Deforestation drivers, carbon emission estimate and setting
forest reference levels
Arief Wijaya1, Lou Verchot1, Martin Herold2, Arild Angelsen3, Erika Romijn2 and John-Herbert
Ainembabazi3
1 Forest and Environment Programme, Center for International Forestry Research (CIFOR), Bogor, Indonesia
2 Center for Geo-Information Science, Wageningen University, Wageningen, The Netherlands
3 Department of Plants and Environmental Sciences, Norwegian University of Life Sciences (UMB), Oslo, Norway
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Background
CIFOR Global Comparative Study (GCS) on REDD+ Component 3: MRV and reference levels
• Monitoring, reporting, verification (MRV) for REDD+
• Setting national reference emission levels (RELs)
• 6 case study countries: Indonesia, Vietnam, Tanzania, Cameroon, Brazil, Peru
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Specific objectives
To detect areas and activities (drivers) of deforestation To calculate carbon emissions and sequestration of
deforested and degraded regions To explore the concepts for developing RELs at national
and sub-national levels
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Stepwise approach for RELs
(Herold, et.al, 2011)
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Carbon biomass estimation approach
Field data (e.g. National forest inventory) Direct remote sensing measurement
• Empirical models where RS data is calibrate to field estimates (Baccini et al. 2004, 2008, Saatchi et al. 2007, Blackard et al. 2008)
Stratify and Multiply (SM) method
• Assign an average biomass value to land cover/vegetation type map (Asner et al. 2010)
Combine and Assign approach
• Extension of SM, GIS and multi-layers information (Gibbs et al. 2007)
Ecological Models approach
• RS data to parameterize the biomass model (Hurtt et al. 2004)
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Mosaic Landsat GLS Data
Source: USGS downloaded from ArcGIS server
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NDVI change 1990 - 2000
Source: USGS downloaded from ArcGIS server
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Direct remote sensing measurement
Biomass map based on study by Baccini et al. (2012) including LIDAR shots data obtained during Biomass mapping training at BIG
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Available national biomass map
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Available national biomass map
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Stratify and Multiply Method
Landuse/cover classification of Indonesia for the years 2000, 2003, 2006 and 2009Data source: LANDSAT satellite data (30 m resolution) (MOF, 2009)
No Classification
1 Primary Upland Forest2 Secondary Upland Forest/Logged Forest3 Primary Swamp Forest4 Secondary Swamp Forest/Logged Area5 Primary Mangrove Forest6 Secondary Mangrove Forest/Logged7 Crop Forest8 Oil Palm and Estate Crops9 Bushes/Shrubland10 Swampy Bush11 Savanna12 Upland Farming
No Classification
13 Upland Farming Mixed with Bush 14 Rice field15 Cultured Fisheries/Fishpond16 Settlement/Developed Land17 Transmigration18 Open Land19 Mining/mines20 Water Body21 Swamp22 Cloud 23 Airport/Harbor
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Deforestation 2000-2009
IslandArea of deforestation (×1000 ha) Area of degradation (×1000 ha)2000-2003 2003-2006 2006-2009 2000-2003 2003-2006 2006-2009
Sumatera 278.95 709.89 1063.01 0.00 5.02 101.66Java 0.27 51.05 2.92 9.08 64.32 0.00Bali & Nusa Tenggara 45.10 38.52 3.74 3.25 3.31 54.08Kalimantan 367.24 963.60 705.48 825.38 304.11 74.57Sulawesi 200.25 283.48 49.62 230.07 1655.13 52.95Maluku 24.24 24.30 18.09 8.48 10.67 0.22Papua 79.15 186.78 728.10 298.20 634.66 777.24Total 995.20 2257.62 2570.96 1374.46 2677.22 1060.72
Annual deforestation 647 thousand ha/year vs FRA FAO Report (2010) 498 thousand ha/year
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Deforestation based on forest typeTh
ousa
nd h
ecta
re
Deforestation Forest degradation
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Deforestation drivers
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Carbon emission estimate
(Sugardiman, 2011 modified from the IPCC report)
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Carbon density by landcover type
Forest classes carbon (ton/ha) SD (ton/ha)Primary dry forest (PF 2001) 179.9 16.9Secondary dry/logged over forest (SF 2002) 173.7 15.2Primary Swamp Forest (PSF 2005) 155.5 19.2Secondary swamp forest(SSF 20051) 143.8 19.7Primary mangrove forest (PMF 2004) 87.4 13.4Secondary mangrove forest (SMF 20041) 62.6 8.9Crop forest (CF 2006) 111.4 17.0
Non-forest classes (vegetated) carbon (ton/ha) SD (ton/ha)Oil Palm and estate crops (PG 2010) 95.6 19.9Bushes/Shrubland (B2007) 123.9 13.7Swampy bush (SB 20071) 77.6 14.1Savanna (S 3000) 63.1 11.3Upland farming (UF 20091) 79.9 14.5Upland farming mixed with bushes (Pc 20092) 115.2 17.2Rice field (Sw 20093) 62.8 12.0
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Carbon stocks change 2000 - 2009
Based on Multiply and Stratify approach. The figure shows only C stocks above ground excluding on peat soils.
Biomass map based on study by Saatchi (2011) and land cover map from the Ministry of Forestry (2009)
2000
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Carbon stocks change 2000 - 2009
2009
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Carbon stocks change 2000 - 2009
2009
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Landcover and carbon density
Landcover 2000 Landcover 2009
(a) (b)
(c) (d)
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Why one map policy is important?
Land cover/land use map Biomass/C stocks map
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Deforestation rates (official data MOF)
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Comparison of different studies
1990-2000
2000-2001
2001-2002
2002-2003
2003-2004
2004-2005
2005-2006
2006-2007
2007-2008
2008-2009
2009-20100
100
200
300
400
500
600
700
800
900
1000
Indonesia MOFOR Indonesia Hansen
Indonesia JRC Indonesia Mean
Annu
al D
efor
esta
tion
(x 1
000
ha)
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Cumulative LUCF carbon emissions
SourceCumulative Emission from
LUCF 2000 -2009(in Gg CO2e)*
Methods Remarks
FAOStat 3,140,033 FRA country report(EF = 138 ton C/ha) Net forest conversion
MoE - Second National Communication to UNFCCC 7,443,064 IPCC Guidelines 2006 Net forest conversion
Winrock International (Harris, 2012) 3,468,150 Carbon Bookkeping model
(RS + Field) Gross deforestation
MOF (official) 1,760,000 Approach 1 + NFI(Tier 1 or 2)
Net forest conversion (peat?) - carbon emissions potential
MOF + Saatchi (CIFOR) 1,811,396 Approach 1 + Global EF(Tier 1 or 2) Net forest conversion
Mean 3,524,529
* does not include peat emissions and peat fire
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Annual LUCF Carbon emissions
1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 20110
100000
200000
300000
400000
500000
600000
700000
800000
900000
1000000
1100000
1200000
1300000
1400000
FAOStat
LUCF - Second NatCom MoE
MOF + Saatchi (CIFOR)
Annu
al C
arbo
n Em
issio
ns f
rom
LU
CF (i
n Pg
CO
2e)
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Combining socio-economic data
Population density
Family ratio below poverty levelAgriculture family ratio
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Combined with forest transition curve?
(Angelsen, 2008)
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Concluding remarks
Stepwise approach is useful to handle data uncertainty and data quality variations in estimating RELs
Carbon density estimated at different land cover types can cause combined errors
Indonesia has capability to implement Tier 3 (or 2.5??) of the REL estimation (given the availability of reliable forest inventory data and spatially explicit datasets)
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