Transcript Measuring Forest Carbon Stocks for Carbon

Measuring Forest Carbon
Stocks for Carbon Financing
Mechanisms
MCT, Phase – IV
1st July, 2013
IGNFA, Dehradun
Uttarakhand
Presentation Outline
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Forests and Climate Change
Key Steps in Measuring Forest Carbon Stocks
Estimation of Carbon Stocks between two data
points – REDD+
Estimation of Carbon Stocks in Trees and Shrubs
at a point of time – A/R CDM
Forests and Climate Change
Globally, forests are at the center stage in the climate
change mitigation and adaptation strategies:
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Act as Carbon Sink – Forests and other terrestrial ecosystems
absorb 2.6 GtC annually
Act as a Carbon Reservoirs - Forests store about 638 GtC,
which accounts more than double of atmospheric carbon
Act as a Source – Deforestation and other land use activities
emit around 1.6 GtC annually. Deforestation accounts for
17.40% of the total anthropogenic GHGs emissions
Dual role of Mitigation as well as Adaptation
Associated ecosystem benefits and poverty alleviation
Low Cost Option
Global Carbon Stocks in Forests
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FAO and UNFCCC are the main sources for the global level
information on the forest carbon stocks
FAO estimates carbon stock along with Global Forest
Resource Assessment (FRA) for every 5 years, while
UNFCCC carried studies through National Communications
as a part GHGs emission
The latest FAO assessment report (2010) released in 2011
has presented the status of forests for 233 countries and
overseas territories which inter alia include C stock of
forests
Source: FAO and GFRA, 2010
Global Carbon Stocks in Forests
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180 countries reported on carbon in tree biomass
72 countries included deadwood
124 countries litter mostly default values (2.1 t/ha)
121 countries reported on soil carbon mostly the default
values as provided in the IPCC 2006 guidelines
For remaining countries and areas, FAO estimated
carbon stocks by taking the average sub regional values
Source: FAO and GFRA, 2010
Global Carbon Stocks in Forests
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Total C stock in forest ecosystem = 652 billion tonnes
C stock in total biomass (all four pools) = 360 billion tonnes
C stock in soil = 292 billion tonnes
C stock per ha in forest ecosystem = 162 tonnes
C stock per ha in soil = 72 tonnes
C stock per ha of India’s forests = 106 t/ha
C stock per ha in India’s forest soil = 62 t/ha
Source: FAO and GFRA, 2010
Sources of GHGs Emissions
Carbon Stock Potential of India’s Forest
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In India, at present total forest and tree cover is 7,81,871 sq. km,
comprising 23.82% of the total geographical area of the country.
However, total forest cover is 6,92,027 sq. km, which is 21.05% of
the total geographical area of the country (FSI, 2011)
Over the past few decades, national policies of the country aimed at
conservation, protection and sustainable management of forests,
which results net increase in carbon stocks (from 1994 to 2004 it
was estimated 592 million tonnes)
Kishwan et al stated that “From 1995 to 2005, carbon stocks stored
in our forests have increased from 6244.78 to 6621.55 m t
registering an annual increment of 37.68 m t of carbon, which is
equivalent to 138.15 m t of CO2e”
This annual removal of CO2 by forests is good enough to neutralize
9.31% of our total annual GHGs emissions of 2000 level
Key Steps for Estimating Forest Carbon Stocks
Step 1
Defining Project
Boundary
Step 2
Selection of
Carbon Pools
Step 4
Developing
Sampling Design
Step 3
Project area
Stratification
Step 5
Plot Types and
Layouts
Step 7
Assessment of
AGB and BGB
Step 6
Field Measurement
Frequency
Step 8
Assessment of
WB and WL
Step 9
Estimating Soil Organic
Carbon
References
References
Step 1: Defining Project Boundaries
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Project area can vary in size
10’s ha  1000’s ha
Project area may be one
contiguous block or many small
blocks of land spread over a
wide area
The Geo coordinates should be
taken at the boundaries of the
project area through GPS and a
base map of the project site
should be prepared
Defining Project Boundaries
Project Area – One block
Project Area – Many parcels of land
Step 2: Eligible Carbon Pools
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Above Ground Biomass (tree
trunk, branches and leaves,
climbers, lianas and shrubs)
Below Ground Biomass (root
system)
Woody Litter
Dead Wood
Soil Organic Carbon
Step 3: Stratification of the Project Area
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Land use (forest, plantation, agro forestry, cropland, etc.)
