Journal of Tropical Forest Science 25(4): 554–565 (2013)
Hamdan O et al.
MANGROVE CARBON STOCK ASSESSMENT BY OPTICAL
SATELLITE IMAGERY
O Hamdan1, *, MR Khairunnisa2, AA Ammar3, I Mohd Hasmadi2 & H Khali Aziz1
1
Forest Research Institute Malaysia, 52109 Kepong, Selangor Darul Ehsan, Malaysia
Faculty of Forestry, Universiti Putra Malaysia, 43400 Serdang, Selangor Darul Ehsan, Malaysia
3
School of Geography Planning and Environmental Management, University of Queensland, Brisbane, St Lucia QLD 4072,
Australia
2
Received November 2012
HAMDAN O, KHAIRUNNISA MR, AMMAR AA, MOHD HASMADI I & KHALI AZIZ H. 2013. Mangrove
carbon stock assessment by optical satellite imagery. Matang Mangrove Forest Reserve or known as Matang
Mangroves is the largest single mangroves in Peninsular Malaysia. Covering an area of about 41,000 ha,
majority of this area is forest reserve. Mangroves have long been known as extremely productive ecosystem
that cycle carbon (C) rapidly, but studies related to carbon in this ecosystem are limited. This study was
carried out to assess the carbon stock and quantify their changes following deforestation, wood extraction
and forest degradation. Landsat-TM and SPOT-5 satellite images for 1991 and 2011 respectively were utilised
to identify mangroves. Vegetation index generated from the images was used as a variable to indicate carbon
stock and it was correlated to forest inventory information through regression. The study showed that carbon
stocks of Matang Mangroves ranged from 1.03 to 263.65 t C ha-1 and 1.01 to 259.68 t C ha-1 for the years 1991
and 2011 respectively. Total carbon stock in Matang Mangroves was estimated at about 3.04 mil t C in year
1991 and 2.15 mil t C in 2011. The study suggested that the traditional use of vegetation index from optical
imagery systems is still relevant and viable in vegetative studies.
Keywords: Landsat-TM, SPOT-5, vegetation index, aboveground carbon
HAMDAN O, KHAIRUNNISA MR, AMMAR AA, MOHD HASMADI I & KHALI AZIZ H. 2013. Penilaian stok
karbon hutan paya laut menggunakan imej satelit optik. Hutan Simpan Paya Laut Matang atau lebih dikenali
sebagai Hutan Bakau Matang merupakan hutan paya laut yang terbesar di Semenanjung Malaysia. Meliputi
kawasan seluas kira-kira 41,000 ha, majoritinya merupakan hutan simpan. Hutan paya laut telah sekian lama
terkenal sebagai satu ekosistem yang amat produktif dalam pengitaran karbon (C). Bagaimanapun, kajian
berkaitan karbon dalam ekosistem ini adalah terhad. Kajian ini dijalankan untuk menentukan stok karbon
serta menilai perubahannya akibat daripada pembasmian hutan, pengeluaran kayu dan juga degradasi
hutan. Imej satelit Landsat-TM dan SPOT-5 masing-masing bagi tahun 1991 dan 2011 telah diguna untuk
mengenal pasti hutan paya laut. Indeks tumbuhan yang dihasilkan daripada imej satelit dijadikan pemboleh
ubah sebagai penunjuk kepada stok karbon dan indeks tersebut dihubungkan kepada maklumat inventori
hutan melalui kaedah regresi. Kajian menunjukkan bahawa stok karbon di Hutan Bakau Matang masingmasing berjulat antara 1.03 t C ha-1 hingga 263.65 t C ha-1 dan 1.01 t C ha-1 hingga 259.68 t C ha-1 bagi tahun
1991 dan 2011. Sementara itu, stok karbon yang terdapat di Hutan Bakau Matang dianggarkan berjumlah
3.04 juta t C pada tahun 1991 dan 2.15 juta t C pada tahun 2011. Kajian menyarankan bahawa kegunaan
indeks tumbuhan masih lagi relevan dan boleh dipraktikkan dalam kajian berkaitan tumbuhan.
INTRODUCTION
Mangrove forests play an important role as a
substantial coastal carbon (C) sink. They are
ranked among the most carbon-rich forests
in the tropics with an average storage of
1023 t C ha-1 (Laffoley & Grimsditch 2009).
Mangroves have been estimated to have higher
amounts of carbon than the other types of forest,
with a storage capacity of between 990 and
*hamdanomar@frim.gov.my
© Forest Research Institute Malaysia
554
1074 t C ha-1 (Donato et al. 2011). It is interesting
to note that although plant biomass in the ocean
and coastal areas comprises only 0.05% of the
total plant biomass on land, it cycles a comparable
amount of carbon each year (Bouillon et al.
