Remote sensing of canadian grasslands: using vegetation biochemistry to monitor grassland health

对植被生化和生物物理性质的评估可以更好地了解草地状况，进一步提高生态系统建模的性能。从遥感数据中估计了一些关键的植被生物物理参数，并将其用作不同空间和时间分辨率的生态系统过程模型的输入。然而，控制生态系统生物地球化学过程的植被生化变量并不总是以空间明确的格式提供，由于缺乏广泛区域的高光谱和空间分辨率的适当数据，因此必须在模型中保持不变。高光谱遥感领域的最新发展为估计植被的生化特性开辟了新的途径。因此，我建议探索和发展前沿的遥感方法，以在多个空间尺度上估计空间显式的草地生化特性(例如，叶绿素浓度和类胡萝卜素浓度)。具体地说，这项研究将1)开发基于指数的方法来准确估计冠层水平上的草地生化性质；2)研究物理模型方法在冠层水平上准确估计草地生物化学物质的能力；3)利用空间高光谱数据从冠层水平向景观水平扩展，并在景观水平上评估基于指数的和基于物理模型的生物化学估计对混杂因子(即裸露土壤和非光合作用植被)的依赖性。拟议的工作将解决有关遥感、生物化学和草原生态系统的重大知识差距。预计这项工作的成果将成为加拿大混合和高大草原生态系统碳汇研究、草原生态系统可持续发展以及气候变化和多变性影响研究的重要投入，也将成为利益攸关方的决策工具。

Assessment of vegetation biochemical and biophysical properties can lead to a better understanding of grassland conditions and further improve performance of ecosystem modeling. Some key vegetation biophysical parameters have been estimated from remote sensing data and used as inputs to ecosystem process models at various spatial and temporal resolutions. However, vegetation biochemical variables that control ecosystem biogeochemical processes, are not always available in a spatially-explicit format and have to be held constant in the models due to a lack of appropriate data with high spectral and spatial resolutions across a broad area. Recent developments in the field of hyperspectral remote sensing have opened new ways for estimating vegetation's biochemical properties. Consequently, I propose to explore and develop cutting-edge remote sensing methods to estimate spatially-explicit grassland biochemical properties (e.g. chlorophyll concentrations and carotenoid concentrations) at multiple spatial scales. Specifically, the research will 1) develop index-based approach to accurately estimate grassland biochemical properties at the canopy level; 2) investigate the capability of physical modelling approach to accurately estimate grassland biochemicals at the canopy level; 3) scale up from the canopy level to the landscape level using space-borne hyperspectral data and assess the dependence of the index-based and the physical modeling based biochemical estimations on confounding factors (i.e. exposed soil and non-photosynthetic vegetation) at the landscape level. The proposed work will address the significant knowledge gap concerning remote sensing biochemistry and grassland ecosystem. It is anticipated that the output from this work will be an essential input for carbon sequestration studies, grassland ecosystem sustainable development, and climate change and variability impact studies in the Canadian mixed and tall grassland ecosystems, as well as a decision making tool for stakeholders.