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Biological Conservation
journal homepage: www.elsevier.com/locate/biocon
Extending the baseline of tropical dry forest loss in Ghana (1984–2015)
reveals drivers of major deforestation inside a protected area
Thomas A.J. Janssen
a,b,c,⁎,1
, George K.D. Ametsitsi
b,d,1
, Murray Collins
a
, Stephen Adu-Bredu
d
,
Imma Oliveras
b,e,1
, Edward T.A. Mitchard
a
, Elmar M. Veenendaal
b
a
School of GeoSciences, University of Edinburgh, Crew Building, The King's Buildings, EH9 3JN Edinburgh, United Kingdom
b
Plant Ecology and Nature Conservation Group, Wageningen University, Droevendaalsesteeg 3a, 6708 PB, Wageningen, The Netherlands
c
Department of Earth Sciences, VU Amsterdam, Boelelaan 1085,, 1081 HV Amsterdam, The Netherlands
d
Forestry Research Institute of Ghana, UPO 63, KNUST, Kumasi, Ghana
e
Environmental Change Institute, School of Geography and the Environment, University of Oxford, South Parks Road, OX1 3QY Oxford, United Kingdom
ABSTRACT
Tropical dry forests experience the highest deforestation rates on Earth, with major implications for the biodi-
versity of these ecosystems, as well as for its human occupants. Global remote sensing based forest cover data
(2000−2012) point to the rapid loss of tropical dry forest in South America and Africa, also, if not foremost,
inside formally protected areas. Here, we significantly extend the baseline of tropical dry forest loss inside a
protected area in Ghana using a generalizable change detection technique. The forest cover change detection is
based on the normalized difference vegetation index (NDVI) derived from historical Landsat data (1984–2015).
Field measurements were carried out in dry semi-deciduous forest and in the adjacent savanna and woodland.
Estimates of the canopy area index and above ground woody biomass were related to NDVI derived from Landsat
8 data. The change detection indicated significant NDVI decrease in a large area initially covered by tropical dry
forest, associated with deforestation. The peak in deforestation was found to have occurred between 1990 and
2002, hereafter, the conservation status of the area was improved. A combination of remote sensing data cor-
roborated by secondary data sources provides evidence for the almost complete clearance of a tropical dry forest
inside a strictly protected area, attributable to logging and land clearing for arable farming. The NDVI change
detection also revealed NDVI increase in the adjacent woodlands from 2002 to 2015, demonstrating woody
encroachment. Historical fire data from the MODIS burned area product indicate that the deforested area ex-
perienced a high frequency of anthropogenic burning since 2004, which may have caused further degradation
and largely prevents forest regeneration. The results show the ongoing destruction of tropical ecosystems even
within ostensibly protected areas and ask for the revision of protection and management strategies of such areas.
1. Introduction
Deforestation and forest degradation (DD) represent a global pro-
blem (e.g. Hansen et al., 2013). West Africa is no exception where DD
has occurred for millennia, principally due to logging, charcoal pro-
duction and slash and burn agriculture (Hawthorne and Abu-Juam,
1995; Lupo et al., 2015). However, agricultural expansion and in-
creasing levels of illegal logging and fire disturbance have dramatically
increased DD since the end of the 19th century (Hansen and Treue,
2008; Hawthorne and Abu-Juam, 1995; Hosonuma et al., 2012; Wardell
et al., 2003). Since 1990, the forest area in Ghana has decreased on
average by 2% every year (FRA, 2010). As a result, timber exports have
dropped markedly over the past decade: from 2008 to 2013 the total
volume of Ghana's timber exports declined by ~50% following decades
of unsustainable exploitation (Hoare and Wellesley, 2014). The loss of
natural forest in Ghana has significant socio-economic and ecological
implications as these forests provide important ecosystem services and
represent hotspots of biodiversity (Brooks et al., 2002; Norris et al.,
2010).
In the forest savanna transition zone of West Africa, savanna and
tropical dry forest occur in close proximity under similar climatic
conditions. Tropical dry forests are particularly vulnerable to anthro-
pogenic disturbances and experience high deforestation rates (Hansen
et al., 2013). In contrast, woody encroachment of open ecosystems and
https://doi.org/10.1016/j.biocon.2017.12.004
Received 4 May 2017; Received in revised form 16 October 2017; Accepted 3 December 2017
⁎
Corresponding author at: Department of Earth Sciences, Earth and Climate Cluster, Vrije Universiteit Amsterdam, Boelelaan 1085, 1081 HV Amsterdam, The Netherlands.
