Mapping of Sicilian Pocket Beaches Land Use/Land Cover with Sentinel-2 Imagery: A Case Study of Messina Province

Giovanni Randazzo, Maria Cascio, Marco Fontana, Francesco Gregorio, Stefania Lanza and Anselme Muzirafuti
Additional contact information
Giovanni Randazzo: Interreg Italia-Malta-Project: Pocket Beach Management & Remote Surveillance System (BESS), University of Messina, Via F. Stagno d’Alcontres, 31-98166 Messina, Italy
Maria Cascio: Interreg Italia-Malta-Project: Pocket Beach Management & Remote Surveillance System (BESS), University of Messina, Via F. Stagno d’Alcontres, 31-98166 Messina, Italy
Marco Fontana: Interreg Italia-Malta-Project: Pocket Beach Management & Remote Surveillance System (BESS), University of Messina, Via F. Stagno d’Alcontres, 31-98166 Messina, Italy
Francesco Gregorio: Interreg Italia-Malta-Project: Pocket Beach Management & Remote Surveillance System (BESS), University of Messina, Via F. Stagno d’Alcontres, 31-98166 Messina, Italy
Stefania Lanza: Interreg Italia-Malta-Project: Pocket Beach Management & Remote Surveillance System (BESS), University of Messina, Via F. Stagno d’Alcontres, 31-98166 Messina, Italy
Anselme Muzirafuti: Interreg Italia-Malta-Project: Pocket Beach Management & Remote Surveillance System (BESS), University of Messina, Via F. Stagno d’Alcontres, 31-98166 Messina, Italy

Land, 2021, vol. 10, issue 7, 1-20

Abstract: Pocket beaches (PBs) are among the most attractive tourist sites and economic development contributors in coastal areas; however, they are negatively impacted by the combined effects of climate change and anthropogenic activities. Generally, research on PBs is conducted from the beach towards offshore. Studies on the land use/land cover (LULC) of PBs are limited and currently lacking. Such studies deserve more investigation due to the importance of LULC in PBs’ functioning. In this study, supervised classification methods were investigated for LULC mapping of the PBs located in the province of Messina. Sentinel-2B satellite images were analyzed using maximum likelihood (MaL), minimum distance (MiD), mahalanobis distance (MaD) and spectral angle mapper (SAM) classification methods. The study was conducted mainly in order to determine which classification method would be adequate for small scale Sentinel-2 imagery analysis and provide accurate results for the LULC mapping of PBs. In addition, an occurrence-based filter algorithm in conjunction with OpenStreetMap data and Google Earth imagery was used to extract linear features within 500 m of the inland buffer zone of the PBs. The results demonstrate that information on the biophysical parameters, namely surface cover fractions, of the coastal area can be obtained by conducting LULC mapping on Sentinel-2 images.

Keywords: land use/land cover; climate change; OpenStreetMap; earth observation satellites; pocket beach; maximum likelihood; minimum distance; mahalanobis distance; spectral angle mapper; image classification (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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