Integrated modelling and assessment of forest harvest patterns

我们的森林正面临越来越大的压力，需要通过提供高质量户外娱乐和旅游的绝佳机会来提供经济安全。在这一目标中，视觉质量是最重要的，任何通过森林采伐而在传统经济投入方面花费很少的视觉质量的提高都是一种双赢的局面。此外，森林的生态健康和功能也是一个非常重要的话题，它直接影响到我们的长期社会抵御短期干扰（如最近的山松甲虫流行）以及气候变化等长期挑战的能力。这个研究项目将帮助我们更好地了解如何平衡这些因素，从而更好地为子孙后代管理加拿大的森林。通过扩展原型应用程序的开发，旨在自动生成潜在的收获设计，并评估它们满足视觉质量目标的能力，我们希望更好地理解给定的非视觉（经济或生态）指标如何与视觉质量相互作用，并确定在哪些景观条件下可能发生协同作用或冲突。本研究分为4个阶段：(I)将提出的视觉效果参数和指标实施到软件程序中，（II）进行偏好研究以评估视觉指标，（III）识别并将非视觉效果指标纳入软件，以及（IV）进行最终偏好研究以确定权衡值。我们有信心，这项工作将提供更深入地了解收割设计的具体方面，以及它们如何影响视觉质量。通过使用可视化软件，我们不仅可以在收获之后简单地调查收获的视觉效果，还可以开始了解在收获发生之前，特定的收获如何出现在景观上。这将允许我们反复检查学科专家评估收获设计的方式，这些设计是他们领域中潜在的兴趣变量，以及这些与视觉设计的关系。此外，我们可以将一些收获设计过程自动化，以提高收获的视觉质量，从而满足各种生态或经济目标。

Our forests are under increasing pressure to provide economic security through their ability to provide excellent opportunities for high quality outdoor recreation and tourism. Visual quality is paramount in this goal and any increase in visual quality that costs little in the way of traditional economic inputs via forest harvesting represents a win-win situation. Additionally, ecological health and function of the forest is also a very important topic and has direct impacts on our long term societal resilience to weather short term disturbances such as the recent mountain pine beetle epidemic as well as longer term challenges such as climate change. This program of research will help us to better understand how to balance these factors and therefore better steward the forests of Canada for future generations. By extending the development of a prototype application designed to automatically generate potential harvest designs, and assess them on their ability to meet visual quality targets we hope better understand how a given non-visual (economic or ecological) metric interact with visual quality and identify under which landscape conditions possible synergies or conflicts are likely to occur. This research is organized into 4 phases: (I) implement proposed visual effect parameters and metrics into the software program, (II) conduct a preference study to evaluate visual metrics, (III) identify and include non-visual effect metrics into software, and (IV) conduct a final preference study to identify trade-off values. We are confident that this work will provide a deeper look into specific aspects of harvest design and how they affect visual quality. More than simply investigating the visual effect of a harvest after it has been conducted, by using visualization software, we can begin to understand how a particular harvest may appear on a landscape before the harvest has occurred. This will allow us to iteratively check the ways in which disciplinary experts evaluate the harvest designs which underlie variables of interest in their area and how these relate to visual design. Furthermore, we can move to automate some of the harvest design process with the intention of increasing the visual quality of a harvest given our desire to meet various ecological or economic objectives.