Case Study: Adapting Global Datasets for Forest Carbon Accounting in Berau. REDD Learning Exchange, November 11, 2014 Peter Ellis & Bronson Griscom

Transcription

1 Case Study: Adapting Global Datasets for Forest Carbon Accounting in Berau REDD Learning Exchange, November 11, 2014 Peter Ellis & Bronson Griscom

2 4 Steps to Adapting Global Datasets 1. Decide on pools and fluxes to include. 2. Conduct accuracy assessments. 3. Calibrate, modify, rebuild. 4. Calculate emissions and overall uncertainty.

3 4 Steps to Adapting Global Datasets 1. Decide on pools and fluxes to include. 2. Conduct accuracy assessments. 3. Calibrate, modify, rebuild. 4. Calculate emissions and overall uncertainty.

4 4 Steps to Adapting Global Datasets 1. Decide on pools and fluxes to include. 2. Conduct accuracy assessments. 3. Calibrate, modify, rebuild. 4. Calculate emissions and overall uncertainty.

5 Step 2: Conduct accuracy assessments - Hansen dataset (AD)

6 Step 2: Conduct accuracy assessments - Hansen dataset (AD)

7 Step 2: Conduct accuracy assessments - Hansen dataset (AD)

8 4 Steps to Adapting Global Datasets 1. Decide on pools and fluxes to include. 2. Conduct accuracy assessments. 3. Calibrate, modify, rebuild. 4. Calculate emissions and overall uncertainty.

9 Step 3: Calibrate, modify, rebuild: biomass map (EF). Saatchi et al. 2011

10 Step 3: Calibrate, modify, rebuild: biomass map. Baccini et al. 2012

11 Step 3: Calibrate, modify, rebuild: biomass map. Saatchi et al Baccini et al. 2011

12 Step 3: Calibrate, modify, rebuild: biomass map. R 2 = 0.83 N = 7575

13 Step 3: Calibrate, modify, rebuild: biomass map. Component Spatial Data: Disturbance (Margono) Elevation (USGS) Forest soils (RePPProT)

14 Step 3: Calibrate, modify, rebuild: biomass map. ANOVA F-Statistic = 311

15 Step 3: Calibrate, modify, rebuild: biomass map.

16 4 Steps to Adapting Global Datasets 1. Decide on pools and fluxes to include. 2. Conduct accuracy assessments. 3. Calibrate, modify, rebuild. 4. Calculate emissions and overall uncertainty.

17 Step 4: Calculate emissions and uncertainty. A l SE EE l SE SF l SE A R SE Monte Carlo Simulation SS R SE A D SE EE D SE Overall Uncertainty

18 Step 4: Calculate emissions and uncertainty. And the magic number is

19 Step 4: Calculate emissions and uncertainty. 8 million tonnes net CO 2 Flux from land use change in Berau every year between 2000 and 2010

20 Step 4: Calculate emissions and uncertainty. Biomass map and Forest Loss

21 Step 4: Calculate emissions and uncertainty. Parameters with known uncertainty

22 Step 4: Calculate emissions and uncertainty. Comprehensive uncertainty (including parameters with unknown uncertainty)

23 Historic LULUCF Carbon Emissions in Berau

24 Terima Kasih! Peter Ellis

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26 Extra Slides

27 4 Steps to Adapting Global Datasets 1. Decide on the pools and fluxes to include. 2. Conduct accuracy assessment of forest loss activity data. 3. Calibrate, modify, rebuild. a. Compile forest strata data to build benchmark biomass map. b. Use GLAS to calculate average biomass per forest strata. c. Collect degradation activity data. d. Gather gain-loss degradation emissions data. e. Review literature for other input parameters. 4. Calculate emissions and uncertainty. a. Assign uncertainty envelope to all input parameters. b. Translate calculations into comprehensive emissions equation. c. Use Monte Carlo simulation to propagate uncertainty. d. Identify opportunities to reduce uncertainty.

28 Gain-Loss Accounting C = C L C G C = ( A l EE l + A D EE D ) ( A l SS l + A R SS R ) Secondary Forest A R, SF R Mature Forest Non- Forest A D, EE D A l, SF l Logged Forest A l, EE l

29 Step 1: Decide on the pools and fluxes to include. Pools Fluxes Above-ground live woody biomass (AGLB) Below-ground live woody biomass (BGLB) Dead woody biomass (DB) Litter (LI) Soil carbon in wetlands (SCw) Soil carbon in uplands (SCu) Natural disturbance Forest Loss Anthropogenic Forest Loss Fire Emissions Decay Emissions Degradation from legal logging Degradation from illegal logging Degradation from fuel wood collection Degradation from low-intensity fire Secondary forest regrowth Regrowth after degradation

30 Forest Loss Emissions Secondary Forest A R, SF R Mature Forest Non- Forest A D, EE D A l, SF l Logged Forest A l, EE l

