The Forest Service, U.S. Department of Agriculture, defines success in the wildland fire response environment as "safely achieving reasonable objectives with the least firefighter exposure necessary while enhancing stakeholder support for our management efforts". However, persistent information and knowledge gaps challenge the agency's ability to measure success in coming fire seasons. In this paper, we outline a roadmap to help fill these gaps, describing progress towards developing meaningful fire response key performance indicators (KPIs). We focus on characterizing suppression resource use and effectiveness as requisite initial steps towards reducing unnecessary exposure. Our intentions are to articulate the rationale for embracing KPIs for fire response operations, briefly review best practices as they relate to organizational performance measurement, and describe recent and emerging analysis techniques designed to ultimately improve responder exposure assessment. Specifically, we review tangible research products that could be operationalized as KPIs in the near future, and illustrate their calculation and interpretation for a set of large fires that occurred in the U.S. in 2017. To conclude, we offer thoughts on productive pathways forward with performance measurement.

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Accounting for externalities generated by fire spread is necessary for managing fire risk on landscapes with multiple owners. In this paper, we determine the optimal management of a synthetic landscape parameterized to represent the ecological conditions of Douglas-fir (Pseudotsuga menziesii) plantations in southwest Oregon. The problem is formulated as a dynamic game, where each agent maximizes their own objective without considering the welfare of the other agents. We demonstrate a method for incorporating spatial information and externalities into a dynamic optimization process. A machine-learning technique, approximate dynamic programming, is applied to determine the optimal timing and location of fuel treatments and timber harvests for each agent. The value functions we estimate explicitly account for the spatial interactions that generate fire risk. They provide a way to model the expected benefits, costs, and externalities associated with management actions that have uncertain consequences in multiple locations. The method we demonstrate is applied to analyze the effect of landscape fragmentation on landowner welfare and ecological outcomes.

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