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

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.</p

Harnessing the NEON data revolution to advance open environmental science with a diverse and data-capable community

It is a critical time to reflect on the National Ecological Observatory Network (NEON) science to date as well as envision what research can be done right now with NEON (and other) data and what training is needed to enable a diverse user community. NEON became fully operational in May 2019 and has pivoted from planning and construction to operation and maintenance. In this overview, the history of and foundational thinking around NEON are discussed. A framework of open science is described with a discussion of how NEON can be situated as part of a larger data constellation—across existing networks and different suites of ecological measurements and sensors. Next, a synthesis of early NEON science, based on >100 existing publications, funded proposal efforts, and emergent science at the very first NEON Science Summit (hosted by Earth Lab at the University of Colorado Boulder in October 2019) is provided. Key questions that the ecology community will address with NEON data in the next 10 yr are outlined, from understanding drivers of biodiversity across spatial and temporal scales to defining complex feedback mechanisms in human–environmental systems. Last, the essential elements needed to engage and support a diverse and inclusive NEON user community are highlighted: training resources and tools that are openly available, funding for broad community engagement initiatives, and a mechanism to share and advertise those opportunities. NEON users require both the skills to work with NEON data and the ecological or environmental science domain knowledge to understand and interpret them. This paper synthesizes early directions in the community’s use of NEON data, and opportunities for the next 10 yr of NEON operations in emergent science themes, open science best practices, education and training, and community building

FIRED CONUS: Events

This dataset is out of date and are preserved for its connection to Balch et al 2020. The more current versions that will be regularly updated are FIRED CONUS + AK (https://scholar.colorado.edu/concern/datasets/d504rm74m) or FIRED US Canada (https://scholar.colorado.edu/concern/datasets/8336h304x) &nbsp; This is the event-level polygons for the FIRED product for the coterminous United States. It is derived from the MODIS MCD64A1 burned area product (see https://lpdaac.usgs.gov/products/mcd64a1v006/ for more details). The MCD64A1 is a monthly raster grid of estimated burned dates. We converted these rasters into events by stacking the entire time series into a spatial-temporal data cube, then used an algorithm to assign event identification numbers to pixels that fit into the same 3-dimensional spatial temporal window. The primary benefit to this dataset over others is the ability to calculate fire spread rate. For each of these products (FIRED CONUS: events and FIRED CONUS: daily) the event identification numbers are the same, but the event-level product has only single polygons for each entire event, while the daily product has separate polygons for each date per event. See the accompanying metadata files for the statistics provided by each data set. See the associated paper (currently in review, preprint DOI: https://doi.org/10.32942/osf.io/nkzpg) for more details on the methods and more.</p

FIRED CONUS: Daily

This dataset is out of date and are preserved for its connection to Balch et al 2020. The more current versions that will be regularly updated are FIRED CONUS + AK (https://scholar.colorado.edu/concern/datasets/d504rm74m) or FIRED US Canada (https://scholar.colorado.edu/concern/datasets/8336h304x) This is the daily level polygons for the FIRED events product for the coterminous United States. It is derived from the MODIS MCD64A1 burned area product (see https://lpdaac.usgs.gov/products/mcd64a1v006/ for more details). The MCD64A1 is a monthly raster grid of estimated burned dates. We converted these rasters into events by stacking the entire time series into a spatial-temporal data cube, then used an algorithm to assign event identification numbers to pixels that fit into the same 3-dimensional spatial temporal window. The primary benefit to this dataset over others is the ability to calculate fire spread rate. For each of these products (FIRED CONUS: events and FIRED CONUS: daily) the event identification numbers are the same, but the event-level product has only single polygons for the entire event, while the daily product has separate polygons for each date. See the accompanying metadata files for the statistics provided by each data set. See the associated paper (currently in review, preprint DOI: https://doi.org/10.32942/osf.io/nkzpg) for more details on the methods and more.</p

