|From||Roger Brugge <firstname.lastname@example.org>|
|Date||Wed, 25 Jul 2018 11:40:02 +0000|
Machine learning approaches to improve retrieval of shelf sea algal biomass from ocean colour remote sensing.
University of Strathclyde
Algal primary production in the marine environment constitutes approximately half of the global total, is therefore a very important element of the global carbon cycle and is the primary source of nutrition for the vast majority of life in the ocean. Various pieces of environmental legislation (EU MSFD, Water Directive etc) require governments to monitor the ecological state of national waters, with algal biomass being used as a proxy for eutrophication. There is also growing interest in monitoring for the presence of harmful algal blooms due to their potential impact on both aquaculture and public health more generally. Ocean colour remote sensing has radically transformed our ability to observe the growth and decay of algal blooms across the globe. However, the performance of standard algorithms for monitoring algal biomass is notoriously variable, with significantly lower performance in optically complex shelf seas. The aim of this project is to use state of the art machine learning approaches to improve understanding of local variability in the optical properties of natural waters and hence to inform interpretation of both historical ocean colour imagery and existing databases of in situ measurements of chlorophyll concentration. This will facilitate construction of a new, water-type specific approach to estimation of algal biomass for Scottish marine waters that will be integrated with regional hydrodynamic and ecosystem models to provide Marine Scotland and other Scottish public bodies with new tools for monitoring and predicting ecosystem status.
Aims & Objectives
In this project, we will use existing state of the art machine learning approaches to categorise optical signals from ocean colour imagery, to develop a database of optical water types. This will inform evaluation of the historic ocean colour time series, with Marine Scotland’s existing database of in situ Chl data used to determine Chl algorithm performance
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