Above Ground Carbon Sequestration in Mangrove Forest Filtration System
The rate of above ground carbon sequestration was examined in mangrove forest filtration system pond within the Kings Royally Initiated Laem Phak Bia Environmental Research and Development Project that located in Thailand. It is divided into two sites: the study site and the reference site. The study site is the mangrove filtration system area where is directly affected from the municipal waste water and the reference site is the mangrove forest area where is indirectly affected from the municipal wastewater at Ban Pranaen is located in the north of the cape. The relationships between tree diameter and tree height were used to evaluate above ground biomass by allometric equations and above ground carbon sequestration was calculated by multiplying the percentage of carbon stock. They were carried out two seasons: Wet and dry seasons. The results were revealed that the highest LAI in study site and reference site were 17.67 and 9.66, respectively. The highest above ground biomass was 1.42 and 2.12 t ha-1 in study site and reference site, respectively. The percentage of carbon content in the parts of sampled tree was slightly difference in study and reference site. The average of highest above ground carbon sequestration was 11.08 and 2.32 t ha-1 in study site and reference site, respectively. Moreover, the results of comparison of LAI, above ground biomass and above ground carbon sequestration confirmed that the mangrove forest filtration system can be high potential of carbon stock source.
Received: May 24, 2012;
Accepted: July 09, 2012;
Published: August 02, 2012
An increased level of atmospheric CO2 through the combustion of
fossil fuel or more precisely its likely effect on the global climate is caused
of concerned (Ayukai, 1998). The storage of carbon reduces
the greenhouse effect that linked to problems of global climate change (Lim,
2007). Thus, many scientists have recognized the carbon sources or sink.
In addition, their studies found that tree can help to reduce the problem of
global warming; carbon dioxide is absorbed by tree that used for photosynthesis.
Tree can absorb CO2 from the atmosphere that transformed to biomass.
This process is called carbon sequestration which is the best effective
on CO2 reduction in atmosphere (Sridang, 2008).
Then, forest is an important role of carbon sink and carbon cycle. Therefore,
more increasing of forest area can help more carbon sequestration sources and
decreasing of CO2 in the air (Schlesinger, 1990).
Mangrove forest ecosystem plays an important role in the global carbon cycle
(Ayukai, 1998). It protects a coast erosion and storms,
encourages sediment deposition and provides the most productive ecosystems and
their carbon stock per unit area can be enormous (Ong, 1993).
To be better understanding the dynamic of carbon cycle in the forest, the amount
of Leaf Area Index (LAI) and biomass is concerned in this cycle. The canopy
structure of a vegetated area is frequently described in terms of leaf area
index. Measurement of LAI is monitored the changing of canopy structure due
to pollution and climate change (Gholz et al., 1991).
It is necessary to measure a LAI with stringent calculation of the amount of
CO2 sequestration by forest (Ishil and Tateda,
2004). The important of LAI stems from the relationship which established
between it and a range of ecological processes (rates of photo synthesis, transpiration
and evapotranspiration) (McNaughton and Jarvis, 1983;
Pierce and Running, 1988); net primary production (Monteith,
1972; Norman, 1980; Gholz, 1982;
Meyers and Paw, 1986; Meyers and
Paw, 1987); rate of energy exchange between plants and the atmosphere (Gholz
et al., 1991). The estimate LAI and biomass is a valuable tool for
modeling of the ecological processes occurring within a forest and in predicting
ecosystem responses. Moreover, this method can be used to estimate LAI and biomass
in all season and it can be applied to calculate other area where has the same
type of vegetations (Domrongsutsiri, 2001). The final
result from the study takes to use for mangrove forest resource management to
help an effective improvement of carbon sequestration in the near future.
