Explain The Meaning Of Forest Biology Essay

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Although forest can be defined as a large area covered highly with trees and undergrowth, there are different legal definition based on countries. Many countries had used the differently definition to explain the meaning of forest. India's Forest Conservation Act of 1980 states that any land recorded as forest in any land record is legally forest land whether or not there is any vegetation on the land. The Philippines has a definition based on the slope of the land - any untitled land having a slope greater than 18% is considered to be forestland.

When we talk about sustainable environment, we cannot reject the facts that forest in the one of important component that contributes to it. Forest have doing its functions for more that thousands years in maintaining the environment. Forests are essential to life on Earth. Without them, the energy of the sun would not be harnessed through the process of photosynthesis, nor would oxygen be released through the same process. Aside from these two vital ingredients to life, they are also important in that they are home to countless species of plants and animals, many of which are endangered.

Though tropical forests cover only 7% of the Earth's land mass, they are home to more than 50% of the world's 10-50 million plant and animal species. Often, the plants in the forests also provide us with medicines. On a more basic level, they have provided the human race with wood for centuries, without which people would not be living at the level of comfort they have today.

The earth's climate is largely controlled by how much of the sun's light and heat is absorbed and reflected. By absorbing the sun's heat, trees cool the air. The interaction of this relationship with an area's topography, latitude, and altitude, can create microclimates, just as trees create microclimates almost anyplace they are by providing a windbreak and shade. Think about it: on a hot, sunny day, it's always cooler beneath a shade tree. A city with a robust tree canopy is cooler in the summer than a similar city with fewer trees, which translates into less energy use and lower cooling costs. Also, in using less energy, less air pollution is created.

The most important role that rural trees and forests play is taking carbon dioxide out of the atmosphere. The living tissue of a tree is a storage vault for carbon, which would otherwise contribute to the greenhouse effect and to global climate change. Simply put, more trees can decrease the rate of climate change and help us withstand its effects, potentially resulting in less intense storms, fewer infectious diseases, a more stable water supply, and fewer wildfires.

Trees, however, aren't immune to the effects of climate warming. Areas once too cold to support trees now can, and as forests migrate north, harmful insects that were once held at bay by winter freezes can wreak havoc on native species. Tropical vines called lianas are now growing faster than the trees they climb, causing trees in the Amazon and other rainforests to die at an alarming rate.

Trees and forests can either be the key to slowing climate change and mitigating its effects. Forests influence climate change largely by affecting the amount of carbon dioxide in the atmosphere. When forests grow, carbon is removed from the atmosphere and absorbed in wood, leaves and soil. Because forests (and oceans) can absorb and store carbon over an extended period of time, they are considered "carbon sinks". This carbon remains stored in the forest ecosystem, but can be released into the atmosphere when forests are burned. Quantifying the substantial roles of forests in absorbing, storing, and releasing carbon is the key to understanding the global carbon cycle and hence climate change.

1.2 Problem statement

Forests play an important role in environmental and economic sustainability. They provide numerous goods and services, and maintain life support systems essential for life on earth. Some of these life support systems of major economic and environmental importance are:

supply of timber, fuel wood, fodder, and a wide range of non-wood products;

natural habitat for bio-diversity and repository of genetic wealth;

provision of recreation and opportunity for ecotourism;

playing an integral part of the watershed to regulate the water regime, conserve soil, and control floods; and

Carbon sequestration and carbon sink.

As carbon sequestration and carbon sink as part of role of forest, the sustainability of forest is important to maintain the carbon cycle in earth as they absorbing the effect of greenhouse gases and reduce the effect of global warming. Forest can be affected by global warming and climate change but still can be the protector to earth by it function. That's why forest must be protected.

Although Malaysia is facing the mission to achieve target of become the developed country in 2020, Malaysia is still committed to ensure the sustainability of forest in Malaysia. With the many kind of forest in Malaysia, it contributes to the economic revenues due to the incoming tourists from inside or outside the country to see the beauty of the natural biodiversity in Malaysia and also from the logging activity that support the woody industry in Malaysia.

According to Malaysia World Wildlife Fund (WWF), Malaysia's land surface was once almost entirely covered with forest. Today, forests still cover about 59.5% of the total land area. However, deforestation is a major concern as the country is still rapidly developing. Based on the data collected by statistical Department, In the 20 years from 1991 to 2010, there was a reduction of about 1.3 million hectare of forest cover in Malaysia (refer to table 1). This is about 2 times the size of Singapore - an average of 68,000 hectare of forest being lost annually.

Table 1: Forest area in Malaysia from 1991-2010

Year

Forest Area ('000 hectares)

1991

19,441

1992

19,296

1993

19,169

1994

18,974

1995

18,903

1996

18,785

1997

18,794

1998

18,902

1999

18,716

2000

18,699

2001

18,459

2002

18,411

2003

18,380

2004

18,338

2005

18,313

2006

18,304

2007

18,225

2008

18,258

2009

18,243

2010

18,081

(Source: Statistical department of Malaysia, 2010)

Apart from deforestation, the remaining forests face threats from unsustainable logging, illegal removal of forest products and encroachment. This problem can be considered as the major problem, not only to sustainable environment, but also to climate as the process of carbon cycle had been disturbed and may cause to increasing of carbon in atmosphere.

1.3 Research questions

The problem statement stated above suggests that there are several questions to be explored. These questions are:

How does aboveground biomass and carbon stock be measured in forest?

What is the amount of aboveground biomass in reserved forest?

What is the amount of carbon stock in reserved forest?

What affect the aboveground and carbon stocks in forest?

1.4 Objectives

The general objective of this study is to estimate aboveground biomass and carbon in selected forest in Selangor. The specific objectives are:

To estimate the above ground forest biomass in 1 hectare area in permanent forest reserve in Selangor.

To estimate the carbon stock in 1 hectare area in permanent forest reserve in Selangor.

To estimate change in carbon stock in reserved forest Selangor.

1.5 Significant of study

From this study, the information on aboveground biomass and carbon will be determined. Estimation about how much the forest change has affected the carbon stock also will come out. These results can provide the information to related agencies that involve in monitoring carbon or concern about things related to climate change. The related agencies also can use this information to make policies or actions together with our prime minister to achieve the 40% reduction of greenhouse gases by 2020. This study also can be as reference and knowledge to others that interesting to do the similar study.

