REDD+ Demonstration Study 2014 – Final Report

MOEJ/GEC REDD+ Demonstration Study 2014
Summary of the Final Report“Improvement of REDD+ implementation using IC technology”( Implementing Entity: Mitsubishi Research Institute, Inc. (MRI) )1．Overview of the Proposed JCM Project
NEC Corporation (NEC)Study partners

Ecological Economic Solutions Sdn Bhd (2ESolutions)
Borneo Orangutan Survival Foundation (BOS-F）

Project site

Republic of Indonesia, East Kalimantan Province

Category of
project

REDD+ (Reducing Emissions from Deforestation and forest Degradation in

Description ofproject

developing countries)
In order to address common issues regarding monitoring activities in REDD+
projects, high-spec MRV methodologies are considered by making the best use ofInformation Communication Technologies (ICTs).
NEC Corporation (NEC)

Expected project
implementerJapan

Mitsubishi Research Institute, Inc. (MRI)
Ecological Economic Solutions Sdn Bhd （2ESolutions）

Host country

Borneo Orangutan Survival Foundation （BOS Foundation）

Initial investment

190,000（Thousand yen）

Date ofgroundbreaking

Annualmaintenance cost

1,067（Thousand yen）

Construction
period20 Years (REDD+ Project

Date of projectcommencement

2016 （ Scheduled

2016（Expected Project Start)

Period)Yes（to be decided based

Willingness toinvestment

upon the results of astudy on institutional

Project

Start)

arrangements)
Initial cost of 190 million yen and operating costs of 32 million yen (total cost for
30 years) are envisaged. Of the initial cost, the amount for acquiring the Ecosystem

Financial plan ofproject

Restoration Concession (ERC) has already been invested, through PT RHOI, by
BOS Foundation, the local counterpart. In the Study Project for the current fiscal
year, studies and surveys will be carried out and capital planning will be worked out
in more concrete terms.
180,000 (tCO2/year)/3,600,000 (tCO2): Total amount for the project period of 20

GHG emission
reductions

years. Assuming deforestation can be controlled by implementing project activities
such as forest patrols and forest conservation etc., the GHG reduction potentials
have been calculated on a trial basis.

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REDD+ Demonstration Study 2014 – Final Report

2. Study Contents(1) Project development and implementation
1) Project planning
Project feasibility will be closely examined on the basis of a project proposal being studied by BOS
Foundation along the lines of the REDD project to be implemented under JCM or the conventional carbon
credit system (VCS). The basic policy has been mapped out to establish a cost-efficient project
implementation method, making best use of the results of the Study Project for the last fiscal year (FY2013),
and applying the MRV methodology, which is to be improved in the Study Project for the current fiscal year
(FY2014).

2) Contribution of JapanThe MRV Methodology to be developed in this Study Project will use Earth Observation Satellites andSatellite Remote Sensing, which incorporate advanced Japanese technologies, and is expected to contributeto further improving the efficiency of REDD+ Project operation. Among the merits and features of satellite
observations are that periodic and homogeneous data can be obtained, because satellites have a wide
high-altitude one-time observation range (swath) in terms of space; long-term continuous observations (interms of several years) are possible; and, there are no constraints on aerial coverage (such as nationalboundaries). The following table summarizes ICT equipment and solutions, whose applicability and usability
at the ERC Site has been confirmed by BOS Foundation and Japanese forestry officials and personnel.

Items
1. Digitalization ofbooks

2. Integration ofelectronicequipment
3.
Assimilating
in-situdatacollected

Table: ICT Functions Required at Observation Spots
Functions
Notes
Weight saving will be achieved with In lieu of illustrated books of flora
portable electronic illustrated reference and maps, etc.
books, etc. for in-situ verification andreference
The books in 1. above will be integrated in n/alieu of individual pieces of equipment
When assimilating data from stationary
observation equipment, memory cards areusually used. A large volume of data can beeasilytransferredfromobservationequipment.

No equipment for storing data isavailable at the present ERC site. In
the future such a system is likely to
become available for tracking
wildlife.

3) MRV structure
The organizational set-up for implementing MRV during the period of Project implementation
envisages the following approximate division of roles.


