MSB-ENSA: Foliar traits and ecosystem variability across NEON domains

准确预测植物物种对全球变化的反应是困难的。物种内部和物种之间的反应各不相同，随着当地和区域环境的变化，以及对诸如虫害等外部压力的反应，使得从单个植物到特定物种的研究难以外推。植物叶片功能性状（如叶片化学、植物色素）最近成为表征和理解植物功能（如生长、胁迫、营养吸收）和植物对环境变化的反应的潜在有用方法。植物叶片性状或“叶性状”已被证明与植物功能的全球变化密切相关，并且可以从遥感图像中检测到。遥感提供了表征和绘制叶面功能性状空间变化的可能性，从而更好地了解对全球变化的生物响应。来自国家生态观测站的遥感和地面测量将用于开发一套来自81个地点的叶面特征，这些地点涵盖了美国发现的生态系统范围。国家生态观测站基础设施的一个组成部分是其空中观测平台，该平台每年使用最新一代成像技术收集地点的远程图像。这些成像传感器具有前所未有的绘制生物功能的能力，包括植物的化学和生理，以及植物的生物量和结构。该奖项将产生第一个全面的数据集和方法，用于绘制美国生态系统类型范围内的植物生物化学和生理学，并将使表征植物性状如何在空间和时间上变化成为可能。叶子的性状、支持数据、地图、使能方程和软件将通过现有的数据库向公众开放。研究生和博士后将参与研究。成像光谱学的一大前景是什么？也被称为高光谱遥感？是绘制驱动陆地生态系统过程的叶面功能性状（如氮浓度、色素、叶片结构、光合能力和次生生物化学）的空间变化的能力。这种叶面特征表征提供了一种组织原则，可用于理解不同分类或系统发育水平上功能的发生和进化分化，并检测生态系统之间的功能差异。国家生态观测站提供了第一个机会来描述和比较这些不同的生态系统类型，包括它们的生物多样性和生态系统服务，如保持空气和水的质量以及隔离和储存碳。国家生态观测站机载观测平台（AOP）上的成像光谱仪将用于定期估计北美主要生物群系的性状变化，提供适合大陆扩展这些性状的高分辨率数据，以及跨域的生态系统建模。该合同将使用于大量植物叶片特征（如氮浓度、色素、LMA、光合能力和次生代谢物）的高光谱制图算法得以开发（或对现有算法进行修改）、验证、应用并公开可用。为了实现这一目标，将需要在遥感领域进行基础研究，以了解光学特性（利用成像光谱学可检测到）如何允许绘制跨生物群系的特征。此外，这些数据将允许评估特征检索算法在不同生物群系之间的差异，以及它们如何受到植被结构、地貌或其他生态系统特性的影响。激光雷达数据将用于测试和控制冠层垂直结构对性状定位的影响。植被性状变异的结果图将被分析，以确定必须取样的必要地理区域，以充分表征跨生态系统类型的性状变异，并随后与全球变异进行比较。最终目标是合成、测试和验证高光谱图像的跨生物群系特征检索模型。通过描述不同景观中多维性状空间是如何被填充的，并将所得到的性状图谱与全球数据进行比较，本研究将确定高光谱图像在多大程度上可以用来推断地球上的性状变化，并填补生物群系到大陆尺度性状变化的现有知识中的地理空白。除了局部研究外，天文台所代表的生物群系的综合性状信息基本缺失，这项工作将有助于更好地了解和预测全球陆地生态系统对干扰、压力和变化的响应。这项工作将为科学界提供必要的数据产品，以更好地了解生态系统功能在地方、区域、大陆和全球尺度上的变化，部分通过与一系列其他数据的联系（例如GPP的通量塔估计）。该项目将培养学生和博士后学者进行跨学科研究，将生态学、遥感和密集数据集的定量分析结合起来，培养能够解决全球生态学交叉问题的新一代研究人员。

Accurately predicting plant species response to global change is difficult. Responses vary within and among species, with variation in the local and regional environment, and in response to external pressures such as insect infestations making extrapolation from individual plant to species-specific studies difficult. Plant leaf functional traits (e.g. leaf chemistry, plant pigments) have recently emerged as a potentially useful way to characterize and understand the variability in plant function (e.g. growth, stress, nutrient uptake), and plant responses to environmental change. Plant leaf traits or "foliar traits" have been shown to be strongly correlated with global variation in plant function and can be detected from remotely sensed imagery. Remote sensing offers the possibility to characterize and map the spatial variation in foliar functional traits to gain a better understanding of biological responses to global change. Remotely sensed and ground based measurements from the National Ecological Observatory will be used to develop a suite of foliar traits from 81 locations that encompass the range of ecosystems found in the United States. One component of the National Ecological Observatory infrastructure is its Aerial Observation Platform, which collects remote imagery annually over the locations using the latest generation imaging technologies. These imaging sensors have an unprecedented ability to map biological function, including plant chemistry and physiology, as well as the biomass and structure of plants. This award will result in the first comprehensive data set and methods for mapping plant biochemistry and physiology across the range of ecosystem types in the US and will enable characterization of how plant traits vary across space and time. The leaf traits, supporting data, maps and enabling equations and software will be made publically available via existing databases. Graduate students and post doctoral candidates will be engaged in the research.One