1
Present address.
E-mail address: t.a.j.janssen@vu.nl (T.A.J. Janssen).
Biological Conservation 218 (2018) 163–172
0006-3207/ © 2017 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/BY/4.0/).
T
in particular the savanna and woodlands in large areas across sub-Sa-
haran Africa is being reported (Eldridge et al., 2011; Mitchard and
Flintrop, 2013). Rising atmospheric carbon dioxide and relief from
anthropogenic pressure are cited as possible drivers of such encroach-
ment (Lambin et al., 2001; Lloyd et al., 2008; Mitchard et al., 2009;
Mitchard and Flintrop, 2013). In the forest-savanna transition zone,
deforestation and woody encroachment can occur simultaneously due
to the action of multiple anthropogenic and biophysical drivers. Dry
forest and transitional forests harbour crucial resources of genetic di-
versity, increasingly important for climate change adaptation in the
context of the predicted warming and drying trend for West Africa
(Boko et al., 2007; Millar et al., 2007).
The Forestry Department of Ghana (FDG) formally recognised the
importance of tropical dry forests in 1962 and established multi-func-
tion “barrier”reserves in the transitional zone between the Guinea sa-
vanna and the fringing dry semi-deciduous forest (Hagan, 1998; Hall
and Swain, 1981; Hawthorne and Abu-Juam, 1995; Swaine, 1992). The
aim was to maintain a high forest cover in order to protect natural
water sources, to provide shelter to agricultural crops from dry season
winds and to provide forest products for the surrounding human po-
pulation (Hagan, 1998). Furthermore, the FDG aimed to preserve a dry
forest belt as a fire break, to prevent the increasingly severe bushfires
from northern Ghana spreading southward (Hagan, 1998). Yet due to
illegal logging and the extreme El Niño drought of 1983, most of these
barrier reserves were degraded by the mid-1990s and needed urgent
protection (Hawthorne and Abu-Juam, 1995).
This paper focuses on one of these barrier reserves, the Kogyae Strict
Nature Reserve (Figs. 1 & 2). Kogyae is the only designated strict nature
reserve in Ghana, being “devoted solely to scientific research”with
entry by humans for tourism or other uses prohibited (Hagan, 1998).
The dry forest and savanna woodlands of Kogyae have received parti-
cular scientific interest recently, in the study of vegetation structure,
fire ecology, plant physiology and carbon dynamics in the forest-sa-
vanna transition zone (Cardoso et al., 2016; Domingues et al., 2010;
Moore et al., 2017; Torello-Raventos et al., 2013; Veenendaal et al.,
2015). Kogyae is classified as category Ia protected area by the Inter-
national Union for the Conservation of Nature (Ofori et al., 2014). The
literature suggests that from 1984 to 1998 the population of commu-
nities inside and surrounding Kogyae tripled, mainly as a result of the
new road access opened in 1984 (Awuku-bor, 1999; Hagan, 1998). In
this period, illegal logging, agricultural expansion, charcoal production
and widespread fire contributed to DD inside Kogyae (Awuku-bor,
1999; Hagan, 1998; Wildlife Department, 1994). According to a recent
survey conducted by the authors among park rangers and local people,
the local communities were removed from Kogyae by the Wildlife De-
partment in 2002. While logging and farming have been limited since
2002, fires are now lit every year, both illegally by poachers as well as
by wildlife officers to provide fresh grass for grazing wildlife (Ayivor
and Ntiamoa-Baidu, 2015). The current reserve management may thus
be in direct contradiction with the original aim, the conservation of
tropical dry forest, for which the reserve was set up.
We assess the changes woody cover that has occurred inside Kogyae
in the past three decades using Landsat-derived NDVI in a multi-decadal
change analysis. Thereby, we add quantitative spatiotemporal data to
the existing, mostly anecdotal, history of Kogyae from 1986 to 2015. In
addition, the climate and fire record are used to provide insights into
these drivers of woody cover change. We aim to answer the following
research questions:
- What woody cover changes are observed and what was the extent of
DD?
