Results of this study - Bureau Waardenburg

Vegetation type
classification and
Distribution of rare species on Mt. Nimba, Guinea
J.W. de Jong
J.M. Reitsma
P.W. van Horssen
Consultants for environment & ecology

Vegetation type classification and distribution of rare species on Mt. Nimba, Guinea
J.W. de Jong
J.M. Reitsma
P.W. van Horssen
commissioned by: SMFG
01 07 2009
report nr 09-069

Status: Final report
Report nr.: 09-069
Date of publication: 01/07/2009
Title: Vegetation type classification and distribution of rare species on Mt.
Nimba, Guinea
Subtitle:
Author:
Authors:
Number of pages incl. appendices: 64
Project nr: 07-567
2
Ir. J.W. de Jong
Ir. J.M. Reitsma
Drs. P.W. van Horssen
Project manager: Ir. J.M. Reitsma
Name & address client: SMFG, Cité Chemin de Fer, BP 2046 Kaloum Conakry, Republic of
Guinea (repr. Jamison D. Suter)
Signed for publication: Director Bureau Waardenburg bv
drs. A.J.M. Meijer
Initials:
Bureau Waardenburg bv is not liable for any resulting damage, nor for damage which results from applying results of work
or other data obtained from Bureau Waardenburg bv; client indemnifies Bureau Waardenburg bv against third-party
liability in relation to these applications.
© Bureau Waardenburg bv / SMFG / BHP Billiton
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Table of contents
Summary ............................................................................................................................................5
1 Introduction ..............................................................................................................................7
1.1 Background...................................................................................................................7
1.2 Objectives ......................................................................................................................7
1.3 This report.....................................................................................................................7
1.4 Acknowledgements......................................................................................................7
2 Satellite image classification.......................................................................................................9
2.1 Materials.........................................................................................................................9
2.2 Methods ......................................................................................................................15
2.3 Results..........................................................................................................................18
2.4 Classification improvements.......................................................................................18
2.5 Discussion / Recommendations.................................................................................23
3 Bioquality map........................................................................................................................25
4 Distribution maps of rare species...........................................................................................27
4.1 Methods ......................................................................................................................27
4.2 Distribution maps........................................................................................................29
5 Literature..................................................................................................................................49
3

Summary
This report describes the results of a GIS analysis of vegetation types and rare species
on Mount Nimba, Republic of Guinea, conducted for the Société des Mines de Fer de
Guinée (SFMG). The study consists of three parts.
The first part is to make a satellite image classification of vegetation types on Mt. Nimba.
The second part is giving an indication of bioquality for the area and the final part is
giving an overview of distribution of rare plant species. All parts of the study were
based on field sampling campaigns in Nov-Dec 2007 and Jul-Aug 2008.
For the image classification several supervised classification techniques on a SPOT 5
image of December 2006 were tested and results were validated and compared. The
image was classified into 13 predefined vegetation classes. It turned out that the best
result was achieved by combining separate maximum likelihood classifications of
different altitude ranges. An overall classification accuracy of 57% was achieved with
this technique. Lower classification accuracies were achieved with a standard maximum
likelihood classification (49% classification accuracy) and by using different SPOT 5
derived image products (NDVI and Principal Component images, 39% classification
accuracy). Other techniques that were studied were image fusion with MODIS EVI time
series and object based image analysis. For the image fusion no improvements could be
made because the pixel resolution was too coarse to be compared on the vegetation
sample level. Pixels at this level often covered several different vegetation types. For the
object based image analysis, similar difficulties in separating the different vegetation
classes were encountered as with the maximum likelihood classifications. Several
variables were calculated for small areas (segments) at different scales but no
improvements could be made when analysing the vegetation classes at segment scale
either.
The different forest classes turned out to be most difficult to distinguish because of
similar reflection characteristics between classes. Savannah vegetation types were
classified more accurately, mainly because of the separately classified altitude ranges at
which they occurred.
The bioquality map that was created for this study was based on the best result of the
satellite image classification. Each pixel within the study area was assigned to the most
resembling vegetation sample with the maximum likelihood classification and the
bioquality score of that corresponding sample used to create the final bioquality map.
In this way, an indication of bioquality is given for the study area.
Distribution maps were made for a selection of rare species found on Mt. Nimba.
Species having a high degree of endemicity were selected to be mapped. Besides the
list of plants found during the field campaigns in Nov-Dec 2007 and Jul-Aug 2008, an
additional dataset from the Herbarium Vadense, Wageningen Agricultural University
was used. Separate maps were created for the selected rare species.
5

