Evidence of fuels management and fire weather influencing fire severity in an extreme fire event

Lydersen, Jamie M.; Collins, Brandon M.; Brooks, Matthew L.; Matchett, John R.; Shive, Kristen L.; Povak, Nicholas A.; Kane, Van R.; Smith, Douglas

doi:10.1002/eap.1586

Cited by 128 publications

(85 citation statements)

References 93 publications

(196 reference statements)

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“…This conclusion echoes that of Lydersen et al. (), who performed a landscape‐level analysis of the drivers of proportion of high‐severity fire within the Rim Fire using weather, vegetation, water balance, and fuel treatment data. Although water balance influenced fire severity in subsets of the Rim Fire (Kane et al.…”

Section: Discussionsupporting

confidence: 81%

“…, Lydersen et al. ). To evaluate the potential influence of water balance on fire severity, we used climatic water deficit (CWD) and actual evapotranspiration (AET) from the 2014 California Basin Characterization Model (Flint et al.…”

Section: Methodsmentioning

confidence: 99%

“…Lydersen et al. () also conducted a study in a broad portion of the Rim Fire, evaluating the effect of recent (since 1995) fuel treatments and fires on the proportion of high‐severity fire at landscape scales within the Rim Fire. Their study focused primarily on the effects of previous fuel treatments on Rim Fire severity at landscape scales and does not consider topographic setting.…”

Section: Introductionmentioning

confidence: 99%

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Previous burns and topography limit and reinforce fire severity in a large wildfire

Harris

Taylor

2017

Ecosphere

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Abstract. In fire-prone forests, self-reinforcing fire behavior may generate a mosaic of vegetation types and structures. In forests long subject to fire exclusion, such feedbacks may result in forest loss when surface and canopy fuel accumulations lead to unusually severe fires. We examined drivers of fire severity in one large (>1000 km 2 ) wildfire in the western United States, the Rim Fire in the Sierra Nevada, California, and how it was influenced by severity of 21 previous fires to examine the influences on (1) the severity of the first fire since 1984 and (2) reburn severity. The random forest machine-learning statistical model was used to predict satellite-derived fire severity classes from geospatial datasets of fire history, topographic setting, weather, and vegetation type. Topography and inferred weather were the most important variables influencing the previous burn. Previous fire severity was the most important factor influencing reburn severity, and areas tended to reburn at the same severity class as the previous burn. However, areas reburned in <15 yr burned at lower severity than expected. Previous fire severity and Rim Fire severity were higher on ridges, at intermediate elevations (~750-1250 m), and on slopes <30°, indicating a consistent effect of topography on fire severity patterns in these forests. Areas burned with low severity prescribed fires burned at low severity again in the Rim Fire, and areas with long fire-free periods burned at higher severity. This fire history effect suggests that prescribed burning was an effective management tool, leading to lower fire severity in the previous burns and the subsequent reburn. Our results show that self-reinforcing fire behavior results mainly from effects of vegetation structure and fuels on fire severity and that this behavior is mediated by topographic setting and the time since last fire.

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Section: Discussionsupporting

confidence: 81%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Previous burns and topography limit and reinforce fire severity in a large wildfire

Harris

Taylor

2017

Ecosphere

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“…This suggests that if practitioners and researchers are interested in only one fire [20,39], it does not matter if dNBR or RBR are produced using the mean compositing approach or using one pre-fire and one post-fire image (e.g., MTBS). However, if RdNBR is the preferred severity metric, our results show that the mean compositing approach substantially outperforms (on average) RdNBR when produced using one pre-fire and one post-fire scene.…”

