**2. Materials and methods**

**1. Introduction**

124 National Parks - Management and Conservation

carbon dioxide (CO2

Tree biomass is a product from photosynthesis as a result of carbon sequestration by tree. A tree can absorb approximately 23 kg of carbon per year. Indeed, a tree can increase biomass as an effect of tree growth and loss biomass through mortality that is due to natural death or logging. Tree biomass can be divided into above (AGB) and below ground biomass (BGB) in which the AGB includes the stem, leaves and branch biomass whereas the BGB is the biomass of tree roots. Each component of the AGB varies in biomass density. The estimation of biomass is significantly important for the environment which is a critical aspect of studies of carbon stocks and the effects of carbon sequestration on the global carbon balance. In recent years,

in the atmosphere has risen to approximately 30% above natural background levels [1]. The need for biomass and carbon stocks estimation is critical and can be measured using destructive or non-destructive sampling method. That is why a field inventory is conducted where the measurement of tree diameter is recorded to estimate the biomass of tree and later the carbon stocks. According to Brown [2], for closed forest such as Pahang National Park (PNP), a minimum diameter of tree to be measured is greater than or equal to 10 cm. However, for

Most of the researches focus on the estimation of the AGB rather than BGB because the process to estimate the AGB is easier and less complicated as compared to BGB. In addition, the above ground tree components are the largest contributor of biomass from the total tree biomass (TTB) whereas the BGB only constitutes a small portion of the TTB. Lajuni and Latiff [4] reported the BGB value in their study plots at Khao Chong forest was one tenth of the AGB. Besides, a study conducted by Mohamad [5] in Kenaboi Forest Reserve, Negeri Sembilan found that root biomass in his study plots was six times smaller than the AGB with

Tree biomass and carbon stocks also varied in accordance to forest types and geographical regions. As such, forest biomass and carbon stocks in tropical forest are higher than temperate forest. This might be due to the different in tree species and climatic condition between both forests. Furthermore, in any forest types, tree biomass and carbon stocks in primary forest are higher than secondary forest. Secondary forest is a forest that has been logged or naturally disturbed whereas primary forest is a forest that has never been logged and free from anthropogenic disturbance. In this case, PNP is considered as a primary forest since anthropogenic activities such as logging have never occurred in this forest. Therefore, it is expected that more

Despite the multi-functional roles of forest biomass, lack of research had been conducted with regards to the extent of AGB, BGB and carbon stocks in lowland dipterocarp (LDF), riparian (RF) and hill dipterocarp forests (HDF) in PNP. In addition, information on biomass estimation and carbon stocks from tree inventory data is currently unavailable for protected forest of PNP. Therefore, this study was conducted to provide the estimation of the AGB, BGB and carbon stocks with respect to different localities in PNP. Considering the fact that biomass represents the role of tree as a key indicator of carbon source and sink, the information from

open or secondary forest, a smaller minimum diameter should be chosen [2, 3].

values of 463.81 and 73.57 t/ha for AGB and BGB, respectively.

carbon can be stored by forest biomass in PNP.

) has received much attention from the world because its concentration

#### **2.1. Study area and field data collection**

This study was conducted in PNP in the state of Pahang. PNP has a tropical climate with an annual rainfall of about 2.260 mm and rich in forest vegetation such as trees, climbers, shrubs, epiphytes and palms. Average temperature throughout the year ranges from 20 to 35°C with more than 80% humidity [6]. There are differences in the soil series in the LDF, RF and HDF mainly due to the variations of parent material between localities [7].

This study was conducted in three types of forests of LDF, RF and HDF of PNP and the location of study area are shown in **Figure 1**. The description for each location is summarized in **Table 1**. A total of 60 plots were set up in which each forest contains 20 plots measuring at 20 × 20 m (0.04 ha). Study plots for LDF were located in Kuala Keniam while plots for RF were scattered; 10 plots were located along Keniam River while another 10 plots were located along Tembeling River near to Kampung Pagi. As for the HDF, data collection was conducted in the Teresek Hill at an elevation around 330 m above sea level.

For the field measurement, diameter at breast height (DBH) tape was used to measure the diameter of sampled trees with DBH ≥ 10 cm which is 1.3 m up from the ground [8]. In the case of big buttressed stems, the tree height was measured just above the upper end of plank buttress [9]. Each tree was permanently tagged using laminated label. Tree height was measured using a clinometer, a device that can be used to measure the slope to points on a tree, which can subsequently be used to determine the tree height. The sampled trees were identified to species level and for unknown species, the botanical specimens (e.g. leaves, flower or fruit) were collected for species identification at herbarium laboratories of Universiti Kebangsaan Malaysia (UKM) and Forest Research Institute Malaysia (FRIM).

