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Ilektra Karasante1, Lazaro Alonso2, Ioannis Prapas3,4

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A new global dataset, SeasFire, aids wildfire prediction by integrating climate, vegetation, and human factors. This Earth observation resource helps scientists understand and anticipate wildfire risks more effectively.

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Area of Science:

  • Earth System Science
  • Environmental Monitoring
  • Remote Sensing

Background:

  • Wildfires pose significant global threats to ecosystems and human populations.
  • Understanding antecedent conditions is crucial for effective wildfire quantification and attribution.
  • Earth system dynamics play a vital role in wildfire occurrence.

Purpose of the Study:

  • Introduce the SeasFire datacube, a spatiotemporal dataset for global wildfire modeling.
  • Facilitate the study of wildfire drivers and their seasonality.
  • Enable prediction of sub-seasonal wildfire patterns using Earth observation data.

Main Methods:

  • Developed a curated spatiotemporal dataset (SeasFire datacube) with 59 variables.
  • Dataset includes climate, vegetation, oceanic indices, and human factors.
  • Utilized 8-day temporal and 0.25° spatial resolutions from 2001-2021.

Main Results:

  • Demonstrated SeasFire's utility in exploring wildfire driver variability.
  • Showcased modeling of causal links between ocean-climate teleconnections and wildfires.
  • Successfully predicted sub-seasonal wildfire patterns using a Deep Learning model.

Conclusions:

  • The SeasFire datacube is a versatile resource for Earth system science research.
  • Public release encourages use by scientists and Machine Learning practitioners.
  • The dataset enhances understanding and anticipation of global wildfire events.