1.6 Limitations of the study

There are some limitations regarding to this study. The first one data collected is only the related to aboveground biomass such as the number of tree and tree diameter. The sampling and laboratory experiment is not used in this study because of lack of knowledge and to preserve the forest from any harm. That is why we select the most non destructive model from Kato et al. 1978 to estimate aboveground which only required DBH and number of tree. This non destructive approach also becomes the priority why this study used the existing model.

This study also used the only 1 hectare sampling plot to estimate the aboveground in total permanent forest reserve. It means that this outcomes or finding cannot assume as it can be apply to other similar study.

1.7 Summarization of Each Chapter

Chapter 1: Introduction

This chapter explains the introduction and the background of the study. It also covers the significant of the study, objective of the study and also the limitation in conducting this study.

Chapter 2: Literature Review

This chapter is reviewed journals and other information sources which are related to this study.

Chapter 3: Methodology

This chapter introduces the study area and the methodology utilized. It also includes the study area map, the steps in conducting the study and data that have been used in conducting this study.

Chapter 4: Results and Discussions

This chapter explains the results for this study which is about the aboveground biomass and carbon stock prediction in Permanent forest reserve Sg. Lalang.

Chapter 5: Conclusions

This chapter explains the conclusion from the study.

CHAPTER 2

LITERATURE REVIEW

2.1 Introduction

This chapter discuss about concepts and past studies of carbon and biomass. This will include the description of biomass and carbon, role of forest on biomass and carbon stock, the methodologies used in past studies, the results of these study and discussion about the advantages and disadvantages of using each methodology. This chapter also will discuss about policies applied and to select the best methodology that will be apply in this study as it will be the best way to achieve the objectives of the study.

2.2 Carbon dioxide

According to Dave Reay et.al (2010), Carbon or specifically carbon dioxide (CO2) is a chemical molecule consisting of one carbon atom covalently bonded to two oxygen atoms. At atmospheric pressure and temperature, carbon dioxide is a colorless, odorless gas that exists naturally as a trace gas in the Earth's atmosphere. It is a fundamental component of the Earth's carbon cycle, with a considerable number of sources, both natural and man-made; moreover, there are a significant number of natural carbon sinks including oceans, peat lands, forests and other biota. All life is based on carbon. Carbon is the major chemical constituent of most organic matter. Yet by weight, carbon is one of the least abundant elements within the Earth's crust.

Generally, Carbon is stored on our planet in the following major sources and sinks: (1) as organic material in living and dead organisms; (2) as the gas carbon dioxide in the atmosphere; (3) as organic matter in soils; (4) in the lithosphere as fossil fuels and sedimentary rock deposits such as limestone, dolomite, and chalk; and (5) in the oceans as dissolved atmospheric carbon dioxide and as calcium carbonate shells in marine organisms.

Carbon from the atmosphere is converted into biological matter by photosynthesis. During decay or combustion, carbon goes back into the atmosphere. This happens over a relatively short period and plant matter used as a fuel can be constantly replaced by planting new growth. Therefore a reasonably stable level of atmospheric carbon results from its use as a fuel.

Carbon is released from ecosystems as carbon dioxide gas by the process of respiration. Respiration takes place in both plants and animals and involves the breakdown of carbon based organic molecules into carbon dioxide gas and some other compound byproducts. All the process can be shown in figure 2.

Figure 1: A sub-cycle within the global carbon cycle. Carbon continuously moves between the atmosphere, plants and soils through photosynthesis, plant respiration, harvesting, fire and decomposition.

2.3 Biomass

Photosynthesis is a process of converting radiant energy from the sun and CO2 from the air into the chemical energy of plant tissue (Hall, 1999). Through photosynthesis, carbon in atmospheric CO2 becomes carbon in plant tissue, also called biomass. When biomass is burned, decays or is otherwise oxidized, the chemical energy is released and the CO2 is placed back into the atmosphere, completing a natural carbon cycle. As long as this cycle is in balance, it has a net zero impact on the carbon in the atmosphere, which is why biomass carbon is often called "carbon neutral."

Biomass can be used repeated in endless number of times, on basic carbon circulation in photosynthesis process compare to fossil resource that limited to a transitory use in principle and can influence the global climate by its irreversible CO2 emission caused by fossil combustion (refer to figure ).

(a)

(b)

Figure 1: Comparison of biomass (a) and fossil (b) system on Carbon cycling

(Source: modified from Asian Biomass Handbook, 2008)

The biomass carbon cycle and carbon neutrality differentiate the carbon in biomass from the carbon in fossil fuels. Fossil fuels contain carbon that has been out of the atmosphere for millions of years. When fossil fuels are burned, therefore, they put carbon in the atmosphere that is in addition to what has been cycling between the atmosphere and the earth, causing the amounts of CO2 in the atmosphere to increase. Indeed, the primary source of increased CO2 in the atmosphere since pre‐industrial times is fossil fuel combustion (Denman, 2007).

Standard accounting protocols measure emissions from fossil fuel at the point of combustion while biogenic carbon emissions and sequestration are accounted for in the context of their impact on the biomass carbon cycle (e.g. IPCC, 2006).

2.4 Role of forest on biomass and carbon stock

According to FAO (2012), Forests have four major roles in climate change: they currently contribute about one-sixth of global carbon emissions when cleared, overused or degraded; they react sensitively to a changing climate; when managed sustainably, they produce wood fuels as a benign alternative to fossil fuels; and finally, they have the potential to absorb about one-tenth of global carbon emissions projected for the first half of this century into their biomass, soils and products and store them - in principle in perpetuity.

According to W. A. Kurz and S.G. Conard (2005), Carbon is continuously cycled between forests and the atmosphere. As trees grow, they remove C (as carbon dioxide, CO2) from the atmosphere and store it in foliage, branches, and other woody biomass. As trees die and decompose, this C is gradually released back into the atmosphere as CO2. Natural or human-induced disturbances contribute to this C cycle. Carbon in biomass and dead organic matter pools is oxidized during fires and released back into the atmosphere. Deforestation or other causes of failed forest regeneration interrupt the cycle of C uptake and release.

2.5 Issues related to forest and biomass

Forests have traditionally been used for many products, including timber, fuel, and fodder. Determining the biomass of forests is a useful way of providing estimates of the quantity of these components. Typically, the quantity of saw timber has been assessed by making volume estimations, but this ignores the other useful components such as smaller size wood for fuel use.

