INTRODUCTION
Deforestation and the southern encroachment of the Sahara desert into sub-Saharan
Africa from anthropogenic and climatic sources have become an increasingly controversial topic
in West Africa. Threats to biodiversity and human livelihood exist, where fragmentation of
tropical forest and a 30-year drought have put considerable strain on water resources (Pearce,
1994). Current estimates put the extent of deforestation in countries like Liberia and Ghana at
69-83% over the last century (Gornitz and NASA, 1985), and the percent of tropical forest loss
per year in areas like Guinea at 1.2% (FAO, 1993). In the tropical forests of West Africa, they
are beginning to ask the question, what is the extent of forest fragmentation in areas that are
literally on the very edge of the sub-Saharan (Sahel) zone and what effect does this have on
biodiversity? In most parts of West Africa lions, elephants, large ungulates and other large game
have been extirpated as human population have increased at a 2.34-3.2% annually (UNSD,
2007). Dry forest and Open (lowland) savannah have been cleared to accommodate subsistence
agriculture. The erosion of the landscape is compounded by drought and the yearly onset of the
dry Harmattan winds off the Sahara desert. This also poses serious health risks as viral and
parasitic infections (malaria, schistosomiasis, arbovirus, Chagas disease) patterns have been
directly and indirectly influenced by loss of tropical forests (Walsh, 1993). Fragmentation of
tropical forests in once contiguous areas of landscape has put considerable pressure on
biodiversity. The existence of a time lag between the destruction of habitat and extinction is
thought to exist (Cowlishaw, 1999). This potential for “extinction-debt” (Tilman et al. 1994) in
taxa that have undergone extensive habitat loss is cause for serious concern (Pimm et al., 1995).
All is not a lost cause, however, there are still areas, particularly national parks in West
Africa, that have the ability to conserve natural biodiversity, and serve as places to study habitat
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fragmentation and land use change. National Parks serve as a good “surrogate” to natural
processes, because they idealistically exist where anthropogenic disturbances are limited. I have
taken one such park, Parc National du Niokola-Koba, Senegal (PNNK) to study habitat
fragmentation of gallery forest at the interface, where the dry Sahel savannah meet the dense
gallery forests of the Guinean highlands. To my knowledge, the most recent land cover maps of
the region exist at a 1km resolution, which is much too coarse for fragmentation analysis. To
accomplish the fragmentation analysis, I performed an Unsupervised Classification of two
Landsat 7 images (encompassing PNNK), mosaiced them and performed an accuracy
assessment, using High-resolution Google Earth images, as reference. From this 28.5-meter
resolution land cover image, I developed several metrics of habitat fragmentation (patch area,
perimeter, etc) to quantify the extent of patch fragmentation that exists across the study area.
Then, I developed several generalizations surrounding these metrics to further support the
current literature’s conclusions about the severity of deforestation and threats to biodiversity in
West African Tropical Forests.
MATERIALS AND METHODS
Study Area
Parc National du Niokola-Koba (PNNK) is located in the southeastern portion of
Senegal, with the Casamance region bordering to the west, Guinea (Conakry) to the south, and
Mali to the east (Figure 1). PNNK is approximately 913,000 hectares in area, (12°57'36.465"W,
12°55'32.264"N) with the waters of the River Gambia flowing along the north-west corner
(Figure 2). Originally created as hunting reserve in 1926 and later designated a National Park in
1954, it was transferred to the UNESCO World Heritage Program as a Biosphere Reserve in
1981. The low elevations of the park (16m to 311m) represent the flat terrain that is commonly
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observed in that part of sub-Saharan Africa. Wide floodplains and small hills reveal the
topography marked by the River Gambia. The vegetation type is dominated by open lowland
savannah, with mostly low woody shrubs and trees dotting the landscape and herbaceous
vegetation and grasses sparsely interspersed. Vegetation changes to seasonally flooded
grasslands and marshy areas created by precipitation during the rainy-season (May-August). In
ravines and areas that are marked by high moisture (stream/river banks) Gallery Forest persists,
where tall trees and a dense understory dominated by lianas, provide a cooler temperature
gradient more indicative of southern Guinean forests.
