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.

野火在加拿大是一个重要的危险，每年都威胁着财产和生计，随着气候变化的影响，这种情况预计会恶化。在过去的30年里，加拿大各省和地区的火灾管理机构一直在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）。各机构每天都使用该系统作为其主要决策工具，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的应急管理战略（EMS）计划将开发下一代CFFDRS（NG-CFFDRS）列为优先事项。NG-CFFDRS的火灾行为模型将使用燃料结构属性的新输入，这些属性在实地很难直接测量，传统的森林清查也没有很好地捕捉到。为了取代耗时耗力的直接测量和破坏性采样，陆地激光扫描仪（TLS）已被确定为提供真实的三维树木和林分信息，用于结构表征和间接的非破坏性估计一些新的燃料结构属性。然而，由此产生的共同注册的TLS点云的处理仍然是复杂的，目前的软件工具往往需要深入了解底层算法，防止这些工具传播给非专家用户在林业和火灾管理。因此，该项目旨在开发新的工具，使用TLS来估计NG-CFFDRS业务实施背景下的燃料结构属性。我们希望开发一个TLS数据处理管道，可以从激光雷达点云中提取燃料结构属性的估计。