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Uncertainty of carbon emissions estimates in Mato Grosso, Brazilian Amazon: implications for REDD projects
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Uncertainty of carbon emissions estimates in Mato Grosso, Brazilian Amazon: implications for REDD projects

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Effectively monitoring deforestation is a crucial component for the success of REDD (Reducing Emissions from Deforestation and forest Degradation). In this presentation, Carlos Souza from IMAZON …

Effectively monitoring deforestation is a crucial component for the success of REDD (Reducing Emissions from Deforestation and forest Degradation). In this presentation, Carlos Souza from IMAZON explores the issue of uncertainty in measuring deforestation and carbon emissions in the Brazilian Amazon, and the implications this has for REDD projects worldwide.

Carlos Souza gave this presentation on 8 March 2012 at a workshop organised by CIFOR, ‘Measurement, Reporting and Verification in Latin American REDD+ Projects’, held in Petropolis, Brazil. Credible baseline setting and accurate and transparent Measurement, Reporting and Verification (MRV) of results are key conditions for successful REDD+ projects. The workshop aimed to explore important advances, challenges, pitfalls, and innovations in REDD+ methods — thereby moving towards overcoming barriers to meeting MRV requirements at REDD+ project sites in two of the Amazon’s most important REDD+ candidate countries, Peru and Brazil. For further information about the workshop, please contact Shijo Joseph via s.joseph (at) cgiar.org

