Post-fire surface fuel dynamics in California forests across three burn severity classes

Eskelson, Bianca N.I.; Monleón, Vicente J.

doi:10.1071/wf17148

Cited by 23 publications

(19 citation statements)

References 43 publications

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“…This addresses the issue of field inventory and remote sensing‐based approach, which are usually for shorter‐term analysis (Bartels et al, ; Eskelson & Monleon, ; Haslem et al, ). Fifth, the lack of pre‐fire data has made the comparison between pre‐fire and post‐fire fuel loads difficult (Eskelson & Monleon, ). The unknown pre‐disturbance forest conditions have also made the estimation of fire‐induced carbon loss challenging (Frolking et al, ; Liu et al, ).…”

Section: Discussionmentioning

confidence: 99%

“…Investigating vegetation recovery through field measurement has been used by many researchers, such as Coppoletta et al (2016) and Miranda et al (2016). Detailed studies involving site-specific repeated measurements are considered the most accurate (e.g., Eskelson & Monleon, 2018); however, there are three drawbacks (Bartels et al, 2016;Eskelson & Monleon, 2018;Miranda et al, 2016;Turner et al, 1999). First, field-based estimations of forest structural recovery are usually an expensive and time-consuming endeavor.…”

Section: Post-disturbance Field Observationmentioning

confidence: 99%

“…Fourth, utilizing the long-term projection capability of FVS, DRAST can simulate long-term forest change (e.g., up to 100 years). This addresses the issue of field inventory and remote sensing-based approach, which are usually for shorter-term analysis (Bartels et al, 2016;Eskelson & Monleon, 2018;Haslem et al, 2011). Fifth, the lack of pre-fire data has made the comparison between pre-fire and post-fire fuel loads difficult (Eskelson & Monleon, 2018).…”

Section: Drast Advantagesmentioning

confidence: 99%

“…Since forest conditions are associated with many ecosystem services such as wildfire risk, carbon sequestration, and wildlife habitat, DRAST's benefit for disturbance management is straightforward in concept (Shifley et al, 2017). For example, regarding fire and fuel issues, post-fire reburn has become a major concern for land managers (Eskelson & Monleon, 2018) because post-fire fuel loading influences the behavior and severity of subsequent fires. However, this information is often restricted to case studies of high-severity fires, prescribed fires, or one-time post-fire measurements across fire severity classes (Coppoletta et al, 2016;Eskelson & Monleon, 2018).…”

Section: Potential Applicationsmentioning

confidence: 99%

“…For example, regarding fire and fuel issues, post-fire reburn has become a major concern for land managers (Eskelson & Monleon, 2018) because post-fire fuel loading influences the behavior and severity of subsequent fires. However, this information is often restricted to case studies of high-severity fires, prescribed fires, or one-time post-fire measurements across fire severity classes (Coppoletta et al, 2016;Eskelson & Monleon, 2018). DRAST can provide spatiotemporal fuel load following disturbance.…”

Section: Potential Applicationsmentioning

confidence: 99%

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Quantifying Spatiotemporal Post‐Disturbance Recovery Using Field Inventory, Tree Growth, and Remote Sensing

Huang

McElhaney

Clark

et al. 2019

Earth and Space Science

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Forest recovery following a disturbance lasts decades to centuries, and the rate depends on pre‐ and post‐disturbance condition and local environmental factors. Existing approaches of field observations, remote sensing, statistical chronosequence, and ecological modeling have one or more drawbacks, including short time frames, generalized details, indirect indicators, hard parameterization, and defective assumptions. Using aboveground live biomass (AGLB) as an example, we developed an approach called “Disturbance and Recovery Assessment across Space and Time (DRAST).” For a specific post‐disturbance year, DRAST utilizes Field Inventory and Analysis data sets and the Forest Vegetation Simulator, as well as pre‐ and post‐disturbance remote sensing to create two rasters: (1) what the AGLB would look like over the disturbed area had the disturbance not occurred and (2) what the AGLB would look like over the disturbed area in the actual presence of the disturbance. These two rasters are compared annually to examine the spatiotemporal recovery pattern. We demonstrated DRAST with the 2013 Rim fire in California, United States, by creating two sets of AGLB for 100 years. Our results showed that (1) the AGLB consumed by Rim fire was 3.52 Tg and (2) 45.9% of the burned area needs <5 years to recover, followed by 6.4% (5–10 years), 6.1% (>95 years), 5.9% (10–15 years), 5.4% (15–20 years), 4.8% (20–25 years), and 4.3% (25–30 years). In conclusion, DRAST can provide spatially explicit and highly detailed ecological indicators for decades under the two scenarios of “no disturbance” and “actual disturbance occurrence” for recovery analysis.