Materials
In the initial data acquisition phase of this project, I gathered relevant primary and
ancillary data layers. I downloaded 2 Landsat 7 ETM+ Images, Bands 1, 2, 3, 4, 5, and 7 from
the Global Land Cover Facility (GLCF) online. Image 1, Path 202 Row 51 was date stamped 1219-2000 at resolution 28.5 meters. Image 2, Path Row 51 was date stamped 12-08-1999 at
resolution of 28.5 meters (Metadata, Appendix). Both of these images represented the least
atmospheric disturbance and cloud cover in the available series. They we also taken during the
dry season, when the evergreen Gallery forest, could be more accurately classified according to a
unique spectral signature. Ancillary data layers, like regional roads and villages were acquired
from University of Maryland, Ph.d student, Karl Wurster. The PNNK boundaries were acquired
from the World Database of Protected Areas (UNESCO), and an African continental polygon
shapefile was obtained through the ESRI Nicholas School C-drive.
Methods
In the data preprocessing phase of the project, I used a combination of ERDAS Imagine9.1 (Leica Geosystems) and Arc-Map 9.2 (ESRI, Redlands, CA). Both ETM images from the
GLCF were imported as GeoTIFF (.tif) and converted to imagine (.img) format. The study area
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was at the intersection of where these two images overlapped. The images were “Layer Stacked”
to form two composite images in ERDAS. Since the images were from two different dates Path
202 Row 51 was “histogram matched” and then “mosaiced” with Path 203 Row 51 in ERDAS.
The mosaiced image was then clipped to a polygon created with a 10-km buffered around PNNK
boundaries. The resultant image contained too much radiometric discrepancy to be useful, so
another approach was undertaken. Upon the recommendation of Joe Sexton (NSOE Ph.d
Candidate, Duke University), I converted each separate band in each image to spectral radiance
values (Watts/(meters squared *ster*µm)) then to a normalized reflectance value, which
represented the actual unit-less planetary reflectance of each band corrected for daily variation in
Sun’s elevation (Landsat 7 Science Data Users Handbook, 2007). This process was conducted in
Arc-Map 9.2 using Single Map Algebra. The 10-km buffer clipped images were then composited
using “Composite Bands” and “Mosaic to New Raster”(Model, Appendix). The final image,
however, was unusable because the borders of the each image picked up a value that did not
allow them to be mosaiced properly. Because of various time constraints and storage necessity
of having 32-bit floating-point images, I used a different approach. This process consumed the
vast majority of my time and patience, and gave me new insight into nuances of using remotely
sensed images (I was insensed!). Finally, for the classification I took the clipped individual
bands of the study area and “Composite Bands” to classify each image individually.
For the Classification, I used a recursive ISOData Cluster tool in Arc-Map 9.2, under 100
iterations for 20 classes to recursively partition the spectral signatures to a signature file (.gsg).
For Path 202 Row 51, I used Bands 1-4, under normal circumstances Band 1 would not be used,
however, the classification “Failed to Execute” when other bands were attempted. Path 203 Row
51 was classified using Band 2, 3, 4, 7. Both signature files were input into a “Maximum
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Likelihood Classification” to output two 13-class raster grids for analysis (Model, Appendix).
Each raster grid was reclassified to 4 classes: Water; Open Savannah, Villages; Dry Forest;
Gallery Forest, using a combination of High-resolution Google Earth images and “best guess
estimates” to partition. The two images were “Mosaic to New Raster” using Path 203 Row 51 to
reach overlap agreement (Model, Appendix). The resultant Land Cover Raster was “Accuracy
Assessed” using a Random grid of 200 sampled “Classified Values” points and cross-validated
using point coordinates on Google Earth High-resolution images. Estimates of Producer’s,
User’s, and Overall Accuracy were produced to quantify attribute accuracy. The Kappa Kstatistic was calculated to determine attribute agreement from a random distribution.