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  • 1. Uncertainty of C Emissions Estimates in Mato Grosso, Brazilian Amazon: implications for REDD Projects Measurement, Reporting and Verification in Latin American REDD+ Projects A CIFOR Workshop, March 8-9, 2012 – Petrópolis, RJ, Brazil Carlos Souza Jr.1, Marcio Sales1, Douglas Morton2, Bronson Griscom3 1 2 3
  • 2. 35000 Annual Deforestation Rate - INPE Acre 30000 Amazonas AmapáArea (km2/year) 25000 Maranhão 20000 Mato Grosso Pará 15000 Rondônia Roraima 10000 Tocantins 5000 Brazilian Amazon 0 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2
  • 3. MRV Case of Study of Mato Grosso, BrazilStudy 1: Morton et al. (2011). Historic Emissions from Deforestation and Forest Degradation in Mato Grosso, Brazil: 1) Source Data Uncertainties. Carbon Balance & Management, 6:18.Study 2: Sales et al. (in prep.) Historic Emissions from Deforestation and Forest Degradation in Mato Grosso, Brazil: 2) Modeling Carbon Emissions Uncertainty.Study 3: Souza Jr. et al. (in prep) Long-term deforestation and forestation degradation C Emissions in Mato Grosso. 3
  • 4. Mato Grosso State • Area: 903.357 km2 • Amazon Biome: 47% • Predominant land uses: mechanized agriculture, ranching and logging• Advanced in REDD preparation
  • 5. Measuring Forest Area and C Stocks Changes http://www.gofc-gold.uni-jena.de/redd/
  • 6. Measuring Gross Carbon EmissionsGross carbon Deforestation Degradationemissions  m   n Cgr _ em =  ∑ Aloss ( i ) ⋅ Closs ( i )  +  ∑ Adgr ( j ) ⋅ Cdgr ( j )     i =1   j =1  Aloss = Area of deforestation (ha) Closs = Carbon emission from deforestation (t/ha) for forest types i … m Adgr = Area affected by degradation (ha) Cdgr = Carbon emission from degradation (t/ha) for degrad. types j … n
  • 7. Deforestation and Forest DegradationSelectively logged forest Sinop-MT, Brazil Deforested area for plantationForest degradation is a type of land modification, whichmeans that the originalstructure and composition istemporarily or permanently changed, but it is not replacedby other type of land cover type (Lambin, 1999).Deforestation replaces the original forest cover by otherland cover type 7
  • 8. Forest Change ProcessesSouza Jr. (in review)Souza Jr. et al., (2009) 8
  • 9. Sources of Deforestation Information for MTMorton et al., (2011), CBM.
  • 10. Spatial Disagreement of Deforestation MapsSpatial differences between PRODES-Digital and SEMA Source; Morton et al. (2011), CBM
  • 11. a 1998 bDynamic of ForestDegradation Logged Old Logged• Degrataion signal Logged changes fast. c d• There is a synergism of forest degradation Logged and Burned Logged and Burned processes that can reduces more C stocks of degraded forests.• Reccurrent forest degratation is expected e f and creates even more loss of C stocks. Old Logged and Old Logged and Burned Burned• Annual monitoring is required to keep track of forest degrataion process. Souza Jr. et al. (2005; 2009)
  • 12. Forest Change Detection R: NDFI02, G: NDFI03 Classification 2002 B: NDFI03 Classificaiton 2003 Logging Old Logging Logging Logging Deforestation DeforestationNon Change Forest loss Old Deforestation Non-forest Regrowth New Deforestation Forest Degradation
  • 13. Forest Change Detection Results 13
  • 14. 25 Yars of Forest Change in Mato Grosso 12000 Annual Forest Change 10000 Deforestation Forest degradation A re a (K m 2 ) 8000 6000 4000 2000 0 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 14 Source: Souza Jr. et (in prep.)
  • 15. Forest Biomass Maps • Total biomass varies from 39 to 93 PgC (1015gC = billions of tons of C). • Maps have high spatial disagreement.Modified from Houghtonal, 20012001 Adaptado de Houghton et et al,
  • 16. Recent Forest Biomass Maps for the Brazilian Amazon Saatchi et al. (2007)Malhi et al. (2006)
  • 17. Stochastic Simulation of Forest BiomassSales et al. (2007), Ecol. ModellingSales (2010), UCSB M.Sc. Thesis
  • 18. Difference in Forest Biomass Maps in Mato GrossoMorton et al., (2011), CBM. 18
  • 19. Carbon Emission Simulator (CES) • CES was used to compute estimates of carbon fluxes and model sources data uncertainties. • Model-based uncertainties were estimated on the variability of emissions factors found in the literature. • Source-data uncertainties were calculated based on the combination forest biomass and deforestation data products. – Run 100 Monte Carlo simulations of the historical carbon releases .Sales et al. (in prep.)
  • 20. Emission Factors and Model Parameters of the Carbon Emissions Simulator (CES). Variable CES model parameters Value Range References nameCarbon Fraction CF 0.47 - 0.5 IPCC, 2006 Nogueira et al. 2008 Malhi et al. 2006Forest Timber Fraction FTF 0.03 - 0.08 of AGLB Feldspauch et al. 2005 , Figueira et al. 2008 Asner et al. 2005, Ramankutty et al. 2007Sawmill Losses SL 0.4-0.6 IMAZON 2003, Winjum et al. 1998Wood Products WP (1-SL) * FTFCombustion CC 0.4 – 0.65 Fearnside et al. 1993, Kauffman et al. 1995Completeness of 1st Guild et al. 1998, Araújo et al. 1999Deforestation Fire Carvalho Jr. et al. 2001, Morton et al. 2008 van der Werf et al. 2009, Righi et al. 2009Elemental Fraction EF 0.03-0.06 Fearnside et al. 1993, Righi et al. 2009(charcoal)Wood debris WD (remaining balance)Heterotrophic k 0.05 – 0.124 Brown 1997, Houghton et al. 2000, van der Werf et al. 2004Respiration Pyle et al. 2008 20
  • 21. Simulations of C Emissions for Mato Grosso, Brasil a) Tier 1/Approach 2 b) Tier 2.a/Approach 3 c) Tier 2.m/Approach 3, Figure 1. Annual deforestation carbon emissions (Tg C) for combinations of deforestation and biomass data. For CES model results, dashed lines indicate model- based uncertainty of ±1 standard deviation of the mean annual deforestation emissions from Monte Carlo simulations.Morton et al., (2011); Sales et al. (in prep.)
  • 22. Summary of C Emissions by IPCC Tier/Approaches Deforestation Emissions (Tg C) Morton et al., (2011); Sales et al. (in prep.)
  • 23. Final Remarks• Forest biomass remains the major source of uncertainty in C emissions;• Deforestation is the most important emissions source;• Degradation from selective logging is not a large net source of C emissions relative to deforestation;• Secondary forest dynamics are poorly known;• Emissions from understory fires are potentially large, but could not be quantified based on available data sources. 23
  • 24. Final Remarks• Baseline and targets for REDD Projects should be defined based on C Emissions.• Forest are change baseline and high uncertainties could limit climate benefits from mitigation actions 24
  • 25. Final Remarks• Apply a continuous process to improve estimates of forest carbon emissions for REDD: – analyze available data, – estimate emissions – quantify uncertainties – build baseline – plan for new data collection and analysis to reduce uncertainties. – Reconstruct baseline and propose new targets 25
  • 26. Aknowledgement• TNC, Washington DC• Gordon & Betty Moore Foundation• Fundo Vale• Skoll Foundation• Climate Land Use Alliance 26

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