Examination of Overall Implementation Policies and Planning and Operation, etc.: Mitsubishi ResearchInstitute, Inc. and NEC
Support for MRV Methodology Operation, Image Analysis, etc.: Mitsubishi Research Institute, Inc. and
NEC
In Situ Field Surveys and In Situ information collection: BOS Foundation, 2ESolutions
Local Cooperative Organizations: East Kalimantan Provincial Government, Research Institutes(Mulawarman Universities and NGO, etc.)

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REDD+ Demonstration Study 2014 – Final Report

Generally, REDD+ Project MRV requires the following tasks of: 1) identifying, through satellite image
analyses, etc., active mass per land-cover classification (Area change data) and 2) identifying emission
factors per land cover classification (Carbon stock per unit area). Moreover, data-collection methods mustalso be examined in terms of (A) whether its own data should be collected and (B) whether existingstatistical data should be used. When reliable data are available, using existing statistical data according to(B) would be conducive to improving operational efficiency. In the FY2013 Study Project, data developed on
our own were used in terms of both land cover change and emission factors. In the Project this fiscal year
(FY2014), the same line will be followed. However, in the actual REDD+ Project, implementation will be inline with what has been agreed bilaterally.

In monitoring activities at the ERC site, forest quadrat surveys were conducted to verify emission
factors. The instruments and tools required for carrying out such surveys were measures, cameras,
illustrated books of flora and stationery, etc. Such items are the same as those used in conventional
vegetation surveys. The duties of quadrat surveys have already been transferred. Compared to the outcome of
the FY2013 survey, the outcome of the FY2014 survey was more stable in terms of contents. The outcome ofthe survey needs to be verified. However, there seem to be no problems that need to be raised and addressedon the survey implemented solely by the local counterpart.

On the other hand, as regards satellite image analysis, transferring duties in the immediate future will bedifficult because of the operational activities, which were unfamiliar to BOS Foundation and because of thedifferent expertise required. A gradual transfer of technological duties in the process of REDD+ Project
implementation is considered to be the proper course of action.
Table: Items of MRVClassification(A) Collection of own data(B) Using existing statistical data

Land cover changes
per Strata (Land coverclassification)

Analysis of satellite images of
the Project Area
【MRI, EC, etc.】

Emission Factor
per Strata (Land coverclassification)

Creation of Emission Factor
data based upon In On-Situ
Sampling Surveys [BOSFoundation, etc.]

Basic data for setting up REL as posted on
RAD-GREK (Availability must be verified)[East Kalimantan Provincial Government,
etc.]
Basic data for setting up REL as posted on
RAD-GREK (Availability must be verified)[East Kalimantan Provincial Government,
etc.]4) Environmental integrity and Sustainable development in host country
The main objective of the REDD+ Project and ecotourism is to conserve ecosystems in tropical rain
forests, and they are expected to have diverse environmental conservation effects on the areas where theProject is implemented. In particular, it is expected to contribute to enhancing conservation activities fororangutans, preventing illegal logging, poaching, and environmentally damaging development, andmaintaining ecosystem services such as water resourcesⅠ- 2 - 3

REDD+ Demonstration Study 2014 – Final ReportConservation of Orangutans
A large part of the monitoring budget of the BOS Foundation is earmarked for activities to monitor

orangutans carried out after they have been released in the ERC area. Therefore, the budget for forest survey
activities is limited. The forest surveys being carried out by BOS Foundation at present are limited tomonitoring under ERC arrangements, as well as those for the purpose of selecting sites to release orangutans
and locating their edible plants. By implementing a REDD+ Project, more comprehensive forest surveying
and monitoring will become possible.
Preventing illegal logging, poaching, and environmentally damaging development
The road infrastructure within the Project Area and its surrounding areas remains undeveloped.

Therefore, development of roads and bridges in these areas will contribute to facilitating orangutanconservation activities now being carried out by BOS Foundation and promoting ecotourism.


Maintaining ecosystem services such as water resources
Telen River, which flows through the Project Area, is situated in the upper reaches of Sungai Mahakam

River flowing through Samarinda, the provincial capital. Sungai Mahakam River is one with the largest basin
areas in Kalimantan. The ERC Area plays an important role in terms of maintaining water resources andflood control in these basin areas. The rain forests, as well as fauna and flora, are an important part of thecultural heritage of the Dayak people living in areas adjacent to the ERC Area. Good examples are theirtattoos featuring local fauna and flora motifs and the hornbill dance. Activities for conserving the ERC Area
through this Project will contribute to the sustainable development of these ecosystem services.

