Special Issue "Decision Support System Development of Wildland Fire"

A special issue of Forests (ISSN 1999-4907). This special issue belongs to the section "Forest Ecology and Management".

Deadline for manuscript submissions: closed (28 May 2021) | Viewed by 14754

Special Issue Editors

Dr. David E Calkin
E-Mail Website
Guest Editor
USDA ARS Rocky Mountain Research Station, US Forest Service, 800 East Beckwith Avenue, Missoula, MT 59801-5801, USA
Interests: wildland fire risk assessment; identification of values-at-risk to wildland fire; decision support system development; performance measurement of wildland fire suppression; modeling and forecasting of wildfire suppression costs; social and managerial tradeoffs among resources affected by fire management
Special Issues, Collections and Topics in MDPI journals
Dr. Matthew P. Thompson
E-Mail Website
Guest Editor
USDA Forest Service, Rocky Mountain Research Station, 240 W Prospect, Fort Collins, CO 80526, USA
Interests: risk and decision analysis; operations research; forest engineering; wildland fire management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Increasingly damaging wildfires around the globe have challenged the fire management community’s ability to achieve organizational objectives and meet societal expectations while protecting responders from the significant hazards of the wildfire environment. The inherent complexity and high levels of uncertainty require innovative partnerships between the research and management communities to create timely, accurate, and appropriate data and models that inform a range of critical management decisions. Decision support systems (DSS) are emerging that support the range of the risk management cycle of planning, deciding, executing, monitoring, and learning. Topics for papers in this Special Issue include development, application, and needs assessment of DSS to inform the range of wildfire management decisions, including wildfire management organizational design, pre-event wildfire response and fuel treatment planning, incident strategy, suppression of resource needs and effectiveness measurement, and multievent prioritization.   

Dr. David E Calkin
Dr. Matthew P. Thompson
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Wildfire management
  • Decision support systems
  • Strategic management
  • Decision making
  • Risk and uncertainty

Published Papers (10 papers)