This study analyzes LAI values, the above ground biomass and the above ground carbon of the two species dominant mangrove trees: Rhizophora mucronata and Avicennia marina located in the mangrove forest filtration system, Phetchaburi province where as municipal wastewater receiving source before flowing into the sea. The hypothesis is the mangrove filtration system site would more potential of carbon sequestration than natural mangrove site. In this experiment, the main objectives of the study are:
||To estimate the rate of LAI, above ground biomass and above
ground carbon in mangrove forest filtration systems
||To determine the distribution pattern of LAI, above ground biomass and
above ground carbon
||To evaluate the carbon sink potential of mangrove above ground filtration
MATERIALS AND METHODS
Description of study area: The study site is the Kings Royally
Initiated Laem Phak Bia Environmental Research and Development Project locates
in Laem Phak Bia Sub-district, Ban Laem District, Phetchaburi Province, Thailand
(100.10°E, 13.05°N) (Fig. 1) where rests on an alluvial
plain that extends the west to the east along the Gulf of Thailand. The total
area is about 10.33 km2. The study area is the mangrove forests filtration
system area where is supported the treated municipal waste water from stabilization
treatment pond. The treated municipal wastewater throughout 4,500-10,000 m3
day-1. The qualities parameters of treated municipal waste water
from the lagoon treatment system: DO was 6.5-9.5 mg L-1, conductivity
was 1116.10-1462.95 msec, the colors less was 5-7 unit, temperature was 25.7-30.3
degree Celsius and pH was 8.1-8.4 unit which the waste water parameters of study
were complied with these standards values of Pollution Control Department (In-on,
Mangrove forests have strong relationships with the surrounding environment
such as tidal inundation, directions and current flow. Directions and current
flow speed mainly depends on the tide. The current flow is two directions in
this area. There are the high tides which flow through in the north the low
tides which flow along the south of costal. Although, the current flow speed
rely on the monsoon influencing in each season follow as the North East monsoon
during November to January and The North East monsoon during February to April.
The highest of high tides speed was 0.61 m sec-1 and the highest
of low tides speed is 0.64 m sec-1 on December whereas the highest
of high tides speed is 0.85 m per a second and the highest of low tides speed
is 0.78 m per a second on May.
||The location of the study area, located within Laem Phak Bia
sub-district, Ban Laem district, Phetchaburi province, Thailand (created
from THEOS satellite image)
|| Sampling plots in the study site and reference site (created
from Landsat TM 5 satellite image)
The average of high-low tide rates are 2.78-7.36 km ha-1. Surface
water is the average of wind speed rating 5.9 and 6.9 km ha-1 during
February, April, November and January, respectively. The highest wind speed
is 40 km ha-1 which to create the average of wave heights are 0.14
and 0.18 m, respectively. The undercurrent is 0.3-1.8 m (Sawangchat,
2001). The study area is divided into two sites (Fig. 2).
There is the study site and the reference site:
||The study site is the mangrove filtration system area where
is directly affected from the municipal waste water. The area is about 7.46
||The reference site is the mangrove forest area where is indirectly affected
from the municipal waste water at Ban Pranaen is located in the north of
the cape. The area is about 2.87 km2
Mangrove forest analysis
Mangrove sampling: The size of each sampling station is 30x30 m2.
A simple random sampling method used for selecting the locations of the sampling
stations. Sampling plots showed in Fig. 2. Mangrove tree sampling
carried out in wet (July-December 2009) and dry (January-June 2010) seasons
for representation. The floristic parameters recorded are species names, tree
heights (H), Diameters a Breast Height (DBH), crown cover area and Differential
Global Positioning System (DGPS) coordinates in the Universal Transverse Mercator
co-ordinate (UTM) system (Vaiphasa et al., 2006).
Tree sampling location: The Kings Royally Initiated Laem Phak
Bia Environmental Research and Development Project where located within Laem
Phak Bia Sub-district, Ban Laem District, Phetchaburi Province, Thailand
(100.10°E, 13.05°N) was selected as study area with a total area of
approximately 10.33 km2. The study area was divided into two sites:
||The study site; the mangrove forest filtration system was
directly by municipal waste water is about 7.46 km2
||The reference site; the natural mangrove forests was indirectly by municipal
waste water is about 2.87 km2 at Ban Pranaen was located in the
north of the cape
Data field measurement: Data were collected in the field from July 2009
to June 2010. Rhizophora mucronata and Avicennia marina were the
two dominant species mangrove trees in this area. Measurement of tree Diameter
at Breast Height (DBH) performed by measurement tape, crown diameter. Tree heights
measured by measuring pole and Haga hypometer. The size of sampling was sampled
within 900 m2 quadrate in the rectangular permanent sample plot by
systematic system and spread around mangrove forest area. LAI was measured by
a total leaf count technique (Ishil and Tateda, 2004)
in each plot.
