Processing terrestrial lidar data to produce vertical profiles of fire fuel for use in the Canadian Forest Fire Danger Rating System

处理地面激光雷达数据以生成火灾燃料的垂直剖面，用于加拿大森林火灾危险评级系统

• 批准号: 576978-2022
• 负责人: Béland, MartinM
• 金额: $ 1.46万
• 依托单位: Université Laval
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=765446
• 关键词: Processing; terrestrial; lidar; data; produce

Wildfire is a hazard of importance in Canada, threatening assets and livelihood on a yearly basis, a situation that is expected to worsen with the impact of climate change. For the last 30 years, Canadian provincial and territorial fire management agencies have been using the Canadian Forest Fire Danger Rating System (CFFDRS), developed by the Canadian Forest Service (CFS). Agencies use this system daily as their main decision-making tool to support fire danger and behaviour assessments. With the evolution of science, technologies, and of the fire management environment, the Emergency Management Strategy (EMS) Program of CFS has prioritized the development of the Next-Generation of the CFFDRS (NG-CFFDRS). The fire behaviour models of the NG-CFFDRS will use new inputs of fuel structural attributes that are hardly directly measurable in the field and that are not well captured by classical forest inventories. To replace time and resource consuming direct measurements and destructive sampling, Terrestrial Laser Scanner (TLS) has been identified to provide realistic 3D tree and stand information for structural characterization and indirect, non-destructive estimation of some of the new fuel structural attributes. However, the processing of the resulting co-registered TLS point clouds remains complex and current software tools often require a deep understanding of the underlying algorithms, preventing the dissemination of these tools to non-expert users in forestry and fire management. This project thus aims at developing new tools to use of TLS to estimate fuel structural attributes in the context of the operational implementation of the NG-CFFDRS. Explicitly we want to develop a TLS data processing pipeline that can extract estimates of fuel structural attributes from lidar point clouds.

野火在加拿大是一种重要的危害，每年威胁资产和生计，这种情况有望随着气候变化的影响而恶化。在过去的30年中，加拿大省和地区消防管理机构一直使用加拿大森林服务局（CFS）开发的加拿大森林火灾危险评级系统（CFFDRS）。机构每天将该系统用作其主要决策工具，以支持火灾危险和行为评估。随着科学，技术和消防管理环境的发展，CFS的紧急管理战略（EMS）计划优先考虑了CFFDRS（NG-CFFDRS）下一代的发展。 NG-CFFDR的火灾行为模型将使用新的燃料结构属性的新输入，这些燃料结构属性几乎无法直接测量，而经典森林库存并不能很好地捕获。为了取代时间和资源消耗直接测量和破坏性抽样，已经确定了地面激光扫描仪（TLS），以提供逼真的3D树，并提供有关结构性表征和间接，非破坏性估计的一些新燃料结构属性的信息。但是，所得共注册的TLS点云的处理仍然很复杂，当前的软件工具通常需要深入了解基础算法，从而阻止了这些工具将这些工具传播给林业和火灾管理中的非专家使用者。因此，该项目旨在开发新的工具来使用TLS在NG-CFFDR的操作实施的背景下估计燃料结构属性。明确地，我们想开发一个TLS数据处理管道，该管道可以从LiDar Point Clouds中提取燃料结构属性的估计。