Computational models for understanding and predicting fire in wildland and managed lands are increasing in impact. Data characterizing the fuels and environment is needed to continue improvement in the fidelity and reliability of fire outcomes. This paper addresses a gap in the characterization and population of mid-story fuels, which are not easily observable either through traditional survey, where data collection is time consuming, or with remote sensing, where the mid-story is typically obscured by forest canopy. We present a methodology to address populating a mid-story using a generative model for fuel placement that captures key concepts of spatial density and heterogeneity that varies by regional or local environmental conditions. The advantage of using a parameterized generative model is the ability to calibrate (or `tune') the generated fuels based on comparison to limited observation datasets or with expert guidance, and we show how this generative model can balance information from these sources to capture the essential characteristics of the wildland fuels environment. In this paper we emphasize the connection of terrestrial LiDAR (TLS) as the observations used to calibrate of the generative model, as TLS is a promising method for supporting forest fuels assessment. Code for the methods in this paper is available.

翻译：用于理解和预测野地及管理用地火灾的计算模型正日益发挥重要作用。表征燃料与环境的特征数据是持续提升火灾模拟精度与可靠性的关键。本文针对中层燃料的表征与填充方法存在的空白——这类燃料既难以通过耗时费力的传统实地调查获取，又难以通过遥感手段观测（因其通常被林冠层遮蔽）——提出一种基于生成模型来填充中层燃料的方法。该方法在燃料布设中融入了随区域或局部环境条件变化的空间密度与异质性等关键概念。参数化生成模型的核心优势在于：可通过与有限观测数据集对比或专家指导对生成的燃料进行校准（即“调优”）。本文展示了该生成模型如何平衡多源信息，从而捕捉野地燃料环境的核心特征。研究中特别强调将地面激光雷达（TLS）作为校准生成模型的观测手段，因为TLS是支撑森林燃料评估的重要新兴技术。本文所述方法的代码已公开提供。