2008). A typical hectare of mangrove has the
potential to release as much carbon as a 3 to
5 ha of terrestrial tropical forest (Ong 1993,
Journal of Tropical Forest Science 25(4): 554–565 (2013)
Hamdan O et al.
Murray et al. 2011). Effective management and
conservation of the mangrove ecosystem would
be vital in any effort to address climate change
(Ong 1993).
However, despite being a crucial component
in the global carbon cycle and having potentially
profound influence on climate change, the
areal extent of mangrove forests has declined
significantly as a result of coastal development,
aquaculture expansion and over-har vesting
(FAO 2007). Malaysia lost a total areal extent
of approximately 111,046 ha of mangrove
forests from 1973 till 2000 due to unsustainable
forestry practices, illegal harvests, agriculture,
aquaculture, construction of airports and
harbours, industrialisation and urbanisation,
land reclamation for coastal development
and waste disposal and pollution (Chong &
Sasekumar 2002). The most significant losses
have been in Peninsular Malaysia where large
areas have been converted into coastal roads,
aquacultural, agricultural land and housing
estates (Giesen et al. 2006, Hamdan et al. 2012).
Mangrove forests sequester carbon faster and
longer than any terrestrial forest but they have
been cleared at a much faster rate. Thus, there
is an urgent and growing need for methods of
quantifying the spatial variation of carbon stock
in the ecosystem. This is essential to form a better
understanding of how carbon dynamics respond
to changes in time and space. It will also enable
the formulation of management strategies that
could incorporate the management of carbon
stock to promote efficient and sustainable use of
ecosystem services.
Satellite remote sensing offers the potential
to methodologically quantify carbon dynamics
in a more detailed and accurate manner.
Recent advancement in technology has not
only improved the accuracy of remote sensing
for mapping and change detection but has
also led to increased capability of estimating
standing biomass (Proisy et al. 2007). It offers the
ability to effectively measure and monitor large
forested areas in a very consistent and robust
manner (Huete 2012). Remote sensing is also a
potentially effective tool because it offers a cost
effective and non-intrusive method in the analysis
of the characteristics of mangroves.
This study aimed to quantify and characterise
carbon dynamics of Matang Mangrove Forest
Reser ve (hereafter Matang Mangroves) in
Malaysia by applying satellite remote sensing
© Forest Research Institute Malaysia
555
techniques. Whilst an extensive range of studies
have focused on estimation of biomass and
the productivity of Matang Mangroves (Putz
& Chan 1986, Gong & Ong 1990, Ong et al.
1995), these studies have primarily focused on
specific date or time. No study has extensively
analysed the carbon flux of Matang Mangroves
over a period of time. This study was designed
to address literature gap in the carbon dynamics
of a managed mangrove forest. It was conducted
to (1) derive an empirical relationship between
mangrove carbon stock and vegetation indices
of optical satellite images, (2) produce spatial
distribution map of carbon stock, (3) quantify
changes of carbon stock that occurred within
20 years from 1991 till 2011 over the whole
Matang Mangroves and (4) identify the variation
of forest carbon in a managed mangrove forest.
MATERIALS AND METHODS
Study area
Matang Mangroves is located in the northwestern coast of Peninsular Malaysia and is
within the administrative districts of Larut
and Matang, and Kerian in the state of Perak.
Currently, the total extent of mangroves in Perak
is approximately 43,292 ha, with the largest tract
in the Matang Mangroves (Hamdan et al. 2012).
Matang Mangroves is a large tract of managed
forest that covers an areal extent of 40,710 ha. It
is an exemplary managed mangrove forest that
has been sustainably and successfully managed
to balance between the production of charcoal
and poles and the preservation of mangrove
ecosystem (Kamaruzaman & Dahlan 2009). The
study area is shown in Figure 1.
The compositions of mangrove species found
in Matang Mangroves are predominantly bakau
minyak (Rhizophora apiculata) and bakau kurap
(Rhizophora mucronata). These are species with
commercial values and they occupy up to 85%
of the total forest area. Despite the dominance,
other forest types within Matang Mangroves
also occur and spread across a wide ranging
area. They can be broadly classified into seven
classes, namely, Avicennia–Sonneratia, Rhizophora,
Bruguiera cylindrica, Bruguiera parviflora, dryland,
transitional new and transitional dryland forests.
A total of 73.6% of the forest has been classified
as productive forest while the remaining 26.4% as
non-productive or protected forest. Most of the
Journal of Tropical Forest Science 25(4): 554–565 (2013)
Figure 1
Hamdan O et al.