Section: Discussionmentioning

confidence: 99%

Mean Composite Fire Severity Metrics Computed with Google Earth Engine Offer Improved Accuracy and Expanded Mapping Potential

et al. 2018

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Abstract:Landsat-based fire severity datasets are an invaluable resource for monitoring and research purposes. These gridded fire severity datasets are generally produced with pre-and post-fire imagery to estimate the degree of fire-induced ecological change. Here, we introduce methods to produce three Landsat-based fire severity metrics using the Google Earth Engine (GEE) platform: The delta normalized burn ratio (dNBR), the relativized delta normalized burn ratio (RdNBR), and the relativized burn ratio (RBR). Our methods do not rely on time-consuming a priori scene selection but instead use a mean compositing approach in which all valid pixels (e.g., cloud-free) over a pre-specified date range (pre-and post-fire) are stacked and the mean value for each pixel over each stack is used to produce the resulting fire severity datasets. This approach demonstrates that fire severity datasets can be produced with relative ease and speed compared to the standard approach in which one pre-fire and one post-fire scene are judiciously identified and used to produce fire severity datasets. We also validate the GEE-derived fire severity metrics using field-based fire severity plots for 18 fires in the western United States. These validations are compared to Landsat-based fire severity datasets produced using only one pre-and post-fire scene, which has been the standard approach in producing such datasets since their inception. Results indicate that the GEE-derived fire severity datasets generally show improved validation statistics compared to parallel versions in which only one pre-fire and one post-fire scene are used, though some of the improvements in some validations are more or less negligible. We provide code and a sample geospatial fire history layer to produce dNBR, RdNBR, and RBR for the 18 fires we evaluated. Although our approach requires that a geospatial fire history layer (i.e., fire perimeters) be produced independently and prior to applying our methods, we suggest that our GEE methodology can reasonably be implemented on hundreds to thousands of fires, thereby increasing opportunities for fire severity monitoring and research across the globe.

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“…However, the higher than expected proportion of high‐severity fire in these forests, combined with evidence that high‐severity fire favors future high‐severity fire in some ecosystems (Lydersen et al. , Prichard et al. ), suggests that rather than restoring ecological resilience, these more severe fires may be facilitating transitions to alternative states (i.e., forest to non‐forested ecotypes, obligate seeders to resprouters, native to invasive species; Hessburg et al.…”

Section: Discussionmentioning

confidence: 96%

The missing fire: quantifying human exclusion of wildfire in Pacific Northwest forests, USA

et al. 2019

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Western U.S. wildfire area burned has increased dramatically over the last half‐century. How contemporary extent and severity of wildfires compare to the pre‐settlement patterns to which ecosystems are adapted is debated. We compared large wildfires in Pacific Northwest forests from 1984 to 2015 to modeled historic fire regimes. Despite late twentieth‐century increases in area burned, we show that Pacific Northwest forests have experienced an order of magnitude less fire over 32 yr than expected under historic fire regimes. Within fires that have burned, severity distributions are disconnected from historical references. From 1984 to 2015, 1.6 M ha burned; this is 13.3–18.9 M ha less than expected. Deficits were greatest in dry forest ecosystems adapted to frequent, low‐severity fire, where 7.2–10.3 M ha of low‐severity fire was missing, compared to a 0.2–1.1 M ha deficit of high‐severity fire. When these dry forests do burn, we observed that 36% burned with high‐severity compared to 6–9% historically. We found smaller fire deficits, 0.3–0.6 M ha, within forest ecosystems adapted to infrequent, high‐severity fire. However, we also acknowledge inherent limitations in evaluating contemporary fire regimes in ecosystems which historically burned infrequently and for which fires were highly episodic. The magnitude of contemporary fire deficits and disconnect in burn severity compared to historic fire regimes have important implications for climate change adaptation. Within forests characterized by low‐ and mixed‐severity historic fire regimes, simply increasing wildfire extent while maintaining current trends in burn severity threatens ecosystem resilience and will potentially drive undesirable ecosystem transformations. Restoring natural fire regimes requires management that facilitates much more low‐ and moderate‐severity fire.

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Evidence of fuels management and fire weather influencing fire severity in an extreme fire event

Cited by 128 publications

References 93 publications

Previous burns and topography limit and reinforce fire severity in a large wildfire

Previous burns and topography limit and reinforce fire severity in a large wildfire

Mean Composite Fire Severity Metrics Computed with Google Earth Engine Offer Improved Accuracy and Expanded Mapping Potential

The missing fire: quantifying human exclusion of wildfire in Pacific Northwest forests, USA

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