Comparison of the Classification of the East African
Coastaline habitats using Loiczview clustering tool and
expert knowledge of the area
Report of Resultsof the LOICZ Workshop -Den Haag, July
2-5, 2001
Amani S. Ngusaru1 and Mwakio P. Tole2
1
University of Dar es Salaam, Institute of Marine Sciences, P.
O. Box 668, Zanzibar, Tanzania
2
School of Environmental Studies, Moi University, P. O. Box
3900, Eldoret, Kenya
Introduction
This work was carried out to test the hypothesis that in many
geospacial data sets there are certain basic variables –
climatological or hydrological, for example, that are related to
other qualities of the environment, such as flora or fauna and
chemical fluxes
(http://www.kgs.ukans.edu/Hexacoral/Tools/Workshops/L
OICZWhitepaperF00.pdf-Mic). According to this
hypothesis, since a typology based on the basic variables
produces a set of typical environments, it should be possible to
develop predictive models using sparce measurements of
biological, physical or chemical variables in each of these
typical environments. This hypothesis has led to the
development of LOICZVIEW clustering tool (LOICZ, 2000;
Maxwell, 2001; Maxwell and Buddemeier, 2000; Rissanen,
1989; Web – LoiczView, 2000) a high power computer
software program and data set for classifying the coasts of the
world.
Goals
 To verify the relevance of the LOICZVIEW clustering
tool to modelling the Eastern Africa Coastal habitats
 To identify the variables that control the habitat
characteristics in the Eastern Africa Coastline.
 To determine the minimum number of parameters that
are able to reproduce the known habitat classifications in
the Eastern Africa coastal region.
Study Area.
Cells 13 and 19 – Western Indian Ocean – the East African
Coastal habitats (figure 1).
Methods.
A number of experiments were run to find out which clusters
can most closely reproduce the known (expert) habitat map of
the Western Indian Ocean Coast (Kamau, 2001).
Coastal Cell types were selected from database.
Experiment 1
In experiment 1 we clustered using all variables in the
database, except basin data .
The MDL analysis gave 27 clusters as being optimal for this
classification.
The result was a very complicated cluster map (figure2) that
was not useful in describing the habitats of the coastline. At
the same time, it was not possible to generate a data summary,
due to limited memory allocation.
Experiment 2
In the second experiment, a limited number of variables,
thought to be important for habitat characteristics
determination were selected. These were:  Windspeed
 Air temperature
 SST
 Precipitation
 Anthropogenic effects (population/ Urban percentage)
 Salinity
 Wave height
 Tidal Range
 Runoff
 Land Cover
MDL analysis gave 23 clusters as being optimal.
The resulting cluster map (figure 3) was not much different
from that of experiment 1. Also it was not possible to generate
a data summary, due to limited memory allocation.
Experiment 3
Going by the experience of experiments 1 and 2, it was
decided to experiment with individual parameters, with the
aim of combining those that most closely reproduced the
desired habitat map. These could then later be combined to
refine the map to match the existing one on habitat
distribution.
In this experiment, Rainfall (Total, Average minimum
monthly, Average maximum monthly) was used to produce a
cluster map.
MDL analysis gave 9 clusters as optimal.
This indicated that the more variables used, the greater the
number of clusters required for optimal classification.
Figure 1. Classification of the East African
Coastline according to Habitats (Kamau, 2001).
Note that the Northern most coastline has been
classified as a Monsoon Coast. This is a habitat that
is affected by climatological factors related to the
Monsoons.
Figure 2 – Results of Experiment 1- All
Variables.
Figure 3 – Results of Experiment 211 variables
Figure 4 – Results of Experiment 3 Rainfall.
Figure 5 – Results of Experiment 7 Salinity.