- How did the drivers of woody cover change develop over time?
- How did the legal protection status of Kogyae and management
efforts contribute to the conservation of dry forest?
We use insights from the case study to understand the processes
driving woody cover changes in the Zones of Transition in Africa and to
evaluate conservation efforts in the past decades.
Table 1
Description of the Landsat images used in the NDVI change detection.
Year Date Sensor Cross-calibration formula
1984 21st of
November
LS 5 TM NDVI
calib1984
= 1.268(NDVI
1984
)−0.025
1990 22nd of
November
LS 5 TM NDVI
calib1990
= 1.073(NDVI
1990
) + 0.118
2002 15th of
November
LS 7 ETM
+
NDVI
calib2002
= 1.158(NDVI
2002
)−0.089
2015 27th of
November
LS 8 OLI
Fig. 1. The location of the Kogyae Strict Nature Reserve within the different vegetation zones of Africa and Ghana. Vegetation zonation of Africa and Ghana adapted from Aubreville et al.
(1958) and Yengoh et al. (2010), respectively. The vegetation zonation within Kogyae is adapted from a vegetation map in the Kogyae Management Plan (Wildlife Department, 1994),
which is based on in situ observations by Schmitt and Adu-Nsiah in April–June 1993 (K. Schmitt, pers. com. 27 August 2015).
T.A.J. Janssen et al. Biological Conservation 218 (2018) 163–172
164
2. Materials and methods
2.1. Study area
The Kogyae Strict Nature Reserve (7° 15′52″N, 1° 04′47″W) is a
330 km
2
protected area located in the north-eastern part of the Ashanti
region in central Ghana. Kogyae experiences a bimodal annual rainfall
distribution with high precipitation from March to July and from
September to October corresponding to the passage of the Inter-
Tropical Conversion Zone across the region (McSweeney et al., 2010).
December and January are dry with 25.2 mm and 16.9 mm average
accumulated precipitation respectively. The pronounced dry season
from December to March is caused by prevailing dry and dusty ‘Har-
mattan’winds from the north east (McSweeney et al., 2010). The mean
annual rainfall is approximately 1350 mm and mean annual tempera-
ture is 28 °C with minimum variation in temperature over the year
(McSweeney et al., 2010; Wildlife Department, 1994).
2.2. Woody cover change
2.2.1. Field data
Field estimates of aboveground woody biomass (AGB) and canopy
area index (CAI) were used to evaluate the suitability of Landsat de-
rived NDVI to detect changes in vegetation cover in the study area.
Thirty nine rectangular vegetation plots of 20 × 20 m were established
in the north-western corner of Kogyae in October 2014. The aim was to
include the entire range of woody cover present in the study area, in
order to evaluate the suitability of the NDVI in detecting gradual
changes in woody cover. The field plots were located in vegetation
types that are different in both their structural characteristics and
floristic composition (Table S1), ranging from open, treeless grassland
to tall forest with a high canopy cover and biomass (Torello-Raventos
et al., 2013; Veenendaal et al., 2015). All trees with a diameter at breast
height (DBH) of > 2.5 cm were tagged, measured and identified at
species level. Tree height was estimated with a laser rangefinder
(TruPulse®200, Laser Technology Inc.). The projected crown dimen-
sion was estimated by measuring the diameter of the crown on two
perpendicular axes with a measuring tape. The edge of the crown was
visually determined by looking up at an angle of 180°. Geographic
coordinates were determined at the centre of each plot using a hand
held GPS device (Etrex Lengend HCx, Garmin Ltd., U.S.). For each plot
the CAI (m
2
canopy area/m
2
ground area) was calculated as the sum of
all crown dimensions divided by the plot area (400 m
2
). AGB was cal-
culated using the general dry forest equation from Chave et al. (2005).
Tree DBH, tree height and species specific wood density retrieved from
the Global wood density database (Chave et al., 2009; Zanne et al.,
2009) were used to calculate the AGB of the individual trees.
