Ocean & Coastal Management 83 (2013) 19e24
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Ocean & Coastal Management
journal homepage: www.elsevier.com/locate/ocecoaman
Mapping of mangrove forest land cover change along the Kenya
coastline using Landsat imagery
K.B. Kirui a, *, J.G. Kairo a, J. Bosire a, K.M. Viergever c, S. Rudra b, M. Huxham b, R.A. Briers b
a
Kenya Marine and Fisheries Research Institute, P.O. Box 80100-81651, Mombasa, Kenya
School of Life, Sport and Social Sciences, Edinburgh Napier University, Sighthill Campus, Edinburgh EH11 4BN, UK
c
Ecometrica Ltd., Top Floor, Unit 3B, Kettle Yards, Edinburgh EH9 1PJ, UK
b
a r t i c l e i n f o
a b s t r a c t
Article history:
Available online 16 December 2011
Mangroves in Kenya provide a wide range of valuable services to coastal communities despite their
relatively small total area. Studies at single sites show reductions in extent and quality caused by
extraction for fuel wood and timber and clearance for alternative land use including saltpans, aquaculture, and tourism. Such studies suggest that Kenyan mangroves are likely to conform to the general
global trend of declining area but there are no reliable recent estimates of either total mangrove extent or
trends in coverage for the country. The total extent of Kenyan mangroves was estimated at four points in
time (1985, 1992, 2000 and 2010) using Landsat satellite imagery. Due to its medium resolution, Landsat
may underestimate mangrove areas in Kenya where relatively small, linear, coastal features occur. There
is also a high frequency of clouds in the coastal areas which can cause data gaps during analysis. However
comparison with aerial photographs taken in 1992 showed satisfactory levels of accuracy (87.5%) and
Cohen’s Kappa (0.54) validating its use in this context. These 1992 data provided an independently
validated baseline from which to detect changes (fore- and hind-casted) in other periods after removing
cloud coverage. We estimated total mangrove coverage in 2010 at 45,590 ha representing a loss of 18%
(0.7% yr1) in the 25 years between 1985 and 2010. Rates of mangrove loss for Kenya varied both spatially
and temporally with variations possibly due to legislative inadequacies and differences in habitat
alteration patterns. Hence freely available Landsat images proved adequate to detect changes in
mangroves and revealed that Kenya shows rates of decline similar to (although slower than) global
estimates.
Ó 2011 Elsevier Ltd. All rights reserved.
1. Introduction
Mangroves are dominant along many tropical and sub-tropical
coastlines and are one of the most productive ecosystems on
earth with a mean production of 8.8 t C/ha/yr (Jennerjahn and
Ittekkot, 2002). They provide a wide range of ecosystem goods
(including fuel wood, medicine, food, construction materials) and
services (including fisheries nursery grounds, sediment trapping
and sewage phytoremediation) of immense value to local, national
and global communities (Barbier et al., 2008). Their ability to
sequester and store carbon makes them important candidates for
conservation efforts under schemes such as Reduced Emissions
from Deforestation and Degradation (REDDþ) (Donato et al., 2011).
Despite this importance, these unique coastal forests are among the
* Corresponding author. Tel.: þ254 41 475151; fax: þ254 20 3533226.
E-mail addresses: bkirui@kmfri.co.ke, kiruib@yahoo.com (K.B. Kirui).
0964-5691/$ e see front matter Ó 2011 Elsevier Ltd. All rights reserved.
doi:10.1016/j.ocecoaman.2011.12.004
most threatened habitats in the world (FAO, 2007). Their estimated
global coverage is 137,760 km2 (Giri et al., 2010) which represents
a decline of 23% in area compared with 1990 (Spalding et al., 1997).
Local communities living adjacent to mangrove ecosystems
have traditionally collected fuel wood, fish and other natural
resources from them at rates that were sustainable (DahdouhGuebas et al., 2000). However, in recent decades many coastal
areas have come under intense pressure from rapid urban and
industrial development, compounded by a lack of effective governance and/or power among responsible government institutions.
