Contributing REDD-plus
through research and development offorest carbon monitoring methodologies-Endeavors of REDD R&D Center森林炭素モニタリング手法の研究・開発を通した、REDDプラスへの貢献

Kiyono Yoshiyuki
REDD R&D Center, FFPRI

What will we do by REDD?
人がREDDでやろうとしていること
Carbon stock in the past
Estimated as scientifically as possible

Forest carbon stock(the possibleamount of emission)

Food and industrial
material production Methods will be
Actual emissions
with REDD
Predictedemissions
without REDD
Hardly to be verified

determinedaccording toperformance andcosts

Reduced emissions/increased removals
The essenceof REDDTime2

Cambodia Logging
Seasonalforest
カンボジア
季節林
Evergreen forest
Agricultural use

Rubber plantations

3

Forest conversion in Malaysia
マレーシアの農地転換
Oil palm and rubber tree plantations

Forest conversion in Paraguay
パラグアイ農・牧地転換

Agricultureandpasturing

Planning
Identification ofarea by forest typeCalculation ofmean carbon stockby forest type

Estimation ofstock changeA flow for estimating GHG emissions/removals
and REDD effect (emission reduction)
森林炭素蓄積量変化とREDD効果の推定手順
Forest carbon stock = Forest area x Averaged carbon stock per land area
基本式は 森林炭素蓄積量＝Σ（森林面積 ｘ 面積当たり平均炭素蓄積量）
Automatic
Base map ← Ground truthclassification The presentchallenges↓自動分類
Series of remote sensing images
Accurate /simplified meth.
↓精度向上/簡素化
DD areas detection by monitoring forest land
↓ ← Carbon stock per land area formain forest types
Estimates of carbon stock change (GHG emissions/removals)↓ ← Scenario emissions without REDD
Estimates of REDD effects (GHG emission reduction)

OctoberNovember

DecemberJanuary

February

MarchApril

Issue of seasonality
季節性の問題Effects of seasonality in automatic classification
自動分類における季節性の影響

The effect on the different stages of dry
season is found in the classification result.
分類結果に乾期の進行の影響が見られる

Standardization of images with algorithm for reducing the effect of seasonality
季節性を軽減するアルゴリズムによる画像の標準化

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

Reduction of effect of seasonality bystandardizing images with developed algorithm.

SPOT衛星画像（上：乾期後半、下：乾期初旬）

画像を開発したアルゴリズムで標準化し、季節性の影響を軽減Producing mosaic image using multiple images within a year
1年以内の複数画像を用いたモザイク画像の作成

Clouds and their shadows
effects on the classification
results.
雲とその影が分類結果に影響を与える。

ForestNon-forestWaterCloudShadow

It is need to produce mosaic image with multiple
images within a year.
1年以内の複数画像を用いたモザイク画像を作成することが必要

Forest cover map and locations of FA’s PSPs in Cambodia
カンボジア森林分布図と長期調査プロット位置

Samreth et al. (in press)

The nationwide forest carbon stock inCambodia (A tentative figure)カンボジアの森林炭素蓄積量（暫定値）
Foresttype

ForestAveragedTotal
area
carbon stock carbon stock
In 2006 In 2000-2001
-1
ha
Tg-C
Mg-C haEvergreen forest* 5,031,540 163.8 ± 7.8 824.2 ± 39.2
Deciduous forest 4,692,098 56.2 ± 6.7 263.9 ± 31.3
Total9,723,638
1,088.1 ± 50.2
*Including Semi-evergreen forest.
Carbon stocks are shown in mean ± standard error.
Samreth et al. (in press)

13

Required number of sample plots for av. carbon stock
at a 5% level of precision and a 95% confidence level
長期調査プロット必要数（5％危険率・95％信頼度）• 336 plots 336プロット≒USD 336,000– 260 for evergreen forest, 76 for deciduous forest• Since most developed countries designed their national forest inventories(NFIs) at 5% level of precision and 95% confidence level, a sampling designusing 336 plots may be acceptable for most countries.
先進国のNFIの設計と同じ精度で、この結果は受け入れられるであろう• Forests in the PSPs are sometimes destroyed. A sufficient number of extra
plots are vital in the region under pressure of land-use change and heavy
wood extraction. Required number of plots should be monitored to beable to add extra plots if necessary.
森林減少・劣化が進む地域ではプロットが破壊される。プロット数は余裕を持たせた方が良い。必要数をモニタリングし、必要に応じて追加する
14

