A weekly, continually updated dataset of the probability of large wildfires across western US forests and woodlands

Miranda E. Gray, Luke J. Zachmann, Brett G Dickson

Research output: Contribution to journalArticle

Abstract

There is broad consensus that wildfire activity is likely to increase in western US forests and woodlands over the next century. Therefore, spatial predictions of the potential for large wildfires have immediate and growing relevance to near-and long-term research, planning, and management objectives. Fuels, climate, weather, and the landscape all exert controls on wildfire occurrence and spread, but the dynamics of these controls vary from daily to decadal timescales. Accurate spatial predictions of large wildfires should therefore strive to integrate across these variables and timescales. Here, we describe a high spatial resolution dataset (250 m pixel) of the probability of large wildfires (> 405 ha) across forests and woodlands in the contiguous western US, from 2005 to the present. The dataset is automatically updated on a weekly basis using Google Earth Engine and a continuous integration/q pipeline. Each image in the dataset is the output of a random forest machine-learning algorithm, trained on random samples of historic small and large wildfires and represents the predicted conditional probability of an individual pixel burning in a large fire, given an ignition or fire spread to that pixel. This novel workflow is able to integrate the near-term dynamics of fuels and weather into weekly predictions while also integrating longer-term dynamics of fuels, the climate, and the landscape. As a continually updated product, the dataset can provide operational fire managers with contemporary, on-the-ground information to closely monitor the changing potential for large wildfire occurrence and spread. It can also serve as a foundational dataset for longer-term planning and research, such as the strategic targeting of fuels management, fire-smart development at the wildland-urban interface, and the analysis of trends in wildfire potential over time. large fire probability GeoTiff products from 2005 to 2017 are archived on the Figshare online digital repository with the DOI https://doi.org/10.6084/m9.figshare.5765967 (available at https://doi.org/10.6084/m9.figshare.5765967.v1). Weekly GeoTiff products and the entire dataset from 2005 onwards are also continually uploaded to a Google Cloud Storage bucket at https://console.cloud.google.com/storage/wffr-preds/V1 (last access: 14 September 2018) and are available free of charge with a Google account. Continually updated products and the long-term archive are also available to registered Google Earth Engine (GEE) users as public GEE assets and can be accessed with the image collection ID "users/mgray/wffr-preds" within GEE.

Original languageEnglish (US)
Pages (from-to)1715-1727
Number of pages13
JournalEarth System Science Data
Volume10
Issue number3
DOIs
StatePublished - Sep 20 2018

Fingerprint

wildfire
woodland
engine
pixel
prediction
timescale
weather
fire management
climate
repository
targeting
spatial resolution
product

ASJC Scopus subject areas

  • Earth and Planetary Sciences(all)

Cite this

A weekly, continually updated dataset of the probability of large wildfires across western US forests and woodlands. / Gray, Miranda E.; Zachmann, Luke J.; Dickson, Brett G.

In: Earth System Science Data, Vol. 10, No. 3, 20.09.2018, p. 1715-1727.

Research output: Contribution to journalArticle

@article{f6e0fc10b3b84bfc96af53bd22c3cb6f,
title = "A weekly, continually updated dataset of the probability of large wildfires across western US forests and woodlands",
abstract = "There is broad consensus that wildfire activity is likely to increase in western US forests and woodlands over the next century. Therefore, spatial predictions of the potential for large wildfires have immediate and growing relevance to near-and long-term research, planning, and management objectives. Fuels, climate, weather, and the landscape all exert controls on wildfire occurrence and spread, but the dynamics of these controls vary from daily to decadal timescales. Accurate spatial predictions of large wildfires should therefore strive to integrate across these variables and timescales. Here, we describe a high spatial resolution dataset (250 m pixel) of the probability of large wildfires (> 405 ha) across forests and woodlands in the contiguous western US, from 2005 to the present. The dataset is automatically updated on a weekly basis using Google Earth Engine and a continuous integration/q pipeline. Each image in the dataset is the output of a random forest machine-learning algorithm, trained on random samples of historic small and large wildfires and represents the predicted conditional probability of an individual pixel burning in a large fire, given an ignition or fire spread to that pixel. This novel workflow is able to integrate the near-term dynamics of fuels and weather into weekly predictions while also integrating longer-term dynamics of fuels, the climate, and the landscape. As a continually updated product, the dataset can provide operational fire managers with contemporary, on-the-ground information to closely monitor the changing potential for large wildfire occurrence and spread. It can also serve as a foundational dataset for longer-term planning and research, such as the strategic targeting of fuels management, fire-smart development at the wildland-urban interface, and the analysis of trends in wildfire potential over time. large fire probability GeoTiff products from 2005 to 2017 are archived on the Figshare online digital repository with the DOI https://doi.org/10.6084/m9.figshare.5765967 (available at https://doi.org/10.6084/m9.figshare.5765967.v1). Weekly GeoTiff products and the entire dataset from 2005 onwards are also continually uploaded to a Google Cloud Storage bucket at https://console.cloud.google.com/storage/wffr-preds/V1 (last access: 14 September 2018) and are available free of charge with a Google account. Continually updated products and the long-term archive are also available to registered Google Earth Engine (GEE) users as public GEE assets and can be accessed with the image collection ID {"}users/mgray/wffr-preds{"} within GEE.",
author = "Gray, {Miranda E.} and Zachmann, {Luke J.} and Dickson, {Brett G}",
year = "2018",
month = "9",
day = "20",
doi = "10.5194/essd-10-1715-2018",
language = "English (US)",
volume = "10",
pages = "1715--1727",
journal = "Earth System Science Data",
issn = "1866-3508",
publisher = "Copernicus Publications",
number = "3",

}

TY - JOUR

T1 - A weekly, continually updated dataset of the probability of large wildfires across western US forests and woodlands

AU - Gray, Miranda E.

