Copulas as a link for modeling stand structure and development

点击翻译按钮获取中文摘要

Good forest management requires good management tools and good forest inventory data. Forest management plans often span 60 or more years. Planning involves accounting for much uncertainty in projections of markets and resource flows. Traditionally forest managers rely on growth and yield models that provide a single estimate for each unique condition within the forest. Errors associated with those predictions are often ignored. A novel statistical approach is proposed here to enhance estimation of current forest conditions using new laser technology and to develop a new type of individual tree growth and yield model that will enable forest managers to directly assess uncertainty and incorporate uncertainty into their planning models. The goal of this project is to extract individual tree information from airborne laser data across an entire forest area, and to use this data as inputs into the growth model to predict forest development, assess species composition change under potential climate change scenarios, design innovative silvicultural options for managing Canada's forests, and to assess risk of forest management planning options. This work will enable forest managers to better anticipate changes in the forest and to plan safeguards to ensure sustainable use of Canada's forest into the future.