“assessing the potency of bromo tengger semeru national ... · statement i, delon marthinus, here...

STATEMENT

I, Delon Marthinus, here by stated that this thesis entitled “Assessing the Potency

of Bromo Tengger Semeru National Park for Carbon Sequestration Project”

is the results of my own work during the period November 2007 to August 2008

and that it has not published before. The content of the thesis has been examined

by the advising committee and the external examiner.

Bogor, August 2008

Delon Marthinus

ABSTRACT

Delon Marthinus (2008). Assessing the Potency of Bromo Tengger Semeru

National Park for Carbon Sequestration Project. Under the supervision of

Dr. Rizaldi Boer, MAgr and Dr. Antonius B. Wijanarto.

The addition of greenhouse gases and aerosols has changed the composition of the

atmosphere. The changes in the atmosphere have likely influenced temperature,

precipitation, storms and sea level (IPCC, 2007), then will give huge impact to

environment and humankind. To mitigate and to adapt the impact, most country in

the world are agree to collaborate and give their commitment to decrease GHG

emission, conducting reforestation/afforestation, and reduce deforestation.

There are a lot of opportunities provided by developed countries for developing

countries to gain additional revenue by conducting mitigation and adaptation

activity. One of the opportunities is carbon trading through reforestation.

Indonesia has huge potency to implement reforestation project. Bromo Tengger

Semeru National Park is one of the potential sites which is has eligible land for

implementing carbon sequestration project is about 1134.5 ha, refer to CDM

mechanism.

The proposed reforestation project activity will reforest the land with Casuarina

junghuhniana and Acacia deccurens. The activity will sequester around of

1.262.648 T CO2e during twenty years, this value shows that Bromo Tengger

Semeru National Park has high potency for carbon sequestration project. Such

activity is nice as a contribution of Indonesia to mitigate and adapt the climate

change impact.

Keyword: Greenhouse Gases (GHG), Clean Development Mechanism (CDM),

reforestation, T CO2e.

SUMMARY

Climate change is one of the global issue that makes people are aware

recently. Many studies show the effect of climate change, most of the studies

describe the extreme climate event such as flood, drought, and windstorm. To

mitigate and to adapt the impact of climate change, most countries in the world

agree to collaborate and give their commitment to against it. United Nation

Framework Convention on Climate Change (UNFCCC) facilitates the countries to

develop and implement the activities that are related to the climate change

mitigation and adaptation in Kyoto, Japan in 1997 that called the Kyoto Protocol.

One of the mechanism that has been arrange by UNFCCC is

Afforestation/reforestation Clean Development Mechanism (AR-CDM)

Considering the large forest areas that need to be reforested and afforested,

Indonesia needs to promote climate change mitigation mechanism including AR-

CDM and other mechanisms of forest carbon project. Bromo-Tengger-Semeru

National Park (TNBTS) is one of the potential sites that suitable for those

mechanisms. The eligible land for AR-CDM in this site is around of 1134.5 ha

(assessment by using satellite imagery).

Through reforestation activity, by planting Casuarina Junghuhniana and

Acacia Decurrens, this site may sequester GHG around of 1.262.648 T CO2e in

twenty years (the calculation is using approved methodology from UNFCCC).

This value shows that TNBTS is quite potential site for carbon sequestration

project and give opportunity for Indonesia to participate globally in mitigating the

climate change.

Copy right © 2008, Bogor Agricultural University

Copy right are protected by law, 1. it is prohibited to cite all or part of this thesis without referring to and

mentioning the source

a. Citation only permitted for the sake of education, research,

scientific writing, report writing, critical writing or reviewing

scientific problem.

b. Citation doesn’t inflict the name and honor of Bogor Agricultural

University

2. It is prohibited to republish and reproduce all or part of this thesis

without the written permission from Bogor Agricultural University.

External examiner: Dr. Ade Komara Mulyana

iv

Research Title : Assessing the Potency of Bromo Tengger Semeru

National Park for Carbon Sequestration Project

Student Name : Delon Marthinus

Student ID : G051060051/ MIT

Study Program : Master of Science in Information Technology for

Natural Resources Management

Approved by,

Advisory Board

Dr. Rizaldi Boer, MAgr

Supervisor

Dr. Antonius B. Wijanarto

Co-supervisor

Endorsed by,

Program Coordinator

Dr. Ir. Hartisari Hardjomidjojo, DEA

Dean of Graduate School

Prof. Dr. Ir. Khairil A. Notodiputro, MS

Date of Examination: Date of Graduation:

August 12, 2008

ACKNOWLEDGEMENT

There are a lot of things that help me to finish this thesis. First of all, of

course I would like to express my gratefulness to Jesus Christ and my Parent and

my family who always gives their blessing to me, and for many people who

helped me in making this thesis.

The first, I would like to thank to Dr. Rizaldi Boer, as my inspire person

who give me a chance to be a great person. My lecturers and my colleague in

Climatology Laboratory, Bapak Ir. Abujamin AN, Dr. Rini Hidayati, Drs.

Bambang Dwi Dasanto MSi. Perdinan, SSi, MSc and Faqih SSi. And thank you to

Program Coordinator and the entire staff of MIT-IPB for their support in

administration, technical and facility campus, Dr. Tania June, MSc, Mas

Bambang and Mba Devi. My colleague in CER Indonesia, Syahrina, Ari Suharto,

Essy, Risyanto, and Hamidah, thanks for the happiness in our office.

My co-supervisor, Dr. Antonius B. Wijanarto, for the scientific guidance,

input and critical reviews during the research.

Special thanks for all friends in MIT-IPB. I spent wonderful unforgettable

moments with all of them, and they all were very important in creating a pleasant

atmosphere here.

My lovely wife, Dea Oktovina for the continuous support and togetherness

during the study. And the others that can’t be written in here thank you very

much.

CURRICULUM VITAE

Delon Marthinus was born in Jakarta, Indonesia at 1st February, 1980. He

received his undergraduate degree from Bogor Agricultural University in 2003 in

the field of Geophysics and Meteorology. After graduated, he worked for

Climatology Laboratory IPB as research assistant and work as outsourcing expert

in various project in Indonesia, now he works at CER Indonesia, consultant of

environmental study.

I. INTRODUCTION

1.1. Background

Climate change is one of the most pressing and difficult challenges we face

today. Climate change is caused by the greenhouse effect, whereby certain gases

(greenhouse gases, GHG) in the atmosphere entrap radiation from the sun. Human

activities have upset the natural balance of greenhouse gases that is resulting in

rapid change to the global climate.

Many studies show the effect of climate change, most of the studies describe

the extreme climate event such as flood, drought, and windstorm.

Intergovernmental Panel for Climate Change (IPCC) is the world scientific body

that assesses climate change. Based on their study, the most common problems

that are effected by climate change are the increase of global mean surface

temperature and average sea level rise. In the next 100 years, the global mean

surface temperature will increase 1.4 oC – 5.8

oC and sea level will rise 9 cm – 88

cm, those problems will give huge impact to environment and humankind

(Watson, 2001).

To mitigate and to adapt the impact of climate change, most countries in the

world agree to collaborate and give their commitment to decrease GHG emission,

by conducting reforestation/afforestation, and reducing deforestation. United

Nation Framework Convention on Climate Change (UNFCCC) facilitates the

countries to develop and implement the activities that are related to the climate

Chapter 1 - Introduction

2

change mitigation and adaptation in Kyoto, Japan in 1997 that called the Kyoto

Protocol.

Nowadays, The Kyoto Protocol is part of this on-going political and

economic debate. Clearly, global warming has the potential to influencing directly

or indirectly on every sentient being on Earth. Hence, many political leaders,

together with the rest of humanity, are concerned over the likely impacts that

global warming will have on society and the environment as well as the costs and

benefits of coping with this human made threat. The scientific and policy debates

is still being continued.

Indonesia has ratified the UNFCCC through Law No. 6/1994 and No.

17/2004, this confirms Indonesia as a party to Kyoto Protocol of United Nation

Framework Convention on Climate Change, UNFCCC. Following this, several

studies and national committee on climate change have been established to seek

the mitigation potential and participate in Clean Development Mechanism

(CDM), and non Kyoto Mechanism. However, most of these studies refer

specifically to energy sector, and consequently, the source of information and

knowledge about mitigation strategies for LULUCF is still very limited and

general (Kirsfianti, 2003).

Considering the large forest areas that need to be reforested and afforested,

Indonesia needs to promote climate change mitigation mechanism including CDM

and other mechanisms such as forest carbon project. Forest carbon project in

Indonesia can be grouped into three categories. First is conservation and forest

management, includes protection forest, enhanced natural regeneration or

enrichment planting, and reduced impact logging. Second is sinks enhancement


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including reforestation, afforestation, timber estate, and agroforestry. Third is the

substitution of fossil fuel-based energy with biomass energy.

It is recommended that tree-based farming systems such as reforestation,

afforestation, timber estate and agroforestry offer a sustainable alternative based

on several aspects: (i) incentives to rehabilitate critical area, (ii) source of

additional income for community, (iii) meet the need of household income for

short, medium, and long term.

Bromo-Tengger-Semeru National Park (TNBTS), is located at 7o54’-8

o13’

South and 112o51’–113

o04’ East. The park lies in four administrative areas; West-

Malang district; East-Probolingo district; North-Pasuruan District and South-

Lumajang District. Based on Minister fo Forestry Decree No. 278/Kpts-VI/1997,

the total of the Bromo-Tengger-Semeru National Park is 50,276 ha (TNBTS,

2007). Large area of this National Park has been degraded and covered by

grassland.

Based on local correspondents, the area was initially covered by ‘cemara

gunung’ (Casuarina junghuhniana) and managed by the Dutch Government. A

small portion of the degraded area at Kandangan Block have been reforested using

government fund (i.e. 30 ha) with Cemara Gunung. Due to the limited funding

and large area of degraded land that need to be reforested, without additional

financial support from other sources, most of this area will remain as grassland.

There are opportunities to gain additional funding from private institution by

implementing Carbon Sequestration Project.


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1.2. Objectives

The objectives of this study are:

• to assess the eligible land area for carbon sequestration project.

• to estimate the baseline of carbon stock change in project area.

• to estimate the actual net GHG Removal by Sinks under the presence of the

carbon sequestration project

• to estimate the leakage of carbon emission at project area, and

• to estimate the net anthropogenic GHG removal by sink under the presence

of the carbon sequestration project.

1.3 Scope and Limitation

The scopes and limitations of this research are:

• study area in this research is Bromo Tengger Semeru National Park,

• the method for defining eligible land is using remote sensing and

Geographic Information System (GIS) technique. For assessing the carbon

stock and carbon sequerestation is using approved methodology by

UNFCCC, AR-AM0001/version2,

• satellite imagery data that used are LANDSAT 5 TM for year 1989 and

ASTER for year 2006, and

• socio-economics are unobserved.

1.4. Outputs:

The outputs of this thesis are:

• the eligible land map for implementing Carbon sequestration Project, and

• the potency of carbon sequestration on the project area.

II. LITERATURE REVIEW

2.1. Climate Change

The climate of the Earth is constantly changing. In the past it has altered as

a result of natural causes. Today, however, the term climate change is generally

used when referring to changes in our climate which have been identified since

the early 20th

century. The changes over recent years and prediction over the next

80 years are thought to be a result of human activities rather than natural changes.

In the atmosphere the greenhouse effect is very important when we talk about

climate change as it relates to the gases which keep the Earth warm (IGES, 2005).

The term 'Greenhouse Effect' is commonly used to describe the increase in

the Earth's average temperature that has been recorded over the past 100 years.

However, without the 'natural greenhouse effect', life on Earth would be very

different to that seen today. The 'natural greenhouse effect', the Earth receives its

life sustaining warmth from the Sun. On its way to the Earth's surface most of the

heat energy passes through the Earth's atmosphere, while a smaller proportion is

reflected back into space. The energy warms the Earth's surface, and as the

temperature increases, the Earth radiates heat energy (infrared energy) back into

the atmosphere. As this energy has a different wavelength to that coming from the

sun, some is absorbed by gases in the atmosphere.

There are four main naturally occurring gases that are responsible for the

Greenhouse Effect; water vapour, carbon dioxide, methane and nitrous oxide.

Although most of the greenhouse gases occur naturally in the atmosphere, some

are man-made and the most well-known of these are the fluorocarbons. Since the

industrial revolution, human activities have also resulted in an increase in natural

greenhouse gases, especially carbon dioxide. An increase in these gases in the

atmosphere enhances the atmosphere's ability to trap heat, which leads to an

increase in the average surface temperature of the Earth (IGES, 2005).

Chapter 2 – Literature Review

6

Source: http://dondandon.com/images/image/p0001164-greenhouse-effect.gif

Figure 2.1. Greenhouse effect.

If the increasing of GHG emission still continues, then in the next 100 years

the seriously climate change impact will occur, the global mean surface

temperature will increase 1.4 oC – 5.8

oC and sea level will rise 9 Cm – 88 Cm,

while the human population is increasing time to time, those problems will give

huge impact to humankind and other species (Watson, 2001). Most of GHG

emission comes from developed countries.

The need to control climate change by reducing the world’s emissions of

greenhouse gases is now accepted throughout the international community. One of

the toughest problems in getting an international agreement to do so has been how

to share the responsibility between the developed and developing world.

2.2. Kyoto Protocol.

The Kyoto Protocol was drawn up in 1997 and came into force in February

2005. The protocol requires that by the period 2008-2012 a group of 40 developed

countries will have reduced their greenhouse gas emissions by five percent below

their 1990 levels. While the Protocol commits the developing countries to adopt


7

appropriate policies to control their emissions, those countries are not committed

to specific emissions targets.

The Protocol hands most of the responsibility for reducing emissions to the

developed countries, but reductions anywhere in the world will ultimately have

the same effect on the atmosphere and threats of global warming. Moreover some

developed countries might find it easier and more cost effective to support

emissions reductions in other developed or developing countries rather than at

home.

Therefore the Kyoto Protocol includes three mechanisms to introduce

flexibility to the developed countries’ approach and allowing them to make the

most cost-effective emissions reductions without softening their commitment to

the Protocol’s overall goals (UNFCCC, 1998).

2.2.1. Clean Development Mechanism (CDM)

CDM is one of the flexible mechanisms contained in the Kyoto Protocol.

Through CDM, developing countries may be able to actively participate in

helping developed countries obligated to decrease the emission of green house

gas, by doing investment in developing countries in various sectors, while the

developing countries benefit in reaching continuous development objective as the

national agenda besides reaching the main objective to stabilize the emission of

green house gas in handling the effect of global warming. Some CDM sectors to

be implemented is CDM in the sectors of energy, transportation, household,

garbage and forestry. Forestry sector CDM is a different mechanism to CDM of

other sectors since the emission decrease is conducted by carbon absorption in the

atmosphere by trees (carbon sequestration) while other sectors apply emission

decrease at the source of emission.

