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REDD-plus after Cancun: Moving from Negotiation to Implementation -Building REDD-plus Policy Capacity for Developing REDD-plus after Cancun: Moving from Negotiation to Implementation -Building REDD-plus Policy Capacity for Developing Country Negotiators and Land Managersat Hotel Nikko 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

General Introduction General Introduction

Net primary production decreased 1% (0. 55 petagrams of carbon over 10 years) globally 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)

Net primary production increased 6% (3. 4 petagrams of carbon over 18 years) globally 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);

Project Introduction Project Introduction

Study Site from 1997 • Central Kalimantan, Indonesia • Peatland • Mega Rice Project Study Site from 1997 • Central Kalimantan, Indonesia • Peatland • Mega Rice Project   Palangkaraya Study Topics: ・Green House Gasses Flux (CO 2, CH 4, N 2 O) ・Fire Detection and Protection ・Water Table Monitoring and Management ・Peatland Ecology ・Integrated Farming

Forest and Peatland Areas in Indonesia Kalimantan 28. 200 (30. 06%) Sulawesi 5. 800 Forest and Peatland Areas in Indonesia Kalimantan 28. 200 (30. 06%) Sulawesi 5. 800 (27. 50%) 8. 900 (9. 45%) na Papua/West Papua 32. 400 (34. 45%) 8. 000 (38. 10%) Sumatera 14. 700 (15. 60%) Maluku/North Maluku 7. 200 (34. 30%) 4. 000 (4. 28%) na Jawa/Madura Bali/Nusa Tenggara 3. 000 (3. 30%) 2. 700 (2. 88%) na na Sumatera 19% Legend: Location Total Forest area 1) Mha (x 1, 000 ha) Sumatera Papua/West 34% Papua 43% Peatland Areas 93, 924. 33 (100%) Peatland area 2) Forest Areas 21, 000 (100%) Sources: Kalimantan 1) Forestry Statistics of Indonesia 2007, Ministry of Forestry, Jakarta 2008. 2) Wetlands International - Indonesia Programme, Bogor July 2008. 38% Kalimantan 28% Papua Pa 38

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

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

(1) Emissions by fires (1) Emissions by fires

Fire Detection New Generation Fire Detection MOD 14 Proposed • Doubled S/N ratio (ASTER Fire Detection New Generation Fire Detection MOD 14 Proposed • Doubled S/N ratio (ASTER comparing to MOD 14, 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 Toshihisa Honma, Hokkaido University, Japan decide firefighting strategy and confirm

Example of Thermograph Image of flight observation RGB IR UAV (Unmanned aerial vehicle) flight Example of Thermograph Image of 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

Fire Expan. Simulation • Simulation Result at 16: 00, June 25 (after 24 hours Fire Expan. Simulation • Simulation Result at 16: 00, June 25 (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

By Hidenori Takahashi, Japan 14 By Hidenori Takahashi, Japan 14

 Count of fire in Central Kalimantan 15000 10000 5000 R 2 = 0. Count of fire in Central Kalimantan 15000 10000 5000 R 2 = 0. 7387 0 0 100 200 PFI 300 400 By Hidenori Takahashi, Japan

Peat Fire Index An indicator of peat fire damage (Carbon emission data is offered Peat Fire Index An indicator of peat fire damage (Carbon emission data is offered by Dr. Erianto Indra Putra) Carbon emission by peat fire (Gt. C/Mha) 0. 15 1 Mha 0. 1 0. 05 R 2 = 0. 8846 0 -600 -300 0 300 600 PFI By Hidenori Takahashi, Japan MRP area in Kalteng

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

The organic matters eluted from burned soil Burned at 220℃ Burned at 350℃ Hydrophilic The organic matters eluted from burned soil Burned at 220℃ Burned at 350℃ Hydrophilic matters Hydrophobic acids 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.

