Dr. Stephen Grebby holds an MPhys (2005) in Physics with Space Science and Technology, an MSc (2006) in Physical Geography and PhD (2011) in Geological Remote Sensing from the University of Leicester. In 2011, he joined the British Geological Survey, where he took up a position as a Remote Sensing Geoscientist. In 2016, Dr. Grebby was appointed as Assistant Professor in Earth Observation, in the Faculty of Engineering. He is affiliated with the GeoEnergy Research Centre (GERC) - a joint venture between The University of Nottingham and British Geology Survey.
Dr. Grebby is a member of the Nottingham Geospatial Institute research group.
Dr. Grebby's expertise is in the acquisition, analysis, investigation and visualisation of Earth Observation data for a wide variety of geoscience applications. Whilst broad-ranging, he has considerable experience in the development of innovative remote sensing techniques for application to geological mapping, natural resources exploration, mapping and monitoring of geohazards, and land use/land cover mapping. He has expertise in utilising a wide variety of Earth Observation datasets and techniques, including InSAR, airborne and terrestrial LiDAR, multi- and hyperspectral imagery, airborne radiometric and geophysical data, field spectroscopy, photogrammetry, geomorphometry, and advanced image classification (i.e., neural networks and other machine learning algorithms). Dr. Grebby is also interested in the application of sensor technologies (e.g., hyperspectral, ultrasound) and machine learning to chemometrics for food quality evaluation.
Dr. Grebby's current research interests are focused on the use of satellite, airborne (manned and UAV) and ground-based Earth Observation techniques for the natural resources exploration (i.e.,… read more
GREBBY, STEPHEN, SOWTER, ANDREW, GLUYAS, JON, TOLL, DAVID, GEE, DAVID, ATHAB, AHMED and GIRINDRAN, RENOY, 2021. Advanced analysis of satellite data reveals ground deformation precursors to the Brumadinho Tailings Dam collapse. COMMUNICATIONS EARTH & ENVIRONMENT. 2, 2 GEE, DAVID, BATESON, LUKE, GREBBY, STEPHEN, NOVELLINO, ALESSANDRO, SOWTER, ANDREW, WYATT, LEE, MARSH, STUART, MORGENSTERN, ROY and ATHAB, AHMED, 2020. Modelling groundwater rebound in recently abandoned coalfields using DInSAR. REMOTE SENSING OF ENVIRONMENT. 249, 112021 YEOMANS, CHRISTOPHER M., SHAIL, ROBIN, K., GREBBY, STEPHEN, NYKÄNEN, VESA, MIDDLETON, MAARIT and LUSTY, PAUL A. J., 2020. A machine learning approach to tungsten prospectivity modelling using knowledge-driven feature extraction and model confidence. GEOSCIENCE FRONTIERS. 11(6), 2067-2081
NOVELLINO, ALESSANDRO and GREBBY, STEPHEN, 2020. Mapping and monitoring of geohazards. APPLIED SCIENCES. 10(13), 4609
Dr. Grebby's current research interests are focused on the use of satellite, airborne (manned and UAV) and ground-based Earth Observation techniques for the natural resources exploration (i.e., geoenergy and minerals) and the monitoring of their exploitation. This includes identifying potential mineral deposits and geothermal energy resources by mapping anomalous mineral assemblages, determining key characteristics (e.g., porosity, permeability, fracture density) for reservoir modelling, and monitoring ground motion at sites associated with gas storage (i.e., CO2), energy production and mining using InSAR.
He also has a strong interest in the mapping and monitoring of geohazards, in particular landslides. His research concerns the development of algorithms to aid the rapid identification and mapping of landslides using airborne LiDAR and optical imagery. Such methods enables comprehensive landslide inventories to be generated, which can then be used to assess the future risk posed by landslides. Dr. Grebby was recently involved in building landslide inventories from very-high resolution satellite imagery, in order to help support the relief efforts following the 2015 Nepal and 2016 Ecuador earthquakes.
More recently, Dr. Grebby's multidisciplinary background has led to a growing interest in the application of sensor technologies (e.g., hyperspectral, ultrasound) and machine learning to chemometrics for food quality evaluation.
I welcome enquiries from potential PhD candidates from Home, EU and International countries who are interested in the following research areas: Geological mapping, natural resources exploration, mapping and monitoring of geohazards, satellite, airborne and ground-based Earth Observation techniques, machine learning.