ISBN 978-4-905304-15-9

Reducing Emissions from Deforestation andforest Degradation and the Role of Conservation,
Sustainable Management of Forests and Enhancement

COOKBOOKHOW TO MEASURE AND MONITOR FOREST CARBON

REDD+ Cookbook

1

Preface
Deforestation and forest degradation in developing countries result in 20 percent of global anthropogenic
CO2 emissions, and are the major source of CO2 emissions after the fossil fuels usage. In light of this, theestablishment of REDD-plus has been discussed as an international framework to reduce emissions fromdeforestation and forest degradation. Initially, REDD-plus was just one item on the agenda concerning futureclimate change mitigation under the United Nations Framework Convention on Climate Change (UNFCCC),
but as UNFCCC discussions have proceeded, REDD-plus has been modified to include bilateral and multilateral
activities by Parties to the Convention and private activities.
The basic concept of REDD-plus is to provide economic incentives such as funding or credits to developing
countries for REDD activities (reducing emissions from deforestation and forest degradation) and "plus"
activities (reducing CO2 emissions and CO2 levels in the atmosphere by carbon sequestration). Thus, in orderto estimate the changes in the amount of carbon stored in forests, monitoring using scientific approach isessential.

This Cookbook is an easy-to-understand technical manual which provide basic knowledge and techniques
required for REDD-plus with the main focus on the forest carbon monitoring methods. It comprises of fourparts: "Introduction", "Planning", "Technical", and "Reference Guide". "Introduction" is designed for the policy
makers and their partner organizations working for the introduction of REDD-plus at national/sub-national
level, "Planning" is intended for the planners and managers of REDD-plus implementing organizations/countries working on REDD-plus at national/sub-national level, and "Technical" for the experts who workon the REDD-plus activities at national/sub-national level. "Reference Guide" provides references of useful
documents which assist users to have a better understanding of "Introduction" and "Planning". In order to havea better understanding of REDD-plus, it is recommended to read "Introduction" in conjunction with "Planning",
or "Planning" with "Technical".

In the sections "Introduction", "Planning", and "Technical", knowledge and techniques required to address
REDD-plus are compiled in units called "recipe". Users can easily go through the items in each recipe to indepth recipe or references in accordance with the "Flow Chart". "Reference Guide" aims to provide users with
useful information by proposing or showing examples that can be used as a guide when exploring realistic
and practical measures for designing and implementing the projects and programs.

This Cookbook is an effort of the REDD Research and Development Center in hopes of contributing topromote REDD-plus in many parts of the world.

November, 2012
REDD Research and Development Center
Forestry and Forest Products Research InstituteREDD-plus Cookbook

1

Contents
REDD-plus Flow Chart

6

Introduction7

Chapter 1 About REDD-plus

8

Recipe - I01 History of REDD-plus and current status10

About REDD-plus / Scientific background / Historical background /Current status of REDD-plus under the UNFCCC / Other measures / Future challenges

Recipe - I02 Key REDD-plus concepts

14

Forest, deforestation, forest degradation, and the "plus" in REDD-plus / Target scale /Reference emission level and reference level /Measurement, reporting and verification of forest carbon stocks / Credits /Phased approach / Safeguards

Chapter 2 Designing a forest monitoring system

20

Recipe - I03 Measurement, reporting and verification (MRV) of forest monitoring

22

MRV / Measurement / Reporting / Verification /Designing a forest monitoring system / Matters to be considered in the system design

Recipe - I04 Designing the forest monitoring system26Compliance with international agreements and rules / Consideration of national circumstances /Capacity of implementation countries and possibility of sustained operation /Construction of a gradual and flexible structure

Planning

29



Chapter 3 Basic knowledge needed for REDD-plus implementation
Recipe - P01 Definitions of forest, deforestation, forest degradation, and the "plus" in REDD-plus

30


32

Forest / Deforestation / Forest degradation / The "plus" in REDD-plus

Recipe - P02 Phased approach34Phase 1: Readiness / Support for readiness phase / Phase 2: Implementation /Phase 3: Full implementation

Recipe - P03 Safeguards

38



Safeguards in REDD-plus / The safeguards in the UNFCCC / Social and environmental safeguards /Effects and significance of safeguards / Institutional challenge for ensuring safeguards

Chapter 4 Measurement, reporting and verification of forest carbon
Recipe - P04 Measurement of forest carbon

2

REDD-plus Cookbook



42
44

What to measure? / How to measure?

Recipe - P05 Reporting of forest carbon

46



What is reporting? / National communications / National greenhouse gas inventory /Biennial update reports / International consultation and analysis / Reporting requirements

Recipe - P06 Verification of forest carbon

50What is verification? / Verification requirements and procedures /Basic approach to verification in the forest sector / Verification of REDD-plus activities

Chapter 5 Monitoring by the stock change method
Recipe - P07 Stock change method

54
56



What is the stock change method? / Area estimation / Estimation of forest area change /Estimation of the carbon stocks per unit area / Estimation of carbon stock change

Recipe - P08 Area estimation using remote sensing

60

Types of remotely sensed data and software / Data pre-processing /Definition of classes / Ground truth / Classification methods /Verifying the accuracy of classification results / Estimation of forest area change

Recipe - P09 Estimation of carbon stocks per unit area64Attainable resources / Direct measurement of carbon stocks (permanent sample plot method) /Indirect measurement of carbon stocks (stand estimation model method)

Recipe - P10 Permanent sample plot method66

Required number of permanent sample plots and their locations /Design and measurement of permanent sample plots / Analysis of permanent sample plot data

Recipe - P11 Using a stand carbon stock estimation model

68Establishment and verification of the model / Overstory height method /Crown diameter method / Community age method /Backscattering coefficient method

Recipe - P12 Calculation of carbon emissions and removals

70

Calculation of carbon stocks and carbon stock changes /Uncertainties of the calculated amounts

Recipe - P13 Reference emission level and reference level

72

Establishment of reference emission level and reference level /Discussions under UNFCCC / Organizational challenges

Technical

75



Chapter 6 Preparation for REDD-plus implementation
Recipe - T01 National forest inventory

76
78



What is national forest inventory? / National forest inventory sampling designs /Conducting the inventory / National forest inventories in tropical countries

Recipe - T02 What greenhouse gases are measured?



80

Which greenhouse gases should be the target of REDD-plus activities / Five forest carbon pools

REDD-plus Cookbook

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Recipe - T03

Attainable resources82

Remote sensing resources / Field survey resources / Continuity of resources

Recipe - T04

Voluntary carbon credit certification system84

The present status of the VER certification system / VCS / Future considerations

Chapter 7 Estimation of forest area using remote sensing

86

Recipe - T05 Remote sensing and sensor types, and image data selection88Platforms / Sensors / Spatial resolution, wavelength resolution, and time resolution /Present, past, and future / Selection of image data / Software

Recipe - T06 Clouds and seasonality differences in images

96



Cloud removal / Seasonality adjustment

Recipe - T07 Definition of a land cover class98



Mutually exclusive and collectively exhaustive classes /Desired and possible classification systems / Integration of classes after classification

Recipe - T08 Ground truth

100

Ground truth / Location of ground truth and its accuracy / Sampling method /Number of samples

Recipe - T09 Classification methods

102

Classification in remote sensing / Classification by pixel and object-based classification

Recipe - T10 Accuracy evaluation

104

The error matrix and accuracy indicators / Uncertainty of the error matrix and ground truth

Recipe - T11 Estimation of forest area change

110Comparison after the classification of images acquired at two different times /Detection of land cover changes by multi-temporal classification /Some notes on the change detection

Chapter 8 Permanent sample plot method

112

Recipe - T12 Selecting the number and arrangement of permanent sample plots



114

Required accuracy / How to calculate the required number of permanent sample plots /Suitable stratification

Recipe - T13 Measurement of permanent sample plots

116

Plot size / Plot shape / Preparation for a tree census / Raising measurement accuracy

Recipe - T14 Analysis of permanent sample plot data120

About allometric equations / Selection of an allometric equation suitable for the target region

Chapter 9 Stand carbon stock estimation models
Recipe - T15 Model design

124
126



Double sampling / Indirect estimation of carbon stocks by correlation models /Evaluation of model accuracy and costs / Notes about applying the models to a target forest

Recipe - T16 Overstory height method
4REDD-plus Cookbook

130

Correlation between overstory height and biomass /Measurement of overstory height by remote sensing / Factors affecting estimation accuracy

Recipe - T17 Crown diameter method132

Measuring tree crown diameters for estimation of carbon stocks /Determining the relationship between tree crown diameter and biomass /Notes on the use of the crown diameter method

Recipe - T18 Community age method134About the community age method / Creation of the relational equation by field survey /Advantages and disadvantages of the community age method

Recipe - T19 Backscattering coefficient method136About the backscattering coefficient method /Creation of the estimation model by field survey / An example of biomass estimation /Advantages and disadvantages of the backscattering coefficient method

Reference Guide

139




Index

63
152



Authors156



REDD-plus Cookbook

5

REDD-plus Flow Chart
I01 History of REDD-plus and current status
About REDDplus

Scientificbackground

Historicalbackground

Reference

Reference

Current statusunder UNFCCC

Other
measures

Futurechallenges

I02 Key REDD-plus concepts
Definitions

Target scale

REL/RL

Reference

To P13

MRV of forest
carbon stocks

PhasedapproachCredits

Safeguards

I03 Measurement, reporting and verification (MRV) of forest monitoring
MRV

Measurement

Reporting

Designinga system

Verification

Matters to beconsidered

Reference

T01 National
forest inventory

Reference

I04 Designing the forest monitoring system
Compliance withinternational
agreements and rules

Consideration ofnational circumstances

deforestation, forest degradation,and the "plus" in REDD-plus

P02 Phased approach

Deforestation

Forestdegradation

Reference

T04 Voluntary carbon
credit certification system

P01 Definitions of forest,

ForestConstruction of a
gradual and flexible
structure

Capacity and possibilityof sustained operation

Support for readiness
phase

Phase 1: Readiness

The "plus" in
REDD-plus

P03 Safeguards

Phase 2: Implementation

Phase 3: Full
implementation

Safeguards in REDD-plus

Social and environmental safeguards

Safeguards in the UNFCCC

Effects and significance of safeguards

Institutional challenge for ensuring safeguards

Reference

P04 Measurement of forest carbon

P06 Verification of forest carbon

P05 Reporting of forest carbon
What isreporting?

How tomeasure?

What tomeasure?

Reference

Reference

National
communications
Reportingrequirements

BUR/ICA

T02 What GHGs
are measured?

Reference

What isverification?

National GHG
inventory

Verification requirements
and procedures

Basic approachto verification

Reference

Verification of
REDD-plus activities

Reference

P07 Stock change method
What is the stockchange method?

Area
estimation

Estimation of forest
area change

Estimation of the carbon
stocks per unit area

P08 Area estimation using remote sensing
Data pre-Types of dataand software
Groundtruth

processing

Classification
methods

T05 Sensor types,
data selection
T08 Groundtruth

Definition ofclasses

Verifying theaccuracy

T09 Classification
methods

T10 Accuracy
evaluation

P12 Calculation of carbon emissions and removals
Calculation of carbon stocks
and carbon stock changes

Uncertainties of thecalculated amounts

P13 Reference emission level and reference level
Establishment of REL/RL

6

Discussions under
UNFCCC

P09 Estimation of carbon stocks
per unit area
T11 Estimationof forest areachangeT03
Attainable
resources

Attainable
resources

Estimation offorest area Change

T07 Definitionof a class

T06 Clouds
and seasonality

Estimation of carbon
stock change

Organizational
challenges

REDD-plus Cookbook

Directmeasurement

Indirectmeasurement

P11 Using a stand carbon
stock estimation model
P10 Permanent sample plot
methodRequired number
and locations

T12 Selecting thenumber andarrangement

Design andmeasurement

Analysisof data

T14 Analysis of
PSP data

T13 Measurementof PSPs

OverstoryheightmethodEstablishment
and verification

Community
age method

T15 Model
designBackscattering
coefficientmethodT16
Overstoryheight method

T18 Community
age method

Crown
diametermethodT17 Crown
diametermethodT19 Backscattering
coefficient method

REDD-plus Cookbook

7

Chapter 1 - About REDD-plus

1
Chapter 1
About REDD-plus

8

REDD-plus Cookbook

An idea of REDD – reducing emissions from deforestation and forest degradation indeveloping countries – was proposed by Papua New Guinea and Costa Rica at the 11th
Conference of the Parties (COP 11) to the United Nations Framework Convention on
Climate Change (UNFCCC), as a climate change mitigation framework. At COP 13, REDD
was expanded to encompass measures for forest conservation, sustainable management offorests, and enhancement of forest carbon stocks. This expanded version is known as REDDplus.
In this chapter, REDD-plus and REDD-plus activities and scope are introduced andexplained, including the history of REDD-plus and important concepts that arose in thecourse of REDD-plus negotiations and the implementation of REDD-plus under the UNFCCC
and related efforts undertaken by other organizations.

I01 History of REDD-plus and current status
I02 Key REDD-plus concepts

REDD-plus Cookbook

9

Chapter 1 - About REDD-plus

Recipe - I01

History of REDD-plus and current status
The main cause of global climate change is increased atmospheric levelsof carbon dioxide (CO 2 ) in association with high CO 2 emissions, anddeforestation and forest degradation in developing countries, which
account for approximately 20 percent of global CO 2 emissions, are thesecond largest source of emissions after fossil fuel combustion. Accordingly,
it is vital to reduce emissions from deforestation and forest degradation.
REDD-plus is an international framework for achieving these reductions.
In this recipe, the scientific and historical background of REDD-plus and its
current status are explained.

About REDD-plus
REDD-plus – reducing emissions from deforestation and forest degradation,

INFO1) Denman KL, Brasseur G,

and the role of conservation, sustainable management of forests, andChidthaisong A, Ciais P, Cox PM,

enhancement of forest carbon stocks – is a climate change mitigation

Dickinson RE, Hauglustaine D, Heinze

framework. Initially, REDD-plus was just one item on the agenda concerning

C, Holland E, Jacob D, Lohmann U,
Ramachandran S, da Silva Dias PL,

future climate change mitigation under the United Nations Framework

Wofsy SC, Zhang X (2007) Couplings

Convention on Climate Change (UNFCCC), but as UNFCCC discussions have

Between Changes in the Climate
System and Biogeochemistry. In:
Climate Change 2007: The PhysicalScience Basis, Contribution of Working
Group I to the Fourth Assessment

proceeded, REDD-plus has been modified to include bilateral and multilateral
activities by Parties to the Convention and private activities. Currently, forest
conservation activities to mitigate climate change in developing countries

Report of the IPCC. Cambridge

come under REDD-plus, and REDD-plus is the acknowledged framework for

University Press, 499-587

providing economic incentives (credits, funds, etc.) for reducing CO2 emissions
from deforestation and forest degradation, or increasing CO2 removals byenhancement of forest carbon stocks.

INFO
2) Nabuurs GJ, Masera O, Andrasko
K, Benitez-Ponce P, Boer R, Dutschke
M, Elsiddig E, Ford-Robertson J,
Frumhoff P, Karjalainen T, Krankina O,

Scientific background

Kurz WA, Matsumoto M, Oyhantcabal
W, Ravindranath NH, Sanz Sanchez MJ,
Zhang X (2007) Forestry. In: Climate
Change 2007: Mitigation of Climate
Change, Contribution of Working
Group III to the Fourth Assessment
Report of the IPCC. Cambridge
University Press, 541-584

The Intergovernmental Panel on Climate Change (IPCC) Third Assessment
Report, issued in 2001, noted that CO2 emissions from deforestation and forest
degradation in developing countries have a large impact on the global carbon
cycle. The IPCC Fourth Assessment Report (AR4), issued in 2007, reported that 80
percent of CO2 was emitted by fossil fuel combustion and cement production,and that the remaining 20 percent was emitted as a result of land-use changes
such as deforestation 1). The IPCC Fourth Assessment Report also reported that

INFO3 ) FA O ( 2 0 0 6 ) E x t e n t o f f o r e s t

about 65% of the total mitigation potential in the forestry sector is located inresources. In: Global Forest Resources

the tropics and about 50% of the total could be achieved by reducing emissions

Assessment 2005. FAO Forestry Paper

from deforestation 2). Data from the Food and Agriculture Organization (FAO)

147: 11-36

show that deforestation rates in Brazil, Indonesia, and tropical Africa is quite high3), and the Stern Review has emphasized that curbing deforestation is a highly

10

REDD-plus Cookbook

Recipe I01
cost-effective way of reducing greenhouse gas (GHG) emissions 4), 5).
The IPCC Fourth Assessment Report devoted many pages to the significanceof REDD-plus activities, which not only mitigate climate change but also
contribute to the livelihoods of indigenous people and other members of local
communities by protecting biodiversity and conserving the ecological services

INFO4) Stern N (2007) Identifying thecosts of mitigation. In: Stern review
on the economics of climate change.
Cambridge University Press, 211-238

provided by healthy forest ecosystems, such as forest products, water resources,and environmental quality. Even though the significance and necessity of REDDplus has been recognized since an early stage of the negotiations, the UNFCCC
does not include a framework for reducing deforestation and forest degradationin developing countries. The REDD-plus framework is being developed in thecontext of this scientific background.

INFO5) This observation is based on theevaluation of the opportunity costof land-use changes; the costs ofmaintaining domestic systems when
implementing REDD-plus and ofmonitoring system development are
not taken into consideration.

Historical background (see I02)
Discussions on REDD-plus began at the 11th Conference of the Parties (COP
11), which was held in Montreal in 2005, when Papua New Guinea and Costa
Rica jointly presented a proposal called "Reducing emissions from deforestationin developing countries: approaches to stimulate action." This proposal, which
was adopted as an agenda item by the Subsidiary Body for Scientific andTechnological Advice (SBSTA), is referred to as REDD – reducing emissions fromdeforestation in developing countries.
Initially, the SBSTA intended to discuss the proposal for two years andthen to report the results of those discussions at COP 13, but in the courseof the discussions, many developing countries requested that REDD should
include mechanisms for the conservation and sustainable management offorests and enhancement of forest carbon stocks (thus, the "plus" in REDDplus) in addition to mechanisms for reducing emissions from deforestationand forest degradation. Therefore, at COP 13, held in Bali in 2007, the proposal
was broadened to include these activities and adopted as an agenda item in
discussions on the development of a post-2013 framework. This broadened
framework is referred to as REDD-plus.
After a further two-year review, the necessity of early development of the
REDD-plus framework including a financing mechanism was noted in the
Copenhagen Accord, adopted by COP 15 in 2009 6). It was also agreed to use the

INFO6) UNFCCC (2009) Decision 2/CP.15,
FCCC/CP/2009/11/Add.1, 4-7, UNFCCC

latest IPCC guidelines to establish a forest monitoring system at national or subnational levels that uses a combination of remote sensing with a ground-based
inventory, and to take into account each respective country's historical and

INFO

present circumstances to establish reference level to serve as standards against

7) UNFCCC (2009) Decision 4/CP.15,

which to evaluate actions. This agreement is the basis of the current REDD-plus

FCCC/CP/2009/11/Add.1, 11-12,

technical methodology 7).

UNFCCCAt COP 16, held in Cancun in 2010, the basic framework for REDD-plus,

REDD-plus Cookbook

11

Chapter 1 - About REDD-plus

INFO

which includes five targeted activities, a phased approach, and consideration of8) UNFCCC (2010) III-C, Decision 1/

safeguards, was proposed 8) (Table I01-1). Thus, under the UNFCCC, this Cancun

CP.16, FCCC/CP/20010/7/Add.1, 12-

Agreement is the basis of REDD-plus.

14, UNFCCC

Table I01-1 Excerpts from the Cancun Agreements related to REDD-plus
1

Affirming Parties should collectively aim to slow, halt and reverse forest cover and carbon loss

2

Encourages developing country Parties to undertake the following activities: (a) Reducing emissions fromdeforestation; (b) Reducing emissions from forest degradation; (c) Conservation of forest carbon stocks;(d) Sustainable management of forests; and (e) Enhancement of forest carbon stocks;

3

Indicated in the Guidance: be consistent with the objective of environmental integrity and take into
account the multiple functions of forests and other ecosystems; respect sovereignty; be results-based;promote sustainable management of forests; etc.

4

Requests developing country Parties to develop the following elements: (a) A national strategy or action
plan; (b) A national forest reference emission level and/or forest reference level; (c) A national forest
monitoring system; and (d) A system for providing information on the safeguards;

5

Decides that the activities should be implemented in three phases: 1st. Readiness; 2nd. Implementation;and 3rd. Results-based actions;

6

Safeguards to be promoted and supported: transparent and effective national forest governancestructures; respect for the knowledge and rights of indigenous peoples and members of local
communities; actions consistent with the conservation of natural forests and biological diversity; etc.

Current status of REDD-plus under the UNFCCC (see I02)
In the Durban Agreement, concluded at COP 17 in 2011, it was agreed that
all countries would participate in the development of a new framework thatwould replace the Kyoto Protocol. This framework should be completed by2015 and put into effect in 2020. So that REDD-plus can be positioned in the
new framework, methodologies and rules for implementing REDD-plus are to
be developed by 2015 with the aim of starting its implementation in 2020.

INFO
9) UNFCCC 2011) Decision 12/CP.17,
FCCC/CP/2011/9/Add.2, 16-18,
UNFCCCAt COP 17, agreement was reached regarding guidance on systems forproviding information on how safeguards are addressed and respected andmodalities relating to forest reference emission levels and forest reference
levels in REDD-plus 9). Still under review are identification of drivers ofdeforestation and forest degradation, development of evaluation methodsof measures and estimation methods for measuring emissions and removals
by forests, modalities for a credible system for measurement, reporting,and verification, and requirements for a national forest monitoring system.
Discussion of mechanisms for financing measures to reduce deforestationand forest degradation in developing countries and to obtain support from

12

REDD-plus Cookbook

Recipe I01
developed countries are also left for the future.

Other measures
Although REDD-plus is still being discussed and developed under the
UNFCCC, it will take time to obtain consensus among participating countries.
In the meantime, deforestation and forest degradation rates are undeniably
increasing. To reverse this trend, early implementation of REDD-plus isrequired. With this in mind, voluntary efforts outside of the UNFCCC are
being made.
The "REDD-plus Partnership" was established in May 2010 as an interim
platform for promoting REDD-plus activities, and as of 24 August 2012, 75
Partner countries joined it. In addition, various organizations have developed
bilateral or multilateral projects. Among the best known are the World
Bank's Forest Carbon Partnership Facility (FCPF); UN-REDD, organized by the
FAO, the U.N. Development Programme (UNDP), and the U.N. Environment
Programme (UNEP); and Australia's International Forest Carbon Initiative. The
Japanese government has proposed the a Bilateral Credit Offset Mechanism(BOCM) and is considering introducing REDD-plus activities.
The Verified Carbon Standard (VCS) is a third-party accreditation standard
certifying voluntary emission reductions (VERs) within the voluntary carbon
market, and it is now widely used to evaluate voluntary REDD-plus activities
at the project level (see T04).

Future challenges
National forest monitoring and measurement, reporting, and verification(MRV) systems (see I03) should be developed on a sound scientific basis,
with a view toward their feasibility for developing countries, and they should
appropriately reflect on-going efforts and experience.
Reference levels should be established by considering practical examples,not just theory. In developing reference levels, it is very important to takeeach country's circumstances and historical emissions trend into account.
Safeguards (see P03) are also important, but their specific attributes stillrequire clarification. Thus, appropriate evaluation and reporting methods stillneed to be developed.
In REDD-plus participating countries, numerous voluntary projects led byprivate sector organizations are being carried out in tandem with REDD-plusactivities at national or sub-national levels under the UNFCCC. Developmentof a consistent way to fit these projects into the REDD-plus framework is achallenge for the future.

REDD-plus Cookbook

13

Chapter 1 - About REDD-plus

Recipe - I02

Key REDD-plus concepts
The preceding Recipe is
Recipe I01 History of REDD-plus
and current status

Discussions of REDD-plus by Parties of the United Nations Framework
Convention on Climate Change (UNFCCC) carried out over the years
from various perspectives have established certain important concepts
necessary to understand the REDD-plus framework. In this recipe, theseimportant concepts are explained.

Forest, deforestation, forest degradation, and the "plus" in REDD-plus (see P01)
REDD was originally proposed at the 11th Conference the Parties (COP
11), and at COP 13, REDD was expanded to encompass forest conservation,
sustainable management of forests, and enhancement of forest carbon
stocks, and the expanded version was called REDD-plus (see I01). Currently,
REDD and "plus" are invariably considered together, but in fact, REDD refers
to activities to curb human-related pressures such as land-use change andlogging, whereas "plus" refers to activities designed to conserve or enhance
forest carbon stocks.
A "forest" is not quantitatively defined in the Intergovernmental Panel on
Climate Change (IPCC) guidelines. Instead, it was agreed that the definition
established by each country should be used for that entire country in time

Forest+/- “plus”- Degradation
Threshold- Deforestation

Non Forest
Figure I02-1 Definitions of forest, deforestation, forest degradation,and the "plus" in REDD-plus

14

REDD-plus Cookbook

Recipe I02
series measurements. However, the Kyoto Protocol and the Global Forest
Resources Assessments (FRA) by the Food and Agriculture Organization (FAO)
have adopted definitions based on quantitative criteria for minimum area,
minimum potential tree height, and minimum crown cover. In the Kyoto
Protocol, "deforestation" is defined as the direct human-caused conversionof an area from "forest" to "non-forest" (Figure I02-1). A similar definitionof "deforestation" is used in REDD-plus, but how to identify anthropogenic
conversion and the definitions of "forest degradation" and the "plus" in
REDD-plus are still under discussion.

Target scale
REDD-plus can be implemented at one of three scales or levels: national,
sub-national, or project level. The implementation of REDD-plus activities
in a certain area can cause leakage to other areas. Here, the concept ofleakage is the same as "displacement" used by the Clean Development
Mechanism (CDM). Leakage occurs when the curbing of deforestation andforest degradation in areas of REDD-plus implementation leads to increasesof deforestation or forest degradation in other areas. The UNFCCC considers
that leakage to different areas of a country can be avoided by implementing
REDD-plus at the national level and that leakage to other countries should be
addressed by increasing the number of countries participating in REDD-plus.
However, at this time, each country that is a Party to the UNFCCC decides
whether to participate in REDD-plus, and leakage to other countries from
REDD-plus participating countries is a concern. For example, if country A
cracks down on illegal logging, then the illegal loggers may move to country
B and harvest trees there illegally. As a result, although REDD-plus activities
successfully reduce graeenhouse gas emissions due to deforestation andforest degradation in country A, emissions might increase in country B, where
the illegal logging is now occurring.
Although implementation of REDD-plus at the national level is best,for some countries immediate national implementation may not be
feasible, depending on the country's capacities and size. For this reason,
implementation at the sub-national level has been proposed. "Sub-national"
has yet to be defined, however, and the size of the target area might vary
according to a country's size and the administrative subdivisions that it uses.
For example, if a certain country consists of three states, A, B, and C, and
REDD-plus is implemented in two states (A and B), and states A and B eachreduce emissions by one and two units, but emissions increase in state C
by four units, then the country is credited although an emission of one unit
totally increases in the country. Thus, the sub-national level can be effective

REDD-plus Cookbook

15

Chapter 1 - About REDD-plus

for initial implementation of REDD-plus, but it is regarded as transitional tofull implementation at the national level 1).

INFO1) Denman KL, Brasseur G et al. (2007)

Although the REDD-plus framework has not yet been finally established

Couplings Between Changes in theunder the UNFCCC, REDD-plus activities are being carried out at the project

Climate System and Biogeochemistry,by private organizations such as NGOs on a voluntary basis. Project-level

Climate Change 2007 - The PhysicalScience Basis (IPCC), 499-587,

implementations of REDD-plus are effective at reducing emissions, but

Cambridge University Press.

emissions data may not be of comparable accuracy among projects, evenamong those carried out in the same country, because different methods
may be used to calculate forest carbon stocks and carbon stock changes.
When REDD-plus is later fully implemented at the national level in countries
where project level activities are being carried out, reconciliation of estimatesof forest carbon stocks obtained at project level by different methods withdifferent degrees of accuracy with the national-level estimates will be a big
challenge.

Reference emission level and reference level (see P13)
The basic concept of REDD-plus is to provide economic incentives suchas funding or credits to developing countries for REDD activities (reducing
emissions from deforestation and forest degradation) and "plus" activities(reducing CO 2 emissions and CO 2 levels in the atmosphere by carbon
sequestration). To quantify reductions in CO 2 emissions due to REDDplus activities, compared with the case where no REDD-plus activities areundertaken, a reference emission level and a reference level have been
established. In expert meetings held at the request of the SBSTA, thedistinction between "reference emission level" and "reference level" has been

予想排出量
Estimated emission without reduction efforts

Emission

Actual emission
排出量

Emission reductions

Reduction efforts

year
Figure I02-2 The concept of emission reductions

16

REDD-plus Cookbook

Recipe I02
made in two ways: in the first, "reference emission level" is defined as net
emission, and "reference level" as net absorption, at the national level, andin the second, the "reference emission level" is defined as the level for use
in evaluating the emissions reductions from REDD activities, and "reference
level" is defined as that to be used to evaluate the emissions reductions from"plus" activities. In some cases, both definitions might be used even in thesame country.
COP 15 concluded that when developing countries establish a reference
emission level or reference level, they should establish it using historical dataaccording to their national circumstances while at the same time ensuringtransparency. COP 16 decided that a forest reference emission level and aforest reference level at the national level, or at the sub-national level as atransitional measure, should be established for implementation of REDD-plus
activities in developing countries. However, the definitions of these reference
levels and details of how to establish them have yet to be settled.

Measurement, reporting and verification of forest carbon stocks (see I03)
In discussions on the framework after the first commitment periodof the Kyoto Protocol in 2012 under the UNFCCC, the importance ofthe measurement, reporting, and verification (MRV) system has beenemphasized. When REDD-plus is implemented, MRV of forest carbon stocks isessential to ensure transparency. In particular, MRV must be highly accurate
before economic incentives such as credits are issued.
A forest monitoring system is indispensable for accurate measurement offorest carbon stocks and their changes at the national level. The guidance forthe methodology determined by COP 15 under the UNFCCC requests thatdeveloping countries build a robust and highly transparent national forest
monitoring system. To estimate the balance of greenhouse gases, forest
carbon stocks, and forest cover changes, a monitoring system that combines
remote sensing with ground-based inventories is recommended.
Credits for REDD-plus activities will be issued based on the results ofthis measurement. To ensure a reliable and transparent credit system
for greenhouse gas reductions, a system for reporting and verifying themeasurement results is indispensable.

Credits
REDD-plus will include a mechanism to provide economic incentives forthe efforts of developing countries to reduce CO2 by reducing deforestation

REDD-plus Cookbook

17

Chapter 1 - About REDD-plus

and forest degradation and conserving existing forests. One type of
economic incentive is carbon credits. Under the UNFCCC, both a fund-based
approach and a market-based approach have been proposed, and a hybrid
approach is under consideration for the implementation of REDD-plus. In
the market-based approach, tradable credits for greenhouse gas emissions
reductions or removals achieved by REDD-plus will be issued.
Although the REDD-plus framework is still being developed under the
UNFCCC, REDD-plus activities at project level have already been initiated byprivate organizations such as NGOs in many parts of the world, along withthe trading of credits in voluntary carbon markets (see T04). When REDDplus is implemented at the national or sub-national level under the UNFCCC,
the credits issued for those REDD-plus activities may not be equivalent tothose being traded in the voluntary carbon market, because of possible
differences in the accuracy level of MRV, and reconciling the differences will
be a challenge. In addition, offset credits are traded in the voluntary market,
whereas credits for achieving national targets, such as Kyoto credits, arecompliance market credits. Which type of credit will be used for REDD-plus
activities under the UNFCCC is a major concern of stakeholders.

Phased approach (see P02)
The basic concept of REDD-plus is to provide performance-based
economic incentives, for which MRV of forest carbon stocks is needed. Many
developing countries, however, do not have adequate historical data, andthey also differ in their implementation capacities. Therefore, a phased
approach that allows REDD-plus to be implemented in steps according toeach country's circumstances has been proposed and discussed during thenegotiation sessions. This phased approach was reviewed and agreed by
COP 16.
According to this agreement, REDD-plus can be implemented in threephases:・Phase 1 is Readiness: National capacity building and strategy
formulation.

・Phase 2 is Implementation: Implement policies and measures of thenational strategy.

・Phase 3 is Full implementation: Full implementation of activities forwhich economic incentives based on the achievement of emission
reductions are provided.

During the first phase, highly accurate MRV of forest carbon stocks by

18

REDD-plus Cookbook

Recipe I02
developing countries is not expected; therefore, phase 1 MRV cannot be
regarded as equivalent to MRV during phase 3. Thus, during phase 1, it isrecommended that Official Development Assistance (ODA) and other funds
be provided.

Safeguards (see P03)
The basic concept of REDD-plus as a climate mitigation framework is theprovision of economic incentives to reward efforts at reducing emissions
form deforestation and forest degradation, and enhancing forest carbon
stocks. To obtain the offered economic incentives, however, indigenous
peoples or local communities might be restricted or banned from using
forests or natural forest might be converted to a fast growing plantation.
Such activities would infringe on the rights of indigenous peoples and local
communities, and cause a loss of biodiversity. Therefore, the discussions
under the UNFCCC have recommended the development of safeguards
designed to reduce the risk of negative impacts on the social and naturalenvironment, and to increase the positive impact on them.
The development of safeguards has received much attention of various
actors, such as the Conference of the Parties to the Convention on Biological
Diversity, international organization, donor countries, NGOs, etc. At COP
17, "guidance on systems for providing information on how safeguards are
addressed and respected" was adopted. However, specific methods andregulations for implementing safeguards have not yet been clarified under
the UNFCCC, because national circumstances vary among countries. For
promoting and supporting safeguards, further discussion to official rules
under the UNFCCC is expected.

REDD-plus Cookbook

19

Chapter 2 - Designing a forest monitoring system

2
Chapter 2
Designing a forest
monitoring system

20

REDD-plus Cookbook

Developing countries are requested to develop a forest monitoring system for theestimation of the greenhouse gas emissions from forests and changes in forest carbon
stocks and forest area. Such a system is essential not only for implementation of REDD-plus
but also for sustainable management of forests. This estimate is then used to estimate thereduction of greenhouse gas emissions attributable to REDD-plus activities, according towhich credits will be issued. Therefore, the monitoring must be conducted in manner that isreliable, transparent, and as accurate as possible, as well as feasible for developing countries
and acceptable by the global community. Moreover, a flexible system is necessary, because
the circumstances of each country are different.
In this chapter, the design of a forest monitoring system that includes measurement,
reporting, and verification (MRV) according to internationally agreed requirements is
considered.

I03 Measurement, reporting and verification (MRV) of forest
monitoring
I04 Designing the forest monitoring system

REDD-plus Cookbook

21

Chapter 2 - Designing a forest monitoring system

Recipe - I03

Measurement, reporting and verification (MRV) of forest monitoring
The preceding Recipe is
Recipe I02 Key REDD-plus concepts

Measurement, reporting, and verification (MRV) is a concept of mechanism
and/or requirements to enable objective evaluation of the implementation
status of REDD-plus policies and emissions and removals for the credit
mechanism. How to implement MRV at national and sub-national levels for
REDD-plus is still under discussion, but some voluntary credit verification
systems used by the private sector (e.g., Verified Carbon Standard (VCS); seeT04) have already proposed the framework for MRV implementation at the
project level, taking into account institutional designs of similar frameworks
such as the Clean Development Mechanism (CDM). In this chapter, what ismeant by "measurement", "reporting", and "verification" is outlined and the
MRV requirements of forest monitoring for REDD-plus are explained.

MRV
The concept of MRV was introduced in the Bali Action Plan agreed at
the 13th Conference of the Parties (COP13) in 2007. According to this plan,
greenhouse gas (GHG) mitigation actions and commitments must be

INFO1) MRV is the concept to realize
domestic and international actions formeasureable, reportable, and verifiable. However, international discussions
on the specific purpose and target of MRV and on who is responsible forthe mitigation of climate change andimplementing it are still in progress 1). As of 2012, MRV modalities of forest

to guarantee the quality of the actions.

monitoring for REDD-plus were also under consideration by the Subsidiary

The measurement and the report in
the National Communications (NCs)

Body for Scientific and Technological Advise (SBSTA). Data on greenhousein the Copenhagen Accord and theirgas emissions and removals obtained by using appropriately designed MRV

verification by the International
Assessment and Review (IAR) arean example of the agreed MRV

will be an important basis for evaluating the effectiveness of REDD-plus
activities.

frameworks.

Measurement (see P04)
REDD-plus activities are evaluated according to the emissions reductions
and removals that result. Thus, these amounts must be measured. In forest
monitoring, "measurement" means the continuous measurement andINFO2) Intergovernmental Panel oncollection of data on anthropogenic forest-related greenhouse gas emissions

Climate Change (IPCC) Nationalby sources and removals by sinks, forest carbon stocks and forest area

Greenhouse Gas Inventories

changes 2).

Programme (2003) Good practiceguidance for land use, land-use

More specifically, REDD-plus participating countries must periodicallychange, and forestry. Technicalmeasure forest cover changes and emissions and removals per unit ofSupport Unit IPCC National
Greenhouse Gas Inventories
Programme, IGES, Hayama, Japanland area where the activities are carried out, in accordance with guidance
provided by the United Nations Framework Convention on Climate Change(UNFCCC), and calculate total forest greenhouse gas emissions and removals

22

REDD-plus Cookbook

Recipe I03
from the acquired data. The measurement system must be transparent,
consistent, and accurate, and uncertainty should be minimized, but it mustalso be feasible for the participating country. In the future, "measurement"
for safeguards and other functions of forest will presumably be required.

Collection of satellite
imagery

Image analysis

Identification and verificationof land-cover from field survey
Monitoring of historicalland-cover change
Creation of a base map

Figure I03-1 Measurement of forest cover changes and emissions andremovals per unit of land area

Reporting (see P05)
Reporting means providing information on the estimated greenhousegas emissions and removals, on the methods and procedures used todetermine them, and on the status and future outlook for measures ofemission reductions and removals by sinks in accordance with prescribed
forms and procedures. In the precedent frameworks of emission reductions,such as the national reports under UNFCCC and the CDM or other voluntary
verification schemes at the project level, the forms and procedures varywith the subject being reported and the purpose of the report. In any
case, reports should include all information needed for verification so that
additional information does not need to be submitted later. The UNFCCC has
obliged the Parties to report a country's national greenhouse gas inventory,
and the report to conform to the following five principles: transparency,
consistency, comparability, completeness, and accuracy. All reports of REDD-

REDD-plus Cookbook

23

Chapter 2 - Designing a forest monitoring system

plus activities should presumably require conforming to these five principles,
too. Transparency is particularly important for developing countries, because
adequate historical data is often lacking and data collection is difficult.