The final step in the project was the fragmentation analysis. I separated the 10kmbuffered images and extracted the PNNK land cover within boundaries. For each raster grid,
Gallery Forest was extracted and then Region grouped to develop resultant outputs of Area,
Perimeter, Thickness, Centroid, and Shape Index (Model, Appendix). Core Area was then
developed by extracting the non-Gallery forest cells and applying a Euclidean distance to a
masked raster. Distances less than 100m were set to NoData, and Values greater than 100m
were set to 1. The raster grids were then Region grouped to quantify the amount of core area that
exists within and outside of PNNK. Finally, the patch with the largest area (Ha) was calculated
and the minimum edge-to-edge distance was calculated. These minimum values were of little
use because of the large number of patches and extent of analysis (Model, Appendix).
Maps were compiled that show the progression through the analysis (Figures 1-7).
Tables were developed to quantify the Accuracy assessment, and relevant landscape patch
metrics.
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In carrying out this analysis I make several assumptions that directly effect the
interpretation of results. One, there is no difference between the 2 Landsat 7 images across the
dates taken. Two, the classification of one image did not affect the classification of the other.
All areas of overlap were in agreement before mosaic to new raster. Three, given time
constraints and the method of unsupervised classification the Kappa-statistic (level of agreement)
was suitably high enough to continue the fragmentation analysis. Four, the levels of
fragmentation and landscape metrics developed are reliable relative to the constraints of the
unsupervised classification.
RESULTS
Classification and Accuracy Assessment
When comparing the classes created from the Unsupervised Classification between the
PNNK and PNNK with buffer, areas (HA) are relatively similar (Table 1). The total % of area in
across classes varies by only 1.41% at most. Water was classified as contributing the smallest
amount of area to the total in both cases. Also, according to spectral signatures it existed in large
patches where it definitely wasn’t (Figure 4). The total land area was dominated by Open
Savannah, 75-77% of the area classified. Dry Forest was the next highest contributor to land
area with approx. 13%, and finally, Gallery forest at the lowest “land” total of approx. 8%.
There is an obvious discrepancy between the Total calculated for PNNK with a raster cell size of
28.5 meters and the 913,000 hectares cited in the introduction. This is a result of the clipping
and rounding that occurred as the PNNK park polygon extracted the cells of the land use raster
(Figure 3).
Upon completion of the accuracy assessment it became obvious that the Unsupervised
classification did not attain the attribute accuracy that I had hoped (Table 2). The Producer’s
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Accuracy for Dry Forest was low at 28.57%, while in the Gallery Forest and Open Savannah it
was between 82-85% (Table 3). The User’s Accuracy for all classes except Savannah (79.39%)
hovered at around 50% (Figure 4).
The Overall Accuracy for this classification was 68.50%, while the Kappa-Statistic was
exceedingly low at 0.4135 (Table 4). Given the method of classification and the user experience,
I would not expect a Kappa-Statistic above 0.50 (Pete Harrell, Pers. Comm.)
Fragmentation Analysis
In continuing with the fragmentation analysis, I assumed that the land cover classification
was adequately better than random. For the analysis, I sampled the extent of Gallery Forest
patches within PNNK and PNNK included within a 10km buffer. The difference in number of
patches in these two cases was 38,723 patches (Table 5). Mean patch area (Ha) and perimeter
(m) are orders of magnitude different between cases, as are the largest two patches (Figure 5-6).
Both patches are located near the southern border of the park (Figure 7). Mean patch thickness
was 103.05 for PNNK, but when the buffer was included that value increased to 165.66. The
Mean Shape Index also increased in the buffer to 21.05 (Table 5). It is pertinent to note that
these values for landscape metrics have standard deviations that are in many cases twice that of
the mean value, this lends considerable concern about the wide variability in estimates and the
strength of the mean values as estimates.