2.2.2. Landsat based change detection
We chose to use the Normalized Difference Vegetation Index (NDVI)
derived from images acquired by three Landsat satellites to detect
changes in woody vegetation cover. Landsat satellites have con-
tinuously acquired multispectral images with a 30 m horizontal re-
solution since 1972. The NDVI was selected as it is a widely used and
validated index of vegetation greenness. NDVI has been used to sepa-
rate woody and grass vegetation in savannas (Archibald and Scholes,
2007) and to monitor woody vegetation changes in the Sahel (Horion
et al., 2014) and in the forest savanna transition zone of Cameroon
(Mitchard et al., 2009). Timing of image capture was chosen to max-
imise the contribution of woody vegetation to the NDVI signal and to
omit the contribution of grasses. In a similar environment in Cameroon,
Mitchard et al. (2009) found that the NDVI from images captured in the
early dry season was most sensitive to woody vegetation greenness. We
confirm this from regular in situ observations in Kogyae, as in the early
dry season (mid-November) all tree species are still in full leaf while
grasses have senesced. Unfortunately, cloud-free historical images from
mid-November covering the study area are rare in the Landsat archive,
making an annual assessment of changes in NDVI impossible. There-
fore, we selected four usable (> 70% cloud-free) images for the change
analysis that would approximate three periods in the recent manage-
ment history of Kogyae: the pre-disturbance (1984–1990), the dis-
turbance (1990–2002) and the recovery period (2002–2015).
Remaining clouds and cloud shadows were masked from the NDVI
images using the Fmask automated cloud detection algorithm version
3.3 using default settings (Zhu et al., 2015; Zhu and Woodcock, 2012).
The NDVI was calculated using the Landsat 8 image and values were
extracted to derive plot averaged NDVI. To evaluate the suitability of
NDVI to assess changes in woody vegetation cover in this environment,
we performed an asymptotic regression to link plot-averaged NDVI to
field estimates of CAI and AGB. Plots 32 and 34 were excluded as they
Fig. 2. A Corona satellite photograph captured in January 1966 (A) and a Landsat 5 NDVI image from November 1984 (B) provide an indication of the initial vegetation cover inside
Kogyae. The dry semi-deciduous forest in Fig. 1 is visible as darker colours in the Corona photograph and as high NDVI (> 0.6) in the Landsat image. The bight white areas in the south
west of the reserve (NDVI < 0.5) are sparsely vegetated rocky outcrops, known as Boval vegetation. The Guinea savanna is visible as greyish colours in the Corona photograph (NDVI
0.5–0.6). The boundary between the forest and savanna is abrupt, accentuated by annual fires that generally extinguish at the forest edge (Swaine, 1992).
T.A.J. Janssen et al. Biological Conservation 218 (2018) 163–172
165
were dominated by a few large trees resulting in unrealistically high
AGB values for this environment of > 700 Mg ha
−1
(see Table S1).
The different Landsat sensors exhibit differences in sensitivity and
spectral band designation that can provide errors in the change detec-
tion. To minimize these errors we use a cross-calibration method to
calibrate the three historical NDVI images to the NDVI image of
November 2015 (Mitchard et al., 2009). For the cross-calibration pro-
cedure, 100 points were selected to represent pseudo-invariant target
areas in undisturbed tropical forest fragments, on roads and rocky
outcrops. A linear regression was then applied to obtain the calibration
equation for the 1984, 1990 and 2002 images (Fig. 3). The calibrated
NDVI images were compared pairwise (i.e. 1984–1990, 1990–2002,
2002–2015) to calculate the relative change in NDVI (ΔNDVI) in every
time period, using the following equation:
∆= −
+
−
−
NDVI NDVI NDVI
NDVI NDVI
nn
nn
1
1
The magnitude and direction of change was established by scaling
ΔNDVI in terms of standard deviations (SDs) away from no change
(ΔNDVI = 0). We use the average SD (0.048) of change in the three
examined periods obtained within the borders of the reserve. For more
details about the change detection method, see Mitchard et al. (2009).
To examine how ΔNDVI differed between the initial land cover
types in our study area, principally savanna and dry forest, we used the
earliest Landsat image available from November 1984 to derive a land
cover map. We used all seven spectral bands of the image in a random
forest supervised classification (Breiman, 2001) with 50 randomly
sampled training samples (100∗100 m). Because there is no field data
available from 1984 to train the classification, we visually assigned the
training samples to either forest, savanna or boval vegetation using the
vegetation map and Corona satellite image in Fig. 1 and 2, respectively,
as a reference.