Verification (see P06)
Verification means reviewing the contents of reports and confirmingthat the specified requirements have been met. Although verification
requirements and procedures differ depending on the purpose and targetof the measurement, verification is essential to ensure that a system andframework of emission reductions and removals by sinks is fair and reliable.
In particular, under the mechanism for providing economic incentives
or trading credits (e.g., CDM or the voluntary carbon credit system), theverification is a mandatory process of ex-post evaluation of the emission
reductions achieved by the project by which the reliability of the credit isensured. By the process, it is required to ensure that the project has been
duly implemented according to the project plan. To guarantee the fairness ofthe verification, it must be executed by a third-party organization. The same
will presumably true when REDD-plus is implemented under the UNFCCC.

Designing a forest monitoring system (see I04)
So that the MRV-based credit estimation will have the confidence of theinternational community, the design of the forest monitoring system must
be based on international agreements and rules. The UNFCCC requests
that forest monitoring at the national level be conducted according to IPCC
guidelines.
Nevertheless, because not only forests but also the political situation,
economy, and culture differ among REDD-plus participating countries,
national circumstances also need to be considered in the design of the forest
monitoring system. In particular, national sovereignty and policies regarding
development goals, sustainable development, and necessary safeguards (seeP03) need to be taken into account.
The forest monitoring system should be developed with consideration of
the capacity of the REDD-implementing country, but if a country's monitoring
capacity is not sufficient, capacity-building actions should be undertaken.
Such a system cannot be completed in a short period; it must be developed
using a progressive and flexible approach to improve a country's readinessfor implementation (phased approach; see P02).

24

REDD-plus Cookbook

Recipe I03

INFO

Matters to be considered in the system design
People involved in REDD-plus activities should keep the two principles,fairness and transparency, in mind. Especially when REDD-plus proponents
select and implement REDD-plus interventions (e.g., when they change
the current land-use pattern), the measurement, reporting, and verificationof the data resulting from these activities need to be fair and transparent.

3) IPCC (2007) Climate Change
2007: Mitigation of Climate Change.
Contribution of Working Group III
to the Fourth Assessment Reportof the Intergovernmental. Panel on
Climate Change.[Metz. B, Davidson
OR, Bosch PR, Dave R, Meyer LA(eds)], Cambridge University Press,

Adherence to these principles will show the stakeholders that their legitimate

Cambridge, United Kingdom and New

interests are consistent with REDD-plus and build their confidence in theYork, NY, USA . 851pp.

program and those who are involved with its implementation.
Moreover, even if a forest monitoring and MRV system is already in place,the ultimate goal of REDD-plus (i.e. mitigating climate change) is unlikely tobe achieved if the objectives of REDD-plus activities are not shared among
the stakeholders. REDD-plus cannot achieve climate change mitigation
cheaply 3), and the potential climate benefits of REDD-plus might not as big
as is sometimes reported 4). Therefore, it is essential for all stakeholders towork together to achieve better implementation of MRV and more effective

INFO4) Harris NL, Brown S, Hagen SC,
Saatchi SS, Petrova S, Salas W, Hansen
MC, Potapov PV, Lotsch A (2012)
Baseline Map of Carbon Emissions
from Deforestation in Tropical
Regions. Science 336: 1573-1575

REDD-plus interventions.

REDD-plus Cookbook

25

Chapter 2 - Designing a forest monitoring system

Recipe - I04

Designing the forest monitoring system
The preceding Recipe is
Recipe I03 Measurement, reporting andverification (MRV) of forest
monitoring

The forest carbon monitoring system for REDD-plus should comply withinternational agreements and rules to gain international recognition.
On the other hand, because the system needs to effectively address
the individual problems of each REDD-plus participating country, it isindispensable for each country's individual circumstances to be taken
into account when the system is designed. Therefore, it is important tobuild the system progressively and flexibly in consideration of a country's
capacity.

Compliance with international agreements and rules
The United Nations Framework Convention on Climate Change (UNFCCC)
requests that developing countries use the latest guidance documents andIntergovernmental Panel on Climate Change (IPCC) guidelines to estimate
the greenhouse gas (GHG) emissions by sources and removals by sinks inthe forest as well as changes in forest carbon storage and the forest area,
and to develop a robust and transparent national forest monitoring systemat the national (or sub-national) level 1) (see I01). In addition, the system

INFO
1) UNFCCC (2009) Decision 4/CP.15,

should (i) combine remote sensing with ground-based inventory, (ii) provide

FCCC/CP/2009/11/Add.1, 11-12,

estimates that are transparent, consistent, and as accurate as possible, and

UNFCCC(iii) be transparent itself, so that the result/outcome of the monitoring becompatible with the reporting and verification processes agreed by the
Conference of the Parties (COP).
The latest IPCC guidelines at the present time (2012) is the 2006 IPCC
Guidelines for National Greenhouse Gas Inventories, which consists of five
volumes. And guidelines relevant to REDD-plus are discussed in Volume 1,

Figure I04-1 The 2006 IPCC Guidelines for National Greenhouse Gas Inventories

26

REDD-plus Cookbook

Recipe I04"General Guidance and Reporting (GGR)" and Volume 4 "Agriculture, Forestry,and Other Land Use (AFOLU)." The forest monitoring system must meet fivestandards, namely transparency, completeness, consistency, comparability,
and accuracy (see I03) of the greenhouse gas inventory specified by theabove-mentioned Guidelines.
REDD-plus will issue credits according to the greenhouse gas emission
reduction and removal results. Therefore, countries implementing andparticipating in REDD-plus shall demonstrate forest carbon monitoring
results accepted as reliable internationally and abide by all agreements andregulations to promote REDD-plus. For this purpose, the design of the forest
carbon monitoring system should include provisions for measurement offorest carbon (see P04), reporting (see P05), and verification (see P06).

Consideration of national circumstances
The circumstances of countries implementing REDD-plus differ withrespect to forest type, politics, economy, and culture. Therefore, the designof the monitoring system should also take into account each country's
individual circumstances. In particular, the country's sovereignty, consistency
with other national policies such as development goals, sustainable
development, and poverty reduction, and implementation of safeguards (seeP03) should be respected 2).
REDD-plus participating countries are responsible for identifying thedrivers of deforestation and forest degradation, the development of

INFO2) UNFCCC (2010) Decision 1/CP.16,
FCCC/CP/20010/7/Add.1, UNFCCC

a national strategy or action plan for REDD-plus, establishment of thereference level or reference emission level (see P13), and development of anational forest monitoring system 1),2). If a participating country has already
implemented a national forest inventory (NFI, see T01), the reference level orreference emission level might be established based on the inventory data.
If it is not the case, necessary information of the past should be estimated byretrospective monitoring with available past data such as satellite images.
Developed countries may provide assistance with the formulation of these,or, if they have already been determined, with their implementation or
improvement.
Furthermore, the capacity of the REDD-plus participating countries andcapacity-building potential should also be taken into consideration (see thefollowing section).
Given limited resources, capacity, and time constraint, REDD-plus
participating countries cannot always contend with all the problems at once.
For this, it is indispensable for developed countries to understand the exact
situation and priorities of the participating countries when they assist.

REDD-plus Cookbook

27

Chapter 2 - Designing a forest monitoring system

INFO

Capacity of implementation countries and possibility of sustained operation

3) Hardcastle PD, Baird D, Harden V(2008) Capability and cost assessment

Majority of prospective participating countries in REDD-plus in the futureof the major forest nations to measure

do not have even the capacity to perform basic monitoring, called Tier 1 (seeand monitor their forest carbon:
Penicuick, UK, LTS International Ltd.

P04) 3), 4). This basic monitoring of Tier 1, however, is argued insufficient for
implementation of REDD-plus.
Therefore, technology transfers from developed countries are encouraged
to promote these countries' readiness for REDD-plus implementation.

INFO4) Herold M (2009) An assessment of

For this purpose, adequate and predictable supports including financial

national forest monitoring capabilities

resources and technical and technological support need to be provided. In

in tropical non-Annex I countries:

addition, the selected technologies should be feasible, taking into accountRecommendations for capacity
building, The Prince's Rainforests

each country's infrastructure and organizational level, and the education

Project and The Government oflevel of its population. In many cases, government authorities do not have

Norwaysufficient number of experienced field workers. One solution is to involve
local communities in monitoring activities 5); involvement of all stakeholders
will also help in the establishment of appropriate safeguards.

INFO
5) Danielsen F et al. (2011) At theMultilateral negotiations regarding funding and REDD-plus credits areheart of REDD+: a role for local

still in progress and thus their technical specifications have not yet beencommunities in monitoring forests?
Conservation Letters 4(2): 158-167

consolidated. The use of voluntary certification systems and VER (Verified
Emission Reduction) credits, however, is increasing at the project level (seeT04). The bilateral offset credit mechanism (BOCM) is a mitigation mechanism

INFO
6) UNFCCC (2009) Cost of
implementing methodologies
and monitoring systems relating

similar to project-level certification systems that the Japanese governmentis designing on the bilateral cooperation basis. For the costs of forest carbon
monitoring technology, UNFCCC 6) should be consulted.

to estimates of emissions fromdeforestation and forest degradation,
the assessment of carbon stocks
and greenhouse gas emissions fromchanges in forest cover, and theConstruction of a gradual and flexible structureenhancement of forest carbon stocks,

It has been agreed among the Parties that the REDD implementation

UNFCCC

structure of each participating country should be gradually consolidated,
given many (prospective) participating countries’ capacities are still

INFO
7) UNFCCC (2011) Decision 12/CP.17,
FCCC/CP/2011/9/Add.2, UNFCCC

insufficient and REDD-plus funding mechanism is not yet in place.
One concept of gradual and flexible consolidation is the phased approach,by which the organizational level and capabilities of each country will bebuilt up gradually (see P02). Another concept is the stepwise approach 7),

INFO8) Herold M, Skutsch MM (2009)

in which monitoring accuracy will be improved gradually as more data are

Measurement, reporting andobtained and expertise increases while a reference level and/or a reference

verification for REDD+ Objectives,emission level (see P13) can be established preliminarily by using the datacapacities and institutions, inAngelsen, A. et al. eds., Realising
REDD+ National strategy and policy
options: Bogor, Indonesia, CIFOR, p.
86-10028

initially available.
A forest monitoring system can be also expanded gradually in accordancewith REDD-plus priorities, while taking into consideration the circumstancesof the participating country 8).

REDD-plus Cookbook

REDD-plus Cookbook

29

Chapter 3 - Basic knowledge needed for REDD-plus implementation

3
Chapter 3
Basic knowledge
needed for REDD-plus
implementation30

REDD-plus Cookbook

In this chapter, basic terms that must be understood for REDD-plus implementation,such as forest, deforestation, forest degradation, and the "plus" in REDD-plus, are definedand explained. In addition, the phased approach (step-by-step implementation of REDDplus activities according to each participating country's capabilities and circumstances) andsafeguards (to prevent REDD-plus climate change mitigation from having adverse effects on
local or indigenous people, the natural environment, or forest governance), both of whichwere proposed during UNFCCC negotiations, are also explained.

P01    Definitions of forest, deforestation, forest degradation,and the "plus" in REDD-plus                                                            P02    Phased approach
P03    Safeguards

REDD-plus Cookbook

31

Chapter 3 - Basic knowledge needed for REDD-plus implementation

Recipe - P01

Definitions of forest, deforestation, forest degradation, and the "plus" in REDD-plus
The preceding Recipe is
Recipe I02    Key REDD-plus concepts

REDD-plus is a framework that provides economic incentives for
reductions in greenhouse gas (GHG) emissions caused by deforestationand forest degradation (REDD), and for conservation of forest carbon
stocks, sustainable management of forests, and enhancement of forest
carbon stocks (plus). Before methods for calculating emissions andcarbon stocks can be presented, the terms "forest", "deforestation","forest degradation", and the "plus" in REDD-plus need to be defined. At
this time, however, final definitions have not been agreed in international
REDD-plus negotiations. Therefore, the definition of "forest" and"deforestation" given here are those of the Marrakesh Accords 1) of the
Kyoto Protocol under the United Nations Framework Convention on
Climate Change (UNFCCC).

INFO
1) Marrakesh Accords: At COP 7, heldin Marrakech in 2001, agreements
were reached on detailed rules, suchas Kyoto Mechanisms, required toForest
The Marrakesh Accords 1) define a "forest" as follows:

implement the Kyoto Protocol and

A forest has a minimum land area of 0.05–1.0 hectares with tree crowncompliance rules. The definition ofcoverage (or equivalent stocking level) of more than 10–30 percent

the "forest" is in Article 3, paragraph
3 and 4 and "ARD (afforestation,

composed of trees that have a minimum potential height of 2–5 meters at

reforestation and deforestation)" ismaturity in situ. A forest may be either closed, where trees occupy various

defined in Article 3, paragraph 3, oflevels and undergrowth covers a high proportion of the ground, or open.

the Kyoto Protocol.
FCCC/CP/2001/13/Add.1 (http://

Young natural stands and all plantations that have yet to reach a crown

unfccc.int/methods_and_science/

density of 10–30 percent or a tree height of 2–5 meters are consideredlulucf/items/3063.php)

forests, as are areas normally forming part of the forest area that are
temporarily do not meet these criteria as a result of human intervention suchas harvesting or of natural causes but which are expected to revert to forest.
Since various types of forests exist in the world, each country may choose
specific values of tree height, tree crown cover rate, and minimum area
within the above criteria.

Deforestation
The Marrakech Accords define "deforestation" as direct human-caused
conversion of forested land to non-forested land. When elements required
to meet the definition of "forest" are lost as a result of reclaiming forest for
agricultural land, mining of underground resources, or construction of cities,
villages, and roads, it is deemed "deforestation."

32

REDD-plus Cookbook

Recipe P01

Forest degradation"Forest degradation" is defined as changes that affect forest production
or forest ecosystem function or adversely affect the structure or function of
the forest. From the viewpoint of providing economic incentives for emission
reductions, forest degradation is considered to be a decrease of forest carbon
stocks. However, forest carbon stocks vary as a result of periodic natural
disturbances and forest management practices such as periodic timber
harvests. In addition, international agreement on the definition of forest
degradation has not yet been reached.

The "plus" in REDD-plus
The 13th Conference of the Parties (COP 13) under the UNFCCC agreed
to add the "plus" - conservation, sustainable management of forests, andenhancement of forest carbon stocks - in REDD-plus. The Marrakesh Accords
define forest management as a series of actions that are undertaken toconserve a sustainable forest ecosystem (including biodiversity) and whichtake into account economic considerations and social functioning. However,the definition has not been concrete. Therefore, it is interpreted differently
by the nations that ratified the Kyoto Protocol, depending on their national
forest management practices. International agreement on the definitions of
the conservation, sustainable management of forests, and enhancement offorest carbon stocks have not been reached, either. Further discussions on
their definitions are needed.
The definitions of forest, deforestation, forest degradation, and the "plus"
in REDD-plus, which are essential for evaluating the outcome of REDD-plus,
have yet to be established. The definitions chosen should be simple, forsmooth implementation of REDD-plus, and their definitions may differ fromcountry to country.

Bibliography
GOFC-GOLD (2011) A sourcebook of methods and procedures for monitoring
and reporting anthropogenic greenhouse gas emissions and removals
caused by deforestation, gains and losses of carbon stocks in forests
remaining forests, and forestation: GOFC-GOLD Report version COP17-1.
GOFC-GOLDREDD-plus Cookbook

33

Chapter 3 - Basic knowledge needed for REDD-plus implementation

Recipe - P02

Phased approach
The preceding Recipe is
Recipe I02    Key REDD-plus concepts

REDD-plus is a framework for reducing greenhouse gas (GHG) emissions
and increasing greenhouse gas absorption through forest-related
activities by developing countries. Implementation of REDD-plus requires
measurement, reporting, and verification (MRV), and positive incentives
will be provided to participating countries based on their performance.
The implementation of REDD-plus in developing countries must take
into consideration their national circumstances and capabilities, bothtechnical and institutional. The "phased approach" defines the steps thatshould be taken to prepare countries for full implementation of REDDplus. This phased approach concept was first put forward at the REDDplus workshop held by the Ad Hoc Working Group (AWG) at the Accra
Climate Change Conference in August 2008 by Papua New Guinea, which
initiated a request to consider allowing REDD-plus to be implemented inthree phases 1).  Since then, a phased approach has been recognized as abasic element of REDD-plus. In this chapter, the phased approach, as laidout in the Cancun Agreements 2), which form the basis of current REDDplus negotiations, is explained.

INFO
1) UNFCCC (2008) FCCC/AWGLCA/2008/CRP.5, UNFCCC

Phase 1: ReadinessAs stated in the Cancun Agreements 2), implementation of REDD-plus
should begin "with the development of national strategies or action plans,

INFO
2) UNFCCC (2011) FCCC/CP/2010/7/
Add.1, UNFCCC

policies and measures, and capacity-building (Figure P02-1)." These will form
the basis not only for successful implementation of REDD-plus but also foraddressing any policy  issues; therefore, it is vital for all countries to develop
appropriate strategies and plans in Phase 1.
The national strategies or action plans, policies, and measures for REDDplus should be developed in a way that is consistent with each respective
country's development goals, land-use planning, and forest planning.
Otherwise, confusion will result during the implementation REDD-plus. To
avoid such confusion, it is dispensable to balance policies by considering therelevant levels of government and the stakeholders.
The Cancun Agreements stipulate that in the development andimplementation of a national strategy and action plan for REDD-plus, thedrivers of deforestation and forest degradation, land tenure issues, forest
governance issues, gender considerations, and the safeguards should betaken into account, and the participation of all stakeholders should besecured (see P03). These processes are integral to the implementation ofemission reduction activities through REDD-plus.
Capacity-building is vital during policy development and REDD-plus
implementation, and it is important to clarify the challenges and targets ofcapacity-building. Capacity-building includes technical training on the MRV

34

REDD-plus Cookbook

Recipe P02
system, monitoring methods, and the establishment of reference levels,and the development of policymaking and law enforcement capabilities.
Capacity-building aims to foster expertise and to train members of REDDplus participating organizations and relevant stakeholders to address theirrespective challenges. Members of concerned organizations, especiallygovernment organizations, and stakeholders are considered to be the targetsof capacity building. Local communities, members of the private sector,
consultants, NGOs, and educational institutions, all of which are important
stakeholders, are also potential targets.
It should be noted that Phase 1 is not merely the phase preceding Phases 2
and 3. The readiness activities of Phase 1 should continue throughout REDDplus implementation (Figure P02-1).

Phase 1

Phase 2Phase 3

Readiness

Implementation

Full Implementation

 Development of
national strategies
or action plans Capacity‐building Implementation ofnational strategies
or action plans (incl.
demonstration
activities

 Result‐based actions(provision of
economic positive
incentives)

Figure P02-1 Flow of the phased approach

Support for readiness phase
Phase 1 activities constitute the foundation of REDD-plus implementation,and they should be continuously modified and improved in response to
new developments. However, carrying out this phase is technically andfinancially challenging for most developing countries. The Readiness Fund of
the Forest Carbon Partnership Facility (FCPF), a World Bank programme, andthe UN-REDD, a United Nations collaborative initiative among the Food andAgriculture Organization (FAO), the U. N. Development Programme (UNDP),
and the U. N. Environment Programme (UNEP), are frameworks already inplace for supporting the readiness phase through financial and technicalaid. National strategies and action plans for REDD-plus, including an annual
implementation schedule by subject, budget, and supporting countries andorganizations, have already been developed by some countries. These canserve as useful references for other countries seeking to move into the first,

REDD-plus Cookbook

35

Chapter 3 - Basic knowledge needed for REDD-plus implementation

readiness phase.
Multilateral and bilateral technical assistance projects conducted bydeveloped countries, interested international organizations, and NGOs will
facilitate REDD-plus activities. Official development assistance agencies, suchas JICA (Japan International Cooperation Agency), GIZ (German Agency forInternational Cooperation), or USAID (United States Agency for International
Development), which implement technical assistance projects, have a vast
amount of technical knowledge and experience, which can be effectively
used to assist developing countries in Phase 1 activities.
Moreover, developed countries are obliged to submit an annual inventory
report on the amount of emissions and removals from the LULUCF sector
based on the Kyoto Protocol under the UNFCCC (see P05). Annual inventory
reports are technically similar to REDD-plus reports regarding forest
greenhouse gas emissions and removals. Accordingly, these reports also canserve as good reference material.

Phase 2: Implementation
Following Phase 1, readiness, the Cancun Agreements specify Phase 2, "theimplementation of national policies and measures and national strategies
or action plans that could involve further capacity-building, technology
development and transfer and result-based demonstration actions". This is

INFOa preparation phase between the readiness phase and the final phase of full3) UNFCCC (2008) FCCC/CP/2007/6/

implementation.

Add.1, UNFCCC

Phase 2 includes "demonstration activities" as a new element.
"Demonstration activities" were first introduced in the Bali Action Plan at
COP 13 "to address the drivers of deforestation relevant to their national
circumstances, with a view to reducing emissions" 3). Its elements are shown
under "Indicative Guidance" in ANNEX. Phase 2 should take these elements
into account to prepare for the final implementation of "activities based on
the fully measured, reported, and verified results".
The Carbon Fund of the World Bank's Forest Carbon Partnership Facility
will be one of the major sources of support for Phase 2 implementation.
In addition, Phase 2 elements such as the "demonstration project" may be
included in bilateral technical assistance projects carried out by developed
countries in some cases. Therefore, these projects are expected to be further
developed and implemented.

36

REDD-plus Cookbook

Recipe P02

Phase 3: Full implementation
The Cancun Agreements specify that in the final phase of REDD-plus,"results-based actions that should be fully measured, reported and verified".
It is understood that incentives will be provided if greenhouse gas emission
reductions and removals are attained as a result of REDD-plus activities
provided that they are fully measured, reported, and verified in a mannerrecognized as credible.
Currently, most REDD-plus participating countries are still in Phase 1 orPhase 2. Few, if any, have reached the final phase. Unlike other sectors suchas the transport sector, REDD-plus implementation faces many challenges,
including limited technology, data, organization, and human resources.
Moreover, the target areas, which are characterized by diverse tree species
with varying growth rates, are affected by weather, which can also delay full
implementation of REDD-plus.
Although there is no way to avoid dealing with these challenges, it ispossible to make them less daunting by implementing REDD-plus first at the
sub-national level. Implementation at the sub-national level allows resources
to be concentrated in a particular region, and early implementation regions
can serve as examples of good practice when implementation is expanded
at the national level. In addition, it is crucial for stakeholders and supporting
countries and institutions to liaise closely to ensure that a certain level ofagreement is reached.

REDD-plus Cookbook

37

Chapter 3 - Basic knowledge needed for REDD-plus implementation

Recipe - P03

Safeguards
The preceding Recipe is
Recipe I03    Measurement, reporting andverification (MRV) of forest
monitoring                                                                                                                                                                                                  The 16th Conference of the Parties (COP 16) agreed on promoting andsupporting "the safeguards" in REDD-plus, and the principles are listed
in Appendix I of the Cancun Agreement. The Forest Carbon Partnership
Facility (FCPF) of the World Bank and the UN-REDD Programme also
request their partners to address environmental and social safeguards.
While specific requirements for "the safeguards" have not yet beenclarified in the United Nations Framework Convention on Climate Change(UNFCCC) negotiations, the FCPF, the UN-REDD Programme, and NGOs
are developing their own principles, criteria, indicators, and guidelines
regarding safeguards.

Safeguards in REDD-plus
Safeguards are policies and measures designed to prevent risks of
impairing effects of climate change mitigation measures as REDD-plus(such as increasing greenhouse gas emissions in another forms and places
or resulting in effects of emission reductions and increased absorption to
be a temporary) and negative impacts on forest governance, the naturalenvironment, and society, and to enhance positive effects. Safeguards are an
important feature of the REDD-plus framework.

The safeguards in the UNFCCC
INFO
1) UNFCCC (2011) Decision 1/CP.16,
FCCC/CP/2010/7/Add.1, UNFCCCThe Cancun Agreement 1), adopted at COP 16 in 2010, stipulates that "thesafeguards" should be promoted and supported in accordance with theprinciples listed in the agreement (Table P03-1). The safeguards cover a range
of issues such as forest governance and social, environmental, and climate

INFO2) This classification is used here forconvenience; it is not based on the
UNFCCC.

concerns 2). These issues were also addressed before COP 16 in the provisionsof various agreements. By listing these principles they have been made clear
and accessible to stakeholders of REDD-plus participating countries.
Moreover, the agreement requests developing countries to develop asystem for providing information on how the safeguards are being addressed
and respected, and also requests that developing countries secure the fulland effective participation of stakeholders, including indigenous peoples andlocal communities, when developing and implementing national strategies
and action plans for addressing "the safeguards" (Table P03-2).

38

REDD-plus Cookbook

Recipe P03
Table P03-1 Safeguards in the Cancun Agreement(after UNFCCC (2011)1))
Safeguards

Category*)(a) Actions should complement or be consistent with the objectives ofnational forest programs and relevant international conventions andagreements(b) National forest governance structures should be transparent andForestgovernanceeffective, taking into account national legislation and sovereignty(c) Actions should respect for the knowledge and rights of indigenous
peoples and members of local communities, by taking into accountrelevant international obligations, national circumstances and laws,
and noting that the United Nations General Assembly has adopted theUnited Nations Declaration on the Rights of Indigenous Peoples

Social(d) The relevant stakeholders, in particular indigenous peoples andlocal communities, should fully and effectively participate in theactions referred to in paragraphs 70 and 72 of this decision(e) Actions should be consistent with the conservation of naturalforests and biological diversity, ensuring that the actions referred to inparagraph 70 of this decision are not used for the conversion of natural

Environmental/

forests, but are instead used to incentivize the protection andSocialconservation of natural forests and their ecosystem services, and toenhance other social and environmental benefits(f) Actions should be taken to address the risks of reversals(g) Actions should be taken to reduce displacement of emissions

ClimateNote: *) “Category” is the author's grouping, not UNFCCC's.

Table P03-2 Paragraphs related to the safeguards in Cancun Agreement	            (after UNFCCC (2011)1))
Paragraph69

Affirms that the implementation of the activities referred to in paragraph
70 below should be carried out in accordance with appendix I to thisdecision, and that the safeguards referred to in paragraph 2 of appendix I
to this decision should be promoted and supported71(d)Requests developing country Parties aiming to undertake the activities
referred to in paragraph 70 above, in the context of the provision ofadequate and predictable support, including financial resources andtechnical and technological support to developing country Parties, inaccordance with national circumstances and respective capabilities, todevelop the following elements:(d) A system for providing information on how the safeguards referred to in
appendix I to this decision are being addressed and respected throughout
the implementation of the activities referred to in paragraph 70 above,
while respecting sovereignty

72

Also requests developing country Parties, when developing andimplementing their national strategies or action plans, to address, inter alia,
the drivers of deforestation and forest degradation, land tenure issues,
forest governance issues, gender considerations and the safeguards
identified in paragraph 2 of appendix I to this decision, ensuring the fulland effective participation of relevant stakeholders, inter alia indigenous
peoples and local communities

REDD-plus Cookbook

39

Chapter 3 - Basic knowledge needed for REDD-plus implementation

Social and environmental safeguards
Social and environment safeguards in particular have been a topic of muchdiscussion.
Social safeguards in the agreement (Table P03-1) provide for (c) respect
for the knowledge and rights of indigenous peoples and members of
local communities; (d) the full and effective participation of relevant
stakeholders, in particular, indigenous peoples and local communities; and(e) enhancement of other social and environmental benefits, including
consideration of gender and vulnerable groups, contributions to sustainable
development, and promotion of poverty alleviation. Environmental
safeguards stipulate that (e) REDD-plus actions should be consistent withthe conservation of natural forests and biological diversity, and should be
used to incentivize protection and conservation of natural forests and theirecosystems services, as well as to enhance other social and environmental
benefits (Table P03-1).
Note that the terms "environmental safeguards" and "social safeguards"
are not defined in the UNFCCC document. At this time, the specific field
activities protected by safeguards often depend on the project planner's  
interpretation of the agreement.

Figure P03-1 Forest management by local community              upper: discussion on management of community forest              lower:  community forest

40

REDD-plus Cookbook

Recipe P03

Effects and significance of safeguards
Ensuring that safeguards are respected will also enhance the feasibilityof REDD-plus activities and their effective implementation, because bysolving social conflicts and reducing political risk in the participating country,
investment by domestic and international markets will increase. In addition,
outside the UNFCCC framework, voluntary initiatives such as the World
Bank's FCPF and the United Nations Collaborative Programme on Reducing
Emissions from Deforestation and Forest Degradation in Developing
Countries (UN-REDD Programme) place the highest priority in ensuring
environmental and social safeguards. It is highly possible that addressing
environmental and social safeguards will be made a requirement for carbon
offsetting with credits generated by REDD-plus activities.

Institutional challenge for ensuring safeguards
The UNFCCC has not yet decided how differences in safeguards due todifferences in each country's circumstances should be handled. Although
the document "Guidance on systems for providing information on how
safeguards are addressed and respected", adopted at COP 17 in 2011,provides basic guidelines 3) , it does not elaborate on what should bereported. Given that situation, the FCPF, the UN-REDD Programme, and NGOs

INFO3) UNFCCC (2012) Decision 12/CP.17,
FCCC/CP/2011/9/Add.2, UNFCCC

have been voluntarily developing their own principles, criteria, and indicators
as well as guidelines for social and environmental safeguards 4). Safeguards
should be addressed by taking these voluntary efforts into consideration,
while also closely watching development in the ongoing discussions.

INFO
4) Several standards are available : The"Strategic Environmental and Social
Assessment (SESA)" by the World
Bank; the "UN-REDD's Social andEnvironmental Principles and Criteria(SEPC)" by the UN-REDD Programme;the "REDD-plus Social andEnvironmental Standards (REDD-plus
SES)" and the "Climate, Community
and Biodiversity Standards (CCBS)" byNGOs.

REDD-plus CookBook

41

Chapter 4 - Measurement, reporting and verification of forest carbon

4
Chapter 4
Measurement, reportingand verification of forest
carbon42

REDD-plus Cookbook

Credible measurement, reporting, and verification (MRV) of climate change mitigation
actions is crucial in REDD-plus. MRV may be required as part of the accounting of all removals
and emissions of greenhouse gases from forests and applied to implement REDD-plus rules.
This chapter focuses on each component of MRV with regard to changes in forest carbon,
which are used to estimate removals and emissions of CO2.
Although REDD-plus is designed for eventual implementation at the national level,
voluntary initiatives at the project level have been gaining momentum. Therefore, therecipes on reporting (P05) and verification (P06) in this chapter also include information thatshould be taken into consideration at the project level.

P04 Measurement of forest carbon
P05 Reporting of forest carbon
P06 Verification of forest carbon

REDD-plus Cookbook

43

Chapter 4 - Measurement, reporting and verification of forest carbon

Recipe - P04

Measurement of forest carbon
The preceding Recipe is
Recipe I03 Measurement, reporting andverification (MRV) of forest
monitoring

Measurement of forest carbon is a vital part of REDD-plus implementation
because CO 2 emission reductions and removals from forests are
estimated by measuring changes in the amount of forest carbon, andcredits are also calculated by using the forest carbon amount. This recipe
explains what is measured and how to measure it, based on the United
Nations Framework Convention on Climate Change (UNFCCC) decisions
and Intergovernmental Panel on Climate Change (IPCC) 2006 Guidelines.

What to measure?

INFO
1) Tier: the level at which the amountof emissions is measured (2006 IPCC

REDD-plus activities are carried out in forests, where "forest" can be

Guidelines).

defined by each participating country as far as within the margin set by the

Tier 1 is the simplest and most basic,

UNFCCC (see P01). To reduce uncertainties in the estimation of emissions,and Tier 3 is the most complex andrequires a great deal of data. Tier 2 is

it is recommended that the total forest area be divided into subclasses ofintermediate between Tiers 1 and 3.

forest, depending on the ecological, legal, economic, cultural, and/or other

When the amount of emissions fromsituations in a particular country. For example, by subdividing a forest bya certain sector of a country is a coreelement (i.e., the absolute quantity,

forest type or by the degree of disturbance and then estimating the carbon

tendency, or uncertainty in thestock by each subclass, the uncertainty will be lower than if it is estimated toamount of emissions in that sector
is remarkable), then Tier 2 or Tier 3

the whole forest.
Land use in the forest is divided into two categories: managed land andshould be used.
In the Agriculture and Forestry andOther Land Use (AFOLU), Tier 1

unmanaged land. "Managed land" is land where human interventions andemissions estimates are made bypractices have been applied to perform production, ecological or social

using the gain-loss method andfunctions, and all other land is classified as "unmanaged". Although REDD-

utilizing default IPCC emission
factors. At higher tiers, emissions

plus includes the role of conservation by definition, a specific definition has

are estimated by using the gain-

not yet been decided (see P01).

loss method or the stock-change
method and the emission factors are

A component of a land use that store, absorb and/or emit carbon is called

determined in situ.

pool. For forest, five pools are defined, namely, above-ground biomass,

For REDD-plus, it is not yet decided

below-ground biomass, dead wood, litter and soil organic matter (See T02).

which tier should be used foremissions estimates. Because theGreenhouse gases (GHGs) other than CO2, such as CH4 and N2O, are subject

AFOLU default emission factors haveto measurement in accordance with the principle of completeness (see I03).

very high uncertainty, some are of
the opinion that Tier 1 should not be

However, the impact of each pool on carbon stocks, and accordingly theused, and a discount should be given

appropriate tier, depends on the size of the pool and the greenhouse gases

for using the conservative factors of

being measured 1). In general, measurements of the above-ground and

AR/D CDM . On the other hand, very
few developing countries have thebelow-ground biomass require higher tiers, since their changes greatly affectcapability to implement REDD-plus

CO2 emissions and removals in forests, while lower tiers for other pools dueeven at Tier 1 (see I04). For this reason,
the phased approach, taking accountto their relatively low affects (See T02).

of capacity-building, is the mostfeasible for developing countries (seeP02).

How to measure?
The UNFCCC recommends the use of remote sensing and ground-based

44

REDD-plus Cookbook

Recipe P04
forest carbon inventory approaches in combination to estimate forest carbon
stocks for REDD-plus 2).
Two methods are available for estimating the amount of forest carbonchange in a pool: the gain-loss method and the stock change method (seeP07). In the gain-loss method, increases (gains) and decreases (losses) in the

INFO2) UNFCCC (2009) Decision 4/CP.15,
FCCC/CP/2009/11/Add.1, 11-12,
UNFCCC

amount of carbon in the pool from various causes are first estimated andsummed. Then their difference (gains minus losses) is the amount of change
in the pool (equation P04-1). For example, the increase caused by the growthof biomass, the decrease caused by withering, and increases and decreases
caused by land-use changes are accumulated:		
where,

ΔC�ΔCG－ΔCL 			(P04-1)ο‫ ܥ‬: the amount of annual carbon change in the pool (t-C/year)

ΔCG : the amount of annual carbon increase (t-C/year),

ΔCL : the amount of annual carbon decrease (t-C/year).

In the stock change method, the amount of forest carbon is estimated in
two different years, and the amount of forest carbon change is estimatedfrom the difference in forest carbon between the years (equation P04-2) (seeP07) :Δ‫ ܥ‬ൌ
where,‫ܥ‬௧మ െ ‫ܥ‬௧భ‫ݐ‬ଶ െ ‫ݐ‬ଵ			(P04-2)ο‫ ܥ‬: the amount of annual carbon change in the pool (t-C/year)‫ܥ‬௧భ : the amount of carbon (t-C) in ‫ݐ‬ଵ (year)‫ܥ‬௧మ : the amount of carbon (t-C) in ‫ݐ‬ଶ (year)

The amount of carbon stocks in a certain year is calculated by measuring thecarbon stocks per unit area and then multiplying that amount by the total
area of the forest (or forest subdivision).
The gain-loss method, can be used to estimate biomass at all tiers (Tiers
1–3), where as the stock change method is used only at Tiers 2 and 3,
because it is more accurate when the amount of carbon change has been
relatively large. Later in this cookbook, the stock change method is describedin detail; P08 explains the area and area change estimations by using remotesensing, while P09 explains the estimation of forest carbon stock per unit
area by ground measurements and/or alternative methods.

REDD-plus Cookbook

45

Chapter 4 - Measurement, reporting and verification of forest carbon

Recipe - P05

Reporting of forest carbon
The preceding Recipe is
Recipe I03 Measurement, reporting andverification (MRV) of forest
monitoring"Reporting" means providing information about the mitigation activities
and their performances in a prescribed manner so as to make themcomparable with other efforts. The modality of "reporting" on REDDplus activities under the United Nations Framework Convention on
Climate Change (UNFCCC) has not yet been agreed. In this recipe, thestatus of international negotiations on the reporting of greenhouse gas(GHG) emissions and removals at the national level is reviewed and basic
requirements and considerations required for reporting greenhouse gas
emissions and removals in the forest sector are explained.

What is reporting?
"Reporting" means providing information about estimated emissions
and removals, estimation methods, procedures, and systems, including
present conditions and future projections in accordance with the prescribedreporting guidelines, formats, and procedures, to designated organizations.
Reporting enable comparison of emission reductions and removals andensures transparency.
The requirements of reporting are different depending on whether
REDD-plus is implemented at the national level under the UNFCCC or at
the project level under clean development mechanism (CDM) or anothervoluntary certification scheme. In this section, the reporting requirements for
implementations at the national level are outlined.

National communications
Article 4, paragraph 1 of the UNFCCC specifies that "All Parties, taking into
account their common but differentiated responsibilities and their specificnational and regional development priorities, objectives and circumstances,
shall: (a) Develop, periodically update, publish and make available to theConference of the Parties, in accordance with Article 12, national inventories
of anthropogenic emissions by sources and removals by sinks of all
greenhouse gases not controlled by the Montreal Protocol, using comparable
methodologies to be agreed upon by the Conference of the Parties, and (b)Formulate, implement, publish and regularly update national and, whereappropriate, regional programmes containing measures to mitigate climatechange by addressing anthropogenic emissions by sources and removals bysinks of all greenhouse gases not controlled by the Montreal Protocol, andmeasures to facilitate adequate adaptation to climate change."46

REDD-plus Cookbook

Recipe P05
Article 12 specifies the information that must be reported and thetimetable for its submission. All Parties are required to submit a national
inventory of anthropogenic greenhouse gases and the relevant measures toachieve the ultimate objective of the Convention. In addition, Annex I Partiesare required to include climate-related policies and measures, greenhousegas projections, and support to the non-Annex I Parties. Each report is calleda "national communication" (NC). Annex I parties must submit an initialnational communication within six months of the entry into force of theConvention for that Party, and non-Annex Parties must submit an initial

INFO

national communications within three years. The frequency of subsequent

1 ) D e c i s i o n 1 7 / C P. 8 G u i d e l i n e s

submissions should be determined according to COP decision 1).

for preparation of national

As of June 2012, most Annex I Parties have submitted their fifth or sixth
national communications. The 16 th Conferences of the Parties (COP 16)

communications from Parties notincluded in Annex I of the Convention:
UNFCCC (2003) Decision 17/CP.8,

calls for submission of the sixth national communications on 1 January

Guidelines for the preparation of

2014. Among non-Annex I Parties, 146 have submitted initial national

Parties not included in Annex I to thecommunications, 74 have submitted their two and three have submitted
three national communications 2). The timetable for submissions by othernon-Annex I Parties and the need to increase their capability level for national
communications submission are still being discussed at COP.
To facilitate the submissions of national communications from non-Annex
I Parties, a user manual 3) and a resource guide containing more detailed

national communications from
Convention, FCCC/CP/2002/7/Add.2
http://unfccc.int/resource/docs/
cop8/07a02.pdf#page=2

INFO
2) Non-Annex I national communications :http://unfccc.int/national_reports/nonannex_i_natcom/items/2979.php

instructions on the procedure have been provided by the UNFCCC.

