Data for Monitoring, Reporting, and Verification: Remote sensing, Inventories, and Intensive sites Richard Birdsey Carbon Monitoring Workshop 17 September, 2010
Measurement, Monitoring, and Verification For. National Greenhouse Gas Inventories REDD and REDD+ Participation in Carbon Markets (Projects) Research and Education
Guías y Requerimientos de Monitoreo Existen varias guías, por ejemplo: IPCC Guías de Buenas Prácticas y reportes especiales Programas como GEO, GOFC-GOLD, UN-REDD Incluir deforestación, degradación forestal y manejo forestal (los métodos pueden ser diferentes) Necesidad de flexibilidad para permitir amplia participación, pero los resultados deben ser consistentes Necesidad de proyecciones confiables de la línea de base para establecer la adicionalidad
El Enfoque Multi-tier de Monitoreo: Observaciones Extensivas con Estudios Intensivos de Procesos del Ecosistema Sensores remotos Inventario nacional forestal Modelaje Sitios de referencia para validación
Selected Land Variables and Measurement Methods Variable Remote Sensing Forest Inventory Intensive Sites Land cover X X X Leaf area X X X Disturbance X X X Live biomass X X Stand structure X X Species composition X X Growth, removals, mortality X X Litter fall Soil CO 2 flux Runoff Dissolved Organic C Net Ecosystem Exchange of CO 2 X X X X X
Forest Type Map from Remote Sensing and Inventory
Basic Forest Inventory Approach Phase I Remote sensing to stratify area Phase 2 Field inventory
Forest Inventory Phase 3 Sample Direct monitoring of additional variables Not yet included in U.S. greenhouse gas inventory Additional variables on subset of P2: soil carbon, down dead wood, forest floor carbon
US Forest Greenhouse Gas Inventory Data: Currently Based on Phase 2 plots and Ecosystem Models Condition A = Forest Land Use Condition B = Nonforest Land Use Old 1/5-acre plot Carbon estimates are based on tree species and dimensions, forest type, volume of growing stock, and stand age.
Use of Data From Intensive Sites Stand structure and composition Diameter and height Tree age Leaf area index Tree density Species composition Carbon pools Live biomass Woody debris Forest floor Mineral soil Carbon fluxes Biomass increment Litterfall Forest floor decomposition Net ecosystem carbon balance Link to forest classification from inventory (Scaling up) Data for empirical models based on inventory data Data for ecosystem process and forest dynamic models
Generalized Biomass Equations by Combining Data from Multiple Studies Aboveground biomass (kg) 8000 6000 4000 2000 0 0 20 40 60 80 100 dbh (cm) aspen/cotton wood hard maple/oak cedar/larch Douglas-fir pine woodland SOURCE: Jenkins and others, 2003
Biomass Related to Volume of Growing Stock: Fitted equation and data points for live trees, Maple- Beech-Birch, NE region 400 Biomass (T/ha dry wt.) 300 200 100 0 0 100 200 300 400 Growing stock volume (m /ha) 3 SOURCE: Smith and others, 2003
Forest floor carbon accumulation, decay, and total Example: Southern pines Carbon mass density (Mg/ha) 30 20 10 0 accumulation decomposition 0 25 50 75 Years TOTAL Mixed or unknown age SOURCE: Smith and Heath, 2002
Some Uses of the Data
CO 2 Emissions from U.S. Forest Fires Compared with Net CO 2 Flux from Forestry and Land-use Change (From Heath and Smith in US Greenhouse Gas Inventory, 2009) Million tons CO 2 yr -1 1,600 1,400 1,200 1,000 Net Forest Sequestration 800 600 400 200 0 Forest Fire Emissions 1990 1995 2000 2005
The Carbon Budget of the U.S. Forest Sector (Forest Ecosystems and Wood Products) Million tons CO 2 per year 4000 3000 2000 1000 0-1000 -2000 Net Emissions Net Sequestration 1700 1800 1900 2000 2100 Year National baseline: -800 MtCO 2 /yr offsetting 12% of fossil fuel emissions From Birdsey 2006
Basic Information: Total Forest Carbon Includes all forest ecosystem carbon components, based on FORCARB2 and 2002 RPA Forest Data
Analyze Activities in the Forest Sector to Increase Carbon Sequestration or Reduce Emissions Increase forest land area Avoiding deforestation Afforestation Increase carbon stocks Mine land reclamation Forest restoration Improved forest management Agroforestry Urban forestry Increase use of wood Biomass energy plantations Use wood residues for energy Substitute wood for other materials
Calculate Emission Factors and Lookup Tables Work well when individual reports are summed over a large domains equivalent to that used for derivation of factors Tend to smooth over interannual variability over time Can be consistently applied at low cost to reporters and verifiers May not matter if estimates are consistently wrong (biased) as long as change is accurately estimated Reporting and verification burden shifts to documentation of activity levels
Sample Output from Landscape Monitoring Series of equations: NEP = f(condition, age) Hundreds of equations required for diverse conditions Example - aggregated estimates for U.S. regions: 6 5 t C per ha per year 4 3 2 1 0-1 -2-3 -4 0-5 5-10 10-15 15-20 20-25 25-30 30-35 35-40 40-45 45-50 Age Class Southeast South Central Northeast North Central Rocky Mountain Pacific Coast
From Pan et al. 2010
Voluntary Reporting Program Project and entity reporting Department of Energy, Energy Information Administration USDA improving agriculture and forestry accounting rules and guidelines 300 250 200 150 100 50 0 age 2 carbon 0 50 100 150 livec_tph from ARG Carbon (t/ha) 250 200 150 100 50 0 Pine plantation, SC 0 10 20 30 40 Years
Carbon OnLine Estimator (COLE) COLE is a web-based decision-support tool that queries the U.S. forest inventory database and estimates forest carbon stocks using national standard methodology. Users may select an area of interest as small as several counties, and target specific forest types or conditions. The principal applications of COLE: Greenhouse gas inventories Regions, states, groups of counties User-defined domains Carbon pools of forest ecosystems Calculations for greenhouse gas registries National registry: 1605(b) Regional and state registries Chicago Climate Exchange
Validation Source of Estimate: Mean NPP (g C m -2 yr -1 ) Std Dev CASA 491 86 PnET CN 417 35 Towers 350 75 FIA 250 100
Teaching and Research Example: Components of NEP Estimation NEP = (ANPP R W ) + (ΔC FR + ΔC CR + ΔC S L) Where ANPP = aboveground NPP R W = respiration from woody debris ΔC FR = net change in fine root C ΔC CR = net change in coarse root C ΔC S = net change in mineral soil C L = annual litterfall (From Law et al. 2004) Potential additional flux terms depending on disturbance and scale (= NBP): DOC, DIC, VOCs, CH 4, particulates,herbivory, tree harvest
Coordination and Consistency for MRV Among Countries With Different Circumstances Silas Little! Parker tract! Tropic of Cancer! Hidalgo
Main products: Summary and Conclusions Statistical estimates and maps of carbon stocks and productivity for representative landscapes Improved ecosystem models at ecoregion and stand scales Decision-support tools for carbon management Carbon management research and demonstration sites Outcomes of improved forest carbon management: Landowners may claim carbon credits Cleaner air and lower risk of climate change Additional benefit: Basis for early warning system to detect initial impacts of climate change