2.3. Precipitation and fire
Monthly accumulated precipitation from January 2000 to January
2016 was retrieved from the tropical rainfall measuring mission
(TRMM) (GIOVANNI, 2015). The monthly precipitation data was area
averaged for a rectangular area around Kogyae (NE = 7° 43′,−0° 83′;
SW = 7° 0′,−1° 24′). Fire data was collected from the Moderate Re-
solution Imaging Spectroradiometer (MODIS) Burned Area product
(MCD45A1) which contains date of detected burning at a 500 m hor-
izontal resolution. The data is acquired since the year 2000 by the
MODIS sensor on-board the Terra and Aqua satellites. The separate
monthly tiles were merged to derive one raster with the day of burning
in the dry season (October –March) of every year. This resulted in 14
raster datasets containing the day of detected burning, from the dry
season 2001–2002 to the dry season of 2014–2015. From these datasets
the total area burned in every month of the dry season from 2000 to
2015 was calculated.
3. Results
3.1. Linking field data to the NDVI
Field estimated AGB was strongly related to field estimated CAI
(R
2
= 0.75, p < 0.0001, n = 37, Fig. 4). AGB increased linearly with
CAI, roughly 100 Mg ha
−1
with every unit of CAI. Plot averaged NDVI
was related to both field estimated CAI (R
2
= 0.66, p < 0.0001,
n = 37) and AGB (R
2
= 0.62, p < 0.0001, n = 37, Fig. 5).
Fig. 3. The cross-calibration regression of 100 pseudo-invariant targets that expectedly did not change in vegetation cover between 1984 and 2015. From left to right: the regression of
November 1984 NDVI against November 2015 NDVI (R
2
= 0.94, p < 0.0001), November 1990 against November 2015 (R
2
= 0.97, p < 0.0001, n = 100) and November 2002 against
November 2015 (R
2
= 0.94, p < 0.0001). The cross-calibration equations are provided in Table 1.
Fig. 4. Field estimated AGB regressed against the field estimated CAI. The correlation is
particularly strong in open ecosystems with low AGB and CAI.
T.A.J. Janssen et al. Biological Conservation 218 (2018) 163–172
166
3.2. Changes in NDVI
The change detection procedure showed both NDVI increase and
decrease between 1984 and 2015 (Fig. 6, Table S2). The detected de-
crease in NDVI occurred almost exclusively in areas that were covered
by dry forest in 1984. Between 1984 and 1990, 10.2% of this forest area
experienced a detectable decrease in NDVI. More than half of this area
(64.7%) decreased in NDVI between 1990 and 2002. In the period
2002–2015 there was a loss of NDVI in 28.3% of the dry forest area.
NDVI increase occurred mainly in the savanna, with 4.4%, 4.8% and
37.6% of this area showing detectable increases in NDVI in the periods
1984–1990, 1990–2002 and 2002–2015, respectively.
The rate of significant NDVI loss in the area covered by dry forest
increased from 1.7% yr.
−1
in the first 6 years (1984–1990) to 5.4%
yr.
−1
in the following 12 years (1990–2002). Hereafter, from 2002 to
2015, the rate of NDVI loss in the forest area decreased again to
2.2 yr.
−1
, cancelled out by a detectable increase of 2.4% yr.
−1
. Based
on the observation that the decrease in NDVI occurred almost ex-
clusively in the central dry forest belt (Figs. 6 & 7) we can conclude that
this NDVI decrease is indicating DD inside Kogyae. The NDVI increase
from 2002 to 2015 in the area initially covered by forest points to a
possible post-disturbance recovery of the vegetation. The area covered
by savanna showed a 2.9% yr.
−1
increase of NDVI in the period
2002–2015. Branching patterns of positive NDVI change were visible in
the NDVI change map of 2002–2015 that closely match the branching
patterns of small streams that flow to the north into the Sene river
(Fig. 7). This suggests that water availability is possibly driving the rate
of woody encroachment in the savanna of Kogyae.
