In recent years, the frequency, intensity, and severity of wildfires have been on the rise due to various environmental factors. Several studies show that the strategic application of fuel treatments is effective at altering fire behavior and its spread patterns. Effective planning for mitigating future expected losses under wildfire risk is a complex challenge that requires the integration of fire spread, simulation, and optimization models as well as the inclusion of multiple objectives into a unified framework. Previous works simplify the analysis by valuing the landscape regions using a unique objective (e.g., minimize the average expected area burned) or a predefined objective function. However, such an assumption is a simplification of the real system as multiple parts of the landscape have different values based on factors such as the presence of human settlements and infrastructure, availability of environmental services, and forest health. In this work, we expand these previous attempts by providing an integrated framework to naturally include and weight multiple objectives into the optimization model and analyze the trade-off between present objectives and future protection against wildfire risk. We study three key regions based on their recent fire history, landscape diversity, and demographic variety to quantify the impact of multiple objectives in landscape management. We obtain treatment plans using various combinations of these layers reflecting how different priorities of the decision-makers could affect treatment policies.

近年来，由于各种环境因素，野火的频率，强度和严重性一直在上升。多项研究表明，燃料处理的战略应用可有效改变火势及其蔓延方式。有效的计划以减轻野火风险下的未来预期损失是一项复杂的挑战，需要整合火势蔓延，模拟和优化模型，以及将多个目标纳入统一框架。先前的工作通过使用唯一目标（例如，将平均预期燃烧面积最小化）或预定义目标函数对景观区域进行评估，简化了分析。然而，这种假设是对实际系统的简化，因为景观的多个部分基于诸如人类住区和基础设施的存在，环境服务的可用性以及森林健康等因素而具有不同的值。在这项工作中，我们通过提供一个集成的框架来扩展这些先前的尝试，以自然地将多个目标包括在内并权衡到优化模型中，并分析当前目标与未来防范野火风险之间的权衡。我们基于最近的火灾历史，景观多样性和人口变化研究了三个关键区域，以量化多个目标在景观管理中的影响。我们使用这些层的各种组合来获得治疗计划，以反映决策者的不同优先级如何影响治疗策略。