5) Toward project realization (planned schedule and possible obstacles to be overcome)As indicated in the figure below featuring the future schedule, the present Project is at the final stage ofdeveloping a methodology. Within approximately one year from now, the Project Design Document (PDD)
will be prepared. After feasibility has been approved, REDD+ activities will move on to the implementation
stage. After initiation of this stage, activities are planned to continue for a period of from 20 to 30 years, witha data review every five years.
The future agenda includes procurement of funding for preparing PDD. This is likely to be done in thenext fiscal year. Activities will continue to look for the necessary financial support. Because no profits are
expected from the period during which financial support, such as subsidies, can be received until crediting
actually takes place.

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REDD+ Demonstration Study 2014 – Final Report
Present
Situation

REDD+
Flows

Featuresof thisProject

Methodology
Development

PDD Preparation

REDD+
Implementation

・ Measuring
・ Reporting・ Verifying

・ Examining Possibility ofConducting an In Situ Survey
・ Examining Safeguards- TransferringTechnologies, Supplementary Item
(Ecotourism)

・ REDD+
Implementation

・Using Satellite
Remote Sensing
・Improving Precision of
Calculations
・Reducing Number of In
Situ Surveys

・Earning Credits

・Timely Monetization
・Examining Potential
use of IT Equipment
・Completing
Implementation Plan
Document
・Lowering Costs withStandardized Image
Analysis Method

Expected
Good
Effects

Crediting・Using Mobile
Equipment at Local
Spots/Ecotourism
・Acquiring Credits

・Encashing Credits

Figure: Future Schedule(2) JCM methodology development
1) Eligibility criteria
The requirements for qualification have been examined for the methodology concerned. The following
Table summarizes the examination results. At present, five requirements have been established as
Requirements for Qualification. It was decided that by applying a methodology that meets thoserequirements, emission reductions achieved when the Project is implemented will be assessed quantitatively.
Table :Equirements for Qualification of Methodology
Item
Requirement 1:
Scope of REDD+
Definition

Requirement 2:
Concession within
Targeted
Project
Area
Requirement 3:
Specifications ofSatellite Images andAnalysis Methods
Requirement 4:
Consideration forContents
Ensuring that the targeted forest shall be in line with the definition of a forest
applicable in the host country, and that the targeted forest conservation activities
shall be in line with the scope of REDD+ in the country.
<Definition of Forest in Indonesia >Crown Ratio: 30%, Minimum Tree Height: 5 m, Minimum Forest Area: 0.25 h＜Scope of REDD+(Example)>(1) Emission Reduction from Forest Decline(2) Emission Reduction from Forest Degradation(3) Conservation of Forest Carbon Stock(4) Sustainable Forest Management(5) Intensification of Forest Carbon StockThat the concession within the Project Area must be in a state of ‘alreadyobrtained’ possession. That approval for REDD+ implementation has already beenobtained from administrative authorities concerned (Provincial Government, etc.)
responsible for granting such concession.
That the resolution of high-resolution satellite image data and analysis methods,
etc. shall meet the following specifications.
High-resolution remote sensing data in the latest year of the project
implementation period (Resolution 5 m or higher resolution: ALOS, PRISM, etc. )Safeguard measures including consideration for biodiversity shall be taken in
REDD+ activities. In case there are stipulations, etc. regulating safeguardⅠ- 2 - 5

REDD+ Demonstration Study 2014 – Final Report

Safeguards
Requirement 5:
No Peat Swamp
Forestmeasures in the host country, there shall be compliance with such stipulations.
No peat swamp forest is included in the Project Area. Or the impact of
deforestation and forest degradation are negligible small in terms of emissions
from soil organic carbon.

2) Calculation of GHG emissions (including reference and project emissions)
1-1 Methods of Calculating Reference Emissions
Based on the rules for setting up the reference area, expansion of the reference area has been examined.
Reference Area 2 includes areas where forests are more developed. The assessment can be carried out,
including future development risks, which may also happen within the ERC Site in the future. The
following figure shows Reference Area 1 and Reference Area 2.