The activities to be proposed for forestry sector CDM are limited only for

afforestation and reforestation activities, known as AR-CDM. Afforestation is the

re-planting on land cleared since 50 years ago while reforestation is the re-

planting of land which is clear of trees before the year 1990. The stages or

procedures of forestry CDM activities, known as forestry CDM project cycle,

begin with project identification activity, project designing, project design


8

documentation, agreement by the CDM National Commission, validation,

registration, implementation and monitoring, verification and certification, and

finally, the issuing of CER (Certified Emission Reduction) by the CDM Board of

Executors. The stage of project designing is the most important stage for the

success of the CDM project since good analysis on accurate project designing,

including methodology and the involvement of related stakeholders, is necessary.

This is intended to produce projects which can sufficiently benefit from CDM,

including sufficient funding provision in order that the transaction cost does not

exceed the available fund.

2.2.2. Joint Implementation (JI)

The second Kyoto Mechanism provides for one developed country helping

another developed country, usually part of the former USSR, to implement

specific projects with low levels of emissions, either to replace or upgrade an

existing high-emission facility or to adjust the design of a new project so as to

lower its expected emissions. 126 project JI proposals were submitted by March

2008.

Two early examples will change from a wet to a dry process at a Ukraine

cement works, reducing energy consumption by 53 percent by 2008-2012; and

rehabilitate a Bulgarian hydropower project, with a 267,000 ton reduction of CO2

equivalent during 2008-2012 (Lightfoot, 2008).

In both these cases including the expected value of the emission reduction

units in the investment analysis made the projects financially viable.

2.2.3. Emission Trading.

The Third mechanism is emissions trading. The Kyoto Protocol provides for

developed countries to buy emissions reduction units if they cannot otherwise

meet their emissions targets, and to sell them if they expect to exceed their targets.

This enables countries for which reducing emissions levels is expensive to buy

emission reduction units from countries where costs are lower, thus lowering the

overall costs of reducing emissions.


9

Transfers and acquisitions of these units are tracked and recorded through

national registry systems. Trading is also open to companies or other non-

governmental organizations under the supervision of their respective countries.

2.2.4. Other mechanisms

Voluntary Basis, a number of other voluntary standards exist.

ISO 14064

This standard operates under the ISO family of standards and is a guideline-

based system of reporting. Its major components are: 1) Project Reporting:

guiding project proponents quantification, monitoring and reporting of greenhouse

gas emissions reductions (ISO 14064 Part 2); and 2) Validation and Verification:

guiding the validation and verification of greenhouse gas assertions from

organizations or projects (ISO 14064 Part 3), Wintergreen, 2007.

Plan Vivo

Plan Vivo is specifically designed for community-based agro forestry

projects. It calls itself “a system for promoting sustainable livelihoods in rural

communities, through the creation of verifiable carbon credits. Although Plan

Vivo projects have been executed successfully in the past, the transactions costs to

verify and monitor this project appear to be prohibitively high for mid-small scale

projects, and the standard has not yet won wide recognition as an industry

standard.

VER + Standard

TÜV SÜD launched the VER+ Standard to certify carbon neutrality and

credits from voluntary carbon offset projects. Although based on CDM and JI

methodology to be “streamlined” with Kyoto, VER+ is fungible on the voluntary

market and compatible with CCX and the Voluntary Carbon Standard. Despite its

ostensible quality, this standard will most likely preclude the use of other

verifiers, likely available at a lower cost in Indonesia. While the chosen voluntary

method will most likely not restrict the options for certification through VER + at


10

a later date, other certifications appear to offer more suitable methodologies

(Kollmuss, et al).

The Voluntary Carbon Standard (VCS)

Voluntary Carbon Standard’s “Version 1 for Consultation” has been

publicly available sine March 2006, and the Climate Group, the International

Emissions Trading Association (IETA) and the World Economic Forum jointly

launched the final version of VCS in 2007. The VCS aims “to provide a credible

but simple set of criteria that will provide integrity to the voluntary carbon market

and underpin the credible actions that already exist.” Mark Kenber, Policy

Director at the Climate Group, described the standard as creating a basic “quality

threshold” in the market. A goal for the VCS is for it to co-exist with other stan-

dards and “reinforce those that are robust and already exist (e.g. WBCSD/WRI

GHG Protocol for Project Accounting, Gold Standard, and CCX). Credits

certified via the VCS are then called Voluntary Carbon Units (VCUs). The 2007

program guidelines include ISO 14064-2:2006, ISO 14064-3:2006, ISO

14065:2007.

The VCS was rated as one of the most promising standards to handle a

significant volume of voluntary carbon trades by Ecosystem Marketplace. VCS

certified projects are intended to be as robust as those under the Kyoto Protocol’s

Clean Development Mechanism (CDM) but they reduce participants’ transaction

costs through added flexibility during the certification, verification and

monitoring process. It has come under intense scrutiny from some groups, most

notably WWF, which allege that the standard will not screen-out low quality

projects and may thereby undermine market credibility. It is unclear if this

standard will live up to its initial potential (Voluntary Carbon Standard, 2007).

Climate, Community and Biodiversity Alliance, CCBA

These Climate, Community and Biodiversity Project Design Standards (the

“CCB Standards”) identify land-based projects that can simultaneously deliver

compelling climate, biodiversity and community benefits. The CCB Standards are

primarily designed for climate change mitigation projects. The CCB Standards


11

were developed by the Climate, Community & Biodiversity Alliance (CCBA).

The CCBA is a global partnership of research institutions, corporations and

environmental groups, with a mission to develop and promote voluntary standards

for multiple-benefit land-use projects.

The CCBA standard offers credible set of criteria to create a premium

carbon project with potential for high social and ecological benefits. The standard

has been well received on the voluntary market since its launch in May 2005. It

was created by a consortium of research institutions, corporations and

environmental groups to develop a credible, comprehensive standard with that

delivers sustainable benefits in emerging economies using both public and private

financing. It is one of the informal ‘best-practice’ methodologies emerging in the

voluntary sector among independently verified standards. More information on

this standard is available at http://www.climate-standards.org/

From all of the mechanism mentioned above, CDM mechanism is the most

applicable mechanism, the methods that have been approved by UNFCCC

(United Nation Frameworks for Climate Change Convention) regarding CDM

mechanism have been adopted by other mechanism. Therefore, the method and

analysis in this study are refer to AR-CDM mechanism

2.3. Terms in AR-CDM project

2.3.1. Additionality

The activity or project under CDM mechanism should be “additional”. It

means that without CDM mechanism the activity or project will not be successful

in sequestering the GHG. There is a tool that prided by UNFCCC to testing

whether the activity or project is additional on not additional, “Tool for the

Demonstration and Assessment of Additionality in A/R CDM Project

Activities (Version 02)”. The document is available at

hppt://cdm.unfccc/int/methodologies/ARmethodologies/approved_ar.html.

The procedure of this tool are (Figure 2.2);

Step 0. Preliminary screening based on the starting date of the A/R project

activity. If the afforestation or reforestation CDM project activity has a starting


12

date after 31 December 1999 but before the date of its registration, then the

project participants shall:

• Provide evidence that the starting date of the A/R CDM project activity

was after 31 December 1999; and

• Provide evidence that the incentive from the planned sale of CERs was

seriously considered in the decision to proceed with the project activity.

This evidence shall be based on (preferably official, legal and/or other

corporate) documentation that was available to third parties at, or prior

to, the start of the project activity.

Step 1. Identification of alternative land use scenarios to the proposed A/R

CDM project activity. This step serves to identify alternative land use scenarios to

the proposed CDM project activity(s) that could be the baseline scenario, through

the following sub-steps: Sub-step 1a. Identify credible alternative land use

scenarios to the proposed CDM project activity, identify realistic and credible

land-use scenarios that would have occurred on the land within the proposed

project boundary in the absence of the afforestation or reforestation project

activity under the CDM3. The scenarios should be feasible for the project

participants or similar project developers taking into account relevant national

and/or sectoral policies4 and circumstances, such as historical land uses, practices

and economic trends. The identified land use scenarios shall at least include:

• Continuation of the pre-project land use;

• Afforestation / reforestation of the land within the project boundary performed

without being registered as the A/R CDM project activity;

• If applicable, forestation of at least a part of the land within the project

boundary of the proposed A/R CDM project at a rate resulting from: Legal

requirements; or extrapolation of observed forestation activities in the

geographical area with similar socioeconomic and ecological conditions to the

proposed A/R CDM project activity occurring in a period since 31 December

1989.

For identifying the realistic and credible land-use scenarios; land use

records, field surveys, data and feedback from stakeholders, and information from


13

other appropriate sources, including Participatory rural appraisal (PRA) may be

used as appropriate.

All identified land use scenarios must be credible. All land-uses within the

boundary of the proposed A/R CDM project activity that are currently existing or

that existed at some time since 31 December 1989 but no longer exist, may be

deemed realistic and credible. For all other land use scenarios, credibility shall be

justified. The justification shall include elements of spatial planning information

(if applicable) or legal requirements and may include assessment of economical

feasibility of the proposed land use scenario.

Outcome of Sub-step 1a: List of credible alternative land use scenarios that

would have occurred on the land within the project boundary of the A/R CDM

project activity.

Sub-step 1b. Consistency of credible land use scenarios with enforced

mandatory applicable laws and regulations (This sub-step does not consider

national and local policies that do not have legally-binding status and local

policies that have been implemented since the adoption by the COP of the CDM

M&P [decision 17/CP.7, 11 November 2001]).

Apply the following procedure:

• Demonstrate that all land use scenarios identified in the sub-step 1a: are in

compliance with all mandatory applicable legal and regulatory requirements;

• If an alternative does not comply with all mandatory applicable legislation and

regulations then show that, based on an examination of current practice in the

region in which the mandatory law or regulation applies, those applicable

mandatory legal or regulatory requirements are systematically not enforced

and that non-compliance with those requirements is widespread, i.e. prevalent

on at least 30% of the area of the smallest administrative unit that

encompasses the project area;

• Remove from the land use scenarios identified in the sub-step 1a, any land use

scenarios which are not in compliance with applicable mandatory laws and

regulations unless it can be shown these land use scenarios result from

systematic lack of enforcement of applicable laws and regulations.


14

Outcome of Sub-step 1b: List of plausible alternative land use scenarios to the

A/R CDM project activity that are in compliance with mandatory legislation and

regulations taking into account the their enforcement in the region or country and

EB decisions on national and/or sectoral policies and regulations. If the list

resulting from the Sub-step 1b is empty or contains only one land use scenario,

than the proposed A/R CDM project activity is not additional.

Sub-step 1c. Selection of the baseline scenario: The baseline methodology

that would use this tool shall provide for a stepwise approach justifying the

selection and determination of the most plausible baseline scenario.

→ Proceed to Step 2 (Investment analysis) or Step 3 (Barrier analysis), as it is

necessary to undertake at least one of them.

Step 2. Investment analysis. Determine whether the proposed project activity,

without the revenue from the sale of temporary CERs (tCERs) or long-term CERs

(lCERs), is economically or financially less attractive than at least one of the other

land use scenarios. Investment analysis may be performed as a stand-alone

additionality analysis or in connection to the Barrier analysis (Step 3). To conduct

the investment analysis, use the following sub-steps:

Sub-step 2a. Determine appropriate analysis method. Determine whether to apply

simple cost analysis, investment comparison analysis or benchmark analysis (sub-

step 2b). If the A/R CDM project activity generates no financial or economic

benefits other than CDM related income, then apply the simple cost analysis

(Option I). Otherwise, use the investment comparison analysis (Option II) or the

benchmark analysis (Option III). Note, that Options I, II and III are mutually

exclusive hence, only one of them can be applied.

Sub-step 2b. – Option I. Apply simple cost analysis. Document the costs

associated with the A/R CDM project activity and demonstrate that the activity

produces no financial benefits other than CDM related income. If the land within

the boundary of the proposed of the A/R CDM project activity was at least

partially forested since 31 December 1989 and the land is not a forest at the

project start, the project participants shall identify incentives/reasons/actions that

allowed for the past forestation and demonstrate that the current legal/financial or

other applicable regulations or socio-economical or ecological or other local


15

conditions have changed to an extent that justifies the conclusion that the activity

produces no financial benefits other than CDM related income.

→ If it is concluded that the proposed A/R CDM project activity produces no

financial benefits other than CDM related income then proceed to Step 4

(Common practice analysis).

Sub-step 2b. – Option II. Apply investment comparison analysis. Identify the

financial indicator, such as IRR8, NPV, payback period, cost benefit ratio most

suitable for the project type and decision-making context.

Sub-step 2b – Option III. Apply benchmark analysis. Identify the financial

indicator, such as IRR9, NPV, payback period, cost benefit ratio, or other (e.g.

required rate of return (RRR) related to investments in agriculture or forestry,

bank deposit interest rate corrected for risk inherent to the project or the

opportunity costs of land, such as any expected income from land speculation)

most suitable for the project type and decision context. Identify the relevant

benchmark value, such as the required rate of return (RRR) on equity. The

benchmark is to represent standard returns in the market, considering the specific

risk of the project type, but not linked to the subjective profitability expectation or

risk profile of a particular project developer. Benchmarks can be derived from:

• Government bond rates, increased by a suitable risk premium to reflect private

investment and/or the project type, as substantiated by an independent

(financial) expert;

• Estimates of the cost of financing and required return on capital (e.g.

commercial lending rates and guarantees required for the country and the type

of project activity concerned), based on bankers views and private equity

investors/funds’ required return on comparable projects;

• A company internal benchmark (weighted average capital cost of the

company) if there is only one potential project developer (e.g. when the

proposed project land is owned or otherwise controlled by a single entity,

physical person or a company, who is also the project developer). The project

developers shall demonstrate that this benchmark has been consistently used

in the past, i.e. that project activities under similar conditions developed by

the same company used the same benchmark.


16

Sub-step 2c. Calculation and comparison of financial indicators (only

applicable to options II and III): Calculate the suitable financial indicator for the

proposed A/R CDM project activity without the financial benefits from the CDM

and, in the case of Option II above, for the other land use scenarios. Include all

relevant costs (including, for example, the investment cost, the operations and

maintenance costs), and revenues (excluding tCER or lCERs revenues, but

including subsidies/fiscal incentives where applicable), and, as appropriate, non-

market cost and benefits in the case of public investors.

Present the investment analysis in a transparent manner and provide all the

relevant assumptions in the CDM-AR-PDD, so that a reader can reproduce the

analysis and obtain the same results. Clearly present critical economic parameters

and assumptions (such as capital costs, lifetimes, and discount rate or cost of

capital). Justify and/or cite assumptions in a manner that can be validated by the

DOE. In calculating the financial indicator, the project’s risks can be included

through the cash flow pattern, subject to project-specific expectations and

assumptions (e.g. insurance premiums can be used in the calculation to reflect

specific risk equivalents).

