EvaSylv：一个用户友好的软件，以评估林业灾害，包括自然风险
EvaSylv: A user-friendly software to evaluate forestry scenarii including natural risk
摘要：
森林管理依赖于造林实践的评估。由于气候变化造成的自然风险的增加，有必要考虑将自然风险考虑在内的评价标准。现有软件中的风险集成需要先进的编程技巧，我们提出了一个用户友好的软件来模拟均匀年龄和单种林分水平，以评估和优化森林管理。该软件提供了可能性，运行管理的风险，或不考虑自然风险的影响。控制变量为间伐日期、间伐率和采伐年龄，风险模型基于泊松过程。福斯特曼方法，包括树木损害风险，用于评估未来的利益，经济或生态系统服务。它依赖于预期值的计算，为此已经进行了专门的数学开发。用于评估各种场景的优化标准是福斯特曼值和平均产量值。我们通过两个案例研究说明了该方法和软件：山毛榉林的经济优化和松树林的碳封存优化。软件界面使用户可以轻松编写自己的（生长-树木损害-经济）模型，无需高级编程技能。在考虑/不考虑自然风险影响的情况下运行管理系统的可能性可能有助于改进造林准则并使其适应气候变化。我们提出了未来的研究和改进路线。
英文摘要：
Forest management relies on the evaluation of silviculture practices. The increase in natural risk due to climate change makes it necessary to consider evaluation criteria that take natural risk into account. Risk integration in existing software requires advanced programming skills.We propose a user-friendly software to simulate even-aged and monospecific forest at the stand level, in order to evaluate and optimize forest management. The software gives the possibility to run management scenarii with or without considering the impact of natural risk. The control variables are the dates and rates of thinning and the cutting age.The risk model is based on a Poisson processus. The Faustmann approach, including tree damage risk, is used to evaluate future benefits, economic or ecosystem services. It relies on the calculation of expected values, for which a dedicated mathematical development has been done. The optimized criteria used to evaluate the various scenarii are the Faustmann value and the Averaged yield value.We illustrate the approach and the software on two case studies: economic optimization of a beech stand and carbon sequestration optimization of a pine stand.Software interface makes it easy for users to write their own (growth-tree damage-economic) models without advanced programming skills. The possibility to run management scenarii with/without considering the impact of natural risk may contribute improving silviculture guidelines and adapting them to climate change. We propose future lines of research and improvement.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