INFONational greenhouse gas inventory3) Reporting on Climate Change: user

A national greenhouse gas inventory consists of estimates of emissions
and removals of greenhouse gases by sector. Non-Annex I Parties must
submit a national inventory as a part of their national communications.
According to the UNFCCC reporting guidelines on annual inventories,
the Annex I Parties are required, by 15 April each year, to provide annual

manual for the guidelines on national
communications from non-Annex I
Parties :http://unfccc.int/files/essential_
background/application/pdf/
userman_nc.pdf

national greenhouse gas inventories covering emissions and removals of
direct greenhouse gases (CO2, CH4, N2O, HFCs, PFCs, and SF6) from six sectors(Energy, Industrial processes, Solvents, Agriculture, LULUCF (land use, landuse change and forestry), and Waste) for all years from the base year or
period to the most recent year.
Under the UNFCCC reporting guidelines for the Annex I Parties, inventory4) CRF of the LULUCF sector of Annex

submissions have two parts:• Common reporting format (CRF) 4) – a series of standardized data tables•

INFO
I Parties :UNFCCC (2005) Decision 14/CP.11,

containing mainly numerical information and submitted electronically

Tables of the common reporting

National Inventory Report (NIR) – a comprehensive description of theand forestry

format for land use, land-use change

methodologies used in compiling the inventory, the data sources,

REDD-plus Cookbook

47

Chapter 4 - Measurement, reporting and verification of forest carbon

the institutional structures, and the quality assurance and control
procedures
The inventory submissions are reviewed by international expert review
teams (ERTs) based on the agreed review guidelines.
The estimation method should be based on the IPCC guidelines agreed
by COP. The non-Annex I Parties should use the IPCC guidelines as revised in
1996, and the Annex I Parties are required to use the Good Practice Guidance
for LULUCF (GPG-LULUCF) published in 2003. Annex I Parties that have madean emissions reduction target pledge under Article 3 of the Kyoto Protocol
are required to provide supplementary information related to the emissions

INFO

and removals of greenhouse gases in the areas subjected to activities under

5) UNFCCC (2007) Decision 6/CMP.3,

Article 3, paragraph 3, and the elected activities under paragraph 4 of theGood practice guidance for land

Kyoto Protocol 5). The supplementary information includes the geographicaluse, land-use change and forestry
activities under Article 3, paragraphs

locations of the boundaries of the areas that encompass lands subjected

3 and 4, of the Kyoto Protocol

to activities under Article 3, paragraphs 3 and 4, and a demonstration thatthese activities are human induced. In-depth reviews are also conducted byan international team of experts from the Annex I and non-Annex I Parties,
selected from the roster of experts. If deficiencies or incompleteness are
identified in the reports, the Parties that submitted the report will receivea "recommendation for improvement". If the Parties fail to improve theindicated parts before the due date, an "adjustment" will be performed by
the expert review team. If the amount of adjustment exceeds a certain level,
penalties, including suspension of eligibility for the Kyoto mechanism, will be
imposed.

Biennial update reports/International consultation and analysis
INFO
6) UNFCCC (2010) Decision 2/

In 2010, in order to promote mitigation actions by developing country

CP.17, Outcome of the work of theParties based on "nationally appropriate mitigation actions of the developing

Ad Hoc Working Group on Long-

country Parties (NAMA)", the Parties agreed at COP 16 that developing

term Cooperative Action under
the Convention, paragraph 39-44,

country Parties should submit national communications every four years

UNFCCC biennial update reporting

and biennial update reports (BURs) 6), which include inventory reports, theguidelines for Parties not included in
Annex I to the Convention

steps the countries are taking for mitigation actions, and support neededfrom developed country Parties, are required to go through "International
Consultation and Analysis" (ICA). The submission date and contents of the

INFObiennial update reports are determined based on the guidelines for biennial

7) Form: Chapter 3, Annex 3 in GPGLULUCF :update reports agreed by COP 17 in 2011. The developing country Parties

http://www.ipcc-nggip.iges.or.jp/

should submit an initial biennial update report by December 2014, and it

public/gpglulucf/gpglulucf_files/

should be updated every two years. Each biennial update report must cover,

Chp3/Anx_3A_2_Reporting_Tables.
pdf

48

at minimum, the inventory for the calendar year no more than four years
prior to the date of the submission, or for a more recent year if information is

REDD-plus Cookbook

Recipe P05
available, and subsequent biennial update reports shall cover a calendar yearthat does not precede the submission date by more than four years.
The inventory included in biennial update report should be calculated andreported based on the 1996 guidelines, GPG2000, and GPG-LULUCF, and for
the LULUCF sector, the use of the form specified in GPG-LULUCF Chapter 3,
Annex 3 7), is recommended.

INFO
8) UNFCCC (2010) Decision 2/
CP.17, Outcome of the work of the

COP 17 also agreed on the modality and guidelines for International
Consultation and Analysis. The agreement specifies that the first roundof International Consultation and Analysis shall be conducted within six
months of the submission of the initial biennial update report and themodality and guidelines 8) will be revised no later than 2017 on the basis of

Ad Hoc Working Group on Longterm Cooperative Action under
the Convention, paragraph 5662, Modalities and guidelines forinternational consultation andanalysisthe experience gained in the first round of International Consultation andAnalysis. Discussions about the team of technical experts that will perform
International Consultation and Analysis and other details are still in progress.

Reporting requirements
The reporting requirements of REDD-plus under measurement,
reporting and verification (MRV) have not yet been agreed. Therefore, adetailed explanation of the reporting requirements is not included in thischapter. Future discussions on the contents and procedures of national
communications and biennial update reports should be carefully followed,
since they will influence the reporting requirements of REDD-plus under MRV
and how REDD-plus activities should carried out at different implementationlevels of either national or project.
Five principles, namely, transparency, accuracy, comparability,
completeness, and consistency, are essential for the reporting of greenhousegases emissions and removals, regardless of the implementation level orwhether the country is developed or developing. To ensure that theseprinciples are followed in the reporting of forest carbon, it is important torigorously specify all elements of the estimation method used, including thedefinition of forest, the main forest stratification, and the main parameters,
and to maintain consistency among various reports.
Unlike other emission sectors, the situation in LULUCF sector varies greatly
among Parties, with regard to the specific aspects of the natural environment
and ecosystem complexity. For reliable reporting, it is important to provide
quantitative information, including about emissions and removals ofgreenhouse gases, and to clearly and fully present qualitative information
about the natural environment, the social and economic situation, and theestimation process, as well as justification for using the selected method,
taking into account the different circumstances of each country.

REDD-plus Cookbook

49

Chapter 4 - Measurement, reporting and verification of forest carbon

Recipe - P06

Verification of forest carbon
The preceding Recipe is
Recipe I03 Measurement, reporting andverification (MRV) of forest
monitoring"Verification" in the context of MRV of greenhouse gas (GHG) emissions
and removals is a procedure for assessing and ensuring, from an
independent standpoint, the reliability of the reporting of estimations,and the adequacy of methods, procedures, and documentation. This
process is a prerequisite for the policy effect evaluation based on thegreenhouse gas inventory and the trade of the credits produced byemission reduction projects. However, verification is often hampered bylimited availabilities of funds, human resources, and data. Such limitations
are particularly pronounced in the forest sector including REDD-plus,
where a verification method that is feasible, within these limitations,and effective, considering the nature of the forest sector, and that alsoachieves these purposes should be selected. Although an international
agreement has not yet been reached on the modality of verification of
REDD-plus activities, it is likely that it would reflect experiences fromthe precedent forest-related emission reduction mechanisms such as
the clean development mechanism (CDM), while taking into accountpeculiarities of REDD-plus.

What is verification?
In general, "verification" is defined as a "process to confirm that the
specified requirements are fulfilled by presenting objective evidence."
Verification is essential both in the case of reported national greenhousegas inventory data under the United Nations Framework Convention on
Climate Change (UNFCCC) and for reporting at project level under the CDM
and other voluntary carbon credit systems. The verification requirements
and procedures, however, vary depending on the purpose of the reported
calculations and the structure of the report.
For national greenhouse gas inventory reporting under the UNFCCC,
verification is performed by using quality control (QC) and quality assurance(QA) procedures, either by those directly involved in the calculation or by athird party. The adequacy of the selected activity data and parameters andthe estimation method are assessed by comparisons with other data sets
and/or inventories submitted by neighboring countries. On the other hand,under the credit trading mechanism, such as the CDM or a voluntary carbon
credit system, the verification is a part of an ex-post assessment processthat confirms the emission reductions that the project has attained and thereliability of the carbon credits. This verification must be done by a third party
to confirm that the project has been conducted according as prescribed in
the project plan.

50

REDD-plus Cookbook

Recipe P06

Verification requirements and procedures
When performing verification, it is important to know the requirements
and procedures specified by the designated reporting scheme. Verificationof national greenhouse gas inventory reports is based on Intergovermental
Panel on Climate Change (IPCC) guidelines (2006 IPCC Guidelines, volume
1, chapter 6; QA/QC and Verification) or IPCC Good practice guidance(IPCC Good practice guidance and uncertainty management in national
greenhouse gas inventories and greenhouse gas for land use, land-use
change and forestry（GPG-LULUCF)). In the case of the CDM, verification
is based on the modality and procedure agreed by COP and a manual
recognized by the CDM executive board. If it is a project-level framework
assuming credit trades, the verification should be based on the respective
guidelines established by each credit-trading framework in addition to the
IPCC guidelines. The EU Emissions Trading System (ETS) and the Verified
Carbon Standard (VCS) have developed specific guidelines for verificationthat comply with international standards such as ISO14064-3, established by
the International Standardization Organization (ISO). In many cases under thesystem developed for credit trading purposes, a certification system pursuant
to ISO14065 is used to ensure the ability of the third parties that conduct theverification.

Basic approach to verification in the forest sector
Estimations of emission reductions and removals for verification are basedon IPCC guidelines. Because limited technical and financial resources areavailable for verification, priorities must be established for determining thetarget and approaches to be verified. For national greenhouse gas inventory
reporting, relevance is the main criterion: the results of key category analysis
and the uncertainty of each category and greenhouse gas are assessed, anditems with higher relevance or uncertainty (see P12) are prioritized for theverification.
The approaches should be selected based not only on their feasibility or
adequacy taking national or regional circumstances into account but also
on the degree of interest, cost, the level of required accuracy, complexity inplanning and implementing verification approach, and the required level ofexpertise.
Applicable approaches for the verification in the LULUCF sector are: (1)comparison with other inventories and independent data sets, (2) overall or
partial comparison with the estimations obtained at a higher tier, (3) direct
measurement, (4) remote sensing, and (5) modeling. (IPCC GPG-LULUCF)

REDD-plus Cookbook

51

Chapter 4 - Measurement, reporting and verification of forest carbon(1) and (2) are the most commonly used methods as they can be carriedout at relatively low cost and do not require special technology or knowledge
as long as an independent data set are available. The cost, human resources,and expertise required for (3), (4), and (5) vary significantly depending on theimplementation method. The applicability of verification approaches for landarea identification and for carbon pools and gases in the LULUCF sector isindicated in Table 5.7.1 of GPG-LULUCF.

Verification of REDD-plus activities
An international agreement has not yet been reached on the modality ofverification of REDD-plus activities, but their verification is indispensable toensure reliability and fairness because financial incentives are provided basedon the achieved emission reductions and removals. It is likely that for projectlevel efforts aimed at credit trading the verification is to be conducted by athird party, based on the experience gained by the CDM or other voluntary
credit certification systems.
Verification criteria for REDD-plus have not yet been decided. Further
discussions will establish the design of the future system of REDD-plus
verification based on in situ experience and by considering the following
points:・

Which implementation level is introduced; national, sub-national, or
project level?

・Have boundaries been appropriately determined (project areas,
reference area, leakage belts, etc.)?

・

Have an appropriate scenario and data been used to establish
baselines (reference levels), considering the drivers of deforestationand forest degradation?

・Has the monitoring system been properly developed (including withthe participation of indigenous and local communities)?

Future discussions will also focus on the effectiveness of emission
reduction activities such as forest conservation, impacts on ecosystems,
especially biodiversity, and socio-economic impacts, and how safeguards are
addressed and respected.

52

REDD-plus Cookbook

Recipe P06
Table P06-1 Applicability of verification approaches for land area identification and forcarbon pools and non-CO2 greenhouse gases (excerpt from GPG-LULUCF 5.7.1)

Land area

Approach 1

Approach 2

Approach 3

Approach 4

Approach 5

Comparison withinventories and otherindependent data sets

Applying higher
tier methods

Directmeasurement

Remotesensing

Modelling

Suitable,, if data are
available

Suitable,, if data are
available

Not applicable

Suitable

Not applicable

Above-ground Suitable, if data arebiomass
available

Suitable, if data are
available

Suitable(resource-intensive)

Suitable (ground dataneeded)Suitable, (regression,
ecosystem andgrowth models)

Below ground
Below-groundbiomass

Suitable, if data are
available

Suitable, if data are
available

Suitable(resource-intensive)

Not applicable

Suitable, (regression,
ecosystem andgrowth models)Dead wood

Suitable, if data are
available

Suitable, if data are
available

Suitable(resource-intensive)

Not applicable

Applicable(ecosystem andinventorybased models)LitterSuitable, if data are
available

Suitable, if data are
available

Suitable(resource-intensive)

Not applicable

Applicable(ecosystem andinventorybased models)

Soil organic
matterSuitable, if data are
available

Suitable, if data are
available

Suitable(resource-intensive)

Not applicable

Suitable (ecosystem
and inventorybased models)

Non-CO 2
greenhousegases

Suitable, if data are
available

Suitable, if data are
available

Suitable(resource-intensive)

Not applicable

Suitable (ecosystemmodels)Emissionfactors

Suitable, if data are
available

Suitable, if data are
available

Suitable(resource-intensive)

Not applicable

Suitable (ecosystemmodels)Forest,Suitable, if data are
grassland,available
cropland, otherland uses

Suitable, if data are
available

Suitable(resource-intensive)

Suitable, particularly
to identify land
cover/land use andtheir changes

Suitable,
Data-intensive,
Can be an alternative
approach whenestimates fromdirect measurements
and remote sensing
are not available

Afforestation,
Reforestation,
Deforestation,
projects

Suitable, if data are
available

Suitable(resource-intensive)

Suitable, particularly
to identify land
cover/land use andtheir changes

Not practical

Carbon poolsl d b d report
Activity/land-based

Suitable, if data are
available

Bibliography
International Standardization Organization (2005) ISO9000:2005
IPCC (2006) 2006 IPCC Guidelines for National Greenhouse Gas
Inventories
IPCC (2003) Good practice guidance for land use, land-use change andforestry. IGES
http://www.ipcc-nggip.iges.or.jp/public/gpglulucf/gpglulucf.htm

REDD-plus Cookbook

53

Chapter 5 - Monitoring by the stock change method

5
Chapter 5
Monitoring by thestock change method

54

REDD-plus Cookbook

The stock change method involves measuring forest carbon stocks at two different
time points and then determining the difference. The stock change method requires two
estimations: forest area estimation and carbon stock estimation per unit area of forest. Area
estimation is achieved by applying remote sensing technologies. Carbon stock per unit area
can be estimated by the permanent sample plot (PSP) method or by using a stand carbon
stock estimation model. In this chapter, general introductions of the stock change method,area estimation and estimation of carbon stocks per unit area are followed by more detailed
explanations of the PSP method and some stand carbon are introduced. Then the calculationof carbon stocks and changes due to emissions and removals and their uncertainties is
described, finally, the establishment of reference levels and reference emission levels on thebasis of historical trends in emissions and removals is discussed.

P07 Stock change method
P08 Area estimation using remote sensing
P09 Estimation of carbon stocks per unit area
P10 Permanent sample plot method
P11 Using a stand carbon stock estimation model
P12 Calculation of carbon emissions and removals
P13 Reference emission level and reference level

REDD-plus Cookbook

55

Chapter 5 - Monitoring by the stock change method

Recipe - P07

Stock change method
The preceding Recipe is
Recipe P04 Measurement of forest
carbonIn the stock change method, as the name suggests, the amount ofemissions and removals is determined from the change in the carbon
stock at different time points. This method is robust and transparent andcan be applied extensively. In this recipe, "forest area" and "carbon stocks
per unit area," parameters that are used by the stock change method, aredefined and explained.

What is the stock change method?
In the 2006 IPCC guidelines, two methods of calculating changes in carbon
stocks (forest biomass) are presented: the gain-loss method and the stockchange method 1). The gain-loss method calculates carbon stock changes
by taking the difference between increases and decreases in emissions,

INFO
1) IPCC (2006) Chapter 2: Genericas described in P04, whereas in the stock change method the change in

Methodologies Applicable to Multiple

carbon stocks (which reflect emissions and removals) is determined from theLand-Use Categories. In: 2006 IPCC

difference in carbon stocks measured at different time points.

Guidelines for National Greenhouse
Gas Inventories Volume 4, Agriculture,
Forestry and Other Land Use

The gain-loss method requires accurate data on carbon stocks lost by
logging and forest disturbances, which is often hard to obtain. For thisreason, the stock change method, in which the difference in carbon stocks at
two different time points is presumed to equal the emissions and removals
during that time interval is more widely applicable.

INFO

At the national level, remote sensing and ground-based inventory used

2) UNFCCC (2009) Reducing emissions
from deforestation in developing

in combination can effectively determine carbon stock changes 2). In the

countries: approaches to stimulate

following subsections, estimation of the forest area from remote sensing data

action. FCCC/SBSTA/2009/L.19/Add.1

and estimation of carbon stocks per unit area by ground-based inventory
methods are explained.

Area estimation (see P08)

INFO3) In addition to forest land, othercategories include cropland,grassland, wetlands, settlements, andTo estimate the amount of carbon stock in a forest, the area of the forest
must be known. Forest is one land-use category 3), but forest land is often
converted to another land use, land in another category may be converted toother.

forest by reforestation. The categories and subcategories can be stratified byclimate, soil, ecological zone, and management system 4).

INFO4) IPCC (2006) Chapter 3: Consistent
Representation of lands. In: 2006 IPCC

Three approaches, which differ in how they deal with conversions between
land-use categories, are used to represent areas of land-use categories 4).

Guidelines for National Greenhouse
Gas Inventories Volume 4, Agriculture,
Forestry and Other Land Use

56

・ Approach 1: Total land-use area, no data on conversions between landuses

REDD-plus Cookbook

Recipe P07
・ Approach 2: Total land-use area, including changes between
categories
・ Approach 3: Spatially explicit land-use conversion data
In Approach 1, the total areas of all domestic land-use categories are
determined, but conversions between the categories are not considered.
Conversions between the categories are tracked in Approach 2, and in
Approach 3 these conversions are tracked systematically using detailed
spatial information 5). In practice, two or three of these approaches may be

INFO5）In Approach 3, to obtain more
spatially explicit information, thetargeted region is divided into gridcells or small polygons. methods
are outlined in IPCC GPG Volume 4,
Chapter 3, Annex 3A.4. 4).

used in combination.
Regardless of which of these approaches is used, a method must be
selected for collecting land-use data. Specific data regarding land-use can beobtained from three sources 4).

INFO6) At the national level, informationon land use available in national
and international databases should

・ Databases prepared for other purposes

be reviewed in advance. One

・ Samplinginternational database is listed in IPCC
GPG Volume 4, Chapter 3; however,・ Complete land inventory

differences in the definitions andresolution of land-use categories

Thus, the first source is existing information (maps and statisticalinformation) 6). Second, if the existing data are not sufficient, new data canbe collected by sampling. The sampling design can use stratification basedon auxiliary data and systematic sampling 7). Third, a complete inventory

should be taken into account whenusing it.

INFO

can be performed periodically to develop land-use maps of the whole

7) This sampling method is outlined incountry. Remote sensing technologies make the acquisition of such mapping

3A.3. 4)IPCC GPG Volume 4, Chapter 3, Annex

information over a wide area feasible 8).

INFO8) Ground-based inventory is

Estimation of forest area change

required to verify classification results
obtained by using remote sensing

Changes in the forested area can be detected by comparing forest areasdata.

estimated by remote sensing at two different time points by one of thefollowing two methods 9):

INFO9) Singh A (1989) Digital change・ Post-classification change detection

detection techniques using remotelysensed data. Int J Rem Sens 10: 989-

・ Pre-classification change detection

1003

Post-classification change detection means that land covers of remotesensing images acquired at two different times are separately classified,
and then the classification results are compared. Pre-classification change
detection means using the old and new images together to detect land-coverchanges. The results obtained by these two methods can differ; therefore, toin order to select the more appropriate method, it is necessary to understandREDD-plus Cookbook

57

Chapter 5 - Monitoring by the stock change method

their differences (see T11).

Estimation of the carbon stocks per unit area (see P09)
Carbon stocks per unit area can be estimated in two basic ways: the firstis to use permanent sample plots (PSPs) and the second involves the use ofa stand carbon stock estimation model 10). Here, five methods are outlined,
including four methods based on indirect estimation models.

INFO

・

Permanent sample plots

Toriyama J, Awaya Y, Asai H, Furuya

・Overstory height model

N, Ochiai Y, Inoue Y, Sato T, Sophal・

Crown diameter modelMatsumoto M (2011) Practicalities

・Community age modelof non-destructive methodologies

・Backscattering coefficient (Synthetic Aperture Radar)

10) Kiyono Y, Saito S, Takahashi T,

C, Sam P Tith B, Ito R, Siregar C.A,

in monitoring anthropogenic
greenhouse gas emissions fromtropical forests under the influence ofEach method has advantages and disadvantages and are applicable underhuman intervention. JARQ 45(2): 233-

different conditions. A ground-based inventory using permanent sample

242

plots has the advantage that it can be used to detect land-use changes,
such as conversion to farmland and selective logging, and to monitor forest

INFO

carbon stocks in a large area over time. For national level measurement,11) Fox JC, Yosi CK, Nimiago P, Oavika

however, many permanent sample plots are needed 11), 12) (see P10).

F, Pokana JN, Lavong K, Keenan RJ(2010) Assessment of abovegroundcarbon in primary and selectively
harvested tropical forest in Papua
New Guinea. Biotropica 42(4): 410-

Estimation of carbon stock change (see P12)

419

The change in carbon stocks is calculated with the following formula (stockchange method):

INFO12) Samreth V, Chheng K, Monda Y,
Kiyono Y, Toriyama J, Saito S, Saito		

H, Ito E (2012) Tree biomass carbon
stock estimation using permanent
sampling plot data in different typesof seasonal forest in Cambodia. JARQ
46(2): 187-192

ο‫ ܥ‬ൌ ‫ܥ‬௧మ െ ‫ܥ‬௧భ‫ݐ‬ଶ െ ‫ݐ‬ଵ			(P07-1)ο‫ = ܥ‬annual carbon stock change in the pool, (t-C/yr)‫ܥ‬௧భ = carbon stock in the pool at time ‫ݐ‬ଵ (t-C)‫ܥ‬௧మ = carbon stock in the pool at time ‫ݐ‬ଶ (t-C)

This simple formula does not take forest stratification into consideration.
In practice, the amount of carbon stocks at each point of time is calculated
by classifying (i.e., stratifying) the forest in the target region (national or
sub-national level) according to climate regime, forest type, and land
management practices. Then, the area and carbon stock per unit area andtheir product (carbon stocks) are determined for each stratum and the results
are summed. Thus, the change in carbon stocks is determined as follows:

58

REDD-plus Cookbook

Recipe P07‫ܥ‬௧మ െ ‫ܥ‬௧భ			 ο‫ ܥ‬ൌ ෍ ൤ ‫ ݐ‬െ ‫ ݐ‬൨
			

௜௝௞

ଶ

ଵ

௜௝௞

			

(P07-2)			

where index ݅ , j and k represent climate, forest, and management types.

When estimating carbon stocks, the uncertainty must also be evaluatedreduced as much as possible. When estimating the area change from satellite
imagery, different definitions and deficiencies in sampling design at thetime of the measurements introduce uncertainty 4). When PSPs are used for
estimating carbon stocks, the uncertainty depends largely on how manyplots are established and where they are established.

Bibliography
Köhl M, Magnussen SS, Marchetti M (2006) Sampling Methods,
Remote Sensing and GIS Multiresource Forest Inventory. Springer-Verlag
Ravindranath NH, Ostwald M (2008) Carbon Inventory Methods:
Handbook for Greenhouse Gas Inventory, Carbon Mitigation andRoundwood Production Projects. Springer-Verlag

REDD-plus Cookbook

59

Chapter 5 - Monitoring by the stock change method

Recipe - P08

Area estimation using remote sensing
The preceding Recipe is
Recipe P07 Stock change method

Estimation of areas of land covers and/or forest types by remotesensing consists of a series of processing and analyses, starting from theselection of appropriate data to summarizing the results. In this recipe,
the following remote sensing topics are explained: data types, software,
pre-processing, the definition of classes, classification methods, groundtruth data, assessment of classification accuracy, and methods of area
estimation.

Types of remotely sensed data and software (see T05)
Optical sensors are the most common sensors for observing the earth's
surface, but other types of sensors, such as the synthetic aperture radar (SAR),
which can penetrate clouds, or the light detection and ranging (LiDAR),
which can measure the tree and/or canopy height, are also available.
Important attributes of remotely sensed data regardless of types of sensor
are spatial and temporal resolutions. Spectral resolution is peculiar to theoptical sensors.
Data source(s) should be selected by considering the purpose andmethods of the use, the cost and the expected accuracy of the products.

Data pre-processing (see T06)
Raw remote sensing data may have various distortions. Accordingly,
they must be pre-processed before analysis to correct for atmosphericand geometric distortions introduced at the time the data are acquired.
Atmospheric correction reduces the impact of atmospheric conditions on
the reflective signal, and geometric correction modifies the image to suit themap projection.
In addition, it is often hard to obtain images of the earth's surface that are
not at least partly obscured by clouds in tropical rain forest areas. To create
cloud-free images, cloudless parts are extracted from the acquired images
and processed into a mosaic image. This procedure requires corrections fordifferences in solar radiance. Furthermore, to create a forest distribution map
at the national level, the images used to construct the mosaic image are likely
to have been acquired at different times of the year. In areas where the forest
undergoes seasonal changes, processing to reduce the influence of theseseasonal differences, differences in the timing of leaf fall, may be performed.
These procedures can be considered pre-processing in the broad sense.

60

REDD-plus Cookbook

Recipe P08

Definition of classes (see T07)
Land cover and forest type classes of a country should be defined to reflect
the forest management regime, ecological characteristics and difference of
biomass, and they must be mutually exclusive to each other and collectively
exhaustive, without any overlap with other classes. When remote sensing
data are used for the classification the definitions of the classes must take
into account the accuracy and resolution of the remote sensing data, and at
the same time they must not contradict existing forest classification criteria.

Ground truth (see T08)
Field survey data on land covers, forest types, and forest biomass collected
for the purpose aiding in the analysis of remote sensing data is called
ground truths (Figure P08-1). These data can be used as training data inthe classification process or as verification data for evaluating the results ofthe classification. Ground truths can be collected by random sampling orby stratified sampling. Stratified sampling is often preferable when funds
or human resources are limited. Another consideration is the possibility oflimited access to some parts of the forest, which can lead to biases if fewer
samples are collected in less accessible places.

Figure P08-1 Confirmation of geographic coordinates in ground truthing

Classification methods (see T09)
Classification is a process by which data are divided by statistical methods
according to their features. Classification processing can be "unsupervised"or "supervised." In unsupervised classification, the data are classified by

REDD-plus Cookbook

61

Chapter 5 - Monitoring by the stock change method

clustering using only statistical features of the spectral information of remotesensing data. Each cluster is labeled by comparison with ground truths. In
supervised classification, spectral features are extracted from the remotesensing data by using ground truths as training data, and statistical methods
are used to assign the remotely sensed data to a class.
Classification of remote sensing data can be either pixel-based or objectbased. In pixel-based classification, each pixel of a remote sensing image
is classified using the spectral feature of each wavelength band. In objectbased classification, a remote sensing image is divided into objects by using
information on color and shape, and the objects are classified by using thespectral information and texture information within each object. In objectbased classification, a computer draws the boundary lines between objects,
and a result similar to a visually interpreted image can be efficiently obtained(Figure P08-2).

Figure P08-2 Object-based classification of Landsat imagery

Verifying the accuracy of classification results (see T10)
The validity of the classification results must be verified. This isaccomplished by creating an error matrix from verification data obtained byground truths. Overall accuracy, user's accuracy, producer's accuracy, and the
kappa coefficient are often used as indices of accuracy. About 50 samples,at least, are required per class for accuracy verification. The area biases areevaluated and corrected by using an error matrix.

62

REDD-plus Cookbook

Recipe P08

Estimation of forest area change (see T11)
As described in P07, forest area change can be delineated either bycomparing classified images (post-classification method) or by comparing
images before classification (pre-classification method). In post-classification,
the classification of each image is performed independently then changes
are identified at where the assigned classes of the two time period are not
identical. Pseudo-changes due to inaccurate image registration or ambiguousclass boundaries should be corrected. Pre-classification, and thus it requires
the next step to understand and assign from which to which classes theyoccurred.

Bibliography
Lillesand TM, Kiefer RW, Chipman JW (2007) Remote Sensing and Image
Interpretation, sixth edition. Wiley
McCoy RM (2005) Field Methods in Remote Sensing. Guilford Press
Alexander R, Millington AC (2000) Vegetation Mapping. Wiley

REDD-plus Cookbook

63

Chapter 5 - Monitoring by the stock change method

Recipe - P09

Estimation of carbon stocks per unit area
The preceding Recipe is
Recipe P07 Stock change method

For Tier 2 and Tier 3 measurements, information on carbon stocks must
be collected periodically at the national level. When the stock change
method is used to estimate changes in carbon stocks over time, theparameter "carbon stocks per unit area" can be estimated by measuringpermanent sample plots, or stand carbon stock estimation models can beused. In this recipe, each of these two estimation methods is outlined.

Attainable resources (see T03)
The estimation of the amount of carbon stocks per unit area 1) is done by two
methods: a method that directly measures the amount of carbon in the forest,
and a method that indirectly measures the amount of carbon stocks using theestimation model. The selection of locations to be surveyed, the number of points

INFO
1) The estimated result is expressed as

to be surveyed, and the skill level of individuals conducting the survey and analysis

forest biomass (t/ha) and it must be

affect the estimation regardless of which method is used. Here, for simplicity,

converted to forest carbon stocks (t-C/

useful information and techniques for accurate estimation of carbon stocks areha) by multiplying the forest biomass
value by the carbon content factor(0.5).

called "resources".
Some countries have already conducted forest and soil surveys as a part of anational forest inventory or local projects. Information from such surveys can be an
important resource for the estimation of carbon stocks. For instance, informationon land use and forest types, which is necessary for application of the stock change
method, can be obtained from existing information on soil and vegetation patterns
and climatic zones. Data on tree diameters and tree species obtained for timber
production are also resources that can be used for estimating forest carbon stocks
per unit area. Information about deforestation and forest degradation, which isused for simulations of forest area trends, can also be used to help select locations
for permanent sample plots (PSPs) or in the design of temporary sampling schemes
for stand carbon stock estimation models. In addition, project teams that havemanaged past forest surveys can help establish a task force for REDD activities.
Each of the various survey methods that have been proposed has advantages
and disadvantages (Table P09-1). The selected methodology should take into
account resources available in the project area.

Direct measurement of carbon stocks (permanent sample plot method) (see P10)
To directly measure carbon stocks, permanent sample plots are establishedin the forest, and the species of trees that grow in those plots, along with thesizes of individual trees and the populations of trees are determined. Establishingpermanent sample plots and conducting the necessary measurements arelabor-intensive activities that are feasible only in areas that are accessible to the64

REDD-plus Cookbook

Recipe P09
individuals conducting the survey. However, both the methods and tools used forthis type of survey are simple and the acquired data are highly accurate.

Indirect measurement of carbon stocks (stand estimation model method) (see P11)
There are a suit of methods indirectly estimating carbon stocks. They are usefulespecially when ground-based survey alone is difficult due to accessibility on the
ground. Indirect methods often involves a trade-off between the cost of equipment
needed and the accuracy of the analysis. Carbon stock can be estimated by thestand age in the case of homogeneous stands where the number of individuals perunit area is already known and where species, stand age, and tree size are uniform,
such as timber plantations. In other cases, remotely measurable stand parameters,such as overstory height, crown diameter, community age, or backscattering
coefficient (SAR data), can be used in such indirect models. There are choices ofremotely sensed data, such as satellite imagery or aerial photograph, dependingon the circumstances of country and/or area.

Bibliography
Kiyono Y, Saito S, Takahashi T, Toriyama J, Awaya Y, Asai H, Furuya N, Ochiai Y,
Inoue Y, Sato T, Sophal C, Sam P Tith B, Ito R, Siregar CA, Matsumoto M (2011)
Practicalities of non-destructive methodologies in monitoring anthropogenic
greenhouse gas emissions from tropical forests under the influence of human

Table P09-1 Matrix of choices of methods for estimating anthropogenic greenhouse gas emissions
from forests under various forms of human intervention (after Kiyono et al., 2011)
Requirements

Costs

Getting data
in a large landarea

PSP data

Measurement on
the groundHigh

DifficultCommunityage

Remote sensor withmedium or higher
Mediumresolution

Crown
diameterRemote sensor withhigh resolution
Aerial photographHigh

MediumMulti-polarization
SAR

Low

MediumHigh

DifficultMediumEasy?

Difficult

Low

MediumApproaches

OverstoryheightAirborne LiDAR
Stereo mapping
remote sensor(DSM)
Measurement on
the ground

SAR with
Backscattering
microwaves longer
coefficients
than L-band

Easy

Applicability of the method in estimating anthropogenic GHG
Technical difficulties

Reducing fallow period
Conversion toof slash-and-burncrop land
agriculture

Limitation in
Possible
representativeness andsecretness of plot.
Applicable to land use
with periodical naked
Impossible
land stages e.g. slashandburn farming.
Not applicable whenclouded. Crowns arePartly possible
hardly recognized insome forests.
Methods are not tested.
?
Applicable to small
parts of globe.
Nothing in particular.
Possible
Not applicable when?
clouded. Methods are
not tested.
Methods are not tested.
Possible
Applicable to small
parts of globe.
Not applicable to areas
with steep slopes. Not
Partly possible
applicable to highbiomass forest.

Logging

Improvement in accuracy
expected by local people
Fuel woodparticipating in thecollection
monitoring

Possible

Possible

PossibleHigh

Possible

Impossible Impossible

Low

Impossible

Partlypossible

Impossible

Low

?

?

Impossible

Low

Possible

Possible

Impossible

Low

?

?

Impossible

Low

Possible

Possible

Impossible

High

Impossible Impossible

Low

Partly possible

This table is applicable to dry land forest.

REDD-plus Cookbook

65

Chapter 5 - Monitoring by the stock change method

Recipe - P10

Permanent sample plot method
The preceding Recipe is
Recipe P09 Estimation of carbon
stocks per unit area

A ground inventory site is called a permanent sample plot when tree
diameter at breast height (DBH) and tree heights are measured every
several years to track forest changes. In this recipe, the number of plots
and their locations required by the selected survey method, other things
to be considered in the design and measurement of permanent sample
plots, and methods of estimating carbon stocks from permanent sample
plot data are explained.

Required number of permanent sample plots and their locations (see T12)
To estimate carbon stocks efficiently in a large area (national or subnational level), the number and locations of permanent sample plots must be
appropriately chosen according to the sampling survey method used. Whenthe population is large enough, the required number of permanent sample
plots ( ݊ ) can be obtained with the following formula 1).
			

INFO
1) Fox JC, Yosi CK, Nimiago P, Oavika F,
Pokana JN, Lavong K, Keenan RJ (2010)
Assessment of aboveground carbon
in primary and selectively harvested
tropical forest in Papua New Guinea.
Biotropica 42(4): 410-419					‫ ݐ‬ȉ ‫ ܸܥ‬ଶ൰ 						 ݊ ൒ ൬‫ܧ‬(P10-1)

where ‫ ܧ‬is the allowable error rate (half the width of the confidence

interval divided by the average value of carbon stock), ‫ ܸܥ‬is the coefficient ofvariation, and ‫ ݐ‬is the ‫ ݐ‬statistic for a significance level of the ‫ ݐ‬-distribution
of 5% with ( ݊ – 1) degrees of freedom. Thus, as ‫ ܧ‬becomes smaller or ‫ܸܥ‬

increases, the required number of permanent sample plots ( ݊ ) increases.
For example, for the national forest inventory carried out in Japan in 1961, ݊was calculated for ‫ = ܧ‬3 %, ‫ = ݐ‬2, and ‫= ܸܥ‬150 % as follows:� � �.� �� � �0000 		
0.03

			 � � �

(P10-2)

Therefore, the required number of plots was 10,000. To determine anappropriate ‫ ܸܥ‬, prior information about the whole forest or each forest typeis required. Its selection should be based on the results of a pilot survey or
past experience.

INFO

To prevent permanent sample plots from being concentrated in readily

2) Gibbs KH, Brown S, Niles OJ, Foley
AJ (2007) Monitoring and estimating

accessible locations such as near roads or villages, permanent sample plots

tropical forest carbon stocks: making

should be established by using either a simple random sampling method 2)

REDD a reality. Environ Res Let 2:

or a systematic sampling method 2). Moreover, a stratified sampling method

0450232) , in which the number of sampling sites and their locations by forest

categories is decided in advance on the basis of remote sensing data, canimprove sampling survey efficiency.

66

REDD-plus Cookbook

Recipe P10

Design and measurement of permanent sample plots (see T13)
Permanent sample plots should be designed to enhance field measurement
efficiency. Generally, permanent sample plots are circular or square. In manycases, one plot consists of nested compartments of different sizes 3). For

INFO3) Pearson T, Walker S, Brown S (2005)
Sourcebook for land use, land-use
change and forestry projects. Winrock
International and the BioCarbon Fundof the World Bank

example, in Japan permanent sample plots are arranged in a 4-km mesh andeach consists of three concentric circles with radii of 5.64, 11.28, and 17.84 m,
in which the minimum diameter of the measurement target (a standing tree) is
1, 5, and 18 cm, respectively 4).

INFO4) Kitahara F, Mizoue N, Yoshida S(2009) Evaluation of data quality inSince measurements are carried out every several years, it is imperative to
install signs at the entrance to the plots and along plot boundaries (Figure P102), to attach an aluminum tag with the tree number to each measured tree,
and to mark breast height on each tree 5). Moreover, because the tree number

Japanese National Forest Inventory.
Environ Monit Assess159: 331-340

INFO

tags often fall off, a map showing the positions of all standing trees is needed.

5) Kitahara F, Mizoue N, Yoshida

Furthermore, to minimize measurement error, a detailed measurement manual

inexperienced surveyors on dataand prior measurement exercises conducted by experts are required 4), 5).