Furthermore, very few to no assessments have been made of the quantity of wood present in forests that appear to have no potential for saw timber production. Assessing the total aboveground biomass of forests, defined as the living and dead matter in standing trees and shrubs and can be classified in foliage, branches, and boles. Bark, hardwood and softwood are timber biomass components, is a useful way of quantifying the amount of resource available for all traditional uses. It either gives the quantity of total biomass directly or the quantity by each component (e.g., leaves, branches, and bole) because their biomass tends to vary systematically with the total biomass.

However, the way the biomass of each forest component varies with total biomass varies by forest type, such as natural or planted forests and closed or open forests. For example, leaves for fodder are about 3-5% and merchantable bole is about 60% of the total aboveground biomass of closed forests.

The quantity of biomass in a forest is a result of the difference between production through photosynthesis and consumption by respiration and harvest processes. Thus it is a useful measure for assessing changes in forest structure. Changes in forest biomass density are brought about by natural succession; human activities such as silviculture, harvesting, and degradation; and natural impacts by wildfire and climate change. Biomass density is also a useful variable for comparing structural and functional attributes of forest ecosystems across a wide range of environmental conditions.

Biomass of forests is also very relevant for issues related to global change. For example, the role of tropical forests in global biogeochemical cycles, especially the carbon cycle and its relation to the greenhouse effect, has heightened interest in estimating the biomass density of tropical forests. The biomass of forests provides estimates of the carbon pools in forest vegetation because about 50% of it is carbon. Consequently, biomass represents the potential amount of carbon that can be added to the atmosphere as carbon dioxide when the forest is cleared and/or burned. Attempts to estimate the biomass density of tropical forests have been made by the scientific community for use in models that assess the contribution of tropical deforestation and biomass burning to the increase in atmospheric carbon dioxide and other trace gases (Brown et al. 1989, Crutzen et al. 1991, Hall and Uhlig 1991, Houghton et al. 1983).

Global interest in climate change led to the establishment of the UN Framework Convention on Climate Change (UNFCCC) at the 1992 UN Conference on Environment and Development (UNCED). Over 130 nations have ratified this convention which means that these nations need to make national greenhouse gas emission inventories. Changes in the cover, use, and management of forests produce sources and sinks of carbon dioxide to and from the biosphere. To estimate the magnitude of these sources and sinks requires reliable estimates of the biomass density of the forests undergoing change.

Biomass density estimates also provide the means for calculating the amount of carbon dioxide that can be removed from the atmosphere by regrowing forests or by plantations because they establish the rates of biomass production and the upper bounds for carbon sequestering. This issue is receiving more attention of late as countries look to forests as a means of mitigating greenhouse gas emissions, particularly carbon dioxide, a major greenhouse gas and the one fixed during photosynthesis. Practices such as sustainable forest management, slowing deforestation, and low-impact-logging decrease emissions or conserve carbon dioxide. Other practices such as plantation establishment or other tree planting programs on previously non-forested land sequester carbon dioxide (Brown et al. 1996). Furthermore, biomass density estimates of forests are extremely relevant for studying other global biogeochemical cycles, such as nitrogen, because the amount of other nutrient elements in forests is also related to the quantity of biomass present.

Another issue related to forest biomass has emerged since the 1980s. In addition to loss of forest area, forest degradation, resulting in biomass density loss, is known to be occurring (Brown et al. 1994, FAO 1993, 1995). Much of this biomass degradation appears to be unrecorded, thus it is in addition to that accounted for by sanctioned harvesting. An example is the illegal logging practices occurring in many forests of tropical Asia (Callister 1992). Clearly, this process of biomass density reduction has implications for the global carbon cycle, other biogeochemical cycles, and biodiversity. Biomass degradation is due to many factors mostly related to social, economic, and political factors.

2.6 Previous studies on aboveground biomass

Estimation of the accumulated biomass in the forest ecosystem is important for assessing the productivity and sustainability of the forest. It also gives us an idea of the potential amount of carbon that can be emitted in the form of carbon dioxide when forests are being cleared or burned. Biomass estimation of the forest ecosystem enables us to estimate the amount of carbon dioxide that can be sequestered from the atmosphere by the forest.

The accurate assessment of biomass estimates of a forest is important for many applications like timber extraction, tracking changes in the carbon stocks of forest and global carbon cycle. Forest biomass can be estimated through field measurement and remote sensing and GIS methods (Ravindranath NH, et.al (2008), Lu D (2006)).

Two methods of field measurement are available. The first one is the destructive method of tree biomass estimation. Among all the available biomass estimation method, the destructive method, also known as the harvest method, is the most direct method for estimation of above-ground biomass and the carbon stocks stored in the forest ecosystems (Gibbs HK et.al 2007).

This method involves harvesting of all the trees in the known area and measuring the weight of the different components of the harvested tree like the tree trunk, leaves and branches (Ravindranath NH, et.al 2008, Hashimotio T, et.al 2000, Nelson BW et.al 1999) and measuring the weight of these components after they are oven dried. This method of biomass estimation is limited to a small area or small tree sample sizes.

Although this method determines the biomass accurately for a particular area, it is time and resource consuming, strenuous, destructive and expensive, and it is not feasible for a large scale analysis. This method is also not applicable for degraded forests containing threatened species (Montès N et.al 2000). Usually, this method is used for developing biomass equation to be applied for assessing biomass on a larger-scale (Navár J, 2009, Segura M, Kanninen M, 2005).

The second method of tree biomass estimation is the non-destructive method. This method estimates the biomass of a tree without felling. The non-destructive method of biomass estimation is applicable for those ecosystems with rare or protected tree species where harvesting of such species is not very practical or feasible.

Montes et al. (2000) developed a non- destructive method for the above-ground biomass estimation of thuriferous juniper (Juniperus thurifera L.) woodlands in the High Central Atlas, South of Morocco. In this study, the biomass of the individual tree was estimated by taking into account the tree shape (by taking two photographs of the tree at orthogonal angles), physical samples of different components of the trees like branches and leaves and dendrometric measurements, volume and bulk density of the different components. Although it is a non-destructive method, to validate the estimated biomass, the trees had to be harvested and weighted.

