Visual Assessment of Surface Fuel Loads Does Not Align with Destructively Sampled Surface Fuels

McColl‐Gausden, Sarah C.; Penman, Trent D.

doi:10.3390/f8110408

Cited by 14 publications

(7 citation statements)

References 31 publications

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“…The destructive measurement approach was employed including the collection, drying, and weighting of the fuel material. It is considered one of the most reliable fuel load measurement methods, although mostly used for research rather than operational inventory purposes due to its time-consuming and cost-inefficient nature [16,17].…”

Section: Field Surveymentioning

confidence: 99%

“…Although destructive sampling is considered one of the most accurate direct methods for quantifying surface fuel load on the field, it is extremely costly and time-consuming, especially when the area to be sampled is large. Given the impracticality of measuring each and every fuel particle over an area of 1000 m 2 , subsampling surface fuels and summarizing the respective measurements (i.e., averaging the measured weights of the subplots) is a commonly adopted method for the reliable description of surface fuel load [6,17,80]. Therefore, within each plot, five subplots of 1 m 2 were randomly placed and the included surface fuels were collected, categorized according to type, and stored in separate paper bags.…”

Section: Field Surveymentioning

confidence: 99%

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Multispectral LiDAR-Based Estimation of Surface Fuel Load in a Dense Coniferous Forest

et al. 2020

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Surface fuel load (SFL) constitutes one of the most significant fuel components and is used as an input variable in most fire behavior prediction systems. The aim of the present study was to investigate the potential of discrete-return multispectral Light Detection and Ranging (LiDAR) data to reliably predict SFL in a coniferous forest characterized by dense overstory and complex terrain. In particular, a linear regression analysis workflow was employed with the separate and combined use of LiDAR-derived structural and pulse intensity information for the load estimation of the total surface fuels and individual surface fuel types. Following a leave-one-out cross-validation (LOOCV) approach, the models developed from the different sets of predictor variables were compared in terms of their estimation accuracy. LOOCV indicated that the predictive models produced by the combined use of structural and intensity metrics significantly outperformed the models constructed with the individual sets of metrics, exhibiting an explained variance (R2) between 0.59 and 0.71 (relative Root Mean Square Error (RMSE) 19.3–37.6%). Overall, the results of this research showcase that both structural and intensity variables provided by multispectral LiDAR data are significant for surface fuel load estimation and can successfully contribute to effective pre-fire management, including fire risk assessment and behavior prediction in case of a fire event.

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Section: Field Surveymentioning

confidence: 99%

Section: Field Surveymentioning

confidence: 99%

Multispectral LiDAR-Based Estimation of Surface Fuel Load in a Dense Coniferous Forest

et al. 2020

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“…These were included as a separate category. We excluded studies where we could not separate grass and understory (near‐surface or elevated fuel layers) from SL (e.g., fuel studies such as Fensham [1992], McColl‐Gausden and Penman [2017]). If data on the mass of dead standing trees were reported, we added this information as an additional class.…”