Available allometry equations for tropical tree biomass
熱帯林木のバイオマス推定式
Generic equations for tropical trees
Specific equations for trees inTropical rainforest
Tropical monsoon forestEvergreen forest
Deciduous forest
Tropical semi- arid forest
Biomes Peat swamp forest
Mangrove forest
OthersSecondary forest of t. rainforest
Shrub community
Palm forest
Bamboo forest

Sufficient
Existing
Less
None
None?
Few
Existing
Existing
Less
Less
Less15

バイオマス調査とマニュアル
Destructive biomass sampling and its manual(5) Remove all branches with leaves

A manual of simplified destructive samplingfrom the stem using a chainsaw and(8) To calculate the volume of the soil

hand saws.

mound, measure its area and height.

Separate stems and branches to avoid
confusing thin stems with branches.

of tree biomass using an excavation machine
Photo2_ 1 Removal of branches with leaves

重機を利用した樹木バイオマスのPhoto3_ 1 Measuring the area and height of the mound(6) Separate leaves from branches on the

簡易化された破壊調査マニュアル vol.1

tarpaulin.

i)May, 2011

Mound shape and measurement point (see solid line)i) Elliptical ; long and short spanii)ii)Trapezoid; long side, short side and height oftrapezoidiii) Square ; 2 sides
The height of the mound are measured at 4-5point foriii)

Khmer andSpanish versions
are under
preparation

average. (see broken line)

Photo2_ 2 Separating leaves from branches(7) Weigh fresh leaves and branches.
Measure the fresh weights of leaves (FWlf) andFig.3_ 2 Measuring the mound’s area and heightbranches (FWbr) using a spring balance or a bowlbalance. Record these weights (FWlf and FWbr) andthe type of tare used.

FWlf must be measured first, because leaves dry
faster than branches.
For large amounts, divide the samples into sizes
convenient for weighing.
Photo2_ 3a Weighing fresh leaves(9) Make 1m×1m sample plots on themound.
The number of sample plots depends on
the size of mound.
Measure the height of sample plots.

Allometry equations for
deciduous forest tree biomass in Indochina
インドシナ落葉林木のアロメトリ式
403,500

Trees in the plot

D-HThis study

35

Published data*

30

3,000

Brown

Sample trees

Chave

2,500
AGB (kg/tree)

25H(m)

Generic equations
make overestimates
汎用式では過大推定

2015Kiyono
2,000
実測値
1,500

101,0005500

002040DBH(cm)6080
0010203040

DBH (cm)

Monda et al. (unpublished)5060

重要排出経路を優先する

Conclusions
••••

••

The nationwide forest carbon stock has rarely been monitored in tropical countries.
However, in wider regions covered by permanent sampling plots (PSPs), we can expect areasonably accurate estimation of carbon stock and its trends by using the PSP data withequations for converting carbon stock and moderately classifying forest types via satellite
imagery.長期調査プロットが広域配置されていれば衛星リモセンで森林炭素を推定できる
As a feasibility study for applying a simplified method to estimate CO2 emissions fromdeforestation and forest degradation in tropical forests, we estimated the nationwide forest
tree biomass carbon stock in Cambodia.カンボジアで始めた

By repeating this calculation, we could monitor the trend of forest carbon stock on anational scale.繰り返し計測すればトレンドが分かる
Technical challenges in forest monitoring include automatic land cover classification byremote sensing (RS) and accurate/simplified methodologies for carbon stock by groundbased measurement (GBM).技術的課題はリモセンの自動計測、正確で簡易な地上調査
For the issue of seasonality in a seasonal forest, we have tried standardization of satellite
images with algorithm for reducing the effect of seasonality.. For GBM, we have selectedbiomes important and less data and devised allometry equations specific to them.季節性の課題については季節の影響を減らす画像処理に取り組んでいる。地上調査では重要であるにもかかわらずデータのないバイオーム（生物群系）のアロメトリ式を開発している
A “Cook-book - How to measure and monitor forest carbon” is under preparation by FFPRI
and will compile our latest knowledge in forest carbon measurement and monitoring.森林総研の技術解説書には森林炭素の計測とモニタリングの最新の知見が収録される予定

Thank you for your attention.
ご清聴ありがとうございました。
kiono@ffpri.affrc.go.jp
This research was supported by the emergency project todevelop the structure of promoting REDD action funded by
the Forestry Agency, Japan