Another way of estimating the above-ground forest biomass by non-destructive method is by climbing the tree to measure the various parts (Aboal J.R. et.al 2005) or by simply measuring the diameter at breast height, height of the tree, volume of the tree and wood density (Ravindranath NH, Ostwald M, 2008) and calculate the biomass using allometric equations (Brown S, et.al 1989). Since these methods do not involve felling of tree species, it is not easy to validate the reliability of this method. These methods can also involve a lot of labour and time and climbing can be troublesome.

2.7 Allometric Equations for Biomass Estimation

The most widely used method for estimating biomass of forest is through allometric equations. The allometric equations are developed and applied to forest inventory data to assess the biomass and carbon stocks of forests. Many researchers have developed generalised biomass prediction equations for different types of forest and tree species (Nelson et al. 1999, Chung-Wang et al. 2004, Montes et al. 2000, Javar j 2009, brown et.al 1989, Basuki et al. 2009).

The allometric equations for biomass estimation are developed by establishing a relationship between the various physical parameters of the trees such as the diameter at breast height, height of the tree trunk, total height of the tree, crown diameter, tree species, etc. Equations developed for single species and for mixture of species give the estimate of biomass for specific sites and for large-scale global and regional comparisons.

Brown et al. (1989) developed allometric regression equations to estimate the above-ground biomass of individual tress for tropical forests as a function of diameter at breast height, total height and wood density and Holdridge life zone. This estimate of Brown's biomass equation takes into account only the live trees and not the fallen litter and the standing dead trees. Nelson et al. (1999) conducted a study to develop species-specific and mixed-species allometric relationships for estimating total above-ground dry weight using eight abundant secondary forest tree species in the Amazon.

Chave et al. (2001) proposed an estimation method for the estimation of biomass in a neo-tropical forest of French Guiana for which they have made use of published data sets providing the biomass and the diameter at breast height of felled and weighted trees. In this study, they have parameterized the regression models using 32 measurements of large trees. Ketterings et al. (2001) also proposed an allometric equation for calculating the biomass of trees in the mixed secondary forest of Sumatra, Indonesia. However, the proposed equation is most suitable for trees having a diameter at breast height of 8-48 cm. Xiao and Ceulemans (2004) conducted a study on a 10-year-old Scots pine to derive allometric relationships of branch and foliage biomass at branch and tree level and confirm the earlier studies conducted by Helmisaari et al. (2002) on Scots pine in Finland.

Segura and Kanninen (2005) conducted a study in the tropical humid forest of Costa Rica to develop allometric models for estimating the stem volume, total volume (stem and branches) and the total aboveground biomass (stem, branches and foliage) for individual trees of that forest. Unlike other allometric equations found in the literature, where only the stem is taken into account for total volume, the model developed by Segura and Kanninen (2005) includes the branches. The models, however, are recommended only when the diameter at breast height is between 60 and 105 cm.

Aboal et al. (2005) also developed allometric equations for estimating tree biomass in the Gomera laurel forest, Canary Islands. The proposed biomass equation is based on the relationship between volume and weight as they relate the diameter at breast height to the above-ground biomass. According to Aboal et al. (2005), the diameter at breast height gives an idea of the volume of the tree.

Kenzo et al. (2009) harvested 136 trees from 23 species to measure the above-ground biomass in various tropical secondary forest trees in Sarawak, Malaysia. They also developed allometric relationships between the stem diameter at breast height, stem diameter at ground and leaf, stem and total root biomass. Their study also showed a relatively high correlation of allometric relationships between the tree height and plant-biomass. Navar (2009) also developed allometric equations to estimate the biomass and carbon stocks for temperate forest and tropical dry forests of Mexico. These allometric equations are useful to estimate biomass of forests with complex diversity structure.

Ryan et al. (2011) carried out a study to quantify the forest carbon stock in Miombo woodland in Mozambique. They developed a new site-specific allometric equation, between stem diameter and tree stem, based on destructive harvest of 29 trees. Djomo et al. (2011) also conducted a study to estimate the total above-ground biomass of a moist tropical forest in South-western Cameroon using a locally developed mixed species allometric equation. The choice of allometric equations has a significant effect on the biomass calculations since the forest biomass estimates vary with age of the forest, site class and stand density.

Hence, the generalized allometric equations available for large landscape scales should be used with caution as the site greatly influences allometric relationships (Muntagu et al. 2005). Kim et al. (2011), in their study, emphasis that the sites specific allomeric equations are more accurate in predicting the forest biomass estimates on the local level as it takes into account the site effects. According to the studies conducted by Vielledent et al. (2012), when biomass allometric models are not available for a given forest site, a simple height-diameter allometry is required to estimate the biomass and carbon stocks accurately from plot inventories.

There have been very few allometric equations developed specifically for lowland dipterocarp forest. Basuki et al. (2009) collected the data from the lowland dipterocarp forest in East Kalimantan, Indonesia and 122 trees were sampled having a diameter at breast height (1.3m) of 6-200 cm. They then developed tree allometric equations for lowland dipterocarp forest by establishing a relationship between tree parameters such as the diameter at breast height, commercial bole height and wood density with above-ground biomass.

The forest carbon stocks are widely estimated from the allometric equations for forest biomass. Generally, the carbon concentration of the different parts of a tree is assumed to be 50% of the biomass (Brown S, 1986) or 45% of the biomass (Whittaker, 1973). However, Losi et al. (2003) in their study estimated the carbon concentration of dry bole sample to be approximately 48% of the dry bole biomass. Djomo et al. (2011) analyses the carbon content in wood with a CNS analyser and found a mean value of 46.53%.

2.8 Selection of methods

The biomass estimation of the forest can be worked out using any of the methods or in combination of the methods mentioned. At the same time, while choosing a method for biomass estimation one should keep in mind the applicability or the suitability of that method for the area or forest type or tree species.

The allometric equations and regression models, for biomass estimation, also should not be used beyond their range of validity (Nelson et al. 1999, Chave et al. 2005). Although, the field measurements give a more accurate estimate of the forest biomass, it is labour and resource intensive and time consuming. Therefore, allometric relationship is often the preferred method for estimating forest biomass as this method provides a nondestructive and indirect measurement of biomass and comparatively, it is less time consuming and less expensive. The estimation of biomass with the help of allometric equation is considered to be a non-destructive method or an indirect method as these equations uses only the indicator parameter obtained from the forest inventories to estimate the biomass. However, the allometric equations developed for biomass estimation need to be validated. And for the validation of the biomass equations, cutting and weighting of tree components are required (Montes et al.2000, Ryan et al. 2011, Djomo et.al 2011, Kim et al. 2011, Araujo et al. 1999).