of the great promises of imaging spectroscopy ? also known as hyperspectral remote sensing ? is the ability to map the spatial variation in foliar functional traits, such as nitrogen concentration, pigments, leaf structure, photosynthetic capacity and secondary biochemistry, that drive terrestrial ecosystem processes. Such foliar trait characterization offers an organizing principle that can be used to understand the occurrence and evolutionary differentiation of function across different taxonomic or phylogenetic levels and to detect functional differences across ecosystems. The National Ecological Observatory provides one of the first opportunities to characterize and compare these different ecosystem types, in terms of their biodiversity and ecosystem services such as maintaining air and water quality and sequestering and storing carbon. The imaging spectrometer on the National Ecological Observatory Airborne Observation Platform (AOP) will be used to regularly estimate trait variation across the major biomes of North America, providing high-resolution data suitable for scaling these traits continentally, as well as ecosystem modeling across domains. The award will enable hyperspectral mapping algorithms for a large number of plant foliar traits (such as nitrogen concentration, pigments, LMA, photosynthetic capacity and secondary metabolites) to be developed (or modified from existing algorithms), validated, applied and made publicly available. To accomplish this will entail fundamental research in remote sensing to understand how optical properties - detectable using imaging spectroscopy - permit mapping traits across biomes. Additionally the data will permit the evaluation of how trait retrieval algorithms differ across biomes and how they are affected by vegetation structure, physiognomy, or other ecosystem properties. Lidar data will be used to test and control for the influence of canopy vertical structure on trait mapping. The resulting maps of vegetation trait variation will be analyzed to determine the requisite geographic area that must be sampled to fully characterize trait variation across ecosystem types and, subsequently, compare to global variation. The ultimate objective is the synthesis, testing and validation of cross-biome trait retrieval models for hyperspectral imagery. By characterizing how multi-dimensional trait space is filled across landscapes and comparing the resulting trait maps with global data, this research will identify the extent to which hyperspectral imagery can be used to both extrapolate trait variation on Earth and fill geographical gaps in existing knowledge of biome- to continental- scale trait variation. Comprehensive trait information for the biomes represented in the Observatory is largely absent except for localized studies, and this work will enable better understanding and prediction of the response of global terrestrial ecosystems to disturbance, stress and change. This work will provide the scientific community with data products necessary to better understand local-, regional-, continental- and global-scale variation in ecosystem function, in part through linkage to a range of other data (e.g. flux tower estimates of GPP). The project will train students and postdoctoral scholars in cross-disciplinary research that merges ecology, remote sensing, and the quantitative analyses of dense data sets, creating a new generation of researchers that can address cross-cutting questions in global ecology.