Assumptions and input data for the investment analysis shall not differ across

the project activity and its alternatives, unless differences can be well

substantiated. Present in the AR-CDM-PDD submitted for validation a clear

comparison of the financial indicator for the proposed A/R CDM project activity

without the financial benefits from the CDM and:

Option II (investment comparison analysis): If one of the other land use

scenarios has the better indicator (e.g. higher IRR), then the A/R CDM project

activity can not be considered as the financially attractive; or

Option III (benchmark analysis): If the A/R CDM project activity has a less

favourable indicator (e.g. lower IRR) than the benchmark, then the A/R CDM

project activity cannot be considered as financially attractive.

→ If it is concluded that the proposed A/R CDM project activity without the

financial benefits from the CDM is not financially most attractive then proceed to

Step 2d (Sensitivity Analysis).


17

Sub-step 2d. Sensitivity analysis. Include a sensitivity analysis that shows

whether the conclusion regarding the financial attractiveness is robust to

reasonable variations in the critical assumptions. The investment analysis provides

a valid argument in favour of additionality only if it consistently supports (for a

realistic range of assumptions) the conclusion that the proposed A/R CDM project

activity without the financial benefits from the CDM is unlikely to be financially

attractive. If the land within the boundary of the proposed A/R CDM project

activity was at least partially forested since 31 December 1989 and the land is not

a forest at the project start, the project participants shall demonstrate that

incentives/reasons/actions that allowed for the past forestation have changed to an

extent that affects the financial attractiveness of forestation of the project area

without being registered as the A/R CDM project.

• If after the sensitivity analysis it is concluded that the proposed A/R CDM

project activity without the financial benefits from the CDM is unlikely to be

financially most attractive (Option II and Option III), then proceed directly to

Step 4 (Common practice analysis).

• If after the sensitivity analysis it is concluded that the proposed A/R CDM

project activity is likely to be financially most attractive (Option II and Option

III), then the project activity cannot be considered additional by means of

financial analysis. Optionally proceed to Step 3 (Barrier analysis) to prove that

the proposed project activity faces barriers that do not prevent the baseline

land use scenario(s) from occurring. If the Step 3 (Barrier analysis) is not

employed then the project activity cannot be considered additional.

Step 3. Barrier analysis. Barrier analysis may be performed as a stand-alone

additionality analysis or as an extension of investment analysis. If this step is

used, determine whether the proposed project activity faces barriers that:

• Prevent the implementation of this type of proposed project activity; and

• Do not prevent the implementation of at least one of the alternative land use

scenarios.

Use the following sub-steps:

Sub-step 3a. Identify barriers that would prevent the implementation of type of the

proposed project activity: Establish that there are barriers that would prevent the


18

implementation of the type of proposed project activity from being carried out if

the project activity was not registered as an A/R CDM activity. The barriers

should not be specific to the project participants. Such barriers may include,

among others:

• Investment barriers, other than the economic/financial barriers in Step 2

above, inter alia:

o For A/R project activities undertaken and operated by private entities:

Similar activities have only been implemented with grants or other non-

commercial finance terms. In this context similar activities are defined as

activities of a similar scale that take place in a comparable environment

with respect to regulatory framework and are undertaken in the relevant

geographical area;

o Debt funding is not available for this type of project activity;

o No access to international capital markets due to real or perceived risks

associated with domestic or foreign direct investment in the country where

the project activity is to be implemented, as demonstrated by the credit

rating of the country or other country investment reports of reputed origin;

o Lack of access to credit.

• Institutional barriers, inter alia:

o Risk related to changes in government policies or laws;

o Lack of enforcement of forest or land-use-related legislation.

• Technological barriers, inter alia:

o Lack of access to planting materials;

o Lack of infrastructure for implementation of the technology.

• Barriers related to local tradition, inter alia:

o Traditional knowledge or lack thereof, laws and customs, market

conditions, practices;

o Traditional equipment and technology.

• Barriers due to prevailing practice, inter alia:

o The project activity is the “first of its kind”: No project activity of this

type is currently operational in the host country or region.

• Barriers due to local ecological conditions, inter alia:


19

o Degraded soil (e.g. water/wind erosion, salination, etc.);

o Catastrophic natural and / or human-induced events (e.g. land slides,

fire, etc);

o Unfavourable meteorological conditions (e.g. early/late frost, drought);

o Pervasive opportunistic species preventing regeneration of trees (e.g.

grasses, weeds);

o Unfavourable course of ecological succession;

o Biotic pressure in terms of grazing, fodder collection, etc.

• Barriers due to social conditions, inter alia:

o Demographic pressure on the land (e.g. increased demand on land due

to population growth);

o Social conflict among interest groups in the region where the project

takes place;

o Widespread illegal practices (e.g. illegal grazing, non-timber product

extraction and tree felling);

o Lack of skilled and/or properly trained labour force;

• Lack of organisation of local communities;

• Barriers relating to land tenure, ownership, inheritance, and property rights, inter

alia:

o Communal land ownership with a hierarchy of rights for different

stakeholders limits the incentives to undertake A/R activity;

o Lack of suitable land tenure legislation and regulation to support the

security of tenure;

o Absence of clearly defined and regulated property rights in relation to

natural resource products and services;

o Formal and informal tenure systems that increase the risks of

fragmentation of land holdings;

o Barriers relating to markets, transport and storage;

o Unregulated and informal markets for timber, non-timber products and

services prevent the transmission of effective information to project

participants;


20

o Remoteness of A/R activities and undeveloped road and infrastructure

incur large transportation expenditures, thus eroding the

competitiveness and profitability of timber and non-timber products

from the CDM activity;

o Possibilities of large price risk due to the fluctuations in the prices of

timber and non timber products over the project period in the absence

of efficient markets and insurance mechanisms;

o Absence of facilities to convert, store and add value to production

from CDM activities limits the possibilities to capture rents from the

land use under A/R CDM project activity.

The identified barriers are only sufficient grounds for demonstration of

additionality if they would prevent potential project participants from carrying out

the proposed project activity if it was not expected to be registered as an A/R

CDM project activity. Provide transparent and documented evidence, and offer

conservative interpretations of this documented evidence, as to how it

demonstrates the existence and significance of the identified barriers. Anecdotal

evidence can be included, but alone is not sufficient proof of barriers. The type of

evidence to be provided may include:

• Relevant legislation, regulatory information or environmental/natural

resource management norms, acts or rules;

• Relevant (sectoral) studies or surveys (e.g. market surveys, technology

studies, etc) undertaken by universities, research institutions,

associations, companies, bilateral/multilateral institutions, etc;

• Relevant statistical data from national or international statistics;

• Documentation of relevant market data (e.g. market prices, tariffs,

rules);

• Written documentation from the company or institution developing or

implementing the A/R CDM project activity or the A/R CDM project

developer, such as minutes from Board meetings, correspondence,

feasibility studies, financial or budgetary information, etc;


21

• Documents prepared by the project developer, contractors or project

partners in the context of the proposed project activity or similar

previous project implementations;

• Written documentation of independent expert judgements from

agriculture, forestry and other land-use related Government / Non-

Government bodies or individual experts, educational institutions (e.g.

universities, technical schools, training centres), professional

associations and others.

If the land within the boundary of the proposed of the A/R CDM project

activity was at least partially forested since 31 December 1989 and the land is not

a forest at the project start, the project participants shall identify,

incentives/reasons/actions/that allowed for the past forestation and shall

demonstrate that the current legal/financial or other applicable regulations or

ecological or other local conditions have changed to the extent that they pose a

barrier which allows for conclusion that repetition of the forestation performed

without being registered as the A/R CDM project activity is not possible.

Sub-step 3 b. Show that the identified barriers would not prevent the

implementation of at least one of the alternative land use scenarios (except the

proposed project activity): If the identified barriers also affect other land use

scenarios, explain how they are affected less strongly than they affect the

proposed A/R CDM project activity. In other words, explain how the identified

barriers are not preventing the implementation of at least one of the alternative

land use scenarios. Any land use scenario that would be prevented by the barriers

identified in Sub-step 3a is not a viable alternative, and shall be eliminated from

consideration. At least one viable land use scenario shall be identified.

• If both Sub-steps 3a – 3b are satisfied, then proceed directly to Step 4

(Common practice analysis).

• If one of the Sub-steps 3a – 3b is not satisfied then the project activity cannot

be considered additional by means of barrier analysis. Optionally proceed to

Step 2 (Investment analysis) to prove that the proposed A/R CDM project

activity without the financial benefits from the CDM is unlikely to produce

economic benefit (Option I) or to be financially attractive (Option II and


22

Option III). If the Step 2 (Investment analysis) is not employed then the

project activity cannot be considered additional.

Step 4. Common practice analysis. The previous steps shall be

complemented with an analysis of the extent to which similar forestation activities

have already diffused in the geographical area of the proposed A/R CDM project

activity. This test is a credibility check to demonstrate additionality that

complements the barrier analysis (Step 2) and the investment analysis (Step 3).

Provide an analysis to which extent similar forestation activities to the one

proposed as the A/R CDM project activity have been implemented previously or

are currently underway. Similar forestation activities are defined as that which are

of similar scale, take place in a comparable environment, inter alia, with respect

to the regulatory framework and are undertaken in the relevant geographical area,

subject to further guidance by the underlying methodology. Other registered A/R

CDM project activities shall not to be included in this analysis. Provide

documented evidence and, where relevant, quantitative information. Limit your

considerations to the period since 31 December 1989.

If forestation activities similar to the proposed A/R CDM project activity are

identified, then compare the proposed project activity to the other similar

forestation activities and assess whether there are essential distinctions between

them. Essential distinctions may include a fundamental and verifiable change in

circumstances under which the proposed A/R CDM project activity will be

implemented when compared to circumstances under which similar forestations

were carried out. For example, barriers may exist, or promotional policies may

have ended. If certain benefits rendered the similar forestation activities

financially attractive (e.g., subsidies or other financial flows), explain why the

proposed A/R CDM project activity cannot use the benefits. If applicable, explain

why the similar forestation activities did not face barriers to which the proposed

A/R CDM project activity is subject.

→ If Step 4 is satisfied, i.e. similar activities can be observed and essential

distinctions between the proposed CDM project activity and similar activities

cannot be made, then the proposed CDM project activity cannot be considered


23

additional. Otherwise, the proposed A/R CDM project activity is not the baseline

scenario and, hence, it is additional.

Figure 2.2. Indicative flowchart of the tool for the demonstration and assessment of

additionality in A/R CDM project activities

2.3.2. Baseline

As stated above, CDM afforestation and reforestation projects enhance

greenhouse gas removals in one country to permit an equivalent quantity of


24

greenhouse gas emissions in another country, without changing the global

emission balance. Technically, the CDM is a baseline-and-credit trade

mechanism, not a cap-and-trade mechanism. Therefore, enhancements of

removals by afforestation and reforestation projects must create real, measurable

and long-term benefits related to the mitigation of climate change (Kyoto

Protocol, Article 12.5b), and must be additional to any that would occur in the

absence of the certified project activity (Kyoto Protocol, Article 12.5c). The “in

the absence” scenario is also referred to as the baseline scenario.

The Marrakech Accords define a baseline scenario as one that “reasonably

represents greenhouse gas emissions that would occur in the absence of the

proposed project activity” and is derived using an approved baseline method. The

Marrackech Accords also state that the project baseline shall be established “in a

transparent and conservative manner regarding the choices of approaches,

assumptions” and that it shall be established “on a project-specific basis”. In

summary, the baseline is the most likely course of action and development over

time, in the absence of CDM financing.

The Figure 2.3 shows the time-path of carbon stocks in the project and

baseline scenarios.

Figure 2.3. Carbon stock in the baseline scenario and in the project.


25

2.3.3. Leakage

Some projects will be successful in sequestering more carbon within the

project area, but the project activities may change activities or behaviours

elsewhere. These changes may lead to reduced sequestration or increased

emissions outside the project boundary, negating some of the benefits of the

project. This is called leakage. A simple example is a project that reforests an area

of poor quality grazing land, but leads to the owners of the displaced livestock to

clear land outside the project boundaries to establish new pastures. The types of

activities that might result in leakage vary with the type of projects, but both

LULUCF and non-LULUCF projects are subject to leakage (Pearson, 2005).

2.3.4. Permanence

During the negotiations leading up to the Kyoto Protocol and subsequently,

there was considerable concern that credits issued for carbon sequestration would

be subject to a risk of re-emission, due to either human action or natural events

such as wildfires. This was called the permanence risk and it is unique to

LULUCF projects under the Protocol. Eventually, Parties agreed that credits aris-

ing from CDM afforestation and reforestation projects should be temporary, but

could be re-issued or renewed every five years after an independent verification to

confirm sufficient carbon was still sequestered within the project to account for all

credits issued.

This deals effectively with the permanence risk and guarantees that any

losses of sequestered carbon for which credits have been issued will have to be

made up through either additional sequestration elsewhere or through credits

derived from non-LULUCF activities. Two types of temporary credits were

agreed: temporary CERs and long-term CERs.

2.4. Remote Sensing in Forestry

There have been many techniques to detect land change developed by many

scientists. For instant spectral mixture analysis, Li-Strahler canopy model, Chi-

Square transformation, artificial neural network, and also from the integration of

several data source.


26

D.lu in his paper summarizes 7 methods that have been used to implement

land change detection;

1) Algebra, in this category contain of image difference, image regression,

image comparison, vegetation index difference, change vector analysis, and

background subtraction. This algorithm has specific character, that character is

threshold selection to detect the changing area. This method relatively not

complicated and simple to be implemented, but unfortunately the change matrix

can not be defined.

2) Transformation, the methods that included in this category are Principal

component analysis (PCA), Gramm-Schmidt (G), and Chi-square transformation.

The advantages of this method are in terms of decreasing redundancy among

bands, and gives fine information on changed area though not to clearly. The

disadvantages are the difficulties on interpretation process and labeling the change

information on the image that have transformed.

3) Classification, this category contain of post classification comparison,

spectral-temporal combination analysis, unsupervised change detection, hybrid

change detection, and ANN. This method based on the image classification, where

the quantity and quality of sample is very crucial to produce a good classification

result. The advantages from this method are able to provide matrix of change and

minimize the external impact such as atmosphere on multy-temporal image.

4) Advance model, model that include in this category are Li-Strahler

reflectance model, spectral mixture model, and biophysical parameter estimation. In

this method, the reflectance value of image is converted into physical parameter

through linear or nonlinear model. The converted parameter is usable to extract the

information from vegetation. The disadvantages of this method are time consuming

and complicated process on the development of model could convert reflectance

value to biophysical parameter.