Next, we can see that the number of patches that have core (>100m of available edge) is
975 in PNNK and 1720 with the buffer are approx. 97% different than the overall Gallery forest
Area (Table 6). The number of patches lost (75,504 PNNK; 113,542 W/Buffer) equates to the
vast majority of the forest area. The largest patches within PNNK and within the buffer (Figure
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7) have drastically different areas, 53.36 Ha and 514.48 Ha respectively (Table 7), and shift
location between analyses depends on the polygon extent (Figure 5, 6).
DISCUSSION
The discrepancy that exists between the total areas calculated in Table 1, and the original
913,000-hectare estimate is detrimental to my analysis only in that the fragmentation analysis
was accurate to the relative area of the land cover raster produced. The consistent values for
each class indicated that similar cover estimates exists both within and outside PNNK. The
results of the accuracy assessment gave me considerable pause before I moved on to the
fragmentation analysis. The Producer’s accuracy indicated that of the observed (Google Earth)
pixels in each class, the classification mis-classed pixels for Dry Forest and Water, but did
relatively good for Open Savannah and Gallery Forest. The User’s accuracy indicated
consistently about a 50% probability that a pixel classified into a category actually represents
that of the sites classified on the ground. This is fairly low, but fair for the method of
classification used. The Overall Accuracy and Kappa statistic under normal circumstances
would cause me to go back to use another classification method. The K-statistic, showed that my
classification was only 41% better than a random distribution, which indicated a low level of
attribute agreement, but also reinforced the applicability of using software like Google Earth to
measure agreement between different vegetation maps (Monserud, 1992).
The results of the fragmentation analysis showed a highly fragmented landscape, with
irregularly shaped Gallery Forest patches of considerably low core area. The interpretations of
these finding can lead to several conclusions. Previous to this analysis, I am unaware of the
extent of Gallery Forest in PNNK, so I cannot make any direct comparison across temporal
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scales. It is obvious that there are highly fragmented small patches that exist, but I have no
indication of contagion. The minimum distance estimates provided a multitude of values that
were of little use in generalization (not included). The shape index and thickness indicated linear
and sinuous patches of gallery forest, which is indicative of ravines and rivers beds where they
are often found adjacent to. It is acceptable to assume that the largest forest patches would be in
the southern region of the study area, where the transition between the savannah and the densely
forested areas near Guinea become more pronounced. However, I cannot conclude that these
patches are Gallery forest, just that they are probably not Savannah or Dry forest. The possibility
exists that the largest patch outside of PNNK may be Mango or Cashew Plantations. Also, one
could assume this also effects the naturally high distribution of savannah and lack of Gallery
forest near the northern boundaries (Figure 7), however, this relativity is hard to quantify by the
data. Among other factors, the southern encroachment of the Sahara may have shifted the
natural distribution of open savannah. If the loss of core area in forest patches (97.34%) is any
indication of the extent of deforestation in PNNK, then the current distribution of Gallery forest
is in sharp decline. This follows the trend found in Guinean Tropical forests, where the percent
original forest remaining is 4.1% (Sayer et al., 1992). The large distances between many of the
core patches do not facilitate the development of corridors. It is my recommendation that further
research be done into the temporal changes associated with deforestation, and the rates of
associated southern encroachment of the Sahara desert. Habitat fragmentation poses a serious
threat to much of the world’s tropical forests biodiversity, and considerable effort should be
placed on research, better land conservation practices in developing nations, and large-scale
replanting of deforested areas.
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Limitations of the analysis existed almost from the beginning. The Landsat 7 images
were hard to work with, and radiometric and phenological correction would require more time
and hard disk space than available. The low K-statistic indicated a high level of error in my
classification estimates and reduced my confidence in making any assumptions regarding ability
of the ISO Data Cluster to fit the image spectral signatures. Although, I did explore the option of
using FragStats as a comparison for my fragmentation analysis results, I did not use it because of
operator constraints. The Patch metrics that were develop (Area, Perimeter, Thickness, Shape
Index) had considerable variation associated with then, which caused me to doubt the inferences
that could be drawn.