Location of the study area, Matang Mangroves
productive forests consist of mainly Rhizophora
stands (Putz & Chan 1986). The productive
forests are assigned for the production of charcoal
and poles while the non-productive forests are
assigned for biodiversity conservation, erosion
mitigation, research and education, recreation,
local community needs and settlement.
Satellite data
In order to obtain carbon dynamics for a period
of 20 years (1991–2011) two series of optical
satellite images were used. The image data sets for
1991 were acquired from the Land Observation
Satellite—Thematic Mapper 5 (Landsat5-TM)
while the 2011 image data sets were obtained
from high resolution geometric sensor onboard
the Systeme Probatoire d’Observation de la Terre
5 (SPOT-5). These optical satellites were selected
as they both shared similar spectral characteristics
that would allow derivation of vegetation indices
that utilised the red and near-infrared spectral
© Forest Research Institute Malaysia
556
bands. Both Landsat-TM and SPOT-5 images
were atmospherically and radiometrically
corrected to obtain synchronised images with
true surface reflectance values. The conversion
of digital numbers into the surface reflectance
were done by using cos-theta method (Chander
et al. 2009) and historical empirical line method
(HELM) (Clark et al. 2010) for Landsat-TM and
SPOT-5 images respectively.
Methodology
This study integrated field inventory data with
the satellite images. In performing this, field
inventor y data were gathered and carbon
stock was calculated on sampling plots basis
prior to image analysis. Analysis was conducted
which involved four major steps, namely, (1)
forest stratification, (2) mangroves changes
detection, (3) derivation of vegetation indices
and (4) correlation analysis. These processes are
explained below.
Journal of Tropical Forest Science 25(4): 554–565 (2013)
Hamdan O et al.
Field inventory data
Forest stratification
The collection of ground sample plots was
carried out from 2010 till 2011 covering all parts
of the study area. A total of 108 sampling plots
with the size of 20 m × 50 m were distributed
randomly and established within the study area.
The stratified random sampling approach was
used in designing the sampling methods, which
was based on the forest strata. Sample plots
covered all important forest types which included
undisturbed virgin jungle reser ve, mature
Rhizophora, regenerating Rhizophora forests and
Avicennia–Sonneratia and Bruguiera. The majority
of the ground sample plots were concentrated in
the dominating Rhizophora forest. All tree stands
measuring 5 cm and above were included in the
inventory process and species of the measured
stands were also recorded.
Besides ground sample plots, this study was
also designed to incorporate an ancillary data
set which contained the spatial information on
the forest classes and management zones. The
data set was derived from the previous 10-year
(2000–2009) working plan of Matang Mangroves
(Azhar & Nik Mohd Shah 2003). They were used
primarily to facilitate the forest stratification
process.
The ecological structure and species composition
of the study area were complex but the
stand structure within a mangrove type was
homogenous. The study area has seven types of
mangrove forest and each type varies in species,
stand structure, size distribution, density and thus
biomass. Due to these conditions, a strategy to
reclassify the study area was considered. Using the
SPOT-5 (2011), Landsat-TM (1991) and
information derived from zoning maps found
in the working plan, the study area was classified
into three categories, namely, (1) mature
Rhizophora forest, (2) Rhizophora forest that was
logged less than 20 years, and (3) non-productive
forest, mainly consisting of Avicennia–Sonneratia
forest and poor mixed forest. The logged-over
category was categorised as less than 20 years
because satellite images used in the study had
20 years difference. Most of the scars within the
harvesting regime that were felled in 1991 were
clearly visible in the image data set and could
be easily demarcated by visual interpretation
and vectorisation. A similar process was also
applied to the Landsat-TM image of year 1991.
Stratification outcomes are depicted in Figure 2.
Mangroves change detection
Plot level carbon stock
Allometric equations from Ong et al. (2004)
and Komiyama et al. (2005) were adopted to
calculate aboveground biomass for R. apiculata
and Avicennia alba respectively. The respective
allometric equations are presented by the
following equations:
R. apiculata: Aboveground biomass (kg)
= 0.235D2.42
(1)
A. alba: Aboveground biomass (kg)
= 0.251ρD2.46
(2)
where ρ = wood density and D = diameter at
breast height (dbh) in cm. The value of wood
density used was 0.506 g cm -3 (Komiyama et
al. 2008) for A. alba (in non-productive forest
strata). Equations 1 and 2 were used for all
trees in Rhizophora and non-productive forests
respectively. The aboveground biomass was
then converted to carbon stock by using a global
default conversion factor of 0.5 (IPCC 2007,
Abdul Rashid et al. 2009).
