Measuring Forest Carbon Stocks for Carbon Financing Mechanisms

Measuring Forest Carbon Stocks for Carbon Financing

Mechanisms

MCT, Phase – IV 1st July, 2013

IGNFA, DehradunUttarakhand

Presentation Outline

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:

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 carbonAct 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 AdaptationAssociated ecosystem benefits and poverty alleviationLow Cost Option

Global Carbon Stocks in Forests

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


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



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


Sources of GHGs Emissions

Carbon Stock Potential of India’s Forest

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

References

References

Step 1: Defining Project Boundaries

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

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 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 designBase map of the entire project area should be developedStratified 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 analysisStep 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 plotsStep V. Calculate the required number of sample plots using following equations:

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 populations = 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

Calculation of Required Number of Sample Plots


Step 5: Types of Sample Plots

Permanent sample plot 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 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)

Tree Plot (500 sq m)

Plot Center

Shrub Plot (25 sq m)

L+S

L+S

Litter (L) + Soil (S) Plot (1 sq m)

L+S

NN N

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)

9m

Layout of Sample Plots - Circular

Layout of Sample Plots – Stem Diameter

Stem Diameter Circular Plot Square Plot

<5 cm dbh 1 m 2m x 2m

5-20 cm dbh 4 m 7m x 7m

20-50 cm dbh 14 m 25m x 25m

>50 cm dbh 20 m 35m x 35m


Layout of Sample Plots – DBH


Step - 6: Measurement Equipment

Step- 6: Measurement Frequency

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

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

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:

Boreal Forest BBD (t/ha) = exp (-1.0587+ 0.8836* In ABD + 0.1874)

Temperate Forest BBD (t/ha) = exp (-1.0587+ 0.8836* In ABD + 0.2840)

Tropical Forest BBD (t/ha) = exp (-1.0587+ 0.8836 * In ABD)

Where:BBD = below ground biomass density (t/ha) andABD = 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

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:

Estimation of Carbon Stocks

Step 8: Carbon Assessment in Dead Wood and Woody Litter

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 tSoil 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 area2. 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 (IR-R) / (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 relation1 tonne of biomass = 0.45 tonne of C1 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:Stratified Random SamplingMean C stock in tree within the project boundary:

Step 1: btree = £ wi * btree,i Step 2: Btree = A * btreeStep 3: Ctree = 44/12 * CFtree * Btree

Where:Ctree = C stock in tree biomass within project boundary; t CO2-eBtree = 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-1wi = Weightage of stratum GO to

ExcelGO to 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-eCFs = 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; habshrub,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)

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 tree species

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 SOCLocation 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 [email protected]

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Measuring Forest Carbon Stocks for Carbon Financing Mechanisms