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。 卫星对地球表面的观测可用于跟踪过去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。 这些重大变化包括生长季节开始较早，以及北方高纬度地区出现"变绿"和"变布朗宁"趋势，这显然是对地表温度升高的反应。 本项目进行的研究将试图利用"功能数据分析"来量化气候-植被关系，这是一种利用一套功能来表示植被在空间和时间上的变化的分析形式。 一旦确定了这些功能，它们的特点就可以与气候的趋势和波动联系起来。 春季开始的日期，生长季节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性质相关联来研究。 例如，生长季节的开始和结束可以由函数的二阶导数的零点或零交叉来定义，而生长季节的峰值出现在一阶导数的零交叉处。 在将遥感植被与气候变化联系起来方面的一个特殊挑战是地表的异质性，因为地表特征在短距离内可能变化巨大。 陆面异质性带来的挑战将通过"混合建模"来解决。 在这个建模框架中，卫星观测中发现的不同陆面类型由概率密度函数的线性叠加表示，可以通过聚类分析来表征。 这项工作将提高我们的能力，以确定和量化植被对气候变化的反应，在地区的土地表面类型是高度可变的。在这项赠款下进行的研究将解决一个问题，这是科学和社会的重要性。 从保护的角度来看，植被如何受到气候变化影响的问题很重要，因为气候变化可能威胁生态系统和生物多样性。 人类福祉还取决于生态系统服务，而生态系统服务可能因气候变化而中断。 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蒸发量、调节陆地对二氧化碳的吸收和调节地表蒸散来影响气候。 此外，本项目开发的工具将适用于统计和地球系统科学交叉领域的各种问题。将向统计学和自然科学课程的学生传播该项目中制定的方法，并向科学界提供软件和样本数据集，以鼓励采用新方法。

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.