Reference area 2(Radius: 50km)

Reference area 1(Radius: 34km)

ForestBushFigure: Set-up for Reference Areas
The calculated reference emissions within Reference Area 2 are shown in the Table below.

Table : Calculated Reference Emissions
Reference Area 1
Reference Area 2（Radius 34 km）（Radius 50 km）
Reference Emissions（＠Project Area）

162,000[tCO2/year]

180,000[tCO2/year]

2-1 Methods of Calculating Project Emissions
As the methodologies for calculating Project Emissions, the following two methods are proposed: (a)Method using moderate resolution optical remote-sensing data and (b) Method using high-resolution
remote-sensing data. The following figure shows the process flow of both methods.

Ⅰ- 2 - 6

REDD+ Demonstration Study 2014 – Final Report(a)Moderate
Resolution Data

In Situ Survey
Data

(b)

High-resolution
Data (More than one
band)

Supervised Classification orClassification based onparameters such as decision
trees, etc.

Land CoverClassification
Map

Elevation
Correction

High-resolution
Data with Elevation
Corrected

Elevation
Correction

Multi-spectral Datawith Elevation
Corrected

Texture Analysis(GLCM)

DEM Data
Multi-spectral Data
(Better thanModerate
Resolution)

Supervised
Classification

In Situ Survey
DataHigh Biomass &Low Biomass
Classification Map
Forest-type
Classification of Primary
Forests and Secondary
Forests

Land CoverClassification
Map

Figure: Flow chart of methods of calculating Project Emissions with high-resolution sensors

Based on the results of the Project conducted in the last fiscal year (FY2013), the texture analysis,
which uses high-resolution images, is adopted as the Method (b). As a result, when developing themethodology, support vector machine is used as a supervised classification. Since the Project site is in a
mountainous and remote area where in-situ surveys are expected to be difficult to undertake, best option ismade of remote sensing data. However, due to the configurations of mountains, it is necessary to correct
images. Because of the possibility that ecological succession in the direction of sparse vegetation will occur
due to elevation changes, sufficient ground survey data featuring diversity and high precision will be
required to ensure the precision of the resulting classification map and the estimation model used for its
calculation. As indicated by the results of the analysis conducted using the RapidEye images (The following
figure), it is inferred that in terms of macro-scale, surface vegetation may change in accordance withelevation changes.

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REDD+ Demonstration Study 2014 – Final Report

(b)

(a)

Figure: Vegetation Change is Likely with Elevation Changes(a) Rapid Eye Regularized Images (R: Band 5, G: Band 4, B: Band 2), (b) DEM Data
Under the conditions, a lot of time, funds, and human resources will be needed to conduct In-situ
surveys that ensure the sufficient level of precision. Therefore, a scheme will have to be developed for ahighly reliable forest-type classification. In the current fiscal year (FY2014), therefore, with a view todeveloping a methodology that ensures efficient operation, the possibility of a methodology by whichdevelopment factors (roads, rivers and villages, etc.) that may aggravate forest decline has been identified
and proxy variables used as evaluation criteria have been verified. Equally, the possibility of a method bywhich classification is undertaken through a forest-canopy-structure analysis using satellite data with a
sub-meter (less than one meter) resolution made available from Worldview 1/2 and ASNARO to be launchedin the future, etc. has been verified. The following figure shows the entire process flow. Each of the two
methods is explained in detail below.

Ⅰ- 2 - 8

REDD+ Demonstration Study 2014 – Final Report(a)Overall project area
High-resolution Data
(More than one band

Elevation
Correction

High-resolution Data with
Elevation Corrected

Elevation
Correction

Multi-spectral Data with
Elevation Corrected

Texture Analysis(GLCM)

DEM Data
Multi-spectral Data
(More than Moderate
Resolution)

High Biomass & Low
Biomass Classification
Map

Supervised
Classification

In Situ Survey
Data

Forest-type Based Classification ofPrimary Forests and Secondary Forest
Land CoverClassification Map

Overall project area
Rivers & Roads Map(b)Overall project area
High-resolution Data
(More than one band

