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，当地和区域环境的变化，以及对外部压力的反应，如虫害，使得从个别植物到特定物种的研究很难外推。植物叶功能性状（如叶化学、植物色素）是表征和理解植物功能（如生长、胁迫、养分吸收）和植物对环境变化响应的一种潜在有用的方法。植物叶片性状或“叶面性状”已被证明与植物功能的全球变化密切相关，并可从遥感图像中检测到。遥感提供了表征和绘制叶片功能性状空间变异图的可能性，以更好地了解生物对全球变化的反应。国家生态观测站的遥感和地面测量数据将用于开发一套来自81个地点的叶片特征，这些地点涵盖了美国的一系列生态系统。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，该平台每年利用最新一代成像技术收集各地的遥感图像。这些成像传感器具有前所未有的绘制生物功能的能力，包括植物化学和生理学，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，用于绘制美国各种生态系统类型的植物生物化学和生理学，并将能够表征植物性状如何在空间和时间上变化。叶片性状、支持数据、地图和使能方程和软件将通过现有数据库以电子方式提供。研究生和博士后候选人将从事这项研究。成像光谱学的巨大前景之一？也被称为高光谱遥感？是能够绘制叶片功能特性的空间变化，如氮浓度、色素、叶片结构、光合能力和次生生物化学，这些特性驱动着陆地生态系统过程。这样的叶片性状表征提供了一个组织原则，可用于了解不同分类或系统发育水平的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，从生物多样性和生态系统服务（如保持空气和水的质量以及固碳和储存碳）的角度来描述和比较这些不同的生态系统类型。国家生态观测站空中观测平台（AOP）上的成像光谱仪将用于定期估计北美主要生物群落的性状变异，提供适合于在大陆上缩放这些性状的高分辨率数据，以及跨领域的生态系统建模。该奖项将使大量植物叶片性状（如氮浓度，色素，LMA，光合能力和次生代谢物）的高光谱映射算法得以开发（或从现有算法修改），验证，应用和公开。为了实现这一目标，需要对遥感进行基础研究，以了解光学特性--使用成像光谱学可检测--如何允许绘制生物群落的特征。此外，这些数据将允许评估性状检索算法在生物群落中的差异，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制图的影响。将对由此产生的植被性状变异图进行分析，以确定必须取样的必要地理区域，以充分表征不同生态系统类型的性状变异，并随后与全球变异进行比较。最终的目标是合成，测试和验证的跨生物群性状检索模型的高光谱图像。通过表征如何在景观中填充多维特征空间，并将由此产生的特征图与全球数据进行比较，本研究将确定高光谱图像可用于推断地球上的特征变化并填补生物群落到大陆尺度特征变化的现有知识中的地理空白的程度。除了局部研究外，观测站所代表的生物群系的全面特征信息基本上是不存在的，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、压力和变化的反应。这项工作将为科学界提供必要的数据产品，以便更好地了解地方、区域、大陆和全球范围内生态系统功能的变化，部分是通过与一系列其他数据（例如全球初级生产力通量塔估计数）的联系。该项目将培训学生和博士后学者进行跨学科研究，融合生态学，遥感和密集数据集的定量分析，创造新一代研究人员，可以解决全球生态学中的交叉问题。

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.