1 Introduction
1.1 Background
SMFG (Société des Mines de Fer de Guinée) is leading exploration to examine the
potential to develop an iron ore mine in the Mount Nimba area (Republic of Guinea,
West-Africa). Results of this exploration will be used for (environmental) impact
assessment studies.
Inventories in the past have proven that the Nimba area can be considered as a centre
of endemism and of high species diversity. Also the area is designated as a World
Heritage Site by UNESCO. Bureau Waardenburg bv has been requested by SMFG to
contribute to the botanical research and GIS analysis of the Nimba area.
1.2 Objectives
This study is part of a botanical assessment project and consists of two sections. The
first part is creating a vegetation class satellite image classification of Mt. Nimba based
on a SPOT satellite image and vegetation samples. The second part of the study is to
give an overview of the distribution of rare plant species within the study area.
Both parts of the study were based on a rapid botanical survey (RBS), with sampling
campaigns in Nov-Dec 2007 and Jul-Aug 2008 (Hawthorne, 2009).
1.3 This report
GIS-files are separately delivered to SMFG and are the main results of this project. In this
report the different steps taken in this project are discussed chronologically and the
different maps are commented briefly. In paragraph 2.1 and 2.2 the materials and a
standard method used for the satellite image classification is described. In Paragraph
2.3 the results of this method are presented and in paragraph 2.4 alternative
classification strategies are discussed, and an improved result is presented. A discussion
and some recommendations concerning the satellite image classification are given in
paragraph 2.5. Based on this satellite image classification, a bioquality map was created
which is presented in chapter 3.
In chapter 4 the methods and results of the rare species distribution are described. For a
selection of different rare species from the RBS, maps are presented in paragraph 4.2.
1.4 Acknowledgements
Drs. M. Zeylmans and Dr. E. Addink from the faculty of Geographical Sciences of
Utrecht University were consulted for data acquisition and defining the methodology
7

for the satellite image classification. Several methods were tested with software at the
university.
For the distribution of rare species, an additional dataset of Dr. C.H Jongkind from the
Herbarium Vadense, Wageningen Agricultural University was used.
8

2 Satellite image classification
2.1 Materials
The satellite image that was used for image classification was multi-spectral SPOT
image, acquired on 26 th of December, 2006. The image was pre-processed to level 2a
(radiometric correction of distortions due to differences in sensitivity of the elementary
detectors of the viewing instrument, geometrical correction done in a standard
cartographic projection (UTM WGS84 by default) not tied to ground control points).
The image contains four bands with reflectance values at different wavelengths of the
electromagnetic spectrum (Green: 0.50-0.59µm, Red: 0.61-0.68µm, Near Infrared:
0.78-0.89µm, Short Wave Infrared: 1.58-1.75µm).
Due to the large spatial error between GPS locations of ground truth points from the
botanical surveys and the corresponding location on the image, no direct comparison
was possible. Orthorectification had to be done first by selecting control points on the
panchromatic image (orthorectified, Processing Level 3) and rectifying the multi-spectral
image with ArcGis 9.2 (‘adjust’ method). To reduce processing time, a subset of the
area directly around the Nimba World heritage site was used for image interpretation.
The ground truth data set consisted of 154 vegetation samples taken in the periods
Nov-Dec 2007 and Jul-Aug 2008 at the Guinean part of Mt Nimba (figure 1). Each of
the samples was assigned to a vegetation class as shown in table 1. More details on
sampling methods and vegetation classes can be found in the report ‘Rapid Botanic
Survey of Mt Nimba’ (Hawthorne, 2009).
Five samples were excluded from further analysis. JRSL04b was excluded because it was
located at the same place as JRSL04. JRFR01 was excluded because it was located at
the same place as CJGF01. WHSA10 (class 10; Lowland Guinea Savannah) was located
in the base camp and had completely different reflection characteristics compared to
other class 10 samples on the satellite image, which would probably have given
distorting effects on the classification; it was, therefore, excluded from further analysis.
WHGF36 was assigned to class 1 (Secondary forest with many pioneer species) but was
located in a savannah area. Sample WHGF20 (a class 7, lower altitude secondary thicket
sample) was located on a road between a strip of forest and lowland savannah, and
has different reflection characteristics than other class 7 samples. It was not used for
classification.
The remaining sample points were converted to polygons to be used as training areas
for the classification. This was done by buffering the sample points with a buffer
distance of 50 and 30 meters for forest/thicket and savannah samples respectively
(savannah polygons are smaller because these samples covered a smaller area in the
surveys than the forest vegetation samples). The areas covered by the polygons were
visually checked to make sure that they represented the right vegetation class. Polygons
were adapted if necessary. Some polygons were slightly moved to make sure that there
were no edge effects (on edges between forest and savannah for example). Polygons
9

that showed a large variation in reflection (e.g. shaded and exposed canopy, burnt
and unburnt savannah) were split into sub-polygons. Samples were analysed and
adapted after discussions with J.M. Reitsma and C.H. Jongkind, who participated in the
field surveys, and comparison with high resolution Quickbird images (Google Earth).
An example of the polygons used for the classification training set is given in figure 2.
10

