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

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.
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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
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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

13. Forest Change Detection Results
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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
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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.
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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
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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
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26. Aknowledgement
• TNC, Washington DC
• Gordon & Betty Moore Foundation
• Fundo Vale
• Skoll Foundation
• Climate Land Use Alliance
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