A generic model of aquatic remote sensing and the incorporation of ecological scenarios using Bayesian statistics

随着人们对全球气候变化影响的日益关注，我们有必要监测大面积生态系统的健康状况。卫星或机载传感器的遥感通常被视为实现这一任务的最具成本效益的手段。许多遥感研究试图提高在地面上可以分辨的分辨率。这是一项复杂的任务，特别是在水生系统中，上面的水柱强烈地减弱了阳光，从而降低了海床特征颜色的“可分离性”。研究项目通常侧重于特定的生态系统和问题/例如，我们能否区分百慕大珊瑚礁上的活珊瑚和海藻？虽然我们可以得出答案是“不”，但我们不能将这个答案推断到其他地方。这是因为我们通常不了解结果的确切原因。在这种情况下，是百慕大珊瑚和海藻的颜色太相似，还是研究地点的水太深或浑浊，海浪太强，使得传感器无法正确地观察海底？为了真正理解我们的结果/并将它们与其他结果进行比较/我们需要一个关于遥感在水生环境中如何工作的通用模型。遥感的某些方面已经被很好地理解了，比如光通过水柱的过程。然而，光与结构和光谱复杂的海床（大多数海床都是如此）之间的相互作用直到最近才被建立起来。我们使用辐射度方法（在《海底总动员》中用于生成珊瑚礁）实现了这种建模。该项目的主要目的是创建和测试一个水生遥感通用模型（GMARS）。为了创建GMARS，我们将扩展现有的辐射模型，然后创建空间逼真的虚拟生态系统，代表两种不同类型的系统：波罗的海的藻床和结构复杂的珊瑚礁。这将是第一次对整个水生生态系统中的所有辐射过程进行建模，并使我们能够测试关于遥感仪器局限性的各种重要假设。我们还将与统计学家合作，以确保参数中的误差和变化可以通过模型传播。重要的是，正式统计框架的使用使我们能够在遥感方面进行进一步的创新：我们的第二个目标是通过添加有关被映射系统的其他知识来源来提高遥感的准确性。遥感算法试图根据其颜色或纹理来识别给定的像素。例如，卫星图像的用户可能试图根据颜色区分一块珊瑚礁是珊瑚、沙子还是海藻。然而，如果可能的话，我们通常有关于生态系统的先验知识。例如，我们可能知道珊瑚礁最近遭到飓风袭击，我们有一个生态模型预测珊瑚死亡并被海藻取代的可能性为70%。我们可能也知道，一个像素的沙子不太可能在两年的时间内变成珊瑚。利用贝叶斯统计的一个分支，我们可以正式地将我们的生态预期与遥感仪器的预测相协调。在之前的NERC拨款中，我们模拟了珊瑚礁种群动态，现在我们将提供一个正式的统计框架，将这些模型预测与遥感数据相结合。结果将改善遥感和生态系统监测。该建议为遥感和生态系统评估提供了两个新的创新：(1)提供了水生系统中完全通用的光模型；(2)将光谱和生态数据结合起来的通用统计环境。综上所述，我们可以确定在遥感过程的每个阶段获取准确数据的价值，这将有助于确定收集实地数据的优先次序。

With rising concerns about the impacts of global climate change, it is important that we monitor the health of ecosystems over large areas. Remote sensing from satellite or airborne sensors is usually seen as the most cost-effective means of achieving this task. Much remote sensing research attempts to improve the resolution of what can be resolved on the ground. This is a complex task, particularly in aquatic systems where the overlying water column strongly attenuates sunlight and therefore reduces the 'separability' in colour of sea bed features. Research projects usually focus on a specific ecosystem and issue / for example, can we distinguish living corals from seaweeds on a reef in Bermuda? Although we may conclude the answer is 'no', we cannot extrapolate that answer elsewhere. This is because we usually do not understand the precise cause of the result. In this case, are the colours of Bermudian corals and seaweeds too similar or was the water too deep or murky at the study site and the waves too strong to allow the sensor to view the seabed properly? To really understand our results / and compare them to others / we need a generic model of how remote sensing works in an aquatic environment. Some aspects of remote sensing are fairly well understood, such as the passage of light through a water column. However, the interaction of light with a structurally and spectrally complex seabed - as most seabeds are - has only recently been modelled. We achieved this modelling using radiosity methods (which were used to generate reefs in 'Finding Nemo'). The main aim of this project is to create and test a Generic Model of Aquatic Remote Sensing (GMARS). To create GMARS we will extend our existing radiosity model and then create spatially realistic virtual ecosystems that represent two contrasting types of system: algal beds of the Baltic Sea and structurally complex coral reefs. This will be the first time that all radiative processes in an entire aquatic ecosystem have been modelled and allows us to test a variety of important hypotheses about the limitations of remote sensing instruments. We will also collaborate with a statistician to ensure that the errors and variation in parameters can be propagated through the model. Importantly, use of a formal statistical framework allows us to make a further innovation in remote sensing: Our second aim is to improve the accuracy of remote sensing by adding other sources of knowledge about the system being mapped. Remote sensing algorithms attempt to identify a given pixel on the basis of its colour or texture. For example, a user of a satellite image may attempt to discriminate whether a patch of reef is coral, sand or seaweed on the basis of its colour. However, we often have prior knowledge about the ecosystem which should be incorporated if possible. We may know, for example, that the reef was recently struck by a hurricane and we have an ecological model predicts a 70% chance that corals will have died and been replaced by seaweeds. We may also know that it is highly unlikely that a pixel of sand will turn into a coral within a period of say 2 years. Using a branch of Bayesian statistics, we can formally reconcile our ecological expectations with the predictions made from a remote sensing instrument. In a previous NERC grant, we modelled coral reef population dynamics and we will now provide a formal statistical framework to combine these model predictions with those of remotely-sensed data. The result will be improved remote sensing and ecosystem monitoring. This proposal provides two new innovations for remote sensing and ecosystem assessment: (1) provision of a fully generic model of light in aquatic systems and (2) a generic statistical environment to combine both spectral and ecological data. Taken together, we can identify the value of acquiring accurate data at each stage of the remote sensing process which will help prioritise the collection of field data.