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Section: Discussionmentioning

confidence: 99%

Section: Post-disturbance Field Observationmentioning

confidence: 99%

Section: Drast Advantagesmentioning

confidence: 99%

Section: Potential Applicationsmentioning

confidence: 99%

Section: Potential Applicationsmentioning

confidence: 99%

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Quantifying Spatiotemporal Post‐Disturbance Recovery Using Field Inventory, Tree Growth, and Remote Sensing

Huang

McElhaney

Clark

et al. 2019

Earth and Space Science

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Matching methods to quantify wildfire effects on forest carbon mass in the U.S. Pacific Northwest

Woo¹,

Eskelson²,

Monleón

2021

Ecological Applications

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Forest wildfires consume and redistribute carbon within forest carbon pools. Because the incidence of wildfires is unpredictable, quantifying wildfire effects is challenging due to the lack of prefire data or controls from experiments over a large landscape. We explored a quasi-experimental method, propensity score matching, to estimate wildfire effects on aboveground forest woody carbon mass in Washington and Oregon, United States. Observational data, including national forest inventory plot measurements and satellite imagery metrics, were utilized to obtain a control set of unburned plots that are comparable to burned plots in terms of environmental conditions as well as spatial locations. Three matching methods were implemented: propensity score matching (PSM), spatial matching (SM), and distance-adjusted propensity score matching (DAPSM). We investigated if propensity score matching with and without spatial adjustment led to different outcomes in terms of (1) balance in covariate distributions between burned and control plots, (2) mean carbon mass obtained from the selected control plots compared to burned and all unburned plots, and (3) estimates of wildfire effects by burn severity. We found that PSM and SM, which use only the environmental covariate set or the spatial distance for estimating propensity scores, respectively, did not appear to produce a comparable set of control plots in terms of the estimated propensity scores and the outcomes of mean carbon mass. DAPSM was the preferred method both in balancing the observed covariates and in dealing with unobservable confounding variables through spatial adjustment. The average wildfire effects estimated by DAPSM showed clear evidence of redistribution of carbon among aboveground woody pools, from live to dead trees, but the consumption of total woody carbon by wildfire was not substantial. Only moderate burn severity led to significant reduction of total woody carbon mass across Washington and Oregon forests (64% of control plots remained on average). This study provides an applied example of a quasi-experimental approach to quantify the effects of a natural disturbance for which experimental settings are unavailable. The study results suggest that incorporating spatial information in addition to environmental covariates would yield a comparable set of control plots for wildfire effects quantification.

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Fire legacies in eastern ponderosa pine forests

Roberts

Donovan

Wonkka

et al. 2019

Ecology and Evolution

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Disturbance legacies structure communities and ecological memory, but due to increasing changes in disturbance regimes, it is becoming more difficult to characterize disturbance legacies or determine how long they persist. We sought to quantify the characteristics and persistence of material legacies (e.g., biotic residuals of disturbance) that arise from variation in fire severity in an eastern ponderosa pine forest in North America. We compared forest stand structure and understory woody plant and bird community composition and species richness across unburned, low‐, moderate‐, and high‐severity burn patches in a 27‐year‐old mixed‐severity wildfire that had received minimal post‐fire management. We identified distinct tree densities (high: 14.3 ± 7.4 trees per ha, moderate: 22.3 ± 12.6, low: 135.3 ± 57.1, unburned: 907.9 ± 246.2) and coarse woody debris cover (high: 8.5 ± 1.6% cover per 30 m transect, moderate: 4.3 ± 0.7, low: 2.3 ± 0.6, unburned: 1.0 ± 0.4) among burn severities. Understory woody plant communities differed between high‐severity patches, moderate‐ and low‐severity patches, and unburned patches (all p < 0.05). Bird communities differed between high‐ and moderate‐severity patches, low‐severity patches, and unburned patches (all p < 0.05). Bird species richness varied across burn severities: low‐severity patches had the highest (5.29 ± 1.44) and high‐severity patches had the lowest (2.87 ± 0.72). Understory woody plant richness was highest in unburned (5.93 ± 1.10) and high‐severity (5.07 ± 1.17) patches, and it was lower in moderate‐ (3.43 ± 1.17) and low‐severity (3.43 ± 1.06) patches. We show material fire legacies persisted decades after the mixed‐severity wildfire in eastern ponderosa forest, fostering distinct structures, communities, and species in burned versus unburned patches and across fire severities. At a patch scale, eastern and western ponderosa system responses to mixed‐severity fires were consistent.

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Post-fire surface fuel dynamics in California forests across three burn severity classes

Cited by 23 publications

References 43 publications

Quantifying Spatiotemporal Post‐Disturbance Recovery Using Field Inventory, Tree Growth, and Remote Sensing

Quantifying Spatiotemporal Post‐Disturbance Recovery Using Field Inventory, Tree Growth, and Remote Sensing

Matching methods to quantify wildfire effects on forest carbon mass in the U.S. Pacific Northwest

Fire legacies in eastern ponderosa pine forests

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