Table 1; Summary of vegetation classes derived from survey results (from Hawthorne, 2009)
Forest or thicket
(Tree dominated, with few grasses)
Savannah
Vegetation
Class No.
No.
samples
Brief vegetation
description/title
Altitudes
range (m)
of samples
Notes
1 16 Secondary forest with many 500-910 Mostly on or near forest edge
pioneer species
– more forest-like than 7
2 16 Moist semi-deciduous, lowland 500-730 Mostly undisturbed,
forest
sometimes riverine or with
streamside flora
3 9 Drier, semi-deciduous forest 500-800 Often on ridges or slopes,
sometimes clearly burnt in
recent past
4 11 Lowland moist riverine or 440-730 Often in locally lower-lying
groundwater forest
riverine or swampy areas
5 11 Moist evergreen forest 500-760 Moister and less disturbed
than class 3 but drier than
class 4 forest
6 11 Nimba Upland evergreen forest 800-1010 A few upland elements, and
at higher altitudes than class
5
7 4 Lower altitude secondary
500-750 In transition between forest
thickets
and savannah, or
farm/fallow, or both
8 8 Gallery forest 980-1320 Often lower canopied and
more narrowly confined in
valley than class 6
9 7 High altitude gallery forest or 1200-1620 The highest types of forest,
thicket
often low canopied
10 6 Lowland Guinea savannah 560-745 Possibly often secondary to
forest. Canopy trees often
present. Often with tall
grasses.
11 9 Lowland edaphic savannah 520-690 Edaphically controlled by
shallow soil or exposed rock,
often with water flow. Some
samples with large trees
12 11 Nimba medium altitude
860-1280 On the slopes or mid altitude
savannah
ridges. Too few trees to
make tree canopy counts
worthwhile, although there
are scattered individuals.
13 31 Nimba Montane savannah 1200-1670 Too few (often no) trees to
make tree canopy counts
worthwhile, although there
are scattered individuals.
11

Figure 1; False colour spot image of Mt. Nimba with locations of vegetation class
samples
12

Figure 2; Example of training areas used for the supervised classification
Besides the satellite image and the vegetation samples, other datasets were available
that could be used to improve the classification. These datasets were: a geological map,
topographical maps, a description of meteorological observations, a Shuttle Radar
Topography Mission (SRTM) 90m resolution Digital Elevation Model (DEM) and a 10m
resolution DEM of the concession area (figure 3). The correlation of the different
thematic maps with occurrence of different vegetation types was analysed. No direct
correlation was found between the vegetation samples and the thematic data other
than the DEM. The DEM can be used to manually correct cells that are misclassified in
terms of elevation.
13

Figure 3; SRTM Digital Elevation Model of Mt. Nimba. The area directly around the
mining concession has a resolution of 10 meters.
14

2.2 Methods
A signature file was created from the remaining 149 samples (91 forest/thicket and 58
savannah) using the Spatial Analyst of Argos 9.2. A unique sample number was used
to create one signature for each of the samples. Classification was done using the
supervised maximum likelihood classification method with equal a-priori probability
weighting on the four spot bands. Maximum likelihood classification means that each
pixel within the study area is assigned one of the 149 samples. For each pixel, the
statistical probability of being a member of a sample is calculated based on the variance
and covariance in the bands reflectance values of the different samples. It is assigned to
the sample with the highest probability.
Besides a classification of the satellite image, a confidence raster is produced in which
the confidence of the classification is given for each cell, based on the spectral similarity
to the class it was assigned to (for details refer to Lillesand et al. 2004).
The classification output was reclassified by grouping the 149 samples according to
their corresponding vegetation class. For class 4 (Lowland moist riverine or
groundwater forest) and 5 (Moist evergreen forest), all samples were grouped into a
single combined class, this was because they are adjacent in the spectrum and the
canopy composition is even more similar (perhaps even not distinguishable) than the
full floristic data suggests. Furthermore, class 4 at least will often be aligned along
streams in narrow, steep-sided valley rather than in extensive blocks (Hawthorne,
2009).
To evaluate the possibility of separating classes and to validate the classification result,
an independent validation was done. There were not enough samples in each class to
randomly select a number from each for validation without also using them as training
samples. This would have certainly lead to a less accurate classification result.
A separate process was set up in which an independent validation data set was used in
four iterations. From the 149 samples, 20% was randomly selected and removed from
the training set (30 samples). The remaining 80% of the samples were used as training
set for classification as described above. The 30 validation samples were then used to
evaluate the classification accuracy on a pixel by pixel level. This process was repeated a
further three times. The average accuracy from these four separate
classifications/validations is presented in the confusion matrix in annex A. For the final
classification, all samples were used to get an optimal result.
15

Figure 4; SPOT satellite image classification based on 149 samples and a supervised
maximum likelihood classification method with equal a-priori probability weighting.
16

Figure 5; Confidence raster of the SPOT satellite image classification. Values represent
the cells’ similarity to the class it was assigned to (1=high similarity, 14=low similarity)
17

