Mapping Forest Canopy Fuels in the Western United States with LiDAR–Landsat Covariance

Moran, Christopher J.; Kane, Van R.; Seielstad, Carl

doi:10.3390/rs12061000

Cited by 11 publications

(6 citation statements)

References 90 publications

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“…The wildfire spread model accuracy could be improved with the refinement of spatial precision of canopy-related features. For example, canopy metrics (such as height or bulk density) can be derived from LiDAR data (Marino et al, 2016;Moran et al, 2020) which were unavailable in the study area. Furthermore, specific fuel spread models (e.g.…”

Section: Discussionmentioning

confidence: 99%

Assessing expected economic losses from wildfires in eucalypt plantations of western Brazil

Galizia

Alcasena

Prata

et al. 2021

Forest Policy and Economics

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Section: Discussionmentioning

confidence: 99%

Assessing expected economic losses from wildfires in eucalypt plantations of western Brazil

Galizia

Alcasena

Prata

et al. 2021

Forest Policy and Economics

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“…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...mentioning

confidence: 99%

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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“…However, LANDFIRE's utility is limited for fine‐scale applications. Canopy fuels are predicted with low accuracy in many vegetation types (Keane et al, 2006; Moran et al, 2020; Reeves et al, 2009), and fuels data must be quality checked and calibrated prior to use (Stratton, 2009). Locally derived maps can improve predictions of fuel characteristics (Engelstad et al, 2019) and fire spread (Krasnow et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

Young forests and fire: Using lidar–imagery fusion to explore fuels and burn severity in a subalpine forest reburn

Braziunas

Abendroth²,

Turner

2022

Ecosphere

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Anticipating fire behavior as climate change and fire activity accelerate is an increasingly pressing management challenge in fire-prone landscapes. In subalpine forests adapted to infrequent, stand-replacing fire, self-limitation of burn severity in short-interval fire is incompletely understood. Spatially explicit fuels data can support assessments of landscape-scale fire risk and fuel feedbacks on burn severity. For a $1450-km 2 largely forested landscape in the US Northern Rocky Mountains, we used airborne lidar and imagery to predict and map canopy and surface fuels. In a fire that burned mature (>125-year-old) and also reburned young ($30year-old) subalpine forest, we then asked: (1) How do prefire fuels and burn severity compare between young and mature forests that burned under similar fire weather conditions? (2) How well do prefire fuels and forest structure predict burn severity under extreme versus moderate fire weather? Lidar-imagery fusion predicted fuel characteristics with high accuracy across forest and shrubland vegetation. Young postfire forests had abundant, densely packed canopy fuels, and both young and mature forests had similar canopy fuel loads and coarse wood biomass.Under similar weather conditions, young and mature forests burned at similar severity. Overall, fuels were weak predictors of burn severity and, surprisingly, better predicted severity under extreme rather than moderate fire weather. Our findings are relevant for subalpine landscapes increasingly dominated by young lodgepole pine (Pinus contorta var. latifolia) forests vulnerable to short-interval fire and provide a benchmark to assess how fuels influence burn severity in future fires. Fire managers should continually reassess fuels and update expectations about fire behavior as landscapes change. Although recovering postfire forests can limit fire spread and severity for a period of time, our results suggest that young subalpine forests in the Northern Rocky Mountains have sufficient fuel loads to burn at high severity and should not be considered effective fire breaks.

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Mapping Forest Canopy Fuels in the Western United States with LiDAR–Landsat Covariance

Cited by 11 publications

References 90 publications

Assessing expected economic losses from wildfires in eucalypt plantations of western Brazil

Assessing expected economic losses from wildfires in eucalypt plantations of western Brazil

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

Young forests and fire: Using lidar–imagery fusion to explore fuels and burn severity in a subalpine forest reburn

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