Reducing Emissions from Deforestation and Degradation (REDD+) requires better monitoring, measurement and verification (MRV) to assess carbon and non-CO2 greenhouse gases. With REDD likely to evolve into a whole landscape accounting approach which includes Agriculture, Forestry and Other Land Uses (AFOLU), reliable and cost efficient MRV across complex landscapes is becoming increasingly important. Experts from the World Agroforestry Centre present four case studies that showcase work on measuring carbon in complex landscapes and agro- ecosystems with trees: Western Kenya; the Peruvian Amazon; the peatlands of West Kalimantan, Indonesia and the Africa Soil Information Service project. There are also insights about choosing the right tools and methods for different contexts, ensuring measurements are accurate, statistically relevant, and can be scaled up.

1. Measuring carbon in
complex landscapes with trees
experiences from the
World Agroforestry Centre and the
Moving the audience to act
ASB Partnership for the Tropical Forest Margins

2. Measuring carbon in complex landscapes with trees
• Introduction: Henry Neufeldt
• REALU approach: Peter A Minang
• Case studies: Johannes Dietz
• Contextualization: Henry Neufeldt
• Questions

3. Reducing Emissions from
All Land Uses:
a framework for global emissions reductions
Peter A Minang
Global Coordinator
ASB Partnership audience toTropical Forest Margins at the
Moving the
for the act
World Agroforestry Centre

4. Why REALU?
• Current forest definition within UNFCCC is
problematic
• Drivers of deforestation not adequately addressed
within REDD+ this far
• Current REDD+ construction ignores high
potential emissions reduction and sequestration in
landscapes
• Copenhagen text on REDD indicates total
accounting within IPCC guidelines

5. • A third of Indonesia’s forest
emissions (total of 0.6 Gt C/yr)
occur outside institutionally
defined forest and is not
accounted for under the current
REDD+ policy
• The famous E. Usambaras
forest in Tanzania 8.8 Mt C
emitted between 1992 and
2006 but no deforestation
occurred according to definition

6. Complex and variable landscapes - the reality

7. REALU in Sync with IPCC
• All carbon pools: living biomass (aboveground and
belowground), dead organic matters (litter and necromass) and
soil carbon
• All 6 land use categories: Forest land, cropland, wetland,
grassland, settlement, other land
• All transition between land use categories (remains and
converted)
• Disaggregation-aggregation, stratification by climatic or other
ecological regions, forest types, land-use or forestry practices,
fuelwood gathering patterns, etc
• Tier definitions for methods in AFOLU: from simplest (Tier 1) to
the most sophisticated (Tier 3)
• Choice of methods (gain-loss vs stock changes) and choice of
activity data

8. The Legend is Key

9. Projects on REALU Approach
• REALU Project
• Reducing Emissions from Deforestation and
Degradation through Alternative Landuses in
Rainforests of the Tropics - REDD ALERT
• Accountability and Local Level Initiative for
Reducing Emissions from Deforestation and
Degradation in Indonesia - ALLREDDI
• Carbon Benefits Project - CBP
• Countries: Cameroon, Indonesia, Peru, Vietnam,
Nepal, Kenya and others

10. Indonesian Soil
Research Institute
Case studies:
1. Counting trees outside forests in the
Peruvian Amazon - Glenn Hyman, CIAT
2. Carbon measured and modeled in
landscapes with trees on farms: an example
from Western Kenya - Johannes Dietz, ICRAF
3. Carbon Budget from the Peatland of West
Kalimantan, Indonesia - Fahmuddin Agus, ISRI
4. Land Degradation Surveillance - Thomas
Gumbricht, AfSIS

11. Case Study 1
About 1/3 of the central Peruvian Amazon study area
available in high resolution imagery
Aguaytia watershed
17,000 km2

12. Counting trees on the farms and in other landscapes

13. Field work
• GPS
• Digital photographs
• Google fusion tables
• Google maps

14. RED REDD
REDD+ REDD++

15. Errors related to scale:
ASTER image

16. Up to 48 times more CO2eq in forests
compared to pastures
Forest with 80% canopy cover
Large cattle ranches (240 Mg/ha) contained within
(5 Mg/ha carbon) large cattle ranches

17. Carbon stock and large cattle ranchesC-stock Detallada
C-stock Hacienda Vs with and without accounting for trees
20000,00
Difference and
carbon stock
18000,00
16000,00
depends on the
14000,00 resolution of the
12000,00
analysis
C-stock Haciendas Vs C-stock detallada
Analyzed on ASTER
10000,00
empleando aster
C-stock Haciendas Vs C-stock detallada
8000,00
Analyzed on Google Earth
empleando GE
6000,00
4000,00
2000,00
0,00
Pastures
Haciendas Pastures
Hacienda+Bosque= C-stock
detallada
with trees

18. Case Study 2
Western Kenya: Complex and
heterogeneous agricultural landscape

19. Rationale
• Mix of land cover types
• Support for remote sensing approaches with
ground based measurements
• Need for reliable and practical approaches for
assessing biomass in trees across such
landscapes:
– Cost reduction
– Quality tiers
– Trade-off Cost:Accuracy