In conclusion, I have found that putting together a workable GIS/Remote Sensing related
project has taken considerable time and effort. It has been rewarding in providing new insight
into the process and applicability of the techniques. Still, to my knowledge, a high-resolution
land use classification map does not exist for this region and many other parts of Africa.
Although, I tried to develop a classification of my own, the accuracy fell well below what is
acceptable, and it would require considerably more knowledge on Remote Sensing to produce
one. Based on the classification that I did produce, gallery forest in PNNK has the lowest land
area and highest fragmentation of any of the classes. This is largely attributed to high levels of
deforestation from land use around PNNK, a severe 30-drought, and the southern encroachment
of the Sahara desert. Further research into the temporal and physical factors behind deforestation
in West Africa should be performed to help develop a comprehensive strategy that
accommodates development and conservation.
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Tables
Land Cover Classification Total for Parc National du Niokola-Koba and a 10km buffer surrounding
PNNK
PNNK with Buffer % of Total PNNK % of Total w/Buffer
Water
Open Savannah, Villages
Dry Forest
Gallery Forest
Total (HA)
32589.58
633550.45
113124.66
63426.25
842690.94
43250.93
1008598.12
162224.93
102869.27
1316943.24
3.87%
75.18%
13.42%
7.53%
100.00%
3.28%
76.59%
12.32%
7.81%
100.00%
Table 1: Land Cover Unsupervised Classification Area totals (Ha) for PNNK and the
surrounding study area.
Accuracy Assessment of Land Use Classification
Google Earth (Observed points)
Water
Savannah
Dry Forest
Gallery Forest
Classified
Points
Water
Savannah
Dry Forest
Gallery Forest
Column Totals
7
3
1
0
11
5
104
15
2
126
1
24
14
10
49
Row Totals
1
0
1
12
14
Table 2: Contingency Table for the Accuracy assessment of 200 random points.
Categorical Attribute Classification Accuracy
Producer's Accuracy
User's Accuracy
Water
Savannah
Dry Forest
Gallery Forest
63.64%
82.54%
28.57%
85.71%
Water
Savannah
Dry Forest
Gallery Forest
50.00%
79.39%
45.16%
50.00%
Table 3: Producer’s and User’s Accuracy estimates produced from the contingency table.
Accuracy Measurments
Overall Accuracy
Kappa Statistic
68.50%
0.4135
Table 4: Overall Accuracy and Kappa Statistic estimates
PNNK Unsupervised Classification.
14
131
31
24
200
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Patch Metrics for Gallery Forest in PNNK
Area
PNNK
PNNK with 10km Buffer
# Patches
76479
115262
Minimum
0.08
0.08
Maximum
6591.00
11700.46
Mean
823.54
2599.98
Standard Dev.
1981.52
4381.98
Perimeter (m)
Minimum
Maximum
Mean
Standard Dev.
114.00
1779369.00
228188.28
534208.10
114.00
2896170.00
663226.65
1087498.01
Thickness (m)
Minimum
Maximum
Mean
Standard Dev.
14.25
302.24
103.05
93.05
14.25
551.85
165.66
182.06
Shape Index
Minimum
Maximum
Mean
Standard Dev.
1.00
54.79
12.23
15.89
1.00
66.94
21.05
25.11
Table 5: Gallery Forest Patch Metric table showing Number of patches and
Minimum, maximum, mean, and standard deviation for patch area, perimeter,
Thickness, and shape index.