2.3 Results
The classification and the confidence raster of the classification are given in figures 4
and 5. The overall classification accuracy is low with 49% of the pixels classified correct
(see confusion matrix in annex A). For most forest types, the percentage of correctly
classified pixels is even below 25% (for savannah types just below 60%). There can be
several reasons for this low accuracy like;
-under sampling of certain classes;
-influence of aspect (shaded or exposed slope) on samples;
-large difference between burnt and unburned savannah on satellite image;
-certain types not sampled;
-classes are difficult to distinguish with available bands due to similar reflection
characteristics.
The last reason is probably the most important for the low classification accuracy. This is
also illustrated in annex C, where average measured reflection (scaled to Digital
Numbers (DN) 0-255) and standard deviation in the four available spot bands is given
per vegetation class. The reflection characteristics for forest types are very similar and are
often overlapping. This makes them difficult to be distinguished based on this
classification strategy, using only the four SPOT bands.
2.4 Classification improvements
After the first classification in which a more or less straightforward image classification
technique was used, some other techniques were tested that could possibly lead to
better results. Several techniques were tried like using Principal Component bands or
Normalised Difference Vegetation Index (NDVI) for the classification. Another technique
that was looked at to improve the classification was image fusion with MODIS
vegetation index time series.
As previously mentioned, there is a strong relation between elevation and occurrence
of certain vegetation classes. Some vegetation classes only occur on certain elevation
levels. In the first classification, many lowland areas are classified as vegetation classes
that only occur on higher elevations and vice versa. By combining elevation data with
the classification some errors can be relatively easy corrected.
The suitability of certain techniques for vegetation mapping in this particular case was
tested and will be shortly discussed.
Classification based on the first three Principal components and the NDVI of the
original SPOT image was applied in the same way as the first classification with the
same validation technique. Although the classification accuracy was slightly higher for
some classes the overall percentage of correctly classified pixels for this classification was
18

39%, which is 10% lower than the classification with the four original SPOT bands. No
possibilities for improvements were found using these image derived products.
It was expected that some of the vegetation classes would have a temporal
development in vegetation density (like Moist or Drier semi-deciduous forest, savannah
classes). By combing the SPOT classification with data that gives an indication of
vegetation density throughout the year, these classes might be better distinguished. To
test this, a data set of one year MODIS Enhanced Vegetation Index (EVI) composite
images was collected from NASA Warehouse Inventory Search Tool (WIST) at
https://wist.echo.nasa.gov. EVI is an index designed to enhance the vegetation signal
with improved sensitivity in high biomass regions compared to NDVI and is available
for free at a spatial resolution of 250 meter.
From the available images, the cloud free images were selected and EVI values from the
sample locations were extracted from the images. In total, seven images from the period
December 2007 to April 2008 were suitable to be compared with the vegetation
samples. The average EVI developments of the different classes for this period are given
in annex D. The expected difference in EVI development for deciduous and evergreen
vegetation classes could not be found in the MODIS EVI images. All vegetation classes
show a more or less similar trend in vegetation development based on these images.
The spatial resolution of MODIS images is probably too coarse to extract these kinds of
differences for this purpose. Many of the vegetation samples occur quite locally and are
mixed with other vegetation classes if looked at a scale of 250 meters.
Another technique that was tested in cooperation with Utrecht University was object
based classification with Definiens Developer software. Object based classification means
that the image is first split up into segments based on spectral information and shape
and scale parameters. When using segments for classification it is possible to use
variables calculated from all cells within a segment or in neighbouring segments instead
of classification based on single pixel values only. In this way it becomes possible to
look at characteristics like segment shape, segment structure, and variability within a
segment or spatial relations to other vegetation types or segments. The SPOT image
was segmented at different scales (from scale 5 to 50) and several variables were
calculated for the segments like variability within segments (standard deviation of
different bands), mean reflections per band, brightness, average NDVI etc. For most of
the scales at which the image was segmented, single segments were sometimes
covering different RBS samples with different vegetation types. This means that the
spectral difference between these classes is not enough to separate these areas in the
first place as different forest patches at appropriate scale. This is illustrated in the
examples of segments at different scales as presented in Annex E.
The different variables of vegetation class samples were compared but no clear
indications of possible improvements were found. Most forest types that were difficult
to distinguish in the first classification (like Moist semi-deciduous (class 2) and Lowland
moist riverine or groundwater forest (class 4)) had similar values for structure and
spectral variability within the segments as well. An overview of calculated variables of
the different vegetation type samples is given as box plots in annex F. No multivariate
19

statistics analysis of different segment variables for vegetation types was done in this
study. Due to similar variable values for the different vegetation classes that are hard to
distinguish, no major improvements are expected using this object based image
analysis. More expert knowledge on structure differences between the different
vegetation classes (canopy structure, tree crown shape, etc.) could be used to further
improve using this technique.
To improve the classification using a DEM, the classification result was first compared
with the 10m resolution DEM of the concession area. For each of the vegetation classes
the range of elevation in which they were found during the field work was described in
the report ‘Rapid Botanic Survey of Mt Nimba’ (Hawthorne, 2009). This range was
compared to the elevation levels in which the different vegetation classes were
classified. An overview is given in table 2.
20