#### **2.2. Data analysis of tree biomass**

Throughout this study, AGB was estimated using Kato et al.'s function [9] (Eqs. (1)–(4)) while BGB using a function from Niyama et al. [10] (Eq. (5)). According to reference [10], the total root biomass is the summation of coarse and fine roots in which fine root is defined as root with diameter less than 5 mm. The TTB is the summation of AGB and BGB. From the values of measured DBH and tree height; the dry mass of stem, branch and leaves of sample trees were estimated. The equations used to estimate these components are as follows:

$$M\_s = 0.0313 \, (\text{D}^2 \, \text{H})^{0.9733} \tag{1}$$

**Figure 1.** Study areas of LDF, RF and HDF in PNP.


**Table 1.** Study area, forest types, locality, coordinates, slope, elevation and soil series in PNP.

$$M\_{\oplus} = 0.136 \, (M\_{\circ})^{1.070} \tag{2}$$

*TTB* = *AGB* + *BGB* (6)

Biomass and Carbon Stocks Estimation of Lowland Dipterocarp, Riparian and Hill Dipterocarp…

http://dx.doi.org/10.5772/intechopen.76699

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Where, Ms, MB, ML were denoted as dry mass of stem, branch and leaves in kg, respectively (Eqs. (1)–(4)). The AGB was computed from the summation of these components as in Eq. (4). The biomass functions developed by Kato et al. [9] can be applied irrespective of tree species since these equations were developed without taking regards of tree species in the study area of Pasoh Forest Reserve [9, 11]. In this study, the dry mass of the tree biomass components was presented in t/ha. The dry mass (kg) for each component was converted into tonne by dividing the values with 1000 then divided with 0.04 ha which is the size of each plot. For an estimation of carbon storage, the biomass value was divided with 0.8 ha which is the total size for each study area. The carbon storage in the forests was calculated in accordance to method

Means of AGB, BGB and TTB between LDF, RF and HDF of PNP were obtained and analyzed using 3 × 5 factorial two-way ANOVA. The PROC GLM was applied in Statistical Analysis Software (SAS) version 9.3 to study the interaction between forests and five similar family and species based on the highest AGB in LDF, RF and HDF of PNP. The normality of the dataset is test using frequency distribution or histogram. Based on the analysis, the data distribution

The total AGB, BGB and TTB for lowland dipterocarp, riparian and HDF are shown in **Table 2**. From the **Table 2**, it appears that HDF recorded the highest AGB, BGB and TTB among study areas. This is because HDF consists of higher trees (*n* = 579) and number of trees with DBH of more than 80 cm was higher than the other two forests (14 trees/ha) (**Table 3**). Furthermore, dominant family in HDF based on basal area was Dipterocarpaceae with tree count of 58 from 579 trees (**Table 3**). These dipterocarp trees have diameter ranges from 10.8 to 103.5 cm. RF recorded the lowest AGB, BGB and TTB among the three forests as it recorded contains less number of trees (*n* = 285) and most of trees in RF have smaller diameter. Big-sized trees in RF with diameter more than 80 cm was lower than LDF and HDF (3 trees/ha) thus less contrib-

As comparison with the previous studies, Cairns et al. [12] presented the AGB for 195 sampled trees with diameter of more than 10 cm in dry forest of Mexico's Yucatan Peninsula with value of 191.5 t/ha. Hikmat [13] conducted a study in three virgin jungle reserves in Mata Ayer, Bukit Bauk and Gunung Pulai each in 2 ha plot. A total of 2341, 2702 and 2070 trees with diameter greater than 5 cm were enumerated in Mata Ayer, Bukit Bauk and Gunung Pulai, respectively. From this study, he found that the AGB of each forest was 402.6, 551 and 320.57 t/ha, respectively. The BGB in Hikmat's [13] study was computed following method

from Brown [2] whereby 50% of the biomass in the forest is assumed as carbon.

is normal and the statistical tests are considered as parametric tests.

**3.1. The total AGB, BGB and TTB for different types of forests**

**2.3. Statistical analysis**

**3. Results and discussions**

uted to tree biomass of RF.

$$\frac{1}{M\_{\text{l.}}} = \frac{1}{0.124 \text{(M}\_{\text{s}}^{\text{
u.}}\text{s}^{\text{
u.}}\text{)}} + \frac{1}{125} \tag{3}$$

$$\text{AGB} = M\_s + M\_\text{g} + M\_\text{L} \tag{4}$$

$$\text{BGB} = 0.0262 \times \text{D}^{2.487} \tag{5}$$

$$\text{TTB} = \text{AGB} + \text{BGB} \tag{6}$$

Where, Ms, MB, ML were denoted as dry mass of stem, branch and leaves in kg, respectively (Eqs. (1)–(4)). The AGB was computed from the summation of these components as in Eq. (4). The biomass functions developed by Kato et al. [9] can be applied irrespective of tree species since these equations were developed without taking regards of tree species in the study area of Pasoh Forest Reserve [9, 11]. In this study, the dry mass of the tree biomass components was presented in t/ha. The dry mass (kg) for each component was converted into tonne by dividing the values with 1000 then divided with 0.04 ha which is the size of each plot. For an estimation of carbon storage, the biomass value was divided with 0.8 ha which is the total size for each study area. The carbon storage in the forests was calculated in accordance to method from Brown [2] whereby 50% of the biomass in the forest is assumed as carbon.

#### **2.3. Statistical analysis**

Means of AGB, BGB and TTB between LDF, RF and HDF of PNP were obtained and analyzed using 3 × 5 factorial two-way ANOVA. The PROC GLM was applied in Statistical Analysis Software (SAS) version 9.3 to study the interaction between forests and five similar family and species based on the highest AGB in LDF, RF and HDF of PNP. The normality of the dataset is test using frequency distribution or histogram. Based on the analysis, the data distribution is normal and the statistical tests are considered as parametric tests.