Tree sampling cutting: Rhizophora mucronata and Avicennia marina were selected to cut for 6 sets and cutting separated to small part with length 1 m by stratified clip technique. All cut samples were weighted and recorded their weighting prior to analyze the percentage of carbon content for each parts of tree samples.
Estimation of tree volume: Estimation of tree volume focused on two dominance species mangroves trees. They were calculated by using the independent variables of algometry equation in D2H by following equations:
BA = πD2/4
BA stand for Tree surface area. D stand for tree diameter at 1.3 cm. The calculation
of tree sample volume used Smalian formula (Phongsuksawat,
Vs = ½(BA1+BA2)xL
Vs stand for the volume of tree log (m3). BA1, BA2 stand for both two ends diameter (m2) L stand for log length (m). For the last end log calculated the volume as following equation:
Vtop = 1/3xBAtopxLtop
Vtop stand for the end of tree stem (m3). BAtop stand for the end side diameter (m2). Ltop stand for the length of the top end (meter).
Estimation of LAI value: Two LAI equations are developed by field data
collection base on two dominance species mangroves trees which a method of the
basic of Japanese national forest surveys and satellite data analysis (Ishii
et al., 2001). The LAI measurements were calculated by measuring
DBH and the total of leaf area at each tree in the field plots and analyzed
the strongly relationship between leaf area, diameter at breast high and tree
height. The results of two algometry equations were used in this research for
calculation of LAI value.
Calculation of biomass: Each tree sampling was registered. Calculations
of crown area and crown diameters were used. The crown area was considered an
ellipse. The harvested trees were subdivided into following compartments: leaves,
branches, stems and roots. Determination of dry weight compartments for each
sampled individual were used simple linear regression between dry weight and
fresh weight. These statistical procedures may be found by Zar
(1996). The obtained regressions were grouped by compartments: leaves; branches,
stems and roots. We pooled the data of two species mangrove trees from the various
regressions to compute common regressions based on the comparison results (Soares
and Schaeffer-Novelli, 2005) then determining the best regression model
to estimate the total above ground biomass and compartments biomass follow by
law of allometric method (Kittredge, 1944; Ogawa
and Kira, 1977). Measurements of net productivity were bases on allometric
techniques (Ong et al., 1984). Sampled plot twelve
trees of two dominance spices mangrove trees were harvested. The allometric
regression equations could be developed. The results of two allometric equations
were used for biomass calculation.
Calculation of carbon content (%): The harvested trees samples were
sent to laboratory at Department of Silviculture, faculty of forestry, Kasetsart
University for analyze the percentage of carbon content by dry combustion method
and using CN corder model MT-700 (Nualngam, 2002).
Calculation of above ground carbon sequestrations: The percentage of
carbon content was analyzed by laboratory. They were taken into the calculation
of above ground carbon sequestration from above ground biomass in term of each
plot (Sridang, 2008) from equation:
Total carbon = %carbonxbiomass
Statistical analysis: Some main statistical parameters were analyzed:
mean, standard deviation variance, coefficients of variation and extreme maximum
and minimum values. ANOVA statistical analysis was used to test the significances
almost of the parameters at p< 0.05. Regression analyses were also used to
develop modeling for above ground carbon sequestration. These statistical parameters
were performed by using EXCEL 2003 and statistical package for the Social Science
(SPSS) program. The statistical relationships between the annual field data
measurements and laboratory results were correlated to various regression models.
The best equation model was selected by its highest coefficient of determination.
Correlation and regression analysis were evaluated the association between two
or more variables and expressing the nature of relationship and determination
the degree of association between variables with coefficient of determination
(R2) (Lim, 2007).