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Research

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Article
Strategic Wildfire Response Decision Support and the Risk Management Assistance Program
Forests 2021, 12(10), 1407; https://0-doi-org.brum.beds.ac.uk/10.3390/f12101407 - 15 Oct 2021
Cited by 2 | Viewed by 911
Abstract
In 2016, the USDA Forest Service, the largest wildfire management organization in the world, initiated the risk management assistance (RMA) program to improve the quality of strategic decision-making on its largest and most complex wildfire events. RMA was designed to facilitate a more [...] Read more.
In 2016, the USDA Forest Service, the largest wildfire management organization in the world, initiated the risk management assistance (RMA) program to improve the quality of strategic decision-making on its largest and most complex wildfire events. RMA was designed to facilitate a more formal risk management process, including the use of the best available science and emerging research tools, evaluation of alternative strategies, consideration of the likelihood of achieving objectives, and analysis of tradeoffs across a diverse range of incident objectives. RMA engaged personnel from a range of disciplines within the wildfire management system to co-produce actionable science that met the needs of the highly complex incident decision-making environment while aiming to align with best practices in risk assessment, structured decision-making, and technology transfer. Over the four years that RMA has been in practice, the content, structure, and method of information delivery have evolved. Furthermore, the RMA program’s application domain has expanded from merely large incident support to incorporate pre-event assessment and training, post-fire review, organizational change, and system improvement. In this article, we describe the history of the RMA program to date, provide some details and references to the tools delivered, and provide several illustrative examples of RMA in action. We conclude with a discussion of past and ongoing program adaptations and of how this can inform ongoing change efforts and offer thoughts on future directions. Full article
(This article belongs to the Special Issue Decision Support System Development of Wildland Fire)
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Article
21st Century Planning Techniques for Creating Fire-Resilient Forests in the American West
Forests 2021, 12(8), 1084; https://0-doi-org.brum.beds.ac.uk/10.3390/f12081084 - 13 Aug 2021
Cited by 2 | Viewed by 1382
Abstract
Data-driven decision making is the key to providing effective and efficient wildfire protection and sustainable use of natural resources. Due to the complexity of natural systems, management decision(s) require clear justification based on substantial amounts of information that are both accurate and precise [...] Read more.
Data-driven decision making is the key to providing effective and efficient wildfire protection and sustainable use of natural resources. Due to the complexity of natural systems, management decision(s) require clear justification based on substantial amounts of information that are both accurate and precise at various spatial scales. To build information and incorporate it into decision making, new analytical frameworks are required that incorporate innovative computational, spatial, statistical, and machine-learning concepts with field data and expert knowledge in a manner that is easily digestible by natural resource managers and practitioners. We prototyped such an approach using function modeling and batch processing to describe wildfire risk and the condition and costs associated with implementing multiple prescriptions for risk mitigation in the Blue Mountains of Oregon, USA. Three key aspects of our approach included: (1) spatially quantifying existing fuel conditions using field plots and Sentinel 2 remotely sensed imagery; (2) spatially defining the desired future conditions with regards to fuel objectives; and (3) developing a cost/revenue assessment (CRA). Each of these components resulted in spatially explicit surfaces describing fuels, treatments, wildfire risk, costs of implementation, projected revenues associated with the removal of tree volume and biomass, and associated estimates of model error. From those spatially explicit surfaces, practitioners gain unique insights into tradeoffs among various described prescriptions and can further weigh those tradeoffs against financial and logistical constraints. These types of datasets, procedures, and comparisons provide managers with the information needed to identify, optimize, and justify prescriptions across the landscape. Full article
(This article belongs to the Special Issue Decision Support System Development of Wildland Fire)
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Article
Is This Flight Necessary? The Aviation Use Summary (AUS): A Framework for Strategic, Risk-Informed Aviation Decision Support
Forests 2021, 12(8), 1078; https://0-doi-org.brum.beds.ac.uk/10.3390/f12081078 - 12 Aug 2021
Cited by 4 | Viewed by 1095
Abstract
Across the globe, aircraft that apply water and suppressants during active wildfires play key roles in wildfire suppression, and these suppression resources can be highly effective. In the United States, US Department of Agriculture Forest Service (USFS) aircraft account for a substantial portion [...] Read more.
Across the globe, aircraft that apply water and suppressants during active wildfires play key roles in wildfire suppression, and these suppression resources can be highly effective. In the United States, US Department of Agriculture Forest Service (USFS) aircraft account for a substantial portion of firefighting expense and higher fatality rates compared to ground resources. Existing risk management practices that are fundamental to aviation safety (e.g., routinely asking, “Is this flight necessary?”) may not be appropriately scaled from a risk management perspective to ensure that the tactical use of aircraft is in clear alignment with a wildfire’s incident strategy and with broader agency and interagency fire management goals and objectives. To improve strategic risk management of aviation assets in wildfire suppression, we present a framework demonstrating a risk-informed strategic aviation decision support system, the Aviation Use Summary (AUS). This tool utilizes aircraft event tracking data, existing geospatial datasets, and emerging analytics to summarize incident-scale aircraft use and guide decision makers through a strategic risk management process. This information has the potential to enrich the decision space of the decision maker and supports programmatic transparency, enhanced learning, and a broader level of accountability. Full article
