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

Cyber​​SEES：类型 2：计算和可视化生态系统管理的最佳策略

• 批准号: 1331932
• 负责人: Thomas Dietterich
• 金额: $ 120万
• 依托单位: Oregon State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-15 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1331932&HistoricalAwards=false
• 关键词: CyberSEES; Type; Computing; Visualizing; Optimal

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.

智力价值许多生态系统都表现出空间扩散过程，我们希望设法促进或阻止这些过程。例如，我们力求防止入侵物种的传播，同时促进濒危物种的传播。对于荒地火灾，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，同时防止高强度树冠火灾的蔓延，从而破坏濒危物种栖息地和宝贵的木材。这些管理问题可以用数学方式表述为在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 非常巨大，无法通过任何现有算法来解决。该项目为空间 MDP 开发新的 MDP 求解算法。这些算法将与生态系统模拟器一起使用（而不需要明确的模型），并且它们还将解决灾难性结果的风险，例如物种灭绝或灾难性野火。为了弥合 MDP 的计算解决方案与政策制定者实际采用此类解决方案之间的差距，本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）能够理解和理解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) 柽柳在河网中的蔓延，(b) 茅草在美国西部牧场的蔓延，(c) 橡树突然死亡在加利福尼亚州和俄勒冈州的蔓延，(d) 决定何时采取措施让野火燃烧而不是扑灭它，以及（e）决定在景观中的何处放置燃料减少处理以降低火灾风险。柽柳、Cheatgrass 和橡树猝死问题将在相关环境特性和成本可能变化的程式化环境中进行研究。这些研究的目的是了解不同的空间扩散过程（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中，并与这些领域的政策制定者进行讨论。野火问题（“让燃烧”和“燃料处理”）将在真实的景观中进行研究——德舒特国家森林中的一个公共场所，其中混合了黄松和黑松。与美国林务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。该项目中开发的问题和技术将构成第一届暑期学校的核心计算可持续性，将由研究团队组织。研究结果还将被纳入 OSU 生态信息学夏季学院（NSF REU 网站）和 OSU 蒙特卡洛 AI 春假课程，供低年级本科生使用。四个博士学生和一名博士后将在该项目期间接受培训。