Photograph by author).
cially for fuelwood) ha s now necessitated the initiation of
changes in perception towards tree ownership as a means
of supplementing fuelwood during shortage periods. This
chan ge i n a tt it ud e i s es pe ci al l y p ro minent i n Nanger e and
its surrounding villages (see Figure 4). The shortage of
fuelwood in the study area has also resulted in a change
of attitude among some farmers, who now also plant
Neem trees to supplement other fuelwood sources.
5.3. Deforestation and Fuelwood Collection
The complexitie s of the cause s of deforestatio n that have
led to the reduction of vegetation in the study area are
varied. The first author’s personal experience of the
stud y are a ha s sho wn that e ve n the vege tatio n c lear ed for
farmland or settlements were used as fuelwood in the
study area. Examples include the new settlements of Sa-
bon Garin Idi Barda and Sabon Garin Bukar Abba, along
Potiskum to Damaturu road (Yobe state) which serve as
fuelwood purcha sing centres for road passengers. Earlier
studies of vegetation in northern Nigeria (see for exam-
ple [7,40]) also identified the absolute reliance on, and
demand for fuelwood as the key factor in deforestation in
the nor ther n arid zones of Ni geri a. The sur vey cond ucted
by Max Lock Group Nigeria (MLGN), ([33]), showed
that there were about six forest reserves within a thirty
mile radius of Potiskum town in 1976. The survey
warned that these forest reserve areas (a source of fuel-
wood) were likely to come under pressure in the future
due to the dependence on fuelwood and population in-
crease of Potiskum, unless a viable substitute for fuel-
wood was found. In particular, they emphasised at that
time the unsustainable exploitation of unreserved bush
areas by fuelwood dealers. During the present field in-
vestigation, the chairman of the fuelwood vendors asso-
ciation of Potiskum confirmed that they now have to go
beyond their local administrative boundaries in order to
procure fuelwood. This is why only two of their present
collection centres (Ngel Kafaje and Gada (near Da wasa);
northeast and northwest of Nangere respectivelysee
Figure 4) appeared on the image results. In the areas
near these collection centres, the vegetation appears to
have drastically declined from 1978 to 2005. At present,
about 95% of the households in Potiskum and its envi-
rons are still dependent on fuelwood for their cooking
Mortimore ([41]) also predicted that the growing
fuelwood demand in the arid north of Nigeria would con-
tinue, because there were no policies in place to discou-
rage the use of fuelwood apart from the existing legisla-
tive measures that denied people the use of the only
available resources affordable to them. This prediction
was confirmed recently (see for example [42,43]) whose
recent studies in Kano and Nigeria respectively revealed
the current state o f deterioration of fossil fuel supplie s in
the northern part of Nigeria and the over-reliance on
fuelwood to meet the cooking energy requirements of the
people. It is worth noting that Nigeria has an abundance
of crude oil in the South-South region of the country
(Niger Delta), which serves as the main source of income
of the country. However, the management of the petro-
leum resources in Nigeria is inefficient and as such, the
supply of refined petroleum products to other parts of the
country is irregular ([42,43]). For example, the decrease
in fossil fuel supply in the last two decades and the con-
tinued shortage of supply due to the various crises in
Nigeria has contributed to the decreased amount of ve-
getation in the study area in the 1990s and 2000s, as a
result of increased demand for fuelwood [43]. Figure 7
is an example of one of the numerous fuelwood markets
where people purchase fuelwood as part of cooking
energy supply options in the study area.
Given the current weak status of the country’s infra-
structure, economic development and lack of alternative
energy sources, the price of fuelwood is far lower than
the alternative energy sources of kerosene, gas and elec-
tricity, which is why most people depend on fuelwood as
their only cooking fuel option [43]. Additionally, with
the recent partial withdr a wal of fuel s ubsid y in Nigeria in
Figure 7. Images of one of the many fuelwood markets in
Poti skum (Source: Photograph by author) .
January 2012, the prices of fossil fuel soared; this has the
implication of committing more people to depend even
more on fuelwood. Therefore, in the future if the situa-
tion does not change, it is likel y that the vegetation in the
study area will decline further.
6. Conclusions
Thi s i n ve st i ga ti o n s e ts out to map t he pattern of veget ation
change in the study area over time using remote sensing
to quantify spatial and temporal rates of vegetation loss.
It has also endeavoured to suggest some of the potential
causes of the observed changes. The interpretation of the
RGB-NDVI image results reported here, revealed an
irregular pattern of vegetation cover in this part of the
Sahel (Potiskum and environs). Periods of remarkable
vegetation decrease as well as increase have been identi-
fied between 1978 and 2005. The overall pattern is one
of vegetation loss, but not as perhaps would have been
expected in a clear linear trend. This study has shown an
improvement in the vegetation of the study area from
1999 to 2005, which coincided with the regional increase
of vegetation as shown by Anyambaa and Tucker ([9] p.
609), Herrmann et al. ([36] p. 398) and Olsson et al. ([8]
p. 559). However, it should be noted that the satellite
sensors used in the two studies are different and the dates
and e xtent of the two studies dif fer markedly.
Understanding the potential causes of these changes
appears to be even more complicated than originally ex-
pected. A range of possible explanations for the observed
patterns of vegetation change have been examined, al-
though it is clear that further research is required to
substantiate some of the findings. Many unanswered
questions remain, which will help focus future scientific
debates on vegetation change in the northern arid zone of
Nigeria in general, and the study area in particular. A
complex pattern of population increase, national and lo-
cal government policies and an increasing demand for
fuelwood are likely to be the most important factors in
explaining the vegetation changes in the study area. In
contrast, t he direc t contribution of rai nfall to t he e xpla na-
tion o f the ve getatio n chan ge in the stud y area d uring the
dry season was not found to be substantial, although in
reality this would have some effect. From field visits, it
is clear that the demand for fuelwood to meet the energy
requirements of the people appeared to be a key factor in
the vegetation decrease observed over the years.
This study has again demonstrated how low cost re-
mote sensing can be used successfully to study vegeta-
tion cover. The RGB-NDVI results also demonstrated
empirically the complex nature of the change in vegeta-
tion pattern in the study area. Such findings have not
been reported in the past for northern Nigeria using the
approach adopted here, although the approach has been
widely used elsewhere (see for example [23,24]). While
the analysis has provided clear evidences of oscillations
in vegetation cover in the past, it is not clear whether
such oscillations will continue in the future in the same
way, because of the progressive loss of forest land due to
agriculture, urbanisation and fuelwood collection. How-
ever, as future imagery becomes available, the same re-
mote sensing methodology can be used to extend the
current analysis for monitoring purpose. The results can
then be disseminated to the relevant policy makers for
necessary action. This type of information is crucial to
the s ucces s ful s ustai nab le manage me nt o f nat ural ve geta-
tion cover in many areas around the world.
Ackno wledgements
This research was funded by the Nigerian Petroleum
Technology Development Fund (PTDF). All the remote
sensing images were freely obtained from the Earth
Science Data Interface (ESDI) and US Geological Sur-
vey (USGS) (http://glovis. us gs. gov/). We also like to
thank the r eviewe rs fo r their comments and suggestio ns.
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