REDD-plus after Cancun: Moving from Negotiation to Implementation

1. REDD-plus after Cancun: Moving from Negotiation to Implementation -Building REDD-plus Policy Capacity forDeveloping Country Negotiators and Land Managers- atHotel Nikko Hanoi, Hanoi, Vietnam, 18-20 May 2011 Developing Robust MRV Systems: Learning from Country Experience in Indonesia Mitsuru Osaki*, Farhan Helmy**, Doddy Skadri**, and Kazuyo Hirose*** *Research Faculty of Agriculture, Hokkaido University, Japan **National Council on Climate Change (DNPI), Indonesia ***Center of Sustainability Science (CENSUS), Hokkaido University, Japan

2. General Introduction

3. Net primary production decreased 1% (0.55 petagrams of carbon over 10 years) globally from 2000 to 2009 The past decade (2000 to 2009) has been the warmest since instrumental measurements began, which could imply continued increases in NPP; however, our estimates suggest a reduction in the global NPP of 0.55 petagrams of carbon. Large-scale droughts have reduced regional NPP, and a drying trend in the Southern Hemisphere has decreased NPP in that area, counteracting the increased NPP over the Northern Hemisphere. Maosheng Zhao, et al.: Drought-Induced Reduction in Global Terrestrial Net Primary Production from 2000 Through 2009 Science 329, 940 (2010)

4. Net primary production increased 6% (3.4 petagrams of carbon over 18 years) globally during 1982 to 1999 We present a global investigation of vegetation responses to climatic changes by analyzing 18 years (1982 to 1999) of both climatic data and satellite observations of vegetation activity. Our results indicate that global changes in climate have eased several critical climatic constraints to plant growth, such that net primary production increased 6% (3.4 petagrams of carbon over 18 years) globally. The largest increase was in tropical ecosystems. Amazon rain forests accounted for 42% of the global increase in net primary production, owing mainly to decreased cloud cover and the resulting increase in solar radiation. Ramakrishna R. Nemani, et al：　Climate-Driven Increases in Global Terrestrial Net Primary Production from 1982 to 1999. Science 300, 1560 (2003);

5. Project Introduction

6. Study Site from 1997 • Central Kalimantan, Indonesia • Peatland • Mega Rice Project Palangkaraya Study Topics: ・Green House Gasses Flux (CO2, CH4, N2O) ・Fire Detection and Protection ・Water Table Monitoring and Management ・Peatland Ecology ・Integrated Farming

7. Forest and Peatland Areas in Indonesia Legend: Sources: 1) Forestry Statistics of Indonesia 2007, Ministry of Forestry, Jakarta 2008. 2) Wetlands International - Indonesia Programme, Bogor July 2008.

8. What Factors Regulate Carbon in Tropical Peat? Ecosystem Change ・Farming/ Vegetation Drainage ・Decrease water table Deforestation ・Dryness of ground surface ・Decrease water holding capacity Water Tree Growth/Mortality Carbon Emission by Fire Carbon Loss through Water Carbon Emission by Microorganism Degradation

9. Terra & Aqua MODIS(2) Landsat, SPOT, Quickbird,TerraSAR, AVNIR-2, ASTER, Hisui, (3), (8) PALSAR, AMSR-E (4), (5), (6), (7) GOSAT (1) Satellite Airborne /UAV LiDAR (4), (5), (7) UAV(1), (3) Lateral CO2 Flux (2)Wildfire detection & Hotspot (1) CO2 concentration Vertical CO2 Flux (3)Deforestation, Forest degradation, Species mapping Tower(1) Chamber(1) Ground DGPS(5) DGPS(5) DGPS(5) (4)Forest biomass change (6)Water level, Soil moisture FES-C (1) Drilling(7) Water Gauge(6) (5)Peat subsidence *FES-C : Fiber Etalon Solar measurement of CO2 (7)Peat dome detection & Peat thickness (8)Water soluble organic carbon Red: Instrument Black: Target Key Elements for Carbon Flux Estimation (Integrated MRV system proposed as Sapporo Initiative)

10. (1) Emissions by fires

11. Fire DetectionNew Generation Fire Detection MOD14 Proposed • Doubled S/N ratio (ASTER comparing to MOD14, and Algorism Improvement) • 80% more HS and & 10% less False Alarm • Smoldering, small fire or slush and burn • Geographical distribution is completely different • Suitable to decide firefighting strategy and confirm extinction Toshihisa Honma, Hokkaido University, Japan

12. Example of Thermograph Imageof flight observation RGB IR UAV (Unmanned aerial vehicle) flight observation and Wireless Sensor Network are indispensable as well as ground observations. Toshihisa Honma, Hokkaido University, Japan

13. Fire Expan. Simulation • Simulation Result at 16:00, June25 (after 24 hours run). • The expansion for the very slow expansion mainly to southward is overestimated. • The rapid expansion toward eastward is underestimated because of the limit of time step. Toshihisa Honma, Hokkaido University, Japan