© Forest Research Institute Malaysia
557
The spatial sizes of images were sharpened using
panchromatic bands and all were classified using
the maximum likelihood classification method
to classify mangroves in the study areas. The
special spectral characteristic of mangroves,
which reside in wet coastal regions and hence
appear darker on satellite images, has made
the selection of training area easy using the
combinations of bands 4, 5 and 3 for Landsat-TM
and 4, 3 and 2 for SPOT-5 images (Khali Aziz et
al. 2009). Changes were detected by comparing
the two classification results. Changes that were
assessed in this study were permanent changes
resulting from landuse changes from mangroves
to agriculture, aquaculture and development
areas. Changes due to clear felling and harvesting
activities were not included in the analysis as the
areas would be replanted with mangroves.
Derivation of vegetation indices
A variety of vegetation indices (VIs) have been
developed for retrieving vegetation density
from optical remote sensing images. The VIs
Journal of Tropical Forest Science 25(4): 554–565 (2013)
Figure 2
Hamdan O et al.
Forest strata of Matang Mangroves for (a) 1991 and (b) 2011
are used to predict the biomass of trees and
the most common one is with the normalised
difference vegetation index (NDVI) (Li et al.
2007). NDVI is based on the characteristics that
vegetation has noticeable absorption in the red
spectrum and very strong reflectance in the near
infrared spectrum. However using NDVI alone
can significantly underestimate the biomass of
some woody mangroves because NDVI represents
canopy properties rather than trunk properties
that are crucial for accurate biomass retrieval
(Foody et al. 2001). Consequently, Kasawani et
al. (2010) also revealed the same and found that
soil-adjusted vegetation index (SAVI) was more
promising in characterising biophysical profile
of mangroves. The combination of several VIs
through canonical correlation analysis (CCA) has
improved the estimation accuracy (Heiskanen
2006, Proisy et al. 2007). Therefore, three
vegetation indices, i.e. NDVI, SAVI and optimised
soil-adjusted vegetation index (OSAVI) were
used in this study to investigate their capability
© Forest Research Institute Malaysia
558
in estimating mangrove biomass in Matang
mangrove forest. Although there are issues raised
relating to the saturation level of biomass on VIs,
they are not critical for mangroves in Malaysia as
the indices only effect very high biomass such as
forests in the Amazon, Brazil (Huete et al. 2002).
The VIs used in this study are as follows:
NDVI
SAVI
NIR – R
NIR – R
=
=
OSAVI
NIR – R
NIR + R + 0.5
=
(3) Rouse et al.(1974)
× 1.5
NIR–R
NIR + R + 0.16
(4) Huete (1988)
(5) Rondeaux et al.
(1996)
where NIR = near infrared spectrum (Band-4 for
Landsat-TM and Band-3 for SPOT-5) and R = red
spectrum (Band-3 for Landsat-TM and Band-2
for SPOT-5).
Journal of Tropical Forest Science 25(4): 554–565 (2013)
Hamdan O et al.
Correlation analysis
41,539.45 ha in 1991 to 40,709.83 ha in 2011)
or an average of 41.5 ha year-1. Most of the
changes occurred along the edge of mangroves
and towards the land area in the eastern region.
There are several factors that contributed to
the loss of mangrove in this area; the main
causes being land conversion to agriculture
and aquaculture which accounted for 50.2 and
16.8% respectively. Mangrove forests were also
lost due to conversion to settlement (13.7%),
coastal erosion (10.8%) and wood extraction
(8.5%). Despite the loss, about 90 ha of land
accretion occurred along the western part of
the study area, especially along the edges of the
Avicennia–Sonneratia forest seaward. This natural
process occurs because sediments suspended on
the mudflat encourage mature trees to produce
new seedlings that will eventually colonise new
grounds (Hamdan et al. 2012).
Plot sampling process was implemented to
extract VI values of the satellite images at the
corresponding locations on the ground. The
2011 SPOT-5 satellite image was utilised for this
process. A ground plot with the size of 20 m ×
50 m can cover exactly 10 pixels (2 × 5 pixels)
of 10-m resolution. However, due to unexpected
errors such as geometric displacement of satellite
image and GPS location readings, a buffer of at
least one pixel or 10 m around the exact size was
taken into account and averaged together, which
made up 28 pixels (4 × 7 pixels). The average
value of VIs contained within the 28 pixels was
used for the correlation analysis. Both VIs and
total carbon stock within each ground sample
plots were correlated to produce regression
functions used to estimate carbon stock over the
entire study area.
Carbon stock distribution in the study area
Validation of estimation
Figure 3 summarises the information gathered
from all the 108 sample plots that have been
sur veyed. Carbon stock of all plots ranged
from 7.04 to 261.62 t C ha-1 with an average of
81.15 t C ha -1 , a value which represented
secondary mangrove forest (Bipal et al. 2009).
