For the sake of the establishmentof reliable forest carbon monitoring system
信頼できる森林炭素モニタリングシステムを構築するために

Yasumasa Hirata
Forestry and Forest Products Research Institute平田泰雅
森林総合研究所

REDD( plus)
Recalling REDD(-plus)…
FForestt degradation
dd ti

Positive incentive
Deforestation

Conservation
MRV
RL/REL

Safeguards
Phased approach

Enhancement

Sustainable management

REDD( plus)
Recalling REDD(-plus)…

Positive incentive

RL/RELMRV of Forest Monitoring

Measurement

MRV

Reporting

Verification

MRV of Forest Monitoring

Measurement(d) To establish, according to national circumstances and capabilities,
robust and transparent national forest monitoring systems and, ifappropriate, sub‐national systems as part of national monitoring
systems that:(i) Use a combination of remote sensing and ground‐based forest
carbon inventory approaches for estimating, as appropriate,
anthropogenic forest‐related greenhouse gas emissions bysources and removals by sinks, forest carbon stocks and forest
area changes;

FCCC/CP/2009/11/Add.1

IPCC method to estimate GHG gas emissions
from activities related with LULUCF sector

Danilo Mollicone, FAO

Forest carbon stock estimation by
y combinationof remote sensing and ground measurements
remote sensing

ground measurements

Total carbon stock = Σ (Forest areai x Averaged carbon stocki)
Forest areai: forest area of forest type i
Averaged carbon stocki : average d carbon stock in forest type i•

The method is the calculation of carbon stock by monitoring
fforestt landl d andd summingi up ththe fforestt area andd itits averaged
dcarbon stock for important forest types.

Remote sensing
Plotless sampling ineach class

Field survey

Decision of classification classes

Plot survey ineach class

Image classificationAllometry

Verification

Calculation ofcarbon stock

Estimation of forest area by forest typeand disturbance degree

Estimation of mean carbon stock byforest type and disturbance degree

Total carbon stock = Σ (Forest areai x mean carbon stocki)

On the road to pphase 2

Different Methods,Methods Different Accuracy• Printout vs. On screen• Interpretation vs. Semi‐automatic• accumulation of practices and know‐howArea Estimation

Data collection

2002
・・・
1990

Image analysis

2014
Identification of historicalchange of land-cover

Confirmation and verificationof land-caver by field survey

Producing a base map

Monitoring of area changeby forest type20062010

Classification of objects into forest types

Whole area

ForestEvergreenDense
evergreen

Non forest

DeciduousDid/otherDegradedevergreen• Generated objects in 1stt
step are classified intoforest types.
types• Classification tree is
one option to get highquality results.
• Experiences of experts,
including interpretationof satellite data and field
conditions,, are needed.

Segmentation → Classification

Sampling (forest / non forest)

Whole image→ forest / non forest

Sampling (Evergreen / Deciduous,Deciduous other)

Forest→ evergreen / deciduous, otherEvergreen→ dense / degradedResultR
lt off fforestt ttype mapping
iby object-oriented classification

Seasonality

OctoberNovember

DecemberJanuary

February

MarchApril

Issue of seasonalityEff t off seasonality
Effects
lit ini automatictti classification
lifi ti

The effect on the different stages of dry
season is found in the classification result.

Standardization of images with algorithm
for reducing the effect of seasonality

SPOT images (upper: the end of dry season,lower: the beginning of dry season)

Reduction of effect of seasonality bystandardizing images with developed algorithm.

Clouds

Destiny of cloud effects on satellite images

• Topographic correction• Atmospheric correction• Mosaic processing

Standardd Deviation((after correcction)
標準偏差差(補正後)• Masking of clouds andtheir shadowsStandard Deviatiion (after correction)
標準偏偏差(補正後)Correction &D t mosaic
Datai

300.0

500.0

Band 3 (red)

200.0

内地林

100.0

アブラヤシ林
ゴム林

0.0
0.0

100.0
200.0
300.0
Standard
Deviation (before correction)
標準偏差(補正前)

Band 4 (IR)

400.0
300.0
200 0
200.0
100.0000.0

内地林
アブラヤシ林ゴム林
0.0 100.0 200.0 300.0 400.0 500.0
標準偏差(補正前)StandardDeviation (before correction)

Preparation of cloud
cloud-free
free data

1 grid = 30 km

2000
2010

Two types of data from field survey• For area estimation– Training data for image classification– Verification
V ifi ti ddata
t ffor th
the result
lt off classification
lifi ti• For estimation of carbon stock per unit area asemission factor– Tree census data

Field survey for estimating carbon stockProblems in field survey• Ownership• Accessibility and road condition• Weather• Topography (steep slope, stream, etc)
• Dangerous animals, insects, and plants• Illegal logger

Publication of REDD-plus Cookbook•

Hands-on manual on REDD-plus forpolicymakers, government officials
and practicionersp•

Based on the UNFCCC decisions
and the IPCC guidelines/guidance•

English, Japanese and Spanish•

151 pages withith 36 units
it off ititems•

National/subnational level•

Reference guideTh k you ffor your attention!
Thanktt ti !