An Unmanned Aerial System for Prescribed Fires

Prescribed fires can lessen wildfire severity and control invasive species, but some terrains may be difficult, dangerous, or costly to burn with existing tools. This thesis presents the design of an unmanned aerial system that can ignite prescribed fires from the air, with less cost and risk than with aerial ignition from a manned aircraft. The prototype was evaluated in-lab and successfully used to ignite interior areas of two prescribed fires. Additionally, we introduce an approach that integrates a lightweight fire simulation to autonomously plan safe flight trajectories and suggest effective fire lines. Both components are unique in that they are amenable to input from the system’s sensors and the fire crew. A preliminary study confirms that such inputs improve the accuracy of the fire simulation to better counter the unpredictability of the target environment. Advisors: Carrick Detweiler and Sebastian Elbau

Fine-tuning the BFOLDS fire regime module to support the assessment of fire-related functions and services in a changing Mediterranean mountain landscape

Fire simulation models are useful to advance fire research and improve landscape management. However, a better understanding of these tools is crucial to increase their reliability and expansion into research fields where their application remains limited (e.g., ecosystem services). We evaluated several components of the BFOLDS Fire Regime Module and then tested its ability to simulate fire regime attributes in a Mediterranean mountainous landscape. Based on model outputs, we assessed the landscape fire regulation capacity over time and its implications for supporting the climate regulation ecosystem service. We found that input data quality and the adjustment of fuel and fire behaviour parameters are crucial to accurately emulating key fire regime attributes. Besides, the high predictive capacity shown by BFOLDS-FRM allows to reliably inform the planning and sustainable management of fire-prone mountainous areas of the Mediterranean. Moreover, we identified and discussed modelling limitations and made recommendations to improve future model applications.A. Sil received support from the Portuguese Foundation for Science and Technology (FCT) through Ph.D. Grant SFRH/BD/132838/2017, funded by the Ministry of Science, Technology and Higher Education, and by the European Social Fund - Operational Program Human Capital within the 2014–2020 EU Strategic Framework. P.M. Fernandes contributed in the framework of the UIDB/04033/2020 project, funded by the Portuguese Foundation for Science and Technology (FCT).info:eu-repo/semantics/publishedVersio

FIRE SUPPRESSION AND IGNITION WITH UNMANNED AERIAL VEHICLES

An unmanned aerial vehicle ( UAV ) can be configured for fire suppression and ignition . In some examples , the UAV includes an aerial propulsion system , an ignition system , and a control system . The ignition system includes a container of delayed - ignition balls and a dropper configured , by virtue of one or more motors , to actuate and drop the delayed - ignition balls . The control system is configured to cause the UAV to fly to a site of a prescribed burn and , while flying over the site of the prescribed burn , actuate one or more of the delayed - ignition balls . After actuating the one or more delayed - ignition balls , the UAV drops the actuated one or more delayed - ignition balls from the UAV onto the site of the prescribed burn

Drawing on fire: children’s knowledge and needs after a wildfire disaster in Portugal

There is growing international concern about the impact of natural hazards and disasters on children and young people. However, very little research has explored children’s views of their wildfire experiences. Using creative methods with two groups of primary school children from an inland area, this qualitative study examines Portuguese children’s experiences of a wildfire disaster that affected their community in 2017. An inductive qualitative approach was used to explore the children’s interpretations of this particular event. The children reported high levels of exposure to the wildfire, either directly (in confronting the actual fire) or indirectly (by observing its impact on nature and their community). The implications of these findings are considered in relation to children’s roles in wildfire risk reduction and response.info:eu-repo/semantics/publishedVersio

Towards integrated fire management - Outcomes of the European Project Fire Paradox

info:eu-repo/semantics/publishedVersio

A review of machine learning applications in wildfire science and management

Artificial intelligence has been applied in wildfire science and management since the 1990s, with early applications including neural networks and expert systems. Since then the field has rapidly progressed congruently with the wide adoption of machine learning (ML) in the environmental sciences. Here, we present a scoping review of ML in wildfire science and management. Our objective is to improve awareness of ML among wildfire scientists and managers, as well as illustrate the challenging range of problems in wildfire science available to data scientists. We first present an overview of popular ML approaches used in wildfire science to date, and then review their use in wildfire science within six problem domains: 1) fuels characterization, fire detection, and mapping; 2) fire weather and climate change; 3) fire occurrence, susceptibility, and risk; 4) fire behavior prediction; 5) fire effects; and 6) fire management. We also discuss the advantages and limitations of various ML approaches and identify opportunities for future advances in wildfire science and management within a data science context. We identified 298 relevant publications, where the most frequently used ML methods included random forests, MaxEnt, artificial neural networks, decision trees, support vector machines, and genetic algorithms. There exists opportunities to apply more current ML methods (e.g., deep learning and agent based learning) in wildfire science. However, despite the ability of ML models to learn on their own, expertise in wildfire science is necessary to ensure realistic modelling of fire processes across multiple scales, while the complexity of some ML methods requires sophisticated knowledge for their application. Finally, we stress that the wildfire research and management community plays an active role in providing relevant, high quality data for use by practitioners of ML methods.Comment: 83 pages, 4 figures, 3 table

Implementing fire history and fire ecology in fire risk assessment: the study case of Canton Ticino (southern Switzerland)

The understanding of the ecological role of wildfires and the knowledge of its past natural and cultural dynamics in different ecosystems have been recognize as a prerequisite for a sustainable land and ecosystem management. The main objective of this work is to propose a methodological approach for implementing the knowledge derived from studies of fire history, fire ecology, and fire suppression strategies in fire risk analyses in a low-to medium fire-prone region such as the Canton Ticino

Extending Mission Duration of UAS Multicopters: Multi-disciplinary Approach

Multicopters are important tools in industry, the military, and research but suffer from short flight times and mission durations. In this thesis, we discuss three different ways to increase flight times and therefore increase the viability of using multicopters in a variety of missions. Alternate fuel sources such as hydrogen fuel and solar cells are starting to be used on multicopters, in our research we simulate modern fuel cells and show how well they currently work as the power source for multicopters and how close they are to becoming useful in Unmanned Aircraft System (UAS) technology. Increasing the efficiency in which the available energy is used can also increase mission duration. Two characteristics that affect the efficiency of a mission are the flight speeds of the multicopter and the payload it carries. These characteristics are well known in larger rotorcrafts but often ignored in smaller multicopters. In our research, we explore the effect of flight speed on the dynamics of a multicopter and show that higher speeds lead to higher flight times due to the effect of translational lift. Lastly, we developed an online updating multi-flight planning algorithm for stop and charge missions, a method that can potentially indefinitely extend a mission. The multi-flight planning algorithm, the variable resolution horizon, reduces the computing resources necessary to 15% to 40% of a typical optimal planner while having a maximum 5.6% decrease in expected future reward, a metric for accuracy. The results of this thesis help guide decisions in fuel type for multicopter missions show examples of how to increase flight time through increasing efficiency and develop the framework for multi-flight missions. Advisers: Justin Bradley and Carrick Detweile