Measuring Forest Carbon Stocks for REDD+

Transcription

1 Measuring Forest Carbon Stocks for REDD+ MCT course of IFS officers -Phase IV Indira Gandhi National Forest Academy, Dehradun 1 July 2013 Dr Devendra PANDEY Fmr DG, Forest Survey of India Fmr PCCF and Prl Secretary Forests, Arunachal Pradesh ID: dpandeyifs@rediffmail.com; pandeyd30@gmail.com

2 Forests consist of five carbon pools as per IPCC Guidelines (2006): i) Above ground biomass (tree trunk, branches and leaves, climbers, lianas and shrubs) ii) Below ground biomass (root system) iii) Deadwood iv) Litter v) Soil organic matter /soil carbon 2

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4 Global Overview of C-Stock 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) now done every 5 years. where as UNFCCC through National Communications as a part GHG emission. The latest FAO assessment (2010) released in 2011 has presented the status of forests for 233 countries and overseas territories which inter alia include C stock of forests. 4

5 Global Overview of C-Stock in forests (source: FAO, FRA 2010)- reliability level 180 countries reported on carbon in tree biomass 72 countries included deadwood and 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 filling the gaps for remaining countries and areas, FAO estimated carbon stocks by taking the sub-regional averages per hectare and multiplying these by the respective forest areas. 5

6 Countries which have well established system of monitoring C Stock Australia (model based & dynamic) Canada (model based & dynamic) India- static Japan New Zealand Russian Federation (model based & dynamic) USA 6

7 Guiding principles (IPCC) for measuring the forest cover and forest carbon stocks consistency in measurements, transparency in reporting, comparability due to common methodological approach completeness in measuring all carbon pools and robustness- based on scientific principles 7

8 Two broad steps in measuring the forest carbon stock and changes within it Measurement of forest area and changes within itdecrease or increase in area, degradation or improvement in the forest stock- by remote sensing Field inventory to measure different carbon pools of the ground 8

9 Measuring forest area change / activity data using remote sensing imagery Measuring changes of forest areas-deforestation Measuring increase of forest area- forestation Measuring forest area change within forests- forest degradation or improvement 9

10 Considerations essential for measuring area change on a scientifically credible basis The national circumstances, particularly existing definitions and data sources Selection and acquisition of satellite imagery and coverage Available skilled staff and soft and hardware resources Sampling based or wall to wall coverage Image interpretational technique Accuracy assessment 10

11 Optical mid-resolution (10-60 m) sensors presently available Nation Satellite &sensor USA Landsat-5 TM 30 m Resolution and coverage 180 X 180 km 2 Cost for data acquisition All data archived at USGS is free Features Images down loadable to any satellite receiving station at repetivity of 16 days USA Landsat-7 ETM+ 30 m 60 X 180 km 2 All data archived at USGS are free Data gaps outside of the central portion of the images due to failure of scan line corrector in April 2003 USA/Japan Terra ASTER 15 m 60 x 60 km 2 60 US$/scene Data acquired on request and is not routinely collected for all areas India IRS-P6 LISS-III AWIFS 23.5 m 141 X141 km 2 56 m 740 X 740 km US$/scene 322 US$/scene Images available from 2003 from NRSC. Images of earlier satellites IRS IC/ID with same resolution also available since 1997 China/Brazil CBERS-2 HRCCD 20 m Free in Brazil and Potentially for other developing countries Experimental: Brazil uses on demand to bolster their coverage France SPOT m 2000 /scene Commercial, Indonesia and Thailand uses HRVIR 60 X 60 km along with Landsat 11

12 Other types of sensors such as Radar (ERS1/2 SAR, JERS-1, ENVISAT-ASAR and ALOS PALSAR) and Lidar are potentially useful and appropriate. Coarse resolution (250 m 1km) data available from 1998 (SPOT VGT) or 2000 (MODIS) have utility because high temporal resolution (1 to 2 day) High resolution data (less than 1m) obtained from IKONOS, QuickBird, Worldview-1/2, Geoeye-I, Cartosat-1/2 but expensive to cover large areas- used to calibrate algorithm and ground truthing 12