14. By Hidenori Takahashi, Japan

15. By Hidenori Takahashi, Japan

16. Peat Fire Index An indicator of peat fire damage (Carbon emission data is offered by Dr. Erianto Indra Putra) 1Mha Carbon emission by peat fire (GtC/Mha) PFI MRP area in Kalteng By Hidenori Takahashi, Japan

17. GHGs Emission by Peat Fire R. Hatano et al. (unpublished)

18. The organic matters eluted from burned soil Burned at 220℃ Burned at 350℃ Hydrophilic matters Hydrophobic acids 30 min 5 min Unburned soil 5 min 30 min ‣Amount of eluviation greatly increases at 220℃burn. ‣Most part of eluted organic matters from burned soil have hydrophilic. (by Kuramitsu et al.) The peat land fire accelerate the eluviation of Carbon.

19. (2) Emission by oxidation of microorganisms

20. Eddy covariance technique CO2 flux (Net ecosystem CO2 exchange) is calculated as the covariance of vertical wind speed and CO2 density. Within the boundary layer, vertical flux is almost constant. If flux is measured at an appropriate height within the boundary layer, we can obtain flux averaged spatially over the fetch. By Takashi Hirano (Hokkaido Univ., Japan)

21. Burnt forest after drainage (BC) Undrained forest (UDF) Drained forest (DF) By Takashi Hirano (Hokkaido Univ., Japan) (Unpublished)

22. Seasonal variation in NEE (net ecosystem CO2 exchange) in DF site • NEE was positive or neutral throughout 3 years (CO2 source). • CO2 emission was the largest in the late dry season, partly due to the shading effect by smoke from farmland fires. • CO2 emission was the largest in 2002, an El Nino year, because of dense smoke from large-scale fires. CO2 source CO2 sink By Takashi Hirano (Hokkaido Univ., Japan) (Unpublished)

23. Inter-site comparison of annual CO2 balance Peat decomposition →-1.4 mm yr-1 →-6.1 mm yr-1 →-11.6 mm yr-1 Results of peat sampling • Peat growth rate in Indonesia：1–2 mm yr-1 (Sorensen 1993) • Carbon accumulation rate in Palangkaraya: 56 gC m-2 yr-1 (0.8 mm y-1) (Page et al. 2004) May 2004 to May 2005, Unit: gC m-2 yr-1 Positive NEE (CO2 source strength): BC> DF> UDF UDF also functioned as a CO2 source to the atmosphere. By Takashi Hirano (Hokkaido Univ., Japan) (Unpublished)

24. Effects of water table (WL) on respiration in forest RE/GPP vs. WL for UDF & DF Soil respiration vs. WL for UDF by automated chamber systems Hirano et al., Ecosystems 2008

25. Some results of greenhouse gases emission from tropical peat soil, Indonesia GWP= CO2 flux + CH4flux×23 + N2Oflux×296 ：CO2 flux： CH4 flux ×23 　　　： N2O flux×296 GWP in forest　　 →　influenced by CO2 GWP in cropland　　→　 influenced by N2O Central Kalimantan, Indonesia; Arai et al., unpublished

26. (3) Carbon Loss through Waterborne Carbon

27. Seasonal Changes of DOC Correlation between Water Table and DOC by I Tanaka et al., Unpublished

28. Hyper sensor for carbon dissolved in water (Example) Hyper sensor N2O N2O *Potential carbon release from peat. Indonesian rivers transfer around 10% DOC of the global riverine DOC oceanic input (Baum et al.,2007). • Monitoring target • Dissolved Organic Carbon (DOC) • Dissolved Inorganic Carbon (DIC) • Particulate Organic Carbon (POC) • Colored Dissolved Organic Matter (CDOM) Colored Dissolved Organic Matter (CDOM) and Dissolved Organic Carbon (DOC) for Southern Finland and the Gulf of Finland by ALI image on14 July, 2002 (Kutser et al., 2005)

29. Robust MRV Systems: Water Table is Key for Measuring!

30. Water Table is Key for Peatland Ecosystem!! 1) Oxidation 2) Fire Factors 3) Tree growth and Mortality 4) DOC

31. Algorism By Wataru Takeuchi, University of Tokyo, Japan

32. By Wataru Takeuchi, University of Tokyo, Japan

33. By Wataru Takeuchi, University of Tokyo, Japan

34. Top-down • satellite • airplane • inverse model Satellite GOSAT “IBUKI” Senescing: CO2 Carbon-Water Simulator Column averaged dry air mole fraction distribution of carbon dioxide for the month of September, 2009, obtained from IBUKI observation data (unvalidated) By JAXA Integrated, practical carbon budget map Simulator: SimCycle-Visit for East Asia ・Carbon Emission by Fire ・Carbon Loss through Water ・Carbon Emission by Microorganisms Degradation ・Tree Growth/Mortality Bottom-up • field survey • flux obs. • process model

35. Biomass Carbon Wet Dry Soil Carbon

36. Thank you for your attention