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TreeSpec: Quantifying the functional role of canopy structural and chemical traits for productivity across experimental tree diversity gradients using a hyperspectral imaging system

TreeSpec：使用高光谱成像系统量化树冠结构和化学特征对实验树木多样性梯度生产力的功能作用

基本信息

批准号：
316733524
负责人：
Professor Dr. Michael Scherer-Lorenzen, since 7/2018
金额：
--
依托单位：
Department of Renewable Resources
依托单位国家：
德国
项目类别：
Research Grants
财政年份：
2016
资助国家：
德国
起止时间：
2015-12-31 至 2020-12-31
项目状态：
已结题

来源：
https://gepris.dfg.de/gepris/projekt/316733524?language=en
关键词：
TreeSpec Quantifying functional role canopy

项目摘要

Quantifying canopy structure and physiology is integral to understanding tree species influences on forest ecosystem function, but the height and complexity of forest canopies makes sampling challenging. To date, next generation tools for canopy research that measure plant traits from a birds-eye-view perspective, such as near-canopy Unmanned Aerial Vehicles (UAVs) equipped with hyperspectral imaging, have predominantly focused on crop monocultures. Few, if any studies have applied these tools to examine more diverse mixtures or forest canopies. Therefore, we propose to address this knowledge gap by testing UAV-based remote sensing methods for the measurement of key traits driving forest productivity and examining their relationship to tree species diversity in experimental communities. Recent analyses suggest forest productivity largely depends on two key canopy traits: 1) leaf area index (LAI), which describes light capture, and, 2) canopy leaf nitrogen (%N), which describes biochemical carbon fixation. Together, these traits may mechanistically link carbon fluxes and forest canopy attributes at broad temporal and spatial scales. However, tests of the generality of these relationships in other regions and for different species pools are needed. Focusing on two of the longest-running tree diversity experiments in the world, we will collect and compare observations of LAI and %N from the ground and the air and test their relationship with productivity. Effects of neighbourhood species and functional composition on crown shapes, LAI and %N relative to monoculture will be detected at high resolution using a state of the art UAV-imaging system. This will allow for novel studies of intraspecific trait variation in relation to architectural complementarity and productivity. Our project will thus accomplish two main goals: the development of novel methods of measuring key canopy traits, and the assessment of their potential role in diversity-productivity relationships.

量化冠层结构和生理是了解树种对森林生态系统功能影响的一个组成部分，但森林冠层的高度和复杂性使取样具有挑战性。到目前为止，下一代冠层研究工具，从鸟瞰角度测量植物性状，如近冠层无人机（UAV）配备高光谱成像，主要集中在作物单作。很少，如果有的话，研究应用这些工具来检查更多样化的混合物或森林冠层。因此，我们建议通过测试基于无人机的遥感方法来测量驱动森林生产力的关键性状，并研究它们与实验社区树种多样性的关系，以解决这一知识差距。最近的分析表明，森林生产力在很大程度上取决于两个关键的冠层特征：1）叶面积指数（LAI），它描述了光捕获，2）冠层叶氮（%N），它描述了生物化学碳固定。总之，这些特征可能在广泛的时间和空间尺度上将碳通量和森林冠层属性机械地联系起来。然而，这些关系在其他地区和不同的物种池的一般性测试是必要的。重点是两个在世界上运行时间最长的树木多样性实验，我们将收集和比较观测的叶面积指数和%N从地面和空中，并测试它们与生产力的关系。邻近物种和功能组成的树冠形状，叶面积指数和%N相对于单一栽培的影响将在高分辨率检测使用最先进的无人机成像系统的状态。这将允许新的研究种内性状变异的建筑互补性和生产力。因此，我们的项目将实现两个主要目标：测量关键冠层性状的新方法的发展，以及评估其在多样性生产力关系中的潜在作用。