Improved LAI estimation and simulation

改进的 LAI 估算和模拟

• 批准号: 249672-2007
• 负责人: Guo, Xulin
• 金额: $ 1.09万
• 依托单位: University of Saskatchewan
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2010
• 资助国家: 加拿大
• 起止时间: 2010-01-01 至 2011-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=455358
• 关键词: Improved; LAI; estimation; simulation

Leaf area index (LAI) is a fundamental parameter for any work related to vegetation, but especially for modeling of natural phenomena. Specifically, it is a key element in climate models, hydrology models, and land surface processing models. The LAI is important because it is strongly related to the exchange of carbon dioxide, water, and energy between vegetation and atmosphere. Unfortunately, the vast majority of current models are based on low accuracy LAI estimates. This greatly limits the model's simulation and prediction accuracy. This alone demonstrates the strong need to improve LAI estimation and to provide a better approach for LAI parameterization. To respond to these concerns, the long term objectives of this program are threefold: 1) to improve LAI estimation accuracy with considerations of scale, heterogeneity, and land cover type; 2) to develop a better LAI parameterization approach for models in hydrology, climatology, and ecosystem; and 3) to investigate the effects of changes in land cover and land use on LAI dynamics. For the proposed five year period, my short term objectives are 1) to investigate spatial variation of LAI under different land cover types in Canadian prairies; 2) to develop a more accurate LAI derivation model from satellite imagery with considerations of topography, land cover, and vegetation condition; 3) to monitor temporal trajectory of LAI over the entire growing season; and 4) to assess the results of improved LAI on two well-established models. To achieve these goals, this program will study the target area at different spatial scales. Data will be collected at the field level and space level, covering different land cover types and spanning the entire growing season. Satellite imagery will range from high (10m), medium (30m), to low (1000m) resolutions. Output from this program will provide a better approach to LAI estimation and simulation for the Canadian prairies and will be applied to studies of the interaction between vegetation, atmosphere, and hydrology. The results of this research will eventually allow for a great improvement in the accuracy of any models relating to vegetation. Finally, this research program will provide a great opportunity for highly qualified personnel training.

叶面积指数（LAI）是任何与植被相关的工作的基本参数，尤其是对于自然现象的建模。具体而言，它是气候模型、水文模型和陆面处理模型的关键要素。叶面积指数是重要的，因为它与植被和大气之间的二氧化碳，水和能量交换密切相关。不幸的是，目前绝大多数模型是基于低精度的LAI估计。这极大地限制了模型的模拟和预测精度。仅这一点就表明，迫切需要改进叶面积指数的估计，并提供一个更好的方法叶面积指数参数化。为了回应这些问题，该计划的长期目标有三个方面：1）提高LAI估算精度，考虑尺度，异质性和土地覆盖类型; 2）开发一个更好的LAI参数化方法，用于水文，气候学和生态系统模型; 3）调查土地覆盖和土地利用变化对LAI动态的影响。本研究的短期目标是：1）研究加拿大大草原不同土地覆盖类型下叶面积指数的空间变化; 2）建立一个更精确的基于地形、土地覆盖和植被状况的卫星遥感叶面积指数模型; 3）监测整个生长季叶面积指数的时间轨迹;（4）在两个已建立的模型上评价改良叶面积指数的结果。为了实现这些目标，该计划将在不同的空间尺度上研究目标区域。将在实地和空间两级收集数据，涵盖不同的土地覆被类型，并涵盖整个生长季节。卫星图像的分辨率将从高（10米）、中（30米）到低（1000米）不等。从这个程序的输出将提供一个更好的方法来叶面积指数估计和模拟加拿大大草原，并将被应用到植被，大气和水文之间的相互作用的研究。这项研究的结果最终将大大提高任何与植被有关的模型的准确性。最后，本研究计划将为高素质人才的培养提供一个很好的机会。