A collaborative, cloud-based decision support system for structured wildfire risk mitigation planning

Multi-stakeholder planning and prioritization for ecosystem management and wildfire risk mitigation are complicated by the need to balance a multitude of values, goals, viewpoints, and interests across large landscapes. Doing so requires quantifying current conditions, defining management feasibility constraints, modeling complex system responses under different management and disturbance scenarios, quantifying outcomes in terms of social values, weighing and assessing tradeoffs, and identifying optimal strategies. Beginning in the 2010s, structured wildfire risk assessment tools were developed to provide a framework for prioritizing management actions based on wildfire hazard, ecological response, and decision-maker values. Yet, more than a decade later, operationalizing risk assessments remains challenging and limited by disconnected tooling, static data, and workflows that are difficult to scale or adapt for collaborative decision-making. Here, we present the Vibrant Planet Platform (VPP), a modular, cloud-based decision-support system that integrates fire simulation, ecological response functions, multi-objective optimization, and user input into a unified planning environment. The platform enables risk-based scenario planning across landscapes up to millions of hectares by linking validated modeling tools (e.g., FSim, FVS, ForSys) with high-resolution, up-to-date vegetation and infrastructure data. We describe the challenges inherent to operationalizing risk assessments, demonstrate how VPP addresses them through architectural and methodological design, and highlight real-world deployments in U.S. risk-exposed landscapes and communities. We outline a multi-tiered validation framework for assessing model relevance, internal coherence, predictive performance, and field alignment. VPP illustrates how structured decision-making can be operationalized at broad scales, offering a model for ecological planning tools that are rigorous, transparent, and participatory.