28. January 2016 - 0:00
Why is information on burned areas needed?
It is estimated, that about 25%-35% of Greenhouse gases (GHG) are resulting from biomass burning and therefore they are considered an important factor in climate change (GTOS 68, T13 Fire Disturbance).
Aims of the Fire_cci project
Current global burned area products already exist but the Fire_cci project aims to improve consistency using better algorithms for both pre-processing and burned area detection while incorporating error characterisation in their product.
The project team lead by University of Alcala (Spain) focuses on the following issues relating to Fire Disturbance:
- Analysis and specification of scientific requirements relating to climate
- Development and improvement of pre-processing and burned area algorithms.
- Inter-comparison and selection of burned area algorithms
- System prototyping and production of burned area datasets
- Product validation and product assessment
More specifically, the project team aims to:
- Develop and validate algorithms to meet as far as possible GCOS Essential Climate Variable (ECV) requirements for (consistent, stable, error-characterized) global satellite data products from multi-sensor data archives
- Produce and validate, within a research and development context, the most complete and consistent possible time series of multi-sensor global satellite data products for climate research and modelling
- Optimize the impact of ESA Earth Observation (EO) missions data on climate data records
- Strengthen inter-disciplinary cooperation between international earth observation, climate research and modelling communities, in pursuit of scientific excellence.
The project focuses on the key variable: burned area. It will incorporate active fire observations as a supplemental variable to improve detection of burned area across varying biomes.
Global Burned Area products - Output of the Fire_cci Project
Two burned area products are delivered as a result of the project:
- Pixel product, with a resolution of ~300 m, including the date of detection, the confidence level and the land cover corresponding to the burned pixel. Each dataset contains one month of information.
- Grid product, with a resolution of 0.5 degrees, and the following information in each grid cell: sum of burned area, standard error, fraction of observed area, number of patches, and sum of burned area for each land cover class.