S (2010) Effects of training for
quality of tree diameter and heightmeasurements. Silva Fennica 44: 657667

Analysis of permanent sample plot data (see T14)
The carbon stock represented by standing trees is calculated as one half thetree biomass 3). Biomass can be estimated from the permanent sample plotmeasurement data 6) by using an allometric equation that relates biomass,
including branches and leaves, of a tree to the tree's DBH or to both tree

INFO
6) Brown S (2002) Measuring carbon
in forests: current status and futurechallenges. Environ Pollut 116: 363372

DBH and tree height. Allometric equations have been created for the various
regions and tree species (or groups of species). For trees in the tropics,

INFO

equations developed by Brown 7) and Chave and colleagues 8) are often used.

7) Brown S (1997) Estimating biomass

Biomass can also be estimated by using biomass expansion factors (BEFs) 6).
A biomass expansion factor is a coefficient that relates dry volume (m3), whichand biomass change of tropicalforests: a primer. FAO Forestry Paper
134. FAO

has been obtained since ancient times by general forest inventory methods,
to biomass, including branches and leaves. It is denoted by the ratio of thebiomass to the dry volume (Mg/m3). biomass expansion factors have also
been determined for various areas and species (or groups of species).

INFO
8) Chave J, Andalo C, Brown S, Cairos
MA, Chambers JQ, Eamus D, Fölster H,
Fromard F, Higuchi N, KiraT, Lescure
J-P, Nelson BW, Ogawa H, Puig H,
Riééa B, Yamakura T (2005) Tree

17 84m
17.84mallometry and improved estimation ofcarbon stocks and balance in tropicalforests. Oecologia 145: 87-99
5.64m
11.28mFigure P10-1 Plot shape used in the Japanese NFI.
Plots are arranged in a grid with a 4-km grid interval

Figure P10-2 Signpost for a plot
in Cambodia

REDD-plus Cookbook

67

Chapter 5 - Monitoring by the stock change method

Recipe - P11

Using a stand carbon stock estimation model
The preceding Recipe is
Recipe P09 Estimation of carbon
stocks per unit area

In addition to the permanent sample plot method (P10), carbon stocks per unit
area can be estimated by using a stand carbon stock estimation model. Several
models with different parameterizations and thus different approaches to theestimation of carbon stocks have been proposed. In this recipe, the assumption ofusing a stand carbon stock estimation model and the characteristics of different
models, based on overstory height, crown diameter, community age, andbackscattering coefficient (SAR), are also explained.

Establishment and verification of the modelWith the permanent sample plot method (see P10 and Chapter 8 ), deforestationand forest degradation due to land-use changes (e.g., conversion to farmland or
disturbance by selective logging) are easy to detect, and because it can be usedregardless of forest or vegetation type, it is very flexible. However, REDD-plus requires
high-accurate data, which means that a large number of plots must be established,which inevitably increases the time and effort and expense of the method. Therefore,

INFO

a technique for indirect estimation of carbon stocks per unit area that uses a relational

1) The models discussed here mainly

equation to relate carbon stocks to some parameter whose value can be obtained

estimate the carbon stocks of above-

without a field survey can be an effective choice. However, model estimates mustground and below-ground biomass.
Not all these methods are suitable forhave their accuracy verified by a field survey to fulfill the measurement, reporting, andestimating the sizes of other carbon

verification (MRV) requirements of REDD-plus. To attain the target level of accuracy,

pools, such as the dead wood andlitter. Therefore, to estimate stocks
in these other carbon pools, a fieldsurvey is also necessary.

the field survey design and model selection must take into consideration the cost ofdata acquisition and the number of samples needed for the model (see T15).
In the following sections, stand carbon stock estimation models with different
stand parameters, i.e. overstory height, crown diameter, community age, or the

INFObackscattering coefficient, are out lined 1).

2) Mascaro J, Asner GP, Muller-Landau
HC, Breugel Mv, Hall J, Dahlin K (2011)
Controls over aboveground forest

Overstory height method (see T16)

carbon density on Barro Colorado
Island, Panama. Biogeosciences 8:
1615-1629

The overstory height method estimates carbon stocks per unit area by assumingthat overstory height is directly proportional to biomass 2), 3). Information on overstory
height is acquired from ground-based inventory or by remote sensing (airborne LiDAR,

INFO
3) Saatchi SS, Harris NL, Brown S,

polarimetric interferometry SAR, or satellite-borne stereoscopic sensors). The cost ofLefsky M, Mitchard ETA, Salas W, Zutta

implementation and the size of the measurement area vary widely and depend on theBR, Buermann W, Lewis SL, Hagen S,

sensor used. According to the precedent studies, the relationship between overstory

Petrova S, White L, Silman M, MorelA (2011) Benchmark map of forest

height and biomass is often stable. Also, significant differences are not observed amongcarbon stocks in tropical regions

different forest types of an area in the relationship between overstory height andacross three continents. PNAS 108:
9899-9904

68

biomass. Tree height data from tree census studies are needed for creating the relational
equation, and its measurement accuracy and the number of samples are also important.

REDD-plus Cookbook

Recipe P11

Crown diameter method (see T17)
The crown diameter method estimates carbon stocks per unit area by assumingthat individual tree biomass increases exponentially with the diameter of the tree

INFO4) Kiyono Y, Saito S, Takahashi T,
Toriyama J, Awaya Y, Asai H, Furuya

crown 4). Information on tree crown diameters is obtained by using aerial photos orN, Ochiai Y, Inoue Y, Sato T, Sophal

high-resolution satellite imagery. The implementation cost is high, and the optical

Matsumoto M (2011) Practicalities

sensor used is affected by clouds, but forest degradation caused by selective loggingof large-diameter trees can be detected and quantified. Because the method depends

C, Sam P, Tith B, Ito E, Siregar CA,
of non-destructive methodologies
in monitoring anthropogenic
greenhouse gas emissions fromon the visibility of the tree crown, it is suitable for forests with large-diameter trees

tropical forests under the influenceor open forests, but not for young secondary forests or bamboo forests. Moreover,of human intervention. JARQ 45: 233-

satellite imagery yields information on the tree crown diameters of upper story trees
only; the biomass of understory trees must be separately estimated.

242

INFO
5) Inoue Y, Kiyono Y, Asai H, Ochiai Y,

Community age method (see T18)

Qi J, Olioso A, Shiraiwa T, Horie T, Saito
K, Dounagsavanh L (2010) Assessing

The community age method estimates carbon stocks per unit area by the community
age assuming that the community biomass increases monotonically as the community
age 5). Temporal and spatial distribution of community ages can be estimated byland-use and carbon stock in slashand-burn ecosystems in tropicalmountain of Laos based on timeseries satellite images. Int J Appl Earth
Obs Geoinf 12(4): 287-297

detecting the emergence of denuded lands using a time series of remotely sensed
images with medium to high resolution sensors (see T05). Since denuded land isassumed as the origin of community age, failure to detect bare land by the time-series
imagery directly reduces the accuracy of the method; thus, monitoring at least once a
year is required. The cost of the method is moderate. It is most effective for land-use
systems in which the land is denuded to reestablish the community, such as slash-andburn agriculture and rubber plantations. In such land-uses, frequency of denuding ismost relevant to the average stand carbon stocks through time.

Backscattering coefficient method (see T19)
The backscattering coefficient method estimates above-ground biomass perunit area from the backscattering coefficient (σ0, units dB) obtained by SAR.
The backscattering coefficient increases monotonically with the above-groundINFO
6) Carreiras J, Vasconcelos MJ,
Lucas RM (2012) Understanding thebiomass, allowing a saturation curve for above-ground biomass to be drawn. For

relationship between abovegroundthe estimation of stands with moderate or high stocks, the relationship must beforests of Guinea-Bissau (West Africa).

determined empirically, because the simple estimation equation is difficult 6). SAR

biomass and ALOS PALSAR data in the
Remote Sens Environ 121: 426-442

microwave radiation passes through clouds; therefore, the method is particularly
useful in regions with year-round cloud cover 7). Long-wavelength microwaves
are advantageous for forests. Implementation costs are small, and it is easy tocompare multiple scenes and is excellent for estimations of biomass in large areas.
On the other hand, analysis is not easy because stand structure, topography, soil
moisture, land-surface conditions, and multiple reflections affect the analysis
result, and there are a number of technical issues that must be overcome.

INFO
7) Ju J, Roy DP (2008) The availabilityof cloud-free Landsat ETM+ data overthe conterminous United States andglobally. Rem Sens Environ 112: 11961211

REDD-plus Cookbook

69

Chapter 5 - Monitoring by the stock change method

Recipe - P12

Calculation of carbon emissions and removals
The preceding Recipe is
Recipe P07 Stock change method
Recipe P08 Area estimation using 		
remote sensing
Recipe P09 Estimation of carbon 		stocks per unit area

In this recipe, a method for calculating carbon stocks and their changes(emissions and removals) is explained. To enhance the reliability of
the carbon credit system, it is important to reduce the uncertainties
of the calculated values 1). A method to combine uncertainties 2) withcalculation examples based on sample data is explained here.

Calculation of carbon stocks and carbon stock change
In the stock change method, the carbon stock (t-C) is obtained by multiplying
the forest area (ha) by the carbon stock per unit area (t-C/ha). The carbon
stocks of the entire project area at a given time are obtained by calculating the
products of the carbon stocks per unit area for each forest type and the area

INFO
1) The uncertainty of the averageoccupied by that type and then summing the results over all forest types:

value calculated here is the ratio (%)

		

of the difference between the 95 %			 ‫ܥ‬௧ ൌ ෍ሺ‫ܣ‬௜ ൈ ‫ܥ‬௜ ሻ 			

confidence interval maximum (orminimum) value and the averagevalue to the average value. If anormal distribution is assumed, thenthe difference between the 95 %confidence interval maximum andaverage value μ corresponds to 1.96
times the standard deviation, andthe uncertainty U (%) is calculated௡

following formula.
U = 100 × (1.96 × σ )/ μ

INFO

(P12-1）௜ୀଵ

Ct : total carbon stock at a certain time ‫( ݐ‬t-C)‫ܣ‬௜ : area occupied by forest type ݅ (ha)‫ܥ‬௜ : carbon stock per unit area of type ݅ (t-C/ha)

Emissions and removals are calculated with equation .

from the average estimate μ andthe standard deviation σ with the				Δ‫ ܥ‬ൌ

‫ܥ‬௧మ െ ‫ܥ‬௧భ‫ݐ‬ଶ െ ‫ݐ‬ଵ			(P12-2)ο‫ ܥ‬: net change in carbon due to emissions and removals (t-C/yr)‫ܥ‬௧భ , ‫ܥ‬௧మ : total carbon stocks at times ‫ݐ‬ଵ and ‫ݐ‬ଶ , respectively (t-C)

2) Instead of combining uncertainties,

The total carbon stocks and the emissions and removals calculated fromthe uncertainty can be also evaluatedforest carbon monitoring data can be used to set the reference level and theby a Monte Carlo method. In thiscase, uncertainty is estimated byreference emission level (see P13).

simulating the statistical distributionof observation using random
numbers generated by computer

Uncertainties of the calculated amounts

and calculating the 95 % confidence
interval of the distribution. With thedata of Table P12-1, the valuesο‫= ܥ‬-622.5 t-C/yr, and ܷο஼ = 30.9 wereobtained by 10,000 iterations.

70

The uncertainty of the carbon stocks for each forest type due tomeasurement errors introduced in determining the area and the carbon
stock per unit area is calculated with equation (P12-3): 		
		

			

ଶ
ଶ
			ܷ௜ ൌ ටܷ஺௜
൅ ܷ஼௜

			

REDD-plus Cookbook(P12-3)

Recipe P12

ܷ௜ = Uncertainty of carbon stock of forest type ݅ (%)ܷ ‫ = ݅ܣ‬Uncertainty of area of forest type ݅ (%)ܷ ‫ = ݅ܥ‬Uncertainty of carbon stock per unit area of forest type ݅ (%)ܷ௜ is calculated for all forest types, and the results are used in equation (P12-4).
௡௡ܷ௧ ൌ ඩ෍ሺܷ௜ ൈ ‫ܯ‬௜ ሻଶ ൙෍ ‫ܯ‬௜
௜ୀଵ
௜ୀଵ

			

（P12-4)

ܷ௧ = Uncertainty of total carbon stock at time ‫( ݐ‬%)‫ܯ‬௜ = Average carbon stock of forest type ݅ (= ‫ܣ‬௜ × ‫ܥ‬௜ ; t-C)

Finally, the uncertainty of net emissions and removals between times ‫ݐ‬ଵand ‫ݐ‬ଶ is calculated with equation (P12-5).

ܷο஼ ൌ ටሺܷ௧మ ൈ ‫ܥ‬௧మ ሻଶ ൅ ሺܷ௧భ ൈ ‫ܥ‬௧భ ሻଶ ൗห‫ܥ‬௧మ െ ‫ܥ‬௧భ ห(P12-5)ܷο஼ = Uncertainty of net emissions and removals (%)
In the example below, hypothetical data are used to calculate emissions
and removals and the uncertainty. Survey data for an evergreen forest and a
deciduous forest are shown in Table P12-1. The net emissions andremovals ο‫ ܥ‬and the uncertainty ܷο஼ is solved as follows by using
equations (P12-2) and (P12-5), respectively.

∆C � ������ � ������ ������ � ����� � ��������� � ����� � ������� � ���� � ������� �|������ � �����| � �������

Bibliography
GOFC-GOLD (2011) A sourcebook of methods and procedures for monitoring
and reporting anthropogenic greenhouse gas emissions and removals
caused by deforestation, gains andlosses of carbon stocks in forests

Table P12-1 Example calculation of forest carbon stock and its change(Result of calculations are in italics)

remaining forests, and forestation:
GOFC-GOLD Report version COP171. GOFC-GOLD
IPCC (2003) Good practice guidance
for land use, land-use change andforestry. IGES

Carbon data

Ai

UAi

Ci

UCi

Ai×Ci

(unit)(ha)(%)(t-C/ha)

(%)

(t－C)

EV in 2000

100

6

150

8

15000

10.0

DD in 2000

93

4

80

7

7440

8.1

EV in 2010

88

8

120

9

11560

9.5

DD in 2010

68

2

83

5

5644

5.4

ଶ
ଶ
ටܷ஺௜
൅ ܷ஼௜

(%)

Ct

Ut

(t－C)

(%)

22440

7.2

16204

6.5

EV = evergreen forest, DD = deciduous forest

REDD-plus Cookbook

71

Chapter 5 - Monitoring by the stock change method

Recipe - P13

Reference emission level and reference level
The preceding Recipe is
Recipe P12 Calculation of carbon
emissions and removals

To quantify reductions in emissions achieved by REDD-plus activities, it isfirst necessary to establish a reference emission level and/or a reference
level and to compare them with the actual emissions. In this recipe,
reference emission levels and reference levels, as defined in discussionsof the meeting of experts held at the request of the Subsidiary Body for
Scientific and Technological Advice (SBSTA) under the United Nations
Framework Convention on Climate Change (UNFCCC), are explained.
In addition, ongoing discussions under the UNFCCC and challenges inestablishing reference emission levels and emission levels are described.

Establishment of reference emission level and reference level
The basic concept of REDD-plus is to provide economic incentives suchas funds or credits to developing countries for REDD activities - reducing
emissions from deforestation and forest degradation in developing countries- and "plus" activities - preventing CO2 emissions and reducing CO2 in theatmosphere by carbon sequestration. To quantify reductions in CO2 due to
REDD-plus activities relative to expected CO2 levels were REDD-plus activities
not undertaken, a reference emission level or a reference level must be
established. In a meeting of experts held at the request of SBSTA1), 2), the"reference emission level" and the "reference level" were distinguished in two
ways:

INFO
1) UNFCCC (2009) FCCC/SBSTA/2009/2, UNFCCC

1. The "reference emission level" is net emission established by the wholecountry , and the "reference level" is net absorption.
2. The "reference emission level" is used evaluate activities to reduce

INFO

emissions resulting from deforestation (REDD activities), and the2) UNFCCC (2011) FCCC/SBSTA/2011/"reference level" is used to evaluate "plus" activities.

INF.18, UNFCCC

In some cases, both a reference emission level and a reference level willbe established in the same country. To enable more REDD-plus activities
to be implement stepwise in the National Strategy and Action Plan ofdeveloping countries, many experts agreed that flexibility should be allowedin establishing the reference emission level and reference levels.
To establish a reference emission level or a reference level, historical dataon deforestation and forest degradation are essential. Deforestation can be
identified relatively easily by using historical time-series satellite data because
deforestation is a clear land-use change. Data on forest carbon stock changes
are indispensable for establishing the reference emission level. Therefore,
it is vital to estimate forest carbon stocks before deforestation occurs. On

72

REDD-plus Cookbook

Recipe P13
the other hand, forest degradation monitoring is more technically difficult.
However, degradation that causes a large decrease in forest carbon stocks
can in many cases be monitored by remote sensing. Thus, during the SBSTA
meeting of experts, some were of the opinion that emissions caused bydeforestation should be taken into account when establishing the reference
emission level or reference level but that emissions from forest degradation
should not necessarily be taken into account. To identify forest degradation,
not only remote sensing but also ground-based carbon stock monitoring andmeasurement are considered to be essential.

Discussions under UNFCCC
The 15 th conference of the Parties (COP 15) concluded that whendeveloping countries establish a reference emission level or reference level,
they should take account of their nation's circumstances, while ensuringtransparency by using their own historical data. COP 16 decided thatthe forest reference emission level and the forest reference level at thenational level, or the sub-national level as a transitional measure, shouldbe established before REDD-plus activities are implemented in developing
countries. However, their definitions and details of how to establish themremain unsettled.
At COP 17, the modality of reference emission levels and reference levels
were discussed and the Parties came to the following conclusions:・The reference emission level and the reference level are criteria forevaluating each country's performance in the implementation of
REDD-plus activities and should be calculated in terms of CO2

・The established reference emission level and reference level should
be consistent with the emissions and removals in the forest that areincluded in national greenhouse gas (GHG) inventories

・ To develop a reference emission level or a reference level, better dataand improved methods and, if appropriate, a phased approach which
incorporates additional carbon pools, is effective
・

A preliminary reference emission level or emission level at the subnational level should be developed during the period of transition tonational level reporting.

Organizational challenges
Historical data are essential for establishing a reference emission level and

REDD-plus Cookbook

73

Chapter 5 - Monitoring by the stock change methodreference level. However, the reference emission level or reference level canvary greatly depending on the period of historical data acquisition and thesort of model used. With a more precise model, data should be available atmore time points. Therefore, full discussions should be held regarding theselection of a model for estimating future trends and the appropriate timeinterval for obtaining the historical data required to create the model.
For forest carbon crediting, highly accurate monitoring is requiredthat must be measurable, reportable, and verifiable (MRV). However, theavailability of historical data may be limited. Establishing an appropriate
reference emission level/reference level in such a way that transparency andreliability are ensured while national circumstances are taken into accountis extremely important for the success of REDD-plus. Capacity building inaccordance with the national circumstances may be required because thetechnology and data needed for establishing the reference emission level
and/or reference level are possessed to a varying degree by developing
countries. Full implementation of REDD-plus in developing countries shouldtake place only after the reference emission level and reference level havebeen provisionally established during demonstration activities and theorganizing challenges have been successfully satisfied.

74

REDD-plus Cookbook

REDD-plus Cookbook

75

Chapter 6 - Premises of REDD-plus implementation

6
Chapter 6
Preparation for REDD-plus
implementation

76

REDD-plus Cookbook

Before REDD-plus can be implemented by a country, the preparation status of that country
must be determined. Are forest distribution data available? What information resources andtechnical expertise does the implementation country have? Which greenhouse gas (GHG)
should be measured preferentially there? The readiness state of a candidate country must
be understood so that an appropriate carbon stock estimation technique can be chosen
according to the resources that are available or can be obtained.

T01    National forest inventory
T02    What greenhouse gases are measured?
T03    Attainable resources
T04    Voluntary carbon credit certification system

REDD-plus CookBook

77

Chapter 6 - Premises of REDD-plus implementation

Recipe - T01

National forest inventory
The preceding Recipe is
Recipe I03    Measurement, reporting andverification (MRV) of forest
monitoring                                                                                                                                                                                                  In many developed countries, a forest resources survey of the entire
country, called the national forest inventory (NFI), is periodically carriedout to assess the forest resources of that country. Here, statisticalsampling design, sampling plot types, and sampling methodology
appropriate for a national forest inventory are described.

What is national forest inventory?
In order to evaluate forest resource of the entire country (e.g. areas volume
and increment of growing stock, etc.), a forest resources survey is periodicallycarried out by the unified technique in most European and North Americancountries. This is called the national forest inventory. Today, sample-based
national forest inventory data can be used for accurate carbon absorption by
the forest.

National forest inventory sampling designs
For statistical sampling, a map grid with a grid interval of 0.5–20 km is
typically used, and sampling plots or groups of plots (plot clusters) areestablished at grid points or at reference points whose position is fixed bya certain rule (Figure T01-1). Because some plot locations may not be in theforest area, it is also possible to use, for example, aerial photographs or fieldobservations to estimate the total forest area by ascertaining whether or notplots are in the forest.
The use of plots of different shapes or of plot clusters reflects differencesin the forest environment of each country. In many countries, circular plots
are used, but plots in the natural forests of China, Brazil, and New Zealand arerectangular. In some countries(e.g., France), circular plots

4km

consisting of two, three, or four
Permanent
P clusters
Temporary
T clusters

concentric circles are used.
In western Europe (e.g., France)
and Japan, a single-plot system istypical (Figure T01-2). In central
Europe (e.g., Germany), small

10km
Germany

Swedenclusters consisting of four plots
about 150–500 m apart andarranged in a square are used,

Figure T01-1 Examples of grid for systematic samplings in national forest inventory78

REDD-plus Cookbook

whereas in northern Europe, large

Recipe T01
clusters that may consist of 10 plots or more arranged in a rectangle, eachside of which is more than 1000 m long, are used. Moreover, in Brazil, a crossshaped cluster is used, and in the United States, a cluster consists of threeplots arranged in an equilateral triangle with a fourth plot at the center of thetriangle.
The advantage of using plot clusters is that it is possible to collect many
data from several plots within a short time period, because the plots are close
together. On the other hand, because plots in the same cluster may not be
statistically independent, the statistical analysis is more complicated.

Conducting the inventory
Forest carbon stocks are estimated by conducting a census of the trees inthe sampling plots. For single circular plots consisting of concentric circles,
standing trees with small diameter are measured in the innermost circle.
For example, in France, each circular plot consists of three concentric circles
with radii of 6, 9, and 15 m, and standing trees with a diameter at breast
height (DBH) of 7, 20, or 37 cm are measured in the innermost, the middle,
and the outermost circle, respectively. Since tree height measurement
requires considerable time and effort, only some standing trees are usuallychosen for this measurement. To measure parameters such as biodiversity,
measurements and other information of understory vegetation, fallen trees,
tree stumps, soil, etc., are also collected and recorded.

INFO
1) Tomppo E, Gschwantner M,

National forest inventories in tropical countries

Lawrence M, McRoberts RE (2010）
National Forest Inventories. Springer-

In tropical countries, because of difficulties of access and technical

Verlagproblems, national forest inventories similar to those conducted in Westerncountries may be hard to carry out. Brazil,
however, first performed an national forest
inventory in 2005 1) . There, a map gridwith a grid interval of about 20 km interval
has been established, and four 20 m ×
50 m plots are arranged in a cross shape

Single plot system(France)

Square cluster(Germany)

centered at each grid point. Standing
trees with a DBH of 10 cm or more aremeasured. Subplots of 10 m × 10 m havebeen established within each plot formeasurement of smaller diameter trees,

Large size rectangular cluster(Sweden)Equilateral triangle cluster(USA)

Cross-shaped cluster(Brazil)and seedlings are censused in a 5 m × 5 m
subplot within each plot.

Figure T01-2 Type classification of the national forest inventory plot
REDD-plus Cookbook

79

Chapter 6 - Premises of REDD-plus implementation

Recipe -- T02
P02

What greenhouse gas are measured?
The preceding Recipe is
Recipe P04    Measurement of forest 		carbon	                                        INFOTo monitor greenhouse gas (GHG) emissions efficiently, it is important
to clarify the measurement target. In this recipe, possible REDD-plus
measurement targets, the greenhouse gases carbon dioxide, methane, andnitrous oxide, and five carbon pools in a forest, above-ground biomass,
below-ground biomass, dead wood, litter, and soil organic matter, aredescribed.

Which greenhouse gases should be the target of REDD-plus activities

1) As one judgment criterion, if agreenhouse gas source accounts for

I n REDD-plus, three greenhouse gases, CO 2 (carbon dioxide), CH 4

more than 25% of total emissions in(methane), and N2O (nitrous oxide), are the most usual monitoring targets.

preliminary calculations, that source
should be measured. Furthermore,

Regardless of the forest ecosystem type, measurement of CO2 is essential. In

the sum of the contributions from allareas of forest where fires are common, CH4 and N2O may also need to bemeasured sources should be morethan 95% of total emissions. A high-measured. In addition, CH4 may also need to be measured in forest areas withpriority source that is a target ofmany peat bogs. Whether measurements of CH4 and N2O sources should bemonitoring is called a key category.

carried out depends on the contribution of each source to total greenhousegas emissions 1). Figure T02-1 shows the results of preliminary calculations

INFO

of the contributions of different sources to total greenhouse gas emissions2) Kiyono Y, Saito S, Takahashi T,

using Intergovernmental Panel on Climate Change (IPCC) default values andHirai K, Saito H, Toriyama J, Monda

observed values in the field 2).

Y, Awaya Y, Shimada M, Inoue T,
Hatano R, Sophal C, Samreth V,

Moreover, once it is determined that measurements are required, theSum T, Kanzaki M, Limin SH, Jaya

implementation of monitoring may be difficult. The stock change method

INS (2011) Important subcategoryof greenhouse gas emissions fromdegraded forestland: CO2 emissions
from biomass in a seasonal forest in
Cambodia and soil organic matter in

is available for monitoring CO2 (see Chapter 5), but a flux method   must
be used for CH4 and N2O. However, training personnel to use an analytical
instrument   such as a gas chromatograph to measure gas fluxes is timea peat swamp forest in Indonesia. The

consuming and expensive. Moreover, field observations and measurements

Kanto Forest Research 62: 167-170

usually need to be made frequently, as often as once every two to four weeks.
To hold down monitoring costs, it may be possible to use flux values already

3%

0%

0% 4%

0%

1% 1%

4%

7%
4%

CO2：Biomass (above- and below-ground)
CO2：Dead organic matter (deadwood, litter)
CO2：Soil
89%

87%

N2O：Fire
N2O：Soil mineralization
CH4：Fire

Figure T02-1 Estimation of contribution of subcategories to total greenhouse gas emissions 2)
Tropical monsoon forest (Left: 425 t-CO2/ha/10 yr in total)
Peat swamp forest (Right: with drainage treatment: 878 t-CO2/ha/10 yr)

80

REDD-plus Cookbook

Recipe T02
obtained by a university or other research institution in the project area, or,if no measurements are available, use of the default IPCC emission factors
should be considered 3). Here, the focus is on the stock change method, theimplementation of which is feasible for many projects. For information about
the flux method, the reader is directed to the literature 4) (Figure T02-2).

INFO
3) The emission factor database isavailable at
http://www.ipcc-nggip.iges.or.jp/
EFDB/main.php

INFO
Figure T02-2 Measurement of greenhouse gas by flux method 4)4) Sakata T, Ishizuka S, Takahashi M(2004) A method for measuring fluxesA) Settlement of chamberof greenhouse gases from forest soils.

B) Suction of greenhouse gas by cylinder

Bulletin of FFPRI 392: 259-265

C) Filling of evacuated vial with greenhouse gas

Five forest carbon pools
In the carbon stock method, forest ecosystems are regarded as consistingof five carbon pools 5) : above-ground biomass, below-ground biomass,
dead wood, litter, and soil organic matter 5). It is important to know the
relative contribution of each pool to forest carbon stocks. In the example
of a tropical seasonal (i.e., deciduous) forest (left pie chart in Figure T02-1),

INFO
5) By the stock change method, thecalculated annual change in a carbon
pool (t-C/ha) is converted into theannual change in CO2 (t-CO2/ha) bymultiplying it by the factor 44/12.

the contributions of CO2 derived from above-ground and below-groundbiomass are large, and the estimation accuracy of total emissions will be
improved by measuring these directly rather than using default emission
factors. On the other hand, the contribution of CO2 from soil (peat) is more
important in a peat bog forest (right pie chart in Figure T02-1). Measurementof above-ground and below-ground biomass for the stock change method is
explained in P10 and P11. Forest carbon stocks of dead plants or soil can beevaluated similarly, by assessing the carbon stocks per unit area (t-C/ha) andmultiplying that value by the forest area (ha). Therefore, if the concept of thestock change method is understood, which carbon pools to measure can bedecided according to the relative contribution (priority) of each greenhousegas source.

Bibliography
GOFC-GOLD (2011) A sourcebook of methods and procedures for monitoring
and reporting anthropogenic greenhouse gas emissions and removals
caused by deforestation, gains and losses of carbon stocks in forests
remaining forests, and forestation: GOFC-GOLD Report version COP17-1.
GOFC-GOLDREDD-plus Cookbook

81

Chapter 6 - Premises of REDD-plus implementation

Recipe - T03

Attainable resources
The preceding Recipe is
Recipe P09    Estimation of carbon 		stocks per unit area	                                        To estimate forest carbon stocks as required in REDD-plus, certain
information and technical resources and a biomass estimation model forthe implementation country are needed. The state of preparation of each
implementation country and the availability of a suitable model must be
ascertained, and then the methods and techniques used for forest carbon
stock estimation should be selected by taking into account the resourcesthat are available or attainable.

Remote sensing resources
As a result of recent advances in remote sensing technologies, many
improvements in ground-surface and wavelength resolution have beenachieved, and many different types of sensors are available. A satellite
equipped with high-resolution optical sensors can now image the groundsurface with a resolution of about 50 cm, which means that it has theability to observe individual tree crowns. The per unit area cost of animage, however, tends to increase with increases with the ground-surface
resolution. For observations of forests in tropical areas, remote sensing withradar technology has the advantage that the radar waves can penetrate
clouds, making observation possible on cloudy days. In forest areas, however,
topographical features may influence the result, and, moreover, radar
technology is not suitable for observations of above-ground biomass in ahigh carbon stock area. Progress in the development of correction factors foratmospheric or geometric distortions, however, have improved the accuracyof observations.
Thus, remote sensing technology has advantages and disadvantages
depending on the sensor and the analysis method used. Therefore, whether
remote sensing technology should be applied and the choice of sensor
should be decided after considering the scale of the necessary observations
and natural conditions in the region (see T05).

Field survey resources
To calculate forest carbon stocks per unit area, it is necessary to knowwhether a biomass estimation equation has been developed for the country
in question and whether National Forest Inventory (NFI) (see T01) data are
available. If both of these are available in the implementation country, they
can be used to calculate the forest carbon stocks.

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REDD-plus Cookbook

Recipe T03
If no NFI data are available in a country that is implementing REDDplus, a permanent sample plot (PSP) survey system must be designed andestablished so that forest data can be collected (see P10 and T12-T14).
Moreover, if a REDD-plusimplementation country lacks a statistical model forcomputing biomass or carbon stocks, a model suited to the country must be
developed (see P11 and T15-T19).

Continuity of resources
In REDD-plus, future emissions are predicted from past changes of forest
carbon stocks. Since the difference between the future predicted value
and the forest carbon stocks at present is the basis for economic incentive
grants by REDD-plus, the past trend and the future monitoring resultsmust be comparable. In choosing the technique to use to determine forest
carbon stocks, it is important to evaluate whether that technique cancontinue to be used in the future, from the point of view of technical andcost considerations. In addition, the consistency and reliability of the remotesensing technique used to detect changes in the forest area serve as thefoundation for a third party review. Such consistency and reliability cannot
be achieved if different techniques are used at different times.

REDD-plus Cookbook

83

Chapter 6 - Premises of REDD-plus implementation

Recipe - T04

Voluntary carbon credit certification system
The preceding Recipe is
Recipe I04    Designing the forest    monitoring system                                                                                       Voluntary certification systems have been developed independently of
the United Nations Framework Convention on Climate Change (UNFCCC)
and the Kyoto Protocol to evaluate emission reduction and removal
activities for the purpose of awarding credits, known as Verified Emission
Reductions (VERs), that can be traded. Among these certification systems,
the Verified Carbon Standard (VCS), which targets actions in the forest
sector, including REDD-plus, has been adopted by many independent
projects.

The present status of the VER certification system
The Kyoto Protocol includes mechanisms for issuing and trading emission
reduction credits; the Clean Development Mechanism (CDM) is typical. In thiscase, the obtained credit is called a Certified Emission Reduction (CER). On
the hand, the credit by other voluntary system is called a Verified Emission
Reduction (VER).
While CERs can be applied toward achievement (compliance) of UNFCCC
or Kyoto Protocol reduction targets, VERs cannot be used for that purpose.
Rather, they are applied toward achievement of voluntarily set reduction
targets or Corporate Social Responsibility (CSR) actions. In Japan, J-VER isa widely recognized type of VER that can be issued when CO2 removal isachieved in a forest.
Although REDD-plus has not yet been formally incorporated into the
UNFCCC, advanced voluntary projects are being carried out in many countries.
The VCS certification system is used by many of these voluntary projects.

VCS
The VCS is a verification and certification standard for voluntary emissions
and removals efforts, and a greenhouse gas accounting program  with the same
name was established in 2005. Its purpose is to promote actions that reduce CO2
emissions through a market mechanism by verifying and issuing carbon credits(Verified Carbon Units or VCUs) that can be traded in voluntary markets. Morerecently, VCS has developed the Jurisdictional and Nested REDD-plus initiative(JNR) as an accounting and crediting framework for REDD-plus-related credits.
The VCS structure consist "VCS standard" 1) which sets out the

INFO

implementation rules of whole system and "Requirements" which provide the1) VCS (2012) VCS Standard v 3.2

necessary information and implementation procedure under the VCS spectral

http://v-c-s.org/sites/v-c-s.org/files/
VCS%20Standard%2C%20v3.2_0.pdf

84

scopes. Project requirements relating to REDD-plus set out in the AFOLU(Agriculture, Forestry and Other Land Use) Requirements 2) . VCS has also

REDD-plus Cookbook

Recipe T04
established "Methodologies" which sets out detailed procedures and equations
for quantifying the greenhouse gas benefits of a specific project.
Table T04-1 lists approved methodologies related to REDD-plus activities
and the applicability by region and forest type. Although development of

INFO
2) VCS (2012) AFOLU Requirements
http://v-c-s.org/sites/v-c-s.org/files/
AFOLU%20Requirements%20v3.2.pdf

methodologies is not easy, those that are already registered are easy to
implement. A guidebook is available 3) that assists project developers in theselection of methodologies.

INFO
3) Shoch D, Eaton J, Settelmyer S

Future considerations(2011) Project Developer’ s Guidebook
to VCS REDD Methodologies

Voluntary projects implementing REDD-plus activities are becoming moresophisticated, and many have adopted VCS. However, under the UNFCCC,

http://www.conservation.org/about/centers_programs/carbon_fund/
Pages/resources.aspx

REDD-plus is targeted at national or sub-national level implementation, not at
individual projects using a VER-based certification system. How to later integratethese voluntary projects, which are carrying out meaningful forest conservation
activities, with REDD-plus under the UNFCCC, is a challenge for the future.
The Japanese government has promoted the bilateral offset credit
mechanism (BOCM) to counteract global warming. Incorporation of bilateral
offset credit mechanism into REDD-plus is being considered, and bilateral offset
credit mechanism guidelines and methodologies are being developed with thatpossibility in mind.

Table T04-1 VCS Methodology and Applicability Conditions
Methodology No.

Land Applicability

Type of REDD-plus Activity

Region

VM0003

Improved Forest Management(Extension in rotation age)No restriction

VM0004

Avoiding Planned Deforestation

TropicalType of Forest
・Forests certiﬁed (or planned to be certiﬁed) by the FSC
・Not including managed peat forest・Tropical peat swamp forests

Southeast Asia
VM0005

・Not including human settlement

Tropical
Improved Forest Management(Conversion of low productive to highproductive forest)

・Evergreen tropical rainforests
・Not including peatland (or emissions associated withpeatland are not significant)

VM0006

Avoiding Mosaic Deforestation andForest Degradation

No restriction

・No restriction

VM0009Avoiding Mosaic Deforestation andForest Degradation(If the soil carbon pool is selected,)
Tropical or semi-arid tropical・Not including organic or peat soil

VM0010

Improved Forest Management(Conversion from logged toprotected forest)

No restriction

・Not including wetland or peatland

VM0011

Improved Forest Management(Conversion from logged toprotected forest)

Tropical・Evergreen tropical forests, Moist deciduous forests, Tropicaldry forests and Tropical upland forests

VM0012

Improved Forest Management(Conversion from logged toprotected forest)

Temperate and Boreal

Avoiding Unplanned Deforestation

No restriction

VM0015

・Not including forest designated for legally sanctioned
logging activities

・Forests designated for sanctioned selective logging
・Private ownership properties
・Not including managed peatland forest
・Not including forested wetland growing on peat

Bibliography
VCS homepage: http://v-c-s.org/
J-VERhomepage: http://www.j-ver.go.jp/

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Chapter 7 - Estimation of forest area using remote sensing

7
Chapter 7
Estimation of forest
area using remotesensing

86

REDD-plus Cookbook

When measurement, reporting, and verification (MRV) of forest carbon stocks are
performed for REDD-plus, forest carbon stocks must ﬁrst be estimated at the national level.
Under the Unaited Nations Framework Convention on Climate Change (UNFCCC), use of
remote sensing and a ground-based inventory in combination is recommended for thispurpose. In particular, remote sensing should be used to clarify the forest types present andarea occupied by each type. This chapter presents the fundamental knowledge and methods
required to determine the area of each forest type by using remote sensing technology. In
addition, methods of combining remote sensing with a field survey and estimating forest
carbon stock per unit area are also introduced. Detailed explanations of these methods are
given in Chapter 9.

T05 Remote sensing and sensor types, and image data
selection
T06 Clouds and seasonality diﬀerences in images
T07 Deﬁnition of a land cover class
T08 Ground truth
T09 Classiﬁcation methods
T10 Accuracy evaluation
T11 Estimation of forest area change

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Chapter 7 - Estimation of forest area using remote sensing

Recipe - T05

Remote sensing and sensor types, and image data selection
The preceding Recipe is
Recipe P08 Area estimation using
remote sensing

To determine forest area, forest type, and degree of forest degradationfrom remote sensing image data, the image must have been acquired bya sensor suitable given the purpose of the measurements and the sizesof the target area and object. In this section, basic information about themain optical satellite sensors, including both technical speciﬁcations andcosts, and the selection of image data and analysis software is presented.

