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

植被的生物化学和生物物理特性的评估可以导致更好地了解草地条件，并进一步提高生态系统模拟的性能。一些关键的植被生物物理参数已从遥感数据中估算出来，并用作各种空间和时间分辨率的生态系统过程模型的输入。然而，控制生态系统生物化学过程的植被生物化学变量并不总是以空间明确的格式提供，并且由于缺乏广泛区域内具有高光谱和空间分辨率的适当数据，因此必须在模型中保持恒定。高光谱遥感领域的最新发展为估算植被的生物化学特性开辟了新的途径。因此，我建议探索和发展先进的遥感方法，以估计空间明确的草地生化特性（如叶绿素浓度和类胡萝卜素浓度）在多个空间尺度。具体而言，本研究将1）开发基于指数的方法，以准确地估计草地冠层水平的生物化学特性：2）调查物理模拟方法的能力，以准确地估计草地冠层水平的生物化学物质; 3）利用天基高光谱数据从林冠层一级扩大到景观一级，并评估指数的依赖性-基于生物化学和基于物理模拟的生物化学评估在景观水平上的混杂因素（即暴露的土壤和非光合植被）。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。预计这项工作的产出将成为碳固存研究、草原生态系统可持续发展、加拿大混合和高大草原生态系统气候变化和变异性影响研究的重要投入，也是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.