Observed versus predicted fire behavior in an Alaskan black spruce forest ecosystem: an experimental fire case study

Drury, Stacy A.

doi:10.1186/s42408-019-0053-9

Cited by 7 publications

(6 citation statements)

References 16 publications

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“…Studies of the self-limiting effects of wildfires also find 10 to 20 years to be an approximate cutoff beyond which previous fire perimeters fail to limit future wildfire spread (Collins et al 2009;Parks et al 2013Parks et al , 2015. Even so, we showed that 20-yearold thin-only treatments substantially reduced bole char height and crown consumption compared to controls, providing evidence that even older thinning treatments can help to reduce crown fire behavior or prevent excessive fire severity (Drury 2019). Treatment longevity is related to site productivity, and in many forests-including those found at this study site-it likely takes longer than 20 years for the seedlings and saplings remaining or establishing after treatment to become canopy fuels (Ritchie 2020).…”

Section: Discussionmentioning

confidence: 50%

Forest thinning and prescribed burning treatments reduce wildfire severity and buffer the impacts of severe fire weather

Brodie,

Knapp,

Brooks

et al. 2024

fire ecol

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Background The capacity of forest fuel treatments to moderate the behavior and severity of subsequent wildfires depends on weather and fuel conditions at the time of burning. However, in-depth evaluations of how treatments perform are limited because encounters between wildfires and areas with extensive pre-fire data are rare. Here, we took advantage of a 1200-ha randomized and replicated experiment that burned almost entirely in a subsequent wildfire under a wide range of weather conditions. We compared the impacts of four fuel treatments on fire severity, including two thin-only, a thin-burn, a burn-only, and an untreated control. We evaluated four fire severity metrics—tree mortality, average bole char height, percent crown volume consumed (PCVC), and percent crown volume affected (PCVA)—and leveraged data from pre-fire surface and canopy fuels to better understand the mechanisms driving differences in wildfire severity among treatments and how they changed with fire weather. Results We found strong mitigating effects of treatments on fire behavior and tree mortality, despite 20 years having elapsed since mechanical thinning and 10 years since the second entry of prescribed fire. The thin-burn treatment resulted in the lowest fire severity across all four metrics and the untreated control the highest. All four fire severity metrics were positively associated with pre-fire canopy and surface fuel loads, with the exception that PCVC (a fire severity metric related to crown fire behavior) was not associated with surface fuel load. The fire weather conditions under which fuel treatment was most effective varied among fire severity metrics. Fuel treatment benefit was maximized at intermediate burning index values for tree mortality, intermediate to high burning index values for PCVA, and high burning index for bole char height and PCVC. Conclusions We conclude that reducing canopy bulk density via mechanical thinning treatments can help to limit crown fire behavior for 20 years or more. However, reducing surface fuels is necessary to limit scorching and the total crown impacts associated with tree mortality. Further, while fuel treatment effectiveness may decline under the most severe fire weather conditions for fire severity metrics associated with tree mortality, it is maximized under severe fire weather conditions for fire severity metrics associated with crown fire behavior (bole charring and torching). Our results provide strong evidence for the use of fuel treatments to mitigate fire behavior and resulting fire severity even under extreme fire weather conditions.

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Section: Discussionmentioning

confidence: 50%

Forest thinning and prescribed burning treatments reduce wildfire severity and buffer the impacts of severe fire weather

Brodie,

Knapp,

Brooks

et al. 2024

fire ecol

Self Cite

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show abstract

“…Studies of the self-limiting effects of wild res also nd ten to twenty years to be an approximate cutoff beyond which previous re perimeters fail to limit future wild re spread (Collins et al, 2009;Parks et al, 2013Parks et al, , 2015. Even so, we showed that 20-year-old thinonly treatments substantially reduced bole char height and crown torching compared to controls, providing evidence that even older thinning treatments can help to prevent or reduce crown re behavior (Drury, 2019). Treatment longevity is related to site productivity, and in many forests-including those found at this study site-it likely takes longer than 20 years for the seedlings and saplings remaining or establishing after treatment to become canopy fuels (Ritchie, 2020).…”