Mangroves have been overexploited or converted to various other
forms of land use, including agriculture, aquaculture, salt ponds,
urban and industrial development and coastal roads and embankments. Analyses of the true economic value of mangroves indicate
that their destruction for short term profit is usually economically
irrational; in fact, the discrepancy between their value as intact
systems and their value after destruction is one of the greatest for
all habitats (Balmford et al., 2004). Given this market failure and the
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K.B. Kirui et al. / Ocean & Coastal Management 83 (2013) 19e24
threats to this ecosystem it is crucial to accurately determine the
current extent, rates of change and distribution of mangroves to
allow effective conservation planning and management (Fatoyinbo
et al., 2008).
In Kenya mangroves are found along the 536 km coastline which
extends over 3 latitude from 1420 south to 4 400 south (Fig. 1).
Mangroves are common features in protected bays, creeks, estuaries,
and river deltas spread all along the Kenya coast. Two communities of
mangroves (fringe and creek) formations occur along the Kenya
coast. The largest formations occur in the north coast around the
Lamu area and at the River Tana delta, (Ferguson, 1993; Kairo et al.,
2001). Nine species of mangroves are found in Kenya with Rhizophora mucronata and Avicennia marina being the dominant species
(Abuodha and Kairo, 2001). The responsibility for the management
of mangrove forests is entrusted to the Kenya Forest Service
(Abuodha and Kairo, 2001). A commonly quoted estimate of
Fig. 1. The coastal area of Kenya showing major mangrove areas.
K.B. Kirui et al. / Ocean & Coastal Management 83 (2013) 19e24
mangrove area in Kenya is 52,000 ha, a figure dating from 1981
(Doute et al., 1981). Subsequent estimates vary greatly and the
methods used to derive them are not always clear (see reviews in
FAO, 2003, 2007). A recent global estimate of mangrove coverage by
Giri et al. (2010) was 12.3% lower than the estimates by Food and
Agriculture Organization (FAO, 2007), implying large potential errors
in previous estimates for individual countries especially those with
relatively small total areas or where change has been rapid. There is
thus a need for up to date estimates of mangrove coverage in the
country, based on clear methodology, which could allow the
assessment of current forest status and changes over time.
Remote sensing has been used to map coastal habitats and is
often a reliable alternative to ground-survey methods of mapping,
particularly in remote or inaccessible regions. Remote sensing
applications have been applied to mangroves for inventory and
mapping, change detection, and for management purposes. Landsat
and SPOT XS data, as well as high spatial resolution airborne
multispectral and SIR-C radar data were recently applied in
mangrove management in Mozambique and Tanzania (Fatoyinbo
et al., 2008; Ferreira et al., 2009). In Kenya remote sensing has
been applied in a number of studies on mangrove status (Doute
et al., 1981; Gang and Agatsiva, 1992; Dahdouh-Guebas et al.,
2004a), species assemblages (Neukermans et al., 2008) and impacts
of human disturbance (Dahdouh-Guebas et al., 2004b; Obade et al.,
2004). However, only Doute et al. (1981) provided an overview of
mangrove coverage for the country as a whole; the other studies
were concerned with the exploration of high resolution imagery at
local spatial extents.
The high costs of much commercially available high resolution
satellite imagery preclude its routine use in many developing
countries. The present study employed Landsat Thematic Mapper
(TM) imagery because the free availability of archived images
makes the development of capacity and techniques in its use with
mangroves potentially transferable to projects without substantial
funding. Landsat spatial (30 m on the ground) and temporal
(16 days return period) resolution is less than for many recent
commercial alternatives; this might cause particular problems in
habitats that are fragmented or linear, such as mangroves.
In addition extensive cloud cover (which is particularly common in
coastal areas) reduces the accuracy and usefulness of the images.
Although Landsat has limitations that might cause underestimation
of mangrove areas, historical data goes back more than 30 years
which makes it ideal for change estimates. Landsat has been used
by several authors in the large scale mapping of mangrove (e.g. Liu
et al., 2008; Giri et al., 2008, 2010). One objective of the current
work was to explore the usefulness of Landsat to mapping
mangroves in Kenya; where the coverage is relatively small and
fragmented and where cloud cover is common along the coast. The
existence of contemporaneous high resolution aerial images unaffected by clouds allowed the validation of the Landsat image classifications in Kenya. Other key objectives, having tested the
feasibility of using Landsat images, were to establish the total
current coverage in the country and to detect any changes over the
last 25 years. Revised data on extent and conditions of mangrove
forests in Kenya could provide critical information needed for
policy-making and resource management.