Table 2; Total area (in hectares) per vegetation class for the different altitude ranges
within the mining concession area. Green marked cells are altitude ranges were a
vegetation class was found during the botanical surveys. Orange marked cells are
probably misclassified in terms of elevation.
Min
(m)
Max
Secondary forest 500 910 0,6 38,2 203 245 157 103 48,5 37,0 24,0 5,4 1,9
Moist semi-deciduous forest 500 730 0,1 30,2 121 142 75,0 51,6 23,4 14,4 5,1 1,3 0,3
Drier semi-deciduous forest 500 800 0,2 23,0 99,9 87,4 44,2 28,1 15,3 10,5 4,8 5,4 1,2
Lowland moist riverine forest 440 730 0,5 14,9 52,3 62,9 39,8 26,0 16,9 11,8 3,9 2,4 0,3
Moist evergreen forest 500 760 0,4 9,8 43,9 45,1 22,4 18,8 10,5 10,6 5,7 1,9 1,0
Nimba Upland evergreen forest 800 1010 1,1 27,1 181 326 331 375 277 231 159 130 40,6
Lower altitude secondary thck. 500 750 0,1 0,7 12,2 42,3 34,1 23,2 22,4 12,9 6,5 2,2 1,7
Gallery forest 980 1320 1,1 9,8 75,6 124 191 283 223 206 143 120 66,3
High altitude forest or thicket 1200 1620 0,0 4,0 36,5 75,1 135 187 186 205 244 251 196
Lowland guinea savannah 560 745 0,0 0,0 0,0 62,4 60,3 5,6 7,3 7,3 16,1 2,7 1,5
Lowland edaphic savannah 520 690 0,0 0,1 8,6 62,5 69,5 55,8 73,2 39,3 41,7 5,0 3,1
Medium altitude savannah 860 1280 0,0 0,2 29,9 165 306 378 396 186 223 98,1 43,9
Montane savannah 1200 1670 0,0 6,4 99,2 576 814 962 1580 2014 2478 1933 895
(m) 600-700
700-800
800-900
900-1000
1000-1100
Total 4 164 964 2018 2283 2501 2883 2988 3357 2560 1253
Many vegetation classes were classified at altitudes where they are not found in the
field according to the botanical survey report. To reduce this error, a new classification
strategy was applied in which the 90 meter resolution DEM (available for the whole
study area) was used. Firstly, the DEM was separated into three altitude ranges
(lowland: 1000m). A separate
classification was made for each of the three altitude ranges, without training areas of
those classes that were not found at that altitude range. To objectively asses the
classification quality and to compare the accuracy with the first classification, the same
validation strategy was applied as described in paragraph 2.2.
The vegetation classes used for the different altitude ranges were the following. The
lowland areas could not be classified as Nimba Upland evergreen forest, Gallery forest,
High altitude forest / thicket, Medium altitude savannah or Montane savannah.
Medium altitude areas could not be classified as Moist semi-deciduous forest, Lowland
moist riverine forest, Lower altitude secondary thickets, High altitude forest / thicket,
Lowland guinea savannah, Lowland edaphic savannah or Montane savannah.
Upland areas could only be classified as Nimba Upland evergreen forest, Gallery forest,
High altitude forest / thicket, Medium altitude savannah or Montane savannah.
After running these classifications a classification map was created with the combined
results of the three separate classifications (figure 6).
According to the separate classification for the validation of the result of this technique
(again with four times a random validation set and a classification with the remaining
samples as described in paragraph 2.2) the percentage of correctly classified pixels is 57.
This is considerably better than the classification without using a DEM which had 49%
pixels classified correct. The confusion matrix of this improved classification is given in
annex B.
1100-1200
1200-1300
1300-1400
1400-1500
21
1500-1600
1600-1700

Figure 4; Improved SPOT satellite image classification based on a supervised maximum likelihood classification method
with equal a-priori probability weighting applied to three altitude ranges separately.

2.5 Discussion / Recommendations
Although the second classification has improved the classification accuracy compared to
the first classification, the reliability of the classification is still quite low. In particular,
forest vegetation classes are difficult to distinguish and even on the improved
classification most classes do not exceed accuracies of about 35%.
Some areas have a rather low classification confidence. This does not directly relate to
the classification accuracy in terms of low confidence areas having low classification
accuracy. The reflection in the different bands of a pixel with the lowest classification
confidence (confidence class 14) has the largest difference with the class it was assigned
to. Overall, areas with savannah cover have a higher classification accuracy than forest
covered areas, but classification confidence is lower because spectral variation in these
classes is much larger (see annex C). This large spectral variation was even larger
because parts of the savannah areas were burnt at the time the satellite image was
acquired (see figure 1, dark and bright green areas represent burnt and unburned
savannah respectively).
Another reason for low classification confidence is under sampling of certain regions in
the image. The lowland areas outside the reserve boundary were not sampled during
the field visits. These areas are possibly covered by vegetation classes that were not
sampled. They are classified to the class with most similar reflection characteristics but
with a very low confidence (see figure 5). Validation was done with samples within the
Guinean part of the reserve only. There is a higher classification uncertainty for the
areas that were not sampled.
There are some options for further classification improvements. The time span between
image acquisition and field visits was 1 and 1,6 year for the first and second field
campaigns respectively. By using an image that was taken in the same period, the
classification accuracy might be improved. Especially for samples that were taken close to
transitional vegetation types between forest and thicket for example, this time span
could lead to errors in the classification.
As already mentioned, the possibility of incorporating multi temporal data was tested
(with MODIS EVI images), however, due to the low spatial resolution of these data, no
improvements could be made. It is however likely that by using images from both the
wet and the dry season, forest classes like evergreen and deciduous forest can be better
distinguished.
The spectral difference between the classes is low for the four bands in the SPOT image
that was used (annex C). This is probably the most important reason for the low
accuracy of the classification. Most of the classes can not be distinguished based on the
spectral information at these wavelengths only. Remote sensing data that contains
more spectral detail, or provides reflectance information at other wavelengths might
lead to a better result. The wavelengths at which SPOT 5 satellite measures reflection
are known to give most information about vegetation cover though (Lillesand et al,
2004).
23