Setting up Regions
Targeted for Analysis

Primary and Secondary
Forest Classification Map

Elevation
Correction

High-resolution Data with
Elevation Corrected

Elevation
Correction

Multi-spectral Data with
Elevation Corrected,

Texture Analysis(GLCM)

DEM Data
Multi-spectral Data
(More than Moderate
Resolution)

Supervised
Classification

In Situ Survey
DataHigh Biomass & Low
Biomass Classification
Map

Limited part of project area
Super Highresolution Data

Primary and Secondary
Forest Classification Map

Forest Canopy
Region Extraction,

Forest-type BasedClassification of Primary
Forests and Secondary Forest

Rivers & Roads Map

Setting up Regions
Targeted for Analysis

Overall project area

Forest-type Based Classification ofPrimary Forests and Secondary Forest

Land CoverClassification Map

Primary and Secondary
Forest Classification Map

Figure: Flow chart of methods of calculating Project Emissions with Super High-resolution data

2-2. Method for Using Proxy Variables
It is difficult to ensure transport routes with logging constituting a disturbance factor, because thetargeted Project Area is located in steep mountainous areas and unless there are rivers or roads wide enoughto be detectable with high-resolution satellite data (A width of approximately several meters) that enable
such transport. These points have also been confirmed through in-situ interviews, etc. There are studies thathave extracted roads and canals for transport to make estimates used as a method of identifying deforestation
areas. This may be a relatively reliable scheme. This area is rugged and it is difficult to penetrate deeper intoⅠ- 2 - 9

REDD+ Demonstration Study 2014 – Final Reportareas more than 250 m from a road. Therefore, the areas for analysis have been limited to the areas within adistance of less than 250 m. Conversely, it will be difficult to transport logged trees in areas more than 250 m
from a road or river. Therefore, these areas have been defined as natural forests where no logging will takeplace. The following figure shows this line of reasoning.

Access Road for In
Situ Surveys
100 m from
Access Road

200 m from
Access Road
Figure: Reasoning of Proxy Variables (Method of Setting up the Scope of Verification)
In the areas mentioned above, forest-type classification is undertaken, as in the last fiscal year, using amethod that applies high-resolution data such as RapidEye. When using this method it is important to get thelay of roads and rivers. These will be identified using remote sensing data with a spatial resolution of lessthan 5 m/pixel, including RapidEye, and by resorting to visual observations or visual observation extraction,
which uses an automatic extraction algorithm. Local information or existing GIS data, if available, may also
be used.

In monitoring activities, the expansion and extension of roads need to be monitored during the project
implementation period. Such monitoring can be carried out by two methods, namely extraction based onremote-sensing data and that based on local information to be provided. In the process of using
remote-sensing data, locations which is subject to environmental changes are identified through time-serieschange extraction method, which will use high-resolution data with a spatial resolution of 5 m/pixel such as
RapidEye, and surveys will focus on such locations. Such a time-series change extraction method will extract
areas where changes occur using second and third principal component analysis images to be computed fromthe principal component analysis. As far as this Project Area is concerned, there will be neither
high-resolution satellite-images of roads being expanded of pre- and post- construction. Therefore, theprecision will have to be verified separately in other areas in the future. Local information will be verified by
the organization responsible for local surveys etc. (BOS Foundation in the case of this Project) throughpatrols, etc. For areas identified as having roads that are likely to be expanded or extended, such verificationis undertaken by visual observations, using sub-meter resolution satellite images. Such data are siphoned into
GIS data. Satellite images from WorldView1/2/3 and ASNARO, etc. are envisaged for sub-meter resolution
images.