2. Data and methodology
2.1. Study region
The Kenyan Coast is situated immediately south of the equator;
it covers a distance of 536 km (Fig. 1) stretching from Ishakani at the
Kenya-Somali boarder in the North to Vanga at the Kenya-Tanzania
boarder in the South (UNEP, 1998). Its notable feature is a well
21
developed fringing reef system running parallel to the coastline
except where major rivers (the Tana and the Athi Sabaki) discharge
into the Indian Ocean (Hamilton and Brakel, 1984). Other features
of the Kenyan coast include mangrove forests (Fig. 1), rocky shores
and sea grass meadows as well as a number of islands to the south.
Approximately three million people inhabit the Kenyan coastal
areas, and the rapidly growing population exerts pressures on most
of the natural environments (Government of Kenya, 2009).
2.2. Data
Landsat images covering the entire Kenyan coastline were
acquired from the US Geological Survey (USGS) Center for Earth
Resources Observation and Science (EROS) website (www.glovis.
usgs.gov). Data collected within the same year and seasons are
best for this kind of study. However simultaneous cloud-free
images of the whole region were not available for all time
periods, prompting the need to combine data taken from images
spanning up to two years into four separate periods for use in
tracking change: a) the ‘1985’ imagery (including images from 1984
to 1985), b) the ‘1992’ imagery (including images from 1990 to
1992), c) the 2000 imagery (including images from 2000 to 2002),
and d) the ‘2010’ imagery (including images from 2008 to 2010).
A total of 5 images were required to cover the entire coastline of
Kenya. Three bands were used (bands 3, 4 and 5) which are in the
red, near-infrared and middle-infrared regions. Other bands were
not used to avoid introduction of atmospheric artefacts that could
cause classification errors (Buchanan et al., 2008).
Medium scale (1:25,000) black and white panchromatic aerial
photographs taken in 1992 exist for the entire Kenyan coastline. The
images are available at the Marine Data Center of the Kenya Marine
and Fisheries Research Institute, Mombasa and provide a very
accurate, cloud free estimate of coverage of mangrove areas along
the whole coast. In the present study, a GIS layer derived from the
aerial photographs was used as reference data for the assessment of
the Landsat classification from the 1992 period; because of the high
accuracy of the distributional data they provided a ‘ground-truthing’ image for the whole coastline at this point in time.
2.3. Image analysis
All images were processed from digital number values to atsatellite reflectance to make them comparable, using the correction procedures detailed in Chander et al. (2009). Individual images
were mosaicked and then initially clipped to a 10 km buffer area
around the coast, to reduce the area of image to be classified. Based
on previous studies it is highly unlikely that mangroves would be
present further away from the coast (Long and Skewes, 1996).
Image classification was undertaken using an unsupervised
maximum likelihood classification algorithm. As only a narrow
strip of coastal land was being classified, the number of classes
specified was kept relatively low. Alternative classifications using
between 10 and 20 classes were undertaken. Classifications with
greater than 20 classes defined failed to converge to a stable classification. Changes in the number of classes had little effect on the
identification of mangrove areas, so the results from a classification
using 12 classes were used in all years. Following initial classification of images, areas classified as cloud cover or cloud shadow were
identified based on examination of color composite images of the
area. These areas were removed from the initial unclassified image,
and replaced with cloud free areas from other images gathered
within the same time period to generate images for analysis with
the minimum cloud cover.
Analysis of the minimum cloud cover images used the same
maximum likelihood technique, with 12 classes. Some areas far from
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K.B. Kirui et al. / Ocean & Coastal Management 83 (2013) 19e24
the coast, which were unlikely to be mangroves, were also classified
in the same class as more obvious mangrove areas. As mangroves are
only found in low-lying coastal areas (FAO, 1994; Long and Skewes,
1996) a height-based filter was used to remove these mis-classified
areas. Elevation data were obtained from the global Digital Terrain
Model (DTM) derived from the Shuttle Radio Topography Mission
(SRTM). SRTM data are influenced by the height of vegetation
overlying the land surface, due to radar scattering in the vegetation
canopy, and this effect has been used to estimate vegetation heights
e.g. Simard et al. (2006). The absolute vertical error of SRTM data has
been estimated as 16 m, although it is likely to be less in the area
under consideration (Rodriguez et al., 2005). For the current application areas with an elevation of 8 m above sea level were defined
as areas that would contain mangroves. This zone was found to
contain 95% of the areas classified as mangrove in the 1992 aerial
data, as was therefore deemed appropriate for use.
