Prior wildfires influence burn severity of subsequent large fires

Stevens‐Rumann, Camille S.; Prichard, Susan J.; Strand, Eva K.; Morgan, Penelope

doi:10.1139/cjfr-2016-0185

Cited by 68 publications

(48 citation statements)

References 42 publications

(77 reference statements)

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“…This effort, undertaken by the US government, is called the Monitoring Trends in Burn Severity (MTBS) program and has mapped the perimeter and severity of over 20,000 fires. The MTBS program has provided data for numerous scientific studies ranging from those involving <10 fires [20][21][22] to those involving >1000 fires [2,23,24] and for topics such as fuel treatment effectiveness, climate change impacts, and time series analyses [25][26][27][28]. The fire severity datasets produced by the MTBS program have clearly advanced wildland fire research in the US.…”

Section: Introductionmentioning

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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Section: Introductionmentioning

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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“…, Stevens‐Rumann et al. ). The lack of effect in this study may be an artifact of our analysis, which only looked at proportion of each landscape sample in general cover type classes.…”

Section: Discussionmentioning

confidence: 99%

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

Lydersen

Collins

Brooks

et al. 2017

Ecological Applications

117

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Following changes in vegetation structure and pattern, along with a changing climate, large wildfire incidence has increased in forests throughout the western United States. Given this increase, there is great interest in whether fuels treatments and previous wildfire can alter fire severity patterns in large wildfires. We assessed the relative influence of previous fuels treatments (including wildfire), fire weather, vegetation, and water balance on fire-severity in the Rim Fire of 2013. We did this at three different spatial scales to investigate whether the influences on fire severity changed across scales. Both fuels treatments and previous low to moderate-severity wildfire reduced the prevalence of high-severity fire. In general, areas without recent fuels treatments and areas that previously burned at high severity tended to have a greater proportion of high-severity fire in the Rim Fire. Areas treated with prescribed fire, especially when combined with thinning, had the lowest proportions of high severity. The proportion of the landscape burned at high severity was most strongly influenced by fire weather and proportional area previously treated for fuels or burned by low to moderate severity wildfire. The proportion treated needed to effectively reduce the amount of high severity fire varied by spatial scale of analysis, with smaller spatial scales requiring a greater proportion treated to see an effect on fire severity. When moderate and high-severity fire encountered a previously treated area, fire severity was significantly reduced in the treated area relative to the adjacent untreated area. Our results show that fuels treatments and low to moderate-severity wildfire can reduce fire severity in a subsequent wildfire, even when burning under fire growth conditions. These results serve as further evidence that both fuels treatments and lower severity wildfire can increase forest resilience.

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“…, Stevens‐Rumann et al. , Prichard et al. ) for a 6–30+ yr period, although abundant snags in areas recently burned at high severity may partly offset this limitation by providing coarse fuels and creating pockets of fuels when fallen (Coppoletta et al.…”

Section: Discussionmentioning

confidence: 99%

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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Prior wildfires influence burn severity of subsequent large fires

Cited by 68 publications

References 42 publications

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

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

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

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

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