(This article belongs to the Special Issue Decision Support System Development of Wildland Fire)
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Article
Hazards of Risk: Identifying Plausible Community Wildfire Disasters in Low-Frequency Fire Regimes
Forests 2021, 12(7), 934; https://0-doi-org.brum.beds.ac.uk/10.3390/f12070934 - 16 Jul 2021
Cited by 4 | Viewed by 1263
Abstract
Optimized wildfire risk reduction strategies are generally not resilient in the event of unanticipated, or very rare events, presenting a hazard in risk assessments which otherwise rely on actuarial, mean-based statistics to characterize risk. This hazard of actuarial approaches to wildfire risk is [...] Read more.
Optimized wildfire risk reduction strategies are generally not resilient in the event of unanticipated, or very rare events, presenting a hazard in risk assessments which otherwise rely on actuarial, mean-based statistics to characterize risk. This hazard of actuarial approaches to wildfire risk is perhaps particularly evident for infrequent fire regimes such as those in the temperate forests west of the Cascade Range crest in Oregon and Washington, USA (“Westside”), where fire return intervals often exceed 200 years but where fires can be extremely intense and devastating. In this study, we used wildfire simulations and building location data to evaluate community wildfire exposure and identify plausible disasters that are not based on typical mean-based statistical approaches. We compared the location and magnitude of simulated disasters to historical disasters (1984–2020) in order to characterize plausible surprises which could inform future wildfire risk reduction planning. Results indicate that nearly half of communities are vulnerable to a future disaster, that the magnitude of plausible disasters exceeds any recent historical events, and that ignitions on private land are most likely to result in very high community exposure. Our methods, in combination with more typical actuarial characterizations, provide a way to support investment in and communication with communities exposed to low-probability, high-consequence wildfires. Full article
(This article belongs to the Special Issue Decision Support System Development of Wildland Fire)
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Article
The Human Dimensions of Spatial, Pre-Wildfire Planning Decision Support Systems: A Review of Barriers, Facilitators, and Recommendations
Forests 2021, 12(4), 483; https://0-doi-org.brum.beds.ac.uk/10.3390/f12040483 - 14 Apr 2021
Cited by 5 | Viewed by 1924
Abstract
Decision support systems (DSSs) are increasingly common in forest and wildfire planning and management in the United States. Recent policy direction and frameworks call for collaborative assessment of wildfire risk to inform fuels treatment prioritization using the best available science. There are numerous [...] Read more.
Decision support systems (DSSs) are increasingly common in forest and wildfire planning and management in the United States. Recent policy direction and frameworks call for collaborative assessment of wildfire risk to inform fuels treatment prioritization using the best available science. There are numerous DSSs applicable to forest and wildfire planning, which can support timely and relevant information for decision making, but the use and adoption of these systems is inconsistent. There is a need to elucidate the use of DSSs, specifically those that support pre-wildfire, spatial planning, such as wildfire risk assessment and forest fuels treatment prioritization. It is important to understand what DSSs are in use, barriers and facilitators to their use, and recommendations for improving their use. Semi-structured interviews with key informants were used to assess these questions. Respondents identified numerous barriers, as well as recommendations for improving DSS development and integration, specifically with respect to capacity, communication, implementation, question identification, testing, education and training, and policy, guidance, and authorities. These recommendations can inform DSS use for wildfire risk assessment and treatment prioritization to meet the goals of national policies and frameworks. Lastly, a framework for organizing spatial, pre-wildfire planning DSSs to support end-user understanding and use is provided. Full article
(This article belongs to the Special Issue Decision Support System Development of Wildland Fire)
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Article
Optimum Sensors Allocation for a Forest Fires Monitoring System
Forests 2021, 12(4), 453; https://0-doi-org.brum.beds.ac.uk/10.3390/f12040453 - 09 Apr 2021
Cited by 4 | Viewed by 792
Abstract
Every year forest fires destroy millions of hectares of land worldwide. Detecting forest fire ignition in the early stages is fundamental to avoid forest fires catastrophes. In this approach, Wireless Sensor Network is explored to develop a monitoring system to send alert to [...] Read more.
Every year forest fires destroy millions of hectares of land worldwide. Detecting forest fire ignition in the early stages is fundamental to avoid forest fires catastrophes. In this approach, Wireless Sensor Network is explored to develop a monitoring system to send alert to authorities when a fire ignition is detected. The study of sensors allocation is essential in this type of monitoring system since its performance is directly related to the position of the sensors, which also defines the coverage region. In this paper, a mathematical model is proposed to solve the sensor allocation problem. This model considers the sensor coverage limitation, the distance, and the forest density interference in the sensor reach. A Genetic Algorithm is implemented to solve the optimisation model and minimise the forest fire hazard. The results obtained are promising since the algorithm could allocate the sensor avoiding overlaps and minimising the total fire hazard value for both regions considered. Full article
(This article belongs to the Special Issue Decision Support System Development of Wildland Fire)
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Article
A Qualitative Study on the US Forest Service’s Risk Management Assistance Efforts to Improve Wildfire Decision-Making
Forests 2021, 12(3), 344; https://0-doi-org.brum.beds.ac.uk/10.3390/f12030344 - 15 Mar 2021
Cited by 7 | Viewed by 999
Abstract
To support improved wildfire incident decision-making, in 2017 the US Forest Service (Forest Service) implemented risk-informed tools and processes, together known as Risk Management Assistance (RMA). The Forest Service is developing tools such as RMA to improve wildfire decision-making and implements these tools [...] Read more.