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

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

Switching on the Big Burn of 2017

Fuel, aridity, and ignition switches were all on in 2017, making it one of the largest and costliest wildfire years in the United States (U.S.) since national reporting began. Anthropogenic climate change helped flip on some of these switches rapidly in 2017, and kept them on for longer than usual. Anthropogenic changes to the fire environment will increase the likelihood of such record wildfire years in the coming decades. The 2017 wildfires in the U.S. constitute part of a shifting baseline in risks and costs; meanwhile, effective policies have lagged behind, leaving communities highly vulnerable. Policy efforts to build better and burn better, in the U.S. as well as in other nations with flammable ecosystems, will promote adaptation to increasing wildfire in a warming world

FIRED CONUS-AK

This is event- and daily-level polygons for the Fire event delineation (FIRED) product for the coterminous United States from November 2001 to January 2022. It is derived from the MODIS MCD64A1 burned area product (see https://lpdaac.usgs.gov/products/mcd64a1v006/ for more details). The MCD64A1 is a monthly raster grid of estimated burned dates. Firedpy (www.github.com/earthlab/firedpy) is an algorithm that converts these rasters into events by stacking the entire time series into a spatial-temporal data cube, then uses an algorithm to assign event identification numbers to pixels that fit into the same 3-dimensional spatial temporal window. This particular dataset was created using a spatial parameter of 5 pixels and 11 days. The primary benefit to this dataset over others is the ability to calculate fire spread rate. For each of these products (events and daily) the event identification numbers are the same, but the event-level product has only single polygons for each entire event, while the daily product has separate polygons for each date per event.&nbsp;</p

FIRED: Hawaii

This is event- and daily-level polygons for the Fire event delineation (FIRED) product for Hawaii from November 2001 to May 2021. It is derived from the MODIS MCD64A1 burned area product (see https://lpdaac.usgs.gov/products/mcd64a1v006/ for more details). The MCD64A1 is a monthly raster grid of estimated burned dates. Firedpy (www.github.com/earthlab/firedpy) is an algorithm that converts these rasters into events by stacking the entire time series into a spatial-temporal data cube, then uses an algorithm to assign event identification numbers to pixels that fit into the same 3-dimensional spatial temporal window. This particular dataset was created using a spatial parameter of 1 pixel and 5 days. The primary benefit to this dataset over others is the ability to calculate fire spread rate. For each of these products (events and daily) the event identification numbers are the same, but the event-level product has only single polygons for each entire event, while the daily product has separate polygons for each date per event. See the accompanying metadata files for the statistics provided by each data set. See the associated paper for more details on the methods and more:Balch, J.K.; St. Denis, L.A.; Mahood, A.L.; Mietkiewicz, N.P.; Williams, T.M.; McGlinchy, J.; Cook, M.C. FIRED (Fire Events Delineation): An Open, Flexible Algorithm and Database of US Fire Events Derived from the MODIS Burned Area Product (2001&ndash;2019). Remote Sens. 2020, 12, 3498. https://doi.org/10.3390/rs12213498</p

FIRED Bolivia

This is event- and daily-level polygons for the Fire event delineation (FIRED) product for Bolivia from November 2001 to May 2021. It is derived from the MODIS MCD64A1 burned area product (see https://lpdaac.usgs.gov/products/mcd64a1v006/ for more details). The MCD64A1 is a monthly raster grid of estimated burned dates. Firedpy (www.github.com/earthlab/firedpy) is an algorithm that converts these rasters into events by stacking the entire time series into a spatial-temporal data cube, then uses an algorithm to assign event identification numbers to pixels that fit into the same 3-dimensional spatial temporal window. This particular dataset was created using a spatial parameter of 1 pixel and 5 days. The primary benefit to this dataset over others is the ability to calculate fire spread rate. For each of these products (events and daily) the event identification numbers are the same, but the event-level product has only single polygons for each entire event, while the daily product has separate polygons for each date per event. See the accompanying metadata files for the statistics provided by each data set. See the associated paper for more details on the methods and more:Balch, J.K.; St. Denis, L.A.; Mahood, A.L.; Mietkiewicz, N.P.; Williams, T.M.; McGlinchy, J.; Cook, M.C. FIRED (Fire Events Delineation): An Open, Flexible Algorithm and Database of US Fire Events Derived from the MODIS Burned Area Product (2001&ndash;2019). Remote Sens. 2020, 12, 3498. https://doi.org/10.3390/rs12213498 Contact: Mahood, Adam L. ** [email protected]</p