Platforms
Remote sensing sensors are mounted on a platform such as a satellite oran aircraft. Continuous earth observations are made from meteorologicalsatellites in geostationary orbit, the platform most distant from the earth.
Geostationary satellites appear to occupy a fixed position in the sky about
35,800 km above the equator. In contrast, an earth observation satellite
follows a circular orbit from 450 to 1,000 km above the surface and observes
the same point periodically. If the earth observation satellite sensor has
the pointing capability, or it can tilt itself perpendicular to the orbit, it canobserve a given point more frequently than the satellite revisit time. Aircraft
observations can be made from a helicopter, which flies at altitudes fromhundreds to thousands of meters above the ground, or from a fixed-wing
aircraft (e.g., a propeller plane or a jet), which ﬂies at altitudes of 1,000‒20,000
m. Furthermore, observations from near the earth's surface can be madefrom a tethered balloon, a radio-controlled machine, an unmanned aerial
vehicle (UAV), a cherry picker, or a person standing at a distance.
If the platform is close to the earth's surface, detailed observations are
possible, but a large area cannot be overlooked at once. In contrast, a large
area can be observed at once from a platform far above the earth, but fewer
details can be distinguished. Thus, there is a trade-oﬀ between the area thatcan be observed and the level of detail that can be captured. Therefore,
remote sensing sensors are mounted on various platforms at different
altitudes, depending the purpose of the observation. Observations fromplatforms other than a satellite are performed as needed, and the sensor
and the observations made can therefore be selected according to typeand amount of data needed. On the other hand, a cost is incurred each timeobservations are made.

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REDD-plus Cookbook

Recipe T05

Sensors
A sensor is a device that observes electromagnetic waves reﬂected fromor emitted by the target. Sensors can be either passive or active. Common
passive sensors are analog and digital cameras, which capture sunlight or
artiﬁcial light reﬂected oﬀ the target (electromagnetic waves emitted fromthe target depending on the observation wavelength band). Optical sensors
used for earth observation are passive sensors. In contrast, active sensors
send electromagnetic waves toward a target and observe their reflection.
Common examples are a weather radar and a laser range finder. For earth
observation, Synthetic Aperture Radar (SAR) and Light Detection And
Ranging (LiDAR) are used.
An optical sensor detects the intensity of visible to infrared light in one
or more wavelength bands. Optical sensors typically detect reflected lightin the visible (wavelength, 0.4‒0.7 μm), near-infrared (0.7‒1.3 μm), shortwavelength infrared (1.3‒3 μm), mid-wavelength infrared (3‒8 μm), or
emitted light in the far or thermal infrared (8‒14 μm) wavelength bands.
Ground resolution, wavelength resolution, and time resolution diﬀer among
sensors. Recently developed sensor can make detailed, high-resolution
observations. Optical sensors can be mounted on a satellite or an aircraft orthey can be handheld by a person. Collection of data by an optical sensor has
many advantages. An optical image can be understood intuitively because
it resembles what we see with the human eye, and if the data are visualized
as a monochrome or an RGB color composite image, land-cover types can beeasily distinguished by their characteristic reﬂections (spectral pattern) andtexture. Thus, optical sensors have been the most-used type of sensor until
now. On the other hand, an optical sensor has the disadvantages that cloud
cover and haze or dust can obscure the earth's surface or cause the data to
be noisy, and that visible and reﬂective infrared wavelength bands cannot be
used for nighttime observations when there is no sunlight.
Synthetic Aperture Radar (SAR) is an active sensor mounted on either asatellite or an aircraft, which emits microwave pulses (wavelength about
7‒1,000 mm) obliquely, then detects and records the intensity, phase, andtime of the reflected pulses from the earth's surface (backscatter). Some
SAR sensors can also observe polarization. Sizes, configurations, densities
and dielectric properties of the objects affect the backscattering's pattern,by which the objects are identified. Characteristics of the backscatter froma target depend on the wavelength band of the sensor. When a forest isobserved using the L band (wavelength about 150‒300 mm) or the P band
(300‒1,000 mm), the backscatter has three main components: reflections
from the earth's surface or a tree crown or forest canopy (surface scattering);
reﬂections from leaves within the canopy (volume scattering); and double-

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Chapter 7 - Estimation of forest area using remote sensing

Figure T05-1 Three components of the SAR backscattering from forest

bounce reflections, when the microwaves are reflected twice (e.g., firstfrom the ground surface to a tree trunk, or from the leaves to the groundsurface, and then back to the sensor) before returning to the sensor (FigureT05-1). Research to interpret the state of the forest (species, biomass, etc.)from this information is ongoing. Moreover, changes in the elevation of
the measurement surface can be detected by interference analysis fromobservation data of a forest obtained at different times. Interpretation ofthis information might allow the detection of deforestation and forest
degradation. Observations can be made both in the daytime and at night
and in all weather conditions, because SAR is an active sensor and themicrowaves can penetrate clouds. Therefore, SAR is expected especiallysuitable for application to tropical forest observation. However, it also has
disadvantages. The resulting data cannot be understood intuitively, thebackscattering mechanism is complicated and not completely understood,
and the angled observation introduces distortion into the resulting images
and also results in the existence of blind spots, portions of the surface thatcannot be observed.
LiDAR emits laser pulses at a frequency of tens to hundreds of kilohertz. It
measures the distance from the sensor to the target by the traveling time andintensity of the reﬂected pulse from the target. Airborne LiDAR typically emits
the laser pulses continuously over an angle of several tens of degrees to both90

REDD-plus Cookbook

Recipe T05
sides of the sensor perpendicular to the direction of movement, which allows
the sensor to measure distribution of the earth's surface elevation threedimensionally. The geometric accuracy of the measurement is assured by theinertial measurement unit (IMU) 1), which measures the position of the sensor
and its inclination. The elevation of the earth's surface can be measured at

INFOhigher resolution by using a higher-frequency laser pulse emitted at lower

1) Inertial measurement unit

altitude. When the LiDAR is aimed at a forest, these laser pulses can be(IMU): Precise position and posturemeasuring device which combinedreflected by the tree canopy surface, the leaves within the canopy, or the

global navigation satellite system

earth's surface. A digital surface model (DSM) of the tree canopy can be(GNSS) and an inertial navigation

created from the firstly returned components of the reflected (first pulse),

European GALILEO, GLONASS ofwhile a digital terrain model (DTM) of the ground surface can be created

Russia, Hokuto Compass in China, etc.

from the lastly returned components (last pulses). Then, by subtracting thesystem. GPS of the United States,name GNSS generically.

DTM from the DSM, a digital crown model (DCM) can be obtained, which canFigure T05-2 Concept of data acquisition by LiDAR

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Chapter 7 - Estimation of forest area using remote sensing

be used to determine the height of a single tree or the stand height (FigureT05-2). This information can then be used to estimate the stand biomass.
Moreover, since the pulses returned between the first and last pulses are
reﬂected by the interior of the tree canopy or by the understory vegetation,
the three-dimensional structure of the forest can be determined in detailfrom them.
To obtain high-resolution data, the aircraft must fly at both low altitude
and low speed, which results in the observations of a large area time
consuming and expensive, or even impossible in mountainous areas. Other
disadvantages of LiDAR include the inability to penetrate clouds or fog andthe data processing time/costs because of the huge amount of observational
data.

Spatial resolution, wavelength resolution, and time resolution
Spatial resolution (how small an object can be recognized), spectral
resolution (how many subdivisions of the wavelength range can beobserved), and temporal resolution (the frequency with which the same area
can be observed) determined the performance of a sensor. A suitable sensor
should be chosen according to the target, the purpose, and the scale (area)of the observation. Spatial resolution typically varies from several centimeters
to about 1 m in aerial photographs, and from 40 cm to several kilometers insatellite sensor images; temporal resolution ranges from one day to tens ofdays; and spectral resolution ranges from one band extending over the entire
wavelength range of the sensor to hundreds of bands.
A high spatial resolution image with a resolution of 1 m or less is required
to acquire information about individual trees. Therefore, if the targeted area
is large, the data volume becomes huge and data processing is difficult.
Moreover, because the area that can be observed at one time becomes
smaller as the spatial resolution becomes higher, it becomes more diﬃcult tocover a large area in a short period as the spatial resolution becomes higher.
For acquisition of information about a stand, a medium-resolution image,
one having a spatial resolution of about 10 m to several tens of meters, may
be suitable, but to be able to distinguish among stands to detect diﬀerent
degrees of degradation, the spectral resolution must be high. If the purpose
is only to distinguish forest from non-forest, a sensor with a visible and anear-infrared wavelength bands is suﬃcient.
When the target is forest, it is often assumed that the temporal resolution
does not need to be very high. For a satellite-borne optical sensor, however,
the time resolution needs to be considered in conjunction with the frequency
over which the data can be actually acquired. If the whole target region

92

REDD-plus Cookbook

Recipe T05
cannot be observed at one time, it is necessary to create a mosaic of multiple
observations obtained at around the same time. Therefore, in the tropics andmonsoon regions, where clouds often obscure the view of the earth's surface
from the sky, a sensor with high temporal resolution is advantageous. The
temporal resolution can be increased if oblique observation is possible or ifthey are made by two or more satellites.

Present, past, and future
Archived remote sensing images can be viewed as a time machinethat travels back into the past. No other data source has recorded suchaccurate temporal and spatial information about the past. Records from
the Multispectral Scanner System (MSS) on-board Landsat 1, the world's
first earth observation satellite, go back to the 1970s. However, naturally,
the farther back in the past the record was obtained, the more likely it isthat the performance of the sensor deteriorated or that the sensor failed.
Thus, the sensor type might change or the images may be acquired lessfrequently with time and the area covered may become smaller. The image
data captured by each sensor may have been stored by the organization that
acquired them or archived in an archive center. Recently, data from many
sensors and platforms have been made available on the Web, where they canbe searched and downloaded.
With regard to future acquisition of image data, orders for data acquisition
can be made in advance for additional cost for certain types of sensors/
satellites.

Selection of image data
Available optical sensor data collected by satellite-borne sensors are
summarized in Table T05-1. Landsat satellite imagery in the archive of theUnited States Geological Survey (USGS) is downloadable for free from the
USGS website. On the landsat.org homepage (http://landsat.org), some oforthorectiﬁed Landsat images are also available for free download. However,
not all Landsat images are stored in the USGS archive, so it is necessary to
search the websites of the organizations distributing image data for eachcountry, and there is usually a charge for such data. Moreover, it may benecessary to purchase satellite imagery acquired by corporations, such as
GeoEye, DigitalGlobe, SpotImage, or from local agencies.

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Chapter 7 - Estimation of forest area using remote sensing

SoftwareGeneral photo retouch software can be used for simple and/or elementary
processing such as color composite displaying. For advanced processing
and analyses, however, specialized remote sensing image-analysis softwareis required. Freeware or open source software, which often work as wellas commercial software, are also increasingly available for this purpose.
Although price may be a constraining factor in selecting software, it isimportant to consider, in addition to the purchase price, the support system,
update frequency such as bug ﬁxes, the existence of a user community, andthe provision of training. In general, it is an advantage for people in the same
work group or organization to use the same software, because they can help
one another and questions can be answered immediately.

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REDD-plus Cookbook

Recipe T05

Table T05-1 Speciﬁcations and prices of major space-borne optical sensors(prices are in Japan, as of July 2012)

Satellite

Landsat 1-3

Landsat 4-5

Landsat 7

EO 1

SensorLaun Terminched ated
year year

Spatialresolution

Swath
(km)

MSS

1972

1983

68×83m185

MSS

1982 1995 b)

TM

1982

ETM+

ALI-Pan, MS

Offnadirobservation

Spectral bands(number of bands)

VIS(2), NIR(2)Revisit
Altitude
Revisit(nadir)
(km)
(day)
(day)

915

18

18

705

16

16

705

16

16

705

16

AcquisPer-areaition A full-scene
price
byprice (JPY)2(JPY/km )order

Operatora) Data for free of charge
available at USGS
NASA/USA （http://glovis.usgs.gov/,
http://earthexplorer.usgs.gov/）

40,740 a)

1.3

40,740 a)

1.3

88,200 a)

2.8

88,200 a)

2.8

NASA/USA d) SLC-off since 2003

00

Data available for free ofNASA/USA charge at
http://eo1.usgs.gov/

68×83m185

VIS(2), NIR(2)

- c)

30m
(Band 6: 120m)

185

VIS(3), NIR(1),MIR(2), TIR(1)

1999

- d)

30m
(Band 6: 60m,
Band 8: 15m)

183

2000

-

MS: 30m
Pan: 10m

37

30m

7.5

VIS - MIR(220)250m: VIS(1), NIR(1)500m: VIS(2), NIR(1),MIR(2)
1km: VIS(7), NIR（5）,
MIR(9), TIR（8)

705

16

16

0

0

NASA/USA

294,000 e)

8.2e) SPOT Scene level 2A, theSpot Image
same price for both XS and Pf)/ Francef) now Astrium

Hyperion

VIS(3), NIR(1),MIR(2), TIR(1),VIS - NIR(1)Pan: VIS(1)MS: VIS(4), NIR(3),MIR(3)

1616b) partially re-started in 2012
NASA/USA

EOS-Terra/
EOS-Aqua

MODIS

1999

-

250m/500m/
1km

2,330

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1986

1993

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M: VIS(1)

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294,000 g)

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France

SPOT 5

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2002-

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60‫ݱ‬

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145.0

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FranceVEGETATION 1998-

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1988

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20

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Data available for free ofcharge at
http://reverb.echo.nasa.gov/
reverb/g) SPOT Scene level 2A, thesame price for both XS and Ph) SPOT Scene level 2A, thesame price for both X (10mresolution) and P (5mresolution)
Data available for free ofcharge at
http://free.vgt.vito.be/i) Archived, standard
product

ISRO/ India

817

2003

IKONOS 1

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ISRO/ Indial) LISS-III Multi 140 x 140km
at EuroMap, 1EUR＝100JPY

NASDA m)/m) now JAXAJapann) forward/ backward
JAXA/ Japano) Level 1 standard products,

GeoEye/USAp) Level 1Bq) Geo Product, Pan+Multiset price; minimum ordering
2

area 25kmr) Standard Image Pan + 4band Multi bundle price;minimum ordering area
225km
GeoEye 1

WorldView 1

WorldView 2

Pan/
Multi

Pan

Pan/
Multi

2008

2007

2009

-

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2

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225kmu) Standard Image Pan + 8band Multi bundle price;
Digital6,400 u)Globe/ USA minimum ordering area
2

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814

0.5

0.5

0

0

GISTDA/
Thailand500kmx) the same price for Pan
and Multi; 100,000 JPY for
the data acquired within thelast 6 months

NOAA/ USA y) Weather satellites

REDD-plus Cookbook

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Chapter 7 - Estimation of forest area using remote sensing

Recipe - T06

Clouds and seasonality diﬀerences in images
The preceding Recipe is
Recipe P08 Area estimation using
remote sensing

Two major problems often arise when optical satellite imagery is used
to observe a large area of the tropical forest: first, clouds may obscure
the ground surface, and second, seasonal vegetation changes may be
mistaken for different forest types when scenes from multiple images
are used. In this recipe, some ways of coping with these obstacles are
explained.

Cloud removal
Clouds and haze and their shadows on the ground are obstacles to groundsurface observation based on optical satellite images. In the tropics, inparticular, overcast skies are common, and cloud-free satellite images are
seldom obtainable.
Cloud removal is a set of processes that create a mosaicked cloud-free
image by patching together the cloudless parts of multiple images acquired
within a targeted time period. The ﬁrst step is to detect and remove clouds,
haze, and their shadows from each image (Figure T06-1). Visual interpretation
on a display is a reliable technique for identifying and masking clouds, but
manual interpretation requires a considerable amount of time and attention,
especially when small clouds are scattered over the image. A realistic solution
can be to combine visual interpretation with the use one of the available

INFO
1) Zhu Z, Woodcock CE (2012) Object-automatic cloud removing algorithms 1).

based cloud and cloud shadow

The next step is to mosaic together the cloudless image parts (Fig. T06-

detection in Landsat imagery. Remote2). Although the mosaicked image shows the entire forest without clouds,

sensing of Environment 118: 83-94

because the images were acquired at diﬀerent times, the image acquisition
conditions, such as the direction and angle of the sun and seasonal
vegetation characteristics, can differ in different parts of the mosaicked
image.

Original image

Clouds are masked out (red)

Figure T06-1 Cloud removal
Clouds are retrieved empirically by detecting appropriate threshold values.
Landsat 7 ETM+ (Malaysia)

96

REDD-plus Cookbook

Recipe T06
Figure T06-2 Cloud-free mosaicked image
This mosaic was created by replacing the cloud-covered parts of
one image by the cloudless parts of three other images obtained forthe same geographic area. Clouds were removed from each image
automatically by using one of the available algorithms 1).
White patches in the mosaicked image are areas covered by clouds
in all of the individual images. Landsat 7 ETM+ images (Malaysia)

Seasonality adjustment
When the target forest area is larger than the area covered by a single
satellite image scene, for example, when the target area is an entire
country, then it is necessary to use multiple scenes. If images of the required
scenes are obtained over a long time period, then the reflectance from

INFO

the vegetation is likely to diﬀer as a result of seasonal vegetation changes,

2) Roy DP, Ju J, Lewis P, Schaaf C, Gaoespecially in the case of forest types that show large seasonal changes each

F, Hansen M, Lindquist E (2008) Multitemporal MODIS‒Landsat data fusion

year. For example, in tropical seasonal forests, the forest canopy leafs out infor relative radiometric normalization,

the rainy season and drops its leaves in the dry season. The leaﬂess forest can

gap ﬁlling, and prediction of Landsat

be misinterpreted or misclassiﬁed as bare soil or grassland, depending on thedata, Remote Sensing of Environment,
112: 3112-3130

amount of understory growth. In addition, a mosaicked image of multiple
scenes might show unrealistically abrupt discontinuities at borders between
adjacent scenes if the scenes were obtained during seasons.
To avoid this problem, the images should beacquired during as short a time period as possible
so that the seasonal changes are small. If that is notpossible, then seasonal adjustment should be applied
to the images before the land cover classiﬁcation of
the area is performed, or the classification should
be applied to each image separately and then theseparate land cover maps can be compiled to createa land cover map of the entire target area.
Histogram matching is a technique for adjustingseasonal diﬀerences among images. In this technique,the spectral reflectances of known and unchanged
objects are compared among the images andthen the reflectance histograms of the images are
empirically adjusted to that of a reference image 2).
Figure T06-3 shows an example of a pair of images
before and after seasonal adjustment.

(a) Original images(b) The upper image was adjusted to the lower

Figure T06-3 Seasonality adjustment (Langner, unpublished)
The upper and lower images were derived during the middle and at the
beginning of dry season, respectively. The deciduous forests are alreadydefoliated in the upper image, while they are still foliated in the lower
image. After seasonal adjustment, the reflectance values of the upper
image were adjusted to ﬁt the lower.
© CNES2007, Distribution Astrium Services/ Tokyo Spot Image

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97

Chapter 7 - Estimation of forest area using remote sensing

Recipe - T07

Deﬁnition of a land cover class
The preceding Recipe is
Recipe P08 Area estimation using
remote sensing

To estimate the forest area by land cover classification of remotesensing data, mutually exclusive and collectively exhaustive land coverclasses, such as forest, farmland, and urban, need to be defined. Here,
what is meant by "mutually exclusive and collectively exhaustive" isexplained and the design of a classiﬁcation system and ways to improveclassiﬁcation accuracy are discussed.

Mutually exclusive and collectively exhaustive classes
Land cover classes must be deﬁned so that they are mutually exclusive andcollectively exhaustive. Here, "collectively exhaustive" means that classes aredeﬁned so that all parts of the classiﬁcation domain can be allotted to a classwithout any part being left unclassiﬁed. To achieve this, not only objects ofinterest but also objects of no interest must be classiﬁed. Moreover, classes
must be "mutually exclusive," which means no overlap can be allowed
between classes. Thus, one class cannot be a subset of another class. For
example, forest and evergreen forest cannot both be defined as classes,
because evergreen forest is just a part of forest.
Land cover classes can be viewed as having a hierarchical structure, andawareness of this structure is helpful for defining mutually exclusive andcollectively exhaustive classes. For example, at the top level of the hierarchy,
the classiﬁcation system might include classes such as forest, farmland, andcity, while in the level below that, the forest class might be divided into an
evergreen forest class and a deciduous forest class 1) (Figure T07-1). By taking
account of this structure, in which the more inclusive classes at the top are

INFO

divided into more detailed classes at a lower level, duplication and overlap

1) Campbell JB (2006) Image

between classes can be avoided.

classification In: Introduction to
Remote Sensing Fourth Edition.
Guilford Press, 324-366

ForestEvergreen

Agriculture

Deciduous

Water

Urban

ͼͼͼͼͼͼ

ͼͼͼͼͼͼ

Figure T07-1 Hierarchical structure of classes

INFO
2) Cihlar J, Ly H, Xiao Q (1996) Land
cover classification with AVHRR

Desired and possible classiﬁcation systems

multichannel composites in northern
environments. Remo Sens Enbiron 58:
36-51

Desired classes and classes that can actually be distinguished in remotelysensed data are not necessarily in agreement 2), and when they are not, it is98

REDD-plus Cookbook

Recipe T07
necessary to reconcile the two without introducing inconsistency into theexisting classiﬁcation system.
One way of accomplishing this reconciliation and meeting the needsof the user is to change the kind of remote sensing data, or the techniqueused to classify those data, from what is currently being used. For example,
classiﬁcation at a more detailed level is possible if data with a higher spatial
resolution are used. In addition, how, for example, a deciduous forest
appears depends on when the image was acquired. Therefore, by using
images acquired at diﬀerent times, classes of forest types such as deciduousand evergreen may become possible. Thus, both the data type and theclassification technique should be selected at the time that the classes aredeﬁned.

Integration of classes after classiﬁcation
When classification based on remote sensing data with user-definedclasses is diﬃcult, classiﬁcation accuracy may become low. In such a case, it
may be possible to increase classiﬁcation accuracy by merging two or moreclasses after the initial classiﬁcation. However, care must be taken so that themerged class is not inconsistent with the existing classiﬁcation system.
In addition, different areas that belong in the same class may not beclassiﬁed into the same class because they appear
diﬀerent on the remote sensing image because of

Shaded slope

Sunny slope

a diﬀerence in, for example, topographic features.
For example, an area of forest on a sun-lit slope
may appear different from a forest area on ashaded slope, and the two areas might be placedin diﬀerent classes even though both areas should
be classified as forest (Figure T07-2). In such acase, the separated classes, forest on a sunlit slope
and forest on a shaded slope, can be merged
during post-classification processing, which will
increase the accuracy of the classiﬁcation.

Figure T07-2 Forests on the sunny andshaded slopes
Bibliography
Franklin SE (2001) Remote Sensing for Sustainable Forest Management. Lewis
Publishers

REDD-plus Cookbook

99

Chapter 7 - Estimation of forest area using remote sensing

Recipe - T08

Ground truth
The preceding Recipe is
Recipe P08 Area estimation using
remote sensing

Ground truths mean the actual terrestrial conditions at the target, and it is used
to confirm classes and estimates based on remote sensing data and verify results.
Collecting ground truths is referred as "ground truthing." Accuracies of not only theterrestrial conditions but also their geographical locations are required so that the
ground truths can be exactly compared with the corresponding locations on the resultsfrom the remote sensing data. Suﬃcient numbers of ground truths should be allocatedin a statistically sound manner so that the results of analysis are robust and unbiased.

Ground truth
Ground truths mean information about actual terrestrial conditions observed, measured, andcollected to confirm the correspondence between remote sensing data and the observation
target. Ground truths are used as ancillary data for creating models to analyze the remote sensing
data and verifying the results, such as collection of training and veriﬁcation data from land-covertypes to be used for land cover classiﬁcation.
When ground conditions or limited accessibility make a ﬁeld survey diﬃcult, higher resolution
satellite imagery, aerial photographs, and existing map information may be substituted for ﬁeld
data. Under such circumstances, these are also ground truths in the broad sense.

Location of ground truth and its accuracy
Geographical coordinates of ground truths are often collected by using Global Positioning
System (GPS), existing maps or interpretation of aerial photos or satellite images.

INFO

When using GPS, it should be noted that the accuracy under the canopy could be more

1) Jones HG, Vaughan RA (2010)

reduced than open areas. Therefore, the position is identified by the combination of above

Remote sensing of vegetation:

methods depending on circumstances. The accuracy of the position information can be

Principles, techniques, andimproved by enhancing the position accuracy of GPS using diﬀerential positioning or averaging

applications. Oxford University Press

positioning results. The required accuracy of position varies depending on the spatial resolutionof the remote sensing data used. For the object based image analysis, it should be notedthat ground truths should not span more than one segment. In addition, since the state of

INFO

forest vegetation changes according to season within a year or from year to year, vegetation

2) Jensen JR (2005) Introductory
digital image processing: a remotephenology (seasonal vegetation differences) must be taken into consideration, particularly

sensing perspective (Third edition).

when a vegetation index is used, or when the target forest is one that undergoes large seasonal

Prentice Hallchanges, such as a tropical deciduous forest.
A camera with GPS, which can record the coordinate of shooting location directly on

INFOphotograph, is a useful device to document the circumstances of ground truths.

3) Hirzel AH, Guisan A (2002) Whichis the optimal sampling strategy for
habitat suitability modelling. Ecol

Sampling method

Model 157: 331-341

Ground truth must be sampled in a statistically appropriate manner. The sampling method100

REDD-plus Cookbook

Recipe T08
has various ways such as a simple random sampling, stratiﬁed sampling,and systematic sampling, and each method has advantages anddisadvantages 1),2) (Figure T08-1, Table T08-1). Stratified sampling isan effective method for obtaining statistically valid results. To keep thenumber of samples from becoming too large while still obtaining arobust result, the number of samples collected from each stratum can be
adjusted according to the total area to be sampled 3). Moreover, to keep
costs from being excessive, stratified sampling can be combined with

Figure T08-1 Sampling strategies.

transect sampling across an environmental gradient 4).

Descriptions of A to G are shown in Table T08-1

Number of samples
INFO
Required number of ground truths as the samples for supervised classification is assumed4) Wessels KJ, Jaarsveld AS, Grimbeek

more than 10 times of the number of the explanatory variables used (e.g. number of spectral

JD, Van Der Linde MJ (1998) An

bands) 1),2). The more heterogeneous a sample is, the more number of samples are required.

evaluation of the gradsect biological

The number of ground truths per class as the samples for verifying the classiﬁcation result is said

1093-1121

survey method. Biodivers Conserv 7:

to be more than 50, determined from a viewpoint of balancing between the statistical adequacy
and the feasibility on the ground 2),5).

INFO

In addition, the ground truth used for veriﬁcation should be independent from the one used
for training, in order not to overestimate the model accuracy with the same training data.

5) Congalton RG, Green K (1999)
Assessing the Accuracy of RemotelySensed Data: Principles and Practices.
CRC Press

Bibliography
McCoy RM (2004) Field Methods in Remote Sensing. Guilford Press
Table T08-1 Advantages and disadvantages of sampling strategies (blue words are modiﬁed after Jones and Vaughan, 2010)
Sampling strategies

Advantages

Disadvantages

A*

Simple random

Statistically optimal.
Minimum operator bias.

Possible inaccessibility of points in difficult terrain.
Small but important categories may be undersampled or missed.
Points not uniformly distributed over the entire study area.

B

Stratified
randomReduce chance of undersampled categories
Largely avoids operator bias.
Often the most efficient strategy.

Possible inaccessibility of points.
A thematic map covering the study area is additionally required for stratification.

C

Systematic(Regular)Ease of sampling.
Evenly spread over area.

The process of selection can interact with hidden periodicities within the population (e.g.,ridge and valley topography).
Possible bias where linear landscape features exist.
Over- or underestimation of categories occurs by their population size.
Orientation and position of samples depend on the selection of the starting point.

D

Systematic
unaligned

Ease of sampling.
Evenly spread over area.
A degree of randomness is maintained
within the constraints of the grid cell.

Possible bias where linear landscape features exist.
Over- or underestimation of categories occurs by their population size.

E

Cluster

Reduces travel cost in the field.
Some combination of cluster and stratified
sampling provides the best balance ofstatistical validity and practical application(Jensen, 2005).

Subject to spatial autocorrelation.

F

Transect

Ease of access and sampling.
Good where pre-existing gradients are
known (e.g., altitude).

Subject to bias depending on how transects are chosen.
Incomplete coverage.

G

Multistage

Smaller sampling cost
Normally more accurate than cluster
sampling with the same sampling size.
Effective for using satellite imagery withdifferent spatial resolution.

Normally less accurate than simple or stratified random sampling with the same sampling
size.

*The letters A to G correspond to panels A to G in Figure T08-1.

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Chapter 7 - Estimation of forest area using remote sensing

Recipe - T09

Classiﬁcation methods
The preceding Recipe is
Recipe P08 Area estimation using
remote sensing

To create a high-precision land cover map from satellite data, selectionof an appropriate classification method is important. The classification
methods are broadly divided into two types: supervised, which uses
training data, and unsupervised. The minimum unit of classiﬁcation canbe a pixel or an object, the more conventional choice. Here, classiﬁcation
methods used for the analysis of satellite data are outlined.

Classiﬁcation in remote sensing
The process of assigning a land cover class to each pixel using its characteristics
such as the spectral reﬂectance in satellite data is called land cover classiﬁcation.
Two statistical approaches used for land cover classification are supervisedclassiﬁcation and unsupervised classiﬁcation. Supervised classiﬁcation uses training
data (a set of data whose characteristics serve as a reference for designating classes),and unsupervised classiﬁcation does not.
In supervised classiﬁcation, a model for identifying a class (classiﬁer) is built byusing training data, and a class is assigned to each unknown pixel according to thismodel. The maximum likelihood method is often used for supervised classiﬁcationof satellite image.
In unsupervised classiﬁcation, the degree of similarity among pixels is determinedby using only satellite image, and all pixels are divided into similar sets of pixels
called clusters (clustering). Because this analysis
method does not assign a class by itself (FigureT09-1), an analyst subsequently needs to interpretthe result and to assign a class to each cluster or
to groups of clusters. The results are interpretedby comparison with ground truths. ISODATA is atypical technique of unsupervised classiﬁcation.
Other techniques, such as construction of a selforganization map, a classiﬁcation tree, or a neural
Figure T09-1 Unsupervised classiﬁcation by ISODATA

network can also be used for classiﬁcation.

(a) source image ‒ Landsat ETM+(b) resulted class map with 9 clusters

Classiﬁcation by pixel and object-based classiﬁcation
In a forest, a pixel of medium-resolution satellite data (for example, 30 m × 30 m)
represents solar radiation reﬂected from various parts of one or more tree crowns.
The pixel may include parts of the crown illuminated by solar radiation and parts
that solar radiation cannot reach. However, in the case of high-resolution satellite

102

REDD-plus Cookbook

Recipe T09
data, a single pixel is smaller than a tree crown, so a pixel represents the reﬂectionof solar radiation from only a small part of a tree crown. As a result, diﬀerent pixels
showing reflections from the same object (e.g., a tree crown) can have various
values. The resulting internal class variance may become excessive, and cause it to
be difficult to obtain a suitable classification result. To avoid this problem, objectbased classiﬁcation, in which groups of spatially adjoining pixels with similar feature
values (obtained by a process called segmentation) are used as the minimum unit in
the classiﬁcation process.
In object-based classiﬁcation, the pixel values used as feature values in pixel-basedclassiﬁcation is used to calculate the average value of an object. In addition, various
other characteristics of objects determined by segmentation, such as the varianceof the pixel values and the texture and shape of the object, can be used in theclassiﬁcation. An advantage of object-based classiﬁcation is that the segmentation
process in unlikely to create a salt-and-pepper pattern scattered over the land covermap. Object-based classification is effective not only for high-resolution satellite
images but also medium-resolution satellite images, and it has been used in the
Global Remote Sensing Survey of the Global Forest Resources Assessment 2010
(FRA2010) 1).

INFO

Since segmentation results diﬀer depending on the initial parameter settings in

1 ) FA O , J R C , S D S U , U C L ( 2 0 0 9 )object-based classification (Figure T09-2), trial and error is used to determine theAssessment Remote Sensing Survey:

optimal parameter values. In addition, parameter values are peculiar to each data set,an outline of the objectives, data,

and thus suitable values must be selected for diﬀerent data sets 2). The average landuse size of the target area, the minimum mapping unit, and other information should

The 2010 Global Forest Resources

methods and approach. Forest
Resources Assessment Working Paper
155. FAO with FRA RSS partners

be also taken into account when the parameters are set.
Parameter values should be set by taking into consideration the average of the

INFO2) A scale parameter is a parameter

patch size of the target area, minimum mapping unit, and other information.

that inﬂuences the size of the object
output.

Bibliofraphy
Bishop C.M. (2006) Pattern Recognition and Machine Learning. Springer

Figure T09-2 Results of segmentation with diﬀerent scale parameters(original image: Landsat ETM+)

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Chapter 7 - Estimation of forest area using remote sensing

Recipe - T10

Accuracy evaluation
The preceding Recipe is
Recipe P08 Area estimation using
remote sensing

It is highly improbable that a land cover map obtained by image
classiﬁcation will perfectly represent the actual land cover distribution on
the ground; rather, such a map is highly likely to contain errors and biases.
The map producer should, therefore, provide accuracy information about
the map so that it can be properly used for estimating land covers andland cover changes. This section outlines the assessment of classiﬁcation
accuracy and correction for biases in area estimates using the error
matrix. In a real mapping project, an appropriate sampling design and acorresponding error estimation method should be applied to reduce theuncertainty of the error matrix.

The error matrix and accuracy indicators
A point is correctly classified if the map class (i.e., the land cover classassigned by the classification procedure) is identical to the reference class(i.e., the real land cover class at that point on the ground). Conversely, a point
is wrongly classified if the map class is not identical to the reference class(Figure. T10-1). Suppose an image is classiﬁed into ‫ ݎ‬classes; then, an error
matrix (or a contingency matrix) is derived as an ‫ ݎ‬ൈ ‫ ݎ‬matrix whose
elements ܰ௜௝ represent the area (i.e., number of pixels multiplied by the area
per pixel) of map class ݅ሺͳ ൑ ݅ ൑ ‫ݎ‬ሻ and reference class ݆ሺͳ ൑ ݆ ൑ ‫ݎ‬ሻ (Table
T10-1). In the matrix, the diagonal element ܰ௝௝ ሺͳ ൑ ݆ ൑ ‫ݎ‬ሻ represents the area௥that is correctly classiﬁed into the reference class j , and ܰ௜‫ ڄ‬ൌ ෍ ܰ௜௝ is the௝ୀଵ௥

area on the map that is classiﬁed into map class ݅ . ܰ‫ڄ‬௝ ൌ ෍ ܰ௜௝ is the area on௜ୀଵ௥௥the ground that is actually in reference class j , and ܰ ൌ ෍ ෍ ܰ௜௝ is the total௝ୀଵ ௜ୀଵ

area of the map.
Overall accuracy A : The ratio of the correctly classiﬁed area on the map,
regardless of the map class, to total map area is 1 when the map perfectly
agrees with the reality on the ground, and 0 when the map totally disagrees
with reality. A target accuracy of A 0.85 is typical for many land cover
mapping projects.
௥

‫ܣ‬ൌ

ͳ
෍ ܰ௝௝
ܰ

(T10-1)

௝ୀଵUser's accuracy ܽ‫ݑ‬௜ : The ratio of the area correctly classified into map
class ݅ to the total area classified into map class ݅ . ͳ െ ܽ‫ݑ‬௜ is called thecommission error.

104

REDD-plus Cookbook

Recipe T10ܽ‫ݑ‬௜ ൌ ܰ௜௜ ോ ܰ௜‫ڄ‬(T10-2)

Producer's accuracy ܽ‫݌‬௝ : The ratio of the area correctly classiﬁed into map
class j to the total area of reference class j . ͳ െ ܽ‫݌‬௝ is called the omission error.

ܽ‫݌‬௝ ൌ ܰ௝௝ ോ ܰ‫ڄ‬௝(T10-3)Kappa coefficient K : An overall classification accuracy indicator, whichtakes into account the eﬀect of coincidentally correct classiﬁcation. The value(a) Real land covers(reference)(b) Classified land covers(map)(c) The map seldom agrees with the reference

Figure T10-1 The real and classiﬁed land covers
The reference (a) is unknown actual mapping projects. To estimate the errors and biases ofthe map (b), the real land covers (i.e. ground truths) should be identiﬁed and compared withthe classiﬁed land covers at sample points.









ͳ

‫ڮ‬

݆

‫ڮ‬

‫ݎ‬



ͳ
‫ڭ‬
݅
‫ڭ‬
‫ݎ‬

ܰଵଵ
‫ڭ‬
ܰ௜ଵ
‫ڭ‬
ܰ௥ଵ

‫ڮ‬

ܰଵ௝

‫ڮ‬



Table T10-1 Error matrix for a whole image ‒ population

‫ڮ‬

ܰ௥௝

‫ڮ‬

ܰଵ௥
‫ڭ‬
ܰ௜௥ 
‫ڭ‬
ܰ௥௥

ܰଵ‫ڄ‬
‫ڭ‬
ܰ௜‫ ڄ‬
‫ڭ‬
ܰ௥‫ڄ‬



ܰ‫ڄ‬ଵ

‫ڮ‬

ܰ‫ڄ‬௝

‫ڮ‬

ܰ‫ڄ‬௥

ܰ



 ‫ڰ‬

REDD-plus Cookbook

105

Chapter 7 - Estimation of forest area using remote sensing

has the range [0,1], and a higher value represents a better classiﬁcation result.