Results showed that rainfall alone could not give the observed
classification as shown in figure 4. The Zanzibar/Tanga sector
was classified as being different from the Malindi and Dar es
Salaam sectors, while in reality these sectors have similar
climatic characteristics. Although the clustering gave
systematic patterns that almost mimiced the observed habitats,
in the Central Mozambique sector, the swamp coast was not
distinguished from the coral coast. This is not unexpected,
because rainfall cannot distinguish between say mangrove
coasts and coral habitats.
Otherwise rainfall seems to be a good parameter for coastal
classifications in Eastern Africa.
Experiment 4
In this experiment, airtemperature (Monthly average,
minimum, and maximum) were added to the rainfall.
MDL gave 14 clusters.
Results produced much wider scatter on the diagram than
shown in figure 4. This indicated that air temperature is not
important in habitat classification in the East African coast.
This is surprising, since air temperature should be important in
habitat (climatic) classification. The reason may be due to the
data used (quality/reliability) or the procedures used in the
clustering for the air temperature parameter when combined
with other parameters.
Experiment 5
In this experiment, air temperature was replaced with runoff
(annual average).
The results were similar to those of rainfall alone.
Experiment 6
In this experiment, Rainfall (Total, Average minimum
monthly, Average maximum monthly) and Salinity (annual
average, gradient) were used to produce a cluster map.
The results could not demarcate the Mozambique swamp coast
as a distinct unit.
Experiment 7
In this experiment, salinity (annual, gradient) was used to
cluster the coasts.
Results showed that the Zanzibar/Tanga coast was similar to
Mombasa/ Dares Salaam sectors in terms of salinity
parameters (figure 5). This casts doubts on the rainfall data in
the database for Zanzibar/Tanga area.
However, the map clearly demonstrated that swamp coasts can
be differentiated from other coasts on the basis of salinity, but
coral coasts cannot be distinguished from dune coasts, which
is expected, because corals are in the oceanic part of the coast
while dunes are terrestrial. Salinity can therefore depict
different aquatic habitats.
Experiments 8, 9, 10, 11, 12.
These experiments respectively tested population density, soil
types (organic and carbonate contents), vegetation (all
parameters in the database), vegetation (mixed forest),
vegetation (wooded grasslands) in reproducing the habitat
map.
Population density has no relationship with habitat types.
Soil types produced complex patterns that were not related to
habitat types.
Vegetation (all types) produced complex patterns that were
not related to habitat types. This was unexpected, so it was
decided to investigate further the different types of vegetation
that were more typical of the East African coastline (mixed
forest, wooded grassland).
Mixed forest completely failed to classify the coast, because
the whole area was classified as one uniform coast type. This
was unexpected, and there is need to find out what type of
mixed forest data exists in the dataset for the East African
region. There are known variabilities in the types of forests in
the region.
The wooded grassland differentiated the north eastern Africa/
Arabian coasts from the south eastern Africa coasts. However,
there was more complexity in terms of clusters in the south
eastern Africa region, to the extent that it was not a useful
parameter in delineating habitats. This is probably due to
paucity of data in the north eastern part of Africa. There were
values of 0 (zero) in the north eastern Africa/Arabian coasts,
and it is not clear what zero means in this database.
Experiments 13
This experiment used Tidal range, Elevation and Wave Height
variables.
MDL analysis gave 16 clusters as optimal for classfication.
Results did not accurately produce the existing habitat map,
but was able to differentiate the coastal areas with no direct
relationship to the habitats.
Experiment 14
This experiment used Tidal range and Wave Height variables.
MDL gave 8 clusters as optimal for classification based on
those variables.
Results most accurately reproduced the habitat map (figure 6).
Experiments 15, 16, and 17
These experiments respectively tested Waves, tides, and bare
ground variables individually.
The results show that waves alone gave a very simple cluster
map, as did the tides alone. Bare ground gave uniform coastal
classification south of Somalia, but fairly detailed and
complex classification north of Somalia. This was not
reflective of the situation on the ground.