5) GIS (Geographic Information System), this category contain of integrated

GIS and remote sensing application, and purely GIS only. The advantages of this

method are, all of the data from different source that represent land changes are

provided by this method. In the other hand, the mixing data process from different

source is influenced by accuracy of each data.


27

6) Visual analysis, this category contain of image visual interpretation and on-

screen digitizing of the changed area. This method is applicable to be used by

scientist and people who have long experience. Texture, shape, form, size and pattern

of the image are key element that used for detection of land cover change. Scientist

uses these entire elements to help him on land cover change analysis.

7) Other land change detection technique, as addition from six category

above, there are few method that exist but not able to be putted on the category above,

such as the use of spatial dependent measurement on TM imagery to detect changes

in savanna, the use of knowledge based vision system to detect land cover change on

the urban edge, the use of vegetation index, surface temperature, and spatial structure

which is derived from AVHRR, to detect land cover change in West Africa, the use

of curve change, and many other method.

2.4.1. ASTER

The Advanced Spaceborne Thermal Emission and Reflection Radiometer

(ASTER) is an advanced multispectral imager that was launched on board NASA’s

Terra spacecraft in December, 1999. ASTER covers a wide spectral region with 14

bands from the visible to the thermal infrared with high spatial, spectral and

radiometric resolution. An additional backward-looking near-infrared band provides

stereo coverage. The spatial resolution varies with wavelength: 15 m in the visible

and near-infrared (VNIR), 30 m in the short wave infrared (SWIR), and 90 m in the

thermal infrared (TIR). Each ASTER scene covers an area of 60 x 60 km.

ASTER consists of three different subsystems: the Visible and Near-infrared

(VNIR) has three bands with a spatial resolution of 15 m, and an additional

backward telescope for stereo; the Shortwave Infrared (SWIR) has 6 bands with a

spatial resolution of 30 m; and the Thermal Infrared (TIR) has 5 bands with a

spatial resolution of 90 m. Each subsystem operates in a different spectral region,

with its own telescope(s), and is built by a different Japanese company. The

spectral bandpasses are shown in Table 2.1, and a comparison of bandpasses with

Landsat Thematic Mapper is shown in Figure 2.4. In addition, one more telescope

is used to view backward in the near-infrared spectral band (band 3B) for

stereoscopic capability.


28

Table 2.1. ASTER Characteristic.

Subsystem Band No. Spectral

Range (µm)

Spatial

Resolution, m

Quantization

Levels

1 0.52-0.60

2 0.63-0.69

3N 0.78-0.86 VNIR

3B 0.78-0.86

15 8 bits

4 1.60-1.70

5 2.145-2.185

6 2.185-2.225

7 2.235-2.285

8 2.295-2.365

SWIR

9 2.360-2.430

30 8 bits

10 8.125-8.475

11 8.475-8.825

12 8.925-9.275

13 10.25-10.95

TIR

14 10.95-11.65

90 12 bits

Source: Abrams, et al. 2005

Figure 2.4 : Comparison of Spectral Bands between ASTER and Landsat-7 Thematic

Mapper. (Note: % Ref is reflectance percent), source: Abrams, et al. 2005.

VNIR has high performance, VNIR subsystem consists of two independent

telescope assemblies to minimize image distortion in the backward and nadir

looking telescopes. The detectors for each of the bands consist of 5000 element

silicon charge-coupled detectors (CCD's). Only 4000 of these detectors are used at

any one time. A time lag occurs between the acquisition of the backward image

and the nadir image. During this time earth rotation displaces the image center.

The VNIR subsystem automatically extracts the correct 4000 pixels based on orbit

position information supplied by the EOS platform.


29

The VNIR optical system is a reflecting-refracting improved Schmidt

design. The backward looking telescope focal plane contains only a single

detector array and uses an interference filter for wavelength discrimination. The

focal plane of the nadir telescope contains 3 line arrays and uses a dichroic prism

and interference filters for spectral separation allowing all three bands to view the

same area simultaneously. The telescope and detectors are maintained at 296 ± 3K

using thermal control and cooling from a platform-provided cold plate. On-board

calibration of the two VNIR telescopes is accomplished with either of two

independent calibration devices for each telescope. The radiation source is a

halogen lamp. A diverging beam from the lamp filament is input to the first

optical element (Schmidt corrector) of the telescope subsystem filling part of the

aperture. The detector elements are uniformly irradiated by this beam. In each

calibration device, two silicon photo-diodes are used to monitor the radiance of

the lamp. One photo-diode monitors the filament directly and the second monitors

the calibration beam just in front of the first optical element of the telescope. The

temperatures of the lamp base and the photo-diodes are also monitored. Provision

for electrical calibration of the electronic components is also provided.

The system signal-to-noise is controlled by specifying the NE delta rho (ρ)

to be < 0.5% referenced to a diffuse target with a 70% albedo at the equator

during equinox. The absolute radiometric accuracy is ± 4% or better.

The VNIR subsystem produces by far the highest data rate of the three

ASTER imaging subsystems. With all four bands operating (3 nadir and 1

backward) the data rate including image data, supplemental information and

subsystem engineering data is 62 Mbps.

The SWIR subsystem uses a single aspheric refracting telescope. The

detector in each of the six bands is a Platinum Silicide-Silicon (PtSi-Si) Schottky

barrier linear array cooled to 80K. A split Stirling cycle cryocooler with opposed

compressors and an active balancer to compensate for the expander displacer

provide cooling. The on-orbit design life of this cooler is 50,000 hours. Although

ASTER operates with a low duty cycle (8% average data collection time), the

cryocooler operates continuously because the cool-down and stabilization time is

long. No cyrocooler has yet demonstrated this length of performance, and the


30

development of this long-life cooler was one of several major technical challenges

faced by the ASTER team.

The cryocooler is a major source of heat. Because the cooler is attached to

the SWIR telescope, which must be free to move to provide cross-track pointing,

this heat cannot be removed using a platform provided cold plate. This heat is

transferred to a local radiator attached to the cooler compressor and radiated into

space.

Six optical bandpass filters are used to provide spectral separation. No

prisms or dichroic elements are used for this purpose. A calibration device similar

to that used for the VNIR subsystem is used for in-flight calibration. The

exception is that the SWIR subsystem has only one such device.

The NE delta rho will vary from 0.5 to 1.3% across the bands from short to

long wavelength. The absolute radiometric accuracy is +4% or better. The

combined data rate for all six SWIR bands, including supplementary telemetry

and engineering telemetry, is 23 Mbps.

The TIR subsystem uses a Newtonian catadioptric system with an aspheric

primary mirror and lenses for aberration correction. Unlike the VNIR and SWIR

telescopes, the telescope of the TIR subsystem is fixed with pointing and scanning

done by a mirror. Each band uses 10 Mercury-Cadmium-Telluride (HgCdTe)

detectors in a staggered array with optical band-pass filters over each detector

element. Each detector has its own pre- and post-amplifier for a total of 50.

As with the SWIR subsystem, the TIR subsystem uses a mechanical split

Stirling cycle cooler for maintaining the detectors at 80K. In this case, since the

cooler is fixed, the waste heat it generates is removed using a platform supplied

cold plate.

The scanning mirror functions both for scanning and pointing. In the

scanning mode the mirror oscillates at about 7 Hz. For calibration, the scanning

mirror rotates 180 degrees from the nadir position to view an internal black body

which can be heated or cooled. The scanning/pointing mirror design precludes a

view of cold space, so at any one time only a single point temperature calibration

can be effected. The system does contain a temperature controlled and monitored

chopper to remove low frequency drift. In flight, a single point calibration can be


31

done frequently (e.g., every observation) if necessary. On a less frequent interval,

the black body may be cooled or heated (to a maximum temperature of 340K) to

provide a multipoint thermal calibration. Facility for electrical calibration of the

post-amplifiers is also provided.

For the TIR subsystem, the signal-to-noise can be expressed in terms of an

NE delta T. The requirement is that the NE delta T be less than 0.3K for all bands

with a design goal of less than 0.2K. The signal reference for NE delta T is a

blackbody emitter at 300K. The accuracy requirements on the TIR subsystem are

given for each of several brightness temperature ranges as follows: 200 - 240K,

3K; 240 - 270K, 2K; 270 - 340K, 1K; and 340 - 370K, 2K.

The total data rate for the TIR subsystem, including supplementary

telemetry and engineering telemetry, is 4.2 Mbps. Because the TIR subsystem can

return useful data both day and night, the duty cycle for this subsystem is set at

16%. The cryocooler, like that of the SWIR subsystem, operates with a 100% duty

cycle.

2.4.2. Satellite Landsat

The Landsat 5 is a Landsate satelite which was released on March 5th 1984

by NASA USA. It has the ability to detect every earth's surface and to send the

data to earth-stations worldwide. The satellite. will recheck the same area in 16-

day cycle, within 185km coverage from northpole to southpole, circling the

earth with sunsyncronous orbit, locating above the equator at 9.30am local time in

descending node.

Landsat 5 was developed from the previous Landsat satelites (1, 2 and 3)

with improvement on the spacial resolution, radiometric varieties, data transfer

with better speed, and vegetation-related focus. Development of Thematic Mapper

(TM) sensor with added thermal channels on the wave lenghth (10.40-12.50

micron). This canal is not available on Landsat 1, 2 and 3 with its MSS sensor.

Landsat 5 is a replica of a high ability Thematic Mapper. Adding a new specialty

that has multifuction and components that are more efficient for the global study

data, to monitor the area coverage and the size of the mapping area more

accurately than the previous design, and to show a stabil radiometric correction


32

with lesser interference. The characteristic of Landsat 5 TM spectrum are shown

on Table 2.2.

Table 2.2. The characteristic of Landsat 5 TM

No.Band Wave length

(Mikron)

Spatial Resolution

(Meter)

1 0.45 to 0.52 30

2 0.52 to 0.60 30

3 0.63 to 0.69 30

4 0.76 to 0.90 30

5 1.55 to 1.75 30

6 10.40 to 12.50 120

7 2.08 to 2.35 30 Source: Julia, 2007

2.5. Geography Information System (GIS) and Satellite imagery

Geography Information System is an information system that was

constructed to work with spatial reference data or those with geographic

coordinates. GIS can be associated as a map with high orde, which can also

operate and save non-spatial data. It was said that GIS has proven its reliability to

collect, save, process, analyze and show the spatial data on either biophysics or

social economy. Star and Estes stated that in general GIS provides the facilities to

take, manage, data manipulation and analysis, and to provide good quality results

on graphic and table, but its main function remains with managing the spatial data.

The advantage of GIS is its ability to attach data from different sources for

changes on detection application. However, combining data sources with different

accuration often affects the detection changes results. Lo (2002) used GIS

approach to calculate the impact of new city development in Hong Kong, through

the multi-temporal air images data integration on land use, and they found that the

overlay image with biner masking may be useful to quantitavely explain the

dynamics of changes on each land use category.

On the last year, the usage of multi-sources data (eg air images, TM, SPOT

and previous thematic map) have been an important method to detect changes on

land use and land cover (LULC), especially if the changes detection is part of a

long interval period connected to different sources, different format,

precision or changes analysis on multi-scale land cover (Muukkonen et al, 2006).


33

Table 2.3. Characteristic of selected existing and proposed satellite platforms and

sensors for forestry

Identification Sensor Numb. of band Spatial Resolution (m)

Operational Satellites (Year 2000)

Landsat-5 TM

MSS

7

4

30-120

82

Landsat-7 ETM+ 7 15-30

SPOT-2 HRV 4 10-20

SPOT-4 HRV

VI

5

4

10-20

1150

RESURS-01-3 MSU-KV 5 170-600

IRS-1B LISS 4 36-72

IRS-1C, -1D LISS PAN

4 1

23-70 5.8

IRS-P4 (Oceansat) OCM 8 360

JERS-1 VNIR, SWIR

SAR

8

1

20

18

Almaz SAR 3 4-40

Radarsat SAR 1 9-100

ERS-1,-2 AMI (SAR)

ATSR

1

4

26

1000

Space Imaging IKONOS-2 5 1-4

NOAA-15 AVHRR 5 1100

NOAA-14 AVHRR 5 1100

NOAA-L AVHRR 5 1100

Orbview-2 (Seastar) SeaWiFS 8 1130

CBERS-1 CCD

IRMSS

WFI

5

4

2

20

80-160

260

Terra (EOS AM-1) ASTER

MODIS

MISR

14

36

4

15, 30, 90

250, 500, 1000

275

Satellites (launch window 2000-2007)

Earthwatch Quickbird 5 0.82-3.2

Orbview-3 Orbview 5 1-4

Orbview-4 Orbview

Hyperspectral

5

200

1-4

8

IRS P5 (Cartosat) Pan 1 2.5

IRS P6 LISS

AWiFS

7

3

6-23.5

80

SPOT 5 HRV, VI 5 5-1150

KVR-100 Camera 1 1.5

TK350 Camera 1 10

EO-1 Hyperion

LAC

ALI

220

256

10

30

250

10-30

WIS EROS 1 1

CBERS-2 CCD IRMSS

WFI

5 4

2

20 80-160

260

Resource21 A, B, C, D 5 20

ADEOS-II GLI 36 250-1000

Source: Adopted from http://www.satimagingcorp.com/characterization-of-satellite-remote-

sensing-systems.html


34

2.6 Biomass and C-Stock

Biomass was the name given to any recent organic matter that had been

derived from plants as a result of the photosynthetic conversion process. Biomass

is the mass (or weight) of living matter per unit area of ground. It is expressed in

units such as grams per square meter or kilograms per hectare. Between different

vegetation types, biomass range from around 100 kg/ha for desert and 500.000

kg/ha for tropical rain forest. In the study of carbon budget, biomass is important

because it directly represents the amount of carbon stored in living plants (Brown

S., 1997).

Aboveground biomass was difficult to quantify over large areas using

traditional techniques and executed the relationship between LAI derived from

NDVI and estimated aboveground biomass based on plant height. The

aboveground biomass of the plant could be easily estimated with some accuracy

from allometric relationship of trunk height (Brown S., 1997). Biomass ton per

hectare depend on the plant height (regression with model LAI) and planting

density.

This natural process was part of the carbon cycle and was known as

sequestration. Half of a tree mass was carbon, so large amounts of carbon were

stored in plants and they are the largest carbon store of terrestrial carbon. In most

ecosystems, most of carbon was stored below ground, either as roots and decaying

biomass or as organic carbon in the soil. The tree carbon calculation used general

allometric relationships to estimate aboveground biomass of the tree.

C-stock means the total Carbon which stored in the biomass component and

nekromass, above and inside soil (soil organic matter, plant root, and

microorganism) per unit area of ground.

