CyberSEES: Type 2: Computing and Visualizing Optimal Policies for Ecosystem Management

智慧价值许多生态系统展示了我们想要设法促进或防止的空间传播过程。例如，我们寻求防止入侵物种的传播，同时促进濒危物种的传播。对于野地火灾，我们寻求促进低强度地面火灾的蔓延，以防止危险燃料的积聚，同时防止破坏濒危物种栖息地和宝贵木材的高强度树冠火灾的蔓延。这些管理问题可以数学地表示为空间区域上定义的马尔可夫决策问题(MDP)。然而，由于这些问题的空间性质，MDP是巨大的，不能用任何现有的算法来解决。该项目为空间MDP开发了新的MDP求解算法。这些算法将与生态系统模拟器一起工作(而不是需要明确的模型)，它们还将解决物种灭绝或灾难性野火等灾难性后果的风险。为了弥合MDP的计算解决方案与政策制定者实际采用这种解决方案之间的差距，这项研究开发了可视化和交互方法，使利益相关者(例如，政策制定者、土地所有者、木材行业代表、保护生物学家)能够理解和批评问题的表述和由此产生的解决方案。更广泛的影响新方法将在五个管理问题上进行测试：(A)罗望鱼在河流网络中蔓延，(B)在美国西部牧场蔓延，(C)橡树猝死在加利福尼亚州和俄勒冈州蔓延，(D)决定何时让野火燃烧，以及(E)决定在景观中的哪里进行燃料减少处理，以降低火灾风险。将在风格化的环境中研究罗望鱼、作弊草和橡树猝死问题，其中相关的环境属性和成本可能会有所不同。这些研究的目的是了解不同的空间传播过程(由这些不同的入侵物种表现出来)如何决定最优管理政策的结构。研究结果将发表在自然资源经济学文献上，并与这些领域的政策制定者进行讨论。野火问题(“让火燃烧”和“燃料处理”)将在一个真实的景观中进行研究--德舒茨国家森林中的一个公共场地，那里有黄皮松和罗奇波尔松树的混合物。一个与美国林业局合作的消防经理将招募一个利益相关者小组，使用我们将开发的可视化和交互工具来分析和批评拟议的管理政策。在这个项目中开发的问题和技术将构成第一个计算可持续发展暑期学校的核心，该暑期学校将由研究团队组织。研究结果还将被整合到俄亥俄州立大学生态信息学暑期学院(NSF REU网站)和俄亥俄州立大学蒙特卡洛人工智能三年级本科生春假课程中。四名博士生和一名博士后将在该项目期间接受培训。

Intellectual MeritMany ecosystems exhibit spatially spreading processes that we would like to manage to either promote or prevent. For example, we seek to prevent the spread of invasive species while promoting the spread of endangered species. For wildland fire, we seek to promote the spread of low-intensity ground fires that prevent the buildup of dangerous fuels while preventing the spread of high-intensity crown fires that destroy endangered species habitat and valuable timber. These management problems can be formulated mathematically as Markov Decision Problems (MDPs) defined over spatial regions. However, because of the spatial nature of these problems, the MDPs are immense and cannot be solved by any existing algorithms. This project develops new MDP solution algorithms for spatial MDPs. These algorithms will work with ecosystem simulators (rather than requiring explicit models) and they will also address the risk of catastrophic outcomes such as species extinction or catastrophic wildfires. To bridge the gap between the computational solution of an MDP and the actual adoption of such solutions by policymakers, this research develops visualization and interaction methods that will allow stakeholders (e.g., policymakers, land owners, timber industry representatives, conservation biologists) to understand and critique both the problem formulations and the resulting solutions. Broader ImpactsThe new methods will be tested on five management problems: (a) Tamarisk spread in river networks, (b) Cheatgrass spread in Western US range lands, (c) Sudden Oak Death spread in California and Oregon, (d) deciding when to let a wildfire burn versus suppressing it, and (e) deciding where to place fuel reduction treatments in the landscape to reduce fire risk. The Tamarisk, Cheatgrass, and Sudden Oak Death problems will be studied in stylized settings where the relevant environmental properties and costs can be varied. The goal of these studies is to understand how the different spatial spreading processes (exhibited by these different invasive species) determine the structure of the optimal management policy. The results will be published in the literature on natural resource economics and discussed with policymakers in these areas. The wildfire problems ("let burn," and "fuel treatment") will be studied in a real landscape-a publicly-owned site in the Deschutes National Forest containing a mix of Ponderosa and Lodgepole pine. A collaborating fire manager with the US Forest Service will recruit a panel of stakeholders to analyze and critique the proposed management policies using the visualization and interaction tools that we will develop.Problems and techniques developed in this project will form the core of the first Summer School in Computational Sustainability, which will be organized by the research team. The results will also be integrated into the OSU Summer Institute in Eco-Informatics (an NSF REU Site) and the OSU Spring Break Course in Monte Carlo AI for junior undergraduates. Four Ph.D. students and one Postdoc will be trained during this project.