Module 2.6 Estimation of GHG emissions from biomass burning REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 1 Module 2.6.

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Module 2.6 Estimation of GHG emissions from biomass burning REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 1 Module 2.6 Estimation of GHG emissions from biomass burning V1, May 2015 Creative Commons License Module developer: Luigi Boschetti, University of Idaho Country examples (regions): 1.Africa 2.Amazonia See also the country example in Module 2.7 for combination of uncertainties for biomass burning

Module 2.6 Estimation of GHG emissions from biomass burning REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 2 How do active fires and burned areas relate?  Remote sensing provides information on biomass burning in two forms: active fires and burned areas.  Both forms have benefits and drawbacks, and both can be profitably used in a fire monitoring system in support of REDD+.  Depending on the vegetation and fire characteristics, one can have different outcomes, exemplified in the following examples: ● In fragmented savannahs, there are small fires that can be detected as active fire, but too small to result in a burned area detection (Africa example). ● Land-clearing fires do not result in a detectable burned area, because the fuel is burned in piles, hence they are detected as active fires (Amazon example).

Module 2.6 Estimation of GHG emissions from biomass burning REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 3 Southern Africa: Burned areas Detection date June 23–Aug Source: Roy et al

Module 2.6 Estimation of GHG emissions from biomass burning REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 4 Southern Africa: Active fires Detection date June 23–Aug Source: Roy et al

Module 2.6 Estimation of GHG emissions from biomass burning REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 5 Amazonia: Burned areas Detection date Aug 1 – Aug Source: Roy et al

Module 2.6 Estimation of GHG emissions from biomass burning REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 6 Amazonia: Active Fires Detection date Aug 1 – Aug Source: Roy et al

Module 2.6 Estimation of GHG emissions from biomass burning REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 7 Active fire detection  Active fires might not be an unbiased estimator of area burned, but they play a fundamental role in identifying in a timely manner fires that are a threat to forested areas.  They play a important role in forest activities such as carbon stock enhancement, sustainable forest management, and forest conservation.

Module 2.6 Estimation of GHG emissions from biomass burning REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 8 Recommended modules as follow-up  Module 2.7 to continue with estimation of uncertainties  Module 2.8 to learn more about evolving technologies for monitoring of forest area changes, carbon stocks and emissions  Modules 3.1 to 3.3 to proceed with REDD+ assessment and reporting

Module 2.6 Estimation of GHG emissions from biomass burning REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 9 Reference  Roy, D.P., Jin, Y., Lewis, P.E., Justice, C.O., Prototyping a global algorithm for systematic fire affected area mapping using MODIS time series data. Remote Sens Environ, 97:137–162.