2.6.1 Biomass Estimation

Biomass, an estimate of the total living or dead organic material expressed

as a weight per area (e.g. kilograms per hectare), has been of greatest interest

when aggregated over regional conditions (Penner et al., 1997; Schroeder et al.,

1997; Fang et al., 1998).


35

Traditionally, stand biomass estimates are derived by the same process as

regional estimation of biomass, by conversion of stem volume estimates from the

forest inventory database (Aldred and Alemdag, 1988). In less-well-inventoried

areas of the world, biomass estimates may be developed through forest cover type

volume tables (Brown and Lugo, 1984). The estimate begins with single tree

estimates by species and site types. The appropriate local allometric equations are

developed to partition the estimate into foliar, branch, stem and root biomass

estimates, or perhaps into two components: aboveground and belowground woody

biomass components (e.g. Lavigne et al., 1996). A recent strategy is to develop a

large-scale system for biomass estimation. Such an approach assumes that better

biomass estimates can be generated by referencing all available information in a

multistage approach: the forest inventory, the available satellite and airborne

imagery, and data collected in the field in permanent sample plots (Czaplewski,

1999; Fournier et al., 1999).

General equation, biomass density can be calculated from VOB/ha by first

estimating the biomass of the inventoried volume and then "expanding" this value

to take into account the biomass of the other aboveground components as follows

(Brown and Lugo 1992):

Aboveground biomass density (t/ha) = V * WD * BEF

where:

V = Volume of tree

WD = Wood density (1 of oven-dry biomass per m3 green volume)

BEF = Biomass expansion factor (ratio of aboveground oven-dry biomass of trees

to oven-dry biomass of inventoried volume)

Volume of tree = 7.0)2/(14.3 2××× hD

where:

D = DBH (Diameter Breast High), m

h = Height of tree, m

0.7 = Correction Factor

The linear regression equation approach requires the selection of the

regression equation that is best adapted to the conditions in the study area. Linear

regression models have been fitted to data in various situations of variable site and


36

ecological conditions globally. The work done by Brown, Gillespie and Lugo

(1989) and FAO (1997) on estimation of above ground biomass of tropical forests

using regression equations of biomass as a function of DBH is central to the use

of this approach. Some of the equations reported by Brown, Gillespie and Lugo

(1989) have become standard practice because of their wide applicability.

2.6.2. Carbon Sequestration

Carbon sequestration is the term describing processes that removes carbon

from the biosphere. A variety of means of artificially capturing and storing

carbon, as well as of enhancing natural sequestration processes, are being

explored. This is intended to support the mitigation of global warming.

A major proportion of the C and nutrients in terrestrial ecosystem is found

in the tree component. In photosynthesis process, trees absorb carbon dioxide

from the atmosphere and store it as carbon (part of carbohydrates) while oxygen is

released back into the atmosphere. Rapidly growing trees absorb a larger amount

of carbon dioxide. Mature trees grow less rapidly and thus have a lower intake of

carbon dioxide. While individual trees burned, die or decay, then it will release

most stored carbon back to the atmosphere, the forest as a whole continues to

store carbon as dying or harvested trees are replaced by natural regeneration

(Wikipedia, 2008).

Figure 2.5. Photosynthesis Process.

Source:

http://www.carbonplanet.com/forestry_carbon_credits


37

The calculation of carbon stock as biomass consists of multiplying the total

biomass by a conversion factor that represents the average carbon content in

biomass. It is not practically possible to separate the different biomass

components in order to account for variations in carbon content as a function of

the biomass component. Therefore, the coefficient of 0.5 for the conversion

biomass to C, offered by IPCC (2003), is generalized here to conversions from

biomass to carbon stock: C = 0.5 × biomass (total). This coefficient is widely

used internationally, thus it may be applied on a project basis.

2.7. Error analysis

Estimating carbon stock changes, emissions and removals arising from Land

Use Land Use Change Forestry (LULUCF) activities have an uncertainties/error

that is associated with area or other activity data, biomass growth rates, expansion

factors and other coefficients. Uncertainty estimates are an essential element of a

complete emissions inventory. Uncertainty information is not intended to dispute

the validity of the inventory estimates, but to help prioritize efforts to improve the

accuracy of inventories in the future and guide decisions on methodological

choice.

In IPCC document (IPCC, 2003), two methods for the estimation of

combined uncertainties are presented: a Tier 1 method using simple error

propagation equations, and a Tier 2 method using Monte Carlo or similar

techniques. Both methods are applicable when dealing with the LULUCF sector.

However, some specific considerations have to be highlighted, because net

emissions can be negative if both emissions and removals are taken into account.

Use of either Tier 1 or Tier 2 will provide insight into how individual

categories and greenhouse gases contribute to the uncertainty in total emissions in

any given year, and to the trend in total emissions between years. Being

spreadsheet based, the Tier 1 method is easy to apply, and it is good practice for

all countries to undertake an uncertainty analysis according to Tier 1. Inventory

agencies may also undertake uncertainty analysis according to Tier 2 or national

methods. The uncertainty estimates of the LULUCF sector can be combined with


38

the uncertainty estimates of the non-LULUCF sector to obtain the total inventory

uncertainty.

Uncertainties should be reported as a confidence interval giving the range

within which the underlying value of an uncertain quantity is thought to lie for a

specified probability. The IPCC Guidelines suggest the use of a 95% confidence

interval, which is the interval that has a 95% probability of containing the

unknown true value. This may also be expressed as a percentage uncertainty,

defined as half the confidence interval width divided by the estimated value of the

quantity (figure 2.6). The percentage uncertainty is applicable when either the

underlying probability density function is known or when a sampling scheme or

expert judgment is used. Furthermore, this notion can be readily used to identify

the categories for which efforts to reduce uncertainty should be prioritized.

Figure 2.6. Uncertainty sample

% uncertainty = 100)widthinterval%95(

21

×

µ

% uncertainty = %20100100

20100

2100

)4(2

1

=×=×=×

µ

σ

µ

σ

Where:

µ = the mean of distribution

σ = standard deviation = 10


39

2.7.1. Monte carlo analysis

The principle of Monte Carlo analysis is to select random values of emission

factor and activity data from within their individual probability density functions,

and to calculate the corresponding emission values. This procedure is repeated

many times, using a computer, and the results of each calculation run build up the

overall emission probability density function. Monte Carlo analysis can be

performed at the source category level, for aggregations of source categories or

for the inventory as a whole (IPCC, 2003).

Monte Carlo analysis can deal with probability density functions of any

physically possible shape and width, can handle varying degrees of correlation

(both in time and between source categories) and can deal with more complex

models (e.g. the 1st order decay for CH4 from landfills) as well as simple

‘emission factor times activity data’ calculations.

Like all methods, Monte Carlo analysis only provides satisfactory results if

it is properly implemented. This requires the analyst to have scientific and

technical understanding of the inventory. Of course, the results will only be valid

to the extent that the input data, including any expert judgments, are sound.

The Monte Carlo approach consists of five clearly defined steps shown in

Figure 2.6. Only the first two of these require effort from the user, the remainder

being handled by the software package.

• Step 1 – Specify source category uncertainties. Specify the uncertainties in the

basic data. This includes emission factors and activity data, their associated

means and probability distribution functions, and any cross correlation

between source categories.

• Step 2 – Set up software package. The emission inventory calculation, the

probability density functions and the correlation values should be set up in the

Monte Carlo package.

The software automatically performs the subsequent steps.

• Step 3 – Select random variables. This is the start of the iterations. For each

input data item, emission factor or activity data, a number is randomly

selected from the probability density function of that variable.


40

• Step 4 – Estimate emissions. The variables selected in Step 3 are used to

estimate total emissions. The example given in Figure 6.1 assumes three

source categories, each estimated as activity multiplied by an emission factor,

and then summed to give total emissions. The calculations can be more

complex. Emissions by gas can be multiplied by GWP values, in order to

obtain total national emissions in CO2 equivalent. Correlations of 100% are

easy to incorporate, and good Monte Carlo packages allow other correlations

to be included. Since the emission calculations should be the same as those

used to estimate the national inventory, the Monte Carlo process could be

fully integrated into the annual emission estimates.

• Step 5 – Iterate and monitor results. The calculated total from step 4 is stored,

and the process then repeats from step 3. The mean of the totals stored gives

an estimate of the total emission. Their distribution gives an estimate of the

probability density function of the result. As the process repeats, the mean

approaches the final answer. When the mean no longer changes by more than

a predefined amount, the calculation can be terminated. When the estimate for

the 95% confidence range is determined to within ± 1%, then an adequately

stable result has been found. Convergence can be checked by plotting a

frequency plot of the estimates of the emission. This plot should be reasonably

smooth. These actions should be handled by the software, with the user

specifying either a number of iterations or convergence criteria.

III. METHODOLOGY

3.1. Time and Location

This research was conducted from November 2007 to August 2008. The

study site for this research is Bromo Tengger Semeru National Park (TNBTS),

East Java, from 7.86 - 8.19 South Latitude and 112.79 - 113.12 East Longitude,

figure 3.1 shows the location of study area. Data processing and analysis were

carried out at the Master of Science in Information Technology for Natural

Resources Management (MSc IT for NRM) laboratory of SEAMEO-BIOTROP

campus, Bogor Agricultural University.

Malang

Pasuruan

Probolinggo

Lumajang

500000

500000

550000

550000

600000

600000

650000

650000

700000

700000

750000

750000

800000

800000

850000

850000

900000

900000

90

500

00

905

000

0

91

000

00

910

000

0

91

5000

0

91

5000

0

92

0000

0

92

0000

0

92

5000

0

92

5000

0

930

000

0

93

000

00

Bromo Tengger SemeruNationla Park

Ce

ntral

Java

H ind ian Ocean

J

ava Sea

Figure 3.1. Study Area

Chapter 3 - Methodology

42

3.2. Data source

There are several kinds of data used in this study as detailed below:

Table 3.1.Tabular data

Tabular data Source

Critical area of TNBTS TNBTS Office

Rehabilitation Planning area of TNBTS TNBTS Office

Table 3.2. Raster data

Raster data Source

Digital Elevation Model / DEM

SRTM_f03_s008e112

SRTM_f03_s008e113

SRTM_f03_s009e112

SRTM_f03_s009e113

Mosaic Data Shuttle Radar Topography Mission

(SRTM) 90- meter data.

http://glcf.umiacs.umd.edu/index.shtml

LANDSAT 5 TM with acquisition date

28th march, 1989.

Path 118 row 65

Downloaded from:

ftp://ftp.glcf.umiacs.umd.edu/glcf/Landsat/WRS2/p

118/r065

ASTER with acquisition date

3rd

Sept, 2006.

BTIC, authorized agent for satellite image.

Table 3.3. Vector data

Vector data Source

Boundary of TNBTS TNBTS Office

Village administration border BAKOSURTANAL

Boundary project Derived from GPS field tracking

Land Cover 1989 Derived from LANDSAT 5 imagery interpretation

Land Cover 2006 Derived from ASTER imagery interpretation


43

3.3. Method

The process of this research is described as figure 3.2.

Figure 3.2. Flowchart of research

3.3.1. Project boundary/area development

Location of project area is in northern part of TNBTS, Blok Keciri is the

largest area for this study case. This location is recommended by TNBTS office,

field tracking by using Global Positioning System (GPS) is needed to derive the

boundary project. Project area can be varying in size from tens of hectares to

hundreds of hectares, and can be confined into several geographic areas. The

project area may be one contiguous block of land under, or many small blocks of

land spread over a wide area. The spatial boundaries of the land need to be clearly

defined and properly documented from the start to aid accurate measuring,

accounting and verification.

3.3.2. Land Cover Classification and Eligible land area development

Satellite image of Landsat 5 and Aster are the data that used for land cover

classification on the project area. Before starting the classification process, those


44

satellite images have to be corrected (geometric and radiometric). To increase the

accuracy of the classification, fieldtruthing is required to conducted, and then

supervised classification process is the liable to be used. To avoid an independent

single pixel on an object, low pass filter will be used, in this method centre pixel

value will be replaced by average value of the surrounding pixel. The default

kernel size is 3 x 3.

After classification process was successfully done, the next step is defining

the eligible land for AR-CDM. Eligible land in this study is the land that suitable

and fulfilled the requirement of implementing reforestation CDM project, which is

the land has not been forested since 31 December 1989. Following Ministry of

Forestry Regulation Number 14/2004 and its Addendum that forest in Indonesia is

defined as land having a minimum area of 0.25 ha, a minimum tree crown cover

of 30%, and three that have minimum height of 5 m, lands which do not meet this

definition are bush/shrubs and savannah. Figure 3.3 shows the step of

classification process and eligible land development.

Figure 3.3. Flowchart of Eligible land Development


45

The A/R CDM project activity may contain more than one discrete parcel of

land. Each discrete parcel of land shall have a unique geographical identification.

The boundary shall be defined for each discrete parcel. The discrete parcels of

lands may be defined by polygons, and to make the boundary geographically

verifiable and transparent, the GPS coordinate for all corners of each polygon

shall be measured, recorded, archived and listed.

3.3.3. Carbon stock change estimation

The approved methodology to calculate and analyze carbon stock for large

scale afforestation/reforestation CDM project is provided in UNFCCC website

“http://cdm.unfccc.int/methodologies/ARmethodologies/approved_ar.html”.

Based on the characteristic of TNBTS which is degraded and covered by

grassland, the methodology that may be used is AR-AM0001/version 2

“Reforestation of degraded land”.

The flows of carbon stock change because of project activity are described

in figure 3.4. In order to asses the carbon stock change

Figure 3.4. Flow change of carbon stock on the project activity

Baseline; Carbon stock change on the project area with the absence of

project activity. For strata without growing trees, this methodology conservatively

assumes that the carbon stock in aboveground and below-ground biomass would

in the absence of the project activity remain constant, i.e., the baseline net GHG

removals by sinks are zero. Only the carbon stock changes in above-ground and

below-ground biomass (in living trees) are estimated.

GHG removal by sinks

Boundary

project Baseline

e

GHG emission outside

Boundary project (L)

GHG emission by sinks GHG removal by sinks


46

.........................................(2)

...............................................(3)

..................................................(4)

.....................(5)

...............................................(6)

......................................(7)

.......................................................(8)

For those strata with growing trees, the sum of carbon stock changes in

above-ground and below-ground biomass is determined based on the projection of

their number and growth, based on growth models (yield tables), allometric

equations, and local or national or IPCC default parameters (detail below in this

section).