(2) Emission by oxidation of microorganisms (2) Emission by oxidation of microorganisms

Eddy covariance technique CO 2 flux (Net ecosystem CO 2 exchange) is calculated as Eddy covariance technique CO 2 flux (Net ecosystem CO 2 exchange) is calculated as the covariance of vertical wind speed and CO 2 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)

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

Seasonal variation in NEE (net ecosystem CO 2 exchange) in DF site CO 2 Seasonal variation in NEE (net ecosystem CO 2 exchange) in DF site CO 2 source CO 2 sink NEE was positive or neutral throughout 3 years (CO 2 source). n CO 2 emission was the largest in the late dry season, partly due to the shading effect by smoke from farmland fires. n CO 2 emission was the largest in 2002, an El Nino year, because of dense smoke from large-scale fires. By Takashi Hirano (Hokkaido Univ. , Japan) n (Unpublished)

Inter-site comparison of annual CO 2 balance May 2004 to May 2005, Unit: g. Inter-site comparison of annual CO 2 balance May 2004 to May 2005, Unit: g. C m-2 yr-1 Site GPP RE NEE Peat decomposition UDF (undrained) 4000 4103 → -1. 4 mm yr-1 DF (drained) 3287 3724 437 → -6. 1 mm yr-1 BC (burnt & drained) 1075 1899 824 → -11. 6 mm yr-1 Positive NEE (CO 2 source strength): BC > DF > UDF also functioned as a CO 2 source to the atmosphere. Results of peat sampling u Peat growth rate in Indonesia: 1– 2 mm yr-1 (Sorensen 1993) u Carbon accumulation rate in Palangkaraya: 56 g. C m-2 yr-1 (0. 8 By Takashi Hirano (Hokkaido Univ. , Japan) mm y-1) (Page et al. 2004) (Unpublished)

Effects of water table (WL) on respiration in forest RE/GPP vs. WL for UDF 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

Some results of greenhouse gases emission from tropical peat soil, Indonesia GWP= CO 2 Some results of greenhouse gases emission from tropical peat soil, Indonesia GWP= CO 2 flux + CH 4 flux × 23 + N 2 O flux × 296 : CO 2 flux    : CH 4 flux × 23    : N 2 O flux × 296 GWP in forest   → influenced by CO 2   GWP in cropland  →  influenced by N 2 O   Central Kalimantan, Indonesia; Arai et al. , unpublished

(3) Carbon Loss through Waterborne Carbon (3) Carbon Loss through Waterborne Carbon

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

Hyper sensor for carbon dissolved in water Hyper sensor (Example) N 2 O *Potential Hyper sensor for carbon dissolved in water Hyper sensor (Example) N 2 O *Potential carbon release from peat. Indonesian rivers transfer around 10% DOC of the global riverine DOC oceanic input (Baum et al. , 2007). Monitoring target 1)Dissolved Organic Carbon (DOC) 2)Dissolved Inorganic Carbon (DIC) 3)Particulate Organic Carbon (POC) 4)Colored Dissolved Organic Matter (CDOM) and Dissolved Organic Carbon (DOC) for Southern Finland the Gulf of Finland by ALI image on 14 July, 2002 (Kutser et al. , 2005)

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

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

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

By Wataru Takeuchi, University of Tokyo, Japan 32 By Wataru Takeuchi, University of Tokyo, Japan 32

By Wataru Takeuchi, University of Tokyo, Japan By Wataru Takeuchi, University of Tokyo, Japan

Top-down • satellite • airplane • inverse model Integrated, practical carbon budget map Satellite Top-down • satellite • airplane • inverse model Integrated, practical carbon budget map Satellite GOSAT “IBUKI” Senescing: CO 2 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 Simulator: Sim. Cycle-Visit for East Asia Bottom-up • field survey • flux obs. • process model ・Carbon Emission by Fire ・Carbon Loss through Water ・Carbon Emission by Microorganisms Degradation ・Tree Growth/Mortality

Biomass Carbon Wet Dry Soil Carbon Biomass Carbon Wet Dry Soil Carbon

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