For this study, the best method is using nondestructive method by using the existing allometric equations and applied in new study environment based on the criteria of forest studies. This method also will ensure no harm to forest and allocate time taken. This method also consistent with the objective to measure the aboveground biomass in forest, not to create any new allometric equation based on the study.

2.9 Laws and policies

2.9.1 Provisions of the constitution

Malaysia constitutional provisions in Article 74 (2) clarify that the forest is under the jurisdiction of State Government. With this provision, the State Government is empowered to carry out the law in the forestry sector and can formulate its own forest policy. However, federal authorities can provide advice and technical assistance, maintenance stations trial and demonstration, training and research.

Under the Constitution of Malaysia, the National Land Council (MTN) is authorized to formulate the National Policy for promote and regulate the use of land, including the coordination and harmonization of forestry affairs through the National Forestry Council (NCC). Therefore, the MPN is a forum for the Federal Government and State Government together to discuss problems and issues related to policy, administration and management of forests, and the responsibility of the State Government to implement the decisions made by MPN Certified by MTN.

2.9.2 Dasar Perhutanan Negara (DPN)

DPN drafted and approved by the MPN and confirmed by MTN on 19 April, 1978, is focused on a comprehensive approach to addressing the challenges faced by the forestry sector to manage, conserve and develop forest resources.DPN was amended on 19 November, 1992 and was adopted by the State Government. This amendment was adopting sustainable management of forest resources for the socio-economic development, forest conservation and environmental interests.

In short, the National Forest Policy 1978 (Revised 1992) has highlighted aspects of

as in the following: -

Hold the Permanent Forest Reserve (HSK) enough as Forest Protection; Forest Production and Recreational Forest;

Manage the HSK with the principles of proper forest management;

Implement development efforts, rehabilitation and reforestation;

Determine harvesting forest resources outside HSK implemented consistent with the rate of land development so that the maximum benefits available;

Improving harvesting and use of all types of forest products and promote the growth of wood-based renewal for a maximum resource utilization, employment opportunities and gain on foreign exchange;

Implement forestry training programs at all levels of public and private sector to produce trained manpower;

Improve programs to meet the needs of socio-economic development, environmental protection environment and conservation, forestry, as well as the sustainable use of forest heritage through: -

(a) Forest legislation that emphasizes the implementation of the National Forestry Act 1984 (amended 1993) for ensure that forest resources are managed and maintained in a sustainable manner;

(b) Encourage the establishment of plantation forests with tree plantations of high quality and involvement increased private sector is active;

(c) Forest Farmers involve local communities in forest fringes in planting fruit trees to protect the forest resources;

(d) Non-Wood Forest Products in particular to promote the production of bamboo and rattan as well as scientific and sustainable management;

(e) Community Forestry meant to cater for recreation and tourism;

(f) Preservation of Biological Diversity to enhance the preservation of flora and fauna to maintain forest biodiversity;

(g) The values ​​that involve the establishment of Special Scientific forest areas dedicated to scientific research, and

(h) International cooperation to forge international cooperation to achieve a common understanding in the management and development of forest resources.

2.10 Forest legislation

2.10.1 Enactment (Application) National Forestry Act 1985

National Forestry Act 1984 (Act 313), which was passed by Parliament has been adopted by the State Government in November, 1985 and gazetted as Enactment (Application) National Forestry Act 1985 on 4 April, 1985. It contains provisions for the Administration, Maintenance, Security and Development in Maharashtra Forest Treasures. Enforcement of this Act is through the Forest Rules 1988.

Due to the development of a more challenging time in the forestry sector, the State Assembly has adopted the National Forest Act (Amendment) Bill 1993 on 12 May 1994. This amendment has been made among others provide for penalties and harsher penalties for preventing the occurrence of forest offenses.

In 2005 amendments to section 10 of the National Forestry Act 1984 have been made to increase the forest functional class of 12 State Parks to proclaim the Selangor State Park in 2007.

2.10.2 Enactment of the 1985 Wood-based Industries

Wood-based Industries Enactment 1985 (Act 314) was passed by the State Assembly in 1987 to coordinate all the activities of the Wood-Based Industries in the state of Maharashtra and its enforcement is through the Rules of the State Wood-based Industries 1990.

CHAPTER 3

METHODOLOGY

3.1 Introduction

This chapter will discuss about the methods that will applied in this study. Generally, this study will be use the allometric equations created by Kato et al. (1978) to estimate the aboveground biomass in forest. Data will be collected in various sources such as secondary data from forestry department, books, reports, journals, articles, and also from related websites. Analysis will be made by using the simple software, using Microsoft Excel. The flowchart below shows the sequence of methodology.

Primary data

Tree measurement

DBH

Secondary data

Data from Foresry department

Books, Journals, articles

websites

Data gathering

Equations by Kato et al. (1978) Using Microsoft Excel to get aboveground biomass

Carbon stock equations

Data analysis

3.2 Study area

Sungai Lalang Permanent forest reserve is located in the district of Selangor. It is 13km from Semenyih and 43km from kuala lumpur if by road. Sungai Lalang forest reserves are under the administration of Selangor forestry department. It was gazetted as a forest reserve since 1999. Forest reserves are managed by the central Selangor district forest office. Forest reserve covers an area of ​​17,027.77 hectares and is divided into 55 compartments.

Figure 3.1 Map of Permanent Forest Reserve Sungai lalang

Sungai Lalang forest reserve more focused on recreational forest and become a tourist attraction because has a waterfall and river clean and beautiful. The area is also close to several interesting locations to visit such Sungai Tekala waterfall, Sungai Gabai, and sungai Batangsi.

In addition, its strategic location on the edge of the road to Hulu Langat and sungai kelawang in nine states make this place easy to reach other basic facilities and infrastructure for the use of visitors. Approximately 3000 visitors will visit this forest at one time, especially over the weekend.

