UAS Marine Habitat Mapping

Unmanned Aerial Systems (UAS), commonly known as drones, provide a highly effective tool for mapping shallow marine habitats with very high spatial resolution. By acquiring high-quality imagery under optimal environmental conditions, UAS enable the detailed identification and delineation of coastal habitats such as seagrass meadows. The collected imagery can be processed into orthophotomaps and spatial datasets that support accurate habitat mapping, and environmental monitoring & management. UAS-based mapping is particularly valuable in shallow coastal environments and works as a complementary method to in-situ surveys and satellite remote sensing, while providing an effective solution in cases where these methods are limited due to environmental, spatial, or resolution constraints. This approach supports marine habitat assessment, environmental monitoring, and coastal management applications.

Satellite Oceanography

Research in satellite oceanography and marine remote sensing focuses on the analysis of oceanic processes using satellite-derived data and products. Emphasis is placed on fisheries applications, including the spatial distribution modeling of small pelagic fish, supporting informed decision-making for marine resource management and conservation.

Additional work includes the detection and prediction of hazardous marine phenomena, such as algal blooms and marine heatwaves. These approaches contribute to environmental monitoring efforts and support marine-related economic activities, including aquaculture.

Coastal Change Monitoring Using Airborne LiDAR

This research focuses on the application of advanced remote sensing and geospatial analysis techniques to monitor shoreline dynamics and coastal geomorphological changes. Multi-sensor datasets, including airborne LiDAR, satellite imagery, and high-resolution aerial data from unmanned aerial systems (UAS), are used to generate high-accuracy digital elevation models and extract shoreline positions.

Particular emphasis is placed on evaluating the accuracy, limitations, and comparative performance of different data acquisition methods for detecting coastal erosion and sediment dynamics. The integration of remote sensing, photogrammetry, and geographic information systems (GIS) enhances shoreline mapping methodologies and supports the sustainable management of vulnerable coastal environments.

Remote Sensing of Marine Debris

This research focuses on the application of optical satellite remote sensing and machine learning for the monitoring of marine and coastal environments. Particular emphasis is placed on the detection and characterization of floating marine litter and floating surface formations using multispectral satellite and unmanned aerial systems (UAS) imagery.

The work combines experimental spectral characterization and deep learning–based image segmentation to support the development of operational monitoring frameworks for environmental applications. More broadly, this research explores Earth observation approaches that translate remote sensing data into practical tools for environmental monitoring, coastal management, and large-scale environmental assessment.

Floating Marine Debris Detection using Satellite and UAV Imagery

This research focuses on the development of multi-scale frameworks for the detection of floating marine debris through the integration of satellite and unmanned aerial systems (UAS) imagery. By combining multi-sensor Earth observation data with high-resolution UAV observations, the work supports improved monitoring of floating debris in marine and coastal environments.

Particular emphasis is placed on remote sensing workflows, image processing, and deep learning methods for the identification, mapping, and analysis of marine debris. These approaches contribute to environmental monitoring practices and support the development of scalable tools for coastal and marine management.