Vegetation species
Slope types (steep, flat)
Drainage (flooded, dry)
Age of vegetation
Stratum - 1
Stratum - 2
Stratum - 3
Step 4: Sampling Design and Variance Analysis
Sampling design
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map of the entire project area should be developed
Stratified Random Sampling - Sample plots should be laid out and distributed randomly
covering all the stratums using standard sampling method or software (eg. Hawths’ tool of Arc
GIS)
Stratified Systematic Sampling – Sample plots should be laid out and distributed
systematically across all stratums of the project area
Variance analysis
Step
I. Identify the desired precision level
(± 10% of the mean at the 95% confidence interval is frequently used)
Step II. Identify the area or preliminary data
(6-10 plots per stratum will suffice for variance analysis)
Step III. Estimate carbon stock per tree, per plot, per ha and mean carbon stock/ha
Step IV. Calculate standard deviation of carbon (tC/ha) of all plots
Step V. Calculate the required number of sample plots using following equations:
Calculation of Required Number of Sample Plots
n=
Where;
E = Allowable error or the desired half-width of the
confidence interval. Calculated by multiplying the
mean carbon stock by the desired precision (that
is, mean carbon stock x 0.1, for 10 per cent
precision)
t = The sample statistic from the t-distribution for the
95 per cent confidence level. t is usually set at 2 as
sample size is unknown at this stage,
N = Number of sampling units for stratum (Total area
divided by plot area)
n = Number of sampling units in the population
s = Standard deviation of stratum
Source: Pearson et al. (2005)
Calculation of Required Number of Sample Plots
Area
5000 ha
Plot size
0.08 ha
Mean C Stock
101.6 tC/ha
Standard deviation
27.1 tC/ha
N
5000/0.08 = 62,500
Desired precision
10%
E
101.6*0.1 = 10.16
Number of sample plots 29
Source: Pearson et al. (2005)
Step 5: Types of Sample Plots
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Permanent sample plot
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Statistically more efficient in estimating changes in forest
carbon stocks
Locations of the plot are known and they could be treated
differently than the rest of the project area
Mapping the trees to measure growth of individuals at each
time interval is critical so that growth of living, dead and in
growth of new trees can be tracked effectively
Temporary sample plot
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Location of the plot is unknown and less chance of treated it
differently
Statistically, less efficient in estimating changes in forest
carbon stocks
Step – 5: Layout of Sample Plots - Rectangular
Source: N H Ravindranath et al. (1992)
Layout of Sample Plots - Circular
N
N
N
L+S
Shrub Plot (25 sq m)
L+S
Tree Plot (500 sq m)
9m
L+S
Plot Center
Litter (L) + Soil (S) Plot (1 sq m)
Radius = 12.62m for 500 m2 plot (tree plot)
Radius = 2.82m for 25 m2 nested plot (shrub plot)
Radius= 0.56m for 1 m2 nested plot (litter and
soil plot)
Layout of Sample Plots – Stem Diameter
Stem Diameter
Circular Plot
Square Plot
<5 cm dbh
1m
2m x 2m
5-20 cm dbh
4m
7m x 7m
20-50 cm dbh
14 m
25m x 25m
>50 cm dbh
20 m
35m x 35m
Source: Pearson et al. (2005)
Layout of Sample Plots – DBH
Source: Pearson et al. (2005)
Step - 6: Measurement Equipment
Step- 6: Measurement Frequency
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Forest processes are generally
measured over periods of five
year intervals
Depending upon the project
activities, biomass or carbon
stocks measurements can be
done annually
Carbon pools that respond more
slowly, such as soil, are
measured every 10 or even 20
years
Step 7: Assessment of Above Ground Tree Biomass
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Measure height and diameter of tree from
the sampled plot
Apply species specific allometric equation
or biomass value from the biomass table
based on the allometric equations
This will provide the volume of tree bole
for each species
Multiply this volume with basic wood
density for each species to convert the
volume into dry mass
Multiplying dry mass with biomass
expansion factor (BEF) of each species,
will provide the Above Ground Tree
Biomass (AGTB) of the tree
Assessment of Below Ground Tree Biomass
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Root - Shoot Ratio for Tees: 0.27 : 1.0 (IPCC, Good
Practices Guidelines, 2006)
Root - Shoot Ratio for Shrubs: 0.40 :1.0 (A/R CDM
TOOL -14, Version 04)
Regression models:
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Boreal Forest
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Temperate Forest
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BBD (t/ha) = exp (-1.0587+ 0.8836* In ABD + 0.1874)
BBD (t/ha) = exp (-1.0587+ 0.8836* In ABD + 0.2840)
Tropical Forest
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BBD (t/ha) = exp (-1.0587+ 0.8836 * In ABD)
Where:
BBD = below ground biomass density (t/ha) and
ABD = above ground biomass density (t/ha)
Calculation of Above Ground Tree Biomass and
Below Ground Tree Biomass
Estimation of Carbon Stocks
Step 1: Calculation of C-stock from above ground tree biomass (AGTB)
Step 2: Conversion of AGTB - C Stock to BGTB – C Stock
Estimation of Carbon Stocks
