A global wildfire dataset for the analysis of fire regimes and fire behaviour
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1.
Directorate-General Joint Research Centre
Description
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).
GlobFire can be used to create statistics which are not commonly found in burnt area products. For instance, the average monthly fire size for country. It can be useful to see the behaviour of the fire season, spatial differences of fire frequency or trends of the maximum daily fire spread. It is interesting to see how there are some regions where the number of fires increased but the total burnt area correlation trend is strongly negative. That fact count point out to successful fuel management techniques or prescribed fires. But the opposite can happen too, an increasing linear trend of burnt area and number of fires as in many regions of the Artic or southestern-western Australia. Fire causes and fire spread behaviour are defined by a large set of different factors like socio-economic factors, weather conditions, fire fighting means, policies, etc . Then, it is hard to point out the reasons of the results obtained from this dataset. However, the datasets like GlobFire could help to detect those areas where fires statistics are worsening potentially by climate change and/or direct anthropogenic factors. Finally, it could help, combined with other datasets, to see in which regions fuel management is working or do research to improve fire danger and risk indexes. See more in Applications section of the manual (GlobFire Manual Method Replication).
The knowledge package includes:
- The first and main publication.
- Code and environment to reproduce the process and the dataset described in the publicacion.
- Manual of the code.
- GlobFire data.