13 RapidEye AG A global imagery and service provider 5 satellite constellation 5 m imagery 5 bands (red edge) Empowering global land-use decisions

14 Deforestation through illegal logging Landsat, 30 meters RapidEye, 5 meters RSS Remote Sensing Solutions GmbH 200

15 Bannerghatta National Park DigitalGlobe Proprietary and Business Confidential

16 Bannerghatta National Park DigitalGlobe Proprietary and Business Confidential

17 Bannerghata National Park DigitalGlobe Proprietary and Business Confidential

18 Wall to wall or sampling approach? Wall to wall approach- covers the full spatial extent of the forested areas and is a common approach A few large countries like India and Brazil have established operational wall-to-wall system since 1980s based on mid-resolution satellite imagery (India-biennial and Brazil- annual) If resources are insufficient, the sampling approach is equally efficient specially for large countries. The recommended sampling approaches are systematic and stratified sampling. 18

19 Analysis of the satellite imagery The selection of the method depends on the available resources including software for image processing. A combination of automated methods (segmentation or classification) and visual interpretation gives the best result. An independent accuracy assessment is an essential component to link area estimates to a crediting system. 19

20 Measuring forest area change- special situations Monitoring of increase of forest area- forestation Identifying increase in forest area with satellite imagery is generally difficult canopy closure slow- better with high resolution Monitoring of forest area change within forestsforest degradation Only those areas can be identified by satellite- where intensity of degradation is high and but not all of them Demands use of more sophisticated algorithm and high resolution imagery Spectral mixture analysis (SMA) has been found to be the robust technique. 20

21 Measuring forest carbon stock Estimation of above ground biomass Woody biomass of living trees above ground Biomass of non tree understory vegetation (herb, shrub, climber) Biomass of deadwood, woody debris and litter Estimation of below ground biomass Below ground biomass (root system) Soil organic carbon 21

22 Data needs for meeting the requirements of the three IPCC Tiers Tiers Tier 1 (basic) Data needs / examples of appropriate biomass data Default values given for all the vegetation-based pools including MAI and biomass of specific forest type Tier 2 (intermediate) MAI and or biomass from existing forest inventories or ecological studies as well as newly collected forest biomass data and default values for all non- tree pools Tier 3 ( most accurate) Repeated measurements of trees from permanent plots and calibrated process models. Can use default data for other pools stratified by in country regions and forest type, or estimate from process models. 22

23 Sample Plots In a District Inventory of 179 districts (total no. of districts 612) No.Sample plots = 21,000

24 Supplementary Study of FSI launched a new biomass study in August 2008 to measure missing components of forest biomass (not measured by NFI) as per REDD requirement The study has followed two approaches (a) measure biomass of herb, shrub, climber, dead wood and litter by laying out sample plots (about 100 plots in each physiographic zone thus in all 1,400 sample plots) (b) select 20 to 30 number of trees for each species in different zones cut and measure their biomass to generate biomass equations for: i) Dbh of NFI trees Vs. biomass of branch for trees above 10 cm dbh. ii) dbh/collar dia Vs. total biomass of trees below 10 cm dbh.