Step 3: Summation of C-stock in AGTB and BGTB of all trees:
Step 4: Calculating mean C-stock in tree biomass for each stratum:
Step 8: Carbon Assessment in Dead Wood and
Woody Litter
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Dead wood and woody litter can
be measure through physical
weighing from the sub plots
Convert the fresh weight into dry
weight by placing the samples in
the oven at 85 degree for 48
hours, if oven capacity is limited,
samples could be sun dried also
Extrapolate the sub plots data on
per hectare basis
Multiply the dry mass weight by
0.45. This will provide the carbon
weight per hectare
Step 9: Estimation of Soil Organic Carbon (SOC)
Estimation of Soil Organic Carbon (SOC)
Example:
How much C stock (Mg/ha) is in the soil layer
sampled at 10 cm depth, if the soil bulk
density is 1.0 kg d/cubic m or 1. 0 Mg/cubic m
and the concentration of C in the soil is 2.0%
Answer:
Soil weight per ha = 100 x 100 x 0.10 x 1.0
Mg/cubic m = 1000 Mg or 1000 t
Soil C stock = 1000 t x 0.02 = 20 Mg/ha or 20 t
Estimating C-Stock Changes between two data points –
REDD+
1. Divide the entire project area into
grids of 1 ha area
2. In Landsat TM datasets, resolution
is 30m x 30m and each grid cell
comprise of 11 pixels
3. Calculate Normalized Difference
Vegetation Index (NDVI) value of
each pixel and average them for
each grid cell
4. NDVI values are calculated as (IRR) / (IR+R)
5. A linear fit equation should develop
through correlating the biomass
values obtained from the field
measurements with the NDVI
values of same coordinates (pixels)
in satellite imageries
Estimating C-Stock Changes between two data points –
REDD+
6. Using this linear fit equation, biomass for
the entire project site would be calculated
for the project monitoring year
7. Similarly, with the help of this regression
equation, biomass values of the same site
for baseline year would be calculated.
8. The difference in the biomass values from
the baseline year and the project
monitoring year would be estimated
9. The grids where an increase in biomass
values are observed with respect to the
baseline year indicate additionality, which
may be due to sustainable forest
management initiatives or other effective
forest management practices
10. Similarly, a decrease in biomass over the
years indicate loss of carbon from the
project area due to unsustainable forest
management practices and/or
anthropogenic pressures
What is traded ?
Certified Emission Reduction (CER)
1 CER = 1 tonne of CO2e
Biomass - Carbon relation
1 tonne of biomass = 0.45 tonne of C
1 tonne of C corresponds to 44/12 (3.667) tonne
of CO2
Estimation of C Stocks in Trees at a point of time –
A/R CDM
Measurement of sample plots:
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Stratified Random Sampling
Mean C stock in tree within the project boundary:
Step 1: btree = £ wi * btree,i
Step 2: Btree = A * btree
Step 3: Ctree = 44/12 * CFtree * Btree
Where:
Ctree = C stock in tree biomass within project boundary; t CO2-e
Btree = Tree biomass within the project boundary; t.d.m.
CFtree = Carbon Fraction of tree biomass; t C ( default value = 0.47)
btree = Mean Carbon stock per hectare in tree biomass within the project boundary;
t.d.m. ha-1
GO to
wi = Weightage of stratum
Excel
Estimation of biomass of a tree in a plot - CDM
Estimating C-stocks in shrubs at a point of time - CDM
Cshrub = 44/12 * CFs * (1 + Rs) * £ Ashrub,i* bshrub,I
bshrub,i = BDRSF * bForest * CCShrub
Where:
Cshrub = C stock in shrub biomass at a point of time; tCO2-e
CFs = Carbon Fraction of Shrub biomass; tC (IPCC default value = 0.47)
Rs = Root shoot ratio for shrubs; dimensionless (Default value of 0.40)
Ashrub,i = Area of shrub biomass stratum; ha
bshrub,i = Shrub biomass per hectare in shrub biomass stratum; t.d.m. ha-1
BDRSF = Ratio of shrub biomass per hectare in land having a shrub crown cover of
100% and the default above ground biomass content per hectare in forest in the
region/Country where project is located. (Default value = 0.10)
bForest = Default above ground biomass content in forest in the region/Country
where project is located. Values from Table 3A.1.4 of IPCC GPG LULUCF 2003 are
to be used.
CCShrub = Crown cover of shrubs in shrub biomass stratum I at the time of
estimation expressed as fraction ( e.g. 10% crown cover implies CCShrub = 0.10)
Format for data collection of tree species
Assessment of Carbon stocks in REDD+ project
Location of plantation:_________Vill:________ Forest Block: _____ Forest Range:_______ Division
/District:______ State __________
Quadrat No.: _______Date: ____/____/2012 Quadrat Size: __________
GPS location of the quadrat:-
Format for data collection of shrubs
Location of plantation:_________Vill:________ Forest Block: _____ Forest Range:_______ Division
/District:______ State __________
Quadrat No.: _______Date: ____/____/2012 Quadrat Size: __________
GPS location of the quadrat:-
Format for data collection of WB, WL and SOC
Location of plantation:_________Vill:________ Forest Block: _____ Forest Range:_______ Division
/District:______ State __________
Quadrat No.: _______Date: ____/____/2012 Quadrat Size: __________
GPS location of the quadrat:-
Thank you for your kind attention
Suresh Chauhan, TERI, New Delhi
[email protected]