Module 2.6 Estimation of GHG emissions from biomass burningREDD+ 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 inModule 2.7 for combination ofuncertainties for biomass burning

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

3

Southern Africa: Burned areas

Detection dateJune 23–Aug 8 2002

Source: Roy et al. 2005.

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

4

Southern Africa: Active fires

Detection dateJune 23–Aug 8 2002

Source: Roy et al. 2005.

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

5

Amazonia: Burned areas

Detection dateAug 1 – Aug 31 2002

Source: Roy et al. 2005.

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

6

Amazonia: Active Fires

Detection dateAug 1 – Aug 31 2002

Source: Roy et al. 2005.

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

9

Reference

Roy, D.P., Jin, Y., Lewis, P.E., Justice, C.O., 2005. Prototyping a global algorithm

for systematic fire affected area mapping using MODIS time series data.

Remote Sens Environ, 97:137–162.