Ⅰ- 2 - 10

REDD+ Demonstration Study 2014 – Final Report2-3. Method of Analysis Based on Super High-resolution Data
To reduce the burden of in-situ surveys, as well as to prevent inconsistency of precision in in-situ
surveys, these prove to be a major advantage for monitoring activities. In recent years, databases fromsatellites with a resolution of less than one, such as those from Worldview1/2/3 and ASNARO to belaunched in the future, have expanded. Taking advantage of these super high-resolution data, a classification
based on a forest canopy structure analysis are carried out, and it is applied as supervised data for theclassification scheme carried out in the last fiscal year using high-resolution data such as those from
RapidEye. This method focuses on the fact that while disturbed forests are restored from secondary forests toprimary forests, there are major differences in tree canopy structures between secondary forests in theprocess of growth and primary forests. This method has the inherent advantage that clear differences in tree
canopy structures can be extracted at a sub-meter class resolution. A method that integrates the macro-texture
structure and spectral data focusing on differences in forest species had been employed because at present
non-sub-meter high-resolution images such as Rapid Eye cannot provide detailed information. It is believedthat classifications with a precision higher than can be realized. Furthermore, in the case of spectralclassifications, forest types per area have to be identified with in-situ surveys, which must be conducted atmany locations. However, super high spatial resolution images will enable photo-interpretation of tree
canopy sizes. Therefore, classification can be conducted just by identifying thresholds that set lines between
forest-type classifications in in-situ surveys. However, unlike multi-spectral data such as those fromLandsat8 and RapidEye, super-high resolution data are limited in terms of the area that can be imaged at one
time and the cost of images is extremely high. Therefore, it is more realistic to expect that the analysis will
be conducted in a very limited part of the whole Project area.
Accordingly, with this methodology, the results of the classification undertaken on the basis of superhigh-resolution data are treated as supervised data for the analysis carried out last fiscal year using
multi-spectral data. The overall operational process is described below.

i)

Forest canopy structures are classified using super-high resolution images. However, as super highspatial resolution images are small range of observation area and therefore they are limited to small
confined areas.

ii)

Using supervised data for areas resulting from the classification mentioned in i) above, supervisedclassification will be undertaken using RapidEye data, which cover the entire ERC site. Data mentionedin i) above will be used for evaluating the process and outcome of developing the classification model.
However, the area of i) for model development and the area i) for verification shall not be overlapped.

iii) Carbon stock can be estimated using the land cover map (primary and secondary forest classification)
derived from applying the proxy variables in (2) to the classification results acquired in ii).

When extracting forest canopy structures using super high-resolution data, first, the configurations oftree canopies are extracted using a morphological analysis. The concept of a morphological analysis as suchis a historic methodology dating back to the 1960s, and is a technique for extracting the configurations of

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REDD+ Demonstration Study 2014 – Final Report

objects from images. Technically, image-processing operations on a configuration basis will be carried out
in the process of image processing. With this methodology, pixel codes are added to the thresholds of objects
within images using dilation, which is the most basic morphological operation. The following figure shows
the images analyzed before and after such extraction.

Figure: Results of Forest Canopy Structure Extraction(a) Super High Spatial Resolution Image (WorldView1) (b) Results of Forest Canopy Extraction

For the forest canopy structures extracted as described above, the following definitions are provided
based on the definitions of primary and secondary forests used in the last fiscal year. Accordingly, areaclassifications are carried out.
Primary Forests: Large-diameter trees (Trees with large trunk shapes) are prevalent.
Secondary Forests: Large-diameter trees are fewer there because of the influences of ecologicaldisturbances and logging.

The following figure shows the above presented in the form of a schematic figure.

Restoration
Disturbance
Logging

Secondary forests
Smalldiametertrees

Primary forests
Largediameter
trees

Figure: Definitions of areas of Primary forests and Secondary forests

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REDD+ Demonstration Study 2014 – Final Report

The following figures show the results of the analysis.
（a）

（b）

Many Smalldiameter Trees

Many Largediameter Trees

Figure: Example of distributional ratio of small-diameter trees by use of Segmentation Data(a) Results of forest canopy extraction, (b) Distributional ratio of small-diameter trees
In order to classify areas between primary forests and secondary forests on this map, a certain thresholdhas to be established in terms of the ratio of small-diameter trees. The threshold can be the basis of theclassification of primary forests and secondary forests in the region, which results from in-situ groundsurveys.
The primary forests and secondary forests within the entire Project Area are classified by conducting a
supervised classification of RapidEye data. In the classification analysis, the results of area classification
between primary and secondary forests by use of the threshold mentioned earlier are applied.
The above-mentioned methodology will enable us to reduce the number of points to be monitored in
in-situ surveys without compromising the precision level of classification. In the actual in-situ monitoring
activities to be done in the future, a sufficient number of points will be monitored during the activities in thefirst fiscal year. Thereafter, the focus will be more on analyses based on super high-resolution data to be
carried out each year. Such arrangements enable us to replace and save a number of actual in-situ activities.

