R&D
Trend about MRV
methods for REDD+in

THAILAND
Anuchit Ratanasuwan
ThailandOverall Condition
ofREDD+
Initiatives in
Department of National Parks,Wildlife and Plant Conservation

Overall condition• Prime Minister of Thailand gave the statement
at UNFCCC COP 15 in Copenhagen inDecember 2009 that“ to increase our carbon sink, Thailand has set
the ambitious target to increase the national
forest cover from 30% in 2006 up to 40% by2020.”

Overall condition• Thailand got R-Pin approval from World Bank
since March 2010 and waiting for funding todevelop RPP.
• To date, Thailand does not have an official
policy on how to implement REDD+.

Overall condition• DNP mission commonly conform with REDD goal :forest protection, forest rehabilitation, forest fire
prevention• To prepare REDD+ : Task force set up andconsidering to conduct REDD+ in project based• Participated in MRV capacity building organized byinternational institution:Winrock International:Wood Hole Research Center (WHRC)

Overall condition• Insufficient of personnel working in RS
area

Forest Cover
Data on forest coverobtained from visualinterpretation using
Landsat data.

Existing Forest1989-1990

Data on forest coverobtained from visualinterpretation using
Landsat data.

Existing Forest 1995

Data on forest coverobtained from visualinterpretation using
Landsat data.

Existing Forest 2000

Data on forest coverobtained from visualinterpretation using
Landsat data.

Existing Forest 2005

Data on forest coverobtained from visualinterpretation using
Landsat data.

Inventory Cluster
Location

National Forest Inventory(ground survey) andMonitoring systemcompletely establishedwith the support of ITTO.

Knowledge obtained
from past experience•The essential data for MRV areforest cover and biomass toestimate historical emission level.
•No standard in historical data onforest cover due to instability ofmethodology• Lack of biomass data

Technical challenges
Calculate C stock directly from RS data
Using mathematical model working
with ground data & RS data for
calculate carbon.

Technical ChallengeLong term using RS data
Normal technique such as visualinterpretation, unsupervisedclassification and supervisedclassification didn’t give accuracy data
enough for calculation C stock. And
take a long time, use a lot of budget
and human resources.

Technical Challenge• RS systems can be used to estimate forest
carbon stocks by applying allometricrelationships to measures of tree volume,crown diameter, or spectral indices, BUT…
– Operational systems still developing– Will require additional ground-based data fordevelopment and verification

Technical challenges
How to gain the right technical in••••••Estimating historic emission levels
Projecting future emission
Monitoring emissions and removals
Monitoring forest degradation
Estimating Biomass (above & below ground live
biomass, litter, dead wood, soil organic carbon)
Establishing National REL/RL

Specific technical challenges (remote sensing): cloud cover,seasonality, topography, remote sensing data
availability and access procedures

Technical Challenge
Stratification of forest C stocks•Efficient stratification of forest carbon stocks
can help reduce the monitoring effort andincrease the accuracy by grouping
homogeneous C stocks and separating
heterogeneous C stocks•This will be dependent on the RS methods thatare chosen and the resources available toacquire and process data

Technical Challenge
Stratification of forest C stocks
Goal::
Increase the accuracy of C stocks
estimates by mapping heterogeneous
stratum, reduce the number of fieldplots, increasing accuracy and reducing
level of effort .

Technical challenges
Accuracy in REL and MRV::
Apply higher tier methods for key categories andsignificant pools
Reliable methods for measuring carbon stocks
are needed
Appropriate methodologies should be used inaccordance with the IPCC to promote accuracy
in inventories and to quantify the uncertainties
in order to improve future inventories

Million ton CO2

Knowledge obtained
from the pastexperience
350

Baseline 1

300

Baseline 2

250
REDD intervention
200

150

100
Year2004

2009

2014

2019

Initiative MRV
Technology /Methodology developing

Evergreen Forest@18.815,98.893

Initiative MRV
Technology /Methodology developing

Evergreen Forest@18.815,98.893
Landsat5-TM
2000-01-25

Initiative MRV Technology /Methodology developing

Evergreen Forest@18.815,98.893
Landsat5-TM
2006-02-26

Initiative MRVTechnology /Methodology developing

Evergreen Forest@18.815,98.893
Landsat5-TM
2010-04-10

Initiative MRV
Technology /Methodology developing

Evergreen Forest@18.815,98.893
ForestType_YYY
Band1 Band2 Band3 Band4 Band5 Band6 Band7
Y