For this classification, some improvements were made by using the 90m SRTM DEM
which was available for the whole study area. Extensive field knowledge (empirical
rules) and auxiliary data like environmental variables could help to further improve
classification accuracy. For most of the variables that probably have a strong influence
on occurrence of vegetation types, no or limited data is available. Knowledge on
relations between these environmental variables and occurrence of vegetation types
was furthermore lacking. If relations between rainfall amounts/distribution, soil depth,
geology or other influencing factors and occurrence of vegetation types are known and
data is available, empirical rules can be made to be used to further improve the
classification.
It is however doubtful whether the vegetation classes as defined for this study can be
identified with remote sensing data. The vegetation classes are characterised based on
mainly species composition of vegetation in both the canopy and the undergrowth
layer. The canopy layer has the strongest influence on reflection characteristics of the
different vegetation classes but spectral differences between vegetation classes are low,
especially for forest classes. The possibilities of using remote sensing data to separate
these different classes turned out to be limited.
24

3 Bioquality map
For each of the samples from the botanical survey, a bioquality score was provided in
the report ‘Rapid Botanic Survey of Mt Nimba’ (Hawthorne, 2009). This bioquality
score is calculated from the occurrence of rare species found within each of the samples.
All species within a sample were rated according to a global rarity rating called Stars.
With these star ratings, an index of degree of endemicity (Genetic Heat Index; GHI) can
be calculated. More details on this method are given in Hawthorne (2009).
The bioquality scores of the samples were used to estimate the bioquality for the rest of
the study area. This was done by linking the DEM adapted satellite image classification
to the bioquality of the sample to which a pixel was assigned in the classification. This
means that each pixel within the study area is given the same GHI value as the
vegetation sample it most resembles. For some of the samples, no GHI was available.
By linking the classification to the different original samples instead of linking it to the
average GHI of the vegetation class it was assigned to, the large variability of GHIs
within vegetation classes is maintained.
It has to be kept in mind that the GHI is purely based on species composition within a
sample and that the maximum likelihood satellite image classification is looking at
surface reflection characteristics due to differences in surface structure and colour. Single
rare species will hardly influence these characteristics.
Linking the GHI of individual samples to the image classification is based on the
assumption that species composition in a given area is related to reflection characteristics
of that area. This means that the GHI of unknown areas is probably most similar to the
GHI of the sample it most resembles. The validity of this assumption is however
questionable and the reliability of the bioquality map made with this approach is
uncertain.
25

Figure 7; Genetic Heat Index map of mount Nimba based on the DEM adapted
maximum likelihood classification and the Genetic Heat Index values of RBS samples.
26

4 Distribution maps of rare species
4.1 Methods
The distribution maps of rare species within the study area are based on two datasets.
The first dataset is list of species found in the field campaigns in Nov-Dec 2007 and Jul-
Aug 2008, on the Guinean part of Mt. Nimba. In this list, rare species are classified as
black and gold star species. These star ratings have the highest degree of endemicity.
The definitions of black and gold stars are given below.
Black Star Endemic to Mt. Nimba and a limited range beyond, occupying on
average 1-2 degree squares globally.
Gold Star Upper Guinea endemics, slightly more widespread than Black Star, or
also in Lower Guinea but very scattered. Occupying about 3 (if
abundant) - 12 (if sparse) degree squares.
For each of the black and gold star species found during the field campaigns, an
abundance score is provided (1=scattered, 2=common, 3= very abundant in the
sample area). These abundance scores are also displayed within the distribution maps.
From the complete list of black and gold star species a selection of relevant species was
made after consulting Dr. C.H Jongkind from the Herbarium Vadense, Wageningen
Agricultural University.
The second dataset is a selection of black and gold star species found on Mt. Nimba,
from the collection of Dr. C.H Jongkind. No abundance score is given for this dataset.
For one species (Kotschya lutea), manually drawn distribution maps were made based
on observations in the field by J.M. Reitsma. These maps were also used to create the
distribution map of this species.
For each of the selected species, a separate map was created. A list of the different
species and the number of RBS plots in which they were found is given in table 3.
27

