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

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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).