Sequential Bayesian inference for spatio-temporal probabilistic models of changes in global vegetation and ocean properties using Earth Observation da

使用地球观测数据对全球植被和海洋特性变化的时空概率模型进行顺序贝叶斯推断

• 批准号: 2438462
• 金额: --
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2438462
• 关键词: Sequential; Bayesian; inference; spatio; temporal

The Earth's vegetation is changing as a result of both human activity and climate change. Large scale shifts in vegetation will fundamentally alter terrestrial ecosystems, with a range of potential consequences - from impacts on biodiversity to altered carbon and hydrological cycling. In northern high latitudes plants are growing more as the climate warms, resulting in a "greening" of the land surface. Within the next 50 years the tundra biome is expected to become climatically suitable for trees, the boreal treeline is already shifting northwards and woody shrub abundance in tundra is increasing. These changes will have a profound impact on ecosystem function and climate feedbacks; while CO2 uptake from the atmosphere through photosynthesis is likely to increase, taller denser plant canopies will decrease the reflectivity of the land surface, resulting in greater warming. To understand the implications of changing vegetation distributions, it is vital we can model important biophysical parameters from space over time.Similarly, ocean currents also vary on both short and long time scales, with attributing differences to longer-term trends, for example climate change, being difficult. It is known that the oceans play a central role in climate change, and are changing rapidly as they absorb large amounts of heat from the atmosphere, but it is often unclear how exactly that is playing out in current conditions.In this project, we focus in developing inferential tools for probabilistic spatio-temporal models with applications in earth observation problems. We consider the challenging problem of estimating biophysical parameters from remote sensing (satellite) observations acquired across time. Just as an example, let us focus in the aforementioned problem where the estimation of the evolving Leaf Area Index (LAI) is key for forecasting the change of Earth's vegetation. It is important to track evolution of LAI through time in every spatial position on Earth because LAI plays an important role in vegetation processes such as photosynthesis and transpiration, and is connected to meteorological/climate and ecological land processes [4, 5]. We also consider oceanography applications by considering complex dynamical models that require sophisticated inferential tools for learning the probabilistic estimates of the evolving states and also the unknown parameters of the model. We will propose novel computational methods in order to overcome current limitations of more traditional IS-based techniques in such a challenging context, including adaptive IS methods for learning static parameters in high dimensional spaces [6] and extensions of [7] to observational spaces with big amount of data. Many applications in earth observation can be benefited from the development of these methodologies. See [5] and [8] for the application of recent IS methodological advances in remote sensing problems.

由于人类活动和气候变化，地球的植被正在发生变化。植被的大规模变化将从根本上改变陆地生态系统，带来一系列潜在后果-从对生物多样性的影响到改变碳和水文循环。在北方高纬度地区，随着气候变暖，植物生长得越来越多，导致地表“绿化”。在未来50年内，苔原生物群落预计将变得气候适合树木生长，北方树木线已经向北移动，苔原的木本灌木数量正在增加。这些变化将对生态系统功能和气候反馈产生深远影响;虽然通过光合作用从大气中吸收的二氧化碳可能会增加，但更高更密集的植物冠层将降低地表的反射率，导致更大的变暖。为了了解植被分布变化的影响，我们必须从空间模拟重要的生物物理参数。同样，洋流也会在短期和长期尺度上发生变化，很难将差异归因于长期趋势，例如气候变化。众所周知，海洋在气候变化中扮演着核心角色，并且由于从大气中吸收大量热量而迅速发生变化，但在当前条件下，海洋的变化究竟是如何发生的，人们往往并不清楚。在本项目中，我们专注于开发概率时空模型的推理工具，并将其应用于地球观测问题。我们认为具有挑战性的问题，估计生物物理参数的遥感（卫星）观测跨时间获得。作为一个例子，让我们关注上述问题，其中不断变化的叶面积指数（LAI）的估计是预测地球植被变化的关键。在地球上的每个空间位置跟踪LAI随时间的演变是很重要的，因为LAI在光合作用和蒸腾作用等植被过程中起着重要作用，并与气象/气候和生态土地过程有关[4，5]。我们还考虑海洋学的应用，考虑复杂的动力学模型，需要先进的推理工具，学习的概率估计的不断变化的状态，也是未知的参数的模型。我们将提出新的计算方法，以克服当前更传统的基于IS的技术在这样一个具有挑战性的背景下的局限性，包括用于学习高维空间中静态参数的自适应IS方法[6]和[7]到具有大量数据的观测空间的扩展。地球观测中的许多应用可以从这些方法的发展中受益。见[5]和[8]关于在遥感问题中应用最新的信息系统方法学进展。