Jennifer Balch, Lise A. St. Denis, Adam Lee Mahood , Nathan P. MietKiewicz, Travis Williams, Joe McGlinchy, Maxwell C. Cook

Harnessing the fire data revolution, i.e., the abundance of information from satellites, government records, social media, and human health sources, now requires complex and challenging data integration approaches. Defining fire events is key to that effort. In order to understand the spatial and temporal characteristics of fire, or the classic fire regime concept, we need to critically define fire events from remote sensing data. Events, fundamentally a geographic concept with delineated boundaries around a specific phenomena that is homogeneous in some property, are key to understanding fire regimes and more importantly how they are changing. Here, we describe FIRED, an event-delineation algorithm, that has been used to derive fire events (N = 51,871) from the MODIS MCD64 burned area product for the coterminous US from January 2001 to May 2019. The optimized spatial and temporal thresholds to cluster burned area pixels into events were an 11-day window and a 5-pixel distance, when optimized against 13,741 wildfire perimeters in the coterminous US from the Monitoring Trends in Burn Severity record. The linear relationship between FIRED and MTBS events size for the US was strong (R2 = 0.92 for all events). Importantly, this algorithm is open source and flexible, allowing the end user to modify the spatio-temporal threshold or even the underlying algorithm as they see fit. We expect the optimized criteria to vary across regions, based on regional distributions of fire event size and rate of spread. We describe the derived metrics provided in a new national database and how they can be used to better understand US fire regimes. The open, flexible FIRED algorithm could be utilized to derive events in any satellite product. We hope that this open science effort will help catalyze a community-driven, data-integration effort (termed OneFire) to build a more complete picture of fire.