View
2.329
Download
0
Tags:
Embed Size (px)
DESCRIPTION
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.
Citation preview
Moving the audience to act
Measuring carbon in complex landscapes with trees
experiences from the
World Agroforestry Centre and the
ASB Partnership for the Tropical Forest Margins
Measuring carbon in complex landscapes with trees
• Introduction: Henry Neufeldt
• REALU approach: Peter A Minang
• Case studies: Johannes Dietz
• Contextualization: Henry Neufeldt
• Questions
Moving the audience to act
Peter A Minang
Global CoordinatorASB Partnership for the Tropical Forest Margins at the
World Agroforestry Centre
Reducing Emissions from All Land Uses:
a framework for global emissions reductions
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
• 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
Complex and variable landscapes - the reality
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
The Legend is Key
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
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
Indonesian Soil
Research Institute
About 1/3 of the central Peruvian Amazon study area available in high resolution imagery
Aguaytia watershed17,000 km2
Case Study 1
Counting trees on the farms and in other landscapes
• GPS• Digital photographs• Google fusion tables• Google maps
Field work
RED REDD
REDD+ REDD++
Errors related to scale:
ASTER image
Large cattle ranches(5 Mg/ha carbon)
Forest with 80% canopy cover (240 Mg/ha) contained within large cattle ranches
Up to 48 times more CO2eq in forestscompared to pastures
0,00
2000,00
4000,00
6000,00
8000,00
10000,00
12000,00
14000,00
16000,00
18000,00
20000,00
Haciendas Hacienda+Bosque= C-stock detallada
C-stock Hacienda Vs C-stock Detallada
C-stock Haciendas Vs C-stock detallada empleando aster
C-stock Haciendas Vs C-stock detallada empleando GE
Pastures Pastureswith trees
Carbon stock and large cattle ranches with and without accounting for trees
Difference and carbon stock depends on the resolution of the analysis
Analyzed on ASTER
Analyzed on Google Earth
Western Kenya: Complex and
heterogeneous agricultural landscape
Case Study 2
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
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
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
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
First results
MethodsLand 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
Case Study 3
Challenges
Including
• Lack of baseline data
• Laborious and intensive sampling
• Remoteness of sites
Policy Applications: CO2 Emissions under BAU and Different
Scenarios
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
Random Hierarchical Field Sampling
Sentinel Site
16 Clusters
10 Plots
4 Sub-plots
Case Study 4
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.
Mount KilimanjaroReflectance corrected anniversary Landsat images
1987
2000
2006
Mapping the landscape from satellite images
Density of woody biomass on the northern slopes of Mount Kilimanjaro Reflectance corrected anniversary Landsat images
Mapping the landscape from satellite images
1987
2000
2006
Moving the audience to actMoving the audience to act
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
World Agroforestry Centre
• 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 toassess and report terrestrial carbon
Challenges with MRV
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
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
Earth Observation System
Satellite Database Data Analysis
Carbon ModelsCarbon Accounts
WWW
MarketsBuyers
Carbon Sellers
An Operational System using EO
GEF managers
Site
0 1 2 3 4
N2O
em
issio
ns (
ng
cm
-2 h
r-1)
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5Forest
Agriculture
Chamber measurements
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
Randomization to minimize local biases that might arise from convenience sampling
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)
Lo
ca
l (s
ite
-le
ve
l) C
ref
Examples from UNEP-ICRAF West Africa Drylands Project
10 km
0.064% measured
Very high resolution
Extrapolation to
Landsat
Lo
ca
l (s
ite
-le
ve
l) C
ref
Examples from UNEP-ICRAF West Africa Drylands Project
160 km
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
Thank you!
Henry Neufeldt
Peter A Minang
Johannes Dietz