Recent trends in fires and land cover change in Western Indonesia

Douglas O. FullerDepartment of Geography and Regional Studies

University of Miami, Florida

Collaborators: T.C. Jessup, Agus Salim, Erik Meijaard, Martin Hardiono

Talk Outline

• Background – Drivers, Ecology, Consequences

• The ENSO-fire relationship – Understanding climate and human actions

• Carbon Emissions, Peat swamp forest, and REDD

• Projecting the future with land change models

Carbon Emissions

• 1.2 Gt yr-1 (12 percent of the global total) from tropical deforestation and forest degradation (Nature Geoscience 2009)

• Pan et al. (Science 2011) report a global forest sink of 1.1 Gt yr-1

• 0.3 Gt yr-1 from tropical peat fires, mostly in Indonesia (4 percent of global total)

• Emissions from Indonesia (Sumatra and Borneo) estimated at 30x during the 2006 El Nino vs. the 2000 La Nina

Study area:

Kalimantan

Vegetation Cover

Social, Economic, and Cultural Consequences of Fire

• 1997 fires cost Indonesia ~ $1 billion in lost tourism, transportation and health impacts.

• Rampant land conversion implicated in the loss of cultural diversity

• Region-wide effects: haze spreads over much of Southeast Asia

The haze dilemma

GHG Emissions

Peat fires important in Indonesia: 1997-98 fires emitted 40 percent CO2 from fossil fuels.Source: Page et al. Nature, 2002

Indonesia forest cover 95-120 million ha, 2-4 percent annual deforestation rate.

Selective Logging Disturbed Lowland Forest

Alang-alang “savanna” Agricultural burning

Process of Land-cover Change in Kalimantan, Indonesia

No fire ??

The fire transition

• A new theory that accounts for anthropogenic changes in tropical fire regimes through time:– Fires are rare in closed forests except during

exceptional climatic events (extreme El Nino for example)

– Fires become more seasonal as forests are converted and remaining high biomass needs to be removed quickly to make way for plantations

– As permanent crops are established and land values rise, fires diminish as people practice fire suppression to protect valued assets

Lit fires

Total active

Nu

mb

er

of

fire

s (r

ela

tive

)

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SP

-OL

S

300 600 900 1200 1500 1800 2100 2400

AV

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orn

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rno

on

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-OL

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Time of day

MO

DIS

EO

S-A

M1

MO

DIS

EO

S-P

M1

AT

SR

-1

Some Satellite Systems for Mapping Fire

Diurnal Patterns of Burning and Satellite Overpass

Fuller, 2000, Prog. Phys. Geogr.

Landsat TM image showing industrial plantations

Fire vs. ENSO indices

1

1.5

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2.5

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3.5

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4.5

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-10

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40A

ug 9

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97

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b 0

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ATSR nighttime fires

SOININO1+2NINO3NINO4NO3.4

LOG

AT

SR

Fire

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unt

EN

SO

Inde

x

Fuller & Murphy, 2006, Clim Change

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-15 -10 -5 0 5 10

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Southern Oscillation Index

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Southern Oscillation Index

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ire c

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/man

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st

Southern Oscillation Index

Non-forest (agriculture, degraded land, pasture)

Tropical moist forest

Swamp and mangrove forest

r = 0.75

Fire-SOI: The influence of land-cover type

Fuller & Murphy, 2006, Clim Change

Annual Time Scale

0

5000

1 x 104

1.5 x 104

2 x 104

2.5 x 104

3 x 104

-40 -30 -20 -10 0 10 20 30

To

tal f

ire

cou

nts

Annual Sum of SOI

1998

1997

2001 1999

2000

Fuller & Murphy, 2006, Clim Change

Extending the Time Series Using MODIS Fire

Overlap

Fuller & Meijaard, 2010, submitted

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Fir

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8-day Count

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8-day Count

TS Models and Decomposition:

Xt = St + Rt + et → additive modelXt = St x Rt x et → multiplicative model

St

Rt

et

Xt

ALL-M PSF-A LOW-A MONT-A P/S-M O/M-M

NINO1+2-M -0.28(-37)-0.21(-45)0.25(-9)-0.19(-2)0.24(6)

0.22(-11)0.21(-11)0.25(-9)0.33(41)0.30(3)

0.19(-11)0.19(-13)0.29(22)0.32(34)0.28(6)

0.20(23)-0.22(27)0.28(23)0.42(41)0.20(-23)

0.24(-37)0.18(12)-0.24(-37)0.16(-17)0.24(6)

-0.30(-37)-0.23(-45)0.26(-8)-0.22(-6)0.20(46)

NINO3-M 0.40(-10)-0.24(-37)0.48(-8)0.18(-7)0.32(-11)

0.42(-11)0.24(4)0.46(-8)0.17(26)0.45(-12)

0.38(-11)0.22(-3)0.46(-9)0.33(8)0.40(-12)

0.23(-7)0.17(6)0.31(-6)0.18(-37)0.22(-10)

0.39(-11)-0.16(-37)0.42(-8)0.17(-7)0.35(-12)

0.38(-10)-0.27(-38)0.48(-8)0.21(8)0.24(-11)

NINO4-M 0.39(-10)0.35(-3)0.40(-10)0.29(-3)0.38(-11)

0.34(-11)0.30(-21)0.36(-6)0.28(-10)0.47(-14)