2-4. Results of Calculating Project Emissions
As indicated in the following figure, this Project Area is mostly covered with forests. Under the generaland traditional land cover classification methodologies, the classification is either for forest or for non-forest.
Therefore, based on the methodologies, this Project Area falls mostly into the category of forest. This meansthat the extent of restoration cannot be assessed in a precise manner with respect to areas where logging hastaken place in the past and forests are now under restoration. Therefore, precision in estimating carbon stock
can be improved using the method described above.

3-1. Methodology by use of proxy variables

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REDD+ Demonstration Study 2014 – Final Report

The rivers and roads within the Project Area have been extracted through in-situ surveys, as well as
through satellite images analyses. The following figure shows the outside view.

Area of project site
River
Road

Figure: Rivers and Roads within the Project Area
By covering forests within a distance of 250 m from roads and rivers, a primary forest or
secondary-forest classification has been undertaken on the basis of RapidEye images and ground in-situ
survey data.

First, a supervised classification was carried out for RapidEye images covering most of the Project Area,
using the images obtained through texture analyses, as well as the ground level data obtained from in-situ
surveys. Furthermore, in accordance with the line of thought followed in the last fiscal year, land coverclassification was carried out to identify types of primary forest and secondary forest. The results covering
the entire Project Area are as shown in the following figure.

Ⅰ- 2 - 14

REDD+ Demonstration Study 2014 – Final Report

(a)

(b)

Area of project site

Primary
forestSecondaryforestFigure: Results of primary or secondary forest classification(a) Results of classification on a pixel basis (b) Results of classification b/w primary or secondary forest
Using the results of the classification mentioned above, areas defined as those of secondary forests
within 250 m of the rivers and roads, were extracted. The results are shown in the following figure.

Area of project site
SecondaryPrimary
forestforestFigure: Results of secondary forest classification carried out using proxy variables
The areas of the primary and the secondary forest in the Project Site are shown in the following table(Areal Space of Primary Forest and Secondary Forest). By multiplying these by the primary-forest emission
factor and the secondary-forest emission factor, the amount of emissions is calculated.

Table: Areal Space of Primary Forest and Secondary Forest)
Primary Forest
Secondary Forest
Area [ha]89,490610

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REDD+ Demonstration Study 2014 – Final Report

Emissions [Mt]

17.5

0.10

4-1. Method of Analysis Based on Super High-resolution Data
One of the main objectives of the methodologies using super high-resolution data is to reduce thenumber of points to be checked in in-situ survey data without compromising the precision of classification.
Here, the two different methodologies have been compared in terms of the precision of the classification toevaluate the validity of this classification method. The results of the primary and secondary forestclassifications carried out using super high-resolution data and the results of the in-situ surveys are shown in
the following figure.

(a)

(b)

In Situ Survey Plots(Primary Forests)
Primary
forest(c)Secondaryforest(d)

In Situ Survey Plots(Secondary Forests)

Figure: Results of primary and secondary forest classification obtained by use of Super
High-resolution Data : (a) and (c) Worldview1; (b) and (d) Classification results
The results of primary and secondary forest classifications carried out with RapidEye images using themap derived from the classification results based on super high-resolution data are shown in the following
figure. It can be said that the precision of the classification is almost at the same level, showing a high degreeof precision. The map also shows more or less the same trends, although there are differences in some areas.
As regards the areas indicated at the right in the figure, it would be appropriate for the results of future in-situ
verification work to be reflected when determining parameters.