ߢൌܰ σ௥௜ୀଵ ܰ௜௜ െ σ௥௜ୀଵሺܰ௜‫ڄܰ ڄ‬௜ ሻܰ ଶ െ σ௥௜ୀଵሺܰ௜‫ڄܰ ڄ‬௜ ሻ(T10-4)

Uncertainty of the error matrix and ground truth
In the preceding section, a complete error matrix without uncertainty isderived under the assumption that the reference class is known at any given
point on the map. That is, however, not the case for real mapping projects,
in which we seldom have complete knowledge of the reference classes‒ otherwise we would not have to use remote sensing! Here, methods tostatistically estimate the error matrix and the accuracy indicators by using
ground truth data (see T08) are introduced. An appropriate sampling
design and calculation procedure should be applied to properly reduce theuncertainty of the error matrix.
There are two basic sampling designs for obtaining ground truth data for
accuracy assessment: simple random sampling (SRS) and stratiﬁed sampling(SS). In SRS, the sampling probability of map class ݅ and reference class ݆ isassumed to be proportional to the area ܰ௜௝ . If available, extensive systematic
sampling, such as a national forest inventory (see T01), can be used as thesubstitute of an SRS ground truth data set. When the sample size is small, theerrors of classes occupying small areas might become relatively large.
In SS, an arbitrary number of samples is randomly allocated to each map
class. In many mapping projects, ground truth data are allocated in thismanner, because it can be a way to eﬃciently utilize the project resources.
Error matrices derived from SRS and SS are shown in Tables T10-2 and T103, respectively.
Let ݊௜௝ the number of samples in map class ݅ and reference class j , thenTable T10-2 Error matrix for simple random samples





106

REDD-plus Cookbook

 



ͳ

‫ڮ‬

݆

‫ڮ‬

‫ݎ‬



ͳ
‫ڭ‬
݅
‫ڭ‬
‫ݎ‬

݊ଵଵ
‫ڭ‬
݊௜ଵ
‫ڭ‬
݊௥ଵ

‫ڮ‬

݊ଵ௝

‫ڮ‬

݊௥௝

‫ڮ‬

݊ଵ௥
‫ڭ‬
݊௜௥ 
‫ڭ‬
݊௥௥

݊ଵ‫ڄ‬
‫ڭ‬
݊௜‫ ڄ‬
‫ڭ‬
݊௥‫ڄ‬



݊‫ڄ‬ଵ

‫ڄ݊ ڮ‬௝

‫ڮ‬

݊‫ڄ‬௥

݊



෡‫ڄ‬ଵ
ܰ

‫ڮ‬

෡‫ڄ‬௝
ܰ

‫ڮ‬

෡‫ڄ‬௥
ܰ

‫ڰ‬
‫ڮ‬

ܰଵ‫ڄ‬
‫ڭ‬
ܰ௜‫ ڄ‬
‫ڭ‬
ܰ௥‫ڄ‬

ܰ

Recipe T10

Table T10-3 Error matrix for stratiﬁed samples





௥

 



ͳ

‫ڮ‬

݆

‫ڮ‬

‫ݎ‬



ͳ
‫ڭ‬
݅
‫ڭ‬
‫ݎ‬

݊ଵଵ
‫ڭ‬
݊௜ଵ
‫ڭ‬
݊௥ଵ

‫ڮ‬

݊ଵ௝

‫ڮ‬

‫ڮ‬

݊௥௝

‫ڮ‬

݊ଵ௥
‫ڭ‬
݊௜௥ 
‫ڭ‬
݊௥௥

݊ଵ‫ڄ‬
‫ڭ‬
݊௜‫ ڄ‬
‫ڭ‬
݊௥‫ڄ‬



෡‫ڄ‬ଵ
ܰ

‫ڮ‬

෡‫ڄ‬௝
ܰ

‫ڮ‬

෡‫ڄ‬௥
ܰ

௥

௥

‫ڰ‬

௥

ܰଵ‫ڄ‬
‫ڭ‬
ܰ௜‫ ڄ‬
‫ڭ‬
ܰ௥‫ڄ‬
ܰ

݊௜‫ ڄ‬ൌ ෍ ݊௜௝ , ݊‫ڄ‬௝ ൌ ෍ ݊௜௝ , and ݊ ൌ ෍ ෍ ݊௜௝ . The total number of samples,௝ୀଵ௝ୀଵ ௜ୀଵ

௜ୀଵ

݊ , in SRS, or in SS the number of samples in map class ݅ , ݊௜‫ ڄ‬, is set arbitrarily
prior to the sampling.
The total map area, ܰ , and the total area of each map class, ܰ௜‫ ڄ‬, are alreadyknown. However, the total area of each reference class, ܰ‫ڄ‬௝ , is unknown, eventhough often the goal of a mapping project is to estimate the areas occupied෡௝ , of ܰ‫ڄ‬௝ must be
by diﬀerent land covers on the ground. Thus, an estimate, ܰdetermined.
Let ߨ௜ the areal proportion of map class ݅ , then ‫݌‬Ƹ௜௝ , an estimate of theareal proportion of a map class ݅ and a reference class ݆ is derived as follows:ߨ௜ ൌ ܰ௜‫ ڄ‬Τܰ(T10-5)‫݌‬Ƹ௜௝ ൌ ߨ௜ ݊௜௝ Τ݊௜‫ڄ‬(T10-6)INFO
1) Card DH (1982) Using Known Map
Category Marginal Frequencies to
Improve Estimates of Thematic Map

Next, the accuracy indicators are estimated with their standard errors (SEs )as follows. Note that the equations marked with ሾሿ or ሾሿ are calculations

Accuracy. Photo Engin Rem Sens 48:
431-439

for the SRS or SS sampling designs, respectively, whereas the equations thatare not marked are calculations common to both the SRS and SS designs.

Estimate of the area of reference class j 1):௥෡‫ڄ‬௝ ൌ ܰ ෍ ‫݌‬Ƹ௜௝ܰ(T10-7)

௜ୀଵ
௥

ଵΤଶ

෡‫ڄ‬௝ ൯ ൌ ܰ ൝෍ ‫݌‬Ƹ௜௝ ൫ߨ௜ െ ‫݌‬Ƹ௜௝ ൯ ോ ሺߨ௜ ݊ሻൡܵ‫ܧ‬൫ܰ

ሾሿ

(T10-8)

௜ୀଵREDD-plus Cookbook

107

Chapter 7 - Estimation of forest area using remote sensingଵΤଶ௥෡‫ڄ‬௝ ൯ ൌ ܰ ൝෍ ‫݌‬Ƹ௜௝ ൫ߨ௜ െ ‫݌‬Ƹ௜௝ ൯ ോ ݊௜‫ ڄ‬ൡܵ‫ܧ‬൫ܰ

ሾሿ

(T10-9)௜ୀଵOverall accuracy 1):௥‫ܣ‬መ ൌ ෍ ‫݌‬Ƹ௜௜

(T10-10)

௜ୀଵ
ଵΤଶ

௥

ܵ‫ܧ‬൫‫ܣ‬መ൯ ൌ ൝෍ ‫݌‬Ƹ௜௜ ሺߨ௜ െ ‫݌‬Ƹ௜௜ ሻ ോ ሺߨ௜ ݊ሻൡ

INFOሾሿ

(T10-11)௜ୀଵ2) Congalton RG, Green K (1999)
Assessing the accuracy of remotelysensed data: principles and practices.

ଵോଶ௥ܵ‫ܧ‬൫‫ܣ‬መ൯ ൌ ൝෍ ‫݌‬Ƹ௜௜ ሺߨ௜ െ ‫݌‬Ƹ௜௜ ሻ ോ ݊௜‫ ڄ‬ൡሾሿ

(T10-12)௜ୀଵ

CRC Press, 137pp

User's accuracy 1):ܽ‫ݑ‬ෞ ௜ ൌ ݊௜௜ ോ ݊௜‫ڄ‬

INFO

ଵ Τଶ

௥

ܵ‫ܧ‬ሺܽ‫ݑ‬ෞ ௜ ሻ ൌ ൛‫݌‬Ƹ ௜௜ ሺߨ௜ െ ‫݌‬Ƹ ௜௜ ሻ ോ ൫ߨ௜ଷ ݊൯ൟ௜ୀଵܵ‫ܧ‬ሺܽ‫ݑ‬ෞ ௜ ሻ ൌ ൛‫݌‬Ƹ௜௜ ሺߨ௜ െ ‫݌‬Ƹ௜௜ ሻ ോ ൫ߨ௜ଶ ݊௜‫ ڄ‬൯ൟͳ3) where, ߠଵ ൌ ෍ ݊௜௜ 、
݊
௥

ߠଶ ൌ

ͳ
෍ ݊௜‫ڄ݊ ڄ‬௜、
݊ଶ
௜ୀଵ

ଵΤଶ

ሾሿ

(T10-14)ሾሿ

(T10-15)

Producer's accuracy 1):௥ͳߠଷ ൌ ଶ ෍ ݊௜௜ ሺ݊௜‫ ڄ‬൅ ݊‫ڄ‬௜ ሻ、݊௜ୀଵ௥(T10-13)

ܽ‫݌‬
ෞ௝ ൌ

௥

ͳ
ଶ
ߠସ ൌ ଷ ෍ ෍ ݊௜௝ ൫݊௜‫ ڄ‬൅ ݊‫ڄ‬௝ ൯݊ܰ௝‫݊ ڄ‬௝௝ ܰ௝‫ڄ‬ൌܽ‫ݑ‬ෞ෡‫ڄ‬௝ ݊௝‫ܰ ڄ‬෡‫ڄ‬௝ ௝
ܰ(T10-16)௜ୀଵ ௝ୀଵ

ଵΤଶ௥ଶ෡‫ڄ‬௝ ିସ෡‫ڄ‬௝ܰ‫݌‬Ƹ௜௝ ൫ߨ௜ െ ‫݌‬Ƹ௜௝ ൯ܰܵ‫ܧ‬൫ܽ‫݌‬ෞ ௝ ൯ ൌ ቐ‫݌‬Ƹ௝௝ ቆ ቇ ቎‫݌‬Ƹ௝௝ ෍൅ ൫ߨ௝ െ ‫݌‬Ƹ௝௝ ൯ ቆ െ ‫݌‬Ƹ௝௝ ቇ ോ ൫ߨ௝ ݊൯቏ቑ
ߨ௜ ݊
ܰ
ܰ

INFO

௜ஷ௝

4) Stehman SV (1996) Estimating theሾሿ(T10-17)

Kappa Coefficient and its Varianceunder Stratiﬁed Random Sampling.
Photo Engin Rem Sens 62: 401-407

ଵΤଶ

௥

ଶ
෡‫ڄ‬௝ ିସ෡‫ڄ‬௝ܰ‫݌‬Ƹ௜௝ ൫ߨ௜ െ ‫݌‬Ƹ௜௝ ൯ܰܵ‫ܧ‬൫ܽ‫݌‬ෞ ௝ ൯ ൌ ቐ‫݌‬Ƹ௝௝ ቆ ቇ ቎‫݌‬Ƹ௝௝ ෍൅ ൫ߨ௝ െ ‫݌‬Ƹ௝௝ ൯ ቆ െ ‫݌‬Ƹ௝௝ ቇ ോ ݊௝‫ ڄ‬቏ቑ
ܰ
ܰ
݊௜‫ڄ‬
௜ஷ௝

INFO
௥

෡ൌ෍
5) where, ‫ܦ‬௜ୀଵ෡‫ڄ‬௜ 、݂௜ ൌ‫ܩ‬෠ ൌ ෍ ܰ௜‫ܰ ڄ‬௜ୀଵ(T10-18)

ܰ௜‫ڄ‬
、
݊௜‫ڄ‬

௥

݊௜‫ڄ‬
、
ܰ௜‫ڄ‬

khat (estimate of the kappa coeﬃcient) 2), 3) ,4) ,5):
ߢƸ ൌ

ܸ෠௜ ൌ

‫ݑ‬௜ଶ െ ݊௜‫ݑ ڄ‬ത௜ଶ݊௜‫ ڄ‬െ ͳሾሿ݊ σ௥௜ୀଵ ݊௜௜ െ σ௞௜ୀଵ ݊௜‫ڄ݊ ڄ‬௜ሾሿ݊ଶ െ σ௞௝ୀଵ ݊௜‫ڄ݊ ڄ‬௜(T10-19)、ଵΤଶ‫ݑ‬ത௜ ൌ ͳȀ݊௜‫ ڄ‬ቐ݊௜௜ ൤ܰܰ ଶ െ ‫ܩ‬෠൨൅෡ െ ܰ൯ܰ൫‫ܦ‬൫ܰ ଶ െ ‫ܩ‬෠ ൯ଶ ෍ ݊௜௝ ܰ௝‫ ڄ‬ቑ௝ஷ௜、ͳ ߠଵ ሺͳ െ ߠଵ ሻ ʹሺͳ െ ߠଵ ሻሺʹߠଵ ߠଶ െ ߠଷ ሻ ሺͳ െ ߠଵ ሻଶ ሺߠସ െ Ͷߠଶଶ ሻܵ‫ܧ‬ሺߢƸ ሻ ൌ ቊ ቈ൅൅቉ቋሺͳ െ ߠଶ ሻଷሺͳ െ ߠଶ ሻସ݊ ሺͳ െ ߠଶ ሻଶሾሿ(T10-20)ܰ෡ െ ܰ൯
ܰ௜‫ܰ ڄ‬൫‫ܦ‬ଶ෡ െ ܰ൯ଶ൫‫ܦ‬ଶଶ‫ݑ‬௜ଶ ൌ ݊௜௜ ൥ ଶ൅ଶ ൩ ൅ܰସ ෍ ݊௜௝ ܰ௝ܰ െ ‫ܩ‬෠൫ܰ ଶ െ ‫ܩ‬෠ ൯ ௝ஷ௜൫ܰ ଶ െ ‫ܩ‬෠ ൯

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REDD-plus Cookbook

Recipe T10ߢƸ ൌ෡ െ ‫ܩ‬෠ܰ‫ܦ‬ሾሿܰ ଶ െ ‫ܩ‬෠(T10-21)ଵΤଶ௥ܵ‫ܧ‬ሺߢƸ ሻ ൌ ൝෍ ܰ௜‫ڄ‬ଶ ሺͳ െ ݂௜ ሻ ܸ෠௜ ൘݊௜‫ ڄ‬ൡ௜ୀଵሾሿ

(T10-22)

Accuracies of classifications can be compared by using these estimates.
For example, assume ߢƸଵ and ߢƸ ଶ are the khats from two diﬀerent classiﬁcation
results; then the null hypothesis ‫ܪ‬଴ ǣ ߢଵ െ ߢଶ ൌ Ͳ is rejected when the test
statistic ܼ ൌȁߢƸଵ െ ߢƸ ଶ ȁඥ‫ݎܽݒ‬ሺߢƸଵ ሻ ൅ ‫ݎܽݒ‬ሺߢƸ ଶ ሻ

is normally distributed, where, is ൒ ܼఈോଶ under the assumption of that Zܼఈോଶ is the minimum value of Z for asigniﬁcance level of 100 (1-α ), which is equal to 1.96 or 2.58 when α is 0.05 or
0.01, respectively.
On empirical grounds, a minimum of 50 samples is recommended for eachland cover. If the area is especially large or there are a large number of land
cover types, then the minimum number of samples should be increased to 75
or 100 per land cover 2). The ground truth data set for accuracy assessment
should be different and independent from the ground truth data used astraining data; otherwise the accuracy will be overestimated.

REDD-plus Cookbook

109

Chapter 7 - Estimation of forest area using remote sensing

Recipe - T11

Estimation of forest area change
The preceding Recipe is
Recipe P07 Stock change method

This recipe outlines approaches to estimating changes in forest areafrom a set of images obtained at different time points and problems
associated with each approach. A single image shows the forest coverat only a single time point. Therefore, images acquired at two or moretime points are needed to detect change in the forest. When a set ofimages obtained at diﬀerent time points are available, two approaches
can be used to reveal changes. In the ﬁrst approach, each image is ﬁrst
classiﬁed, and then the classiﬁcation results are compared to determine
their diﬀerences (the change). In the second approach, the set of multitemporal images is all classiﬁed together, then the changes are derived
as a group of classes.

Comparison after the classiﬁcation of images acquired at two diﬀerent times
To detect changes by comparing the classification results obtainedby separately classifying images acquired at two different times, a crosstabulation table (e.g., Table T11-1) is created and used to identify land
covers before and after a change. This is key to understanding the amountof change that occurred. However, changes can be falsely identified along
boundary between land covers when registration of the images to eachother is not accurate enough (Figure T11-1).

Table T11-1 Error matrix for stratiﬁed samples
Land covers in 2004Land
coversin
1990

ForestNon-forest

Water

Total

Forest

606.9

90.5

0.0

697.4

Non-forest

47.9

169.3

0.8

218.0

Water

0.0

9.6

12.2

21.8

Total

654.8

269.3

13.0

937.1

Detection of land cover changes by multi-temporal classiﬁcation
There are two multi-temporal classiﬁcation methods that can be used toclassify a set of multi-temporal images all together: in one, classification is
performed on a differentiated image over the time period (in most cases,by calculating the diﬀerence between the two images, and in the other, theimages are combined into a single multi-layer image, which is then used forthe classiﬁcation (Figure T11-2).
If multi-temporal classification of images in a block is performed bydetecting changes and the training data are sampled from only changed

110

REDD-plus Cookbook

Recipe T11
areas, then the land covers before and after the change cannot be identiﬁed.
On the other hand, if the sampling is conducted to include land covers

INFO
1）Dai X, Khorram S (1998) The eﬀects

both before and after the change, a larger number of training samplesof image misregistration on theis needed. Moreover, because the acquisition times of the images differ,accuracy of remotely sensed changechanges of luminance due to seasonal diﬀerences (phenology diﬀerences) or

36: 1566-1577

detection. IEEE Trans Geosci Rem Sens

topographic features may be mistaken for land cover changes.

INFO

Some notes on the change detection2) http://www.gofcgold.wur.nl/redd/
sourcebook/Sourcebook̲Version̲To use images acquired at different times to detect change, geometricNov̲2011̲cop17̲1.pdf

registration of the images is extremely important. For a change detection
error of less than 10 %, an accuracy of position of less than 0.2 pixel is needed1). However, in GOFC-GOLD, it is acceptable if the relative error of the two

images is less than 1 pixel 2).

INFO3) An object is a group of spatiallyadjoining pixels that have a

In the Remote Sensing Survey of Global Forest Resources Assessment(FRA) by the FAO, an object-based 3) classiﬁcation was applied to each of thehomogeneous spectrum. The process
dividing an image into objects isreferred to as "image segmentation."time-series set of images, then the changes in forest area were determinedby comparing the classiﬁcation results from diﬀerent years 4). In contrast, in
GOFC-GOLD, an object-based classification was applied to the time-series
set of images all together to determine the forest changes through the timeINFO4) http://www.fao.org/docrep/012/
k7023e/k7023e00.pdf

period 2).

Figure T11-1 Comparison after the classiﬁcation

Figure T11-2 A multi-temporal image of two diﬀerent datesof images at the two times

Changes appear in diﬀerent colors on a multi-temporal image in which

Green: forest, yellow: non-forest, light blue: water,bands of different acquisition dates are assigned to different color

light green: change from non-forest to forest, red:channels. In this example, two Landsat TM images of diﬀerent dates arechange from forest to non-forest

used; red for Band 3 of the latter image, green for Band 5 of the former
image, and blue for Band 5 of the latter image. Changes from forest to
non-forest appear yellowish, while changes from non-forest to forest
appear reddish, on this image.

Bibliography
Campbell JB（2007）Introduction to Remote Sensing. Guilford Press

REDD-plus Cookbook

111

Chapter 8 - Permanent sample plot method

8
Chapter 8
Permanent sample
plot method

112

REDD-plus Cookbook

The most traditional and direct measurement technique for determining forest carbon
stock involves the use of permanent sample plots (PSPs). The permanent sample plot
method consists of 3 steps; design, field survey, and data analysis. In this chapter, firstly, how
to design the permanent sample plot method, such as how many plots should be used andhow to set up plots is explained. Secondly, how to determine the plot areas and the shapeof plots, as well as technologies that will be useful for field survey are introduced. Lastly,
important things that should be bore in mind when estimating carbon stocks per unit area
using tree census data, especially how to select allometric equation that have a big influenceon the calculation result, are explained.

T12 Selecting the number and arrangement of permanent
sample plots
T13 Measurement of permanent sample plots
T14 Analysis of permanent sample plot data

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Chapter 8 - Permanent sample plot method

Recipe - T12

Selecting the number and arrangement of permanent sample plots
The preceding Recipe is
Recipe P10 permanent sample plot
methodTo estimate carbon stocks efficiently in a large area (national or subnational level), stratified sampling based on a suitable forest classification
method is effective. Here, how to obtain the required accuracy level,
calculation of the required number of permanent sample plots forstratified sampling, and creating a matrix for suitable stratification are
explained.

Required accuracy
The required accuracy of a sampling method, that is, the permissible error ‫ ܧ‬,

is calculated as follows, provide that the population is sufficiently large 1).

		

INFO
1) Avery TE, Burkhart HE (eds.) (1994)
Forest measurements, Fourth edition.
McGraw-Hill

���							��

√�(T12-1)

Here, ‫ ݐ‬is the ‫ ݐ‬statistic value that is necessary for a 5% significance level ofthe ‫ ݐ‬-distribution, ‫ ݏ‬is standard deviation, and ݊ is the number of samples. It

INFO2) Brown S (2002) Measuring,monitoring, and verification of carbon
benefits for forest-based projects. Phil
Trans R Soc Lond A 360: 1669-1683

is expressed as the error rate (%) which is calculated by dividing ‫ ܧ‬by theaverage . According to the equation T12-1, the larger the number of samples( ݊ ) becomes, the smaller the error ( ‫ ) ܧ‬becomes, but since a larger number ofsamples also increases the cost of the investigation, the permissible error rateshould be selected after considering cost effectiveness. Previous experiencesuggest that the permissible error rate in a tropical forest is 10% or less 2).
Moreover, it is clear from this equation that the smaller the standard
deviation ( ‫ ) ݏ‬becomes, the smaller the error ( ‫ ) ܧ‬becomes. Thus, the numberof samples ( ݊ ) can be fewer and the estimation will be more cost efficient if

INFO3) Pearson T, Walker S, Brown S (2005)
Sourcebook for land use, land-use
change and forestry projects. Winrock
International and the BioCarbon Fundof the World Bank

the standard deviation ( ‫ ) ݏ‬is small. Stratified sampling, described below, is one
way to reduce the size of the standard deviation ( ‫ ) ݏ‬3).

How to calculate the required number of permanent sample plots
When the target forest is large, it is likely to include different forest types (e.g.,
needle-leaf or broadleaf and evergreen or deciduous). Moreover, different
parts of the forest may have been affected by different degrees of disturbance,or there may be differences in the successional stage among forests of thesame type. Under these circumstances, stratified sampling is a very effective
sampling strategy. In stratified sampling, the forest is classified into strata(layers) that are relatively homogeneous with respect to the forest type andstate, and then the required number of permanent sample plots and theirdistribution in each stratum is considered.
Suppose that a forest is divided into L strata, the total required number ( ݊ )114

REDD-plus Cookbook

Recipe T12of permanent sample plots is obtained as follows 4):

INFO4) UNFCCC "Calculation of the number

�

�

ℎ��

ℎ��

� �
� � � � �� �ℎ �ℎ ��ℎ � �� �ℎ �ℎ /��ℎ � 		
�of sample plots for measurements(T12-2)

within A/R CDM project activities"
http://cdm.unfccc.int/
methodologies/ARmethodologies/

ൌ the
ܰℎ Ȁܰ
Here, ܹℎ isratio of the number of sampling units in each stratum ܰℎ totools/ar-am-tool-03-v2.1.0.pdf

the whole sample population ܰ ( ܹℎ ൌ ܰℎ Ȁܰ ), t is the value of the t statistic at

the 5% significance level of the t -distribution with ( ݊ - L ) degrees of freedom,‫ݏ‬ℎ is the standard deviation of each stratum, and ‫ܥ‬ℎ is the cost of measuring

the plots in each stratum. The number of samples ( ݊ℎ ) assigned to eachstratum is calculated as follows 5):

INFO5) An Excel sheet that can actuallycalculate the required number of�ℎ � ��ℎ �ℎ /��ℎ∑�ℎ�� �ℎ �ℎ /��ℎpermanent sample plots by stratified			(T12-3)

sampling is available the following
URL:
http://www.winrock.org/ecosystems/

If no information on costs is available, then ‫ܥ‬ℎ = 1, and if the sampling ratio

files/Winrock_Sampling_Calculator.
xls( ݊ / ܰ ) is 5% or more the population is considered to be limited, then therequired number of permanent sample plots is computed as the compensation
value ݊௔ , which is calculated as follows, using the value of ݊ computed above
6).

INFO
6) Wenger KF (ed.) (1984) Forestry
handbook, 2nd edition. John Wiley

ܰ݊
݊௔ ൌ
ܰ൅݊

and Sons				(T12-4)

Suitable stratification
The purpose of using stratified sampling is to raise the estimated accuracy
or to decrease the number of sampling plots needed ( ݊ ) 3). Therefore, theforest should be stratified in such a way that the variation in each stratum ( ‫ݏ‬ℎ )INFO7) Gibbs KH, Brown S, Niles OJ, Foley

is a small as possible compared with the variation in the whole population ( ‫) ݏ‬.

AJ (2007) Monitoring and estimatingin Figure T12-1. This matrix constructed with two axes: one axis shows forest

045023

An effective means of stratification is to create a stratification matrix 7) shown

tropical forest carbon stocks: making
REDD a reality. Environ. Res. Lett. 2:

types and the other shows the state or condition of the forest. Before
constructing the matrix, it isimportant to understand what
factors (e.g., altitude, soil type,land-use history) relate to spatialchanges in carbon stocks 7) .
Moreover, a stratification matrix
is also an effective way toquantify degrees of forest
degradation by forest type.

Forest Condition
Forest type

MatureLoggedSecondary(young)Secondary(med. aged)

Evergreen
Deciduous
Mixed

Figure T12-1 Stratification matrix using forest type and forest condition( modified from Gibbs et al 7))

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Chapter 8 - Permanent sample plot method

Recipe - T13

Measurement of permanent sample plots
The preceding Recipe is
Recipe P10 permanent sample plot
methodAs suggested by the name, permanent sample plots (PSPs) are measured
periodically over a long period of time. In this recipe, permanent sample
plot design (e.g., size and shape), how to conduct a tree census, and waysof improving measurement accuracy are described.

Plot size
The larger the area of each permanent sample plot, the larger the numberof trees that must be measured, and the larger the time and expense

INFO

required for the measurement. On the other hand, if the area of each1) Although the practice of advanced

permanent sample plot is small, measurement requires relatively little timenations is the mainstream, Tomppo

and expense, but the error in the carbon stock measurement result increases.

et al. (2010) summarizes themethodology used for national forest

Moreover, plot area should be chosen with reference to the national forest

inventories by country.

inventory 1) if one has been carried out in the past in the country, and after
researching the forest structure and other characteristics of the forest . As arough standard, an area of 0.1–0.5 ha is often used.

Plot shape
In general, permanent sample plots are either circular or rectangularshaped, plots are often used (Figure T13-1). Each shape has advantages
and disadvantages with respect to both set-up and use. For example, theexistence and location of a plot is marked with stakes. For a circular plot, astake is required only at the central point, but it may be difficult to judge
whether individual trees near the plot boundary are within the plot andtherefore should be a measurement object. On theother hand, a rectangular plot requires a stake in eachof the four corners, at least, but ascertaining whether atree should be measured is easy, because the boundary
is a straight line. Moreover, because measurements will
be carried out again and again, a rectangular plot has
the advantage that it is easy to locate , because there
are at least four stakes.
Stakes should be driven into the ground as deeply
as possible so that they will not be easily dislodged by
Figure T13-1 Shape of plot

soil movement or animal activities. Moreover, in case a

Surveyor can measure the size of trees effectively, i.e., >5 cm in diameter

fire should occur, it is desirable to make the stakes out

at breast height within a, >10 cm within b and >20 cm within c.

of a fireproof material such as concrete (Figure T13-2).

116

REDD-plus Cookbook

Recipe T13
It is best, however, to talk with the local person in charge of the forest areain advance of material selection, because if a valuable material is used, there
is a risk of theft. In addition, when the plot is surveyed, for example by usinga laser range finder and a compass, it is important to measure horizontal
distances (not slope distances), so that estimations of the carbon stocks perunit area will be accurate 2).

INFO2) In addition , the disturbance history
can be investigated in the field. For
example, local residents may havecut firewood or carried out selective
logging. Information on land-use
changes, such as the conversion

One stake in each plot (in the case of a circular plot, the central stake) isof forest into rubber or oil palm
plantations, is also important.

defined as a reference point , and its position information is determinedby GPS and recorded. This information is indispensable for revisits (forremeasurement) and for locating the plots on remote sensing images duringlater analyses.

Preparation for a tree census
The number of established plots and their spatial distribution can also
affect the estimation accuracy (see T12 for details). When monitoring over along period of time is expected, development of a measurement manual can
reduce measurement error caused by the measurements being performedby different people. Moreover, if periodic field investigations are planned, it isbest to use tools that can be obtained locally.
The main purpose of performing a tree census in REDD-plus is to estimate
carbon stocks. For this purpose, at the minimum, tree diameter at breast
height (DBH), tree height (of at least some trees), and the species measured

Figure T13-2 Example of stake
which is resistant to disturbance(a concrete stake)must be determined and recorded 3). These data are required by allometricequations for the calculation of biomass (see T14). Measurements of DBH andINFOtree height are best performed by teams of at least two persons, one to do3) It may be necessary to measure

the measuring and the other to record the result. In addition, the services ofother carbon pools, such as deadan expert to identify the species measured must be secured.
Next, the minimum size of plants (excluding lianas) or trees to bemeasured is set: For example, only trees with a DBH of 10 cm or more mightwood and soil carbon, in addition toplant biomass. See Ravindranath &Ostwald (2008) for methodologies tomeasure these other pools.

be measured. In some cases, species such as bamboo and palms may be
included in the tree census, and judgments may need to be made in thefield about whether marginal species with pseudo-woody stems should beconsidered as measurement objects.

Raising measurement accuracy
For accurate determination of the forest structure in a plot, all living trees
within the plot could be measured, but that is unrealistic with regard to timeand effort. Time and effort can be saved by using a nested plot structure

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117

Chapter 8 - Permanent sample plot method(e.g., concentric circles). For example, the rectangular plot shown in Figure13-1 has three frames (a–c) whose sizes differ. This structure can reduce labor
requirements by measuring in each frame trees with a different minimum
DBH.
The basic measurement procedure is as follows :•Record the position of a tree selected as a measurement object•

Assign a unique identifying number to the tree and attach a tag withthat number to the tree.

•

Measure the tree's diameter at breast height•Mark the measurement height on the tree•

Identify and record the species•

Measure the tree height (when required)•

Record other pertinent information
The position within the plot of each tree being
used as a measurement object must be accurately
determined. By dividing a plot into sub-quadrats, andidentifying each sub-quadrat by a code number, as
shown in Figure T13-3, it is easy to record the positionof individual trees on the resulting grid. Moreover,
if the trees are numbered in a regular pattern, forexample in a counterclockwise direction from the stakeused as the starting point, it will be is easy to tell if any
tree has disappeared when future measurements (remeasurements) are carried out.

Figure T13-3 Example of compartment of plot

All individual trees designated as measurement

For instance, the gray area has the code

objects must be assigned a unique number, and then

"F3-2"

the number must be engraved on a tag, such as acommercial aluminum tag 4), and attached to the tree. Because tags are
sometimes lost (including removal by people making mischief), it will save

INFO
4) For example, Racetrack-Shaped

time and effort when the tree is measured subsequently if the number is also

Aluminum Tags from Forestry

written directly on the stem with paint, or if the rough position of the tree

Suppliers.

in the sub-quadrat and the sub-quadrat code are recorded along with theassigned identification number.
Tree diameter is usually measured at 1.3 m above the ground. Before
measuring the diameter of an individual tree, the measurement location

INFO
5) For trees that have two or moreshould be checked. If there is a swelling that would distort the true

trunks or a bent trunk, please refer

diameter, then that place should be avoided. Similar, if a climbing liana is

to Condit (1997) or Ravindranath &wrapped around the tree, it should be removed before measurement. In

Ostwald (2008), who show concrete
examples of how to measure DBH.

118

such a situation, field annotations such as "measured without climber" and"measuring position moved to avoid swelling" should be written down

REDD-plus Cookbook

Recipe T13
immediately before they are forgotten 5). In addition, in the case of a tree
growing on a slope, the diameter should be measured from the upper side,and the position of the measurement should be marked with paint for the
next time (Figure T13-4).
Also, caution is required in the determination of a measurement position if
the tree has buttress roots. The usual practice is determine the measurement
height from a point 50 cm above the top of the buttress (Figure T13-5).
Although this measurement requires that a ladder be used, it is important
to make these measurements in the correct manner, or an error will be
introduced into the estimation of biomass.
Caution is also required to use the correct tool for measurement of theDBH. For example, with calipers, the DBH is measured not once, but twice,with the second measurement being made at right angles to the first, andthen the average value is used. If a tape measure is used, the DBH might

Figure T13-4 Clarification of measuring

become confused with the girth at breast height (GBH). It is important to beheight (a case in Indonesia)

sure that the value recorded is the correct one. In addition, the same type ofmeasurement tool should be used for all measurements of the same type.
For example, some DBH measurements should not be made with an ordinary
tape measure and others with slide calipers, or a diameter tape and an
ordinary tape measure should not both be used.
In a tropical forest, it is often difficult to measure tree height, because it ishard to observe the tops of the trees. If measurement objects are selectedby a sampling procedure that takes the size structure into consideration,then the height of only some individual trees needs to be measured andthe tree height of unmeasured trees can be determined froma DBH–tree height curve. Also, it is important to verify thedefinition of tree height used, because some engineers may
use commercial tree height.
For species identification, it is best to include an expert
who can identify tree species in the field on the field crew.
Although the list of possible species can be narrowed down
to some extent during a field survey, accuracy will be higher if
specimens are collected for later identification by a botanist.

Bibliography
Condit R（1997）Tropical Forest Census Plots. Springer-Verlag
Ravindranath NH, Ostwald M (2008) Carbon Inventory
Methods: Handbook for Greenhouse Gas Inventory, Carbon
Mitigation and Roundwood Production Projects. SpringerVerlag

Figure T13-5 Measurement of diameter of treewith buttress root
Surveyor must measure the diameter at the lowest height
without buttress root (solid line) rather than the diameterat breast height (1.3 m, dotted line).

Tomppo E, Gschwantner M, Lawrence M, McRoberts RE（2010)
National Forest Inventories. Springer-Verlag

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Chapter 8 - Permanent sample plot method

Recipe - T14

Analysis of permanent sample plot data
The preceding Recipe is
Recipe P10 permanent sample plot
methodThe carbon stocks in a forest are generally considered to be half the forest
biomass. Therefore, to determine forest carbon stocks, the forest biomass
is first estimated, often by using allometric equations. In this recipe,
biomass estimation using tree census data obtained from a permanent
sample plot as explained in T12 and T13 is described.

About allometric equations
Trees of a certain type grow in a predictable manner and the sizes of thedifferent parts of a tree are proportionate. As a result, the size of a part thatis difficult to measure can be estimated from the size of another part that

INFO

is more easily measured. Scaling equations that relate the sizes of different1 ) I n a d d i t i o n , i f n e c e s s a r y, a nparts of a tree are called allometric equations.

allometric equation can be selectedon the basis of the forest classification

Because allometric equations reflect plant growth characteristics, the(e.g., lowland forest, montane

estimated results vary greatly with the forest type or the growth environment

forest, shrubland, secondary forest),(Figure T14-1). Therefore, it is important to choose an allometric equation

the growth environment, or the
succession stage.

that is suitable for the environmental conditions in the region and the forest
type (e.g., evergreen or deciduous) 1). Many equations for estimating aboveground biomass (AGB) have been proposed, and data for estimation of

INFO2) Cairns et al. (1997) and Mokany

below-ground biomass (BGB) are also being collected 2).

et al. (2006) have proposed genericequations for below-ground groundbiomass. These equations use
above-ground biomass (t/ha) as
an independent variable, and theestimated result is also biomass per
hectare (t/ha). In addition, the Hozumi
equation is available for tropicalevergreen and tropical seasonal
forests (Hozumi et al. 1969) andthe Niiyama equation can be used
for dipterocarp forests in tropicallowlands (Niiyama et al. 2010) for
estimation of below-ground biomasson an individual tree level .

Figure T14-1 Comparison of above-ground biomass (AGB) estimations made byusing different allometric equations with tree diameter at breast height (DBH).
A wood density of 0.57 t/m3, which is the FAO default value for tropical tree species in
Asia, was used to calculate the AGB. The different estimation models are described andcompared in Figure T14-2

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REDD-plus Cookbook

Recipe T14

Selection of an allometric equation suitable for the target region
An equation that relates a trait (see T13) that can be measured easily
and repeatedly in the field to, for example, AGB, is selected from equations
developed for use in a forest in a similar environment or of the same forest
type 3) (Figure T14-2). At present, various equations, both generic equations(or models) and models developed for specific localities have been developedfor every forest type by using data from trees around the world 4). Many local
models are species-specific or specific to a local forest stand in a specificlandscape . Each has advantages and disadvantages (Table T14-1).

INFO
3) Many estimation equations
include wood density (t/m 3 ) as anindependent variable. Wood density
is the weight per unit volume of
the trunk. The biomass is estimatedby multiplying the wood density
by the volume calculated from theDBH, obtained from the tree census.
Note that WD may be reported withunits of kg/m3 instead of t/m3. Wooddensity values at the species or genus
level have been reported by the IPCC

An allometric equation should be selected from among equations suitable(2003, 2006) and in various papers. In

for the forest type and region. If two or more applicable allometric equations

is often difficult. If the species cannot

are available, then information about tree size (e.g., diameter at breast height(DBH) and tree height) and dominant species in the target forest should be
used with each equation and the results should be compared with available
biomass data for the target forest (e.g., reported in the literature) to evaluate
the estimation error of each. If there is a correlation between the estimation

tropical forests, species identification
be identified, default values (Asia 0.57,
America 0.60, Africa 0.58 in tropicalcountries) (e.g., Brown, 1997) can beused. Moreover, some papers reportwood density of parts other than thetrunk.

error and the DBH, that is an indication that the allometric relationshipassumed in the equation differs from that in the target forest. In this case, as

INFO

big the error of overestimation and underestimation anyway as large diameter4) Typical generic equations havetree arises. If no suitable equation is available, the biomass in the target area
should be measured to create a suitable allometric equation. On the otherbeen proposed by Brown (1997)
and Chave et al. (2005). The basal
area (ba: cm 2) is used in the Brown

hand, if the variation within error is large but no correlation exists between

equation; and wood density andthe estimated error and the DBH, the reason is probably that different forest

tree height, are used in the Chavetypes are intermingled in the target area. In this case, the classification into
forest types based on species or site conditions should be re-examined,
and further subdivision or stratification may be necessary. Moreover, if theDBH, or wood density, DBH, andequation as independent variables.
Both equations are used to estimate
the above-ground biomass on an
individual tree level.

estimation error is large for only a certain species, then that species probably
has a different life history (e.g., it may be lower canopy tree), or the allometric
relationship may differ from the usual one because that species has a unique
DBH:height ratio (e.g., baobab). Whensuch species are abundant in a forest, theirinfluence on the forest biomass will also
be large. Therefore, a different equation
should be used for trees of that species.
However, if that is not considered costeffective, the error may be unavoidable.

Table T14-1 The characteristics comparison between generic model(generic equation) and local model
Contents
Data set

Generic modelCollected from a range oftropical tree species

Applicable area

Applicable to forests ofthe same type as the data
set. Application in many
areas is possible.
Medium in an applicable
forest.
The accuracy may be
improved by using two
independent variables(e.g., DBH and tree height)

Examples of the estimation of biomass
per unit area from permanent sample plot

Estimation error

data and the calculation of forest biomass

Options for decreasing alarge estimated error

and carbon stocks at the national level areavailable for Cambodia 5) and Papua New

Local modelSpecies-specific orlandscape-specific data arecollected
Applicable to only forests
with the same landscape orsame species.
Small, if used in an applicable
forest, but large otherwise.
Do not use in an inapplicable
forest.

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Chapter 8 - Permanent sample plot method

Guinea 6). The biomass obtained by using an allometric equation is expressed

INFO
5) Samreth V, Chheng K, Monda Y,

as dry weight. That value is converted to carbon stocks by multiplying it by 0.5.

Kiyono Y, Toriyama J, Saito S, Saito

Since species, forest age, and forest management practices are constantH, Ito E (2012) Tree biomass carbon

in the case of a plantation, it can be assumed that tree size and growthstock estimation using permanent
sampling plot data in different typesof seasonal forest in Cambodia. JARQ
46(2): 187-192

conditions are also constant. In this case, the biomass expansion factor (BEF ) 7)
can be used as follows:

		 C =[V ×WD ×BEF ](1+R )×CFINFO
6) Fox JC, Yosi CK, Nimiago P, Oavika F,
Pokana JN, Lavong K, Keenan RJ (2010)
Assessment of aboveground carbon
in primary and selectively harvested
tropical forest in Papua New Guinea.
Biotropica. 42(4): 410-419

Here, C is carbon stock per unit area (t-C/ha), V is stand volume (m3/ha),WD is wood density (t/m3), BEF is the biomass expansion factor, and CF is the
carbon content ratio (t-C/m3).

