An Experimental Methodology for Automated Detection of Surface Turbulence Features in Tidal Stream Environments.

Tidal stream environments are important areas of marine habitat for the development of marine renewable energy (MRE) sources and as foraging hotspots for megafaunal species (seabirds and marine mammals). Hydrodynamic features can promote prey availability and foraging efficiency that influences megafaunal foraging success and behaviour, with the potential for animal interactions with MRE devices. Uncrewed aerial vehicles (UAVs) offer a novel tool for the fine-scale data collection of surface turbulence features and animals, which is not possible through other techniques, to provide information on the potential environmental impacts of anthropogenic developments. However, large imagery datasets are time-consuming to manually review and analyse. This study demonstrates an experimental methodology for the automated detection of turbulence features within UAV imagery. A deep learning architecture, specifically a Faster R-CNN model, was used to autonomously detect kolk-boils within UAV imagery of a tidal stream environment. The model was trained on pre-existing, labelled images of kolk-boils that were pre-treated using a suite of image enhancement techniques based on the environmental conditions present within each image. A 75-epoch model variant provided the highest average recall and precision values; however, it appeared to be limited by sub-optimal detections of false positive values. Although further development is required, including the creation of standardised image data pools, increased model benchmarking and the advancement of tailored pre-processing techniques, this work demonstrates the viability of utilising deep learning to automate the detection of surface turbulence features within a tidal stream environment.

Capturing Gas Fluxes on Your Phone: An iOS- and Android-based Data-Logging Setup for EGM-4 Environmental Gas Monitoring Systems.

Mobile devices have become increasingly important for field monitoring to improve data capture efficiency, increase storage capacity, and replace heavy equipment. We introduce a quick and straightforward protocol to capture greenhouse gas (GHG) emission rates on mobile devices. We developed our setup on the widely used infrared gas analyzer (IRGA) EGM-4 by PP Systems. This IRGA has a limited internal storage capacity and requires an external device such as a laptop to conduct even modest field sampling. Furthermore, when raw data storage is required, carbon dioxide concentration resolution is reduced by the internal EGM-4 software settings, making the equipment less suitable for high-frequency measurements. Our protocol lets the user bring either an iOS or Android mobile device in to the field to connect to the EGM-4's data stream. For both platforms, a mobile console application was used to read, log, and share flux data. The raw data can be processed in either Python, R, or Matlab using the provided scripts that give the user flexibility to amend further postprocessing steps to obtain GHG fluxes. We demonstrate the flexible applicability of mobile devices for field recording and show that a cost-effective solution can enhance the operational life of superseded field equipment while also increasing the quality of the captured data.