CMG: Functional Data Modeling of Climate-Ecosystem Dynamics

该项目的目标是开发更好的方法来分析植被对气候变化的反应。对地球表面的卫星观测可以用来跟踪过去30年来植被的变化，将这些变化与气候变化联系起来的任务既重要又具有挑战性。其中显著的变化包括生长季的提前开始，以及在北部高纬度地区出现的“变绿”和“变黄”趋势，显然是对地表温度上升的反应。在这个项目中进行的研究将尝试使用“功能数据分析”来量化气候-植被关系，这是一种分析形式，其中使用一组函数来表示植被在空间和时间上的变化。一旦定义了这些功能，它们的特征就可以与气候的趋势和波动相关。春季开始的日期、生长季的长度和其他物候变化的变化可以通过将它们与函数及其导数的属性相关联来研究。例如，生长季的开始和结束可以由拐点或函数的二阶导数的零点来定义，而生长季的峰值出现在一阶导数的零点。将遥感植被与气候变化联系起来的一个特别挑战是陆地表面的异质性，因为地表特征在短距离内可能会有巨大的变化。陆面异质性带来的挑战将通过“混合建模”来解决。在这个模型框架中，卫星观测中发现的不同地表类型被表示为概率密度函数的线性叠加，这可以通过聚类分析来表征。这项工作将提高我们在陆地表面类型高度不同的地区识别和量化植被对气候变化的反应的能力。在这项拨款下进行的研究将解决一个具有科学和社会重要性的问题。从保护的角度来看，气候变化如何影响植被的问题很重要，因为气候变化可能威胁到生态系统和生物多样性。人类福利还依赖于生态系统服务，而生态系统服务可能会因气候变化而中断。此外，植被变化可以作为气候变化的反馈，因为植被可以通过改变地表反照率、调节陆地对二氧化碳的吸收以及调节地表蒸散来影响气候。此外，该项目开发的工具将适用于统计学和地球系统科学交叉领域的各种问题。该项目制定的方法将在统计学和自然科学课程中向学生传播，并将向科学界提供软件和样本数据集，以鼓励采用新的方法。

The goal of this project is to develop better methods for analyzing the response of vegetation to changes in climate. Satellite observations of the earth's surface can be used to track changes in vegetation over the last three decades, and the task of relating these changes to changes in climate is both important and challenging. Among the significant changes are the earlier onset of the growing season and "greening" and "browning" trends occurring in high northern latitudes, apparently in response to increases in surface temperature. Research conducted in this project will attempt to quantify climate-vegetation relationships using "functional data analysis", a form of analysis in which a set of functions is used to represent variations of vegetation in space and time. Once these functions are defined, their characteristics can be related to trends and fluctuations in climate. Changes in the date of spring onset, the length of the growing season, and other phenological changes can be studied by associating them with properties of the functions and their derivatives. For example, the onset and termination of growing seasons may be defined by points of inflexion, or zero-crossings of the second derivatives of the functions, while the peak of the growing season occurs at a zero-crossing of the first derivative. A particular challenge in relating remotely-sensed vegetation to climate variations is the heterogeneity of the land surface, as surface characteristics can vary tremendously over short distances. The challenges posed by land surface heterogeneity will be addressed by "mixture modeling". In this modeling framework the different land surface types found in satellite observations are represented by a linear superposition of probability density functions, which can be characterized through cluster analysis. The work will lead to improvements in our ability to identify and quantify the vegetation response to climatic changes in regions where land surface type is highly variable.Research conducted under this grant will address a question which is both scientifically and societally important. The question of how vegetation is affected by climate change is important from a conservation standpoint, since climate change can threaten ecosystems and biodiversity. Human welfare also depends on ecosystem services which may be interrupted by changes in climate. In addition, vegetation changes can act as a feedback on climate change, since vegetation can affect climate by changing surface albedo, regulating terrestrial uptake of carbon dioxide, and modulating surface evapotranspiration. In addition, the tools developed in this project will be applicable to a variety of problems at the intersection of statistic and earth system science. Methods developed in the project will be disseminated to students in courses on statistics and natural sciences, and software and sample datasets will be made available to the scientific community to encourage adoption of new methodologies.