ORCID as entered in ROS
![orcid_icon](/themes/resgate8/images/icons/ORCIDiD_icon24x24.png)
Select Publications
2025, 'Application of SAR-Optical fusion to extract shoreline position from Cloud-Contaminated satellite images', ISPRS Journal of Photogrammetry and Remote Sensing, 220, pp. 563 - 579, http://dx.doi.org/10.1016/j.isprsjprs.2025.01.013
,2024, 'Offset integrity reduces environmental risk: Using lessons from biodiversity and carbon offsetting to inform water quality offsetting in the catchments of the Great Barrier Reef.', Sci Total Environ, 951, pp. 175786, http://dx.doi.org/10.1016/j.scitotenv.2024.175786
,2024, 'Predicting Ground Cover with Deep Learning Models—An Application of Spatio-Temporal Prediction Methods to Satellite-Derived Ground Cover Maps in the Great Barrier Reef Catchments', Remote Sensing, 16, http://dx.doi.org/10.3390/rs16173193
,2023, 'Benchmarking satellite-derived shoreline mapping algorithms', Communications Earth and Environment, 4, http://dx.doi.org/10.1038/s43247-023-01001-2
,2023, 'Reconstructing cloud-contaminated NDVI images with SAR-Optical fusion using spatio-temporal partitioning and multiple linear regression', ISPRS Journal of Photogrammetry and Remote Sensing, 198, pp. 115 - 139, http://dx.doi.org/10.1016/j.isprsjprs.2023.03.003
,2022, 'Global coastal geomorphology – integrating earth observation and geospatial data', Remote Sensing of Environment, 278, http://dx.doi.org/10.1016/j.rse.2022.113082
,2021, 'Efficient measurement of large-scale decadal shoreline change with increased accuracy in tide-dominated coastal environments with Google Earth Engine', ISPRS Journal of Photogrammetry and Remote Sensing, 181, pp. 385 - 399, http://dx.doi.org/10.1016/j.isprsjprs.2021.09.021
,2021, 'Determining the Shoreline Retreat Rate of Australia Using Discrete and Hybrid Bayesian Networks', Journal of Geophysical Research: Earth Surface, 126, http://dx.doi.org/10.1029/2021JF006112
,2019, 'Mapping the sandy beach evolution around seaports at the scale of the African continent', Journal of Marine Science and Engineering, 7, http://dx.doi.org/10.3390/jmse7050151
,Predicting long-term shoreline response to sea-level rise on continental and global scales with data-driven models, http://dx.doi.org/10.14264/d566c79
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