Module 2.5 Estimation of carbon emissions from deforestation and forest degradation REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 1 Module 2.5 Estimation of carbon emissions from deforestation and forest degradation Module developers: Sandra Brown, Winrock International Lara Murray, Winrock International Country example:  Combining emission factors and activity data for Guyana V1, February 2015 Creative Commons License

Module 2.5 Estimation of carbon emissions from deforestation and forest degradation REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 2 Country Example: Guyana  A high forest cover, low deforestation (HFLD) country  Historically, lack of data on forest cover and deforestation  Main driver of emissions from deforestation is mining  Currently developing national-level REDD+ system

Module 2.5 Estimation of carbon emissions from deforestation and forest degradation REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 3 Changes in C stocks from deforestation Potential C storage in wood products CO 2 & non- CO 2 emissions from combustion and decomposition of dead biomass and soil Future land use C stocks Deforestation Pre-deforestation C stocks

Module 2.5 Estimation of carbon emissions from deforestation and forest degradation REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 4 Gathering data to estimate AD in Guyana GIS and Remote Sensing (RS) data collection and processing for the monitored years including: Mapping areas of forest change (per activity/driver) Mapping areas of forest loss due to wildfire Example of AD from satellite imagery analyzed by Indufor and Guyana Forestry Commission (GFC) Data for are from Landsat Data for 2011 are from Landsat and RapidEye Data for 2012 and going forward are wall-to-wall RapidEye

Module 2.5 Estimation of carbon emissions from deforestation and forest degradation REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 5  C stocks in forest carbon pools were estimated through: ● Collecting data from a well-designed sampling plan ● Data derived from multiple measurement plots ● All species and all 5 IPCC pools were included in inventories ● Minimum diameter at breast height (DBH) was 5 cm  Stratified by threat to ensure cost-effective sampling while producing results with low uncertainties ● target precision set at 95% confidence interval <15% of mean Gathering Data to estimate deforestation EFs in Guyana

Module 2.5 Estimation of carbon emissions from deforestation and forest degradation REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 6 Estimating EFs - Forest Carbon Stratification map Forest type Map Potential for Future Change Map Forest Carbon Stratification Map

Module 2.5 Estimation of carbon emissions from deforestation and forest degradation REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 7 Estimating emission factors for deforestation EFs from field measurements of 35 cluster plots established in sampling design Precision of C stocks was <12% of mean at 95% confidence) Data collected and analyzed by Winrock and GFC

Module 2.5 Estimation of carbon emissions from deforestation and forest degradation REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 8 Estimated emissions (t CO 2 e) from deforestation in Guyana: 2010 and 2011 Emissions estimated as the product of AD and EF for each stratum by driver, and summed across strata to given annual emissions Assumes all emissions occur in year of event = committed emissions

Module 2.5 Estimation of carbon emissions from deforestation and forest degradation REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 9 Recommended modules as follow up  Module 2.6 to continue with estimating GHG emissions from biomass burning  Module 2.7 to continue with estimation of uncertainties  Modules 3. to proceed with REDD+ assessment and reporting