Bibliography
Cairns MA, Brown S, Helmer EH, Baumgardner GA (1997) Root biomass

INFO

allocation in the world's upland forests. Oecologia 111: 1–11

7) A biomass expansion factor is afactor that converts the trunk volume

IPCC (2003) Good practice guidance for land use, land-use change andinto the volume of the whole tree,

forestry. IGES

including branches, leaves, andhttp://www.ipcc-nggip.iges.or.jp/public/gpglulucf/gpglulucf.htm

roots. This factor depends on the tree
species and the forest stand age.

IPCC (2006) 2006 IPCC Guidelines for National Greenhouse Gas Inventories
Volume 4, Agriculture, Forestry and Other Land Use
Mokany K, Raison R, Prokushkin A (2006) Critical analysis of root: shootratios in terrestrial biomes. Global Change Biology 12: 84–96
Niiyama K, Kajimoto T, Matsuura Y, Yamashita T, Matsuo N, Yashiro Y,
Ripin A, Kassim AR, Noor NS (2010) Estimation of root biomass basedon excavation of individual root systems in a primary dipterocarp forest in
Pasoh Forest reserve, Peninsular Malaysia. J Trop Ecol 26: 71–284

Generic
model
G
Generic
i models
d l

Local
model
L
Local
l modesdTropical forest

Tropical forest
SecondaryforestNatural forest/Primary forest

High annual
precipitation(more than 4000mm)

Wet model

Middle annual
precipitation(1500‐4500mm)

Low annual
precipitation(less than1500mm)

Moist model

Dry model

Humid

湿潤熱帯
tropics

model A, B

Tropicallowlandforestmodel C

Tropical seasonal
forestEvergreen
forestDeciduous
forestmodel Dmodel E

Figure T14-2 Flow for estimating allometric equation

122

REDD-plus Cookbook

model F, G, H

Recipe T14
Generic models
Brown

Allometric equation
Wet model

AGB=21.297 - 6.953 × DBH + 0.740 × DBH

Recommended forest type and climate condition
Annual precipitation more than 4000mm. No dry
season.
Annual precipitation 1500-4500mm. No or few dry
season.
Annual precipitation less than 1500mm, several
months drt season.

2

Moist model AGB=exp(-2.134 + 2.530 × ln (DBH ))Chave*

Dry model

AGB=exp(-1.996 + 2.32 × ln (DBH ))Wet model

AGB=WD × exp ( -1.302 + 1.980 × In(DBH )+ 0.207 × In(DBH ) ) - 0.0281 × (In(DBH ))2

3

Moist model AGB=WD × exp ( -1.562 + 2.148 × In(DBH )+ 0.207 × In(DBH ) )2 - 0.0281 × (In(DBH ))3
Dry model

Kiyono2

AGB=WD × exp ( -0.730+ 1.784 × In(DBH )+ 0.207 × In(DBH ) ) - 0.0281 × (In(DBH ))3

Annual precipitation more than 3500mm. No dry
season. High-rainfall lowland forest.
Annual precipitation 1500-3500mm, ont to 4
months dry season for lowland forests.
Annual precipitation less than 1500mm, over 5
months dry season.

Moist model Stem=2.69 × ba 1.29×WD 1.35
Moist model Branch=0.217 × ba 1.26 × WD 1.48
Moist model Leaf=173 × ba 0.938

Allometric equation

Recommended forest type

Yamakura

Local models
model A

AGB=exp(-2.30 + 3.62 × ln(DBH ))Tropical rain forest（Borneo）

Chambersmodel B

AGB=exp(-2.010 + 2.55 × ln(DBH ))（Central Amazon）

Dojomomodel C

AGB=exp(-2.05 + 2.33 × ln(DBH ))Tropical lowland forest（Africa）

Hozumimodel D

Stem=0.072 × (D 2 H )0.9326

Tropical seasonal evergreen forest（Cambodia）

2

Branch=0.01334 × (D H )1.027

Leaf=0.031 × (D 2H )0.7211
Tropical seasonal deciduous forest（Indochina
region）

Monda

model EAGB=0.3510 × DBH

2.3855

Kenzo

model F

AGB=0.0829 × DBH

2.43

Ketterings

model G

AGB=exp(-2.75 + 2.59 × ln(DBH ))Mixed secondary forest（Indonesia・Sumatra）

AGB=exp(-2.51 + 2.44 × ln(DBH ))Secondary forests dominated by pioneer species（Indonesia・Eastern Kalimantan）

Hashimotomodel H× WD

1.7827

Secondary forest （Malaysia・Sarawak）*Chave has also proposed the allometric equation which uses DBH, H abd WD for independent variables.
Units：Biomass（AGB, Stem, Branch, Leaf）：kg/tree, WD：t/m3 (Kiyono model use kg/cm3), DBH：cm, ba：m2, D2H：DBH2(cm)×H(m)The sources of the flow for estimating allometric equation
Brown S (1997) Estimating biomass and biomass change of tropical forests, a primer. FAO Forestry paper No.134. FAO, RomeChambers JQ, Santos J, Ribeiro RJ, Higuchi N (2001) Tree damage, allometric relationships, and above-ground net primary production incentral Amazon forest. Forest Ecology and Management. 152: 73-84
Chave J, Andalo C, Brown S, Cairns MA, Chambers JQ, Eamus D, Fölster H, Fromard F, Higuchi N, Kira T, Lescure JP, Nelson BW, Ogawa H, Puig H,
Réra B, Yamakura T (2005) Tree allometry and improved estimation of carbon stocks and balance in tropical forests. Oecologia. 145: 87-99
Dojomo AN, Knohla A, Gravenhorstb G (2010) Estimations of total ecosystem carbon pools distribution and carbon biomass currenrt annual
increment of a moist tropical forest. Forest Ecology and Management. 261: 1448-1459
Hashimoto T , Tange T , M asumori M , Y agi H , Sasaki S , K ojima K ( 2004) Allometric equations for pioneer tree species and estimation of theaboveground biomass of a tropical secondary forest in East Kalimantan. TROPICS. 14(1) 123-130
Hozumi K, Yoda K, Kokawa S, Kira T (1969) Producing ecology of tropical rain forests is southwestern Cambodia I. Plant biomass. NATURE AND
LIFE IN SOUTHEAST ASIA.Ⅵ. 1-51
Kenzo T, Ichie T, Hattori D, Itioka T, Handa C, Ohkubo T, Kendawang JJ, Nakamura M, Sakaguchi M, Takahashi N, Okamoto M, Tanaka-Oda A,
Sakurai K, Ninomiya I (2009) Development of allometric relationships for accurate estimation of above- and below-ground biomass intropical secondary forests in Sarawak, Malaysia. Journal of Tropical Ecology. 25: 371-386
Ketterings QM, Coe R, Noordwijk M, Ambagau Y, Palm CA (2001) Reducing uncertainty in the use of allmetric biomass equations for predicting
above-ground tree biomass in mixed secondary forests. Forest Ecology and Management. 146: 199-209
Kiyono Y, Furuya N, Sum T, Umemiya C, Ito E, Araki M, Matsumoto M (2010) Carbon stock estimation by forest measurement contributing tosustainable forest management in Cambodia. JARQ. 44(1): 81-92
Yamakura T, Hagihara A, Sukardjo S, Ogawa H (1987) Tree form in a mixed Dipterocarp forest in Indonesian Borneo. EcologicalResearch. 2: 215-227

Figure T14-2 Flow for estimating allometric equation (cont.)

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Chapter 9 - Estimation models for forest carbon stocks

9
Chapter 9Stand carbon stock
estimation models

124

REDD-plus Cookbook

In addition to the method of forest carbon stock estimation based on permanent
sample plots (PSPs), discussed in the preceding chapter, carbon stocks can be estimatedby using a model that relates a certain parameter to biomass. In this chapter, model designand evaluation, including the calculation of costs, is first explained. Subsequently, four
estimation models, based on overstory height, tree crown diameter, community age, or thebackscattering coefficient from SAR, are described. Because these models differ in termsof their applicability and estimation accuracy, their individual characteristics and theirapplicability to different forests are explained.

T15 Model design
T16 Overstory height method
T17 Crown diameter method
T18 Community age method
T19 Backscattering coefficient method

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Chapter 9 - Estimation models for forest carbon stocks

Recipe - T15

Model design
The preceding Recipe is
Recipe P11 Using a carbon stock
estimation model

Forest carbon stocks per unit area can be indirectly estimated by using astatistical model that relates an easily measureable parameter to carbon
stocks. The optimal design combines measurement of that parameter ina large sample population with direct measurement of carbon stocks ina subset of the sample population. The numbers of indirect and direct
measurements can be chosen so as to minimize the measurement cost,
and the efficiency of the method can be compared with that of thepermanent sample plot method. This sampling design, which is called"double sampling," is introduced here as a framework for indirect forest
carbon stock estimation using specific parameters. These methods aredescribed in recipe T16–T19.

Double sampling
In the permanent sample plot method, the mean and variance of theforest carbon stocks per unit area are estimated by direct field measurementsof the carbon stocks in sampling plots permanently established in theforest (see P10 and Chapter 8). These field measurements are often costly
and time-consuming, however, especially in a large forest area withlimited accessibility, or they may not even be possible because of the lackof infrastructure or for security reasons. Thus, some countries have notestablished a network of permanent sample plots, nor do they have thecapability to establish such a network in the near future.
Less costly yet accurate "double sampling" methods take advantage ofmodels that relate forest carbon stocks to easily measurable parameters,
including some that can be measured by remote sensing without requiring
any field studies to be carried out on the ground.
Double sampling methods involve two stages of measurements. In the firststage, an ancillary parameter that can be measured easily and at low cost ismeasured in a large number of samples. The most often used parameters areoverstory height (see T16), tree crown diameter (see T17), community age(see T18), and radar backscattering (see T19). In the second stage, accurate,
and more costly, measurements are performed on a subset of the samples
measured in the first stage. In this chapter, the second-stage measurement isassumed to be a tree census.
Double sampling methods are not always more suitable than thepermanent sample plot method in terms of accuracy and cost, even if
straightforward methods such as those described in T16–T19 are used.
Methods being considered for the target forest should be evaluated andcompared, particularly with regard to their accuracy and cost, before one
is chosen for implementation. If estimates of accuracy and cost are not

126

REDD-plus Cookbook

Recipe T15
available, they should be derived by examining earlier studies in similar areas
or by conducting small-scale preliminary experiments.

Indirect estimation of carbon stocks by correlation models
In the first stage, a large sample population of size ݊ଵ is selected, and an
ancillary parameter, ‫( ݔ‬e.g., overstory height) is measured in that sample

population. In the second stage, a subsample of size ݊ଶ ሺ൏ ݊ଵ ሻ is selectedfrom the first stage samples, and the carbon stocks( ‫ ) ݕ‬are measured by atree census. The two stage sample with regression estimators of the
population mean for a single variable of interest and a single ancillaryparameter is‫ݕ‬ത௧௥ ൌ ‫ݕ‬തଶ ൅ ܾሺ‫ݔ‬ҧଵ െ ‫ݔ‬ҧଶ ሻ 			(T15-1)

where ‫ݔ‬ҧଵ and ‫ݔ‬ҧଶ are the sample-based estimates of the mean of ‫ ݔ‬in the first-

and second-stage samples, respectively, and ‫ݕ‬തଶ is the estimate of the
population mean of ‫ ݕ‬from the second-stage sample, ܾ is the regression� ��� ��⁄���� ��� ,(ܿ‫ݒ݋‬ෞ ሺ‫ݔ‬ǡ ‫ݕ‬ሻ is the covariance of ‫ݔ‬coefficient of ‫ ݕ‬on ‫ ݔ‬, � � ���ෞ ሺ‫ݔ‬ሻ is the variance of ‫ ݔ‬. A better estimate of ‫ݎܽݒ‬ෞ ሺ‫ݔ‬ሻ can beand ‫ ݕ‬, and ‫ݎܽݒ‬
derived from the first-stage samples.

ෞ ሺ‫ݕ‬ത௧௥ ሻ , is approximated as;
Thus, the variance of ‫ݕ‬ത௧௥ , ‫ݎܽݒ‬‫ݎܽݒ‬ෞ ሺ‫ݕ‬ത௧௥ ሻ ؆ଶଶෞ ሺ‫ݕ‬ሻߩො௫௬൯ ‫ݎܽݒ‬‫ݎܽݒ‬ෞ ሺ‫ݕ‬ሻ൫ͳ െ ߩො௫௬
		
൅
݊ଶ
݊ଵ

(T15-2)ଶ

where ‫ݎܽݒ‬ෞ ሺ‫ݕ‬ሻ is the variance of ‫ ݕ‬, ߩො௫௬ is the coefficient of determinationଶൌ ܿ‫ݒ݋‬ෞ ሺ‫ݔ‬ǡ ‫ݕ‬ሻଶ ൗ൫‫ݎܽݒ‬ෞ ሺ‫ݔ‬ሻ‫ݎܽݒ‬ෞ ሺ‫ݕ‬ሻ൯ .
between ‫ ݔ‬and ‫ ݕ‬, and ߩො௫௬Evaluation of model accuracy and costs
The larger the variance of the measurement, the lower the accuracy ofthe carbon stock estimation, and the bigger the sample size, the higher thelevel of accuracy, but also the higher the cost of the estimation. The optimal
allocation of samples to the first and second stages can be estimated byminimizing the overall variance of the estimate (Eq. T15-2). Moreover, thecost of each model can be compared against that of the permanent sample
plot method.
Suppose the measurement cost of a first-stage plot (i.e., ‫ ݔ‬measurement)

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Chapter 9 - Estimation models for forest carbon stocks

is ܿଵ , and that of a second-stage plot (i.e., ‫ ݕ‬measurement) is ܿଶ ሺܿଶ ൐ ܿଵ ሻ ,then the total cost, ܿ , is determined as follows:			 ܿ ൌ ݊ଵ ܿଵ ൅ ݊ଶ ܿଶ 			(T15-3)ᇱIn double stage sampling, when ܿଵ and ܿଶ are given and ܿ is constant, ݊ଵᇱᇱෞ ሺ‫ݕ‬ത௧௥ ሻ can beand ݊ଶ that minimize Eq. 15-2 and the total variance ‫ݎܽݒ‬
calculated as follows;݊ଵᇱ ൌܿଵ ൅ ܿଶ ඨܿଶ ൯ܿଵ ൫ͳ െ ߩො௫௬ଶܿଶ ߩො௫௬‫ݎܽݒ‬ෞ ᇱ ሺ‫ݕ‬ത௧௥ ሻ ൌ ‫ݎܽݒ‬ෞ ሺ‫ݕ‬ሻ

݊ଶᇱ ൌ

ǡ

ܿଵ ඨ

ܿ

ଶ
ܿଶ ߩො௫௬൅ ܿଶଶ ൯ܿଵ ൫ͳ െ ߩො௫௬ଶଶ ൯൅ ܿ ߩଶ ቇቆටܿଶ ൫ͳ െ ߩො௫௬ඥ ଵ ො௫௬

ܿ		

(T15-4)(T15-5)In the permanent sample plot method, on the other hand, ݊ଶ ൌ ܿ ോ ܿଶ� � ����� �PSP can be calculated as
because ݊ଵ =0, and the total variance ���follows;			���� � ����� �PSP � ���
� ���

��
				
�

(T15-6)Comparing Eqs. T15-5 and T15-6, double sampling is more accurate thanଶ
permanent plot sampling when ���� � ����� � � ���� � ����� �PSP . Thus, when ߩො௫௬ isgiven and if
		

ଶ

ଶ ൯
ܿଶ ൫ͳ ൅ ඥͳ െ ߩො௫௬
				
൐
ଶ
ܿଵ
ߩො௫௬

(T15-7)then double sampling with the model should be employed. Or, when ܿଵand ܿଶ are given and ifଶ൐			 ߩො௫௬INFO
1) Köhl M, Magnussen SS, Marchetti
M (2006) Sampling Methods, RemoteͶܿଵ ܿଶ
				ሺܿଵ ൅ ܿଶ ሻଶ(T15-8)then double sampling should not be used.

Sensing and GIS Multiresource Forest
Inventory. Springer-Verlag

Notes about applying the models to a target forestଶIn large forest areas or in forests with large spatial variability, ߩො௫௬values ofଶaround 0.4 are not uncommon. On the other hand, ߩො௫௬larger than 0.9 is128

REDD-plus Cookbook

Recipe T15
unrealistic or even questionable 1).
Results from models using remote sensing data are sometimes unstableand poorly reproducible because of the complexity of remotely sensed data,
which are influenced not only by the observation target but also by manyother factors and conditions between the sensor and the object.
Because forests are complex and ancillary parameters are measured
indirectly, for example, by remote sensing, the model used should be as
simple as possible, and the accuracy and cost of the model should be
evaluated and compared with the accuracy and cost of the permanent

Simulated error relative to the error of a single sampling plot

sample plot method.

0.18
0.16
0.14
PSP

0.12

RS1

0.10

RS2

0.08
0.06
0.04
0.02
0.00
0

200

400

600

800

1000

Number of sample plots

Figure T15-1 Errors by two simulated double samplings
Simulated errors of two double sampling scenarios (RS1, RS2) are plotted against theestimated error of permanent sampling plot measurements (PSP). The horizontal axis
represents the number of sampling plots surveyed for the double samplings, whilethe vertical axis represents ratio of the total error of survey to the error of a single PSP
measurement.
Suppose that the total cost available for the survey, ܿ , is JPY10,000,000.–, and thecost of ground measurement per sampling plot, ܿଶ , is JPY10,000.–, thus the possible
number of sampling plots for PSP is 1,000, estimated error of which is plotted in the
figure as a dashed line.

Further suppose that, for RS1, the cost of measurement per area of a sampling plot,ܿଵ , is JPY100.– and the coefficient of determination between two sampling methods,ଶଶߩො௫௬, is 0.8, while, for RS2, ܿଵ is JPY1.– and ߩො௫௬ is 0.3.

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Chapter 9 - Estimation models for forest carbon stocks

Recipe - T16

Overstory height method
The preceding Recipe is
Recipe P11 Using a carbon stock
estimation model

INFO

Carbon stocks per unit area can be estimated from the overstory heightof a forest stand on the basis of a correlation between overstory heightand biomass. The method is applicable to many types of standing stock,from degraded stands with low carbon stocks to mature stands withhigh stocks. This recipe presents an overview of the method's underlying
principles and of the challenges it poses, as well as of remote sensing
techniques that can be used for overstory height estimation.

Correlation between overstory height and biomass

1) There are many definitions of theoverstory height in the literature

The overstory height method for estimating carbon stocks per unit area in(e.g., the average height of all

a forest stand is based on the existence of a power-law relationship between

standing trees, the average heightof canopy layer trees, or a weighted
average). The definition of overstory
height being used must be clearly
established when the model foroverstory height 1) and biomass (Figure T16-1). A power-law function relating
the two parameters has been reported by many studies 2), 3).
For example, the carbon stocks on Barro Colorado Island, Panama 2), were

estimation by the overstory height

estimated on the basis of a relationship between ground-based measurement

method is developed.

of biomass and overstory height measured by airborne LiDAR. In this study,
the overstory height was defined as the median height within each grid cell

INFO

ranged from 30 m to 100 m widths. Another study 3) estimated the carbon

2) Mascaro J, Asner GP, Muller-Landaustocks in tropical forests across three continents at a resolution of 1 km by

HC, BreugelMv, Hall J, Dahlin K (2011)using space-borne LiDAR to estimate Lorey's height (i.e., the basal area-

Controls over aboveground forest
carbon density on Barro Colorado
Island, Panama. Biogeosciences 8:
1615-1629

weighted height of all trees) and the correlation between Lorey's height andabove-ground biomass.
These two studies demonstrated that the applicability of overstory height
method depends on whether overstory height estimation by remote sensing ispractical.

INFO
3) Saatchi SS, Harris NL, Brown S,
Lefsky M, Mitchard ETA, Salas W, Zutta

1500

BR, Buermann W, Lewis SL, Hagen S,
Petrova S, White L, Silman M, MorelB
Biomasss (Mg/ha))A (2011) Benchmark map of forest
carbon stocks in tropical regions
across three continents. PNAS 108:
9899-9904

1000

500

0

0

5

10

15

20

25

Lorey’s height (m)Figure T16-1 Relationship between overstory height weighted by basal area

(Lorey's height) and biomass
130

REDD-plus Cookbook

Recipe T16

Measurement of overstory height by remote sensing
Airborne or satellite-borne LiDAR and stereoscopic images are remotesensing techniques that can be used to estimate overstory height.
Specifically, the overstory height is estimated by taking the difference
between a digital surface model (DSM; i.e., a surface elevation map of theforest canopy) and a digital elevation model (DEM) of the ground surface
derived from Airborne LiDAR data. This method can be used for forest stands
with both high and low levels of carbon stocks. However, the measurement
costs are relatively high. In addition, it is difficult to extrapolate from theresults of one area to other areas.
ICESat/GLAS (Ice, Cloud, and Land Elevation Satellite/ Geoscience Laser
Altimeter System) was a satellite-borne LiDAR system that was in operation
during 2003–2010, and its data are available for free. A successor, ICESat-2, isINFO

scheduled for launch in 2016. The LiDAR pulses from ICES at were expected

4) INFO 3) estimated the overstory

to strike the ground every 170 m with a footprint diameter of 70 m. Wall-towall satellite images can thus be combined with GLAS data to interpolate

height by maximum entropy
modeling using a combination of
MODIS, SRTM, and QSCAT data in
addition to GLAS data.

between dispersed height information on the ground 4).
Stereo pairs of aerial photographs or high-resolution satellite images 5) havebeen used to obtain stereoscopic information about the ground surface. If a

INFOreliable digital terrain model (DTM) is available for the study area, the stereo

5) Panchromatic Remote-sensing

pairs can be used to derive overstory height as well.

Instrument for Stereo Mapping(PRISM) is an optical sensor on ALOS
(Advanced Land Observing Satellite),
which can observe the ground surface
simultaneously in three directions –Factors affecting estimation accuracy

forward, nadir, and backward – along
the satellite track. ALOS terminatedPrevious studies have suggested that, in general, the overstory height–
biomass relationship does not differ significantly among forest types or
succession stages 1), 6). However, the relationship for bamboo stands issignificantly different from the relationships for other forest types.

operation in April 2011. Other types ofhigh-resolution satellite imagers, e.g.,
Worldview and IKONOS, can provide
stereo pairs of an area of interest on
demand.

The accuracy of carbon stock estimation by this method depends on thenumber of field measurement plots used to derive the power-law relationship

INFO

between overstory height and carbon stocks (see T15). The resolution of the6) In INFO 3), significant differenceswere found among the continents

remote sensing data also affects the accuracy of the method.
The derived overstory height–biomass relationship depends on the
allometric equation that is used to estimate the biomass. Thus, if theallometric equation is replaced with a new, more reliable one, the overstory(SE Asia, S America, and Africa),
but no significant differences were
found among forest types within acontinent.

height–biomass relationship should be recalculated.

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Chapter 9 - Estimation models for forest carbon stocks

Recipe - T17

Crown diameter method
The preceding Recipe is
Recipe P11 Using a carbon stock
estimation model

INFO

In the crown diameter method, carbon stocks per unit area are estimated bymeasuring tree crown diameters on aerial photographs or on high-resolution
satellite images and then using those values to estimate tree biomass, because
the biomass of each individual tree increases as its crown size (diameter and area)
increases. To estimate the carbon stocks per unit area, the estimated biomass of
the individual trees determined by measuring the crown diameter of every single
tree could be summed, but it is impossible to directly observe the tree crowns of
the middle and lower layers of a multilayer forest stand. Therefore, a method of
estimating the carbon stocks per unit area in all trees, including the middle andlower layer trees, from the tree crown size of the measurable upper layer trees isneeded. One of the requirements of the method, is that the individual tree crowns
must be visible. Therefore, the method is most suitable for a forest consisting oftrees with large crowns such as an open forest, whereas it is unsuitable for young
secondary forests or bamboo forests.

Measuring tree crown diameters for estimation of carbon stocks

1) In this recipe, tree crown diameter isused as shorthand for tree crown size (i.e.,
both diameter and area).

1)

Although the biomass of an individual tree increases predictably with the tree
crown size (diameter and area) 2) (Figure T17-1), the measurable diameter of a tree
crown depends on the ground resolution of the remote sensing imagery being used.

INFO

The crown diameter of an upper layer tree can be measured on an aerial photograph2 ) K i y o n o Y, S a i t o S , Ta k a h a s h i T ,or a satellite image with high ground resolution 3) (Figure T17-2), and then the

Toriyama J, Awaya Y, Asai H, Furuya
N, Ochial Y, Inoue Y, Sato T, Sophal
C , S a m P, T i t h B , I t o E , S i r e g a r C A ,Matsumoto M (2011) Practicalities of nondestructive methodologies in monitoring

diameter information is converted to an index value that is used to estimate the forest
carbon stocks.
A measurable tree crown is a tree crown located in the upper layer. The tree crownanthropogenic greenhouse gas emissions

diameters of middle and lower layer trees cannot be measured directly because theyfrom tropical forests under the influence ofare wholly or partially obscured by the upper layer trees, nor can the small tree crowns

human intervention. JARQ 45(2): 233-242

in a young secondary forest be measured. To deal with this situation, equations for
estimating the carbon stocks of all of the trees, including the middle and upper layer

INFO

trees that cannot be observed, directly from the tree crown diameter measurement

3) In addition, the measurement of tree
crown diameters is influenced by thedata for the upper layer trees have been proposed 4), 5).

availability of color information, the data

In addition, decreases in carbon stocks caused by selective logging of upper layer

acquisition time, and whether a stereo

trees with large tree crown diameters can be estimated directly by measuring thepair of images which makes it easier todiscriminatetree stratumand adjacenttree crowns using height information

tree crown diameters on the image obtained before the logging and comparing theresults with the measurement results for the images obtained after the logging 6).

available.

Determining the relationship between tree crown diameter and biomass

INFO4) Broadbent EN, Asner GP, Marielos PeñaClaros, Palace M, Soriano M (2008) Spatial

It is not easy to measure the diameter of a tree crown in a forest. Therefore, forpartitioning of biomass and biodiversity inpractical reasons it is desirable to derive a relationship between tree crown diametera lowland Bolivian forest: Linking field andmeasured on a remotely sensed image and biomass. Because DBH (diameter at breast

remote sensing measurement. For EcolManage 255: 2602-2616

132

height) – tree height curves generally differ among forest types, the relationshipbetween tree crown diameter and biomass should be derived for every forest type.

REDD-plus Cookbook

Recipe T17
The forest may be stratified by forest types and by the succession stage of forest
stands. Therefore, the forest should be classified by forest type before the crowndiameter method is implemented. In addition, the amount of dead wood shouldbe estimated separately by using long-term observational data from permanent
sample plots.

INFO
5) In general, the ratio of the whole upper
tree biomass is high . Moreover, the crowndiameter of upper level trees is used as
an index of the developmental stage of astand structure.

Notes on the use of the crown diameter method

INFO

The crown diameter method is suitable for a forest consisting mainly of large trees
with large crowns such as an open forest, whereas it is unsuitable for young secondary6) See, for example,
Win RN, Suzuki R, Takeda S (2012) Remotesensing analysis of forest damage byforests or bamboo forests. Because a tree crown is a three-dimensional structure, it

selection logging in the Kabaung Reserved

is best to measure the tree crown diameter by a three-dimensional measurement

Res 17: 121-128

Forest, Bago Mountains, Myanmar. J For

technique on a stereo pair of images or to make precise orthogeometric corrections 7).
The crown of a tall tree might become damaged by lightning or wind during astorm. In this case, the biomass of the tree determined from the diameter of its crownwould be underestimated. On the other hand, a group of two or more tree crowns
might be interpreted as a single tree crown, which would lead to an overestimation

INFO
7) Many commercial satellites carrying
high-resolution sensors have a feature
called a pointing function that allows

of biomass. Conversely, branches belonging to a single tree crown might be mistaken

angled observations, thus increasing thefor two or more trees, which would cause underestimation. Errors from these causes

due to the observation angle, however,

together constitute the estimation error factor 8). It is difficult to distinguish treesthat have died a natural death from selectively cut trees on a remotely sensed image,although there may be clues that make it possible to infer that selective logging has

observation field. Because of distortions
tree crown diameters at the edges of such
images cannot be accurately measured,
even if orthogeometric corrections are
made by referring to a precise digitaltaken place 9). The felling of trees for firewood or charcoal in the middle and lower

elevation model. Tree crown diameters inlayers, however, is undetectable.

when the sensor is pointing directly below

Because high-resolution remote sensing data are needed for the crown diameter
method, the measurement cost may be large. On the other hand, spatial differences incarbon stocks can be determined even in areas where ground access or measurement

the central part of such images (obtained
the aircraft in the case of aerial photos
or by using a small pointing angle in thecase of satellite images) can be measured
accurately.

at many points is difficult.

INFO
8) In addition, although wood density(wood specific gravity)also influences
biomass estimation, it is technicallydifficult to identify species on remotesensing images at present. For simplicity,
therefore, timber volume density is set toa constant value or it is set according to
the forest type.

INFO

Figure T17-1 Relationship between

Figure T17-2 Extraction of crown

crown diameter and single tree biomass

polygon by stereoscopy of digital

9) Selective logging may be indicated by(Modified from Kiyono et al. 2))

aerial photograph (dry dipterocarp

been logged, or by identifying tracks,( ◇ ：Kalimantan, Indonesia

forest in Kampong Thom, Cambodia)

detection of areas where many trees havealong which logs have been dragged.

△ : Cambodia, × : Java, Indonesia)

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Chapter 9 - Estimation models for forest carbon stocks

Recipe - T18

Community age method
If the forest was cleared in the past and the vegetation is in the process ofrecovery, community age can be used as an index value for estimation ofcarbon stocks. Here, community age method for carbon stock estimation
is outlined and the circumstances under which it may be applicable aredescribed.

The preceding Recipe is
Recipe P11 Using a carbon stock
estimation model

INFO

About the community age method

1) The number of years after harvest,
i.e., the number of fallow years, isWhere slash-and-burn agriculture is practiced, for example, land is cleared

used as the index value in slash-and-when the crops are harvested and then the natural vegetation is allowedburn agriculture.

to gradually recover. Land may also be cleared to establish a plantation ofa perennial crop such as rubber or oil palm or a forest plantation. In each

INFO

case, the vegetation community, whether it consists of natural vegetation or2) Inoue Y, Qi J, Olioso A, Kiyono
Y, Ochiai Y, Horie T, Asai H, Saito K,

plantation trees, has a uniform age, and it is possible to estimate the carbon

Shiraiwa T, Douangsavanh L (2007)

stocks of the ecosystem by using community age 1) in regions where suchTraceability of slash-and-burn land-

land uses are practiced.

use history using optical satellite
sensor imagery: a basis for chrono-

Moreover, because the area of each community age can be determinedsequential assessment of ecosystemfrom a time series of satellite images 2), the carbon stocks in those areas cancarbon stock in Laos. Int J Rem Sens

be calculated by multiplying the total area by the carbon stocks per unit

28: 5641-5648

area determined by the community age method 3). In addition, the method

INFO

can be used to estimate forest degradation resulting from, for example, a3) Inoue Y, Kiyono Y, Asai H, Ochiai Y,

reduction of the fallow period in slash-and-burn agricultural systems, or

Qi J, Olios, A, Shiraiwa T, Horie T, Saito

carbon stock changes caused by the conversion of natural forest to rubber or

K, Dounagsavanh L (2010) Assessing

oil palm plantations 3).

land-use and carbon stock in slashand-burn ecosystems in tropicalmountain of Laos based on timeseries satellite images. Int J Appl Earth
Obs Geoinf 12: 287-297

Creation of the relational equation by field survey
The relationship between carbon stocks and theCarbon stocks in biomass,
Deadwood, and litter (t-C/ha)60

number of fallow years during which the forest is allowed

50

Alt. 500 m

40

Alt. 1200 m

30

Alt. 500 m with grazing
Alt. 1200 m with grazing20

to recover after the harvest in slash-and-burn agriculture
has been determined empirically in northern Laos andnorthern Vietnam 4). These recovery curves show a rapid
increase in carbon stocks in first few years, after which the

10
0
0

5

10

15

20

Years since the last cropping

Figure T18-1 Relationship between the years since the last
cropping and the ecosystem carbon stocks(Modified from Kiyono et al. 2007)

increase slows and eventually plateaus (Figure T18-1).
To estimate carbon stocks by the community age
method, the carbon stocks in communities of different
ages must be determined by a field survey, and thenthe data can be used to create a relational expression.

134

REDD-plus Cookbook

Recipe T18
To accomplish this, sampling plots in areas with vegetation of uniform andknown age should be established. Then, in a plot in which herbaceous plants
are dominant, just after clearing, the plants can be cut and weighed 5), 6)
and then their weight can be converted into carbon stocks. In plots in which
woody plants have become established, carbon stocks can be estimated by a

INFO4) Do TV, Osawa A, Thang NT (2010)
Recovery process of a mountain
forest after shifting cultivation in
Northwestern Vietnam. For. Ecol.
Manage. 259: 1650-1659

tree census.
The rate of recovery of vegetation after crops are harvested in slash-andburn agriculture may differ under different environmental conditions (e.g.,
disturbances during recovery or elevation differences). For example, therecovery of carbon stocks after harvest in northern Laos depends on whether
livestock are grazed on the land during the fallow period and on whether
the site is in the lowlands or in the mountains 2) (Figure T18-1). Factors suchas these need to be taken into consideration when a relational equation is

INFO5) The biomass of all herbaceous
plants growing in a plot of a certain
size (e.g., 1 m 2 ) is determined by
cutting the plants at ground level anddetermining their total dry weight.
This technique is mainly used todetermine biomass in grasslands.

developed, and information about them should be collected to improve

INFO

estimation accuracy.

6) Mannetje L.'t (2000) Measuringbiomass of grassland vegetation. In:

Advantages and disadvantages of the community age method

Field and Laboratory Methods for
Grassland and Animal Production
Research. Mannetje L.'t, Jones RM

This technique is applicable where management of land is local, and(ed.) CABI Publishing Univ Press,
Cambridge, 151-177

where unexpected vegetation changes seldom occur. It is particularly
applicable where land use is rotated at fixed intervals, so that a mosaic ofcommunities of different ages exists (Figure T18-2). Under these conditions,the field data that are needed to develop the relational equation can beacquired comparatively easily. It is important to know exactly when each landparcel was cleared, so monitoring at least once for a year for that purpose isnecessary.
If the community age method is to be applied in a large
area (e.g., at the national or sub-national level), two or more
relational expressions will be needed that take into accountdifferent uses of the land after harvest (slash-and-burn
agriculture) and species differences among plantations.
Another disadvantage is that the felling of trees in the target
area or the collection of firewood cannot be detected by thecommunity age method. If either of these commonly occur,
their impact on the carbon stocks needs to be determinedby a separate investigation.

Figure T18-2 Slash-and-burnagricultural fallows ina mosaic-like pattern of different fallow ages innorthern Laos (Photo taken by Naoyuki Furuya)

Bibliography
Condit R (1997) Tropical Forest Census Plots. Springer-Verlag

REDD-plus Cookbook

135

Chapter 9 - Estimation models for forest carbon stocks

Recipe - T19

Backscattering coefficient method
The preceding Recipe is
Recipe P11 Using a carbon stock
estimation model

Satellite-borne Synthetic Aperture Radar (SAR) is a technology of remotesensing which can observe the surface of the earth even when thesurface is obscured by clouds. The backscattering coefficient 1) of thereflected radiation observed by SAR is correlated with above-groundbiomass up to a total biomass of about 100 t/ha, although above thatvalue saturation occurs. Thus, by modeling the relationship between
the backscattering coefficient and biomass, the carbon stocks can bemeasured in, for example, forests where a large disturbance has occurred
and the forest is still in an early stage of succession, such as recovery afterslash-and-burn. Here, the backscattering coefficient method is outlined
and the limits of its application are described.

About the backscattering coefficient method
SAR is an active sensor which transmits microwave pulses to the earth's

INFO1) The received digital number

surface then detect the reflected pulses back from the earth's surface.

(sigma-naught) or a corrected

The backscattering coefficient is derived from the reflected signals 2). The

value, where the dependence

correlation between the backscattering coefficient and biomass is high foron the incidence angle has been
rectified by geographic correction

long-wavelength (L band, about 23 cm), but saturation occurs at a biomass(gamma-naught), can be used as theof about 100 t/ha 3). For comparison, the above-ground biomass of a maturebackscattering coefficient 2).

tropical forest can be as high as 400–500 t/ha and usually exceeds 200 t/ha 4).
For this reason, it is difficult to estimate the biomass of a mature forest.
The method is suitable, however, for mapping biomass changes over a large

INFO
2) Shimada M (2010) Ortho-

area of forest that is recovering from some kind of large-scale disturbance

rectification and slope correction of(e.g., slash-and-burn or plantation agriculture). In hilly terrain, topographic

SAR data using DEM and its accuracy

distortion should be corrected.

evaluation. IEEE J Select Topic Appl
Earth Obs Remo Sens 3(4): 657-671

Creation of the estimation model by field survey

INFO3) Lucas RM, Cronin N, Lee A,

The backscattering coefficient initially increases rapidly as biomass

Moghaddam M, Witte C, Tickle

increases, and then the increase slows gradually until saturation occurs. Thus,

P (2006) Empirical relationships

the relationship between the backscattering coefficient and biomass can be

between AIRSAR backscatter and
LiDAR-derived forest biomass,

approximated by an exponential function 3) (Figure T19-1). The saturation

Queensland, Australia. Rem Sens

level is reached sooner with like-polarized waves (HH, VV) than with cross-Environ 100: 407-425

polarized (HV, VH) waves. The relational expression (model) is created bycollecting biomass data in the field, converting them to carbon stocks, andINFO

then comparing the results with backscattering coefficients from the same4) Roy J, Saugier B, Mooney, HA

area. To obtain high-precision biomass estimates, the field data should be(2001) Terrestrial Global Productivity.
Academic Press.