Table 3: Selected rare species found on Mt. Nimba with their star rating, vegetation
type in which they were found, total number of samples containing this species and
number of samples in which they were found within the concession area.
Species Star Vegetation type
Brachycorythis paucifolia GD
Savannah high
Bulbophyllum scariosum Summerh. GD
Forest (epiphyte) high
Croton aubrevillei J.Léonard BK
Forest medium and low
Dolichos nimbaensis Schnell BK
Savannah medium and high
Dracaena calocephala Bos BK
Forest medium and low
Gladiolus praecostatus BK
Savannah high
Guibourtia leonensis J.Léonard BK
Forest low
Gynura micheliana GD
Savannah medium and high
Heterotis jacquesii GD
Savannah high
Hibiscus comoensis A.Chev. ex Hutch. & Dalz. BK
Forest low
Hypolytrum cacuminum Nelmes BK
Savannah high
Kotschya lutea BK
Savannah medium and high
Nemum bulbostyloides (S.S.Hooper) J.Raynal BK
Savannah high to low
Osbeckia porteresii Jacq.-Fél. BK
Savannah & rock slopes high
Psychotria ombrophila (Schnell) Verdcourt BK
Forest low
Sabicea harleyae Hepper BK
Savannah and forest edge medium and high
Trichoscypha barbata Breteler BK
Forest low
Uapaca chevalieri Beille BK
Forest medium and high
Vernonia nimbaensis BK
Savannah medium and high
Xyris festucifolia Hepper BK
Savannah medium
28
Total number of plots
Plots within mining
concession area
% plots within mining
concession area
13 5 38
4 3 75
3 1 33
12 6 50
15 0 0
10 3 30
4 0 0
28 11 39
11 6 55
2 0 0
16 6 38
5 5 100
9 1 11
10 7 70
1 0 0
5 3 60
6 0 0
8 4 50
20 8 40
1 0 0

4.2 Distribution maps
29

5 Literature
Lillesand, T.M., Kiefer, R.W. and Chipman, J.W., 2004, Remote Sensing and Image
Interpretation. 5th ed. New York: John Wiley & Sons, Inc.
Hawthorne, W., 2009, Botanical Survey of Mt Nimba. Unpublished report to SMFG,
Guinea.
49

Annexes
Annex A: Confusion matrix of the satellite image classification
Annex B: Confusion matrix of the improved satellite image classification
Annex C: Average reflection characteristics of vegetation classes
Annex D: EVI development of vegetation classes
Annex E: Examples of the segmented image at different scales
Annex F: Box plots of different variables of segments per vegetation type
51

Annex A: Confusion matrix of the satellite image classification
Values in green are numbers of pixels classified correct. Values in orange are classes with more misclassified pixels than correct
classified pixels in either of the two directions. Values in red are classes with more than twice as many misclassified pixels,
compared to the number of correct classified pixels. Overall fraction of correct classified pixels is given in the lower right corner.
User and producer accuracy are calculated by dividing the total number of pixels in a class by the number of correctly classified
pixels.
11 Lowland edaphic savannah Reference data
891 364 1255 0,71
12 Medium altitude savannah 1 2 4 3 4 5 6 7 8 72 9 10 30 11 1248 12 1131 13 2485 0,50
13 Montane savannah 2 21 32 330 396 372 9698 10851 0,89
total 1197 2516 1401 676 2385 2364 392 3640 3339 560 2321 1692 11557 34040
producer accuracy 0,29 0,13 0,25 0,27 0,11 0,57 0,13 0,20 0,28 0,36 0,38 0,74 0,84 fraction correct 0,49
user accuracy
total
Montane savannah
Medium altitude savannah
Lowland edaphic savannah
Lowland guinea savannah
High altitude forest or thicket
Gallery forest
Lower altitude secondary thickets
Nimba Upland evergreen forest
Moist evergreen forest
Lowland moist riverine forest
Drier semi-deciduous forest
Moist semi-deciduous forest
Secondary forest
1 Secondary forest 350 962 276 108 565 168 217 208 360 3214 0,11
2 Moist semi-deciduous forest 188 324 204 80 315 108 63 56 45 55 26 1464 0,22
3 Drier semi-deciduous forest 125 238 348 172 305 168 72 108 1536 0,23
4 Lowland moist riverine forest 210 510 375 180 605 54 35 72 108 2149 0,08
5 Moist evergreen forest 74 172 51 92 260 84 168 117 1018 0,26
6 Nimba Upland evergreen forest 151 148 102 40 250 1350 7 1736 1143 4927 0,27
7 Lower altitude secondary thickets 45 104 20 24 49 18 13 273 0,18
8 Gallery forest 41 42 33 60 324 712 423 1635 0,44
Classification data
9 High altitude forest or thicket 13 10 12 4 5 84 584 945 72 104 1833 0,52
10 Lowland guinea savannah 200 979 221 1400 0,14
52