Experiment 18 and 19
This experiment tested Sea Surface Temperatures (mean
monthly, maximum, minimum, and range) as a variable to
clasify the Eastern African Coast.
The result was a good reproduction of the habitat map as
shown in figure 7. The use of mean SST alone produced
fyrther details of the cluster map, especially in the central
areas of the Mozambiquian coast, which was difficult to
explain.
Figure 6: Experiment 14 – Tides and
Wave Height variables
Figure 7: Experiment 18 – SST (Mean
monthly, Min, Max) Range
DESCRIPTION Fig. 6
 The coastal area south of the horn
of Africa down to Somali Kenya border is
classified as a single coastal entity that seems to
reflect the Monsoon Coast.
 The Grey cluster in between could not be
explained
 The bright green cluster seem to indicate the coral
coast stretching from Mombasa to the Northern
coast of Mozambique.
 The blue cluster in central Mozambique coast seen
to indicate the swamp coast.
 The yellow cluster down the southern coast of
Mozambique seem to indicate the dune coast in
that area.
Figure 8 – Experiment 21 – Combined variables
that produced good fit with the habitat map (No.
Of Islands, Precip.(total, intraann. Std) Elevation
(Std. Dev), SST (Inta ann. Std.dev), Salinity
(Ann. Avg.), Wave Height, Tidal Range)
Experiment 20
This experiment tested wind as a variable that would be
expected to be important in the classification of the East
African coast, especially due to the influence of the monsoons.
However, the results produced a highly fragemented cluster
map that did not lend itself to meaningful interpretation of the
coastal habitats.
Experiment 21
Having systematically identified the important parameters that
seem to be controlling the distribution of coastal habitats in
the East African coastline, these were combined together to
test their collective impact on the habitats.
This experiment tested the following variables: Number of Islands (indicative of coral reef islets)
 Precipitation (12 months total, intra annual std. Dev.)
 Elevation (std. Dev. – G30 value)
 Sea Surface Temperature (Intra annual Std. Dev.)
 Salinity (annual average)
 Wave height
 Tidal range
MDL analysis produced 16 clusters, but 11 were applied as
this was the point of most rapid decrease in the number.
The results (figure 8) were the best match to the habitat map.
Conclusions
These results indicate that the LOICZVIEW modelling tool is
easy to use, and yet powerful in terms of characterisation of
different coastal ecosystems.
Based on this analysis, it appears that 5 variables
(Precipitation, Salinity, Sea surface temperature, Wave height
and tidal range) are the most important factors in determining
the habitat characteristics in the Western Indian Ocean region.
Air – sea interactions, more than land influence, influence the
ecological and coastal processes responsible for creation and
maintenance of the coastal habitats and ecosystems (coral
reefs, mangrove swamps, dune fields, tidal).
It appears that two parameters (Wave heights and Tidal range)
are able to reproduce as good a habitat map as the five
parameters in combination. This requires further analysis
using more comprehensive wave and tide data.
References
1. Kamau, I. (2001) Preliminary assessment of the Western
Indian Ocean sensitive marine ecosystems. Eco-Region
Project. WWF, Dares Salaam country Office.
2. LOICZ (2000) Land Ocean Interactions in the Coastal
Zone, http://www.nioz/loicz.
3. Maxwell, B. A. (2001) Visualising Geographic
Classifications Using Colour. J. of Cartography (in
Press).
4. Maxwell, B. A. and Buddemeier, R. (2000) Coastal
Typology Development with heterogeneous Data Sets.
Kansas geological Survey Open-File Report 2000-53.
5. Rissanen, J. (1989) Stochastic Complexity in Statistical
Inquiry. World Scientific Publishing Co. Ptc. Ltd.
Singapore.
6. Web – LoiczView (2000):
http://www.palantir.swathmore.edu/loicz
7. Whitepaper (2000)
http://www.kgs.ukans.edu/Hexacoral/Tools/Workshops/L
OICZWhitepaperF00.pdf-Mic