Abstract
Global fire monitoring systems are crucial to study fire behaviour, fire regimes and their impact at the global scale. Although global fire products based on the use of Earth Observation satellites exist, most remote sensing products only partially cover the requirements for these analyses. These data do not provide information like fire size, fire spread speed, how fires may evolve and joint into single event, or the number of fire events for a given area. This high level of abstraction is very valuable; it makes it possible to characterize fires by types (either size, spread, behaviour, etc.). Here, we present and test a data mining work flow to create a global database of single fires that allows for the characterization of fire types and fire regimes worldwide. This work describes the data produced by a data mining process using MODIS burnt area product Collection 6 (MCD64A1). The entire product has been computed until the present and is available under the umbrella of the Global Wildfire Information System (GWIS).
Highlights
Background & SummaryWildfires are an important phenomenon at the global scale, as they are responsible for large amounts of economic and environmental damage
Global fire products based on the use of Earth Observation satellites exist, most remote sensing products only partially cover the requirements for these analyses
We present and test a data mining work flow to create a global database of single fires that allows for the characterization of fire types and fire regimes worldwide
Summary
Wildfires are an important phenomenon at the global scale, as they are responsible for large amounts of economic and environmental damage. We present a global single wildfire database and the resulting data, the GlobFire Database, based on an algorithm that relies on encoding in a graph structure a space-time relationship among patches of burned areas This approach, used in other studies[15,16], allows us to have wildfires with several initial ignition points and merge them even if burnt areas are non-spatially contiguous such as spotting fires. It is be possible to search for general patterns of wildfires at diverse spatial scales (including global), providing critical information to fire spread and fire behaviour analysis, only available at local, regional or country level[17] This information can be valuable for fire regime characterization at global scale. The proposed method works with raster or vector patch based burnt area products such as those derived from MODIS19, Copernicus Proba-V20 or Fire CCI21
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