EG001_2000

6323

2155

40

113

13

EG001_2006

48

23

179351129

14EG001_2010

8940368667118

23

Initiative MRV Technology /Methodology developing

Evergreen Forest@18.815,98.893
NDVI2000 = 0.447
NDVI2006 = 0.690
NDVI2000 = 0.409

Initiative MRV
Technology /Methodology developing

Evergreen Forest@18.810,98.894

Initiative MRV
Technology /Methodology developing

Evergreen Forest@18.810,98.894
ForestType_YYYY

Band1 Band2 Band3 Band4 Band5 Band6 Band7

EG001_2000

6223205841115

14EG001_2006

4623158946130

14

EG001_2010

9141369571118

23

Initiative MRV
Technology /Methodology developing

Evergreen Forest@18.810,98.894
NDVI2000 = 0.487
NDVI2006 = 0.711
NDVI2000 = 0.450

Initiative MRV
Technology /Methodology developingLU Changed@18.979,98.811

Initiative MRV
Technology /Methodology developing

Changed from Mixed Deciduous Forest to Agriculture Area
@18.979,98.811
ForestType_YYYY

Band1Band2Band3Band4Band5Band6Band7MX001_2000

6524225053118

20AG001_2006

73374580

128

144

56

AG001_2010

974347

71

131

13962

Initiative MRV
Technology /Methodology developing

Changed F-NF
@18.979,98.811
NDVI2000 = 0.389
NDVI2006 = 0.280
NDVI2000 = 0.203

Initiative MRV Technology /Methodology developing

RS Data 2006

Initiative MRV
Technology /Methodology developing

Stratifiedby Pattern ofDN Value

Initiative MRV Technology /Methodology developing

Stratified
by DN ValueRS Data 2006

Initiative MRV
Technology /Methodology developing

Stratified
4 Class
Class3::C = High Equation
Class2::C = Low Equation

Class1::C = Very Low Equation Class0::C = 0

Initiative MRV
Technology /Methodology developing

Landsat2000
2000-01-25

Initiative MRV
Technology /Methodology developing

CarbonMapping
2000

Initiative MRV
Technology /Methodology developing

Landsat2006
2006-02-26

Initiative MRV
Technology /Methodology developing

CarbonMapping
2006

Initiative MRV
Technology /Methodology developing

Landsat2010
2010-04-10

Initiative MRV
Technology /Methodology developing

CarbonMapping2010

Initiative MRV
Technology /Methodology developing

CarbonChanged2000-2006

Initiative MRV Technology /Methodology developing

CarbonChanged2006-2010

Issues to be addressedto promote REDD+
initiatives
Develop, prove, technology transfer, capacity
building.
 Requirements for national MRV system:
International: principles and procedures specified by theIPCC Good Practice Guidelines and UNFCCC guidance.
National: needs and priorities of the national REDD policy
and implementation strategy.
Bridging the capacity gap.
Assessment: of existing national forest monitoring
technical capabilities versus the requirements for the MRVsystem.
Develop and implement a roadmap: to build sustained incountry capacities for MRV

Suggestion for
promotion of REDD+
Collaboration•••

Data Sharing, RS data
National REL/RL establishment– Forest definition– Stratification– Drivers of deforestation– Sampling design, where and what tomeasure
MRV– Calculating emission removals– Verification of land cover change

Suggestion for
promotion of REDD+
Collaboration
A major effort concerning capacity
building is also required and identified as apriority by participants.
Enhancing current collaboration willbenefit both the implementation of REDD+
strategies and exchange of experiences.

Monitoring carbon
stock enhancements• GIS data for plantations• RS data for regrowth and degradation via
change in canopy cover combined withground plots• Permanent or temporary sample plots forenhancement and degradation

49

Coordination between GIS/RSand forest carbon inventory
teams• Benchmark map creation• Stratification by type of signal DN value• Field measurement plan sampling design50Thank You

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