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Terrestrial laser scanning (TLS) data can offer a means to estimate subcanopy fuel characteristics to support site characterization, quantification of treatment or fire effects, and inform fire modeling. Using field and TLS data within the New Jersey Pinelands National Reserve (PNR), this study explores the impact of forest phenology and density of shrub height (i.e., shrub fuel bed depth) measurements on estimating average shrub heights at the plot-level using multiple linear regression and metrics derived from ground-classified and normalized point clouds. The results highlight the importance of shrub height sampling density when these data are used to train empirical models and characterize plot-level characteristics. We document larger prediction intervals (PIs), higher root mean square error (RMSE), and lower R-squared with reduction in the number of randomly selected field reference samples available within each plot. At least 10 random shrub heights collected in situ were needed to produce accurate and precise predictions, while 20 samples were ideal. Additionally, metrics derived from leaf-on TLS data generally provided more accurate and precise predictions than those calculated from leaf-off data within the study plots and landscape. This study highlights the importance of reference data sampling density and design and data characteristics when data will be used to train empirical models for extrapolation to new sites or plots.
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Terrestrial Laser Scan Metrics Predict Surface Vegetation Biomass and Consumption in a Frequently Burned Southeastern U.S. Ecosystem
Fire-prone landscapes found throughout the world are increasingly managed with prescribed fire for a variety of objectives. These frequent low-intensity fires directly impact lower forest strata, and thus estimating surface fuels or understory vegetation is essential for planning, evaluating, and monitoring management strategies and studying fire behavior and effects. Traditional fuel estimation methods can be applied to stand-level and canopy fuel loading; however, local-scale understory biomass remains challenging because of complex within-stand heterogeneity and fast recovery post-fire. Previous studies have demonstrated how single location terrestrial laser scanning (TLS) can be used to estimate plot-level vegetation characteristics and the impacts of prescribed fire. To build upon this methodology, co-located single TLS scans and physical biomass measurements were used to generate linear models for predicting understory vegetation and fuel biomass, as well as consumption by fire in a southeastern U.S. pineland. A variable selection method was used to select the six most important TLS-derived structural metrics for each linear model, where the model fit ranged in R2 from 0.61 to 0.74. This study highlights prospects for efficiently estimating vegetation and fuel characteristics that are relevant to prescribed burning via the integration of a single-scan TLS method that is adaptable by managers and relevant for coupled fire–atmosphere models.
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- Award ID(s):
- 2134904
- PAR ID:
- 10472982
- Publisher / Repository:
- Fire
- Date Published:
- Journal Name:
- Fire
- Volume:
- 6
- Issue:
- 4
- ISSN:
- 2571-6255
- Page Range / eLocation ID:
- 151
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Williamson, Grant (Ed.)Terrestrial LiDAR scans (TLS) offer a rich data source for high-fidelity vegetation characterization, addressing the limitations of traditional fuel sampling methods by capturing spatially explicit distributions that have a significant impact on fire behavior. However, large volumes of complex, high resolution data are difficult to use directly in wildland fire models. In this study, we introduce a novel method that employs a voxelization technique to convert high-resolution TLS data into fine-grained reference voxels, which are subsequently aggregated into lower-fidelity fuel cells for integration into physics-based fire models. This methodology aims to transform the complexity of TLS data into a format amenable for integration into wildland fire models, while retaining essential information about the spatial distribution of vegetation. We evaluate our approach by comparing a range of aggregate geometries in simulated burns to laboratory measurements. The results show insensitivity to fuel cell geometry at fine resolutions (2–8 cm), but we observe deviations in model behavior at the coarsest resolutions considered (16 cm). Our findings highlight the importance of capturing the fine scale spatial continuity present in heterogeneous tree canopies in order to accurately simulate fire behavior in coupled fire-atmosphere models. To the best of our knowledge, this is the first study to examine the use of TLS data to inform fuel inputs to a physics based model at a laboratory scale.more » « less
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Wildfire activity is increasing in boreal forests as climate warms and dries, increasing risks to rural and urban communities. In black spruce forests of Interior Alaska, fuel reduction treatments are used to create a defensible space for fire suppression and slow fire spread. These treatments introduce novel disturbance characteristics, making longer-term outcomes on ecosystem structure and wildfire risk reduction uncertain. We remeasured a network of sites where fuels were reduced through hand thinning or mechanical shearblading in Interior Alaska to assess how successional trajectories of tree dominance, understory composition, and permafrost change over ∼ 20 years after treatment. We also assessed if these fuel reduction treatments reduce modeled surface rate of fire spread (ROS), flame length, and fireline intensity relative to an untreated black spruce stand, and if surface fire behavior changes over time. In thinned areas, soil organic layer (SOL) disturbance promoted tree seedling recruitment but did not change over time. In shearbladed sites, by contrast, both conifer and broad-leaved deciduous seedling density increased over time and deciduous seedlings were 20 times more abundant than spruce. Thaw depth increased over time in both treatments and was greatest in shearbladed sites with a thin SOL. Understory composition was not altered by thinning but in shearbladed treatments shifted from forbs and horsetail to tall deciduous shrubs and grasses over time. Modeled surface fire behavior was constant in shearbladed sites. This finding is inconsistent with expert opinion, highlighting the need for additional fuels-specific data to capture the changing vegetation structure. Treatment effectiveness at reducing modeled surface ROS, flame length, and fireline intensity depended on the fuel model used for an untreated black spruce stand, pointing to uncertainties about the efficacy of these treatments at mitigating surface fire behavior. Overall, we show that fuel reduction treatments can promote low flammability, deciduous tree dominated successional trajectories, and that shearblading has strong effects on understory composition and permafrost degradation that persist for nearly two decades after disturbance. Such factors need to be considered to enhance the design, management, and predictions of fire behavior in these treatments.more » « less
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Abstract Black spruce forest communities in boreal Alaska have undergone self‐replacement succession following low‐to‐moderate severity fires for thousands of years. However, recent intensification of interior Alaska's fire regime, particularly deeper burning of the soil organic layer, is leading to shifts to deciduous‐dominated successional pathways, resulting in many socioecological consequences. Both fuel load quantity and quality (or “burnability”) influence black spruce plant communities' potential to burn. Even relatively low fuel loads, such as those seen in black spruce forest understory, can be highly influential drivers of fire behavior due to their high flammability. Additionally, black spruce community self‐replacement following fire can be largely attributed to the suite of functional and life history traits possessed by the species dominating these communities. We used fuel load (quantity and quality) and amount of within‐population plant trait variation (coefficient of variation; CV) as community‐level emergent properties to investigate black spruce forest vulnerability and resilience to a changing fire regime across the landscape. Our burn severity potential index (BSPI), calculated from fuel load quantity and quality measurements, indicates that drier, higher elevation stands with thicker active layers were the most vulnerable to fire‐induced vegetation shifts under a changing fire regime. Forest resilience to fire‐induced vegetation shift, represented by higher CV, was negatively associated with BSPI and greatest in ecoregions dominated by lowland black spruce forests. Together, these analyses provide critical information for determining the likelihood of stand‐replacing shifts in dominant vegetation following fire and for implementing appropriate ecosystem management practices.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/fire6040151