In this study, it was found that SAVI and
OSAVI provided similar responses to the sampled
biomass, with coefficient of determination
(R2) of around 0.65 for all strata. The NDVI
on the other hand had produced a slightly
higher R 2 when compared with the rest of
the VIs used in this study (~0.72). Linear and
non-linear regressions were tested on samples
and both produced different relationships
In order to validate results, a total of 25 check plots
distributed within the study area were randomly
selected. Of these, 20 were distributed within
mature and logged-over Rhizophora forests (10
plots each) and 5 within non-productive forest.
These plots were established independently,
specifically for the validation process.
RESULTS AND DISCUSSION
Changes of mangroves extents
Within the observed 20-year period (1991–2011),
Matang Mangroves had lost about 830 ha (from
Percentage (%)
60.0
55.3%
50.0
38.5%
40.0
Minimum no. of trees per plot:
Maximum no. of trees per plot:
Minimum tree size, dbh (cm):
Maximum tree size, dbh (cm):
Total no. of trees:
Total C stock of all plot (t C):
37
212
5
48.8
10,942
416,587.90
30.0
20.0
10.0
5.7%
0.5%
0.0
5 to 10 cm
10 to 20 cm 20 to 30 cm
> 30 cm
Tree size, dbh (cm)
Figure 3
Summary of plot inventory information (n = 108); dbh = diameter at breast height
© Forest Research Institute Malaysia
559
Journal of Tropical Forest Science 25(4): 554–565 (2013)
Hamdan O et al.
between aboveground biomass and NDVI.
Linear regression produced higher correlation
coefficients but did not represent the real
distribution, especially when the NDVI value
approached 0. Based on the comparison of
both methods, the equations derived from the
non-linear regression were selected to quantify
aboveground biomass of each forest strata for
the whole study area. The relationship between
NDVI and carbon stock is shown in Figure 4 and
the derived models are summarised in Tables 1
and 2.
Figure 4
The equations from Table 2 were applied
to both SPOT-5 and Landsat-TM images that
were transformed into NDVI values to predict
aboveground biomass over the whole study area
for 2011 and 1991. Aboveground biomass value
generated for each pixel on the images was
converted to aboveground carbon stock. The
patterns of distribution and changes of carbon
stocks in the study area are shown in Figure 5.
Basic statistic of carbon stocks distribution in the
study area produced from spatial estimations of
satellite images are summarised in Table 3.
Scatterplots of carbon stock against difference vegetation index; (a) and (b) represent linear and
non-linear regressions respectively for mature Rhizophora, logged-over Rhizphora and non-productive
strata; 1 = mature Rhizophora, 2 = logged-over Rhizophora, 3 = non-productive
Table 1
Linear regression models and coefficients of determination (R2) for
carbon stock and difference vegetation index
Carbon stock (t C ha-1)
Forest strata
Model
Mature Rhizophora
-798.83x + 435.14
R2
Sample size (n)
0.724
82
Logged-over Rhizophora
1239.9x – 608.58
0.782
17
Non-productive
392.61x – 23.933
0.787
9
All correlations are significant (p < 0.05)
Table 2
Non-linear regression models and coefficients of determination (R2) for carbon
stock and difference vegetation index
Carbon stock (t C ha-1)
Forest strata
Model
Mature Rhizophora
2
Sample size (n)
0.728
82
Logged-over Rhizophora
18.63
6E + 06x
0.652
17
Non-productive
75.485ln(x) + 180.35
0.775
9
© Forest Research Institute Malaysia
817.84x – 1468.6x + 569.34
R2
560
Journal of Tropical Forest Science 25(4): 554–565 (2013)
Hamdan O et al.
C stock distribution of 2011
8000
8000
6000
No. of pixels
No. of pixels
C stock distribution of 1991
4000
2000
2000
1.01
30.43
59.85
89.27
118.68
148.10
177.52
206.93
236.35
Carbon stock (t C ha-1)
Figure 5
4000
0
1.03
27.81
54.58
81.36
108.14
134.91
161.69
188.47
215.24
242.02
0
6000
Carbon stock (t C ha-1)
Distribution of carbon stock in the study area for (a) 1991 (b) and 2011
Changes of carbon stock in the study area
Carbon stocks of 1991 and 2011 ranged
from 1.03 to 263.65 t C ha -1 and 1.01 to
259.68 t C ha-1 respectively (Figure 5). Dominant
carbon stock for both years was around
100 t C ha-1. A slightly higher average in carbon
stock in 1991 (76.62 t C ha-1) as opposed to that
in 2011 (69.89 t C ha-1) could be attributed to
the harvesting that took place within the period.
Carbon stock of each strata is reported in Table
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561
3. Statistic shows that each strata has different
profile of carbon stock. The increased extent of
production areas from 14.4% in 1991 to 36.3%
in 2011 as shown in Figure 3 also explains these
changes.