Ⅰ- 2 - 16

REDD+ Demonstration Study 2014 – Final Report

(a)

(b)

SecondaryforestClassification

Accurancy

Classification

Accurancy

Primary forest

71.4 %

Primary forest

96.3 %

Secondary forest

87.5 %

Secondary forest

72.2 %

Figure Comparison between results of two classification results: (a) Methodology using
High-resolution Data, (b) Methodology using Super High-resolution Data

3) Data and parameters fixed ex ante
The Methodology stipulates that, as regards emission factor data used for calculating GHG emissions,either using existing emission factor data, or using emission factor data compiled by the project proponents
on its own may be freely selected. In this Study, the official emission factor data compiled by the Indonesian
Ministry of Forestry and widely used in Indonesia are used. This database covers areas of 22 land-coverclassifications as a GIS database. The emission factor database stipulated in RAD-GRK (Emission Reduction
Plan of the Province) published by the East Kalimantan Provincial Government is shown in the Table below.
Based on the outcome of the Study Project for the last fiscal year, the possibility of application focusing on
two forest classifications marked red and six non-forest classifications marked green, has been studied.

Table: Emission Factors per Land Cover Classification of RAD-GRK in East Kalimantan ProvinceNo.

Land Cover

Proposed carbon stock [t/ha]

1

Primary dry land forest

195.4

2

Secondary/former logged dryland forest

169.7

3

Primary mangrove forest

170

4

Primary swamp forest

196

5

Plantation forest

64

6

Bush

15

7

Plantation/garden

638Settlement

19Open land

0

10

Grass

4.5

11

Water area0
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REDD+ Demonstration Study 2014 – Final Report

12

Secondary/former logged mangrove forest

120

13

Secondary/former logged swamp forest

155

14

Swamp bush

15

15

Dryland agriculture

8

16

Mixed dryland agriculture shrubs/garden

10

17

Field

5

18

Embankment

019

Airport/port

5

20Transmigration

10

21

Mining

0

22

Swamp

0

Source: Provincial Government of East Kalimantan "Local Action Plan for Greenhouse Gas Emission
Reduction East Kalimantan Province"

In addition to the land-cover database, additional information has been collected within the Project Area
to improve the precision of analyses.
Table: Outline of Own Information Collection within the Project Area
Pre-set Point

Contents of Information Collected

Informationonroad conditions
within the Project
Area
Informationonrivers and streams
within the Project
Area

GIS Data on roads suitable for car traffic within the Project Area. Development such as
deforestation starts with roads. In that context, information will be collected to verify
areas surrounding roads because it is highly likely that further development will takeplace there.
GIS data obtained on rivers and streams flowing within the Project Area. Rivers arenavigable by vessels and are used for accessing gold mines in adjacent areas and fortransporting goods. Log transport using rivers is taking place and further development islikely. For that reason information has been collected.
Information has been collected on DHB, tree species, understory species, and rakes, etc.,
and photographs were taken of survey spots during in-situ surveys. Information has also
been collected to obtain training data and identity data with a view to developing abiomass estimation model for the Project Area.

Information
Collected by In
Situ Surveys4) Elaboration of the JCM methodology based on applying the draft to measure GHG
emission reductions
As mentioned earlier, the reference emissions in the Project Area in the case of Reference Area 2 with arelatively large area coverage are 180,000 [tCO2/year]. In this trial calculation, this value has been adopted
for reference emissions resulting from the Study.
As regards the expected value of Project emissions, the precision of the calculation will have to befurther improved. However, when it is presumed that the ratio of decline of forest carbon stock can be
decreased to 0% through Project activities such as forest patrols, including control of illegal loggings,
re-vegetation, and forest conservation, etc., the emissions reduction resulting from the REDD+ Project canreach 180,000 [tCO2/year] at maximum. Furthermore, if the Project is implemented over a period of 20 years,
the total reduction of GHG emissions will reach 3.6 [MtCO2].

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REDD+ Demonstration Study 2014 – Final Report(3) Activities for acquiring international understanding of REDD+ under the JCM
In the process of Project implementation in the future, cooperation with the Indonesian side andcontributions in the following fields may be possible and may be considered effective.
【Capacity building in the area of MRV methodologies, etc.】
As repeatedly stated in this Report, studies have been ongoing in Indonesia with a view to developing
legal frameworks for REDD+. Among these studies, those on methodologies for quantitatively assessing
emission reductions and on establishing monitoring methods are essential.
【Information dissemination on study outcome through actual Project Activities】
It is increasingly important in the future for information on the fruits of activities at the stage of afeasibility study and at a more advanced stage of demonstrative studies to be actively communicated, in order
to contribute to promoting capacity building at a local level and enhancing local awareness on andunderstanding of JCM.

End

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