To support improved wildfire incident decision-making, in 2017 the US Forest Service (Forest Service) implemented risk-informed tools and processes, together known as Risk Management Assistance (RMA). The Forest Service is developing tools such as RMA to improve wildfire decision-making and implements these tools in complex organizational environments. We assessed the perceived value of RMA and factors that affected its use to inform the literature on decision support for fire management. We sought to answer two questions: (1) What was the perceived value of RMA for line officers who received it?; and (2) What factors affected how RMA was received and used during wildland fire events? We conducted a qualitative study involving semi-structured interviews with decision-makers to understand the contextualized and interrelated factors that affect wildfire decision-making and the uptake of a decision-support intervention such as RMA. We used a thematic coding process to analyze our data according to our questions. RMA increased line officers’ ability to communicate the rationale underlying their decisions more clearly and transparently to their colleagues and partners. Our interviewees generally said that RMA data analytics were valuable but did not lead to changes in their decisions. Line officer personality, pre-season exposure to RMA, local political dynamics and conditions, and decision biases affected the use of RMA. Our findings reveal the complexities of embracing risk management, not only in the context of US federal fire management, but also in other similar emergency management contexts. Attention will need to be paid to existing decision biases, integration of risk management approaches in the interagency context, and the importance of knowledge brokers to connect across internal organizational groups. Our findings contribute to the literature on managing change in public organizations, specifically in emergency decision-making contexts such as fire management. Full article
(This article belongs to the Special Issue Decision Support System Development of Wildland Fire)
Article
A Deep Learning Approach to Downscale Geostationary Satellite Imagery for Decision Support in High Impact Wildfires
Forests 2021, 12(3), 294; https://0-doi-org.brum.beds.ac.uk/10.3390/f12030294 - 03 Mar 2021
Cited by 1 | Viewed by 1440
Abstract
Scarcity in wildland fire progression data as well as considerable uncertainties in forecasts demand improved methods to monitor fire spread in real time. However, there exists at present no scalable solution to acquire consistent information about active forest fires that is both spatially [...] Read more.
Scarcity in wildland fire progression data as well as considerable uncertainties in forecasts demand improved methods to monitor fire spread in real time. However, there exists at present no scalable solution to acquire consistent information about active forest fires that is both spatially and temporally explicit. To overcome this limitation, we propose a statistical downscaling scheme based on deep learning that leverages multi-source Remote Sensing (RS) data. Our system relies on a U-Net Convolutional Neural Network (CNN) to downscale Geostationary (GEO) satellite multispectral imagery and continuously monitor active fire progression with a spatial resolution similar to Low Earth Orbit (LEO) sensors. In order to achieve this, the model trains on LEO RS products, land use information, vegetation properties, and terrain data. The practical implementation has been optimized to use cloud compute clusters, software containers and multi-step parallel pipelines in order to facilitate real time operational deployment. The performance of the model was validated in five wildfires selected from among the most destructive that occurred in California in 2017 and 2018. These results demonstrate the effectiveness of the proposed methodology in monitoring fire progression with high spatiotemporal resolution, which can be instrumental for decision support during the first hours of wildfires that may quickly become large and dangerous. Additionally, the proposed methodology can be leveraged to collect detailed quantitative data about real-scale wildfire behaviour, thus supporting the development and validation of fire spread models. Full article
(This article belongs to the Special Issue Decision Support System Development of Wildland Fire)
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Article
Prototyping a Geospatial Atlas for Wildfire Planning and Management
Forests 2020, 11(9), 909; https://0-doi-org.brum.beds.ac.uk/10.3390/f11090909 - 20 Aug 2020
Cited by 9 | Viewed by 1829
Abstract
Wildland fire managers are increasingly embracing risk management principles by being more anticipatory, proactive, and “engaging the fire before it starts”. This entails investing in pre-season, cross-boundary, strategic fire response planning with partners and stakeholders to build a shared understanding of wildfire risks [...] Read more.
Wildland fire managers are increasingly embracing risk management principles by being more anticipatory, proactive, and “engaging the fire before it starts”. This entails investing in pre-season, cross-boundary, strategic fire response planning with partners and stakeholders to build a shared understanding of wildfire risks and management opportunities. A key innovation in planning is the development of potential operational delineations (PODs), i.e., spatial management units whose boundaries are relevant to fire containment operations (e.g., roads, ridgetops, and fuel transitions), and within which potential fire consequences, suppression opportunities/challenges, and strategic response objectives can be analyzed to inform fire management decision making. As of the summer of 2020, PODs have been developed on more than forty landscapes encompassing National Forest System lands across the western USA, providing utility for planning, communication, mitigation prioritization, and incident response strategy development. Here, we review development of a decision support tool—a POD Atlas—intended to facilitate cross-boundary, collaborative strategic wildfire planning and management by providing high-resolution information on landscape conditions, values at risk, and fire management resource needs for individual PODs. With the atlas, users can rapidly access and assimilate multiple forms of pre-loaded data and analytics in a customizable manner. We prototyped and operationalized this tool in concert with, and for use by, fire managers on several National Forests in the Southern Rocky Mountains of the USA. We present examples, discuss real-world use cases, and highlight opportunities for continued decision support improvement. Full article
(This article belongs to the Special Issue Decision Support System Development of Wildland Fire)
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Review

