Characterising forest landscapes using a stationary and a roving tower eddy covariance system: linking point data to landscape-level characterisations of growth and yield

使用固定和流动塔涡流协方差系统表征森林景观：将点数据与生长和产量的景观级表征联系起来

• 批准号: 170415-2010
• 负责人: Bourque, Charles
• 金额: $ 2.11万
• 依托单位: University of New Brunswick
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2012
• 资助国家: 加拿大
• 起止时间: 2012-01-01 至 2013-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=506966
• 关键词: Characterising; forest; landscapes; using; stationary

Eddy covariance-based net ecosystem productivity (NEP) measurements taken over growing forests provide the best possible measure of stand wood production. Stand-level forest models based on forest transition rules or modelled interactions between individual trees (gap models) provide generalisations of stand growth and wood production at various levels of accuracy. Most of these models rely on historical tree data for model formulation-calibration, for which growth behavior is well documented. In a changing climate regime, tree growth behavior is most likely to change, rendering future forest growth projections with forest transition models unsuitable. Also, landscape application of gap models is prohibitive because of the huge amount of computer resources needed to calibrate and run the model for a landscape of potentially tens of thousands stands, many with different attributes. Landscape application of existing ecosystem-based NEP models is also not practical, because of the models' complexity and data requirements. Because of these limitations, the proposed work is to develop a forest G&Y by extending an existing 10-parameter NEP model to the landscape by linking the model's monthly growth projections to local biophysical conditions. These conditions are to be derived by combining remote sensing-based images of temperature and leaf area with landscape-level simulations of solar radiation and soil water content. Forest ecosystem characterisation and model parameter setting are to be based mostly on information collected by a stationary base station and roving tower system. The 37-m stationary tower will provide long term base weather and ecological data. This information along with NEP and weather data collected at forest sites with a roving tower will be used to derive the required forest-specific growth parameters for different forest cover, and extend 3-4 week-long time series of NEP for application at the landscape level. The proposed work provides a major scientific innovation in G&Y modelling.

基于涡动协方差的净生态系统生产力（NEP）的测量，在不断增长的森林提供了最好的措施，林分木材生产。 林分一级森林模型的基础上，森林过渡规则或模拟的相互作用，个别树木（差距模型）提供了概括的林分生长和木材生产在不同的精度水平。 这些模型中的大多数依赖于历史树数据来进行模型制定-校准，其中生长行为有很好的记录。 在不断变化的气候状况下，树木的生长行为最有可能发生变化，从而使未来的森林生长预测与森林过渡模型不适用。 此外，景观应用的差距模型是禁止的，因为需要大量的计算机资源来校准和运行模型的景观可能有数万个立场，许多不同的属性。 由于模型的复杂性和数据要求，现有的基于生态系统的NEP模型在景观中的应用也不现实。由于这些局限性，拟议的工作是开发一个森林G&Y的扩展现有的10个参数的NEP模型的景观模型的每月增长预测当地的生物物理条件。这些条件将通过将温度和叶面积的遥感图像与太阳辐射和土壤含水量的卫星级模拟相结合而得出。森林生态系统的特征描述和模型参数的设定将主要以固定基站和流动塔系统收集的信息为基础。37米高的固定塔将提供长期的基本天气和生态数据。这一信息沿着NEP和气象数据收集的森林网站与一个巡回塔将被用来推导出所需的森林特定的生长参数为不同的森林覆盖，并延长3-4周的时间序列的NEP在景观水平上的应用。拟议的工作提供了一个重大的科学创新G&Y建模。