3.3. Interactions of fire and climate
The MODIS burned area product showed that fires were very fre-
quent inside Kogyae (Fig. 7). In 52.3% of the reserves surface area
(180.7 km
2
)afire was recorded every 1 to 2 years, while in another
24.1% of the area (83.3 km
2
)afire was recorded at an interval of 2 to
3 years. The remaining 23.6% of the area (81.8 km
2
) experienced a fire
return interval of 3 to 14 years. The area inside the reserve that burned
annually varied significantly over the years (Fig. 8). The first four years
of the record show relatively small burned areas. However, in the dry
season of 2004–2005, 56.3% of the reserve area (194.6 km
2
) burned.
Thereafter, the area that burned annually remained large, with the
exception of 2010–2011 (51.2 km
2
) and 2013–2014 (35.2 km
2
). There
was a significant linear correlation between the extent of area burned in
January and February and the accumulated precipitation in January
(R
2
= 0.45, p < 0.001, n = 14).
4. Discussion
We observed a number of changes in the study area that are im-
portant for the long term resource exploitation and future management
of the area and offer insights into long term dynamics of protected
areas. The NDVI change detection procedure reveals the previously
unidentified and complete clearance of a dry forest inside Ghana's IUCN
Category Ia Kogyae Strict Nature Reserve between 1984 and 2015. The
Landsat archive offered us the possibility to detect woody cover
changes in three time periods that correspond to changes in land use,
resource management and human pressure inside the reserve (Fig. 9).
The estimated rate of deforestation more than doubled from 1.7% yr
−1
in the pre-disturbance period to 5.4% yr
−1
in the disturbance period
(1990–2002). From 2002 to 2015, referred to as the recovery period,
the deforestation rate declined again to 2.2% yr
−1
, somewhat higher
than the national deforestation rate of 2% yr
−1
in Ghana in the same
period (FRA, 2010).
Three different procedures were performed to reduce uncertainties
and prevent errors of commission in the NDVI change detection ana-
lysis. First, by selecting images captured on similar times in the dif-
ferent years intra-annual or seasonal variation in NDVI is largely ex-
cluded from the NDVI change detection. Secondly, the cross-calibration
of the NDVI images from the different years ensures that distortions due
to sensor differences are excluded while the cross-calibration procedure
also reduces some of the remaining seasonal variation. Finally, by
measuring NDVI change in terms of standard deviations of change
within Kogyae, only areas that show a significant magnitude of change
are detected. The observed changes in tree cover were found to coincide
with a number of historical events in the region. (Fig. 9). The opening of
road access in 1984 contributed to the influx of migrant farmers into
Kogyae, engaged in arable farming (Mertens and Lambin, 2000;
Wildlife Department, 1994). Low initial population density and land
Fig. 5. Landsat 8 NDVI captured on the 27th of November 2015 regressed against field estimated CAI (A) and AGB (B). CAI and AGB showed an asymptotic relationship with NDVI with
the response of NDVI to woody cover saturating around CAI = 2 m
2
m
−2
and around AGB = 200 Mg ha
−1
.
T.A.J. Janssen et al. Biological Conservation 218 (2018) 163–172
167
availability in protected areas have been found to act as pull factors to
migrants in Ghana and elsewhere in Africa (Awuku-bor, 1999; Hartter
et al., 2014; Zommers and Macdonald, 2012). The rate of forest clearing
for arable farming accelerated in the early 1990s (Fig. 9)(Awuku-bor,
1999; Hagan, 1998; Wildlife Department, 1994).
Two factors have been contributed to the failing of the reserve
management in protecting the dry forest of Kogyae. First, a lack of
definite policies and management guidelines led to a situation in which
the local wildlife officers were responsible for setting out their own
management priorities (Wildlife Department, 1994). Secondly, the
community of settlers used to see the reserve as potential farmland that
was to be released to them and therefore they did not recognise the
status of Kogyae as a protected area (Awuku-bor, 1999; Hagan, 1998;
Wildlife Department, 1994). The history of Kogyae provides a textbook
example of how imposing protected areas on local communities without
strong enforcement rarely leads to successful conservation (e.g. Infield,
2001). The extensive deforestation observed is in line with the general
trend of DD in Ghana's barrier reserves in the 1990s. Another docu-
mented example is the Tain II tributaries barrier reserve (Hawthorne
and Abu-Juam, 1995; Kyereh et al., 2007). Kogyae's watershed pro-
tection function is now lost with the effects felt by local communities.
