The traditional approach for coastal monitoring consists in ground investigations that are burdensome both in terms of logistics and costs, on a national or even regional scale. Earth Observation (EO) techniques can represent a cost-effective alternative for a wide scale coastal monitoring. Thanks to the all-weather day/night radar imaging capability and to the nationwide acquisition plan named MapItaly, devised by the Italian Space Agency and active since 2010, COSMO-SkyMed (CSK) constellation is able to provide X-band images covering the Italian territory. However, any remote sensing approach must be accurately calibrated and corrected taking into account the marine conditions. Therefore, in situ data are essential for proper EO data selection, geocoding, tidal corrections and validation of EO products. A combined semi-automatic technique for coastal risk assessment and monitoring, named COSMO-Beach, is presented here, integrating ground truths with EO data, as well as its application on two different test sites in Apulia Region (South Italy). The research has shown that CSK data for coastal monitoring ensure a shoreline detection accuracy lower than image pixel resolution, and also providing several advantages: low-cost data, a short revisit period, operational continuity and a low computational time.
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http://dx.doi.org/10.3390/s19061399 | DOI Listing |
Mar Pollut Bull
January 2025
University of Victoria, 3800 Finnerty Road, Victoria, BC V8W 2Y2, Canada. Electronic address:
Marine pollution poses significant risks to both marine ecosystems and human health, requiring effective monitoring and control measures. This study presents the Ocean Pollution Monitoring System (OPMS), a web application designed to visualize the seasonal and annual fluctuations of marine pollutants along coastal regions in Canada. The pollutants include fecal coliform and biotoxins such as paralytic shellfish poisoning (PSP), and amnesic shellfish poisoning (ASP).
View Article and Find Full Text PDFSci Rep
January 2025
Department of Geomorphology and Quaternary Geology, Faculty of Oceanography and Geography, University of Gdańsk, Bażyńskiego 4, 80-952, Gdańsk, Poland.
This study introduces a novel methodology for estimating and analysing coastal cliff degradation, using machine learning and remote sensing data. Degradation refers to both natural abrasive processes and damage to coastal reinforcement structures caused by natural events. We utilized orthophotos and LiDAR data in green and near-infrared wavelengths to identify zones impacted by storms and extreme weather events that initiated mass movement processes.
View Article and Find Full Text PDFMar Pollut Bull
January 2025
Third Institute of Oceanography, Ministry of Natural Resources, Xiamen 361005, China; Observation and Research Station of Coastal Wetland Ecosystem in Beibu Gulf, Ministry of Natural Resources, Beihai 536015, China. Electronic address:
The temporal variation and transport of Cs in the Beibu Gulf (BG) are still poorly understood. Here we measured Cs concentrations in the BG water column and surface sediments during 2022. We found that Cs in the BG water column was controlled by the movement and mixing of local water masses.
View Article and Find Full Text PDFFront Plant Sci
December 2024
Collage of Forestry, Northeast Forestry University, Harbin, China.
The diversity of liverworts in China is rich. It is of great significance to study the species distribution pattern of liverworts in China for the protection of liverworts diversity, flora research and biodiversity monitoring. On the basis of records from national and provincial liverwort lists, herbaria and online databases, a dataset of liverwort distributions was created to analyze the geographical distribution patterns of liverwort species diversity in China.
View Article and Find Full Text PDFEnviron Monit Assess
January 2025
Waste and Resource Management, Rostock University, Justus-Von-Liebig-Weg 6, 18059, Rostock, Germany.
We conducted surveys of Mediterranean beaches in Egypt, Morocco, and Tunisia including 37 macro-litter (> 25 mm) and 41 meso-litter (5-25 mm) assessments. Our study identified key litter items and assessed pollution sources on urban, semi-urban, tourist, and semi-rural beaches. Macro-litter concentration averaged 5032 ± 4919 pieces per 100 m or 1.
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