Supervisors: Professor Martin Solan (lead, Ocean & Earth Science, UoS), Dr Julian Leyland (Geography & the Environment, UoS), Dr Charlie Thompson (Ocean & Earth Science, UoS), Dr Sasan Mahmoodi (Electronics & Computer Science, UoS), Dr. John Howe (SAMS)
Understanding how alterations to environmental properties, including sediment type, hydrodynamic conditions, and the distribution of biodiversity, affect coastal dynamics is central to understanding regional changes in ecosystem functioning and coastal heritage. However, the extent to which system behaviour alters in response to dynamic lithological and biological activity, and their interactions, is largely unknown. Upscaling to the temporal and spatial scales necessary to detect change is a fundamental challenge, requiring a remote solution that can monitor subtidal, intertidal and terrestrial habitat whilst avoiding sampling disturbance.
UAVs and AUV’s combine flexible platforms with digital imaging, offering novel opportunities to collect highly resolved spatial and temporal remotely sensed data. However, many coastal terrestrial and marine landscapes are visually homogeneous (intertidal sediments, pasture), making monitoring particularly challenging because (i) image analysis techniques capable of extracting pertinent physical and biogenic features have not been developed, (ii) there is a high computational overhead for high-resolution multi-parameter imagery covering macro-scales and, (iii) data collection conducive to rapidly deriving evidence in support of management decisions is absent. This studentship seeks to provide a remote monitoring solution to aid understanding of natural variability and the detection of directional change that pre-empts secondary cascading effects in coastal systems.
The studentship will develop a set of data processing techniques that will enable the extraction of a suite of metrics capable of detecting and monitoring evolving organism-sediment interactions. UAVs equipped with multispectral cameras will be deployed, in conjunction with terrestrial LiDAR, combined with shallow-water AUV for bathymetry, side-scan and photo-mosaics, over sub-tidal and supra-tidal field sites and appropriate artificial (experimental) plots to develop a monitoring system which is self-adapting over time (by learning from previous images and data) and becomes increasingly sensitive to changes occurring at varying spatial and temporal scales. Image features (texture, color) from multiple field locations can be extracted and used to train the monitoring system. Key foci will be:
The NEXUSS CDT provides state-of-the-art, highly experiential training in the application and development of cutting-edge Smart and Autonomous Observing Systems for the environmental sciences, alongside comprehensive personal and professional development. There will be extensive opportunities for students to expand their multi-disciplinary outlook through interactions with a wide network of academic, research and industrial / government / policy partners. The student will be registered at University of Southampton and hosted at Ocean and Earth Science. Specific training includes: (i) safe operation of UAVs and AUV’s associated sensors, (ii) application of laser scanning equipment and data processing, (iii) image & bathymetric data processing and analysis (iv) knowledge of how to work with large datasets, (v) competence in experimental design and statistical analysis, and (vi) an understanding of ecological & sedimentological patterns and processes in intertidal environments. This suite of training is relevant to careers in governmental or non-governmental organisations, industry or academia.
Background Reading:
Brito, Benyoucef, Jesus, Brotas, Gernez, Mendes, Launeau, Dias, Barillé. 2013. Seasonality of microphytobenthos revealed by remote-sensing in a South European estuary, Continental Shelf Research. doi: 10.1016/j.csr.2013.07.004.
Hale, R., Boardman, R., Mavrogordato, M.N., Sinclair, I., Tolhurst, T.J., Solan, M. (2015) High-resolution computed tomography reconstructions of invertebrate burrow systems. Scientific Data 2: 10.1038/sdata.2015.52
To apply for this project, use the: apply for a NEXUSS CDT studentship