It has been reported that the highest carbon
stock for > 80-year-old R. apiculata-dominated
mangrove forest was 230.0 t C ha -1 (Putz &
Chan 1986) while those of 20- and 28-year-old
Rhizophora forests (also aciculate) were 114 and
105.9 t C ha-1 respectively (Ong et al. 1995). The
Journal of Tropical Forest Science 25(4): 554–565 (2013)
Table 3
Basic statistics of carbon stock in the study area over the years 2011 and 1991
Year
Strata
2011
Mature Rhizophora
1991
Hamdan O et al.
Statistics
(t C ha-1)
Total
(t C)
Min
Max
Mean
Mode
20.35
260.70
64.08
75.36
1,367,439.00
Logged-over Rhizophora (≤ 20 years)
0
244.09
49.49
29.53
736,620.84
Non-productive
0
122.80
18.16
21.35
48,095.26
Subtotal
2,152,155.10
17.57
259.68
65.14
73.07
2,048,352.70
Logged-over Rhizophora
0
231.02
47.56
28.94
702,287.24
Non-productive
0
161.06
25.31
38.25
285,442.75
Mature Rhizophora
average carbon values derived for 1991 and 2011
in this study were in agreement with previous
researches which were performed at Matang
Mangroves.
In order to quantify the growth rate of
mangroves in logged-over forest, a comparison
was made between 645.3 ha of the clear-cut
regime in 1991 (where carbon stock was relatively
0 t C ha-1) and the 20-year-old forest in the same
area in 2011 (Figure 6). In 2011, the total carbon
accumulation in that area was 53,234 t C (ranged
between 0 and 184.3 t C ha-1) with an average of
136.7 t C ha-1. This indicates that the mangrove
trees in that particular area have sequestered
about 2662 t year-1 of carbon with an average of
4.1 t C ha-1 year-1. Within the next 10 years (after
2011), this regime will undergo the next rotation
of a 30-year clear felling cycle that is practised
by the Forestry Department for mangrove forest
production. It can be concluded that even
though the 20-year mangrove is considered as
mature forest, its carbon stock will never be the
same as natural mangrove stands such as that
found in virgin jungle reserve which can store
up to 250 t C ha-1.
Considering the spatial distribution over the
entire study area of about 41,540 ha in year 1991
and 40,710 ha in 2011, total carbon stocks in
Matang Mangroves were 3.04 and 2.15 mil t C in
1991 and 2011 respectively. This meant that the
total change of carbon stock within the period
was 883,928 t C, with an average loss of 44,196 t
C ha1 year-1. In this case, the released carbon can
be considered as a source of carbon emission
that equals 3.2 million t CO2e or about 0.2 t
CO2e per year (Ong 1993). These estimates were
© Forest Research Institute Malaysia
562
Subtotal
3,036,082.69
Total changes
-883,927.59
derived from individual pixels obtained from
the image data sets of the mangrove forest. The
estimates took into account the variation of each
carbon stock value of each pixel on the satellite
images. As the approach did not apply the biome
average method which involved the application
of an estimated average carbon stock value to
the entire areal extent, the estimates produced
in this study presented a more accurate estimate
that reflected the profile of the forest.
It has been estimated that the carbon stock
in Matang Mangroves with acreage of about
40,800 ha was at 4.13 million t C (Gong & Ong
1990). Comparing this estimate with the result
of this study (3.04 million t C in year 1991), this
study has underestimated about 1.1 million t C.
However, as explained earlier, the biome average
method was susceptible to issues that arose from
generalisation of the mangrove forest. It could
provide erroneous estimates because only a single
average value was applied. For example, if this
study uses an average carbon stock of 114 t C ha-1
as reported by Ong et al. (1995), the yield will be
about 4.6 million t C.
Validation process carried out using root
mean square error indicated that magnitude of
errors for the 25 check plots ranged from 0.1
to 18.25 t C ha-1 (Table 4). The huge variation
within the distribution reflected the randomness
of the check plots. Negative values in mean
square error indicated that the generated carbon
stock from the equation had underestimated the
carbon stock and vice versa. Average carbon stock
in Matang Mangroves was, therefore, 76.62 ±
7.56 t C ha-1 and 69.89 ± 7.56 t C ha-1 for the years
1991 and 2011 respectively.
Journal of Tropical Forest Science 25(4): 554–565 (2013)
Hamdan O et al.
