Hierarchically scaled remote sensing and field datasets for three-dimensional wildland fuel characterization

Fire Ecology

卷:22

Brief Report

Susan J. Prichard¹  Deborah G. Nemens¹  Eric Rowell¹  Jonathan L. Batchelor²  Michelle S. Bester³  Benjamin C. Bright⁴  Gina R. Cova¹  James B. Cronan⁵  Brian Drye¹  Andrew T. Hudak⁴  Paige C. Eagle¹  MaureenC. Kennedy⁶  E. Louise Loudermilk⁷  HannahE. Redford¹  RussellA. Parsons⁸  Carlos A. Silva⁹  Nicholas S. Skowronski¹⁰  Jessie Thoreson¹ 

[1] Forest Biometrics, Remote Sensing and Artificial Intelligence Laboratory (SilvaLab), School of Forest, Fisheries, and Geomatics Sciences, University of Florida, Gainesville, FL, USA

[2] School of Environmental and Forest Sciences, University of Washington, Seattle, WA, USA

[3] Sandia National Laboratories, Albuquerque, NM, USA

[4] USDA Forest Service Pacific Northwest Research Station, Pacific Wildland Fire Science Laboratory, Seattle, WA, USA

[5] University of Montana, Missoula, MT, USA

[6] USDA Forest Service Rocky Mountain Research Station, Missoula Fire Sciences Laboratory, Missoula, MT, USA

[7] USDA Forest Service Northern Research Station, Morgantown, WV, USA

[8] USDA Forest Service Southern Research Station Athens Prescribed Fire Science Laboratory, Athens, USA

[9] USDA Forest Service Rocky Mountain Research Station, Moscow Forestry Laboratory, Moscow, ID, USA

[10] University of Washington, Tacoma, WA, USA

关键词: Wildland fuels;    Computational fluid dynamics modeling;    Longleaf pine;    Loblolly pine;    Ponderosa pine;    TLS;    ALS;    Structure-from-motion photogrammetry;   

DOI  :  10.1186/s42408-026-00507-2

 received in 2026-03-07, accepted in 2026-05-05,  发布年份 2026

来源: Springer

【 摘 要 】

BackgroundNext-generation models of fire behavior and smoke production rely on gridded, 3D inputs of wildland fuel complexes. We used a hierarchically scaled sampling design to characterize canopy and surface fuels that are common to prescribed burning programs in the southeastern and western US. Sampling included airborne laser scanning, terrestrial laser scanning, close-range photogrammetry, and destructive field sampling. The objective of this study was to use a combination of airborne laser scanning (ALS), terrestrial laser scanning (TLS), structure-from-motion photogrammetry (SfM), and field observations to create co-located 3D datasets of live and dead understory fuels for use in wildland fuel mapping and prescribed burn decision support.ResultsUsing our integrated, co-located methods, we detail methods used to produce hierarchically scaled datasets of the structure and composition of canopy and surface fuels across 9 southeastern pine sites, 5 western pine sites, and 4 western grassland sites. These are now publicly available within the Wildland Fire Science Initiative data repository (https://doi.org/10.60594/W4859C).ConclusionsThe study demonstrates that 3D fuel characterizations can provide consistently scaled and labeled datasets for model training and evaluation. More specifically, machine learning models can be used to parse 3D point clouds collected from ALS, TLS, and SfM photogrammetry into fuel objects and metrics. Calibration with field plots will allow our hierarchically scaled datasets to be used as the foundation for synthetic fuelbed mapping, starting with fine-scale objects such as individual shrubs or downed wood and scaling to vegetation patches and operational burn units.

【 授权许可】

CC BY   
© The Author(s) 2026

【 图 表 】

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