Abstract The spatial pattern of surface fuelbeds in fire-dependent ecosystems are rarely captured using long-standing fuel sampling methods. New techniques, both field sampling and remote sensing, that capture vegetation fuel type, biomass, and volume at super fine-scales (cm to dm) in three-dimensions (3D) are critical to advancing forest fuel and wildland fire science. Such scales are particularly important for some computational fluid dynamics fire behavior models that operate in 3D and have implications for wildland fire operations and fire effects research. This study describes the coupling of new 3D field sampling data with terrestrial laser scanning (TLS) data to infer fine-scale fuel mass in 3D. We found that there are strong relationships between fine-scale mass and TLS occupied volume, porosity, and surface area, which were used to develop fine-scale prediction equations using TLS across vegetative fuel types, namely grasses and shrubs. The application of this novel 3D sampling technique to high resolution TLS data in this study represents an advancement in producing inputs for computational fluid dynamics fire behavior models that will improve understanding fire-vegetation feedbacks in highly managed fire-dependent ecosystems.

中文翻译：

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将地面激光扫描与 3D 燃料生物质采样相结合，以推进荒地燃料表征

摘要 依赖火的生态系统中地表燃料床的空间格局很少使用长期存在的燃料采样方法来捕获。在三维 (3D) 中以超精细尺度（cm 到 dm）捕获植被燃料类型、生物量和体积的现场采样和遥感新技术对于推进森林燃料和荒地火灾科学至关重要。这种尺度对于一些在 3D 中运行的计算流体动力学火灾行为模型特别重要，并且对野火操作和火灾影响研究有影响。这项研究描述了新的 3D 现场采样数据与地面激光扫描 (TLS) 数据的耦合，以推断 3D 中的精细燃料质量。我们发现细尺度质量与 TLS 占据体积、孔隙率和表面积之间存在很强的关系，用于开发使用 TLS 跨植物燃料类型（即草和灌木）的精细预测方程。在这项研究中，将这种新颖的 3D 采样技术应用于高分辨率 TLS 数据代表了计算流体动力学火灾行为模型输入的进步，这将提高对高度管理的依赖火灾的生态系统中的火灾植被反馈的理解。