Figure 6
Changes of carbon stock in logged-over areas; cutting regime is clear on the satellite image of (a)
1991 and (b) the logged-over areas after 20 years in 2011, (c) and (d) are spatial distributions of
carbon stock for 1991 and 2011 respectively
Table 4
Absolute accuracy measure (t C ha-1) of each strata in 2011
Strata
Range of magnitude
error
Mean square error
RMSE
Average estimation error
Mature Rhizophora
0.10–18.25
-5.85
11.35
7.56
Logged-over Rhizophora (≤ 20)
0.32–9.73
-3.85
5.62
Non-productive
0.18–7.96
3.18
5.71
RMSE = root mean square error
CONCLUSIONS
The study showed that optical imager y had
successfully predicted aboveground carbon stock
over all forest types in a mangrove environment.
The use of NDVI is still relevant for biomass and
carbon stock estimation in a mangrove ecosystem.
© Forest Research Institute Malaysia
563
Homogeneity, in terms of stand structure, has
also made the estimation of carbon stock using
optical remote sensing viable.
The study had also successfully quantified
carbon stock over the entire Matang Mangroves
using empirical equations derived from the
relationship between carbon stock estimated on
Journal of Tropical Forest Science 25(4): 554–565 (2013)
Hamdan O et al.
the ground and the NDVI of satellite image. Total
carbon stocks in Matang Mangroves were estimated
at 3.04 in 1991 and 2.15 mil t C in 2011 with the
reduction of about 0.9 mil t C. It was estimated
that about 3.2 mil t CO2e of carbon emission had
been released within 20 years, which equalled to
0.2 t CO2e per annum.
Overall, the study indicated that the use of
vegetation indices from optical remote sensing
data to assess carbon stock (and changes) in a
mangrove ecosystem is still a viable technique.
Multispectral optical data offer choices for
carbon stock in mangrove forest where it has
capability to derive a number of indices. This
study, thus, suggested for replication of carbon
stock assessment on mangroves in different parts
of the world.
REFERENCES
ABDUL RASHID AM, SHAMSUDIN I, ISMAIL P & FLETCHER SC.
2009. The role of FRIM in addressing climate-change
issues. Research Pamphlet No. 128. Forest Research
Institute Malaysia, Kepong.
AZHAR M & NIK MOHD SHAH NM. 2003. A Working Plan for
the Matang Mangrove Forest Reserve, Perak: The Third
10-Year Period (2000–2009) of the Second Rotation. State
Forestry Department of Perak, Ipoh.
BIPAL KJ, BISWAS S, MAJUMDER M, PANKAJ KR & MAZUMDAR A.
2009. Carbon sequestration rate and aboveground
biomass carbon potential of four young species.
Journal of Ecology and Natural Environment 1: 15–24.
BOUILLON S, BORGES AV, CASTAÑEDA-MOYA E, DIELE K, DITTMAR
T, D UKE NC, K RISTENSEN E, L EE SY, M ARCHAND C,
M IDDELBURG JJ, R IVERA -M ONROY VH, S MITH TJ &
TWILLEY RR. 2008. Mangrove production and carbon
sinks: a revision of global budget estimates. Global
Biogeochemical Cycles 22: 1–12.
CHANDER G, MARKHAM BL & HELDER DL. 2009. Summary
of current radiometric calibration coefficients for
Landsat MSS, TM, ETM+, and EO-1 ALI sensors.
Remote Sensing of Environment 113: 893–903.
CHONG VC & SASEKUMAR A. 2002. Coastal habitats (mangroves,
coral reefs and seagrass beds) of the ASEAN region:
status, utilization and management issues. Fish Science
68: 566–571.
CLARK B, SUOMALAINEN J & PELLIKKA P. 2010. A comparison of
method for the retrieval of surface reflectance factor
from multitemporal SPOT HRV, HRVIR, and HRG
multispectral satellite imagery. Canadian Journal of
Remote Sensing 36: 397–411.
D ONATO DC, K AUFFMAN JB, M URDIYARSO D, K URNIANTO S,
STIDHAM M & KANNINEN M. 2011. Mangroves among
the most carbon-rich forests in the tropics. Nature
Geosciences 4: 293–297.
FAO (FOOD AND AGRICULTURE ORGANIZATION). 2007. The World’s
Mangrove 1980–2005. FAO Forestry Paper 153. FAO,
Rome.
© Forest Research Institute Malaysia
564
FOODY GM, CUTLER ME, MCMORROW J, PELZ D, TANGKI H,
BOYD DS & DOUGLAS I. 2001. Mapping the biomass of
Bornean tropical rain forest from remotely sensed
data. Global Ecology and Biogeography 10: 379–386.
G IESEN W, W ULFFRAAT S, Z IEREN M & S CHOLTEN L. 2006.
Mangrove Guidebook for Southeast Asia. FAO and
Wetlands International, Bangkok.
