SeasFire as a Multivariate Earth System Datacube for Wildfire Dynamics

• URL: http://arxiv.org/abs/2312.07199v2
• Date: Fri, 22 Dec 2023 09:27:38 GMT
• Title: SeasFire as a Multivariate Earth System Datacube for Wildfire Dynamics
• Authors: Ilektra Karasante, Lazaro Alonso, Ioannis Prapas, Akanksha Ahuja, Nuno Carvalhais and Ioannis Papoutsis
• Abstract summary: We release the SeasFire datacube, a meticulously curated dataset tailored for global sub-seasonal wildfire modeling via Earth observation. The SeasFire datacube comprises of 59 variables encompassing climate, vegetation indices, and human factors, has an 8-day temporal resolution and a spatial resolution of 0.25$circ$, and spans from 2001 to 2021. We showcase the versatility of SeasFire for exploring the variability and seasonality of wildfire drivers, modeling causal links between ocean-climateconnections and wildfires, and predicting sub-seasonal wildfire patterns across multiple timescales with a Deep Learning model.
• Score: 1.019446914776079
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The global occurrence, scale, and frequency of wildfires pose significant threats to ecosystem services and human livelihoods. To effectively quantify and attribute the antecedent conditions for wildfires, a thorough understanding of Earth system dynamics is imperative. In response, we introduce the SeasFire datacube, a meticulously curated spatiotemporal dataset tailored for global sub-seasonal to seasonal wildfire modeling via Earth observation. The SeasFire datacube comprises of 59 variables encompassing climate, vegetation, oceanic indices, and human factors, has an 8-day temporal resolution and a spatial resolution of 0.25$^{\circ}$, and spans from 2001 to 2021. We showcase the versatility of SeasFire for exploring the variability and seasonality of wildfire drivers, modeling causal links between ocean-climate teleconnections and wildfires, and predicting sub-seasonal wildfire patterns across multiple timescales with a Deep Learning model. We publicly release the SeasFire datacube and appeal to Earth system scientists and Machine Learning practitioners to use it for an improved understanding and anticipation of wildfires.

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