25 Outcome of supplementary Study Based on the data collected in the supplementary study FSI has developed new allometric equations about 200 new regressions equations for small sized trees/ seedlings for different species below 10 cm diameter growing in to estimate their biomass Similarly new equations have been developed to biomass of branch wood and leaf of trees above 10 cm diameter measured during regular NFI 25

26 An example- Biomass allometric equations of a few species Species Name Eqn Descri Equation R 2 1 Careya arborea small wood->10cmdbh y = 460.4x x Careya arborea foliage->10cmdbh y = 15.21x x Careya arborea small wood-<10cmdbh y = 0.108x x Careya arborea foliage-<10cmdbh y = x x Schima wallichii small wood->10cmdbh y = 1319.x x Schima wallichii foliage->10cmdbh y = 57.1x x Schima wallichii small wood-<10cmdbh y = 0.127x x Schima wallichii foliage-<10cmdbh y = 0.006x x Shorea robusta small wood->10cmdbh y = 111.8x Shorea robusta foliage->10cmdbh y = 31.40x Shorea robusta small wood-<10cmdbh y = 0.093x x Shorea robusta foliage-<10cmdbh y = 0.019x x Tectona grandis small wood->10cmdbh y = 302.2x Tectona grandis foliage->10cmdbh y = 10.72x Tectona grandis small wood-<10cmdbh y = 0.177x x Tectona grandis foliage-<10cmdbh y = 0.009x x

27 2-Eastern Himalayas S. No. Species Name Eqn Descri Equation R2 1 Alnus nepalensis small wood-<10cmdbh y = 0.178x x Alnus nepalensis foliage-<10cmdbh y = 0.008x x Eurya japonica small wood-<10cmdbh y = 0.101x x Eurya japonica foliage-<10cmdbh y = x x Ficus species small wood-<10cmdbh y = 0.145x x Ficus species foliage-<10cmdbh y = 0.012x x Macaranga species small wood-<10cmdbh y = 0.127x x Macaranga species foliage-<10cmdbh y = 0.002x x Machilus species small wood->10cmdbh y = 16.99ln(x) Machilus species foliage->10cmdbh y = 1.421ln(x) Machilus species small wood-<10cmdbh y = x x x Machilus species foliage-<10cmdbh y = 0.007x x Michelia species small wood-<10cmdbh y = 0.169x x Michelia species foliage-<10cmdbh y = x x Quercus species small wood->10cmdbh y = 132.5x x Quercus species foliage->10cmdbh y = 6.755x x Quercus species small wood-<10cmdbh y = 0.137x x Quercus species foliage-<10cmdbh y = 0.003x x Rhododendron arboreum small wood->10cmdbh y = x x Rhododendron arboreum foliage->10cmdbh y = 9.354ln(x) Rhododendron arboreum small wood-<10cmdbh y = 0.119x x Rhododendron 2 27

28 National average of India s forest Carbon stock in tone/ha in different pools ABG, SOM, BGB, 9.79 Litter, 1.79 DW,

29 Carbon stock in t/ha in different forest types Tr Wet Evergreen Tr Semi Evergreen Tr Dry/moist Decidous Littoral & swamp Tr Thorn Sub-tropical pine/broadleave Montane Temperate 30

30 Highest carbon density in some areas of India s forests Montane temperate very dense( Himachal Pradesh Jammu &Kashmir, Sikkim) =268 t/ha (four pools = 169 t/ha, SOM= 99t/ha) Tropical wet evergreen very dense (Andaman & Nicobar, Kerala, Tamilnadu, Karnataka) =200 t/ha (four pools = 106 t/ha, SOM=94 t/ha) 31

31 Carbon-stock in t/ha in different pools by major forest types Forest types ABG BGB DW Litter SOM Total C Tropical Wet Evergreen Tropical Semi Evergreen Tropical Dry/moist Deciduous Littoral & swamp Tropical Thorn Sub-tropical pine/broadleave Montane Temperate

32 Thank you for your attention Questions? 33

33 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 Co ordinates should be taken from the boundaries of the project area through GPS and a base map should be prepared

34 Project Boundaries Project Area One block Project Area Many parcels of land

35 Step 2: Stratification of the project area Land use (forest, plantation, agro forestry, cropland, etc.) Vegetation /forest type Slope types (steep, flat) Drainage (flooded, dry) Age of vegetation