This forest area is also frequently became an area of ​​educational activities for secondary and tertiary levels where there are studies done by those related forest species found here. Forest reserves have been used for forest management demonstration center where there is herb plots and forest ecology made. These forests are classified as Lowland Dipterocarp forest with an altitude of about 300 meters above the sea level. For this study, the Compartment 24 is selected to do the study on aboveground biomass and carbon stock.

Table Compartments in Sungai Lalang forest eserve

Compartment

Area (hectare)

1

179.73

2

123.43

3

165.11

4

149.61

5

36.46

11

131.43

12

198.43

13

237.63

14

209.51

15

126.55

16

213.27

17

222.99

18

288.26

19

146.98

20

270.18

21

122.36

22

204.21

23

205.87

24

208.7

24D

91.04

25

245.09

26

96.39

27

146.49

28

88.58

29

115.16

30

190.34

31

531.94

32

244.06

33

495.22

34

287.28

35

583.81

36

842.03

37

519.26

38

857.65

39

306.85

40

249.79

41

396.58

42

616.21

43

720.07

44

463.07

45

420.11

46

641.13

47

169.35

48

224.77

49

541.24

50

597.71

51

452.25

52

836.43

83

264.1

84

201.26

85

197.46

86

332.57

87

129.99

88

238.17

89

227.28

116.33

Total

17,027.77

(Source: Selangor Forestry Department, 2011)

3.3 Data gathering

This study used the non destructive method to collect the data from forest. As the sampling site is the permanent reserve forest, any activities that can harm the forest is forbidden. All the data gathering activities only including create sampling plot, slope correction, and measure the tree diameter.

3.3.1 Create the sampling plot

The data gathered from the primary data by collecting data from sampling site in compartment 24 in Sungai Lalang forest reserve. Sampling site is about 100m x 100m (1 ha) area. This sampling plot has been divided into subplot of 10m x 10m each one. The red rod was piled in each corner of sampling plot and yellow rod was piled in each corner of subplot.

Survey in trees has been done using zigzag method from subplot 1 until subplot 100 and each trees sampled was numbered according to first and next tree until all trees had been surveyed. Figure 3.1 show the design of sampling plot and how survey was done. Data gathered from them is actually data on the tree Dbh, species names, families, and others data relevant. The data collected by help form Department of forestry Selangor.

U

Y

10 m

10

11

30

31

50

51

70

71

90

10 m91

9

12

29

32

49

52

69

72

89

92

8

13

28

33

48

53

68

73

88

93

7

14

27

34

47

54

67

74

87

94

100 m6

15

26

35

46

55

66

75

86

95

5

16

25

36

45

56

65

76

85

96

4

17

24

37

44

57

64

77

84

97

3

18

23

38

43

58

63

78

83

98

2

19

22

39

42

59

62

79

82

99

1

20

21

40

41

60

61

80

81

100

X

100 m

Figure 3.1 Structure of sampling plot and survey method.

3.3.2 Slope correction and DBH measurement

Slope correction also measured to make sure the measurement of sampling plot area is correct. Clinometers had been used to measure slope. The reading of slope is in degree. The increasing slope read as positive and the decreasing slope read as negative. The slope correction is based on trigonometry concept and this correction usually done before make sampling plot. All the measurement was recorded in table. Figure 3.1 show how slope correction been done and the regression equation that been used.

Horizontal distance

Ï‘

B

A

Slope distance

C

Formula: AB = AC Cos Ï‘

Figure 3.2 Slope Correction and formula

The trees with the DBH more than 10cm in 1.3m height were recorded. The methods to measure DBH is different because of the different nature of tree stand and this measurement is based on Husch et al. (1972) and Manokaran et al. (1990). Figure 3.3 show how to measure DBH on different nature of tree stand.

Figure 3.3 BDH measurement on different nature of tree stand.

3.4 Data analysis

3.4.1 Aboveground and belowground biomass

To estimate the aboveground biomass, Kato et al. (1978) model will be use. It contain the estimation on the stem, branch and leaf biomass and these components will be the total of aboveground biomass.

Before analysis had been made, the tree height will be measured by using the following equation. The equation is:

Tree height (H) = (122 x D) / 2D + 61), unit in m

Where D = diameter on 1.3m of tree breast

After the tree height had been measured, the aboveground equations will be used to estimate the total aboveground biomass. The equation can be divided to stem biomass, branch biomass and leaf biomass.

These 3 components will make the total aboveground biomass. The equation will be estimate aboveground biomass in kilogram (kg). So after estimate biomass, the data will be converted to tone to get the actual data needed. The equations are:

Ws = Stem biomass

= 0.0313 (D2H) 0.9733

Wb = branch biomass

= 0.136 Ws1.070

Wl = leaf biomass

= (1.25 x 124 Ws0.794) / (0.124Ws 0.794 + 125)

Aboveground biomass = Ws + Wb + Wl,

The same data will be used for estimate belowground biomass. For the belowground biomass, the equations from the Ogawa et al. (1965) as follow:

Root (WR) = 0.0264(D2H)0.775

This belowground biomass results also must be converted from kg to tonne. This aboveground and belowground biomass estimation will be combining to get the total tree biomass.

3.4.2 Carbon stock

The carbon stock will be estimate based on carbon content in the biomass data. The default value of the carbon content on biomass is 0.47. Because of no specific value of carbon in biomass made by this country, this default value will be used. So the carbon stock will be calculated as:

Cb = B x % C organic

Where:

Cb is the carbon content from biomass, expressed in kilograms (kg);

B is the total biomass, expressed in kilograms (kg);

%C organic is the percentage value of carbon content, amounting to 0.47 or using the value obtained from measurements in the laboratory.

3.4.3 Forest value

Forest value will be calculated by using the value of carbon. The C/tonne will be converted to monetary value that been using in estimate carbon price. Because of no local reference of carbon price that have been used in Malaysia, the carbon price will based on the international prices rate.

CHAPTER 4

RESULT AND DISCUSSION

4.1 Introduction

This chapter shows all the result and discusses the result from analysis performed; aboveground biomass allometric model and carbon conversion on the data collected in sampling plot. The results of these will be used in the measure entire Permanent forest reserve to get the result of total aboveground biomass and carbon stock. The outputs from these two analyses are very important as the result will be used in managing and improvement to forest management in Permanent Forest Reserve Sungai Lalang, Selangor and become the example of management to entire Selangor forest.