The minimum mapping unit for mangrove classification was
chosen to be 0.18 ha. This was based on the distribution of
mangrove areas found in the 1992 aerial-photograph data. Therefore any areas classified as mangroves, but smaller than this, were
removed from the image, provided that they were not coincident
with areas that were mangrove in the previous time period and
hence could represent remaining areas that had been degraded.
Once this had been completed, the total areas of mangrove for each
of the four periods were determined.
The 1992 aerial photograph data provided an opportunity to
assess classification accuracy. Therefore an error matrix was constructed based on comparisons of the 1992 aerial photograph and
Landsat data from the same period. This was used to derive the
percentage classification accuracy (i.e. the percentage of the area
being considered that was classified correctly, either as mangrove
or as non-mangrove) and Cohen’s Kappa (k, range 0e1), an alternative measure used to determine the extent to which the classification is better than random (Jensen et al., 1996).
Total area classified as mangrove could not be directly compared
between periods due to variation in the areas covered by clouds in
different periods. To allow between periods comparisons layers
containing all areas classified as cloud in either period of each
period e pair (i.e. 1985e1992, 1992e2000 etc.) were created. These
areas were then removed from the mangrove layers for both
periods, leaving images that were cloud free over the same locations at both points of time and thus allowing detection of change
in these areas un-confounded by clouds. The cloud free areas for
each year pair were between 68 and 73% of the aerial derived data
from 1992.
The estimates of mangrove area derived from the Landsat
images for each period did not represent the total area present due
to the residual cloud coverage. In order to estimate the actual area
of mangrove at each time point, the total area of mangrove from
the 1992 high accuracy aerial derived data was determined. The
proportion of this area which was classified as mangrove using the
Landsat data was used to derive a total area estimate for the 1992
Landsat classification. Percentage changes calculated between
periods after the removal of clouds were then applied to the 1992
baseline Landsat area to allow calculation of total areas in other
time periods. In addition to estimating total area in this way,
changes in coverage in different major mangrove areas (see Fig. 1)
were also determined to compare rates of change in different areas.
Fig. 2. Changes in mangrove at Gazi-Vanga system in south Coast Kenya between 1985 and 2010.
K.B. Kirui et al. / Ocean & Coastal Management 83 (2013) 19e24
3. Results and discussion
The Landsat classification of mangroves in 1992 had
a percentage accuracy of 87.5% and a kappa coefficient of 0.54, and
was therefore assumed to have performed adequately for the
purposes of assessing temporal change in mangrove extent in
Kenya.
Temporal changes in mangrove cover at GazieVanga system
located in south Coast Kenya is shown in Fig. 2 and the temporal
change in the extent of mangrove in different areas is shown in
Fig. 3. The total area of mangrove has reduced continually in Kenya
since 1985 although the overall rate of loss is not uniform and is
slowing. In 1985 there was an estimated 55,280 ha of mangrove. By
1992 this had reduced to 51,880 ha, a loss of 6.2% over the period or
an average of 0.89% yr1. Total coverage in 2000 was 46,930 representing a further loss of 9.5% (1.19% yr1) and in 2010 it was
45,590 ha (2.8% loss or 0.28% yr1). Hence in the 25 years between
1985 and 2010 Kenya lost 18% of its mangroves at an average rate of
loss of 0.7% yr1. Rates of mangrove loss varied between areas, and
over time (Fig. 3.), although average rates of loss were highest in the
1992e2000 period. Mangroves in the Kilifi area only make up
a small proportion of the total area, but have seen the highest rate
of loss, with an overall loss of w76%, mostly in the period between
1985 and 2000. The large areas of mangrove in the Tana river and
Lamu-Kiunga regions also showed higher rates of loss than other
areas, with an overall reduction of 38% and 12% respectively. Loss
rates across all areas were lowest in the 2000e2010 period. While
no site- specific factors can be attributed to the observed variations
in mangrove cover change, the main drivers of cover loss at local
and national levels in Kenya have been identified as habitat alteration and inadequate legislation (UNEP, 2009). Whereas several
factors may be responsible for the varying rates in mangrove cover
loss, it is noteworthy that the period between 2000 and 2010
witnessed the lowest rate of loss which coincided with the presidential ban on harvesting of mangroves for domestic market. An
earlier ban on mangrove export from Kenya was implemented in
1982; however this did not affect local harvesting until 2000
(Abuodha and Kairo, 2001).