The baseline net greenhouse gas removals by sinks can be calculated by:

∑∑∆=∆

i j

tijtBSL CC ,, ….. ...................................... (1)

where:

∆CBSL,t = the sum of the changes in carbon stocks in the living biomass of trees for

year t, tonnes CO2 yr-1 for year t

∆Cij,t = average annual carbon stock change in living biomass of trees for stratum i

species j, tonnes CO2 yr-1 for year t

∆Cij,baseline,t = average annual carbon stock change in living biomass of trees for stratum i species j in the absence of the project activity, tonnes CO2 yr-1 for year t

i = strata

j = tree species

t = 1 to length of crediting period

7.0)(14.3

12/44)(

2

,,

,

,,

,1,1

,2,2

,1,2,

⋅⋅⋅=

⋅=

⋅⋅=

+=

⋅⋅=

⋅⋅=

⋅÷−=∆

ijijij

jijABijBB

jjijijAB

ijBBijABij

jijijij

jijijij

ijijtij

HrV

RBiomassdryBiomassdry

BEFDVBiomassdry

BiomassdryBiomassdryBiomassdry

CFBiomassdryAC

CFBiomassdryAC

TCCC

where: ∆Cij,t = average annual carbon stock change in living biomass of trees for stratum I species

j, tonnes CO2 yr-1

for year t

C2,ij = total carbon stock in living biomass of trees for stratum i species j, calculated at

time 2, tonnes C

C1,ij = total carbon stock in living biomass of trees for stratum i species j, calculated at time 1, tonnes C

T = number of years between times 2 and 1

Cij = total carbon stock in living biomass of trees for stratum i species j, tonnes C

Aij = area of stratum i species j, hectare (ha)


47

Biomassdry2,ij = biomass of trees for stratum i species j, calculated at time 2, t ha-1

Biomassdry1,ij = biomass of trees for stratum i species j, calculated at time 1, t ha-1

BiomassdryAB,ij = Above gorund biomass of trees for stratum i species j, t ha-1

BiomassdryBB,ij = Below ground biomass of trees for stratum i species j, t ha-1

Vij = Volume of trees for stratum i species j m3. ha

-1

Dj = basic wood density for species j, tonnes d.m. m-3

merchantable volume

BEFj = biomass expansion factor for conversion of merchantable volume to aboveground

tree biomass for species j, dimensionless

Rj = Root-shoot ratio species j, dimensionless

rij = 0.5 of dimeter of trees for stratum i species j, m

Hij = Height of trees for stratum i species j, m

CFj = the carbon fraction for species j, tonnes C (tonne d.m.)-1

GHG emissions by sources/project area; the A/R CDM project activity

may cause GHG emissions within the project boundary, in particular. The

emission of CO2, CH4 and N2O from following sources may occur as a result of

the proposed A/R CDM project activity:

• Emissions of greenhouse gases from combustion of fossil fuels for site

preparation, thinning and logging; (No emission from this part, human

resources were used for this activities)

• Decrease in carbon stock in living biomass of existing non-tree vegetation,

caused either by competition of planted trees or site preparation including

slash and burn; (No emission from this part, the existing vegetation are still

living on the project area and there are no slash and burn activity for the site

preparation)

• Emissions of non-CO2 greenhouse gases from biomass burning for site

preparation (slash and burn activity); (No emission from this part, no slash and

burn activity)

• N2O emissions caused by nitrogen fertilization application.

Based on the information above, the GHG emission as a result of the

implementation of the proposed A/R CDM project activity within the project

boundary is estimated as follows:


48

.......... (10)

............................................ (11)

............................................... (12)

fertilizerNdirectE ONGHG−

= 2 ......................................................... (9)

where:

GHGE = the GHG emissions as a result of the implementation of the A/R CDM project

activity within the project boundary, tonnes CO2-e yr-1

N2Odirect-Nfertilizer = N2O emission as a result of direct nitrogen application within the project

boundary, tonnes CO2-e yr-1

[ ]

)1(

)1(

28/44)( 212

GASMfertONON

GASFfertSNSN

ONSNNdirect

FracNF

FracNF

OGWPNEFFFONfertilizer

−⋅=

−⋅=

⋅⋅⋅+=

−

−

−

where:

N2O = the direct N fertilizer N O 2 − the direct N2O emission as a result of nitrogen

application within the project boundary, tonnes CO2-e yr-1

FSN = mass of synthetic fertilizer nitrogen applied adjusted for volatilization as NH3

and NOX, tonnes N yr-1 FON = [Annual] mass of organic fertilizer nitrogen applied adjusted for volatilization

as NH3 and NOX, tonnes N yr-1

NSN-Fert = mass of synthetic fertilizer nitrogen applied, tonnes N yr-1

NON-Fert = mass of organic fertilizer nitrogen applied, tonnes N yr-1

EF1 = Emission Factor for emissions from N inputs, tones N2O-N (tonnes N input)-1

FracGASF = the fraction that volatilises as NH3 and NOX for synthetic fertilizers,

dimensionless

FracGASM = the fraction that volatilises as NH3 and NOX for organic fertilizers,

dimensionless

44/28 = ration of molecular weights of N2O and nitrogen, dimensionless

GWPN2O = Global Warming Potential for N2O, kg CO2e (kg N2O)-1 (IPCC default = 310, valid for the first commitment period)

As noted in GPG 2000, the default emission factor (EF1) is 1.25 % of

applied N, and this value should be used when country-specific factors are

unavailable. The default values for the fractions of synthetic and organic fertilizer

nitrogen that are emitted as NOX and NH3 are 0.1 and 0.2 respectively in 1996

IPCC Guideline. Project participants may use scientifically-established specific

emission factors that are more appropriate for their project.


49

....................................(14)

................................................................... (15)

Actual net GHG removals by sinks/project area; the actual net greenhouse gas

removals are calculated as follows:

∑∑ −∆=∆

i j

EjACTUALGHGCiC .................................................13

where: ∆CACTUAL = actual net greenhouse gas removals by sinks, tonnes CO2-e yr-1

∆Cij = average annual carbon stock change in living biomass of trees for stratum i

species j, tonnes CO2 yr-1.

GHGE = GHG emissions by sources within the project boundary as a result of the

implementation of an A/R CDM project activity, tonnes CO2-e yr-1

Leakage; the changes may lead to reduced sequestration or increased

emissions outside the project boundary, negating some of the benefits of the

project. The status of the project area is protected area, where by regulation it is

not allowed to enter the site without having permission from TNBTS authority. It

means that, there is no logging or getting fuelwood activity outside the project

area because of the presence of the project.

The identified potential leakage of the proposed A/R CDM project activity is

GHG emissions that caused by vehicle fossil fuel combustion due to

transportation of seedling, labours, and staff to and/or from project sites. The CO2

emissions can be estimated using bottom-up approach described in GPG 2000.

ijijij

i j

ijijCOVehicle

eknptionijFuekConsum

ptionFuelConsumEFLK

⋅⋅=

⋅=∑∑ 1000/)(2,

where:

LKVehicle,CO2 = total GHG emissions due to fossil fuel combustion from vehicles, tonnes

CO2-e yr-1

i = vehicle type j = fuel type

EFij = emission factor for vehicle type i with fuel type j, kgCO2/litre

FuelConsumptionij = consumption of fuel type j of vehicle type i, litres

nij = number of vehicles

kij = kilometres travelled by each of vehicle type i with fuel type j, km

eij = average fuel consumption of vehicle type i with fuel type j, litres/km

Country-specific emission factors shall be used if available. Default

emission factors provided in the IPCC Guidelines and updated in the GPG 2000

may be used if there are no locally available data.


50

Ex ante net anthropogenic GHG removal by sinks/project; the net

anthropogenic GHG removals by sinks is the actual net GHG removals by sinks

minus the baseline net GHG removals by sinks minus leakage, therefore, the

following general formula can be used to calculate the net anthropogenic GHG

removals by sinks of an A/R CDM project activity (CARCDM), in tonnes CO2-e

yr-1

2,COVehicleBSLACTUALCDMAR LKCCC −−=−

......................................... (16)

where:

CAR-CDM = net anthropogenic greenhouse gas removals by sinks, tonnes CO2-e yr-1

CACTUAL = actual net greenhouse gas removals by sinks, tonnes CO2-e yr-1

CBSL = baseline net greenhouse gas removals by sinks, tonnes CO2-e yr-1

LKVehicle,CO2 = total GHG emissions as leakage due to fossil fuel combustion from vehicles,

tonnes CO2-e yr-1

3.3.4. Error analysis on Carbon stock change estimation

The size area that already defined by using remote sensing and GIS analysis

is contain some error, because there area several area that miscounted (area that is

not counted in the calculation process, in fact the area is suppose to be counted)

and over counted (area that is counted in the calculation process, but in fact the

area is not suppose to be counted), and also the volume of Casuarina

Junghuhniana and Acacia Decurens that have been calculated from diameter and

height of trees measurement to estimate the actual GHG removal by sinks is

contain some error. Crystal Ball tool that integrated with Microsoft Excel

application was used to analyze the error. The errors of area definition are

assumed as 5%, 10%, 15%, and 20%, which means that the errors is occur on the

processing of defining the 1134.5 ha of eligible land area. For growth error

estimation are also assumed as 5%, 10%, 15%, and 20%. The scenario of those

process are shown in Figure 3.5. The simulation estimation will take 2500 trials

and the error value will be modified as normal distribution.


51

Figure 3.5. Error analysis of carbon stock change estimation

Figure 3.6. Error on size area definition

Area (error 5%) Volume (error 5%)




vector

grid

Overcounted area

miscounted area

IV. RESULTS & DISCUSSIONS

4.1. Location information

Geographically, the Bromo Tengger Semeru National Park lies between

7°51’ - 8°11’ S, and 112°47’-113°10’E with elevation of between 750 - 3.676 m.

a.s.l. Most of the area is undulating and hilly with slopes of 0 - 40%; most of the

remainder is mountainous with slopes of more than 20-30% (see Figure 4.1).

Types of ecosystem of the Bromo Tengger Semeru National Park consist of

sub-montana, montana and sub-alphin covered by big trees with age of more than

100 years such as ‘cemara gunung’ (Casuarina Junghuhniana), jamuju

(Dacrycarpus imbricatus), eidelweis (Anaphalis javanica), and various kind of

orchids and endemic grasses such as Styphelia pungieus.

The rainfall pattern is unimodal with one peak occurs around January.

Months with rainfall of less than 100 mm normally occurs between June-

September (Figure 5). The maximum temperature is about 22.° C and the

minimum temperature around 5° C. The relative humidity during the day is quite

dry about 43% during the day and during the night about 94 %.

Figure 4.1. Mean Regional Rainfall at Bromo Tengger Semeru National Park

Fires occur almost every year in this Park. The period with high fire risk is

from July-September (dry season). Area affected by fires in 2000-2005 is given in

0

50

100

150

200

250

300

350

400

1 2 3 4 5 6 7 8 9 10 11 12

Month

Rain

fall (

mm

)

Chapter 4 - Analysis

53

Table 1. Main cause of fires comes from communities who made fires for heaters

during their travel to jungle for collecting mushroom and making charcoal.

People who are looking for mushroom in forests normally travel up to 7 days. In

the night they normally make fires.

4.2. Land Cover Classification and Eligible land area development.

The location that recommended by Kantor Taman Nasional Bromo Tengger

Semeru (TNBTS) for the project is in block Keciri, estimated area is

approximately 1200 ha. In order to delineate the boundary project, GPS tracking

directly to the field was conducted (Figure 4.2).

Figure 4.2. GPS field tracking.

Total area of target location after field tracking process has finished is about

1586 ha. During field tracking process, it is seen that most of the land cover type

on the location is savannah and few is bush/shrubs. Fieldtruthing process was

Field tracking GPS To define boundary project


54

conducted together with field tracking process, this process is needed to

increasing classification accuracy level. Total of fieldtruthing points are 61, 19 of

fieldtruthing point were selected as a reference point for classification process

(supervised classification) and the rest are as independent reference point for

accuracy measurement (Figure 4.3).

Figure 4.3. Fieldtruthing point map

After the boundary of the target location has defined, and fieldtruthing

points have collected, the next step is the classification process using satellite data

(ASTER, acquisition date 2006-09-03). Supervised classification process is liable

to be used, an then to avoid an independent single pixel on an object, low pass

filter method was used, in this method centre pixel value will be replaced by

average value of the surrounding pixel. The accuration level of the classification is

quite good (83 %), it can be shown at Appendix 1. Land cover type of 1989 was

derived by using satellite data (LANDSAT 5, acquisition date 1989-03-28), in

order to increase the accuration level, additional information from local

correspondent who experienced well about the location is needed. The result of

both analyses is shown in Figure 4.4.


55

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0Inset

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Project Boundary

Legend

Bush/Shrubs

Forest

Savana

Cloud

MAP OF LAND COVER TYPETNBTS1989

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MAP OF LAND COVER TYPETNBTS2006

Inset

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702000

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000

9180

000

9270

000

9270

000

TNBTS

Project Boundary

Legend

Bush/Shrubs

Forest

Savana

Cloud

Figure 4.4. Result of Land-cover classification of project area on 1989 and 2006.

Tabel 4.1. Land cover area in project site on 1989 and 2006

LAND COVER 1989 LAND COVER 2006

TYPE HECTARES HECTARES

Bush/Shrubs 194.56 121.03

Forest 111.67 223.61

Savannah 1230.59 1241.65

Cloud 49.23 -

TOTAL 1586 1586


56

702000

702000

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705000

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708000

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911700

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Project Boundary

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Inset

MAP OF ELIGIBLE LANDFOR AR-CDM

TNBTS

Bush/Shrubs - Bush/Shrubs

Bush/Shrubs - Savana

Cloud - Bush/Shrubs

Cloud - Savana

Savana - Bush/Shrubs

Savana - Savana

Figure 4.5. A/R CDM Eligible Land

Tabel 4.2. Landcover change area on eligible land

LCC HECTARE

Bush/Shrubs - Bush/Shrubs 19.4

Bush/Shrubs - Savannah 118.4

Savannah - Bush/Shrubs 65.4

Savannah - Savannah 1016.1

TOTAL 1219.3

Following Ministry of Forestry Regulation Number 14/2004 and its

Addendum that forest in Indonesia under the KP is defined as land having a

minimum area of 0.25 ha, a minimum tree crown cover of 30%, and three that

have minimum height of 5 m, the lands which do not meet this definition are

bush/shrubs and savanna. Thus lands eligible for CDM are lands which are

bush/shrubs or savanna before 1990 and now are still bush/shrubs or savanna.

The total of eligible land for the project is actually about 1219.3 ha where

about 1134.5 ha covered by savannah and 84.8 ha by bush/shrubs. At the

methodology mentioned before, the biomass increment at baseline level has

described already, bush/Shrub is a growth species that can not be defined as a

forest, as this species can not reach a height of 5 m. It is difficult to define the

growth rate of Bush/Shrubs, because the data of this species is limited. The size

area of eligible land that covered by this species is only 84.8 ha, and it is not give


57

a significant influence. Therefore the eligible land that covered by bush/shrubs

was avoided, and the land that eligible for this project only 1134.5 ha which is

covered by savannah. The proposed project location is located at elevation

between 1440-2665 m a.s.l.