Streams dry up completely during the dry season (Hagan, 1998) and the
water supply from boreholes is reduced, resulting in water shortages
(Ofori et al., 2014). Furthermore, dry forest tree species constitute an
essential part of rural livelihoods as many species are used for charcoal
and household fuel, fodder, construction wood, medicine and food
(Paré, 2008). The effects of DD on biodiversity inside the reserve are
expected to be substantial (Barlow et al., 2007; Green et al., 2013; Koh
and Sodhi, 2010; Norris et al., 2010).
Some of the larger mammal species previously common inside the
reserve, for example Elephant (Loxodonta africana) and Black-and-
white Colobus (Colobus polykomos) have become locally rare or extinct
(Ayivor and Ntiamoa-Baidu, 2015). Tree species that occur in the dry
semi-deciduous forest inside Kogyae are growing at the very limit of
their ecological distribution. Economically important transitional tree
species in Kogyae include Afzelia africana and Khaya senegalensis, both
classified as vulnerable in The IUCN Red List of Threatened Species.
Genotypes found in these populations are expected to represent
Fig. 6. Map showing the 1984 land cover in Kogyae derived from the random forest classification (top left). Major rivers and streams in the study area are depicted in blue: the Afram
river tributaries in the south-west and the Sene river tributaries in the north-east. Negative change in NDVI occurred mainly in the dry forest belt in the centre of the reserve, particularly
in 1990–2002. Large areas of the savanna in the north-east of the reserve show a significant increase in NDVI in 2002–2015 associated with woody encroachment. The increase of NDVI in
this period shows branching patterns, following the course of multiple streams that intersect the savanna. Clouds were masked and shown in white. (For interpretation of the references to
colour in this figure legend, the reader is referred to the web version of this article.)
T.A.J. Janssen et al. Biological Conservation 218 (2018) 163–172
168
valuable genetic resources, especially in the context of climate change
adaptation (Gonzalez, 2001; Millar et al., 2007). It would be a tre-
mendous loss for the biodiversity, rural livelihoods and the forestry
sector of Ghana if these genotypes would disappear completely.
A recent survey among (retired) wildlife officers and people from
local communities (results not shown) confirmed that in 2002, all the
farming communities residing within the reserve were expelled by the
Wildlife Department. This is confirmed by the interpretation of aerial
images (Google Earth™), as the area that was deforested within the
reserve is presently not cultivated and has been clearly abandoned. The
decline of forest loss observed after 2002 (Fig. 6) can therefore be at-
tributed with confidence to a tighter control on arable farming within
Fig. 7. Fire return interval from 2000 to 2015 derived from
the MODIS burned area product. The deforested area shows
frequent burning as this area experiences at least one fire
every two years. Also the savanna in the north experiences
a high fire frequency. Areas in the south-west are char-
acterised by a longer fire return interval of 3 to 5 years.
Note that the fire return interval inside Kogyae is sig-
nificantly shorter compared to the fire return interval in the
direct surroundings.
Fig. 8. The extent of area burned in the three dry season months (above) and the accumulated precipitation in January (below). Associations are observable between the detected burned
area and January precipitation. In 2004, 2006 and 2014 the accumulated precipitation in January peaks above 35 mm. In these years the recorded burned area in the dry season was also
relatively low. From 2007 to 2010, January was dry and these years also show a large area burned in January.
T.A.J. Janssen et al. Biological Conservation 218 (2018) 163–172
169
Kogyae by the reserve management. Yet the rate of woody cover loss in
the north of the reserve remained high until 2015 (Fig. 6). This can be
attributed to the damage caused by recurrent annual fires, as fire is
known to accelerate forest degradation in areas where forest cover has
been previously reduced and a high fire frequency is maintained
(Hawthorne, 1994; Hosonuma et al., 2012; Swaine, 1992).