4.2 Results

4.2.1 Aboveground biomass

Descriptive analysis was used to discuss all the results collected from the allometric model created by Kato et al. (1978) to better understanding of results. The tables and graphs will be the presentation items to explain the whole result.

Generally, the total number trees in sampling plot that been measured are 418 trees from 50 different family and species. The total aboveground biomass in this sampling plot is 410.64 tons. The total aboveground biomass for the stem is 332.46 tons/ha or 80.96% of total aboveground biomass in sampling plot. The total aboveground biomass for stem is 78.12 tons/ha (19.02%) and the total aboveground biomass for leaf is only 0.0604 tons/ha (0.02%). With this result this shows almost 0.98 tons/tree average.

Table 1. Total Aboveground biomass for stem, branch, and leaf in sampling plot

Plot size

location

number of tree

AGB stem

total (tons)

AGB branch total

(tons)

AGB

leaf

total (tons)

AGB Grand Total (tons)

1 hectare (100m x 100m)

Compartment 24, Permanent

Forest Reserve Sungai Lalang

418

332.46

(80.96%)

78.12

(19.02%)

0.0604

(0.02%)

410.64

(100%)

According to Table 2, for all the families of trees in plot sampled, the highest contribution of AGB is come from the Dipterocarpaceae which about 103.14 tons AGB come from this family tree. This followed by Euphorbiaceae families, about 59.22 tons AGB. Lauraceae families also show the high contribution to AGB, about 41.37 tons. The other families that contribute high to AGB are Guttiferae (17.45 tons), Olacaceae (16.95 tons), Leguminosae (12.86 tons), Sterculiaceae (12.71 tons), Moraceae (11.83 tons), Tiliaceae (10.50 tons), and Chrysobalanaceae (10.28 tons).

Table 2. 10 family that contribute highest aboveground biomass value

ID

Famili

ni/ha

AGB stem

AGB branch

AGB leaf

AGB total

1

Dipterocarpaceae

31

82.40

20.73

0.0089

103.14

2

Euphorbiaceae

73

48.25

10.96

0.0104

59.22

3

Lauraceae

26

33.21

8.16

0.0045

41.37

4

Guttiferae

12

14.10

3.34

0.0023

17.45

5

Olacaceae

9

13.66

3.29

0.0020

16.95

6

Leguminosae

16

10.51

2.34

0.0025

12.86

7

Sterculiaceae

10

10.34

2.37

0.0021

12.71

8

Moraceae

18

9.67

2.15

0.0023

11.83

9

Tiliaceae

5

8.48

2.02

0.0013

10.50

10

Chrysobalanaceae

5

8.25

2.03

0.0010

10.28

Based on the species measured in sampling plot, the highest contribution of AGB is from Shorea leprosula species which contribute the total AGB of 34.22 tons/ha. It contribute 27.21 tons/ha of stem AGB, 7 tons/ha of branch AGB, and 0.0023 tons/ha for the leaf AGB. The second highest species contribution of AGB shows by shorea curtisii ssp. Curtisii species. it contribute 28.46 tons/ha, which contain 22.64 tons/ha of stem AGB, 5.81 tons/ha of branch AGB and 0.002 tons/ha of leaf AGB. Cryptocarya costata species contribute the third highest of AGB, on 19.7 tons/ha of total AGB. It contain about 15.66 tons/ha of stem AGB, 4.04 tons/ha of branch AGB, and 0.0014 tons/ha of leaf AGB. The other high contribution species are Elateriopermum tapos (13.16 tons/ha), Shorea acuminate (12.41 tons/ha), Elateriospermum tapos (10.96 tons/ha), Ochanostachys amentacea (10.81 tons/ha), Maranthes corymbosa (8.93 tons/ha), Agrostistachys gaudichaudii (8.15 tons/ha) and Garcinia parvifolia (7.82 tons/ha).

Table 3. 10 species that contribute highest aboveground biomass value

ID

Species

ni/ha

AGB stem

AGB branch

AGB leaf

AGB total

1

Shorea leprosula

5

27.21

7.00

0.0023

34.22

2

Shorea curtisii ssp. curtisii

5

22.64

5.81

0.0020

28.46

3

Cryptocarya costata

5

15.66

4.04

0.0014

19.70

4

Elateriopermum tapos

11

10.68

2.49

0.0020

13.16

5

Shorea acuminata

1

9.86

2.55

0.0007

12.41

6

Elateriospermum tapos

11

8.95

2.02

0.0020

10.96

7

Ochanostachys amentacea

6

8.73

2.08

0.0014

10.81

8

Maranthes corymbosa

2

7.13

1.79

0.0007

8.93

9

Agrostistachys gaudichaudii

9

6.63

1.53

0.0013

8.15

10

Garcinia parvifolia

2

6.26

1.56

0.0007

7.82

To show the result of AGB based on the DBH trees, the DBH have been divided to several class; lower than 20cm, 20.1cm-40cm, 40.1cm-60cm, 60.1cm-80cm, 80.1cm-100cm, and higher than 100cm.

Figure 1. Stem Above ground biomass based on DBH class

For AGB for stem in sampling plot, the highest AGB is measured from the DBH 40.1cm-60cm where 94.88 tons/ha AGB has been measured. This value was following by the DBH 60.1cm-80cm which about 77.64 tons/ha. DBH 20.1cm-40cm become third highest AGB about 67.80 tons/ha followed by DBH 100cm and higher (24.22 tons/ha) and DBH 10cm-20cm (25.68 tons/ha). The lowest AGB come from DBH 80.1cm-100cm (24.22 tons/ha). (Refer to figure 1)

For AGB for branch in sampling plot, the result is slightly similar with the AGB stem. The highest AGB is measured from the DBH 40.1cm-60cm. 22.09 tons/ha AGB has been measured in this DBH class. The DBH 60.1cm-80cm class become the second highest class which show the result about 19.14 tons/ha. DBH 20.1cm-40cm become third highest AGB about 14.59 tons/ha followed by DBH 100cm and higher (11.22 tons/ha) and 80.1cm-100cm (6.19 tons/ha). The <20cm class become the lowest AGB with only 4.89 tons/ha contribution.

