Mapping firescapes for wild and prescribed fire management: a landscape classification approach

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 interpret 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 it is 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.

Technical info

1. R8 Firescapes Input Variable Dataset:

The 73 variables included in large-scale data synthesis for forested landscapes in 13 states in the USDA Forest Service Southern Region. Each row provides data corresponding to a 1,000-hectare landscape, as described in Gould et al. (2023). The dataset was used in factor analysis to generate synthetic factor variables used in the cluster analysis. See main text for data source references.

2. R8 Firescapes Input Variable Descriptions:

The variable descriptions file provides full names and descriptions linked to short names in the data file. Data sources for all variables are provided in Gould et al. (2023).

3. Firescapes - Factor and Cluster Analysis R Script.R:

Annotated R script detailing the factor and cluster analyses used in Gould et al. (2023) with the above-referenced dataset. Can be opened using free R statistical software.