Overview

28. January 2016 - 0:00
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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.