20. Rationale
• Mix of land cover types
• Support for remote sensing approaches with
ground based measurements
• Need for reliable and practical approaches for
assessing biomass in trees across such
landscapes:
– Cost reduction
– Quality tiers
– Trade-off Cost:Accuracy

21. Approach
• Destructive sampling of randomly selected
trees across 5 size classes
• Additional parameters recorded:
– Below ground biomass
– Wood density (http://www.worldagroforestry.org/sea/Products/AFDbases/WD/Index.htm)
– Canopy projection
– Canopy cover
– Fractal branching
• Development of calibrated non-destructive
ground sampling methods

22. First results
• Size does matter:
– < 25cm diameter = 10% BM
– > 40 cm diameter = 75% BM
– 10% largest trees = 45% BM
• Exisiting formulas apply but complex
landscapes seem to resemble a mix of forest
types

23. First results

24. Case Study 3
Methods
Land use (2008) Future arable peatland
• Overlay of peat depth and land
cover maps
• Time series land cover maps
for developing land use change
matrix
• Some measurement of peat C
stock, and use of default
values of emission. removal
factors

25. Challenges
Including
• Lack of baseline data
• Laborious and
intensive sampling
• Remoteness of sites

26. Policy Applications:
CO2 Emissions under BAU and Different
Scenarios

27. Next steps
• Combine repeated CO2 gas flux
measurement and C stock data
• Test the scenarios at local level, evaluate
for the legal and institutional constraints.
• Evaluate the abatement costs and have a
sensitivity test for willingness to accept

28. Case Study 4
Random Hierarchical Field Sampling
Sentinel Site
16 Clusters
10 Plots
4 Sub-plots

29. Spectral libraries and Soil Mapping
The spectral properties of different
vegetation and soils, and even soil
physical and chemical properties can
be used for “fingerprinting” complex
landscapes.
Using reflectance corrected satellite
imagery, soil conditions can be
inferred from the spectral
information.

30. Mapping the landscape from satellite images
Mount Kilimanjaro
Reflectance corrected anniversary Landsat images
1987
2000
2006

31. Mapping the landscape from satellite images
Density of woody biomass on the northern slopes of Mount Kilimanjaro
Reflectance corrected anniversary Landsat images
1987
2000
2006

32. Carbon Benefits and Costs
Comparing the costs for measuring and monitoring within the CBP
with the benefits obtained on the markets
Henry Neufeldt,
Climate Change Research Leader
Moving the audience to act
World Agroforestry Centre

33. Challenges with MRV
• High costs for project-based carbon estimation
• Unreliable measurements on the ground
• Problems related to scaling from plot to landscape level
• Problems related to carbon leakage
• Lack of comprehensive, standardized, robust methodology to
assess and report terrestrial carbon

34. The Carbon Benefits Project aims to
provide a cost-effective end-to-end
estimation and support system for
showing carbon benefits in GEF and
potentially other natural resource
management projects
The system will be applicable to a
wide range of soils, climates and land
uses

35. Component 1:
Development of standardized and integrated tools for
quantification and assessment of carbon (including C
accounting) and GHG benefits, both above and below
ground
Component 2:
Test Cases and capacity building using existing GEF
projects in five countries
Component 3:
Best practice toolbox for project design using
socioeconomic and biophysical appraisal
Component 4:
Integrated and easy-to-use frontend for carbon
management, accounting and exchange

36. An Operational System using EO
Carbon Sellers
GEF managers
Earth Observation System
WWW
Satellite Database
Data Analysis
Markets
Carbon Models Buyers
Carbon Accounts

37. Chamber measurements
3.5
Forest
N2O emissions (ng cm hr )
Agriculture
-1
3.0
-2
2.5
2.0
1.5
1.0
0.5
0.0
-0.5
0 1 2 3 4
Site

38. Community Based Carbon Measurement
Benefits of community – based
carbon measurements
– Access to local knowledge
– Community buy-in
– More project resources go to
communities
– Transparency for the
community and others
– Cost effective extension of
sampling resources
The CBP will develop community-
based measurement materials for
– Extension personnel
– Community members

39. Sentinel Site based on the
Land Degradation Surveillance
Framework
a spatially stratified,
hierarchical, randomized
sampling framework
Sentinel site (100 km2)
16 Clusters (1 km2)
10 Plots (1000 m2)
4 Sub-Plots (100 m2)
Randomization to minimize local biases that might arise from convenience sampling

40. Local (site-level) Cref Examples from UNEP-ICRAF West Africa Drylands Project
10 km
0.064% measured
Very high resolution
Extrapolation to
Landsat

41. Local (site-level) Cref Examples from UNEP-ICRAF West Africa Drylands Project
160 km

42. Conclusions
• MRV costs are likely to decrease strongly with scale but are
potentially very high for small projects
 need to use different methodological tiers
• Management costs will decrease moderately with scale
• Significant costs for third-party verification of standards
• Significant setup costs
• Carbon benefits for small farmers are generally small
 need for co-benefits (e.g. tree products; better market connection;
time)
• Benefits for farmers will not likely increase strongly through
bundling

43. Thank you!
Henry Neufeldt
h.neufeldt@cgiar.org
Peter A Minang
a.minang@cgiar.org
Johannes Dietz
j.dietz@cgiar.org