Annex B: Confusion matrix of the improved satellite image classification
Values in green are numbers of pixels classified correct. Values in orange are classes with more misclassified pixels than correct
classified pixels in either of the two directions. Values in red are classes with more than twice as many misclassified pixels,
compared to the number of correct classified pixels.
13 Montane savannah 351 6916 7267 0,95
Reference data
total 2368 2328 1088 441 913 1706 360 1560 1991 560 2272 1347 8467 25401
1 2 3 4 5 6 7 8 9 10 11 12 13
producer accuracy 0,14 0,17 0,43 0,64 0,29 0,61 0,93 0,23 0,46 1,00 0,78 0,69 0,82 fraction correct 0,57
user accuracy
total
Montane savannah
Medium altitude savannah
Lowland edaphic savannah
Lowland guinea savannah
High altitude forest or thicket
Gallery forest
Lower altitude secondary thickets
Nimba Upland evergreen forest
Moist evergreen forest
Lowland moist riverine forest
Drier semi-deciduous forest
Moist semi-deciduous forest
Secondary forest
1 Secondary forest 341 280 75 27 80 34 2 839 0,41
2 Moist semi-deciduous forest 276 386 124 26 104 4 4 924 0,42
3 Drier semi-deciduous forest 324 324 468 69 231 138 1554 0,30
4 Lowland moist riverine forest 580 664 272 284 212 8 2020 0,14
5 Moist evergreen forest 250 345 135 35 265 10 1040 0,25
6 Nimba Upland evergreen forest 516 1038 840 774 3168 0,33
7 Lower altitude secondary thickets 14 329 14 21 336 14 728 0,46
8 Gallery forest 56 496 360 200 1112 0,32
Classification data
9 High altitude forest or thicket 360 909 72 27 1368 0,66
10 Lowland guinea savannah 560 490 180 1230 0,46
11 Lowland edaphic savannah 11 1782 66 1859 0,96
12 Medium altitude savannah 108 924 1260 2292 0,40
53

Annex C: Average reflection characteristics of vegetation classes
Average reflection RED (0.61-0.68_m) +/- StDev
Average reflection GREEN (0.50-0.59_m) +/- StDev
140
140
120
120
100
100
80
80
60
60
40
40
20
20
0
0
Montane savanna
Medium altitude
savanna
Lowland edaphic
savanna
Lowland guinea
savanna
High altitude
forest or thicket
Gallery forest
Lower altitude
secondary
thickets
Nimba Upland
evergreen forest
Moist evergreen
forest
Lowland moist
forest
Drier semideciduous
forest
Moist semideciuous
forest
Secondary forest
Montane savanna
Medium altitude
savanna
Lowland edaphic
savanna
Lowland guinea
savanna
High altitude
forest or thicket
Gallery forest
Lower altitude
secondary
thickets
Nimba Upland
evergreen forest
Moist evergreen
forest
Lowland moist
forest
Drier semideciduous
forest
Moist semideciuous
forest
Secondary forest
Average reflection NIR (0.78-0.89_m) +/- StDev
Average reflection SWIR (1.58-1.75_m) +/- StDev
250
160
140
200
120
150
100
80
100
60
40
50
20
0
0
Montane savanna
Medium altitude
savanna
Lowland edaphic
savanna
Lowland guinea
savanna
High altitude
forest or thicket
Gallery forest
Lower altitude
secondary
thickets
Nimba Upland
evergreen forest
Moist evergreen
forest
Lowland moist
forest
Drier semideciduous
forest
Moist semideciuous
forest
Secondary forest
Montane savanna
Medium altitude
savanna
Lowland edaphic
savanna
Lowland guinea
savanna
High altitude
forest or thicket
Gallery forest
Lower altitude
secondary
thickets
Nimba Upland
evergreen forest
Moist evergreen
forest
Lowland moist
forest
Drier semideciduous
forest
Moist semideciuous
forest
Secondary forest
54

Enhanced Vegetation Index (EVI)
Enhanced Vegetation Index (EVI)
Annex D: EVI development of vegetation classes
0,7
0,6
0,5
0,4
0,3
0,2
0,1
0,7
0,6
0,5
0,4
0,3
0,2
0,1
0
0
1-Dec-07
1-Dec-07
55
1-Jan-08
1-Jan-08
average EVI development of savannah samples
1-Feb-08
average EVI development of forest and thicket samples
1-Feb-08
1-Mar-08
1-Mar-08
1-Apr-08
1-Apr-08
Lowland guinea savanna
Lowland edaphic savanna
Medium altitude savanna
Montane savanna
Secondary forest
Moist semi-deciuous forest
Drier semi-deciduous forest
Lowland moist forest
Moist evergreen forest
Nimba Upland evergreen
forest
Lower altitude secondary
thickets
Gallery forest
High altitude forest or thicket

Annex E: Examples of the segmented image at different scales
The training areas are projected on top of the segments labelled with their vegetation
type code (vegetation type code 2 = Moist semi-deciduous forest; 3 = Drier semideciduous
forest; 4 = Lowland moist forest; 7= Lower altitude secondary thickets).
Images that were segmented at scales > 10 (which seems an appropriate scale for
uniform forest patches in this image), single segments are covering different vegetation
type samples, which illustrates their spectral similarity.
Scale 5
Scale 10
56

Scale 15
Scale 50
57

Annex F: Box plots of different variables of segments per vegetation type
Segment values are based on segments (created at scale 10) that cover at least 50% of a
training area.
58

Consultants for environment & ecology
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Tel +31 345 512 710
Fax + 31 345 519 849
E-mail: info@buwa.nl
Website: www.buwa.nl