4.3. Carbon stock change estimation

Baseline; For strata without growing trees (savannah), the carbon stock in

aboveground and below-ground biomass would remain constant, the baseline net

GHG removals by sinks are zero.

0, =∆ tBSLC ….(for savannah)

Actual net GHG removals by sinks/project area; the actual net

greenhouse gas removals are calculated as follows:

∑∑ −∆=∆

i j

EjACTUAL GHGCiC

The estimates of the actual net GHG removals by sinks include the annual

carbon stock change in aboveground and belowground biomass of living trees

(∆Cij) and the direct N2O emission caused by fertilizer. Carbon stock change is

represented by the growth of trees (Casuarina Junghuhniana and Acacia

Decurrens). The growth are assumed and modified based on study literature,

Casuarina junghuhniana is a fast-growing deciduous tree 15-25 (max. 35) m tall;

trunk diameter 30-50 (max. 65) cm. The growth of this tree on the several sites in

east java can be shown on Table 4.3, the modified growth of this species is follow

the equation of Volume (m3/ha) = 449/(1+EXP(4.96-0.4123*E121)), described in

Figure 4.6. For Acacia Decurrens, no exact information about this species, IPCC

document on Average Annual above Ground Net Increment in Volume in

Plantations by Species quotes that Acacia Decurrens has an annual aboveground

volume increment around of 14 m3/ha/year, the modified growth of this species is

follow the equation of Volume (m3/ha) = 386/(1+EXP(5.14-0.4548*E121)),

described in Figure 4.7.


58

Table 4.3. DBH and height of Casuarina Junghuhniana

DBH (cm) Mean Height (m) No Location

22 (months) 48 (months) 22 (months) 48 (months)

1 Mt. Willis, East Java 1.67 6.48 3.16 7.03

2 Mt. Kawi, East Java 1.55 6.45 3.14 7.14

3 Mt. Arjuno, East Java 1.37 6.22 2.84 6.96

4 Mt. Bromo, East Java 1.95 7.33 3.59 7.7

5 Mt. Argopuro, East Java 1.42 5.68 2.98 6.2

Source: http://search.sabinet.co.za/images/ejour/forest/forest_n194_a2.pdf

0

50

100

150

200

250

300

350

400

450

500

0 5 10 15 20 25 30

Vo

lum

e (

m3/h

a)

Figure 4.6. The modified growth of Casuarina Junghuhniana

0

50

100

150

200

250

300

350

400

450

0 5 10 15 20 25 30

Vo

lum

e (

m3/h

a)

Figure 4.7. The modified growth of Acacia Decurrens


59

GHG emissions by sources/project area; the emission that may occur as a

result of the proposed A/R CDM project activity is only N2O emissions that is

caused by nitrogen fertilization application.

The indigenous vegetation on the proposed area is Casuarina Junghuhniana,

while Acacia Decurrens is exotic vegetation. Therefore, the composition of trees

that will be planted is 70% for Casuarina Junghuhniana and 30% for Acacia

Decurrens. Planting distance is 5m x 5m, this value comes from field survey on

the primary forest near by the project area, whether it is Casuarina Junghuhniana

and Acacia Decurrens. Total numbers of trees that will be planted on the proposed

area are: 10000/25 x 1134.5 = 453.800 trees, consists of 317.660 Casuarina

Junghuhniana and 136.140 Acacia Decurrens.

Planting process will be implemented in five years, each year around 227

Ha of proposed area will planted. Before planting the trees, fertilization will be

implemented, manure and urea will be used to fertilize the soil. Each hectare of

the land will be fertilized by 5000 Kg of manure mixed by 150 Kg of urea. In

1000 Kg manure contains about 5 Kg of N, and in 100 Kg urea contains about 46

Kg of N. Following the equation in chapter 3, total emission from fertilizer on the

proposed area are 107.4 t CO2e/year, this emission only occur in planting phase (5

years).

Based on the calculation (Appendix 2), the actual net GHG removals by the

project activity in the next 20 years is around 1.262.858 T CO2 (Figure 4.8, and

Table 4.4.).

-

200

400

600

800

1,000

1,200

1,400

1 3 5 7 9 11 13 15 17 19

Thousands

Age (Year)

AN

GR

S (

t C

O2)

Figure 4.8. Actual net GHG removals by sinks on project activity


60

Table 4.4. Actual net GHG removals by sinks on project activity

Year Actual net GHG removals

by sinks in tonnes of CO2 e

2009 2698

2010 4249

2011 6419

2012 9662

2013 14458 2014 18749

2015 27165

2016 38768

2017 53758 2018 71765

2019 91359

2020 109973

2021 124391

2022 131719 2023 130390

2024 120724

2025 104810

2026 85742

2027 66572 2028 49488

Total estimated actual net GHG removals

by sinks (tonnes of CO2 e) 1262858

Total number of crediting years 20

Annual average over the crediting period of

estimated actual net GHG removals by

sinks (tonnes of CO2 e)

63143

Leakage; The identified potential leakage of the proposed A/R CDM project

activity may be GHG emissions caused by vehicle fossil fuel combustion due to

transportation of seedling, labours, and staff to and/or from project sites on

planting and monitoring phase.

Vehicles that will be used in this project are; truck (2 units), car (2 units),

and motorcycle (3 units). Distance from base camp to proposed area is 20 Km,

average fuel consumption of truck and car is 0.17 liters/km, while motorcycle is

0.03 liters/km, emission factor for each vehicle is 2.63 kgCO2/litre. Planting

activity will be conducted four months, regarding the equation in chapter 3, total

leakage or GHG emission outside boundary on planting phase (5 years) that

would occur under the presence of the project is about 17 t CO2e/year, while after


61

five years the annual leakage is only 50% of planting phase because the activity

will only focus on monitoring (Table 4.5).

Table 4.5. Leakage on project activity

Year Leakage in tonnes of CO2 e

2009 17

2010 17

2011 17

2012 17

2013 17

2014 8

2015 8

2016 8

2017 8

2018 8

2019 8

2020 8

2021 8

2022 8

2023 8

2024 8

2025 8

2026 8

2027 8

2028 8

Total estimated leakage (tonnes of CO2 e) 210

Total number of crediting years 20

Annual average over the crediting period

of leakage (tonnes of CO2 e) 10.5

Net anthropogenic GHG removal by sinks/project; the net anthropogenic

GHG removals by sinks is the actual net GHG removals by sinks minus the

baseline net GHG removals by sinks minus leakage, therefore, the following

general formula can be used to calculate the net anthropogenic GHG removals by

sinks of an A/R CDM project activity (CARCDM), in tonnes CO2-e yr-1

2,COVehicleBSLACTUALCDMAR LKCCC −−=−

Based on the calculation process, the net anthropogenic GHG removals by

sinks in the next 20 years is around of 1.262.648 T CO2e see Figure 4.9 and Table

4.6.


62

0

200

400

600

800

1000

1200

1400

1 3 5 7 9 11 13 15 17 19

Thousands

Year

NA

GR

S (

T C

O2e)

Figure 4.9. Net Anthropogenic GHG removals by sinks on project activity

Table 4.6. Net Anthropogenic GHG removals by sinks on project activity

Year

Estimation of

baseline net

GHG removals

by sinks (tonnes

of CO2 e)

Estimation of

actual net GHG

removals by sinks

(tonnes of CO2 e)

Estimation of

leakage

(tonnes of

CO2 e)

Estimation of net

anthropogenic GHG

removals by sinks

(tonnes of

CO2 e)

2009 0 2698 17 2681

2010 0 4249 17 4232

2011 0 6419 17 6402

2012 0 9662 17 9645

2013 0 14458 17 14441

2014 0 18749 8 18741

2015 0 27165 8 27156

2016 0 38768 8 38759

2017 0 53758 8 53749

2018 0 71765 8 71757

2019 0 91359 8 91351

2020 0 109973 8 109965

2021 0 124391 8 124382

2022 0 131719 8 131711

2023 0 130390 8 130381

2024 0 120724 8 120715

2025 0 104810 8 104801

2026 0 85742 8 85734

2027 0 66572 8 66564

2028 0 49488 8 49479

Total (tonnes of

CO2 e) 0 1262858 210 1262648


63

4.4. Error analysis on Carbon stock change estimation

Conservative approach was used in choosing parameters and making

assumptions, i.e. if different values for a parameter are plausible, a value that does

not lead to an overestimation of the actual net GHG removals by sinks or

underestimation of the baseline bet GHG removals by sinks should be applied.

From the error analysis process, can be seen that if the error of the size area is

bigger because of the resolution of satellite image is low, then the number of GHG

removal by sinks will decrease, because the low figure (red line) is the most

plausible unit that can be claimed as a carbon credit. This result is suggested that,

by using good resolution satellite imagery, the output/result is getting better. On

the scenario 5% error of area definition and 5% error of volume measurement, the

minimum actual GHG removal by sink is 1.196.844 T CO2e (Figure 4.10), while

in the scenario 20% error of area definition and 5% error of volume measurement,

the minimum actual GHG removal by sink is only 1.007.297 T CO2e (Figure

4.11). The difference is quite significant 189.547 T CO2e. So, it is necessary to

put more attention when we want to use satellite imagery.

-

20

40

60

80

100

120

140

160

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Thousands

Year

T C

O2e

Mean

Minimum

Maximum

Figure 4.10. ANGRS on scenario 5% error of area definition and 5% error of

volume measurement


64

-

20

40

60

80

100

120

140

160

180

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Thousands

Year

T C

O2e

Mean

Minimum

Maximum


volume measurement.

High error on tree’s volume does not give significant difference to the value

of GHG removal by sinks, see Figure 4.12 is similar with Figure 4.10. But the

result of measurement will generate high variance of output, it is not liable by

having high variance on the output. Then, the standard operational procedure on

measuring diameter and height of trees are needed.

-

20

40

60

80

100

120

140

160

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Thousands

Year

T C

O2

e

Mean

Minimum

Maximum


volume measurement.

V. CONCLUSIONS AND RECOMMENDATIONS

5.1. Conclusions

There are several conclusions that can be drawn related to the research

objectives:

1. The eligible land area for carbon sequestration project on Bromo Tengger

Semeru National Park is around of 1134.5 Ha, which is covered by

savannah.

2. Carbon stock change at baseline in Bromo Tengger Semeru National Park is

zero, because the species that living on the proposed area is not growing

trees.

3. The actual net GHG removal by sinks under the presence of carbon

sequestration project at Bromo Tengger Semeru National Park in twenty

years is around of 1.262.858 T CO2e .

4. The leakage that occur under the presence of carbon sequestration project at

Bromo Tengger Semeru National Park is around of 17 t CO2e/year, while

after five years the annual leakage is only 50% of planting phase because the

activity will only focus on monitoring.

5. The net anthropogenic GHG removal by sink under the presence of the

carbon sequestration project at Bromo Tengger Semeru National Park in

twenty years is around of 1.262.648 T CO2e.

Chapter 5 – Conclusion and Recommendation

66

5.2. Recommendations

Recommendations as the result of this research are as follows:

1. Big/high error of size area definition caused by low resolution of satellite

image will decrease the value of GHG removal by sinks, because the low

figure (red line) is the most plausible unit that can be claimed as a carbon

credit. Using high resolution satellite imagery will increase the potency to

get high carbon credit.

2. Big/high error in trees volume measurement will make the output calculation

of GHG removal by sinks is not liable, because of the high variance.

Standard Operational Procedure is needed to avoid the error.

3. Databases of tree’s growth of Casuarina Junghuhniana and Acacia

Decurrens are necessary to define the biomass of trees.

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Mwihomeke, S. T., et al. 2002. Early performance of Casuarina junghuhniana

provenances/land races at Lushoto, Tanzania. Southern African Forestry

Journal – No. 194, July 2002. Available:

http://search.sabinet.co.za/images/ejour/forest/forest_n194_a2.pdf.

Muukkonen, P. and Heisnaken, J., 2006. Biomass Estimation over a large area

based on standwise forest inventory data and ASTER and MODIS satellite

data: A possibility to verify carbon inventories. Remote Sensing and

Environment 06703:8. [http://www. Elsevier.com/locate/rse]

Pearson, T., et al. 2005. Sourcebook for Land Use, Land-Use Change and Forestry

Projects. BIO-CF and Winrock International.

Pearson, T., et al. 2006. Guidebook for the Formulation of Afforestation and

Reforestation Projects under the Clean Development Mechanism.

International Tropical Timber Organization (ITTO).

Penner, M.,K. Power C. Muhairwe, R. Tellier, and Y.Wang. 1997. Canada’s

Forest Biomass Resources: Deriving Estimates from Canada’s Forest

Inventory. Canadian Forest Service Inf. Rep. BC-X-370. Pacific Forestry

Centre, Victoria, British Columbia

Satellite Imaging Corporation. 2008. Characterization of Satellite Remote Sensing

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satellite-remote-sensing-systems.html

Sitorus J., et al. 2006. Kajian Model Deteksi Perubahan Penutup Lahan

Menggunakan Data Inderaja Untuk Aplikasi Perubahan Lahan Sawah.

Bidang Pengembangan Pemanfaatan Inderaja Pusbangja, LAPAN.