Figure 2. Branch Above ground biomass based on DBH class

Figure 3. Leaf Above ground biomass based on DBH class

Figure 3 show the result for AGB leaf in sampling plot based on DBH class. 20.1cm-40cm class shows the highest contribution of AGB for leaf, about 0.0185 tons/ha. The second highest contribution AGB for leaf is 40.1cm-60cm DBH class. It show the AGB about 0.0169 tons/ha. It followed by lower than 20cm DBH class (0.0114 tons/ha), 60.1cm-80cm DBH class (0.0091 tons/ha) and higher than 100cm DBH class (0.0025 tons/ha). The lowest AGB show in 80.1cm-100cm DBH class, about 0.0021 tons/ha.

Figure 4. Total Above ground biomass based on DBH class

For the total AGB in sampling plot, 40.1cm-60cm DBH class show the highest contribution to AGB on 166.98 tons/ha. 60.1cm-80cm DBH class become the second contribution to AGB of the total 96.79 tons/ha. For the 20.1cm-40cm DBH class, the AGB measured was 82.41 tons/ha and it become the third highest contribution of AGB. The other highest DBH classes are higher than 100cm DBH class, which about 53.47 tons/ha and lower than 20cm DBH class, which about 30.49 tons/ha. The lowest contribution for AGB is shows in 80.1cm-100cm. this DBH class is show the 30.41 tons/ ha AGB contributions.

Table 4. Above ground biomass in each compartment in Permanent Forest Reserve Sungai Lalang

Comp.

area (ha)

AGB stem (tons)

AGB branch (tons)

AGB leaf (tons)

AGB total (tons)

1

179.73

59753.86

14040.40

10.86

73805.11

2

123.43

41036.10

9642.28

7.46

50685.83

3

165.11

54893.22

12898.29

9.97

67801.49

4

149.61

49740.02

11687.44

9.04

61436.50

5

36.46

12121.66

2848.23

2.20

14972.09

11

131.43

43695.82

10267.23

7.94

53970.99

12

198.43

65970.94

15501.23

11.99

81484.16

13

237.63

79003.55

18563.51

14.35

97581.42

14

209.51

69654.65

16366.79

12.65

86034.10

15

126.55

42073.39

9886.01

7.64

51967.04

16

213.27

70904.72

16660.52

12.88

87578.12

17

222.99

74136.27

17419.84

13.47

91569.59

18

288.26

95836.24

22518.70

17.41

118372.34

19

146.98

48865.64

11481.99

8.88

60356.51

20

270.18

89825.28

21106.30

16.32

110947.89

21

122.36

40680.36

9558.69

7.39

50246.44

22

204.21

67892.59

15952.76

12.33

83857.68

23

205.87

68444.48

16082.44

12.43

84539.35

24

208.7

69385.35

16303.52

12.61

85701.48

24D

91.04

30267.57

7111.99

5.50

37385.06

25

245.09

81483.74

19146.28

14.80

100644.83

26

96.39

32046.26

7529.93

5.82

39582.01

27

146.49

48702.73

11443.71

8.85

60155.29

28

88.58

29449.71

6919.82

5.35

36374.88

29

115.16

38286.62

8996.23

6.96

47289.80

30

190.34

63281.31

14869.25

11.50

78162.05

31

531.94

176851.20

41554.83

32.13

218438.16

32

244.06

81141.30

19065.82

14.74

100221.86

33

405.22

134721.29

31655.54

24.48

166401.31

34

287.28

95510.42

22442.14

17.35

117969.91

35

583.81

194096.14

45606.88

35.26

239738.28

36

842.03

279945.14

65778.87

50.86

345774.87

37

519.26

172635.55

40564.28

31.36

213231.19

38

857.65

285138.23

66999.10

51.80

352189.13

39

306.85

102016.75

23970.94

18.53

126006.22

40

249.79

83046.32

19513.44

15.09

102574.85

41

396.58

131848.80

30980.59

23.95

162853.34

42

616.21

204867.99

48137.95

37.22

253043.16

43

720.07

239397.76

56251.43

43.49

295692.68

44

463.07

153954.37

36174.75

27.97

190157.08

45

420.11

139671.69

32818.74

25.37

172515.80

46

641.13

213153.01

50084.69

38.72

263276.42

47

169.35

56302.87

13229.52

10.23

69542.62

48

224.77

74728.06

17558.90

13.58

92300.53

49

541.24

179943.12

42281.34

32.69

222257.15

50

597.71

198717.39

46692.74

36.10

245446.24

51

452.25

150357.10

35329.50

27.32

185713.91

52

836.43

278083.34

65341.40

50.52

343475.26

83

264.1

87803.89

20631.33

15.95

108451.17

84

201.26

66911.82

15722.31

12.16

82646.28

85

197.46

65648.45

15425.46

11.93

81085.83

86

332.57

110567.74

25980.17

20.09

136567.99

87

129.99

43217.07

10154.74

7.85

53379.66

88

238.17

79183.09

18605.70

14.39

97803.17

89

227.28

75562.55

17754.98

13.73

93331.25

116.33

38675.60

9087.63

7.03

47770.26

Total

17027.77

5661130.13

1330199.07

1028.47

6992357.68

By using the result of sampling plot, the entire Permanent forest reserve can be measured. For the compartment 24 which the sampling plot located, it stated that the total AGB for stem is about 69385.35 tons. The total AGB for branch for compartment 24 is 16303.52 tons and the total AGB for leaf is 12.61 tons. It makes the grand total of AGB in compartment 24 is 85701.48 tons.

For all the compartments in the Permanent forest reserve, compartment 38 show the highest contribution to AGB, with the total AGB is 352189.13 tons. It contains 285138.23 tons of stem AGB, 66999.10 tons of branch AGB, and 51.80 tons of leaf AGB. The lowest AGB contribution comes from compartment 5. Compartment 5 contains 12121.66 tons of the total stem AGB, 2848.23 tons of the total branch AGB and 2.20 tons of the total leaf AGB. The grand total of AGB in compartment 5 is 14972.09 tons.

For the entire compartments in Permanent Forest reserve Sungai Lalang, the grand total of AGB is 6,992,357.68 tons. The total AGB for stem is about 5661130.13 tons. The total AGB for branch is 1330199.07 tons and the total AGB for leaf is 1028.47 tons.

4.2.2 carbon stock

This section will show the results on the carbon stock on sampling plot and the entire permanent forest reserve Sungai Lalang. The tables and graphs will be the presentation items to explain the whole result.

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