Coastal and Littoral Observation Using Remote Sensing

Dear Colleagues,

Remote sensing offers unvaluable capabilities for earth observation. The use of present satellite/airborne systems working on the microwave spectrum, such as SAR (synthetic aperture radar) and PolSAR (polarimetric SAR), and on other wavelengths (visible, infrared, hyperspectral), in addition to the use of laser systems (LIDAR: laser imaging detection and ranging) makes it possible to better monitor the earth. These capabilities are of great importance for providing information with regard to coastal and littoral observation, where even low-cost systems (high/low-resolution cameras) can be useful and provide extra functionalities through convenient fusion strategies. Such systems offer huge amounts of data to researchers and to final users that can be analyzed to assist with the monitoring/planning of coastal and littoral uses.

There are several problems to tackle such as coastal and beaches dynamic evolution, maritime traffic and oil spill detection, sand deposits observation, land use pressure (over-exploitation, surface-water contamination), land cover (deforestation, cover vegetation, soil moisture), sea-level and sea temperature changes, ocean observation, smart tourism, among other applications of interest (local biodiversity management).

To fully extract information from the data, new methods and strategies are strongly required. Fortunately, computing machines have also experienced a notable increase in their capabilities, allowing us to process huge amount of data in a more efficient way through multicore/GPU resources, especially in coastal and litoral spaces where edge computing is required.

This Special Issue focuses on exploring new techniques for the data-to-information process used to acquire remote sensing data from coastal and littoral areas. Deep learning approaches, pattern recognition, machine learning methods built on suitable models closely linked to the data, image processing techniques (for instance segmentation and classification) and data fusion methods in general are the main interests of this Special Issue.

Dr. Luis Gómez Déniz
Dr. María Elena Buemi
Dr. Nelson Monzón López
Guest Editors

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