Integrating ecological data into the spatial distribution mapping model:
Spatial relationships between mangroves and the environment are well known (Clough
et al., 1983). These relationships result in the mangrove zonations
that are usually found in tropical mangrove forest (Tomlinson,
1986). Moreover, Vaiphasa et al. (2006) tested
whether mangrove environment relationships can be exploited in order to improve
mapping accuracy. The study confirmed that the mangrove environment relationships
into the mapping process that can be used for mapping mangrove at the species
All of the results were used for the Kring method to produce the spatial distribution
map of study and reference sites. For the spatial interpolation, a cell size
of 100x100 m was chosen to divide the study area into a grid system. The final
result of this spatial interpolation process was shown as values spatial distribution
maps. Geostatistics (Matheron, 1963) uses the semi-variogram
to quantify the spatial variation of a regionalized variable and provides the
input parameters for the spatial interpolation method of Kriging (Krige,
1951). The Geostatistical analyses and the interpolated map were produced
with the Geographic Information System (GIS) software.
RESULTS AND DISCUSSION
Tree parameters inventory data: From the research field work, the data demonstrated structures characteristics of mangrove trees follow as Table 1. Mean of tree height were 4.40 and 2.58 m for study site and reference site, respectively. These results showed significant of tree height which found that the study site higher than reference site approximately twice times and mean of DBH for study site and reference site were 9.07 and 6.23 cm, respectively. Finding of DBH showed the slightly higher value in study site and crown area (m) found that the similar value in study and reference sites with no significant of different were 4.10 and 4.25 m2, respectively. Volumes of tree found slightly higher in study site than reference sites were 0.75 and 0.54 m3, respectively (Table 1).
Estimation of LAI
LAI equations: Two LAI equations were developed by field data collection
which a method of the basic of Japanese national forest surveys and satellite
data analysis (Ishii et al., 2001). LAI measurements
were calculated by measuring DBH and the total of leaf area in each tree and
analyzed the strongly relationship between leaf area, DBH and tree height. The
results of two algometry equations have shown in Table 2.
||Structural characteristics of the studies mangrove forests
|DBH: Diameter at breast height, values are Mean±SD
||Allometry equations for leaf area estimation of the two species
of dominance mangrove trees
|U: Leaf area (m2), DBH: Diameters at breast height
(cm), H: Height (m)
|| Comparison of the average LAI values
||Statistic values of leaf area (m2)
|1N = 80, 2N = 15
LAI values: Two algometry equations were used to estimate value of mangrove
LAI values. LAI ranged from 0.00-28.01, with a mean value of 4.75 and 0.00-10.86
and mean value of 1.909 in study site whereas Green et
al. (1997) found mangrove LAI ranged between 0.8 and 7.0, with a mean
of 3.96. In addition to, these results demonstrated that the LAI in study site
was higher than reference site. From Fig. 3, results of seasonal
variation showed that LAI was the highest in dry season in the both area. Finding
of LAI was 0.00 value because of the death of mangrove trees which can see in
clear spaces in Fig. 1. From Table 3, comparison
of LAI results with the previous studies, it demonstrated that this research
has the highest and lowest LAI value with the nearly value of mean. Values of
this research has the maximum average with 17.67 and minimum average with 0.00
and a mean of 4.76 meanwhile study of Clough et al.
(1983) showed previous publish of LAI values for mangrove from the west
coast of peninsular Malaysia. They obtained indices ranging from 2.2-7.4 (mean
4.9) by direct measurement and a mean value of 5.1 when LAI was estimated indirectly
from light transmission measurements over four transects. Values of LAI derived
from satellite data of Caribbean mangroves (0.83-7.00, mean 3.96). Clough
et al. (2000) has studied canopy LAI of the mangrove Rhizophora
apiculata in the Mekong data, Vietnam. They found that LAI ranging from
3.3-4.9 shown in Table 3 whereas Ishil
and Tateda (2004) found that these differences are due to a high plantation
density and the absence of thinning.
LAI statistics values: From Table 4 showed statistic values of LAI. The results found that mean of LAI in study site from wet and dry seasons were 11.94 and 21.85, respectively and standard deviation was 11.64 and 25.50, respectively. Testing of LAI values different between wet and dry seasons by t-test. The LAI in dry season is significantly higher (t-test, p<0.01) than in wet season.
Meanwhile, mean of LAI in reference site from wet and dry season were 7.34 and 0.38, respectively and standard deviation was 9.40 and 0.39, respectively.
|| Histograms of leaf area index
Testing of LAI values different between wet and dry season by t-test. The LAI in wet season is significantly higher (t-test, p<0.05) than in dry season (Table 4).