PNNK
# Patches
Total Forest Area (Ha)
# Patches w/ Core
Area as Core (Ha)
% Difference
Patches Lost
Area Lost (Ha)
76479
63426.247
975
1687.94
97.34%
75504
61738.31
PNNK with 10km Buffer
115262
102869.2694
1720
3987.17
96.12%
113542
98882.10
Table 6: Core Metrics from the fragmentation analysis shown as the number of core patches,
Area (Ha) as core, and % difference from original values.
Largest Core Gallery Forest Areas
Core
Area (HA)
PNNK with 10km Buffer
PNNK
514.48
53.36
Table 7: Two largest patches from the fragmentation analysis of gallery forest
Within PNNK and within a 10km buffer of PNNK.
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Literature Cited
Cowlishaw, G. 1999. Predicting the Pattern of Decline of African Primate Diversity: an
Extinction Debt from Historical Deforestation. Conservation Biology 13(5): 1183-1193.
FAO. 1993. Tropical Resources Assessment 1990. FAO Forestry Paper 112, Food and
Agricultural Organization of the United Nations, Rome.
Gornitz, V. and NASA (1985). A survey of anthropogenic vegetation changes in West Africa
during the last century - climatic implications. Climatic Change 7: 285-325.
Global Land Cover Facility. 2007. http://glcf.umiacs.umd.edu/index.shtml. University of
Maryland.
Pete Harrell. 2007. Personal Communication. Geospatial Specialist, Duke University.
Monserud, R.A. & R. Leeman. 1992. Comparing global vegetation maps with the Kappa
Statistic. Ecological Modeling 62(4):275-293.
NASA. 2007. Landsat 7: Science Data Users Handbook.
http://landsathandbook.gsfc.nasa.gov/handbook.html.
Pearce, D. W. & Brown, K. in The Causes of Tropical Deforestation (eds Brown, K. & Pearce,
D. W.) 2−26 (University College London Press, 1994).
Pimm, S.L., G.J. Russell, J.L.Gittleman, & T.M Brooks. 1995. The future of biodiversity.
Science 269:347-350.
Sayer, J.C. & T.C Whitmore. 1992. Tropical Deforestation and Species Extinction. Springer,
New York.
Joe Sexton. 2007. Personal Communication. Ph.D Candidate, NSOE Duke University.
Tilman, D.R., M. May, C.L. Lehman & M.A. Nowak. 1994. Habitat destruction and the
Extinction Debt. Nature 371:65-66.
UNESCO. 2007. World Database of Protected Areas. http://sea.unep-wcmc.org/wdbpa/.
United Nations Statistics Division 2007. Common Database.
http://unstats.un.org/unsd/cdb/cdb_help/cdb_quick_start.asp.
Walsh, J.F., Molyneux, D.H. & M.H. Birley. 1993. Deforestation: Effects on vector-borne
Disease. Parasitology 106:55-75.
Karl Wurster. 2007. Personal Communication. Ph.D. Candidate, University of Maryland.
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Acknowledgments
I would like to thank the Global Land Cover Facility (GLCF) for providing the Landsat 7
Imagery, and the UNESCO World Database on Protected areas for boundary information.
Google Earth provide the high-resolution imagery I used to cross-validate while accuracy
assessing the classification. Karl Wurster, University of Maryland, provided valuable insight
into my study area. Joe Sexton gave me guidance through the arduous process of Radiometric
correction. John Fay and Pete Harrell provided classification advice. Jennifer Swenson guided
me through the initial frustration of working with remotely sensed images. I would also like to
thank Leica Geosystems and ESRI, Redlands, CA for the use of ERDAS Imagine and Arc Map
9.2, respectively, and the NSOE, Duke University for providing computers to work on. I would
like to thank my fiancé, Maggie Davis, for providing vital food while completing the project. I
would most like to thank His Excellency Lieutenant Colonel Dr. President Yahya Alphonse
Jemus Jebulai Jammeh, Commander In Chief of The Armed Forces and the Chief Custodian of
the Sacred Constitution of The Gambia for providing me the opportunity to live and work in the
Gambia, which gave me the interest to complete this project.