The document discusses ICCC activities related to developing methodologies for monitoring drivers of fires and haze in Indonesia and estimating greenhouse gas emissions. Key outputs include protocols for monitoring drivers, an early warning system, and more accurate estimates of emissions, human health impacts, and patterns of drivers. It also summarizes challenges in detecting smoldering peatland fires using satellites and presents preliminary findings from a project using nighttime satellite data to estimate peatland fire emissions.

1. ICCC MRV-Cluster Activities on Methodology Development
Dadang Hilman
ICCC Coffee Morning Meeting, Apr 15th, 2014
www.iccc-network.net

2. Monitoring
Underlying drivers
Mediating factors
Impacts
Fire/Haze
Multiple drivers
Law enforcement
Social negotiations
Local/national politics
Incentives
Conservation interventions
REDD+
Governance & institutional arrangement
Effectiveness and shortcomings of institutional arrangements
Impacts on temperature & human health (rural and urban)
Outputs:
•Protocol to monitor drivers
•Early warning system
•More accurate GHG emissions estimation
•More accurate estimate human health impacts
•Better understanding of patterns of drivers and causality
•Science supports evidence based interventions
Fewer fire/haze
Burnt areas&smoke plumes landscapes dynamics mapped.
Haze composition & emissions rate known
Socio-economic drivers
•Land tenure
•Conflict
•Finance and thecapital
•Migration policy
•Expanding OP market
•Poverty
•Demographic (population density & migration)
Climatic, Soil, Landcover Drivers
•Drought/rainfall
•El Niño occurrence
•Indian Ocean Dipole
•Wind speed/direction
•Peat lands
•Degraded lands
•Peat soil draining
Mortality rate Others?
Temperature
Others?
Health
Climate
Spatio-temporal
variations of drivers mapped comprehensively at finer scales

3. Remaining issue on REDD+ implementation readyness

4. PERPRES 61 / 2011
RAN-GRK
PERPRES 71 / 2011
INVENT GRK
KONTRIBUSI EMISI (SNC)
(1) agriculture, forestry & peat land,
(1) agriculture, forestry & peat land, and other land uses,
5 + 47 (LUCF) + 13 (PF) = 65%
(2) Energiy & transportation,
(2) Energy supply and use;
20% (incl Transp)
(3) industry,
(3) Industri al Process & Product uses;
3%
(4) Waste Management.
(4) Waste Management.
11%
(5) Other supporting activities.
Indonesia Context
Indonesia’s commitment: to reduce GHG emission 26 – 41% from BAU in 2020
Source: Bappenas

5. Various PL Data
Source
Peat land area
(M ha)
Polak (1952)
16.35
Nugroho et al (1992)
15.43
Rieley et al (1996)
20
Puslitbangtanak (1997)
16.27
Subagyo et al (2000)
14.89
Wetland International (2004; 2005)
20.60
BBSDLP (2011)
14.91
Source: Nursyamsi and Maswar (2013) modified

6. Emission Estimates from PL & Peat Fire
Source
Emission
(M ton CO2-e/ yr)
Notes
Bappenas (2003) in Ai Dariah
903
From 2000 - 2006
Including peat fire;
Rieley et al (2008)
20 – 40
Per ha ; natural forest
DNPI (2009)
1,034
In 2005 (55% of LUCF)
Source
Emission
(ton CO2-e/ha)
Notes
Haranto (2004)
275
Ave Carbon content: 50 kg/m3
Depth: 15 cm;
Van der Werf (2007) in SNC
466
Ave from 2000-2006 from peat and forest
World Bank (2008)
1,270 Mt CO2/yr
53% from LUCF

7. ICCC MRV Cluster On-going Activities
I.Methodology Development For Estimating GHGs Emissions From Peatfire
II.ICCC – LAPAN - NOAA - NGDC (National Geophysical Data Center)’s ‘Estimating Peatland Fire Emissions Using Nighttime Satellite Data’
III.Training workshop on Application of IPCC methodology on GHG emission estimation for peat fire from recently burned area in Riau

8. The Challenges of Peatland Burning
•Peatland burning often presents a mixture of flaming and smoldering phases within a single VIIRS. 600 K is considered the break point between flaming (above 600 K) and smoldering (below 600 K).
•Emissions are distinctly different for flaming versus smoldering peatland fires.
•Satellite detection works well for detection of flaming peatland fires.
•Detection and characterization of smoldering peatland fires from satellites is challenging:
–Much of the burning is underground – while satellites observe the surface.
–Detection of low temperature sources requires large source areas to yield sufficient infrared emissions.
–Many satellite fire detection algorithms rely on a background radiance subtraction derived from analysis of pixels surrounding suspected fire pixels. Undetected smoldering fires can corrupt the background radiance subtraction.

9. Why Nighttime Satellite Data?
•Smoldering peatland fires go on day and night for extended periods.
•The flaming phase of burning is common in the day, but subsides at night.
•The infrared emissions from the flaming phase can easily overwhelm the signal from smoldering in pixels with both.
•Therefore night is a better time to observe smoldering.
•In addition, daytime imaging bands can be used for multispectral detection and Planck curve fitting.

10. Nightfire hot pixel detections for June 19, 2013
Detections are color coded based on temperature.
Hot gas flares are red and yellow. Fires are cooler colors. Sizes are set based on radiant heat of the source. Full set of detections are in the csv file. KMZ has local maxima.

11. Project II: Estimating peatland fire emissions using nighttime satellite data
•Phase 1 is retrospective, focused on 2013 burning in Sumatra (Riau). To be completed by June 2014.
•Phase 2 is focused on the 2014 burn season.

12. Project II: Objectives
•To modify an existing fire emission model to accept the enhanced information content of the Nightfire product (temperature, source area, radiant heat). Phase 1.
•To validate Nightfire detections through comparison with other satellite fire products and a combination of high resolution satellite data and field surveys. Phase 1 and 2.
•To evaluate the utility of nighttime Landsat data for peatland fire detection and characterization. Phase 2.
•To produce an authoritative set of emission estimates (trace gases and particulates) for the 2013 Sumatra fires based on VIIRS Nightfire (VNF) data. Phase 1.
•To expand the emission modeling to all or Indonesia for the 2014 burn season. Phase 2.

13. Proj II: two primary tasks:
a)explore the detection and characterization of peatland fires in Indonesia with nighttime satellite data collected by VIIRS and Landsat 8;
b)modify an existing atmospheric emission model to make use of the VIIRS temperature and source size estimates.

14. FINN Fire Emission Model
Ei = A(x,t) * B(x) * FB * efi
Assume
1 km2
Look up table of carbon stocks based on landcover
Assume 60% consumed for forest fires
One set of coefficients for each landcover type
Current configuration. Runs globally on a daily basis with MODIS fire detections as input

15. (Temporary) Findings
•Riau had 91% of the fire detections and 97% of the burn area.
•82% of the fires in Riau were on areas mapped as peatlands. The total area of burning detected by VNF for Riau was 13.3 km2

16. Thank you