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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
35000
                              Annual Deforestation Rate - INPE
                                                                                        Acre
                  30000                                                                 Amazonas
                                                                                        Amapá
Area (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
MRV Case of Study of Mato Grosso, Brazil

Study 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
Mato Grosso State


            • Area: 903.357 km2
          • Amazon Biome: 47%
       • Predominant land uses:
        mechanized agriculture,
           ranching and logging
• Advanced in REDD preparation
Measuring Forest Area and C Stocks Changes




           http://www.gofc-gold.uni-jena.de/redd/
Measuring Gross Carbon Emissions

Gross carbon            Deforestation                   Degradation
emissions



                 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
Deforestation and Forest Degradation
Selectively logged forest                    Sinop-MT, Brazil
                               Deforested area for plantation




Forest degradation is a type of land modification, which
means that the originalstructure and composition is
temporarily or permanently changed, but it is not replaced
by other type of land cover type (Lambin, 1999).
Deforestation replaces the original forest cover by other
land cover type
                                                                7
Forest Change Processes




Souza Jr. (in review)
Souza Jr. et al., (2009)                             8
Sources of Deforestation Information for MT




Morton et al., (2011), CBM.
Spatial Disagreement of
           Deforestation Maps




Spatial differences between PRODES-Digital and SEMA




                                        Source; Morton et al. (2011), CBM
a                       1998   b
Dynamic of Forest
Degradation                       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)
Forest Change Detection

                                 R: NDFI02, G: NDFI03
  Classification 2002                 B: NDFI03                      Classificaiton 2003

                                               Logging
                                                                              Old Logging

                                                                 Logging

                                                           Logging
                                                                                          Deforestation




                                     Deforestation




Non Change         Forest loss                       Old Deforestation           Non-forest


                   Regrowth                          New Deforestation           Forest            Degradation
Forest Change Detection Results




                                  13
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.)
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,
Recent Forest Biomass Maps for the
           Brazilian Amazon

                         Saatchi et al. (2007)




Malhi et al. (2006)
Stochastic Simulation of Forest Biomass




Sales et al. (2007), Ecol. Modelling
Sales (2010), UCSB M.Sc. Thesis
Difference in Forest Biomass Maps in Mato Grosso




Morton et al., (2011), CBM.                               18
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.)
Emission Factors and Model Parameters of the Carbon
                  Emissions Simulator (CES).
                         Variable
 CES model parameters                  Value Range                                  References
                          name
Carbon Fraction            CF           0.47 - 0.5        IPCC, 2006

                                                          Nogueira et al. 2008

                                                          Malhi et al. 2006
Forest Timber Fraction    FTF       0.03 - 0.08 of AGLB   Feldspauch et al. 2005 , Figueira et al. 2008

                                                          Asner et al. 2005, Ramankutty et al. 2007


Sawmill Losses             SL             0.4-0.6         IMAZON 2003,

                                                          Winjum et al. 1998
Wood Products              WP          (1-SL) * FTF
Combustion                 CC           0.4 – 0.65        Fearnside et al. 1993, Kauffman et al. 1995

Completeness of 1st                                       Guild et al. 1998, Araújo et al. 1999
Deforestation Fire                                        Carvalho Jr. et al. 2001, Morton et al. 2008

                                                          van der Werf et al. 2009, Righi et al. 2009




Elemental 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. 2004

Respiration                                               Pyle et al. 2008                                    20
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.)
Summary of C Emissions by IPCC Tier/Approaches
               Deforestation Emissions (Tg C)




 Morton et al., (2011); Sales et al. (in prep.)
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
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
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
Aknowledgement
•   TNC, Washington DC
•   Gordon & Betty Moore Foundation
•   Fundo Vale
•   Skoll Foundation
•   Climate Land Use Alliance




                                      26

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

  • 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 Amapá Area (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, Brazil Study 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 Emissions Gross carbon Deforestation Degradation emissions  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 Degradation Selectively logged forest Sinop-MT, Brazil Deforested area for plantation Forest degradation is a type of land modification, which means that the originalstructure and composition is temporarily or permanently changed, but it is not replaced by other type of land cover type (Lambin, 1999). Deforestation replaces the original forest cover by other land cover type 7
  • 8. Forest Change Processes Souza Jr. (in review) Souza Jr. et al., (2009) 8
  • 9. Sources of Deforestation Information for MT Morton et al., (2011), CBM.
  • 10. Spatial Disagreement of Deforestation Maps Spatial differences between PRODES-Digital and SEMA Source; Morton et al. (2011), CBM
  • 11. a 1998 b Dynamic of Forest Degradation 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 Deforestation Non Change Forest loss Old Deforestation Non-forest Regrowth New Deforestation Forest Degradation
  • 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 Biomass Sales et al. (2007), Ecol. Modelling Sales (2010), UCSB M.Sc. Thesis
  • 18. Difference in Forest Biomass Maps in Mato Grosso Morton 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 name Carbon Fraction CF 0.47 - 0.5 IPCC, 2006 Nogueira et al. 2008 Malhi et al. 2006 Forest Timber Fraction FTF 0.03 - 0.08 of AGLB Feldspauch et al. 2005 , Figueira et al. 2008 Asner et al. 2005, Ramankutty et al. 2007 Sawmill Losses SL 0.4-0.6 IMAZON 2003, Winjum et al. 1998 Wood Products WP (1-SL) * FTF Combustion CC 0.4 – 0.65 Fearnside et al. 1993, Kauffman et al. 1995 Completeness of 1st Guild et al. 1998, Araújo et al. 1999 Deforestation Fire Carvalho Jr. et al. 2001, Morton et al. 2008 van der Werf et al. 2009, Righi et al. 2009 Elemental 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. 2004 Respiration 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