Vegetation structure parameters determine high burn severity likelihood in different ecosystem types: A case study in a burned Mediterranean landscape

Fernández‐Guisuraga, José Manuel; Suárez‐Seoane, Susana; García-Llamas, Paula; Calvo, Leonor

doi:10.1016/j.jenvman.2021.112462

Cited by 42 publications

(30 citation statements)

References 105 publications

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“…Importantly, the strength of ladder fuel densities to predict RdNBR were stronger in all analyses when CBH was included. This finding agrees with those of Fernández-Guisuraga et al (2021) who found that severe ecosystem damage was mainly driven by vegetation structure rather than topography or patch size, with different roles of pre-fire fuel structure parameters. Many studies have accurately estimated CBH from ALS data (Andersen et al, 2005;Kelly et al, 2017;Luo et al, 2018;Moran et al, 2020;Stefanidou et al, 2020;Chamberlain et al, 2021), and a few studies have estimated CBH with TLS data (García et al, 2011;Novotny et al, 2021), so ideally these forest structure variables could be estimated via remote sensing instead of a field-based approach, to maintain a continuity in data collection.…”

Section: Modeling the Relationship Between Ladder Fuels And Burn Seve...supporting

confidence: 92%

Comparing Remote Sensing and Field-Based Approaches to Estimate Ladder Fuels and Predict Wildfire Burn Severity

Forbes

Reilly

Clark

et al. 2022

Front. For. Glob. Change

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While fire is an important ecological process, wildfire size and severity have increased as a result of climate change, historical fire suppression, and lack of adequate fuels management. Ladder fuels, which bridge the gap between the surface and canopy leading to more severe canopy fires, can inform management to reduce wildfire risk. Here, we compared remote sensing and field-based approaches to estimate ladder fuel density. We also determined if densities from different approaches could predict wildfire burn severity (Landsat-based Relativized delta Normalized Burn Ratio; RdNBR). Ladder fuel densities at 1-m strata and 4-m bins (1–4 m and 1–8 m) were collected remotely using a terrestrial laser scanner (TLS), a handheld-mobile laser scanner (HMLS), an unoccupied aerial system (UAS) with a multispectral camera and Structure from Motion (SfM) processing (UAS-SfM), and an airborne laser scanner (ALS) in 35 plots in oak woodlands in Sonoma County, California, United States prior to natural wildfires. Ladder fuels were also measured in the same plots using a photo banner. Linear relationships among ladder fuel densities estimated at broad strata (1–4 m, 1–8 m) were evaluated using Pearson’s correlation (r). From 1 to 4 m, most densities were significantly correlated across approaches. From 1 to 8 m, TLS densities were significantly correlated with HMLS, UAS-SfM and ALS densities and UAS-SfM and HMLS densities were moderately correlated with ALS densities. Including field-measured plot-level canopy base height (CBH) improved most correlations at medium and high CBH, especially those including UAS-SfM data. The most significant generalized linear model to predict RdNBR included interactions between CBH and ladder fuel densities at specific 1-m stratum collected using TLS, ALS, and HMLS approaches (R2 = 0.67, 0.66, and 0.44, respectively). Results imply that remote sensing approaches for ladder fuel density can be used interchangeably in oak woodlands, except UAS-SfM combined with the photo banner. Additionally, TLS, HMLS and ALS approaches can be used with CBH from 1 to 8 m to predict RdNBR. Future work should investigate how ladder fuel densities using our techniques can be validated with destructive sampling and incorporated into predictive models of wildfire severity and fire behavior at varying spatial scales.

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Section: Modeling the Relationship Between Ladder Fuels And Burn Seve...supporting

confidence: 92%

Comparing Remote Sensing and Field-Based Approaches to Estimate Ladder Fuels and Predict Wildfire Burn Severity

Forbes

Reilly

Clark

et al. 2022

Front. For. Glob. Change

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“…Additionally, canopy metrics were computed at landscape level across the study sites with a grid size of 30 m. A minimum height and cover threshold of 0.2 m was implemented to remove the influence of laying woody debris and other ground features such as rocks on the computed metrics. The metrics comprised: (i) 95th percentile of LiDAR 1st returns and (ii) average height of LiDAR 1st returns, both as a representative measure of canopy mean height in the plot [77][78][79]; and, (iii) LiDAR canopy cover, computed as the proportion of 1st returns above the cover threshold [80,81].…”

