Deforestation drivers, carbon emission estimate and setting forest reference levels Arief Wijaya 1 , Lou Verchot 1 , Martin Herold 2 , Arild Angelsen 3 , Erika Romijn 2 and John-Herbert Ainembabazi 3 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 THINKING beyond the canopy
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Deforestation drivers, carbon emission estimate and setting forest reference levels
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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
THINKING beyond the canopy
THINKING beyond the canopy
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
THINKING beyond the canopy
Available national biomass map
THINKING beyond the canopy
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)
Family ratio below poverty levelAgriculture family ratio
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Combined with forest transition curve?
(Angelsen, 2008)
THINKING beyond the canopy
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)