Advancing 3D Fuel Mapping for Wildfire Behaviour and Risk Mitigation Modelling

野火在世界许多地区(如北美洲北部和温带的北美或地中海盆地)是一种自然现象，但在其他地区(如大西洋欧洲)，它们大多是人为造成的。无论起源如何，野火平均每年燃烧的面积相当于英国的20倍左右。当它们烧毁人口稠密的地区时，它们可能是致命的。例如，2018年，它们导致希腊100人死亡，葡萄牙99人死亡，仅加利福尼亚州就有104人死亡。在英国，到目前为止，火灾很少造成生命损失，但平均每年用于应对野火的资金约为5500万GB，它们威胁到了基础设施和社区(例如，去年夏天的几场野火导致疏散)。气候和土地利用的变化已经增加了英国国内外许多地区的野火风险，而且这种趋势预计还会恶化。为了开发更有效的工具来减轻和扑灭极端的野火，我们需要提高我们理解、预测并在可能的情况下控制火灾行为的能力。在这个项目中，我们的目标是通过开发新的自动化方法(算法)来提高野火行为模型的能力，以提高对野火的理解和缓解，首次将开创性的真实3D燃料数据转化为基于物理的野火行为模型。就预测火灾行为的能力而言，这些模型是最先进的，但它们仍然受到缺乏详细燃料投入信息(即易被焚烧的活植物和死亡植物的数量和结构)的限制。我们旨在提供的进步将在物理火灾建模能力方面带来阶段性的变化。新算法将在功能强大的燃料模型Fuel3D和STANDFIRE中实施，这两个模型为基于物理的火灾行为模型FIRETEC和WFDS提供燃料输入。我们将把这些应用到英国、欧洲西北部和北美一些最常见的易燃针叶林的森林中。生成的算法将公之于众，因此可以适用于世界各地的许多其他森林类型。三维燃料数据集将在实地活动中使用一系列最先进的激光扫描(地面、可穿戴和基于空中无人机的激光扫描仪)和“运动结构”方法获取，并进行传统燃料库存测量以进行比较和模型验证。我们的案例研究将集中在英格兰、苏格兰、威尔士和美国的针叶林。在英国，针叶林占英国320万森林的一半。它们最有可能引发树冠大火，沿着树梢蔓延，是最危险和最具挑战性的灭火对象。在美国，这项工作将包括出于研究目的进行的真实森林火灾，这将提供有价值的火灾行为和燃料消耗数据集，以验证改进后的燃料和火灾模型。火灾行为取决于天气、地形和植被燃料的类型和数量，后者是唯一可以通过管理努力有意义地影响的因素。通过管理燃料，我们可以降低极端火灾行为及其影响的风险。我们的项目为设计和测试“虚拟燃料处理”提供了一种新的方法，目的是在当前和预测的未来气候和土地使用情景下，减少燃料危险，从而减少火灾风险。英国主要终端用户(林业委员会、英国气象局、威尔士自然资源局和南威尔士消防与救援服务局)作为合作伙伴的参与将最大限度地提高项目成果的适用性和影响力。新的3D燃料数据和算法也将为其他林业应用(如林业调查、木材预测、森林碳预算、生态系统服务评估)带来重大进展。

Wildfires are a natural phenomenon in many regions of the world (e.g. the boreal and temperate North America or the Mediterranean Basin) but, in others (e.g. Atlantic Europe), they are mostly human-caused. Irrespective of their origin, wildfires burn, on average, an area equivalent to about 20 times the size of the UK every year. When they burn through populated areas they can be deadly. For example, in 2018, they resulted in 100 deaths in Greece, 99 in Portugal, and 104 in California alone. In the UK, fires have to date rarely resulted in losses of life but, on average, ~£55M are spent annually in wildfire responses and they have threatened infrastructures and communities (e.g. several wildfires last summer led to evacuations). A combination of climate and land use changes is already increasing wildfire risk in many areas, both inside and outside the UK, and this trend is expected to worsen. In order to develop more effective tools for mitigating and fighting extreme wildfires, we need to advance our ability to understand, predict and, where possible, control fire behaviour. In this project we aim to improve understanding and mitigation of wildland fire by advancing wildfire behaviour model capabilities through the development of new automated methods (algorithms) to implement, for the first time, ground-breaking real 3D fuel data into physics-based wildfire behaviour models. These models are the most advanced in terms of their ability to forecast fire behaviour, but they remain constrained by the lack of detailed fuel input information to work with (i.e. the amount and structure of live and dead vegetation susceptible to burn). The advancement we aim to deliver will provide a step-change in physical fire modelling capabilities. The new algorithms will be implemented in the powerful fuel models FUEL3D and STANDFIRE, which provide fuels inputs for the physics-based fire behaviour models FIRETEC and WFDS. We will apply these to forest stands that typify some of the most common flammable conifer forests in the UK, NW Europe and North America. The algorithms produced will be made publicly available and, therefore, can be adapted and applied to many other forest types around the world.Three-dimensional fuel datasets will be acquired in field campaigns using a range of state-of-the-art laser scanning (terrestrial, wearable and aerial UAV-based laser scanners) and 'Structure from Motion' methods, with traditional fuel inventory measurements being carried out for comparison and model validation. Our case studies will focus on conifer stands in England, Scotland, Wales and the US. In the UK, conifer forests comprise half of the UK's 3.2 Mill. ha of forested land, and they have the greatest potential for crown fires, which spread along treetops and are the most dangerous and challenging to fight. In the US, the work will include real forest fires, carried out for research purposes, which will provide valuable fire behaviour and fuel consumption datasets to validate the improved fuel and fire models. Fire behaviour depends on weather, topography, and on the type and amount of vegetation fuels, with the latter being the only factor that can be meaningfully influenced through management efforts. By managing fuels, we can reduce the risk of extreme fire behaviour and its impacts. Our project provides a novel approach for designing and testing of 'virtual fuel treatments' aimed at decreasing fuel hazard and, thus, fire risk, under current and predicted future climatic and land use scenarios. The involvement of key UK end-users (Forestry Commission, Met Office, Natural Resources Wales and South Wales Fire & Rescue Service) as partners will maximise the applicability and impact of the project's outputs. The novel 3D fuel data and algorithms will also present a major advance for other forestry applications (e.g. forestry inventory, timber forecasting, forest carbon budgeting, ecosystem services assessment).