Global distribution of muddy coasts using a hybrid classification model: An automated method that employs multispectral satellite imagery and globally available coastal datasets

The coastal area provides important services such as valuable habitats for wildlife, resources for regional development, and buffer zones for the land against natural disasters such as storm surges. But these narrow coastal areas experience pressure from both land and ocean side. In order to regulate sustainable coastal development, protect...

Machine learning based time-series postprocessing for the interferometric SAR remote sensing data

A satellite remote sensing technique, Interferometric Synthetic Aperture Radar (InSAR), is able to provide surface displacement information on a millimeter level. In this study, data from the TerraSAR-X satellite collected in the years 2009-2018 over the area of Amsterdam is used. Even though radar data is a subject to multi-step processing,...

A machine learning assessment of multi-resolution remote sensing data for Natura 2000 dune habitat classification

Coastal dunes habitats are in the transition zone of marine and terrestrial ecosystems and therefore have high biodiversity in terms of animal and vegetation species. These species provide important ecosystem services to the human kind. Due to the development and operation of Maasvlakte 2, an increase of nitrogen emission is expected in the...