36 Stratification based on canopy cover Forest Crown Cover Strata 1-10% Open Shrub Land 11-40% Degraded Forest 41-70% Moderate Dense Forest % Dense Forest Stratum - 1 Stratum - 2 Stratum - 3

37 Field Inventory Step 3: 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. Estimate carbon stock per tree, per plot, per ha and mean carbon stock/ha Step III. Calculate standard deviation of carbon (tc/ha) of all plots Step IV. Calculate the required number of sample plots using following equations:

38 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)

39 Plot Layout

40 Step 5: Plot Layout Stem Diameter Circular Plot Square Plot <5 cm dbh 1 m 2m x 2m 5-20 cm dbh 4 m 7m x 7m cm dbh 14 m 25m x 25m >50 cm dbh 20 m 35m x 35m Source: Pearson et al. (2005)

41 Step 4: Field measurements- tree inventory Normally, size of rectangular plot is 20m x 25m and radius of circular plot is m, but can vary depending upon various factors such as site topology, forest types, slope, vegetation species etc. If the plot is rectangular or square, take co ordinates from all four corners or if it circular, take co ordinate from the center point by using GPS For rectangular or square plot, all four corners of the plot are marked using pegs of about 6 inches length For circular plot, a string or rope is used to mark the perimeter of the circular plot

42 Dead wood sampling Draw at least three sub plots of 2m x 2m within the square or rectangular sample plot Or draw a sub plot of 2.82 m radius within the circular sample plot Collect all dead wood vegetation from the sub plot and place it in a bag Weight directly to get fresh weight To get the dry weight, place the samples in the oven at 85 degree for 48 hours or sun dry Record all the data into separate worksheet

43 Leaf litter sampling Draw atleast three sub plot of 1m x 1m or 0.5m x 0.5m within the square or rectangular sampled plot of tree species or circular plots Collect samples of all leaf and woody litter, un decomposed or semi decomposed plant material Subsequently, may collect the fine litter in the organic layer (0-5 cm) and dry sieve the roots and partly decomposed dark litter and weight it

44 Soil sampling Collection of soil sample is generally done from the centre of the each sample plot by using augur or making pit which is at least 30 cm deep or more Entire soil taken by augur or from pit are thoroughly mixed and then then 250 gm or 500 gm soil is taken in a bag for analysis in the lab.

45 Measurement Equipment

46 Step 5: Assessment of Above Ground Biomass (AGB) 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 Biomass (AGB) of the tree

47 Assessment of Below Ground Biomass (BGB) Root Shoot Ratio: 0.27 : 1.0 (IPCC, Good Practices Guidelines, 2006) Regression models: Boreal Forest BBD (t/ha) = exp ( * In ABD ) Temperate Forest BBD (t/ha) = exp ( * In ABD ) Tropical Forest BBD (t/ha) = exp ( * In ABD) Where: BBD = below ground biomass density (t/ha) and ABD = above ground biomass density (t/ha)

48 Estimation of Carbon Stocks Step 1: Calculation of C-stock above ground biomass (AGB) Step 2: Conversion of C-stock AGB to C stock BGB

49 GOFC-GOLD REDD Sourcebook A sourcebook of methods and procedures for monitoring and reporting anthropogenic greenhouse gas emissions and removals caused by deforestation, gains and losses of carbon stocks in forests remaining forests, and forestation Latest version published for COP16 in Cancun, 29 Nov-10 Dec, Mexico

50 GOFC-GOLD Global Observation of Forest and Land Cover Dynamics- A technical panel of the Global Terrestrial Observing System (GTOS) and supported by FAO, UNEP, START, ESA and NASA. 51

51 Web resources GOFC-GOLD: GOFC-GOLD land cover project office: GOFC-GOLD REDD sourcebook: IPCC background paper on use of remote sensing in LULUCF sector (GOFC-GOLD 33): UNFCCC/SBSTA technical paper on costs of monitoring for REDD