To measure the success of REDD (Reducing Emissions from Deforestation and forest Degradation), it is crucial to first set baseline emissions from which the reduction can be measured in each project or region. In this presentation, Fabiano Godoy from Conservation International shared experiences with applying the VCS VM0015 model in the Alto Mayo protected forest of Peru in order to set baseline emissions. Fabiano Godoy 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. Alto Mayo
Protected
Forest
REDD
Initiative
Peru
Fabiano Godoy
March- 2012
Photo 1 Photo 2
4.2” x 10.31” 5.51” x 10.31”
Position Position
x: 4.36”, y: .18” x: 8.53”, y: .18”

2. REDD initiative profile
Alto Mayo Protected Forest – Department San Martin –Peru
National protected area with highest deforestation rate in Peru (0.34% yr-1)
~ 5000 families live within the AMPF
AMPF size: 182,000 ha
project start date: 2008
main threat: forest conversion to coffee plantation
co-benefit: provision of water supply
strategy: capacity building and incentives
to improve coffee production through
conservation agreements

3. Major Steps and Inputs – VM0015
1996
Trans. Potent.
2001
2006 2020
Historical Land change CO2 emission
deforestation modeling reductions
Ref area
Elevation Spatial
boundaries Carbon map
tC ha-1
Dist to roads
Drivers of
deforestation
Defor rate

4. Historical land cover and change
In-house processing
Image acquisition - Landsat 5 & 7
1996-2001-2006
(path-row 8-64 and 9-64)
Interpretation and classification
Ortho, cloud removal
Decision tree algorithm (See5-ERDAS)
Forest, non-forest, cloud and water
Post-processing and map accuracy
MMU 2ha
Field visit – high resolution satellite images – aerial photos
accuracy 92% forest-non forest

5. Spatial Boundaries
Spatial Boundaries
Project Area
forested area inside AMPF
153, 929 ha
Reference Region
similarity with project area
same drivers & agents of
deforestation
Leakage Belt
mobility analysis
MCE
Fuzzy based on
hist deforestation

6. Carbon Pools
Included / TBD /
Carbon pools Justification / Explanation of choice
Excluded
Represents the pool where the greatest carbon stock change will
Above-ground tree included
occur.
The baseline land use in the project area is conversion of forest
Above-ground non-tree included to perennial crops (coffee), therefore the carbon stock in this pool
is likely to be relatively large compared to the project scenario.
Recommended by the methodology as it usually represents
Below-ground included
between 15% and 30% of the above-ground biomass.
Conservatively excluded (the carbon stock in this pool is not
Dead wood excluded expected to be higher in the baseline compared to the project
scenario).
Under the baseline scenario, illegal selective logging occurs in
Harvested wood products excluded very small scale and, therefore, harvested wood products have
been considered insignificant.
Not to be measured according to the latest VCS AFOLU
Litter excluded
Requirements (version 3.0).
The baseline land-use of the project area is conversion of forest
to perennial crop (coffee) followed by conversion to pasture. The
Soil organic carbon excluded
soil organic carbon is not to be measured in such cases
according to the latest VCS AFOLU Requirements (version 3.0).

7. Sources of GHG emissions
Sources Gas Included/ excluded Justification / Explanation of choice
CO2 Excluded counted as carbon stock change
The major baseline activity is conversion of forest
to conventional coffee plantation using slash and
burn techniques. The project aims to reduce this
Biomass activity by providing technical assistance to
CH4 Excluded
burning establish sustainable, shade-grown organic coffee
plantations and therefore, the non-CO2 emissions
related to biomass burning are conservatively
excluded.
N2 O Excluded See above explanation.
Raising livestock is not a widespread baseline
activity and the AMCI project will not promote the
CO2 Excluded raising of livestock or result in an increase of this
Livestock activity compared to the baseline. Therefore,
emissions livestock emissions are conservatively excluded.
CH4 Excluded See above explanation.
N2 O Excluded See above explanation.

8. Drivers and Agents of Deforestation
Identify the main drivers of deforestation, the agents and the underlying causes
compilation of relevant scientific publications + public consultation
Drivers of deforestation
conversion to coffee
plantation
conversion to pastureland
conversion to agriculture of
subsistence
conversion to infrastructure
clearance to illegal land trade
illegal logging

9. Drivers and Agents of Deforestation

10. Drivers and Agents of Deforestation
Identify the main drivers of deforestation, the agents and the underlying causes
Map the threat distribution
Understand the deforestation dynamic and provide a comprehensive list of variables to
be used in the modeling of future deforestation
Past Future

11. Deforestation Rate
The major drive of deforestation in the project area is conversion to coffee plantation
deforestation rate was model as function of coffee production over time.
direct correlation between deforestation and coffee production in the past
constant (increasing) coffee production (1996-2007)
coffee production do not follow the price trends
Deforestation as function of Coffee Production
(in Rioja, Moyobamba and Huallaga proportional to reference
area)
5.000
deforestation as
y = 0,1188x - 36,338
4.000 function of coffee
R² = 0,9417
production
3.000
Linear
2.000
(deforestation as
function of coffee
1.000 production)
-
Annual Coffee Production in Rioja + Moyobamba + 0 10.000 20.000 30.000 40.000
Huallaga Coffee Price in Peru
(proportional to reference area)
16.000 1996-2001 & 2001-2006 3000
14.000 y = 604,47x - 1.200.357,57
R² = 0,86 2500
12.000 Annual
Coffee
10.000 Production 2000
8.000 Linear 1500
(Annual
6.000 Coffee
1000
4.000 Production)
2.000 500
0 0
1995 2000 2005 2010 1996 1998 2000 2002 2004 2006 2008

12. LCM Tool Concept – IDRISI Taiga
2006 actual
1996 2001
Land Cover 1996 Land Cover 2001 Land Cover 2006 NO
Drivers
Change 96-01 2006 proj Validation
Change 96-01 Land Proj. 2006 YES
Suit. Map
Trans. Potential
Modeling Suitab. Map 2020
2025
2030
Elevation Land Proj 2012
Elevation 2020
2040
Dist to villages
Dist. Villages
Dist to roads
Dist. Roads
Input Output Process

13. Projected Deforestation
Transition Potential Map
(Neural network)
Model future land use change
P-FOM = 60% cloud forest
= 8% pre montane

14. Carbon Map
based on forest classification
89% cloud forest (1000-2500masl)
7% pre montane forest (below 1000masl)
4% dwarf forest (above 2500masl)
biomass measurement
107 plots
above ground biomass
root to shoot ratio
weighted-area average non-forest
Next Steps - REDD project is under VCS validation
currently addressing the findings (NIR, CAR)
verification (monitoring report 2008-2011) by Sept
CCBS validation and verification by December

15. Photo 1 Photo 2
4.2” x 10.31” 5.51” x 10.31”
Position Position
x: 4.36”, y: .18” x: 8.53”, y: .18”