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

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.