Mapping Firescapes for Wild and Prescribed Fire Management: A Landscape Classification Approach

Nicholas P. Gould (), Lars Y. Pomara, Sandhya Nepal, Scott L. Goodrick and Danny C. Lee
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Nicholas P. Gould: Eastern Forest Environmental Threat Assessment Center, Southern Research Station, United States Forest Service, Asheville, NC 28804, USA
Lars Y. Pomara: Eastern Forest Environmental Threat Assessment Center, Southern Research Station, United States Forest Service, Asheville, NC 28804, USA
Sandhya Nepal: Eastern Forest Environmental Threat Assessment Center, Southern Research Station, United States Forest Service, Asheville, NC 28804, USA
Scott L. Goodrick: Southern Research Station, United States Forest Service, Athens, GA 30602, USA
Danny C. Lee: Southern Research Station, United States Forest Service, Knoxville, TN 37919, USA

Land, 2023, vol. 12, issue 12, 1-35

Abstract: Risks associated with severe wildfire are growing in forest landscapes due to interactions among climate change, fuel accumulation from fire suppression, an expanding wildland–urban interface, and additional factors. People, infrastructure, ecosystem services, and forest health all face varying degrees of risk. The spatial distributions of the many social and ecological factors that influence wildfire, its impacts, and management responses are an important landscape-level context for managing risks and fostering resilient lands and communities. Decision-support tools that integrate these varied distributions can provide a holistic and readily interpreted characterization of landscapes, helping fire management decision making be appropriate, efficient, and effective. Firescapes—landscape types defined in relation to fire, its drivers, and its effects as a socioecological system—fill this role, providing a way to organize and interpret spatial variation along multiple relevant dimensions. We describe a quantitative approach for classifying and mapping firescapes for decision support, using the southeastern United States as a case study. We worked with regional partners to compile relevant large-scale datasets and identify 73 variables for analysis. We used factor analysis to reduce the data to eight factors with intuitive interpretations relevant to fire dynamics, fire history, forest characteristics, climate, conservation and ecosystem service values, social and ecological landscape properties, and social vulnerabilities. We then used cluster analysis on the factors to generate quantitative landscape classes, which we interpreted as nine distinctive firescape classes. The firescapes provide a broad-scale socioecological information context for wildfire risk management and planning. The analytical approach can accommodate different data types at a variety of scales, incorporate new monitoring data as they are available, and can be used under data-driven scenarios to assess possible consequences of future change. The resulting firescape maps can provide decision support to forest managers, planners, and other stakeholders, informing appropriate strategies to manage fire and associated risks, build community and forest resilience to fire, and improve conservation outcomes.

Keywords: cluster analysis; factor analysis; fire planning; firescape; forest resilience; prescribed fire; risk management; social vulnerability; wildfire (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2023
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