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

Ten simple rules for working with high resolution remote sensing data

Researchers in Earth and environmental science can extract incredible value from high- resolution (sub-meter, sub-hourly or hyper-spectral) remote sensing data, but these data can be difficult to use. Correct, appropriate and competent use of such data requires skills from remote sensing and the data sciences that are rarely taught together. In practice, many researchers teach themselves how to use high-resolution remote sensing data with ad hoc trial and error processes, often resulting in wasted effort and resources. In order to implement a consistent strategy, we outline ten rules with examples from Earth and environmental science to help academic researchers and professionals in industry work more effectively and competently with high-resolution data

earthlab/earth-analytics-open-gis-python-workshop: Earth Analytics Workshop on Get Started With GIS in Open Source Python

This workshop provides lessons on open source packages that can be used to plot and manipulate spatial data in Python including GeoPandas, Rasterio and Matplotlib

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

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

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