In Fig. 3 showed LAI values. The results discovered that
LAI values in dry season higher than in wet season twice times. Accordingly,
highs LAI value was indicated the ability of rates of energy exchange plants
and the atmosphere (Gholz et al., 1991).
Estimation of above ground biomass
The percentage of carbon content in each part of tree: From Table
5 showed the average percentage of carbon content of Rhizophora mucronata.
The results showed carbon content percentage found in each part of tree in both
sites have similar values when compared carbon content percentage between study
and reference sites also found similar value in all parts.
From Table 6 showed the average percentage of carbon content of Avicennia marina. The results found slightly difference between study and reference sites.
Comparison of the average percentage of carbon content between Rhizophora mucronata and Avicennia marina. The results showed that the average percentage of carbon content in all parts of Rhizophora mucronata have slightly higher than Avicennia marina.
|| Average of carbon content (%) of Rhizophora mucronata
|| Average of carbon content (%) of Avicennia marina
||Algometry equations for biomass estimation of the two species
of dominance mangrove trees
|DBH: Diameters at breast height (cm), H: Height (m), WS:
Biomass of stem (kg), WB: Biomass of branches (kg), WL:
Biomass of leaf (kg), WR: Biomass of tree roots (kg)
Algometry equations of the two species of dominance mangrove trees: Allometric equations of two species of mangroves trees were developed from the field data of two dominance species mangrove trees for the various regressions to compute common regressions based on the comparison results. Determining of the best regression model was selected to estimate the total above ground biomass and the compartments of biomass in each part follow by law of allometric method. The results of two allometric equations were created follow as Table 7.
Above ground biomass value: The results were calculated the average
above ground biomass values from allometric equations. Estimation of the average
above ground biomass have maximum valve with 154.62 and 35.09 t ha-1
for study and reference sites, respectively. These results discovered that the
average above ground biomass in study site has higher than reference site about
five times. From Fig. 4, the seasonal variation of total biomass
found that significantly between wet and dry seasons. Comparison of above ground
biomass between wet and dry seasons found that dry season higher than wet season
approximately twice times. The significant result showed that the net productivity
of mangrove forest depend on seasons. The net productivity of dry season has
higher than wet season. According to the result of Aksornkoae
et al. (1989), they found that the net productivity of dry season
has higher than wet season about 40-50%. Moreover biomass of mangrove forest
was varied in species and zone. The highest above ground biomass, 460 tonnes,
was found in a forest dominated (Putz and Chan, 1986).
Above ground biomass (AGB) (tonnes per hectare) statistics values:
Table 8 showed that above ground 206.93 t ha-1
in wet and dry season, respectively biomass statistic values.
||Histograms of above ground biomass
|| Statistics values of above ground biomass (AGB) (tonnes ha-1)
|1N: 80, 2N: 15
The results showed mean of above ground biomass in study site was 102.30 and
and standard deviation was 121.33 and 290.59 t ha-1, respectively.
Testing of AGB values different between wet and dry seasons by t-test. The AGB
in dry season is significantly higher (t-test, p<0.01) than in wet season.
Whereas, mean of Above Ground Biomass in reference site was 67.68 and 2.50 t ha-1 in wet and dry season, respectively and standard deviation was 90.56 and 2.66 t ha-1, respectively. Testing of AGB values different between wet and dry season by t-test. The AGB in wet season is significantly higher (t-test, p<0.05) than in dry season.
Chukwamdee and Anunsiriwat (1997) estimated biomass
of mangrove forest at Changwat Samut Songkhram. Their results showed above ground
biomass sequestration with 75.96 tonnes mean while above ground biomass of more
than 300 tonnes was also reported in mangrove forests in Indonesia (Komiyama
et al., 1988). An above ground biomass of 341 tonnes was reported
for an Avicennia marina forest (Mackey, 1993).
The lowest above ground biomass reported was 7.9 tonnes for Rhizophora mangle
forest in Florida, USA (Lugo and Snedaker, 1974).
Estimation of above ground carbon
Above ground carbon sequestration: Above ground biomass and the percentage
of carbon content were conducted to estimate of above ground carbon sequestration.
