ICCC MRV-Cluster Activities on Methodology Development

Dadang Hilman

ICCC Coffee Morning Meeting, Apr 15th, 2014

www.iccc-network.net

Monitoring U

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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

Fe

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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

Remaining issue on REDD+ implementation readyness

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

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

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

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

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.

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.

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.

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.

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.

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.

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

(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

Thank you