AU - Zachmann, Luke J.

AU - Dickson, Brett G

PY - 2018/9/20

Y1 - 2018/9/20

N2 - There is broad consensus that wildfire activity is likely to increase in western US forests and woodlands over the next century. Therefore, spatial predictions of the potential for large wildfires have immediate and growing relevance to near-and long-term research, planning, and management objectives. Fuels, climate, weather, and the landscape all exert controls on wildfire occurrence and spread, but the dynamics of these controls vary from daily to decadal timescales. Accurate spatial predictions of large wildfires should therefore strive to integrate across these variables and timescales. Here, we describe a high spatial resolution dataset (250 m pixel) of the probability of large wildfires (> 405 ha) across forests and woodlands in the contiguous western US, from 2005 to the present. The dataset is automatically updated on a weekly basis using Google Earth Engine and a continuous integration/q pipeline. Each image in the dataset is the output of a random forest machine-learning algorithm, trained on random samples of historic small and large wildfires and represents the predicted conditional probability of an individual pixel burning in a large fire, given an ignition or fire spread to that pixel. This novel workflow is able to integrate the near-term dynamics of fuels and weather into weekly predictions while also integrating longer-term dynamics of fuels, the climate, and the landscape. As a continually updated product, the dataset can provide operational fire managers with contemporary, on-the-ground information to closely monitor the changing potential for large wildfire occurrence and spread. It can also serve as a foundational dataset for longer-term planning and research, such as the strategic targeting of fuels management, fire-smart development at the wildland-urban interface, and the analysis of trends in wildfire potential over time. large fire probability GeoTiff products from 2005 to 2017 are archived on the Figshare online digital repository with the DOI https://doi.org/10.6084/m9.figshare.5765967 (available at https://doi.org/10.6084/m9.figshare.5765967.v1). Weekly GeoTiff products and the entire dataset from 2005 onwards are also continually uploaded to a Google Cloud Storage bucket at https://console.cloud.google.com/storage/wffr-preds/V1 (last access: 14 September 2018) and are available free of charge with a Google account. Continually updated products and the long-term archive are also available to registered Google Earth Engine (GEE) users as public GEE assets and can be accessed with the image collection ID "users/mgray/wffr-preds" within GEE.

AB - There is broad consensus that wildfire activity is likely to increase in western US forests and woodlands over the next century. Therefore, spatial predictions of the potential for large wildfires have immediate and growing relevance to near-and long-term research, planning, and management objectives. Fuels, climate, weather, and the landscape all exert controls on wildfire occurrence and spread, but the dynamics of these controls vary from daily to decadal timescales. Accurate spatial predictions of large wildfires should therefore strive to integrate across these variables and timescales. Here, we describe a high spatial resolution dataset (250 m pixel) of the probability of large wildfires (> 405 ha) across forests and woodlands in the contiguous western US, from 2005 to the present. The dataset is automatically updated on a weekly basis using Google Earth Engine and a continuous integration/q pipeline. Each image in the dataset is the output of a random forest machine-learning algorithm, trained on random samples of historic small and large wildfires and represents the predicted conditional probability of an individual pixel burning in a large fire, given an ignition or fire spread to that pixel. This novel workflow is able to integrate the near-term dynamics of fuels and weather into weekly predictions while also integrating longer-term dynamics of fuels, the climate, and the landscape. As a continually updated product, the dataset can provide operational fire managers with contemporary, on-the-ground information to closely monitor the changing potential for large wildfire occurrence and spread. It can also serve as a foundational dataset for longer-term planning and research, such as the strategic targeting of fuels management, fire-smart development at the wildland-urban interface, and the analysis of trends in wildfire potential over time. large fire probability GeoTiff products from 2005 to 2017 are archived on the Figshare online digital repository with the DOI https://doi.org/10.6084/m9.figshare.5765967 (available at https://doi.org/10.6084/m9.figshare.5765967.v1). Weekly GeoTiff products and the entire dataset from 2005 onwards are also continually uploaded to a Google Cloud Storage bucket at https://console.cloud.google.com/storage/wffr-preds/V1 (last access: 14 September 2018) and are available free of charge with a Google account. Continually updated products and the long-term archive are also available to registered Google Earth Engine (GEE) users as public GEE assets and can be accessed with the image collection ID "users/mgray/wffr-preds" within GEE.

UR - http://www.scopus.com/inward/record.url?scp=85053902211&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85053902211&partnerID=8YFLogxK

U2 - 10.5194/essd-10-1715-2018

DO - 10.5194/essd-10-1715-2018

M3 - Article

VL - 10

SP - 1715

EP - 1727

JO - Earth System Science Data

T2 - Earth System Science Data

JF - Earth System Science Data

SN - 1866-3508

IS - 3

ER -