FIRED (Fire Events Delineation): An Open, Flexible Algorithm and Database of US Fire Events Derived from the MODIS Burned Area Product (2001–2019)

Abstract

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 spatial and temporal boundaries around a specific phenomenon that is homogenous in some property, are key to understanding fire regimes and more importantly how they are changing. Here, we describe Fire Events Delineation (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 (CONUS) from January 2001 to May 2019. The optimized spatial and temporal parameters to cluster burned area pixels into events were an 11-day window and a 5-pixel (2315 m) distance, when optimized against 13,741 wildfire perimeters in the CONUS from the Monitoring Trends in Burn Severity record. The linear relationship between the size of individual FIRED and Monitoring Trends in Burn Severity (MTBS) events for the CONUS 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 approach 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.

U.S. fires became larger, more frequent, and more widespread in the 2000s

“…Should they be temporally biased, errors in MTBS could influence our results. For this reason, we compared annual time series generated from MTBS with Integrated Reporting of Wildland-Fire Information [IRWIN ( 11 )], Landsat Burned Area [LBA ( 12 )], and Fire Events Delineation [FIRED ( 13 ); see the Supplementary Materials for details].…”

“…Should they be temporally biased, errors in MTBS could influence our results. For this reason, we compared annual time series generated from MTBS with Integrated Reporting of Wildland-Fire Information [IRWIN (11)], Landsat Burned Area [LBA (12)], and Fire Events Delineation [FIRED (13); see the Supplementary Materials for details]. Improvements in fire detection capabilities arising from better quality and/or more satellite imagery could be expected to be associated with Landsat 7 and its Enhanced Thematic Mapper Plus sensor (ETM+).…”

Assessing the Accuracy of MODIS MCD64A1 C6 and FireCCI51 Burned Area Products in Mediterranean Ecosystems

“…At the cost of medium to low nominal resolution (250-1000 m), these sensors provide the advantage of daily or near-daily imagery acquisitions that can be used for operational activities including early detection, fire suppression and direct impact assessment [12]. Global BA products provide the essential pixel-level information that can be further processed to provide the size and distribution of single fire events over space and time [10,13]. Though finer resolution BA assessments from Landsat or Sentinel-2 satellites are now becoming available [14,15], yet these cannot compete with the systematic and long-term coverage provided by the former systems.…”

Interannual climate variability mediates changes in carbon and nitrogen pools caused by annual grass invasion in a semiarid shrubland

“…Time since initial introduction may not always be feasible to ascertain, but annual grass invasions initiated during the satellite record may be detected from seasonal differences in NDVI (Boyte et al, 2019;Bradley & Mustard, 2006). Time since the loss of woody plant cover may be determined from satellite-derived datasets of fire occurrence (Balch et al, 2020;Hawbaker et al, 2017) and functional group fractional cover (Jones et al, 2018).…”