Fire-prone landscapes found throughout the world are increasingly managed with prescribed fire for a variety of objectives. These frequent low-intensity fires directly impact lower forest strata, and thus estimating surface fuels or understory vegetation is essential for planning, evaluating, and monitoring management strategies and studying fire behavior and effects. Traditional fuel estimation methods can be applied to stand-level and canopy fuel loading; however, local-scale understory biomass remains challenging because of complex within-stand heterogeneity and fast recovery post-fire. Previous studies have demonstrated how single location terrestrial laser scanning (TLS) can be used to estimate plot-level vegetation characteristics and the impacts of prescribed fire. To build upon this methodology, co-located single TLS scans and physical biomass measurements were used to generate linear models for predicting understory vegetation and fuel biomass, as well as consumption by fire in a southeastern U.S. pineland. A variable selection method was used to select the six most important TLS-derived structural metrics for each linear model, where the model fit ranged in R2 from 0.61 to 0.74. This study highlights prospects for efficiently estimating vegetation and fuel characteristics that are relevant to prescribed burning via the integration of a single-scan TLS method that is adaptable by managers and relevant for coupled fire–atmosphere models.
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A review of optimization and decision models of prescribed burning for wildfire management
Abstract Prescribed burning is an essential forest management tool that requires strategic planning to effectively address its multidimensional impacts, particularly given the influence of global climate change on fire behavior. Despite the inherent complexity in planning prescribed burns, limited efforts have been made to comprehensively identify the critical elements necessary for formulating effective models. In this work, we present a systematic review of the literature on optimization and decision models for prescribed burning, analyzing 471 academic papers published in the last 25 years. Our study identifies four main types of models: spatial‐allocation, spatial‐extent, temporal‐only, and spatial–temporal. We observe a growing number of studies on modeling prescribed burning, primarily due to the expansion in spatial‐allocation and spatial–temporal models. There is also an increase in complexity as the models consider more elements affecting prescribed burning effectiveness. We identify the essential components for optimization models, including stakeholders, decision variables, objectives, and influential factors, to enhance model practicality. The review also examines solution techniques, such as integer programming in spatial allocation, stochastic dynamic programming in probabilistic models, and multiobjective programming in balancing trade‐offs. These techniques' strengths and limitations are discussed to help researchers adapt methods to specific challenges in prescribed burning optimization. In addition, we investigate general assumptions in the models and challenges in relaxation to enhance practicality. Lastly, we propose future research to develop more comprehensive models incorporating dynamic fire behaviors, stakeholder preferences, and long‐term impacts. Enhancing these models' accuracy and applicability will enable decision‐makers to better manage wildfire treatment outcomes.
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- Award ID(s):
- 2230869
- PAR ID:
- 10600980
- Publisher / Repository:
- Wiley
- Date Published:
- Journal Name:
- Risk Analysis
- ISSN:
- 0272-4332
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Although prescribed fire is frequently used in the Southeastern United States, land managers in the region and across the country plan to expand burning to mitigate wildfire and achieve other ecological goals. However, smoke management is often considered a barrier to prescribed fire. Additionally, climate change will likely affect the frequency of acceptable meteorological conditions for prescribed burning, potentially restricting the use of the practice. Here, we examine the air quality impacts from prescribed fire and wildfire in the Southeastern U.S., the populations affected by smoke in the region, and how these impacts may change under climate change. We rely on projections of wildfire burn area and climate-driven shifts in the frequency of meteorological conditions adequate for prescribed burning, as well as a survey of Southeastern land managers investigating their anticipated response to these changes. Based on this information, we use chemical transport modeling to assess the contributions of wildfire and prescribed fire to air pollution, and project how smoke impacts may vary due to climate change and different land manager responses. We find that prescribed fire is responsible for a significant fraction of regional particulate matter pollution. Populations exposed to the most smoke tend to have higher fractions of people of color and low income. Depending on how land managers respond to changes in atmospheric conditions under climate change, prescribed fire smoke may decrease slightly in the areas with the heaviest burning or increase across much of the Southeast. Projections also show that climate-driven changes in wildfire and prescribed burning may impact compliance with recently updated air quality standards. The analysis assesses the potential consequences of climate change on air pollution over a region in which wildland fire is extensively managed, providing insight into land management strategies that call for increased application of prescribed fire.more » « less
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