The annual average rate of mangrove loss in Kenya is lower than
that in many other parts of the world. For example, the tsunamiimpacted region of South and Southeast Asia lost about 25% of
mangrove forests from 1975 to 2005 (Giri et al., 2008). At the global
scale, the estimates of total habitat loss within the past century vary
Fig. 3. Changes in areal extent of mangrove in Kenya over 25 years as estimated from
Landsat imageries.
23
from 25% to 50% (FAO, 2007). Conversion to aquaculture is the
single largest driver of forest destruction in most Asian and Latin
American countries; similar aquaculture developments are more
recent and rarer in Kenya and other Western Indian Ocean region
countries (Giri et al., 2008). The average annual rate of loss of 0.7% is
similar to the estimated rate of loss of all types of forest in the
country which currently stands at 0.8% (Karyn et al., 2010).
Earlier estimations of the extent of mangrove cover within
Kenya vary widely ranging from 32,378 ha to 96 ha (summarized in
FAO, 2003, 2007; Giri et al., 2010). Most of these studies- provide an
estimate of total mangrove forest area of the country but do not
provide information on the spatial distribution of forests. Variations in estimation of total cover of mangroves could be attributed
to differences in estimation techniques, time of the survey and how
areas considered to be mangroves were classified. For instance
using aerial photography, the forest department of Kenya estimated
the total area of mangroves in the country as 64,426.90 ha (Forest
Department of Kenya, 1983) which is substantially higher than
our estimate for 1985, but is likely to include smaller, fragmented
mangroves which would not be well captured by the relatively
coarse resolution Landsat data. Giri et al. (2010) recently mapped
global mangrove cover using Landsat data from 1997 to 2000. Their
estimate for Kenya was 32,378 ha which is much lower than our
estimate for 2000 (46,930 ha). A number of reasons may be
responsible for the observed variations. For instance, given the
global approach they employed, they probably had to use
a conservative mapping method, so as not to add areas that are not
classified as mangroves and also their choice of minimum mapping
unit may have excluded the small patches that occur in places in
Kenya.
4. Conclusion
The information on the current status and rates of change of
mangrove forest cover in Kenya presented here will be of value to
forest managers, conservators and other stakeholders in a number
of ways. The findings have improved our understanding of the
spatial distribution of Kenya’s mangroves and assessed their rates
of deforestation. This can support countrywide decision making on
the distribution of resources for the conservation and rehabilitation
of mangrove forests. The new opportunities provided by payments
for ecosystem services schemes, for the conservation and restoration of mangrove forests can be realized only with accurate data on
their rates of historical loss and current extent.
Monitoring deforestation at a country level using moderate
resolution satellite images over a long period of time requires the
processing of large volumes of data. We used simple but efficient
methods to analyze these data. Our approach applied semiautomated image analysis techniques to assess present status and
to monitor the rates of change over a large area covering the entire
country. Our analyses show the potential for producing consistent
and timely mangrove forest databases, sufficient to show the
baseline rates of deforestation required in REDDþ type analyses,
using the historical archive of Landsat data. Crucially such data are
freely available hence a similar approach could be adopted in other
developing countries without requiring expensive commercial
imagery. The full potential of remote sensing technology for identifying mangrove forests, measuring their biophysical properties,
and detecting forest cover changes can only be realized through
a robust and operational mangrove forest assessment and monitoring program. Future research is needed to map mangrove areas
using high resolution satellite images combined with detailed
ground truthing, which could go well beyond the current estimates
of coverage alone and include data on species distributions and
rates of degradation.
24
K.B. Kirui et al. / Ocean & Coastal Management 83 (2013) 19e24
Acknowledgments
This study was supported through the Natural Environment
Research Council (NERC) and the Ecosystem Services for Poverty
Alleviation (ESPA) consortium of UK, grants NE/G008078/1 and NE/
I003401/1. Additional support was through WIOMSA-MASMA
Grant (MASMA/CC/2010/08). We gratefully acknowledge all
support.
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