1. Recent trends in fires and land cover change in Western Indonesia Douglas O. Fuller Department of Geography and Regional Studies University of Miami, Florida Collaborators: T.C. Jessup, Agus Salim, Erik Meijaard, Martin Hardiono

2. 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

3. 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

4. Study area: Kalimantan

5. Vegetation Cover

6. 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

7. The haze dilemma

8. 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.

9. Selective Logging Disturbed Lowland Forest Alang-alang “savanna” Agricultural burning Process of Land-cover Change in Kalimantan, Indonesia No fire ??

10. 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

11. Some Satellite Systems for Mapping Fire Diurnal Patterns of Burning and Satellite Overpass Fuller, 2000, Prog. Phys. Geogr.

12. Landsat TM image showing industrial plantations

13. Fire vs. ENSO indices Fuller & Murphy, 2006, Clim Change

14. 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

15. Annual Time Scale Fuller & Murphy, 2006, Clim Change

16. Extending the Time Series Using MODIS Fire Overlap

17. Fuller & Meijaard, 2010, submitted

18. TS Models and Decomposition: X t = S t + R t + e t → additive model X t = S t x R t x e t → multiplicative model StSt RtRt etet XtXt

19. ALL-MPSF-ALOW-AMONT-AP/S-MO/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

20. 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)

21. Peat = carbon = $$$ $2 billion pledged to help Indonesia implement REDD+ “Soros wants to turn Indonesia into a pilot project for his carbon trading plan. ”

23. Evidence of change from Landsat

24. 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)

25. Hooijer et al., 2010, Biogeosciences

26. Peat depths from core samples Jaenicke et al. (2009), Geoderma

27. Change in carbon stocks C conversion = Σ i {(C AFTERi − C BEFOREi ) · ∆A TO OTHERSi } → gross emissions where: C conversion = change in carbon stocks on land converted to another land category, t C yr −1 ; C AFTERi = carbon stocks on land type i immediately after the conversion, t C ha −1 ; C BEFOREi = 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.

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

29. 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

30. Research Design Pre-process GIS data Develop validation (reference) data set Determine values for C BEFOREi /C AFTERi Simulated land cover maps Perform validation LUCC Models Model calibration Simulate forward X time steps Simulated land cover based on model calibrations END PRODUCT REL CURVES Modeling loop

31. Fires 1997 Fires 2005 Deforestation 1995-2005 Rivers Reforestation Local roads

32. 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

33. reforestation/regeneration (RR) between 2005- 2010 and protection of Sebangau NP ~48,000 ha of regrowth through replanting or natural regeneration National Park

34. BAU vs. Some Regeneration 2020: Regeneration scenario: 2.28 million ha 2020 BAU (no PSF regeneration Considered) 1.86 million ha

35. Forest Loss Projections Fuller et al., 2011, Env. Mgmt

36. 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.

37. 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 (Gt net ) Extend fire analysis to continue testing fire transition theory using cross-border comparisons

38. THANK YOU!