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Measuring Forest Carbon Stocks for Carbon Financing Mechanisms

Measuring Forest Carbon Stocks for Carbon Financing Mechanisms. MCT, Phase – IV 1 st July, 2013 IGNFA, Dehradun Uttarakhand. Presentation Outline. Forests and Climate Change Key Steps in Measuring Forest Carbon Stocks Estimation of Carbon Stocks between two data points – REDD+

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Measuring Forest Carbon Stocks for Carbon Financing Mechanisms

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  1. Measuring Forest Carbon Stocks for Carbon Financing Mechanisms MCT, Phase – IV 1st July, 2013 IGNFA, Dehradun Uttarakhand

  2. 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

  3. 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 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

  4. Global Carbon Stocks in Forests Source: FAO and GFRA, 2010 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 

  5. Global Carbon Stocks in 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

  6. Global Carbon Stocks in Forests Source: FAO and GFRA, 2010 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

  7. Sources of GHGs Emissions

  8. 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

  9. Key Steps for Estimating Forest Carbon Stocks

  10. References

  11. References

  12. 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

  13. Defining Project Boundaries Project Area – One block Project Area – Many parcels of land

  14. 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

  15. Step 3: Stratification of the Project Area Stratum - 1 Stratum - 2 Stratum - 3 Land use (forest, plantation, agro forestry, cropland, etc.) Vegetation species Slope types (steep, flat) Drainage (flooded, dry) Age of vegetation

  16. Step 4: Sampling Design and Variance Analysis Sampling design Base 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:

  17. 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)

  18. Calculation of Required Number of Sample Plots Source: Pearson et al. (2005)

  19. 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

  20. Step – 5: Layout of Sample Plots - Rectangular Source: N H Ravindranath et al. (1992)

  21. Tree Plot (500 sq m) Plot Center Shrub Plot (25 sq m) L+S L+S L+S Litter (L) + Soil (S) Plot (1 sq m) Layout of Sample Plots - Circular N N N 9m 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)

  22. Layout of Sample Plots – Stem Diameter Source: Pearson et al. (2005)

  23. Layout of Sample Plots – DBH Source: Pearson et al. (2005)

  24. Step - 6: Measurement Equipment

  25. 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

  26. 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

  27. 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) and ABD = above ground biomass density (t/ha)

  28. Calculation of Above Ground Tree Biomass and Below Ground Tree Biomass

  29. 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

  30. 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:

  31. 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

  32. Step 9: Estimation of Soil Organic Carbon (SOC)

  33. 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

  34. Estimating C-Stock Changes between two data points – REDD+ • Divide the entire project area into grids of 1 ha area • In Landsat TM datasets, resolution is 30m x 30m and each grid cell comprise of 11 pixels • Calculate Normalized Difference Vegetation Index (NDVI) value of each pixel and average them for each grid cell • NDVI values are calculated as (IR-R) / (IR+R) • 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

  35. Estimating C-Stock Changes between two data points – REDD+ Using this linear fit equation, biomass for the entire project site would be calculated for the project monitoring year Similarly, with the help of this regression equation, biomass values of the same site for baseline year would be calculated. The difference in the biomass values from the baseline year and the project monitoring year would be estimated 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 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

  36. 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

  37. Estimation of C Stocks in Trees at a point of time – A/R CDM GO to Excel Measurement of sample plots: 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 wi= Weightage of stratum

  38. Estimation of biomass of a tree in a plot - CDM

  39. 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)

  40. 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:-

  41. 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:-

  42. 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:-

  43. Thank you for your kind attentionSuresh Chauhan, TERI, New Delhisureshc@teri.res.in

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