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Review
Decision Support System Development of Wildland Fire: A Systematic Mapping
Forests 2021, 12(7), 943; https://0-doi-org.brum.beds.ac.uk/10.3390/f12070943 - 17 Jul 2021
Cited by 2 | Viewed by 1060
Abstract
Wildland fires have been a rising problem on the worldwide level, generating ecological and economic losses. Specifically, between wildland fire types, uncontrolled fires are critical due to the potential damage to the ecosystem and their effects on the soil, and, in the last [...] Read more.
Wildland fires have been a rising problem on the worldwide level, generating ecological and economic losses. Specifically, between wildland fire types, uncontrolled fires are critical due to the potential damage to the ecosystem and their effects on the soil, and, in the last decade, different technologies have been applied to fight them. Selecting a specific technology and Decision Support Systems (DSS) is fundamental, since the results and validity of this could drastically oscillate according to the different environmental and geographic factors of the terrain to be studied. Given the above, a systematic mapping was realized, with the purpose of recognizing the most-used DSS and context where they have been applied. One hundred and eighty-three studies were found that used different types of DSS to solve problems of detection, prediction, prevention, monitoring, simulation, administration, and access to routes. The concepts key to the type of solution are related to the use or development of systems or Information and Communication Technologies (ICT) in the computer science area. Although the use of BA and Big Data has increased in recent years, there are still many challenges to face, such as staff training, the friendly environment of DSS, and real-time decision-making. Full article
(This article belongs to the Special Issue Decision Support System Development of Wildland Fire)
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