GONG WK & ONG JE. 1990. Plant biomass and nutrient flux
in a managed mangrove forest in Malaysia. Estuarine,
Coastal and Shelf Science 31: 519–530.
HAMDAN O, KHALI AZIZ H, SHAMSUDIN I & RAJA BARIZAN RS.
2012. Status of Mangroves in Peninsular Malaysia.
Forest Research Institute Malaysia, Kepong.
HEISKANEN J. 2006. Estimating aboveground tree biomass
and leaf area index in a mountain birch forest using
ASTER satellite data. International Journal of Remote
Sensing 27:1135–1158.
HUETE AR. 1988. A soil-adjusted vegetation index (SAVI).
Remote Sensing of Environment 25: 295–309.
H UETE AR. D IDAN K, M IURA T, R ODRIGUEZ EP, G AO X &
FERREIRA LG. 2002. Overview of the radiometric and
biophysical performance of the MODIS vegetation
indices. Remote Sensing of Environment 83: 195–213.
HUETE A. 2012. Vegetation indices, remote sensing and forest
monitoring. Geography Compass 6: 513–532.
IPCC (INTERGOVERNMENTAL PANEL ON CLIMATE CHANGE). 2007.
Climate Change 2007: Synthesis Report. Contribution
of Working Group I, II and III to the Fourth Assessment
Report of the Intergovernmental Panel on Climate Change.
(eds). IPCC, Geneva.
KAMARUZAMAN J & DAHLAN HT. 2009. Managing sustainable
mangrove forests in Peninsular Malaysia. Journal of
Sustainable Development 1: 88–96.
KASAWANI I, NORSALIZA U & MOHD HASMADI I. 2010. Analysis
of spectral vegetation indices related to soil-line for
mapping mangrove forests using satellite imagery.
Applied Remote Sensing Journal 1: 25–31.
KHALI AZIZ H, HAMDAN O, SHAMSUDIN I & ISMAIL H. 2009.
Digital change detection of mangrove forest in
Selangor using remote sensing and geographic
information system (GIS). The Malaysian Forester 72:
61–69.
K OMIY AMA A, P OUNGPARN S & K ATO S. 2005. Common
allometric equations for estimating the tree weight
of mangroves. Journal of Tropical Ecology 21: 471–477.
K OMIYAMA A, O NG JE & P OUNGPARN S. 2008. Allometr y,
biomass, and productivity of mangrove forests: a
review. Aquatic Botany 89: 128–137.
L AFFOLEY D & G RIMSDITCH G. 2009. The Management of
Natural Coastal Carbon Sinks. International Union for
Conservation of Nature, Gland.
LI XA, YEH GO, WANG S, LIU K, LIU X, QIAN J & CHEN X. 2007.
Regression and analytical models for estimating
mangrove wetland biomass in South China using
Radarsat images. International Journal of Remote Sensing
28: 5567–5582.
M URRAY B, P ENDLETON L, J ENKINS WA & S IFLEET S. 2011.
Green Payments for Blue Carbon Economic Incentives
for Protecting Threatened Coastal Habitats. Nicholas
Institute Report NI R 11-04. Duke Nicholas Institute
for Environmental Policy Solutions, Durham.
O NG JE. 1993. Mangroves—a carbon source and sink.
Chemosphere 27: 1097–1107.
Journal of Tropical Forest Science 25(4): 554–565 (2013)
Hamdan O et al.
O NG JE, G ONG WK & C LOUGH BF. 1995. Structure and
productivity of a 20-year-old stand of Rhizophora
apiculata Bl. mangrove forest. Journal of Biogeography
22: 417–424.
O NG JE, G ONG WK & W ONG CH. 2004. Allometr y and
partitioning of the mangrove, Rhizophora apiculata.
Forest Ecology and Management 188: 395–408.
PROISY C, COUTERON P & FROMARD F. 2007. Predicting and
mapping mangrove biomass from canopy grain
analysis using Fourier-based textural ordination of
IKONOS images. Remote Sensing of Environment 109:
379–392.
© Forest Research Institute Malaysia
565
PUTZ F & CHAN HT. 1986. Tree growth, dynamics, and
productivity in a mature mangrove forest in Malaysia.
Forest Ecology and Management 17: 211–230.
RONDEAUX G, STEVEN M & BARET F. 1996. Optimisation of
soil-adjusted vegetation indices. Remote Sensing of
Environment 55: 95–107.
R OUSE JW, H AAS RH, S CHELL JA & D EERING DW. 1974.
Monitoring vegetation systems in the Great Plains
with ERTS. Pp 310– 317 in Freden SC et al. (eds)
Proceedings of Third Earth Resources Technology Satellite-1
Symposium. 10–14 December 1973, Greenbelt.