Peer-reviewed articles 17,970 +


Mateo Gasparovic; Almin Dapo; Bosko Pribicevic
•    Prof. DSc. Oleksandr Trofymchuk, UKRAINE 
•    Prof. Dr. hab. oec. Baiba Rivza, LATVIA
Nowadays, remote sensing techniques play an important role in the rapid acquisition of a large amount of spatial environmental data. The development of sensors in the last decade has led to the development of Earth observation satellite missions, e.g., Sentinel, PlanetScope. Numerous machine learning and deep learning methods are used in nowadays research to classify satellite imagery to enable rapid environmental mapping (e.g., land cover and land use, water bodies). Posidonia oceanica is considered the most important and best-studied seagrass species in the Mediterranean Sea. The objective of this preliminary research is to test the applicability of machine learning image classification methods for rapid seagrass mapping based on Sentinel-2 imagery. The research was conducted in the study area located in the north part of Dugi Otok in the central Adriatic in Croatia. Accuracy assessment of the mapped seagrass emphasises that Cart, Random Forest (RF), and Support vector machine (SVM) overperformed Naive Bayes (NB) method. Further, detailed visual analysis of seagrass map and accuracy assessment shows that RF and Cart give the best results. This research was done as part of the project Climate HIDROLAB (KK. – Integrated hydrographic system for sustainable development of the marine ecosystem.
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[2] Dattola, L., Rende, S. F., Dominici, R., Lanera, P., Di Mento, R., Scalise, S., ... Aramini, G., Comparison of Sentinel-2 and Landsat-8 OLI satellite images vs. high spatial resolution images (MIVIS and WorldView-2) for mapping Posidonia oceanica meadows. In Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions SPIE, vol. 10784, pp. 252-262, 2018.
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[9] Gasparovic, M., Zrinjski, M., & Gudelj, M., Automatic cost-effective method for land cover classification (ALCC). Computers, Environment and Urban Systems, vol. 76, 1-10, 2019.
[10] Gasparovic, M., Singh, S. K., Urban surface water bodies mapping using the automatic k-means based approach and Sentinel-2 imagery, Geocarto International, accepted for publishing, 2023.
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This research was done as part of the project Climate HIDROLAB (KK. – Integrated hydrographic system for sustainable development of the marine ecosystem.
Proceedings of 22nd International Multidisciplinary Scientific GeoConference SGEM 2022
22nd International Multidisciplinary Scientific GeoConference SGEM 2022, 06-08 December, 2022
Proceedings Paper
STEF92 Technology
International Multidisciplinary Scientific GeoConference SGEM
SWS Scholarly Society; Acad Sci Czech Republ; Latvian Acad Sci; Polish Acad Sci; Serbian Acad Sci and Arts; Natl Acad Sci Ukraine; Natl Acad Sci Armenia; Sci Council Japan; European Acad Sci, Arts and Letters; Acad Fine Arts Zagreb Croatia; Croatian Acad Sci and Arts; Acad Sci Moldova; Montenegrin Acad Sci and Arts; Georgian Acad Sci; Acad Fine Arts and Design Bratislava; Turkish Acad Sci.
06-08 December, 2022
remote sensing, image classification, Sentinel-2, Posidonia oceanica, Adriatic Sea

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