Section: Methodsmentioning

confidence: 99%

Dynamics of necromass in woody Australian ecosystems

Neumann

Turner²,

Lewis³

et al. 2021

Ecosphere

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Litterfall (LF) is the major contributor to aboveground necromass in ecosystems. Litter decomposition or litter decay (LD) then offsets deposition in LF, with the balance of LF and LD determining the standing litter (SL). SL together with fine and coarse woody debris (FWD, CWD) are the largest necromass pools. The interactions of LF, SL, and LD at continental scales reflect carbon and nutrient cycling and other ecosystem processes. We compiled data on leaf, twig (<2.6 cm), and other material (mostly bark and reproductive tissue) for SL and LF for the fire-prone Australian continent, where SL is also a major "fuel load" and important for fire spread and fire intensity. We extracted data from 498 published and unpublished works (1825 LF observations; n SL = 3914; n LD = 629). We used Olson's (mass-balance) approach (k˜LF/ SL) to calculate LD for sites long undisturbed with both LF and SL data. We compiled LF and SL by component (leaves, twigs, other material) and metainformation such as sampling location, tree species, or time since fire from literature and/or scientists. Most data were available from warm-seasonal (36% for SL) and cool-wet (31%) climates, linking the locations of our data with a bio-climate classification. Warm-wet (20%) and hot-seasonal (8%) climates followed, while other climate zones each contributed <2% of the data. Across all climatic zones, average SL (1100 g/m 2 ) was roughly twice that of LF (468 gÁm −2 Áyr −1 ). SL was greatest in cold climates (2334 g/m 2 ), compared to warm-wet (1168 g/m 2 ) and hot-seasonal conditions (499 g/m 2 ). Important drivers of SL are LD (e.g., slow under cold conditions) and fire frequency. Olson's k varied with type of decomposing material ("composition"). For example, across the continent, k˜1.942 yr −1 for leaves but was 0.504 yr −1 for twigs. SL varied strongly in composition according to climate type (e.g., seasonal vs. wet climates). Robust models of necromass dynamics must distinguish between the litter components (such as leaves and twigs) and consider the complex and non-linear effects of climate, stand structure, and stand history on litterfall and decomposition.

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“…The fuel load of the litter bed was measured by collecting all dead fuel particles < 25 mm in diameter (leaves, twigs, bark and woody fragments) inside a 0.1 mP 2 P sampling ring at each of the four corners of the plot [44]. Fuel was separated into dead fine (<6 mm diameter) and dead coarse (6 ≥ diameter) size classes.…”

Section: Field Measurementsmentioning

confidence: 99%

Mechanical Mastication Reduces Fuel Structure and Modelled Fire Behaviour in Australian Shrub Encroached Ecosystems

Grant

Duff

Penman

et al. 2021

Forests

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Shrub encroachment of grassland and woodland ecosystems can alter wildfire behaviour and threaten ecological values. Australian fire managers are using mechanical mastication to reduce the fire risk in encroached ecosystems but are yet to evaluate its effectiveness or ecological impact. We asked: (1) How does fuel load and structure change following mastication?; (2) Is mastication likely to affect wildfire rates of spread and flame heights?; and (3) What is the impact of mastication on flora species richness and diversity? At thirteen paired sites (masticated versus control; n = 26), located in Victoria, Australia, we measured fuel properties (structure, load and hazard) and floristic diversity (richness and Shannon’s H) in 400 mP2 plots. To quantify the effects of mastication, data were analysed using parametric and non-parametric paired sample techniques. Masticated sites were grouped into two categories, 0–2 and 3–4 years post treatment. Fire behaviour was predicted using the Dry Eucalypt Forest Fire Model. Mastication with follow-up herbicide reduced the density of taller shrubs, greater than 50 cm in height, for at least 4 years. The most recently masticated sites (0–2 years) had an almost 3-fold increase in dead fine fuel loads and an 11-fold increase in dead coarse fuel loads on the forest floor compared with the controls. Higher dead coarse fuel loads were still evident after 3–4 years. Changes to fuel properties produced a reduction in predicted flame heights from 22 m to 5–6 m under severe fire weather conditions, but no change in the predicted fire rate of spread. Reductions in flame height would be beneficial for wildfire suppression and could reduce the damage to property from wildfires. Mastication did not have a meaningful effect on native species diversity, but promoted the abundance of some exotic species.

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Visual Assessment of Surface Fuel Loads Does Not Align with Destructively Sampled Surface Fuels

Cited by 14 publications

References 31 publications

Multispectral LiDAR-Based Estimation of Surface Fuel Load in a Dense Coniferous Forest

Multispectral LiDAR-Based Estimation of Surface Fuel Load in a Dense Coniferous Forest

Dynamics of necromass in woody Australian ecosystems

Mechanical Mastication Reduces Fuel Structure and Modelled Fire Behaviour in Australian Shrub Encroached Ecosystems

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