While fires are lit every dry season at the edges of the reserve
(Ayivor and Ntiamoa-Baidu, 2015), the fire record shows that a rela-
tively high precipitation in January (> 35 mm) can prevent the fires
from burning a large area of the reserve (Fig. 8). This effect of dry
season precipitation via fuel moisture on the annual variability of fire
extent is well described on larger spatial scales for the African continent
(Andela and van der Werf, 2014), Amazonia (Aragão et al., 2008) and
equatorial Asia (van der Werf et al., 2008). The MODIS fire record
suggests that the area burned inside the reserve has increased since
2004, this is confirmed by observations by the reserve management
(Ayivor and Ntiamoa-Baidu, 2015). The fragmentation of dry forest, the
opened canopy and the increase of savanna grasses in open areas have
increased fire fuel loads with poaching and park management sus-
taining a high fire frequency. Logged and burned forests are found to be
susceptible to renewed burning due to a substantial and dry fuel load
(Kyereh et al., 2007) and a similar effect is found when analysing long
term fire experiments(Veenendaal et al., in press). Eventually, much of
the original forest changed into the treeless tall grasslands and Chro-
molaena odorata thickets existing there today. The annual early
burning management likely prevents or slows down the return of tro-
pical dry forest previously present.
NDVI increase was widespread in the savanna and woodland inside
Kogyae from 2002 to 2015. Our results suggest that regular fires were
not able to prevent woody encroachment in the savanna as the NDVI
increase occurred in the north eastern savanna zone that experienced a
relatively short fire return time of 1 to 2 years in the period 2000–2015
(Fig. 7). Finding woody encroachment along streams and in the savanna
ads to the increasing amount of evidence reporting woody encroach-
ment in the woodlands and savanna of sub-Saharan Africa (Mitchard
and Flintrop, 2013) and in the western Sahel (Horion et al., 2014).
Deforestation of tropical dry forests is a major global issue. From
2000 to 2012 tropical dry forests in Latin America, Africa and Eurasia
experienced the highest deforestation rates of all forest ecosystems
(Hansen et al., 2013). Forest clearing for agriculture is recognised as the
main driver of DD in the tropics (Hosonuma et al., 2012). We show how
dry forests are extremely susceptible to DD even if they are located in a
protected area in the strictest sense. Formal de jure protection can be
totally disconnected from de facto status, leading to creation of so-
called ‘paper parks’(Figueiredo, 2007; Joppa et al., 2008). Dry forest
patches are still present in Kogyae and the areas surrounding these
patches are showing signs of forest recovery. However, annual fires are
hindering forest recovery. This suggests that Kogyae's management
should re-assess the current practice of deliberate burning. Interna-
tional mechanisms that will provide funding for reforestation activities
in the context of climate change (REDD+), and biodiversity con-
servation schemes may provide an avenue for the restoration of Ko-
gyae's forest. Most broadly, this study contributes to the ongoing debate
about the effectiveness of protected areas (Di Minin and Toivonen,
2015; Joppa et al., 2008; Symes et al., 2016). We have demonstrated
the application of remote sensing techniques to provide robust change
detection using freely-available data, and have revealed a hitherto
undocumented and near-complete loss of Ghana's most strictly pro-
tected forest.
Acknowledgements
We would like to thank the Wildlife Division of the Forestry
Commission of Ghana for granting us research permission to work in
the Kogyae Strict Nature Reserve (KSNR). Our appreciation also goes to
the Park Manager, Mr. Dwoben Nyantakyi, and his staffat Dome Camp
for providing security and technical assistance in the field, particularly
Isaac Sarpong, Kwaku Yinye and Yaw Agyeman, who in addition or-
ganized various scattered settler communities in KSNR to participate in
interviews to verify our findings. Finally, we would like to thank the
three anonymous reviewers for their comments and suggestions. EM
was funded by a research fellowship from NERC (grant ref.: NE/
I021217/1). TJ was partly funded by the Netherlands Earth System
Science Centre (NESSC), a program of the Ministry of Education,
Culture and Science (OCW) of the Netherlands. EV and GA were funded
by the EU FP7 GEOCARBON project (grant agreement no. 283080) in
Ghana.
Appendix A. Supplementary data
Supplementary data associated with this article can be found in the
online version, at doi: https://doi.org/10.1016/j.biocon.2017.12.004.
Fig. 9. History of Kogyae, as described in the literature (Awuku-bor, 1999; Hagan, 1998; Ofori et al., 2014; Wildlife Department, 1994) and from the interpretation of the NDVI change
detection and MODIS fire record results.
T.A.J. Janssen et al. Biological Conservation 218 (2018) 163–172
170
These data include the Google maps of the most important areas de-
scribed in this article.
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