The Alberta Wildland Fuels Inventory Program (AWFIP): data description and reference tables

Phelps, Nathan; Cameron, Hilary; Forbes, Air M.; Schiks, Tom; Schroeder, Dave; Beverly, Jennifer L.

doi:10.1186/s13595-022-01144-w

Cited by 3 publications

(2 citation statements)

References 19 publications

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“…Overstory structural attributes, including canopy base height and canopy bulk density, are not currently represented in provincial forest inventories, but can be approximated using Light Detection and Ranging (LiDAR), if calibrated to sufficiently dense field plot data (Riaño et al 2004 ; Andersen et al 2005 ; Zhao et al 2011 ; Jeronimo et al 2018 ; Engelstad et al 2019 ; Chamberlain et al 2021 , 2023 ). The great heterogeneity of subcanopy attributes describing surface and ladder fuels cannot currently be derived from remote sensing products and will necessitate applications of existing field inventory data (e.g., Hanes et al 2021 ) and the development of new field-based fuel inventories (e.g., Phelps et al 2022 ) to train products. Thus, fuel attribute data can be enhanced by using remote sensing products and reduced reliance on manual photointerpretation, but will require a significant complementary focus on field surveys and plot-level data.…”

Section: Discussionmentioning

confidence: 99%

“…Although the CFFDRS was developed based on experimental crown fires, future data collection will likely need to rely on wild and prescribed fires due to the magnitude of data required to characterize modern-day shifting dynamics. To do so would require a coordinated effort in the systematic collection, storage, and integration of observational fuels and fire behavior data using field and remotely sensed approaches (e.g., Perrakis et al 2014 ; Hart et al 2021 ; Phelps et al 2022 ). Simply put, the relationship between fuels and fire behavior cannot be understood or predicted without data for the systems we seek to represent.…”

Section: Discussionmentioning

confidence: 99%

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Fuel types misrepresent forest structure and composition in interior British Columbia: a way forward

Baron,

Hessburg,

Parisien

et al. 2024

fire ecol

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Background A clear understanding of the connectivity, structure, and composition of wildland fuels is essential for effective wildfire management. However, fuel typing and mapping are challenging owing to a broad diversity of fuel conditions and their spatial and temporal heterogeneity. In Canada, fuel types and potential fire behavior are characterized using the Fire Behavior Prediction (FBP) System, which uses an association approach to categorize vegetation into 16 fuel types based on stand structure and composition. In British Columbia (BC), provincial and national FBP System fuel type maps are derived from remotely sensed forest inventory data and are widely used for wildfire operations, fuel management, and scientific research. Despite their widespread usage, the accuracy and applicability of these fuel type maps have not been formally assessed. To address this knowledge gap, we quantified the agreement between on-site assessments and provincial and national fuel type maps in interior BC. Results We consistently found poor correspondence between field assessment data and both provincial and national fuel types. Mismatches were particularly frequent for (i) dry interior ecosystems, (ii) mixedwood and deciduous fuel types, and (iii) post-harvesting conditions. For 58% of field plots, there was no suitable match to the extant fuel structure and composition. Mismatches were driven by the accuracy and availability of forest inventory data and low applicability of the Canadian FBP System to interior BC fuels. Conclusions The fuel typing mismatches we identified can limit scientific research, but also challenge wildfire operations and fuel management decisions. Improving fuel typing accuracy will require a significant effort in fuel inventory data and system upgrades to adequately represent the diversity of extant fuels. To more effectively link conditions to expected fire behavior outcomes, we recommend a fuel classification approach and emphasis on observed fuels and measured fire behavior data for the systems we seek to represent.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Fuel types misrepresent forest structure and composition in interior British Columbia: a way forward

Baron,

Hessburg,

Parisien

et al. 2024

fire ecol

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Classification of forest fuels in selected fire-prone ecosystems of Alberta, Canada—implications for crown fire behaviour prediction and fuel management

Phelps

Beverly

2022

Annals of Forest Science

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Key message We used clustering to construct fuel classes from fuel inventory data based on three stand attributes relevant to crown fire behaviour: surface fuel load (SFL), canopy base height (CBH) and canopy bulk density (CBD). Resulting fuel classes explained more of the stand-to-stand variability in predicted crown fire behaviour than fuel types of the Canadian Forest Fire Behaviour Prediction (FBP) System. Context Wildfire behaviour is partly determined by stand structure and composition. Fuel characterization is essential for predicting fire behaviour and managing vegetation. Currently, categorical fuel types based on associations with major forested or open vegetated landcovers are used nationally in Canada for fire research and management applications. Aim To provide an alternative description of selected forest fuels in Alberta, Canada, using direct classification in which fuel categories are constructed from data using analytical methods. Methods Fuel inventory data for 476 stands were used to construct fuel classes with clustering. Potential crown fire behaviour was modelled for resulting fuel class clusters (FCCs) and FCCs were compared with assigned FBP System fuel types. Tree-based modelling was used to identify stand characteristics most influential on FCC membership. Fuel treatment effects on FCC and modelled crown fire behaviour were explored for the FCC most susceptible to crown fire. Results Four FCCs were identified: Red (low SFL, low CBH, low CBD); Green (high SFL, low-moderate CBH, low CBD); Blue (low SFL, high CBH, low-moderate CBD); and Black (low SFL, moderate CBH, high CBD). Stand density of live conifers and FBP System fuel type were the most important variables influencing FCC membership; however, FCCs did not align directly with assigned FBP System fuel types. Fuel reduction treatments in the Black FCC were effective at shifting the stand to a less flammable FCC. Conclusion FCCs explained more of the stand-to-stand variability in predicted crown fire behaviour than assigned FBP System fuel types, which suggests FCCs could be used to improve fire behaviour predictions and aid fire managers in prioritizing areas for fuel treatments. Future technological and remote sensing advances could enable mapping FCCs across large regions.

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Delineating and Reconstructing 3D Forest Fuel Components and Volumes with Terrestrial Laser Scanning

Xi,

Chasmer,

Hopkinson

2023

Remote Sensing

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Predictive accuracy in wildland fire behavior is contingent on a thorough understanding of the 3D fuel distribution. However, this task is complicated by the complex nature of fuel forms and the associated constraints in sampling and quantification. In this study, twelve terrestrial laser scanning (TLS) plot scans were sampled within the mountain pine beetle-impacted forests of Jasper National Park, Canada. The TLS point clouds were delineated into eight classes, namely individual-tree stems, branches, foliage, downed woody logs, sapling stems, below-canopy branches, grass layer, and ground-surface points using a transformer-based deep learning classifier. The fine-scale 3D architecture of trees and branches was reconstructed using a quantitative structural model (QSM) based on the multi-class components from the previous step, with volume attributes extracted and analyzed at the branch, tree, and plot levels. The classification accuracy was evaluated by partially validating the results through field measurements of tree height, diameter-at-breast height (DBH), and live crown base height (LCBH). The extraction and reconstruction of 3D wood components enable advanced fuel characterization with high heterogeneity. The existence of ladder trees was found to increase the vertical overlap of volumes between tree branches and below-canopy branches from 8.4% to 10.8%.

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The Alberta Wildland Fuels Inventory Program (AWFIP): data description and reference tables

Cited by 3 publications

References 19 publications

Fuel types misrepresent forest structure and composition in interior British Columbia: a way forward

Fuel types misrepresent forest structure and composition in interior British Columbia: a way forward

Classification of forest fuels in selected fire-prone ecosystems of Alberta, Canada—implications for crown fire behaviour prediction and fuel management

Delineating and Reconstructing 3D Forest Fuel Components and Volumes with Terrestrial Laser Scanning

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