136

obtained from forest stands with various biomass. It is particularly important
to collect data from low-biomass stands. Although the estimation model

REDD-plus Cookbook

Recipe T19
may depend on the forest
type, the model should beapplicable to two or moresatellite scenes, because thechange in the backscattering
coefficient caused byseasonal differences is small
in closed tropical rain forests.
Figure T19-1 Relationship between
backscattering coefficient and total
above-ground biomass

An example of biomass estimation
Figure T19-2 illustrates biomass estimation by the backscattering
coefficient method in a peat bog forest in central Kalimantan, Indonesia. The
biomass is about 300 t/ha and saturation of the backscattering coefficient isreached in the natural forest in this area. In an open forest in which the forest
floor is covered by standing water (a bog), after the SAR electromagnetic
pulses reflect off the water surface, they may reflect off the trunk of a tree oranother object, before being reflected back to the SAR sensor. This is calleda double bounce, and can cause overestimation of the biomass. Bias of the

Figure T19-2 Estimated distribution of the above-ground biomass by the backscattering coefficient methoda)Study area by Landsat 7 ETM+ image, August, 2007, b) Study area by ALOS/PALSAR, October, 2007, c) Estimated distribution of theabove-ground biomass, d) Improved estimation by correcting effects from the double bounce.
Forests are colored green in both a) and b), while burnt areas and sparse swamp forests are colored beige in b).
Overestimation of biomass due to the double bounce c) is reduced by the correction d).
ALOS/PALSAR: © JAXA, METI

REDD-plus Cookbook

137

Chapter 9 - Estimation models for forest carbon stocks

double bounce can be eliminated by using dual polarization as indicated in
Figure T19-2.

Advantages and disadvantages of the backscattering coefficient method
Since L-band SAR penetrates clouds, data can be acquired even in anarea that is cloudy for much of the year. Thus, the backscattering coefficientmethod is expected in especially useful in tropical rainforests where
deforestation is a big problem.
Although carbon stocks of low-biomass forests can be estimated with
sufficient accuracy, the method's accuracy is low in high-biomass forests, so
the results are not reliable. For this reason, the use of the method is restricted
to forest in an early stage of succession, or it can be used to monitor an areathat has been clear-cut .
Excessive noise, called speckle noise, occurs irregularly in SAR data. This
noise can be reduced by averaging the values across groups of neighboring
pixels, instead of using per-pixel values, but the ground resolution of themeasurement deteriorates as a result.
At present, topographic correction in mountainous areas is not sufficiently
accurate, so the backscattering coefficient method cannot be applied to aforest on medium to steep slopes. In general, the accuracy of the biomass
estimate decreases when the inclination of the slope exceeds 10 º to 15º.
The low accuracy of even corrected values may be caused in part by the useof elevation data with low accuracy for the correction, or it may be causedby using a correction model that cannot account for the scattering by thecrowns of trees growing on slopes. In the future, these problems may besolved, thus allowing the method to be used even for forests growing on asteep slope.

138

REDD-plus Cookbook

REDD-plus Cookbook

139

Reference Guide【Reference Guide】
This reference guide introduces further reading materials (reports, guidelines, manuals, papers, etc.) to help Cookbook
users to deepen their understanding of recipes presented in Introduction and Planning (Chapters 1‒5). Furthermore,this guide includes references that contain recommendations, suggestions and case studies relevant to each recipe,
which will be of help to users who seek practical solutions for the challenges they encounter in designing andimplementing projects and programs.

Anticipated audiences✓Policy makers, program developers, and their partners engaging REDD-plus activities at national/subnational levels✓REDD-plus project proponents✓People who wish to expand their knowledge of REDD-plus beyond forest carbon measurement

How to use the guide
References are introduced in the following form :No.

Recipe*1
Implementationlevel*2

Title

Language*3

Year

Lead author

Organization or publisher

Overview(Here we discuss multiple aspects of each reference: e.g., objective, main points,
comprehensiveness, level of detail, practical suggestions and case studies, andusefulness)

Source of original materials*1 Related recipe in the Cookbook*2 Implementation level for which the reference will be most useful: international, national, sub-national, or project*3 Key of language. CH: Chinese, EN: English, FR: French, IN: Indonesian, JP: Japanese, PO: Portuguese, SP: Spanish, VI: Vietnamese

Further information
Users who wish to learn more about a specific recipe should visit The REDD Research and Development Center's
Reference Data Base for additional reference information.

http://www.ﬀpri.aﬀrc.go.jp/redd-rdc/en/reference/reference.html

140

REDD-plus Cookbook

Reference Guide

Chapter 1: About REDD-plus

No. 1

I01
International,
National,
Sub-national,
Project

Reducing Emissions from Deforestation and Forest
Degradation

EN

2011

Agrawal
et al.

Annual Review
of Environment
and Resources

This review assesses trends of REDD-plus discussions mainly from a social science perspective.
After introducing the history of REDD-plus negotiations and explaining environmental and social
aspects of REDD-plus, it briefly reviews actors in international and national initiatives, markets, andcivil society as well as problems of carbon monitoring, with the challenges of carbon monitoring
as a crosscutting theme. This multi-faceted review is one of best introductions to REDD-plus.

http://www.annualreviews.org/doi/abs/10.1146/annurev-environ-042009-094508

REDD-plus (Reducing Emissions from Deforestation
and Forest Degradation in developing countries; andNo. 2

the role of conservation, sustainable management offorests and enhancement of forest carbon stocks inJapan International
EN

2012

Cooperation Agency (JICA)JP

2nd Ed.

International Tropical Timber
Organization (ITTO)

developing countries)

I01

This booklet published by JICA and ITTO is designed for the general public with the aim of raisingawareness and increasing understanding of REDD-plus. In addition to a brief introduction on thecontext and concept of REDD-plus, it gives an overview of Japanese government policies on

International,

climate change and the involvement of JICA and ITTO in REDD-plus. This publication will be of

National,

most benefit to newcomers to REDD-plus who wish to understand the basic concept of REDD-plus

Subnational,

and to people involved in REDD-plus who wish to get the "big picture" of ongoing REDD-plus

Project

related projects and activities implemented by JICA and ITTO.

http://www.jica.go.jp/publication/pamph/pdf/redd.pdf

No. 3

I02

What is the right scale for REDD? The implications ofENnational, subnational and nested approaches

VI

2008

Angelsen
et al.

CIFOR

This CIFOR brief gives a clear idea of three approaches at three levels or scales of REDD accounting
and incentive mechanisms: national, sub-national or project, or a nested approach. The three
approaches are briefly introduced and assessed by the three criteria of effectiveness, efficiency,

National,

and equity. Although this brief doesnʼt provide case studies, it is useful for understanding theSub-national,

concept and pro & cons of each approach in a short time. It is highly recommended, particularly for

Project

people who wish to understand the concept of linking REDD-plus projects with REDD-plus
activities at national level.

http://www.cifor.org/online-library/browse/view-publication/publication/2595.html

REDD-plus Cookbook

141

Reference Guide

No. 4

I02

REDD+ at project scale: Evaluation and Development

EN

Guide

FR

2010

Calmelet al.

FFEM, ONFI, etc.

This is a guide to REDD-plus project development and project evaluation designed for project
developers, investors and funding agencies. This guidebook covers a range of topics, such as site
selection, design and implementation of activities, legal and institutional aspects, economic andfinancial aspects, and environmental and social safeguards, in a balanced manner. It also presents

Projectcase studies and gives specific suggestions for each topic. This guide is highly recommended for
project proponents and program/policy makers of REDD-plus.

http://www.onfinternational.org/en/publications/313-qguide-redd-a-lechelle-projetq-guide-devaluation-et-de-develo
ppement.html

1) Olander
Building Forest Carbon Projects: Step-by-Step
No. 5

Overview and Guide1) In: Building Forest Carbon
Projects2)& EbelingEN

20112) Ebeling

Forest Trends& Olander
(ed.)

I02
I03
I04

This series of guides helps project proponents by giving tips on making a project successful. Basedon Forest Trendsʼ experience in forest carbon projects, it covers multiple aspects of REDD-plus,
including technical issues, environmental and social considerations, and financial matters. This
series covers eight topics: overview, REDD technical project design, afforestation/reforestation
technical project design, carbon stock assessment, community engagement, legal issues, business,

Projectsocial impacts, and biodiversity impacts. Each guide will be a useful reference for project
proponents. These are all freely downloadable.

http://forest-trends.org/publication̲details.php?publicationID=2555

142

REDD-plus Cookbook

Reference Guide

Chapter 2: Designing a forest monitoring system
Cost of implementing methodologies andmonitoring systems relating to estimates ofemissions from deforestation and forest degradation,
No. 6

the assessment of carbon stocks and greenhouse gas

EN

2009

UNFCCCemissions from changes in forest cover, and theenhancement of carbon stocks. Technical paper( FCCC/TP/2009/1)

I02

This technical paper prepared by the UNFCCC secretariat gives an overview of the requirements,

I03

steps, and cost of developing and implementing a GHG monitoring system. It contains informationon the current state of monitoring capacity of non-Annex I countries and the required capacity for
implementing the system, cost estimation by country, by each step of system development and byeach country's land area, and the relationship between cost and accuracy and precision. This
report also present a case study of development of the Indian national forest monitoring system as

National

well as further references to studies conducting detailed cost analyses of the development. For
people in charge of a national forest monitoring system for REDD-plus and for those who wish tofollow this topic, this paper is a useful introduction.

http://unfccc.int/documentation/documents/advanced̲search/items/6911.php?priref=600005151

An assessment of national forest monitoring
No. 7

capabilities in tropical non-Annex I countries:

EN

2009

HeroldRecommendations for capacity building

I03

GOFC-GOLD
Project OfficeThis study aims to identify, for 99 tropical non-Annex I countries, the near-term
capacity-development activities that are necessary for implementing a forest area change andcarbon stock monitoring system. Readers get an overview of the status of forest monitoring
system development in 99 countries and the challenges they face. In addition, the study provides

International,

detailed country reports on current capacity and suggestions on further capacity development for

National

30 of those countries. The study will be of most benefit to project managers who wish tounderstand the status of forest carbon monitoring systems in the countries of their interest.

http://unfccc.int/methods̲science/redd/methodologies/other/items/4542.php

REDD-plus Cookbook

143

Reference Guide

Estimating the cost of building capacity in rainforest
No. 8

nations to allow them to participate in a global REDD

EN

2008

mechanism

I04

Hoare
et al.

Chatham House

This report estimates funds required for ʻreadinessʼ activities needed for 25 rainforest countries toparticipate in a REDD mechanism. It calculates the costs of concrete activities such as developing anational REDD strategy, development of reference levels & forest inventory, land use planning &International,
National

zoning, and forest policy & legislation and reform. Annex 1 gives project case studies and theircosts, surveyed for this report. This report will be of most benefit to policy makers or donors as areference on the funds necessary for readiness, as well as a catalog of case studies.

http://www.illegal-logging.info/item̲single.php?it̲id=744&it=document

A stepwise framework for developing
No. 9

REDD+ reference levels. In: Analysing REDD+

EN

2012

Challenges and Choices

Heroldet al.

CIFOR

I04

This chapter in the CIFOR book Analysing REDD+ Challenges and Choices introduces a stepwise

P13

approach necessary for developing forest reference (emission) levels. It explains a three-step
approach and the different technical capacities required by each step, and points out problems of
uncertainty and different ways of coping with it. This chapter also contains case studies from Brazil,

National,Indonesia and Viet Nam in which multiple regression analyses are applied (Step Two). In addition,

Sub-national,

Box 16.3 is very useful for people who are confused about REDD-plus terminology and the

Project

differences between ʻthree phasesʼ, ʻthree approachesʼ, ʻthree tiersʼ and ʻthree stepsʼ!

http://www.cifor.org/online-library/browse/view-publication/publication/3805.html

EN
No. 10What is needed to make REDD+ work on the ground?
Lessons learned from pilot forest carbon initiatives

SP
PO
FR

2010

HarveyConservation

et al.

International

CH

This report provides a summary of the key factors needed to ensure that REDD-plus projects are
I04

successful at reducing greenhouse gas emissions, while also providing biodiversity and social
co-benefits. It is based on the initial experiences of 12 forest carbon projects (5 REDD-plus and 7
AR/CDM). The reports highlight five key factors for success: 1) strong partnerships and local
capacity; 2) robust technical and scientific information and analysis; 3) sufficient funding for project
development 4) strong stakeholder participation both in project design and implementation; and5) full support from the government. The report provides an overview of how the 12 forest carbon

Project

projects were designed and implemented, and includes a summary of the key lessons learned. It
also provides recommendations on how to ensure forest carbon projects provide the desired
climate, biodiversity and social benefits. The report will be of most benefit to project managers
who are initiating feasibility studies.

http://www.conservation.org/publications/Pages/REDD̲lessons̲learned.aspx

144

REDD-plus Cookbook

Reference Guide

Chapter 3: Basic knowledge for REDD-plus implementation

No. 11

Legal Frameworks for REDD :Design and Implementation at the National Level

EN

2009

Costenbader
(ed.)

IUCN

I04

This IUCN report on legal frameworks for REDD presents the legal requirements that need to be

P03

addressed at national level (such as ownership of land, use rights, participation, benefit sharing,
additionally, and permanence) and makes suggestions about framework development. As
appendices, it includes a checklist of these requirements and examples of REDD-plus legal

National,

frameworks from four countries (Brazil, Cameroon, Guiana, and Papua New Guinea), which haveSub-national,

been carefully chosen by considering geographic condition, forest area, deforestation rate, etc.

Project

This report will be useful for policymakers and project proponents who wish to investigate topics
that should be considered in developing the legal framework in their own country.

http://www.iucn.org/about/work/programmes/environmental̲law/elp̲resources/elp̲res̲publications/?uPubsID=39
43

The Importance of Defining ʻForestʼ: Tropical Forest
No. 12Degradation, Deforestation, Long-term Phase Shifts,

EN

2010

and Further Transitions

P01

Putz &

Biotropica

Redford

Vol 42 Issue 1

This paper is a comprehensive discussion about the history of the term ʻforestʼ and its use by
diverse actors in the social and natural sciences. The paper includes discussion of ʻforestʼ as definedby the UNFCCC and FAO, and of the definitions ʻdeforestationʼ and ʻforest degradationʼ in tropicalregions used for REDD-plus. This paper is particularly useful for REDD-plus negotiators and scholars

International,

who are interested in REDD-plus issues and who wish to understand the definitions used in

National

REDD-plus discussions and the problems with those definitions.

http://onlinelibrary.wiley.com/doi/10.1111/j.1744-7429.2009.00567.x/abstract

Global and national REDD+ architecture
No. 13

Linking institutions and actions. In: Realising REDD+
National Strategy and Policy Options

P01

EN
IN
SP

Wertz-

2009

FR

Kanounnikoff
&

CIFOR

Angelsen

The one of topics of this chapter, in a CIFOR book entitled Realising REDD+ National Strategy and

Policy, is a discussion of the ʻ-plusʼ of REDD. The chapter briefly outlines the history on of thenegotiations and how ʻREDʼ evolved to ʻREDD-plusʼ. In addition, Box 1.1 in Chapter 1 of this bookInternational

summarizes the definition of REDD-plus. These parts of the book will be useful for newcomers to
REDD-plus or people who wish to revisit the discussion about the definition of REDD-plus.

http://www.cifor.org/online-library/browse/view-publication/publication/2871.html

REDD-plus Cookbook

145

Reference Guide(A) REDD+ Institutional Options Assessment(Chapter 2)
No. 14(B) Reducing Emissions from Deforestation andForest Degradation (REDD): An Options
Assessment Report

P02(Chapter 2)

EN
FR
SP

(A) Streck

2009

PO

et al.

Meridian(B) Angelsen

Instituteet al.

These two reports (A) and (B) introduce the phased approach. Chapter 2 of report (A) summarizes
the three phases. In particular, Table 2.1 is a useful introduction to specific topics such as activities,
costs, performance indicators, and capacity for MRV, and an overview of the requirements of eachphase. Chapter 2 of report b explains the relationship between the phased approach and financeInternational,

options in detail. It describes activities that require financial support and proposes options for

National

mobilization of international finance for each phase. We suggest people who wish to understandthe basics of the phased approach to read the two chapters together.

http://www.redd-oar.org/rl.html

REDD+ safeguards in national policy
No. 15

discourse and pilot projects. In: Analysing REDD+

EN

2012

Challenges and Choices

P03

Jaggeret al.

CIFOR

This chapter in the CIFOR book is an overview of the current state of environmental and social
safeguards and highlights challenges and choices for implementing REDD-plus safeguards. This
brief consists primarily of an analysis of the safeguards established in several ongoing pilot projects

International,in Brazil, Cameroon, Indonesia, Tanzania, and Viet Nam, and also discusses core safeguard issues

National,

covered by the national media. This chapter will be useful for people such as project proponents

Sub-national,

who wish to get information necessary to understand the current UNFCCC negotiations about how

Project

safeguards for REDD-plus activities are to be addressed and respected.

http://www.cifor.org/online-library/browse/view-publication/publication/3805.html

Safeguarding and enhancing
No. 16the ecosystem-derived benefits of REDD+ Multiple

UNEP-WCMC,
EN

2010

Miles et al.

Benefits Series 2

P03

UN-REDD
Programme

This issue paper provides concrete ideas on how national REDD-plus programs can safeguard
ecosystem services and biodiversity and proposes options for enhancing these benefits. It
describes approaches and methods to enhance ecosystem services and benefits in acomprehensive manner and introduces tools available for the purpose. This paper will be

National,

particularly useful for program developers who need to incorporate the consideration of

Sub-nationalsafeguards for ecosystem services and biodiversity into a REDD-plus program.

http://www.unep-wcmc.org/multiple-benefits-series-2̲629.html

146

REDD-plus Cookbook

Reference Guide

Multiple Benefits Series 5 & 6(A) Ecosystem services and biodiversity from new
No. 17and restored forests: tool development(B) Methods for assessing and monitoring change in(A) MilesEN2010

the ecosystem-derived benefits of afforestation,

et al.
(B) Doswald
et al.

UNEP-WCMC,
UN-REDD
Programme

reforestation and forest restoration

P03

These two documents (A) and (B) address safeguard issues of projects and programs that include
afforestation, reforestation, and forest restoration activities. Document (A) provides useful
information about assessing the potential impacts of these activities on biodiversity, water, soil,
and non-timber forest products by providing a visual ʻscore cardʼ. It also lists many papers that can

National,
Sub-national,
Project

be consulted to obtain additional information. Document b summarizes the requirements andsteps of designing a monitoring system to assess the impacts and introduces several guides andmethods for designing such a system. The two documents together will help people who wish tounderstand basic approaches to avoiding adverse impacts of afforestation, reforestation, andforest restoration activities.

http://www.un-redd.org/MultipleBenefitsPublications/tabid/5954/Default.aspx

No. 18

P03

Climate Change & the Role of Forests

EN

A Trainerʼs Manual

etc.

2010

Stone et al.

Conservation
International

The Climate Change and the Role of Forests Training of Trainers materials are designed to expand
local communitiesʼ knowledge about issues related to climate science, the forest carbon cycle,
climate policy, Payment for Ecosystem Services, and REDD-plus. The main objective of thesematerials is to expand the number of skilled trainers who are able to effectively deliver information
on issues related to climate change and REDD-plus to communities and other local stakeholders.
The methodology includes guidance on training design and facilitation skills and, along with the

Projectmanual, the training toolkit also includes flashcards, posters, and training activities. The materials
are available in seven languages (English, Spanish, French, Bahasa (Indonesian), Malagasy, Khmer(Cambodian), and Mandarin (Chinese)) and have been used in 12 countries. The toolkit is available
for not-for-profit organizations upon request.

http://www.conservation.org/publications/Pages/climate̲change̲and̲the̲role̲of̲forests.aspx

REDD-plus Cookbook

147

Reference Guide

Chapter 4: Measurement, reporting and veriﬁcation (MRV) of forest carbon

Emissions factors. Converting land use change to
No. 19CO2 estimates. In: Analysing REDD+ Challenges and

EN

2012

choices

P04

Verchot
et al.

CIFOR

This chapter in Analysing REDD-PLUS Challenges and Choices introduces the measuring of forest
carbon in REDD-plus, and describes the current state of non-Annex I countries with regard tocapacity and information available for the measurement, and concludes by summarizes futurechallenges. It gives detailed explanations of the Gain-Loss Method (including an approach forpeatland) and the Tier 1 approach, which the Cookbook discusses only briefly. Furthermore, thisNational,

chapter covers a range of topics, from the currently available emission factors and the possibility ofSub-national,

improving them to a potential integration of community carbon monitoring with national carbon

Projectmonitoring. This chapter is particularly recommended for people who wish a concise presentationof forest carbon measurements not covered by the Cookbook.

http://www.cifor.org/online-library/browse/view-publication/publication/3805.html(A) Reporting on Climate Change Use manual for theguidelines on national communications fromNo. 20non-Annex I Parties(B) UNFCCC Resource Guide For Preparing The
National Communications of Non-Annex I Parties.

EN
FR
SP

(A) 2004(B) 2009

UNFCCC

Module 3 National greenhouse Gas Inventories

Manual (A) was prepared to help non-Annex I countries develop a national communication (NC)P05

based on information currently available. To ensure that the parties fully document all that needs
to be reported, it provides detailed explanations and tips and points out relevant articles of theconvention. In addition, for further explanation, four sets Resource Guides are available. The third
guide (B) provides guidance on developing a national GHG inventory. Since REDD-plus rules are

International,

being negotiated, we donʼt know what countries should report or how they should structure the

National

report. Nevertheless, these documents help us visualize what a national level report should looklike.

http://unfccc.int/national̲reports/non-annex̲i̲natcom/guidelines̲and̲user̲manual/items/2607.php

148

REDD-plus Cookbook

Reference Guide

The IPCC
No.21

IPCC Inventory Software

EN

2012

Task Force on

National Greenhouse Gas
Inventories

P05

This software has been developed for supporting countries to develop a national GHG inventory (see recipe
P05 in the Cookbook) and in a national communication for submission to the UNFCCC. This softwareʼs
structure follows the Tier 1 and Tier 2 methodologies of 2006 IPCC Guidelines. Since default data for Tier 1
methodology has already been embedded in the software, users can use that data to perform a simulation.
In addition, a user manual for the software can also be downloaded from the same website. Because specificInternational,

reporting requirements have not yet been decided, this software will be useful for people who wish to begin

Nationalplanning the development of an inventory and the probable reporting requirements.

http://www.ipcc-nggip.iges.or.jp/software/index.html

No. 22P06

8. Verification and Issuance. In: Building Forest
Carbon Projects. Step-by-Step Overview and Guide

Olander
EN

2011

&

Forest Trends

Ebeling

This guide is an introduction to verification and credit issuance at the project level (this guide isalso in the ʻBuilding Forest Carbon Projectsʼ series introduced in No. 5). Because the UNFCCC has
not agreed on the REDD-plus verification modality, this chapter explains basic steps for verificationof GHG benefits from a project, the possible costs of verification, and the importance of the timing

Projectof the initial verification, giving examples of both VCS and CDM. In addition, it briefly describes therelationship between credit issuance and verification. This section of the guide will be useful for
people who need information about the verification modality of REDD-plus at the project level.

http://forest-trends.org/publication̲details.php?publicationID=2555

Chapter 5: Monitoring by the stock change method

No. 23

P08

Remote sensing and image interpretation Sixth
EditionEN

2007

Lillesandet al.

John Wiley &Sons, Inc.

This book is the sixth edition (the first edition was published in 1979) of a well-known textbook on
remote sensing and image interpretation that is used globally. The book is designed as a reference
for practitioners who deal with geospatial information in various fields. Since the book is intended
as a general textbook about remote sensing, it provides relatively little information specific to

National,

forest ecosystems. Nevertheless, it is highly recommended for people wish to acquire a

Sub-national,

fundamental understanding of the remote sensing topics introduced in P08 and Chapter 7, such as

Project

types of remote sensing data, selection of data, pre-processing of data, classification methods, andaccuracy verification.

Book available for purchase ISBN978-0470052457

REDD-plus Cookbook

149

Reference Guide

No. 24

Field Methods in Remote Sensing

EN

2005

MaCoy

Guilford Press

P08

This book provides guidance on ground truthing methods used in remote sensing applications. It
includes detailed explanations on project planning, sampling methods, steps for identifying
locations in the field using a GPS, and various measurement methods. In particular, theNational,
Sub-national,
Project

explanations on the measurement methods of vegetation and on identifying and measuring
various surface features, as well as the included field note forms, will be useful for people
conducting field surveys for REDD-plus. This book will be of most benefit to people who have an
understanding of remote sensing but little practical knowledge about ground truthing.

Book available for purchase ISBN978-1593850791

Carbon Inventory Methods: Handbook forNo. 25

Greenhouse Gas Inventory, Carbon Mitigation and

EN

2008

Roundwood Production Projects

Ravindranath
et al.

Springer

This handbook on the development of forest carbon inventory was written by two experts in thisP04−P10arena, one of whom has been involved in the preparation of the IPCC guidelines. The handbook
includes detailed explanations on the topics introduced in the Cookbook (P04‒P10). In particular, it
describes methods for measuring carbon pools that are not covered in detail in our Cookbook:

National,

below-ground biomass, dead wood, litter, and soil. This book is highly recommended for carbon

Sub-national,

inventory planners and people who will conduct field surveys. It should be a useful companion

Project

book to our Cookbook.

Book available for purchase ISBN978-1-4020-6546-0

No. 26

P10

Tropical Forest Census Plots

EN

1998

ConditSpringer-Verlag

This book gives detailed explanations on methods for establishing plots for large-scale ecologicalsurveys in tropical forests. It includes a comprehensive description of the requirements ofpermanent sample plots (PSPs), introduced in the Cookbook, and provides tips on conducting plot
surveys, including field survey methods and database development, ways of coping with problems

National,that might arise during a survey, and scheduling and labor estimation for the survey. It also

Sub-national,

includes case studies about surveys in Barro Colorado Island, Panama, and 11 other large forest

Project

areas in the world. This handbook will be quite useful for planners involved in plot design andpeople conducting field surveys.

Book available for purchase ISBN978-3-540-64144-5

150

REDD-plus Cookbook

EN

2007

WalkerWinrock

No. 27Winrock Terrestrial Sampling Calculator

P10

This Excel tool calculates the number of PSPs necessary for each forest type, based on Stratified

T12

Sampling (introduced in T12 of the Cookbook). The user enters the area of each forest type; theet al.

International

carbon stocks per unit area for each, with standard deviation; the plot size; and costs (e.g., of travel,
National,

equipment, sample analysis). The output is an estimate of the number of plots required and of the

Sub-national,

cost of conducting the sampling. This is a useful tool for obtaining a quick estimate of the number

Projectof plots needed and the costs of the sampling.

http://www.winrock.org/ecosystems/tools.asp

2.7 Estimation of Uncertainties. In: A sourcebook of
methods
No. 28and proceduresfor

monitoring andreporting anthropogenic greenhouse gas emissions
and removals associated with deforestation, gains

EN

2012

GOFC-GOLD

GOFC-GOLD
Project Officeand losses of carbon stocks in forests remaining
forests, and forestation

This chapter in the GOFC-GOLD Sourcebook explains how to estimate uncertainties in area andP12

carbon stocks. It includes a brief explanation of the concept of ʻuncertaintiesʼ, requirements for
estimating uncertainties in both area and carbon stocks, methods for combining these two
uncertainties, and the reporting uncertainties. Helpful figures and tables make the material easier

National,
Sub-national,
Project

to understand. It also contains many useful references for further reading. This chapter will bebenefit not only to people who wish to go beyond the explanations in the Cookbook but also topeople who are just learning the basic concepts related to uncertainties and the combining of
uncertainties.

http://www.gofcgold.wur.nl/redd/

No. 29

P13

Guidelines for REDD+ Reference Levels:
Principles and Recommendations

EN

2011

Angelsen
et al.

Meridian
Institute, The
Gov. of Norway

This 20-page report proposes guidelines for developing reference levels under the UNFCCC. It
covers the selection of activity data and emission factors for reference level development andmethods for analyzing the data. It also introduces the basic ideas and points to consider withregard to national circumstances and setting reference levels at sub-national level. Appendix 2

National,

contains examples of reference level development in Brazil and Guiana. This report will be usefulSub-national,

for policy makers and people who wish to understand the theory of reference level development

Project

at the national level with practical examples.

http://www.forestcarbonportal.com/resource/guidelines-redd-reference-levels-principles-and-recommendations

REDD-plus Cookbook151

IndexAFOLU

I04, P04, T04

CDM

I03, P04
P05, P06, T12

CER (Certiﬁed Emission Reduction)

I01, I02, P04, P13, T04

COP 13

I01, I02, I03, P01, P02

COP 15

I01, I02, P13

COP 16

I01, I02, P03, P05, P13

COP 17

I01, I02, P03, P05, P13

DSM

T05, T16

DTM

T05

FAO

I01, I02, P10, T09, T11
T11

GPG

P05, P06

GPS

T05, T08, T13

GPS camera

T08

IPCC

I01, P04, P06, P07, T02, T14

IPCC guideline

LULUCF
MRV

VCS（Veriﬁed Carbon Standard）
I01, I03, P06, T04
VCU（Veriﬁed Certiﬁcation Units）
T04
VER

I04, T04

above-ground biomass

P04, P11, T02

T03, T14, T16, T19
accuracy

I03, P05

accuracy assessment

T10

airborne LiDAR

P11, T05, T16

allometric equation
area changes

P10, T13, T14, T16

I03, P04, P07, P08, T11

area estimation

P07, P08

atmospheric correction

P08

backscattering coeﬃcient

P11, T19
bambooP11, T13, T16, T17

T04

basal area

T14,T16

below-ground biomass

P04, T02, T14

P08, P11, T05, T16

Bilateral Oﬀset Credit

P02, P05, P06

Mechanism (BOCM)

I01, I04, T04

biodiversity

I01, I02, P01

I01, I02, I03, P02
P05, P06, P11, P13

NFI

I04, P10, T01, T03

REDD-plus

I01, I02, I03, I04, P01, P02
P03, P04, P05, P06, P11

P03, P06, T01
biomass expansion factor
buttress root
capacity building

P13, T02, T03, T04, T13
SAR

P07, P08, P09, P11, T05, T19

SBSTA

I01, I02, I03, P13

UNFCCC

I01, I02, I03, I04,
P01,P02,P03, P04

152

P07, P09

P05, P06, P07

P08, T05, T06, T09, T11, T19

LiDAR

I01, P02, P03

I01,I02,I03,I04

J-VER
Landsat

UN-REDD

T04

CO2 emission

GOFC-GOLD

P05, P06, P13, T04

REDD-plus Cookbook

P10, T14
T13
I02, I03, I04
P02, P04, P13

carbon credit

I02, I03, P06
P12, P13, T04

carbon stocks

I01, I02, I03, P01
P04, P07, P09, P10

P11, P12, P13, T01

classes

T02, T03, T12, T13, T14

environment safeguard

T15, T16, T17, T18, T19

estimation model

P08, T07, T08, T09, T10, T11

Clean Development Mechanism (CDM)
I 02, I03, T04
cloud removal
cloud-free image
cluster

P07,P09, P11, T03, T19
estimation of forest area

P08, T11

ﬁeld survey/measurement

I01, I02, I04

T06

P04, P07,P08, P10, P11,T03

P08, T06

T08, T13, T15, T16, T18, T19

P08, T01, T09

community age

P03

ﬂux method

T02

P07, P09

Food and Agriculture Organization (FAO)

P11, T15, T18

I01community age method

P11, T18

forest area

I02, I03, I04

comparability

I03, I04, P05

P01, P04,P07, T03

completeness

I03, I04, P04, P05

Forest Carbon Partnership Facility (FCPF)

Conference of the Parties
consistency

I01, P02, P03

I04, P05
I03, I04, P05

forest conservation

I01, I02, P06, T04

I01, I03

forest degradation

I01, I02, I04, P01

Copenhagen Accord

P02, P03, P06, P09

corporate social responsibility (CSR)

P11, P13, T05, T12, T18

T04
I01, I02, I03, I04, P03

forest monitoring

P04, P06, P12, P13, T04

forest planningcredit (credits)credit mechanism

I03, T04

cross-polarized (HV, VH) wave
crown diameter

I01, I02, I03, I04
P02

Forest Resources Assessment (FRA)
I02, T09, T11

T19

P07, P09, P11, T15, T17

forest type

I04, P04, P07, P08

P11,T17

P09, P10, P11, P12

I01, I02, I04, P01, P02

T04, T05, T06, T 07

P06, P09, P11, P13, T05, T19

T12, T14, T16, T17, T19

crown diameter method
deforestation

diameter at breast height

P10, T01

Fourth Assessment Report (AR4)

T13, T14, T17

framework convention ontree height curve

T13, T17

climate change (UNFCCC)

double sampling

T15

emission reduction

I02, I03, I04

gain-loss method

I01

I01
P04, P07

geometric correction/ registration
P08, T11, T17

P01, P02, P05, P06, T04

REDD-plus Cookbook

153

girth at breast height

T13

greenhouse gas

P05, P06,P13

I01, I02, I03, I04

multiple reﬂection

P11

P04, P05, P06, T02

multi-layer image

T11

P08, T08, T09, T10

National Forest Inventory (NFI)

ground truth
haze

T05, T06

illegal logging

I02

I04, P09, P10, T01, T03, T10, T13
national forest monitoring system

Intergovernmental Panel on

I01, I02, I04

Climate Change (IPCC)

I01

national greenhouse gas inventory

International Organization

I03, I04, P06

for Standardization (ISO)inventory

P06

I01, I03, I04, P04

National Inventory Report (NIR)
national levelI02, I03, I04, P05

P05, P06, P07, P09

P07, P08, P09, P10

P10, P11, T01, T10, T13

P13, T04, T12,T14, T18

kappa coeﬃcient
Kyoto Protocol

land cover

P08, T10

speckle noise

T19

I01, I02, P01

object-based

T11

P02, P05, T04

object-based classiﬁcation

P07, P08, T05, T06, T07
T08, T09, T10, T11

land surface condition
land use

P11

P08, T09, T11
oil palm

T13, T18

optical sensor

P08, P11

I01, I02, I03, I04
P02, P04, P07, P09, P11, P13

T03, T05, T16
overstory height

T04, T09, T12, T13, T18

P07, P09, P11, T15, T16

laser pulse

T05

overstory height method

like-polarized wave

T19

parameter

Marrakesh accords

P01

measurement

I01, I02, I03, I04, P02

P11, T16

P05, P06, P07, P09
P11, T01, T09, T15, T16

Permanent Sample Plot (PSP)P04, P06, P07, P09, P10

P07, P09, P10, P11, T03

P11, P12, P13, T01, T02, T05

T12, T13, T14, T15, T17

T08, T13, T15, T16, T17, T19

phased approachmeasurement reporting and veriﬁcation
I01, I02, I03, P02, P11
microwave
modality/modalities

154

P05

P11, T05, T19
I01, I03

REDD-plus Cookbook

I01, I02, I03
I04, P02, P04, P13

pixel
platform

P08, T09, T10, T11, T19
T05

stratified sampling

Polarimetric Interferometry
SAR (POLinSAR)

T10, T12

P11

producer's accuracy
project level

P08, P10, T08

P08, T10

sub-national levelI01, I02, I03, I04
P02, P06, P07

I01, I02, I03, I04, P05, P06

reference emission levelP12, P13
reference level

P13, T04, T18

I01, I02, I04

I01, I02, I04, P02

supervised classiﬁcation
sustainable management of forests

I01, I02, P01

P06, P12, P13
remote sensing

I01, I02, I04, P04, P06

Synthetic Aperture Radar (SAR)
P07, P08, P11,T05, T19

P07, P08, P10, P11, P13
T03, T05, T07, T09, T10
T11, T13, T15, T16, T17, T19
remote sensing data

P07, P08, P10

T07, T08, T15, T16, T17
reporting

I01,I02, I03, I04
P02, P05, P11

rubberP11, T13, T18

safeguard

I01, I02, I03, I04
P02, P03, P06

sampling design
seasonality
sensorP07, T01, T10, T15
T06
P08, P11, T03, T05
T15, T16, T17, T19

slash-and-burnP11, T18, T19

social safeguard

P03

softwarespace-borne LiDAR
spatial resolution

P08, T09

temporal resolution

P08, T05

Third Assessment Report (TAR)
overall accuracy

I01
P08, T10transparency

I02, I03, I04, P05, P13

tree census

P11, T13, T14, T15, T18

tree crown cover
uncertainty

I02, P01
I03, P04, P06, P07, P12, T10

U. N. Development Programme (UNDP) I01
U. N. Environment Programme (UNEP)

I01

unsupervised classiﬁcation

P08, T09

user's accuracy

P08, T10

veriﬁcation data

P08

Veriﬁed Emission Reduction credit

I04

wavelength resolution

T03, T05

REDD-plus Cookbook

155

P08, P12, T05
T16
T05, T07

stand carbon stock estimation model
P07, P09, P11
stereoscopic sensors/
images(information)

P11, T16

stock change method

P04, P07, P09
P12, T02

Authors
Shinichi Aikawa 1)
Satoshi Akahori 5)
Yoshio Awaya 2)

Reference Guide

Kei Suzuki 6）

Recipe T03

Recipe P02

Gen Takao 1)

Recipe I04, P04, T10, T15
Recipe T09

Recipe T19

Shinya Tanaka 1)

Makoto Ehara 1)

Recipe P03, Reference Guide

Takeshi Toma 1)

Recipe P01, T03, Reference Guide

Yasumasa Hirata1)

Recipe I02, P13

Jumpei Toriyama 1)

Recipe P12, T02

Naoyuki Furuya 1)

Recipe T17

Naoko Tsukada 1)

Recipe P05, P06

Kimihiko Hyakumura 3)

Recipe P03

Satoshi Tsuyuki 4)

Recipe T05

Toshiro Iehara 1)

Recipe T01

Mitsuo Matsumoto 1)

Recipe I01, T04

Recipe P11, T16

Toshiya Matsuura 1)

Recipe T08

Recipe T11

Nobuya Mizoue 3)

Recipe P10, T12
Recipe P09, T14

Eriko Ito 1)
Tsuyoshi Kajisa 3)
Satoko Kawarasaki 1)

Reference Guide

Yukako Monda 1)

Yoshiyuki Kiyono 1)

Recipe I03

Tetsushi Ohta 3)

Recipe T07

Hideki Saito 1)

Recipe P08, T06

Yasuhiro Yokota 1)

Recipe P03

Akinobu Sato 6）

Recipe T03

Tamotsu Sato 1)

Recipe P07, T13, T18

1) Forestry and Forest Products Research Institute, Japan
2) Gifu University, Japan3) Kyusyu University, Japan4) The University of Tokyo, Japan
5) Forestry Agency, Ministry of Agriculture, Forestry and Fisheries, Japan
6) Japan Forest Technology Association

Editors
Yasumasa Hirata・Gen Takao・Tamotsu Sato・Jumpei Toriyama

Editorial staﬀ
Makoto Araki・Yasumasa Hirata・Gen Takao・Tamotsu Sato
Jumpei Toriyama・Naoko Tsukada・Tomomi Fukumi・Atsuko Naka

Citation
This document may be cited as:
Hirata Y, Takao G, Sato T, Toriyama J (eds) (2012) REDD-plus Cookbook. REDD Research and Development
Center, Forestry and Forest Products Research Institute Japan, 156pp.
ISBN 978-4-905304-15-9
A digital copy of this report can be downloaded at
http://www.ﬀpri.aﬀrc.go.jp/redd-rdc/en/reference/cookbook.html

2013.6

156

REDD-plus Cookbook

Forestry and Forest Products Research Institute
1 Matsunosato, Tsukuba, Ibaraki, 305-8687 JAPAN
REDD Research and Development Center
TEL: +81-29-829-8365

FAX: +81-29-829-8366

URL: http://www.ﬀpri.aﬀrc.go.jp/redd-rdc/ja/index.html
E-mail: redd-rd-center@ﬀpri.aﬀrc.go.jp