Section: Discussionmentioning

confidence: 56%

Forest thinning and prescribed burning treatments reduce wildfire severity and buffer the impacts of severe fire weather

Brodie,

Knapp,

Brooks

et al. 2023

Preprint

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Background: The capacity of forest fuel treatments to limit the behavior and severity of subsequent wildfires depends on weather and fuel conditions at the time of burning. We compared the impacts of five fuel treatments—including two thin-only, a thin-burn, a burn-only, and a control—on fire severity using a 1200 hectare randomized and replicated experiment that burned almost entirely in a subsequent wildfire. We evaluated four fire severity metrics (mortality, average bole char height, percent crown volume torched [PCVT], and percent crown volume affected [PCVA]) and leveraged pre-fire surface and canopy fuels data to better understand the mechanisms driving differences in wildfire severity among treatments and how they changed with fire weather. Results: We found strong mitigating effects of treatments on fire behavior and tree mortality, despite 20 years having elapsed since mechanical thinning and 10 years since the second entry of prescribed fire. The thin-burn treatment resulted in the lowest fire severity across all four metrics and the untreated control the highest. Prefire canopy and surface fuel loads were associated with all four fire severity metrics, with the exception that surface fuel loads were not associated with PCVT, a fire severity metric associated with crown fire behavior. The relationship between fuel treatment effectiveness and fire weather varied among fire severity metrics, with maximum fuel treatment benefit at intermediate burning index values for tree mortality, intermediate to high burning index values for PCVA, and high burning index for bole char height and PCVT. Conclusions: We conclude that reducing canopy bulk density via mechanical thinning treatments can help to limit crown fire behavior for 20 years or more. However, reducing surface fuels is necessary to limit scorching and the total crown impacts associated with tree mortality. Further, while fuel treatment effectiveness may decline at the most severe fire weather for some fire severity metrics (total crown impacts and mortality), it is maximized under severe fire weather conditions for others (bole charring and torching). Our results provide strong evidence for the use of fuel treatments to mitigate fire behavior and resulting fire severity even under extreme fire weather conditions.

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“…Fire is a fire behavior prediction simulator that calculates fire effect on stand characteristics [72]. Stacy A Drury [73] compared predicted fire behaviors from four models (BehavePlus, RedAPP, CanFIRE, and Crown Fire irritation and Spread System) with the observed fire behavior on the Alaskan black spruce forest. He studied the rate of fire movement, predicted flame length, energy, and ecological impact, and concluded that Canadian models, including RedAPP and CanFIRE provide more accurate predictions than BehavePlus.…”

Section: Scope Of ML Algorithms In Forest Fire Management Frame-workmentioning

confidence: 99%

Role of Machine Learning Algorithms in Forest Fire Management: A Literature Review

Arif¹,

K²,

A³

et al. 2021

J Robotics Autom

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Forest fire disasters are recently getting lots of attention due to climate change globally. Globally, climate changes are rapidly changing the fire patterns on Earth. Effective fire management requires accurate information about the fire occurrence, its spread, and impact on the environment. Prediction of fire activities in the forest guides the authorities to make optimal, efficient, and sound decisions in fire management. This paper aims to summarize recent trends in the forest fire events prediction, detection, spread rate, and mapping of the burned areas. Furthermore, fire emissions in terms of smoke also put the Earth's public health and ecological system at greater risk. Hence, future policymaking can be more accurate in saving billions of dollars, improving the healthy environment and ecological cycle for the inhabitants of this Earth. This paper provides a comprehensive review of the usage of different machine learning algorithms in forest fire or wildfire management. Furthermore, we have identified some potential areas where new technologies and data can help better fire management decision making.

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Observed versus predicted fire behavior in an Alaskan black spruce forest ecosystem: an experimental fire case study

Cited by 7 publications

References 16 publications

Forest thinning and prescribed burning treatments reduce wildfire severity and buffer the impacts of severe fire weather

Forest thinning and prescribed burning treatments reduce wildfire severity and buffer the impacts of severe fire weather

Forest thinning and prescribed burning treatments reduce wildfire severity and buffer the impacts of severe fire weather

Role of Machine Learning Algorithms in Forest Fire Management: A Literature Review

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