Schroeder, P.,S. Brown, J. Mo, R. Birdsey, and C. Cieszewski. 1997. Biomass

estimation for temperate broadleaf forest of United States using inventory

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Framework Convention On Climate Change (Konvensi Kerangka Kerja

Perserikatan Bangsa Bangsa Mengenai Perubahan Iklim). KLH Republik

Indonesia. Available : http://www.menlh.go.id/i/art/pdf_1038298068.pdf

Undang-Undang Republik Indonesia Nomor 17 Tahun 2004 Tentang Pengesahan

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70

Change (Protokol Kyoto Atas Konvensi Kerangka Kerja Perserikatan

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Perundangan/Undang_Undang/2004/17-04.pdf

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Wikipedia. 2008. Carbon dioxide sink. Available:

http://en.wikipedia.org/wiki/Carbon_sequestration#Forests

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Conference Raleigh, NCThursday, 26 April 2007. Available:

http://www.epa.gov/ttn/chief/conference/ei16/session13/wintergreen_pres.p

df

APPENDICES

66

Appendix 1. Independent sample points

NO Reference LONGITUDE LATITUDE CLASSIFICATION RESULT

1 Bush/Shrubs 706843.405 9122061.928 Bush/Shrubs




5 Forest 703475.295 9122630.458 Bush/Shrubs*



8 Forest 705769.515 9123224.831 Forest

9 Forest 709499.260 9119866.569 Forest

10 Forest 710430.210 9119485.216 Forest

11 Forest 710979.807 9123825.910 Forest

12 Forest 710267.574 9123646.450 Forest

13 Savana 702516.260 9122222.725 Forest

14 Savana 702372.691 9122478.276 Forest

15 Savana 706093.979 9123661.278 Bush/Shrubs*

16 Savana 705422.080 9123977.128 Forest

17 Savana 706329.431 9123687.120 Forest

18 Savana 706958.260 9122977.893 Forest

19 Savana 706843.405 9122153.812 Forest



22 Savana 707925.909 9120537.234 Savana

23 Savana 708063.735 9120712.387 Savana

24 Savana 708508.796 9120778.429 Savana

25 Savana 709992.775 9120595.626 Savana

26 Savana 710609.670 9119350.621 Savana

27 Savana 710890.077 9119530.081 Savana

28 Savana 710991.023 9119956.299 Savana

29 Savana 710418.993 9121661.171 Savana

30 Savana 710261.966 9121150.831 Savana

31 Savana 711450.890 9121470.495 Savana


33 Savana 711989.271 9122395.837 Savana

34 Savana 711349.943 9119193.593 Savana

35 Savana 712280.893 9118189.737 Savana

36 Savana 712673.463 9117909.330 Savana

37 Savana 713486.642 9117769.127 Savana

38 Savana 714002.590 9117836.425 Savana

39 Savana 714254.956 9118739.334 Savana

40 Savana 714479.281 9119238.458 Savana

41 Savana 713666.102 9119698.325 Savana

42 Savana 712729.544 9120146.975 Savana

67

Appendix 2. Carbon stock change estimation

Emmision on fertilizer process

Manure (kg) 5000

NPK (kg) 150

TOTAL (kg/ha) 5000 150

N_cont(%) 0.5 46

N Applied (kg/ha) 25 69 Total area fertilized 226.9 226.9 226.9 226.9 226.9 Age 1 2 3 4 5

NON-fert(ton) 5.7 5.7 5.7 5.7 5.7

NSN-fert(ton) 15.7 15.7 15.7 15.7 15.7

FON 5.1 5.1 5.1 5.1 5.1

FSN 12.5 12.5 12.5 12.5 12.5

EF1 0.0125 N2O direct-Nfertil (tCO2/year) 107.4 107.4 107.4 107.4 107.4 Cumulative emission (tCO2) 107.4 214.7 322.1 429.4 536.8

68

INPUT Name of Species and Area allocated for each Species

depend on Name % area Luas No of trees/ha Planted within ? Years Fixed Input

project design (ha) (pohon/ha) (tahun) WD C-Content Root:Shoot

Species 1 Casuarina junghuhniana 70.00 794

280 5 0.9 0.5 0.42

Species 2 Acacia decurens 30.00 340

120 5 0.65 0.5 0.42

Luas Strata-1 100.00 1134.50 400.00

INPUT Age 1 2 3 4 5

From Field Casuarina junghuhniana DBH (D) m 0.00 0.02 0.05 0.07 0.10

Height (H) m 1.4 3.6 5.6 7.7 9.7

V=3.14*(D/2)^2*H*0.7 Volume m3/ha 0.0009 0.2100 1.9687 6.3654 14.6285

BEF 1.30 1.30 1.30 1.30 1.30

NA(t) tC/ha 0.00 0.12 1.15 3.72 8.56

NB(t) tC/ha 0.00 0.05 0.48 1.56 3.59

NA(t)+NB(t) tC/ha 0.00 0.17 1.64 5.29 12.15

Acacia decurens DBH (D) m

Height (H) m

Volume m3/ha 0.0010 0.5000 2.0000 7.0000 15.0000

BEF 1.30 1.30 1.30 1.30 1.30

NA(t) tC/ha 0.00 0.21 0.85 2.96 6.34

NB(t) tC/ha 0.00 0.09 0.35 1.24 2.66

NA(t)+NB(t) tC/ha 0.00 0.30 1.20 4.20 9.00

62

69

6 7 8 9 10 11 12 13 14 15

0.12 0.15 0.18 0.20 0.23 0.25 0.28 0.29 0.30 0.31

11.0 12.0 14.0 16.0 18.0 19.0 20.0 21.0 22.0 22.5

26.1493 41.4315 66.1938 99.2597 141.8298 185.3492 236.6222 271.1702 308.8525 342.4124

1.30 1.30 1.30 1.30 1.30 1.30 1.30 1.30 1.30 1.30

15.30 24.24 38.72 58.07 82.97 108.43 138.42 158.63 180.68 200.31

6.42 10.18 16.26 24.39 34.85 45.54 58.14 66.63 75.89 84.13

21.72 34.42 54.99 82.46 117.82 153.97 196.56 225.26 256.56 284.44

30.0000 44.0000 70.0000 100.0000 140.0000 180.0000 220.0000 275.0000 310.0000 330.0000

1.30 1.30 1.30 1.30 1.30 1.30 1.30 1.30 1.30 1.30

12.68 18.59 29.58 42.25 59.15 76.05 92.95 116.19 130.98 139.43

5.32 7.81 12.42 17.75 24.84 31.94 39.04 48.80 55.01 58.56

18.00 26.40 42.00 60.00 83.99 107.99 131.99 164.99 185.98 197.98

63

70

16 17 18 19 20

0.33 0.34 0.34 0.34 0.34

22.5 22.6 23.0 23.2 23.5

369.9458 400.3150 409.8052 415.8017 423.4699

1.30 1.30 1.30 1.30 1.30

216.42 234.18 239.74 243.24 247.73

90.90 98.36 100.69 102.16 104.05

307.31 332.54 340.43 345.41 351.78

340.0000 345.0000 355.0000 360.0000 365.0000

1.30 1.30 1.30 1.30 1.30

143.65 145.76 149.99 152.10 154.21

60.33 61.22 62.99 63.88 64.77

203.98 206.98 212.98 215.98 218.98

Luas Tnm (ha) Ceking Luas 1

2

3

4

5

Casuarina junghuhniana 794 794

158.8

158.8

158.8

158.8

158.8

Acacia decurens 340 340

68.1

68.1

68.1

68.1

68.1

Total Area 1,135

1,135

226.9

226.9

227

227

227

64

71

Casuarina junghuhniana

Umur sistem

N(t)=Ton C/ha

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

(ton Carbon) 1 0.00 0

0

0

0

0

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

2 0.17 28

28

28

28

28

-

-

-

-

-

-

-

-

-

-

-

-

-

-

3 1.64 260

260

260

260

260

-

-

-

-

-

-

-

-

-

-

-

-

-

4 5.29 840

840

840

840

840

-

-

-

-

-

-

-

-

-

-

-

-

5 12.15 1,930

1,930

1,930

1,930

1,930

-

-

-

-

-

-

-

-

-

-

-

6 21.72 3,450

3,450

3,450

3,450

3,450

-

-

-

-

-

-

-

-

-

-

7 34.42 5,466

5,466

5,466

5,466

5,466

-

-

-

-

-

-

-

-

-

8 54.99 8,734

8,734

8,734

8,734

8,734

-

-

-

-

-

-

-

-

9 82.46 13,096

13,096

13,096

13,096

13,096

-

-

-

-

-

-

-

10 117.82 18,713

18,713

18,713

18,713

18,713

-

-

-

-

-

-

11 153.97 24,455

24,455

24,455

24,455

24,455

-

-

-

-

-

12 196.56 31,220

31,220

31,220

31,220

31,220

-

-

-

-

13 225.26 35,778

35,778

35,778

35,778

35,778

-

-

-

14 256.56 40,750

40,750

40,750

40,750

40,750

-

-

15 284.44 45,178

45,178

45,178

45,178

45,178

-

16 307.31 48,811

48,811

48,811

48,811

48,811

17 332.54 52,818

52,818

52,818

52,818

18 340.43 54,070

54,070

54,070

19 345.41 54,861

54,861

20 351.78

55,873

65

72

Acacia decurens

Umur sistem

N(t)=Ton C/ha

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

1 0.00 0

0

0

0

0

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

2 0.30 20

20

20

20

20

-

-

-

-

-

-

-

-

-

-

-

-

-

-

3 1.20 82

82

82

82

82

-

-

-

-

-

-

-

-

-

-

-

-

-

4 4.20 286

286

286

286

286

-

-

-

-

-

-

-

-

-

-

-

-

5 9.00 613

613

613

613

613

-

-

-

-

-

-

-

-

-

-

-

6 18.00 1,225

1,225

1,225

1,225

1,225

-

-

-

-

-

-

-

-

-

-

7 26.40 1,797

1,797

1,797

1,797

1,797

-

-

-

-

-

-

-

-

-

8 42.00 2,859

2,859

2,859

2,859

2,859

-

-

-

-

-

-

-

-

9 60.00 4,084

4,084

4,084

4,084

4,084

-

-

-

-

-

-

-

10 83.99 5,717

5,717

5,717

5,717

5,717

-

-

-

-

-

-

11 107.99 7,351

7,351

7,351

7,351

7,351

-

-

-

-

-

12 131.99 8,984

8,984

8,984

8,984

8,984

-

-

-

-

13 164.99 11,231

11,231

11,231

11,231

11,231

-

-

-

14 185.98 12,660

12,660

12,660

12,660

12,660

-

-

15 197.98 13,477

13,477

13,477

13,477

13,477

-

16 203.98 13,885

13,885

13,885

13,885

13,885

17 206.98 14,089

14,089

14,089

14,089

18 212.98 14,498

14,498

14,498

19 215.98 14,702

14,702

20 218.98

14,906

66

73

N(t) ton C 0

20

102

388

1,001

2,226

4,002

6,779

10,577

15,682

N(t) ton CO2 0

75

375

1,423

3,669

8,161

14,675

24,857

38,783

57,501

1 2 3 4 5 6 7 8 9 10

tCO2 Casuarina junghuhniana 0

102

1,054

4,134

11,211

23,861

43,803

74,874

119,814

181,352

tCO2 Acacia decurens 0 75

375

1,423

3,669

8,161

14,675

24,857

38,783

57,501

TOTAL t CO2 1 177

1,429

5,557

14,880

32,022

58,478

99,731

158,597

238,853

t C 0 48

390

1,515

4,058

8,733

15,948

27,199

43,254

65,142

GHG emission T CO2 107 107 107 107 107

(107)

70

1,322

5,449

14,772

32,022

58,478

99,731

158,597

238,853

ANGRS t CO2 (107)

176

1,252

4,128

9,323

17,250

26,456

41,253

58,866

80,255

21,808

28,995

37,367

45,943

53,703

60,237

65,342

68,609

70,651

72,080

79,962

106,316

137,013

168,459

196,910

220,869

239,586

251,566

259,053

264,294

11 12

13 14

15 16 17 18

19

20

258,370 352,799

451,963

553,359

650,397

739,702

818,893

885,962

937,702

976,916

79,962 106,316

137,013

168,459

196,910

220,869

239,586

251,566

259,053

264,294

338,332 459,116

588,976

721,819

847,308

960,570

1,058,479

1,137,528

1,196,755

1,241,210

92,272 125,213

160,630

196,860

231,084

261,974

288,676

310,235

326,388

338,512

338,332 459,116

588,976

721,819

847,308

960,570

1,058,479

1,137,528

1,196,755

1,241,210

99,479 120,784

129,861

132,843

125,489

113,263

97,909

79,048

59,227

44,455

67

74

Leakage

vehicle nij kij (km) eij (L/km frequency FuelCons EF (kgCO2/L) Lkvehicle.CO2 (CO2e/year)

Car 2 40 0.166667 104 1386.666667 2.63121 3.65

Truck 2 40 0.166667 104 1386.666667 2.63121 3.65

Motor 3 40 0.033333 104 416 2.63121 1.09

8.39

vehicle nij kij (km) eij (L/km Frequency FuelCons EF (kgCO2/L) Lkvehicle.CO2 (CO2e/year)

Car 2 40 0.166667 52 693.3333333 2.63121 1.82

Truck 2 40 0.166667 52 693.3333333 2.63121 1.82

Motor 3 40 0.033333 52 208 2.63121 0.55

4.20

vehicle nij kij (km) eij (L/km Frequency FuelCons EF (kgCO2/L) Lkvehicle.CO2 (CO2e/year)

Car 2 40 0.166667 52 693.3333333 2.63121 1.82

Truck 2 40 0.166667 52 693.3333333 2.63121 1.82

Motor 3 40 0.033333 52 208 2.63121 0.55

4.20

Year 1 2 3 4 5 6 7

Planting 8.39 8.39 8.39 8.39 8.39

Controling 8.39 8.39 8.39 8.39 8.39 8.39 8.39

Total 16.78 16.78 16.78 16.78 16.78 8.39 8.39

75

Appendix 3. Error analysis figures

-

20

40

60

80

100

120

140

160

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Thousands

Year

T C

O2e

Mean

Minimum

Maximum

error: area 5%, volume 5%

-

20

40

60

80

100

120

140

160

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Thousands

Year

T C

O2

e

Mean

Minimum

Maximum


-

20

40

60

80

100

120

140

160

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Thousands

Year

T C

O2

e

Mean

Minimum

Maximum


-

20

40

60

80

100

120

140

160

180

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Thousands

Year

T C

O2e

Mean

Minimum

Maximum


-

20

40

60

80

100

120

140

160

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Thousands

Year

T C

O2

e

Mean

Minimum

Maximum


-

20

40

60

80

100

120

140

160

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Thousands

Year

T C

O2e

Mean

Minimum

Maximum

error: area

5%, volume

15%


-

20

40

60

80

100

120

140

160

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Thousands

Year

T C

O2

e

Mean

Minimum

Maximum


-

20

40

60

80

100

120

140

160

180

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Thousands

Year

T C

O2

e

Mean

Minimum

Maximum


76


-

20

40

60

80

100

120

140

160

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Thousands

Year

T C

O2

eMean

Minimum

Maximum


-

20

40

60

80

100

120

140

160

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Thousands

Year

T C

O2

e

Mean

Minimum

Maximum


-

20

40

60

80

100

120

140

160

180

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Thousands

Year

T C

O2

e

Mean

Minimum

Maximum


-

20

40

60

80

100

120

140

160

180

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Thousands

YearT

CO

2e

Mean

Minimum

Maximum

-

20

40

60

80

100

120

140

160

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Thousands

Year

T C

O2

e

Mean

Minimum

Maximum

error: area 5%, volume 20%error: area 10%, volume 20%

-

20

40

60

80

100

120

140

160

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Thousands

Year

T C

O2

e

Mean

Minimum

Maximum


-

20

40

60

80

100

120

140

160

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Thousands

Year

T C

O2

e

Mean

Minimum

Maximum


-

20

40

60

80

100

120

140

160

180

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Thousands

Year

T C

O2

e

Mean

Minimum

Maximum