The highest of above ground carbon was found in study site with maximum 14.58
t ha-1 in wet season whereas found only 2.78 t ha-1 in
dry season for reference site. From Fig. 5, above ground carbon
sequestration of dry season has higher than wet season.
||Histograms of above ground carbon
|| Statistics values of above ground carbon (AGC) (t ha-1)
|1N: 80, 2N: 15
Comparison of the average of above ground carbon between study and reference
site in both seasons found the study site have higher above ground carbon than
reference site and when we compared between wet and dry season seasons found
that dry season have the average above ground carbon higher than wet season.
Calculation of the total of above ground carbon in study area found the above
ground carbon sequestration in this area was 320.11 tonnes. When we compared
with the previously researcher, Fujimoto et al. (2004)
found above ground carbon sequestration rate with 208 tonnes. Besides, Sridang
(2008) estimated above ground carbon sequestration on mangrove forest, found
an average of it about 71.10 tonnes. Then our results showed the above ground
carbon sequestration higher than that.
Above ground carbon (AGC) (tonnes ha-1) statistics values: Mean of Above Ground Carbon in study site was 51.15 and 103.38 t ha-1 in wet and dry seasons, respectively. Standard Deviation was 60.67 and 145.31 t ha-1, respectively. Testing of AGC values different between wet and dry season by t-test. The AGC in dry season is significantly higher (t-test, p<0.01) than in wet season.
Mean while mean of Above Ground Biomass in reference site was 33.84 and 1.24 t ha-1 in wet and dry seasons, respectively. Standard Deviation was 45.28 and 1.32, respectively. Testing of AGB values different between wet and dry seasons by t-test. The AGC in wet season is significantly higher (t-test, p<0.05) than in dry season.
From Table 9 showed above ground carbon statistics values.
The results showed above ground carbon sequestration in study site has higher
than reference site in both seasons. Especially, the rate of above ground carbon
sequestration in dry season has the highest because sediment organic matter
was rapidly and efficiently decomposed in mangrove forest for dry season. Accordingly,
Bouillon et al. (2004) found that the degree
of utilization of mangrove derived food sources depends partially on the degree
of material exchange with adjacent system.
Integrating ecological data into the spatial distribution mapping model
Integrating leaf area index (LAI) into the spatial distribution mapping
model: A technique was presented by thematic maps of mangrove LAI. It was
derived accurately and precisely from remote sensed satellite data. The final
result of this spatial interpolation process was shown as Fig.
6. From the spatial distribution map of LAI showed that LAI values have
the highest lay on in Western and Eastern, respectively.
||Spatial distribution map of leaf area index values
||Spatial distribution map of above ground biomass
The density of LAI found the highest in dry season. Trend of LAI spatial distribution
showed low density of LAI in area where has less of tree and showed value of
LAI with 0.00 in death of tree area.
Integrating above ground biomass into the spatial distribution mapping model:
The final result of this spatial interpolation process was shown as Fig.
7. From the spatial distribution map of AGB showed that AGB values have
the highest lay on in Western and Eastern, respectively. The density of AGB
found the highest in dry season.
||Spatial distribution map of above ground carbon
Trend of AGB spatial distribution showed the same result of LAI values.
Integrating above ground carbon into the mapping model: Figure 8 showed the trend of the spatial distribution. This map found that above ground carbon values have the highest in fresh water outlet area where found the content of above ground carbon the highest in dry season whereas slightly in wet season same as the spatial distributions of LAI and above ground biomass.
The study of biological parameters of the two species of dominance mangrove trees in study area demonstrated that study site (mangrove filtration system) has higher biological values than the natural mangrove in the reference site when we calculate the above ground carbon sequestration, the results showed the positive potential for the above ground carbon sequestration. In this context, sequestration is the removal of CO2, either directly from the atmosphere and disposing of it either permanently for geologically significant time periods. Out data suggest that mangrove forest filtration system was not only wastewater construction but the results of this study showed advantages on a function as the sink of carbon.
This research was funded by The 90th Anniversary of Chulalongkorn University
Fund (Ratchadaphiseksomphot Endowment Fund), Chulalongkorn University, Thailand.
We are extremely thankful for the comments and suggestion from the reviewers.
The authors would also like to thank the numerous workers of The Laem Phak Bia
Environmental Study, Research and Development Project for their assistance in
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