0.31(-11)0.27(-21)0.41(-9)0.25(-10)0.41(-11)

0.22(-5)0.19(-21)0.28(-4)0.23(-18)0.26(-8)

0.39(-10)0.31(-3)0.41(-10)0.26(-2)0.38(-11)

0.37(-10)0.33(-3)0.39(-10)0.27(-3)0.33(-10)

NINO3.4-M 0.41(-10)0.33(-8)0.47(-8)0.16(1)0.39(-12)

0.41(-12)0.34(0)0.44(-7)0.25(1)0.50(-14)

0.37(-12)0.30(-9)0.47(-9)0.23(1)0.45(-12)

0.25(-6)0.34(2)0.33(-5)0.27(2)0.26(-10)

0.40(-10)0.29(-4)0.43(-8)0.17(31)0.40(-12)

0.39(-9)0.31(2)0.45(-8)0.15(1)0.32(-12)

Cross-correlations between fire and ENSO, 2001-2010

Black = whole series, red = 2001-2006, blue = 2007-2010 (May)

Fuller & Meijaard, 2010, submitted

Evidence consistent with the decoupling hypothesis:

1) Maximum cross-correlations decreased across the two time segments (except for PSF);

2) Time lags between fires and ENSO increased noticeably;

3) Seasonality increased in certain transitional land

cover types (especially fire-susceptible forests)

Peat = carbon = $$$$2 billion pledged to help Indonesia implement REDD+

“Soros wants to turn Indonesia into a pilot project for

his carbon trading plan.”

Evidence of changefrom Landsat

Some background on peat deposits:

– About 55 percent of PSF have been logged and drained, which exposes peat surfaces that burn readily during droughts (seasonal or otherwise)

– Range in age from 2-26 Kyr– Range in thickness from 1-20 meters– Contain up to 18x the carbon of the above-ground

biomass– Total carbon store of 55 (+/-10) Gt in Indonesia– Largest deposits in Central Kalimantan– When drained, they subside due to oxidation (60

percent) and shrinkage (40 percent)

Hooijer et al., 2010, Biogeosciences

Peat depths from core samples

Jaenicke et al. (2009), Geoderma

Change in carbon stocks

Cconversion = Σi{(CAFTERi − CBEFOREi ) · ∆A TO OTHERSi } → gross emissions

where: Cconversion = change in carbon stocks on land converted to another land category, t C yr−1;

CAFTERi = carbon stocks on land type i immediately after the conversion,t C ha−1;

CBEFOREi = carbon stocks on land type i before the conversion, t C ha−1;

∆A TO OTHERSi = area of land use i converted to another land use category in a certain year, ha yr−1; i = type of land use converted to another land use category.

Source: IPCC, 2006, IPCC Guidelines for National Greenhouse Gas Inventories.

More to the point….how REDD is supposed to work

0.18

0.2

0.22

0.24

0.26

0.28

0.3

0.32

0.34

2006 2008 2010 2012 2014 2016 2018 2020 2022

Baseline (BAU)REDD intervention

Em

issi

ons

(Gt

yr-1

)

Year

Potential incomefrom emissionsreductions ($ / t C / yr)

Hutan Rawa circa 1995

Hutan Rawa – MoF map 2006

Hutan Rawa 2005

3,505,425 ha of Hutan Rawa 2,660,692 ha of Hutan Rawa

Ministry of Forestry Maps

Both maps derived from interpretation of Landsat imagery

Research Design

Pre-process GIS data

Develop validation

(reference) data set

Determine values for

CBEFOREi/CAFTERi

Simulatedland covermaps

Perform validation

LUCC Models

Model calibration

Simulate forward X time steps

Simulated land coverbased on model calibrations

0.18

0.2

0.22

0.24

0.26

0.28

0.3

0.32

0.34

2006 2008 2010 2012 2014 2016 2018 2020 2022

Baseline (BAU)REDD intervention

Em

issi

on

s (G

t yr

-1)

Year

Potential incomefrom emissionsreductions ($ / t C / yr)

END PRODUCT REL CURVES

Modeling loop

Fires 1997

Fires 2005 Deforestation 1995-2005

Rivers

Reforestation

Local roads

GEOMOD - 2020

LCM - 2020

0.8 million ha lost

1.39 million ha lost

Constrained 3x3

2005

0.9 million ha lost

Dinamica EGO - 2020

Fuller et al., 2011, Environmental Management

reforestation/regeneration (RR) between 2005-2010 and protection of Sebangau NP

~48,000 ha of regrowth through replanting or natural regeneration

National Park

BAU vs. Some Regeneration

2020: Regeneration scenario:2.28 million ha

2020 BAU (no PSF regenerationConsidered) 1.86 million ha

Forest Loss Projections

Fuller et al., 2011, Env. Mgmt

Conclusions

• LUCC models are useful to explore possible outcomes given a range of scenarios

• Our results indicate that Indonesia can meet between 36-81 percent of its 2020 target for reduced greenhouse gas emissions of 0.78 Gt CO2 equivalent (e) by implementing peatland restoration and other REDD interventions in Central Kalimantan.

Research Frontiers

• Results reflect emissions from deforestation only not degradation (RED not REDD)

• Fluxes from oxidizing peat not well known, so emissions baselines are difficult to establish

• More accurate accounting will include degradation and carbon sequestration (Gtnet)

• Extend fire analysis to continue testing fire transition theory using cross-border comparisons

THANK YOU!