Measuring Carbon in Complex Landscapes with Trees - ICRAF and ASB at UNFCCC SB32
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The document discusses the challenges and methodologies for measuring carbon in complex landscapes, particularly in agroforestry contexts, emphasizing the need for more inclusive definitions of forest within international frameworks like REDD+. It presents various case studies focusing on innovative approaches to assess biomass and carbon stocks, highlighting the roles of local initiatives and remote sensing in enhancing measurement accuracy. The overall goal is to develop a standardized, cost-effective system for carbon accounting that supports emissions reduction and sustainable land use practices across diverse regions.
Measuring Carbon in Complex Landscapes with Trees - ICRAF and ASB at UNFCCC SB32
- 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