31 Forest Loss Emissions = Biomass Map EE D TNC/Baccini

32 Forest Loss Emissions = Biomass Map * Forest Loss 8.4 million tonnes CO 2 per year EE D A D TNC/Baccini * Hansen

33 Area of Loss (Activity Data) Secondary Forest A R, SF R Mature Forest Non- Forest A D, EE D A l, SF l Logged Forest A l, EE l

34 Emissions Factor (Biomass Map) Secondary Forest A R, SF R Mature Forest Non- Forest A D, EE D A l, SF l Logged Forest A l, EE l

35 Step 5: Collect degradation activity data. Logging Concession Name Average PT INHUTANI 1 UNIT LABANAN ,087 PT INHUTANI 1 UNIT SAMBHARATA ,366 PT KARYA LESTARI ,631 PT WANABHAKTI PERSADA UTAMA ,903 PT UTAMA DAMAI INDAH TIMBER ,453 PT MARDHIKA INSAN MULYA ,839 PT MARDHIKA TABALAR ,118 PT RIZKI KACIDA REANA ,235 PT DAISY TIMBER ,146 PT SUMALINDO LESTARI JAYA IV ,250 PT Amindo Wana Persada ,743 PT ADITYA KIRANA MANDIRI ,887 PT. Inhutani I unit Mera'ang ,469 PT.Inhutani I (Unit Segah Hulu) ,202 PT.Widya Artha Perdana ,136 PT.Puji Sempurna Raharja ,119 PT.KEDUNG MADU TROPICAL WOOD ,120 PT.HANURATA COY ,614 PT. SEGARA INDOCHEM & PT SEGARA TIMBER ,417 PT.GUNUNG GAJAH ABADI ,097 Total 10,065 10,889 12,659 6,699 6,070 6,619 7,378 11,484 10,399 9,569 9,622 11,158 8,864

36 Step 6: Gather gain-loss degradation emissions data.

37 Step 6: Review literature for other input parameters. Step 7: Assign uncertainty envelope to all input parameters.

38 Emissions Drivers Derived from "Spatial Plan" produced by the Ministry of Forestry in GIS data layer used for analysis comes via the World Resources Institute. ** Degradation in logging concessions includes forest loss detected by Hansen associated with Haul Roads (669 ha)

39 Emissions Drivers Berau district boundary Forest loss APL (mostly oil palm) HTI (timber plantation) HPH (logging concession) Protection forest

40 Reference Imagery: Landsat 2000

41 Reference Imagery: SPOT 2009

42 Hansen Change

43 Forclime Change

44 Hansen Accuracy Assessment Results Error of Comission red Error of Omission orange No Error green

45 Forclime Accuracy Assessment Results Error of Comission red Error of Omission orange No Error green

46 Forest Loss Accuracy Assessment: Results

47 Step 4: Use GLAS to calculate average biomass per forest strata. Geoscience Laser Altimeter System (GLAS)

48 Step 4: Use GLAS to calculate average biomass per forest strata. r 2 = 0.83

49 Deforestation Over Time

50 9.9 Emissions Over Time

51 Logging Emissions Secondary Forest A R, SF R Mature Forest Non- Forest A D, EE D A l, SF l Logged Forest A l, EE l

52 Step 9: Translate calculations into comprehensive emissions equation. C = C L C G C = ( A l EE l + A D EE D ) ( A l SS l + A R SS R ) Secondary Forest A R, SF R Mature Forest Non- Forest A D, EE D A l, SF l Logged Forest A l, EE l

53 TNC Approach: Gain-Loss Method IPCC Gain Loss Equation 2.4: C = C L C G Carbon Flux = (Defor Emissions + Logging Emissions) (Defor Uptake + Logging Uptake) C = (A D EE D + A l EE l ) (A R SS R + A l SS l ) Approach 3* Tier 3 Approach 2/3 Tier 3 Approach 3* Tier 2 Approach 2/3 Tier 3 Hansen TNC-Baccini Hansen/GOI TNC Hansen Lit Review Hansen/GOI STREK Tier 3 Uncertainty Analysis

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56 Step 4: Calculate emissions and uncertainty.

57 Step 11: Identify opportunities to reduce uncertainty. AD EF

58 Compare to Other Estimates

59 Compare to Other Studies Forclime TNC MOFOR Method Stock-difference Gain-loss Stock-difference Activity Data Foclime Landcover Maps Hansen + Benchmark Biomass Map MOFOR Landcover Maps C Stocks Data MOFOR plots, other? GLAS footprints (Baccini) MOFOR Plots Fluxes Loss, Logging, Regrowth Loss, Logging, Regrowth Loss, (Logging), Regrowth Pools AG, BG AG, BG, SC, DC AG, BG? Loss Factor Conversion-based Process- Based Conversion Based

60 Historic LULUCF Carbon Emissions in Berau

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