Section: Lidar Data Acquisition and Processingmentioning

confidence: 99%

Short-Term Recovery of the Aboveground Carbon Stock in Iberian Shrublands at the Extremes of an Environmental Gradient and as a Function of Burn Severity

Fernández‐Guisuraga

Calvo

Fernandes

et al. 2022

Forests

Self Cite

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The degree to which burn severity influences the recovery of aboveground carbon density (ACD) of live pools in shrublands remains unclear. Multitemporal LiDAR data was used to evaluate ACD recovery three years after fire in shrubland ecosystems as a function of burn severity immediately after fire across an environmental and productivity gradient in the western Mediterranean Basin. Two large mixed-severity wildfires were assessed: an Atlantic site, dominated by resprouter shrubs and located at the most productive extreme of the gradient, and a Mediterranean site, dominated by obligate seeders and located at the less productive extreme. Initial assessment of burn severity was performed using the differenced Normalized Burn Ratio index computed from Landsat imagery. Thresholds for low and high burn severity categories were established using the Composite Burn Index (CBI). LiDAR canopy metrics were calibrated with field measurements of mean shrub height and cover at plot level in a post-fire situation. Pre-fire and post-fire ACD estimates, and their ratio (ACDr) to calculate carbon stock recovery, were computed from the predictions of LiDAR grid metrics at landscape level using shrubland allometric relationships. Overall, ACDr decreased both with high burn severity and low productivity, although the burn severity impact was not homogeneous within the gradient. In the Atlantic site, ACDr was similar under low and high burn severity, whereas it decreased with burn severity in the Mediterranean site. These results suggest that carbon cycling models could be biased by not accounting for both fire severity and species composition of shrublands under different environmental conditions.

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“…Shorter-canopied vegetation is more susceptible to satellite-observable vegetation scorch and consumption resulting from understorey fire (Chafer et al 2004;Hammill and Bradstock 2006), and NBR indices, therefore, reflect this expected fire severity effect. Findings from studies in Mediterranean and North American forest types have suggested that vegetation height also plays an important role in determining remotely sensed fire severity in other environments (Alexander et al 2006;García-Llamas et al 2019;Fernández-Guisuraga et al 2021;Taylor et al 2021), although the relative strength of vegetation height effects may be influenced by its heterogeneity within a fire-affected area (Mitsopoulos et al 2019).…”

Section: Vegetation Height Effects On Fire Severitymentioning

confidence: 99%

What determines variation in remotely sensed fire severity? Consideration of remote sensing limitations and confounding factors

Gale

Cary

2022

Int. J. Wildland Fire

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Analyses of the effects of topography, weather, land management, and fuel on fire severity are increasingly common, and generally apply fire severity indices derived from satellite optical remote sensing. However, these indices are commonly interpreted with insufficient appreciation for their limitations and may be inappropriately invoked as representing physical fire effects and fire behaviour. For a large wildfire in southeast Australia, we investigated three considerations for inferring robust insights from fire severity analyses – the potentially confounding influences of pre-fire vegetation height and tall vegetation cover, and the choice of fire severity response variable. Using nonparametric regression, we found that different fire severity indices gave rise to substantially different modelled relationships with commonly invoked environmental predictors, as is consistent with dissimilarities in index design. Further, pre-fire vegetation height was a strong control of fire severity, with equivalent importance to weather. Importantly, strong covariation between vegetation height and environmental predictors suggests that modelled fire severity effects are strongly influenced by variation in vertical distance between flames and vegetation, and this confounds fire behaviour insights. To enable more robust and mechanistic insights into the determinants of fire severity, we recommend greater consideration of the meaning and limitations of optical remote sensing indices.

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Vegetation structure parameters determine high burn severity likelihood in different ecosystem types: A case study in a burned Mediterranean landscape

Cited by 42 publications

References 105 publications

Comparing Remote Sensing and Field-Based Approaches to Estimate Ladder Fuels and Predict Wildfire Burn Severity

Comparing Remote Sensing and Field-Based Approaches to Estimate Ladder Fuels and Predict Wildfire Burn Severity

Short-Term Recovery of the Aboveground Carbon Stock in Iberian Shrublands at the Extremes of an Environmental Gradient and as a Function of Burn Severity

What determines variation in remotely sensed fire severity? Consideration of remote sensing limitations and confounding factors

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