Coastline Zones Identification and 3D Coastal Mapping Using UAV Spatial Data

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dc.contributor.authorPapakonstantinou, Apostolos
dc.contributor.authorTopouzelis, Konstantinos
dc.contributor.authorPavlogeorgatos, Gerasimos
dc.date.accessioned2019-10-27T23:09:44Z
dc.date.available2019-10-27T23:09:44Z
dc.date.issued2016
dc.departmentEge Üniversitesien_US
dc.description.abstractSpatial data acquisition is a critical process for the identification of the coastline and coastal zones for scientists involved in the study of coastal morphology. The availability of very high-resolution digital surface models (DSMs) and orthophoto maps is of increasing interest to all scientists, especially those monitoring small variations in the earth's surface, such as coastline morphology. In this article, we present a methodology to acquire and process high resolution data for coastal zones acquired by a vertical take off and landing (VTOL) unmanned aerial vehicle (UAV) attached to a small commercial camera. The proposed methodology integrated computer vision algorithms for 3D representation with image processing techniques for analysis. The computer vision algorithms used the structure from motion (SfM) approach while the image processing techniques used the geographic object-based image analysis (GEOBIA) with fuzzy classification. The SfM pipeline was used to construct the DSMs and orthophotos with a measurement precision in the order of centimeters. Consequently, GEOBIA was used to create objects by grouping pixels that had the same spectral characteristics together and extracting statistical features from them. The objects produced were classified by fuzzy classification using the statistical features as input. The classification output classes included beach composition (sand, rubble, and rocks) and sub-surface classes (seagrass, sand, algae, and rocks). The methodology was applied to two case studies of coastal areas with different compositions: a sandy beach with a large face and a rubble beach with a small face. Both are threatened by beach erosion and have been degraded by the action of sea storms. Results show that the coastline, which is the low limit of the swash zone, was detected successfully by both the 3D representations and the image classifications. Furthermore, several traces representing previous sea states were successfully recognized in the case of the sandy beach, while the erosion and beach crests were detected in the case of the rubble beach. The achieved level of detail of the 3D representations revealed new beach characteristics, including erosion crests, berm zones, and sand dunes. In conclusion, the UAV SfM workflow provides information in a spatial resolution that permits the study of coastal changes with confidence and provides accurate 3D visualizations of the beach zones, even for areas with complex topography. The overall results show that the presented methodology is a robust tool for the classification, 3D visualization, and mapping of coastal morphology.en_US
dc.description.sponsorshipEuropean Regional Development Fund (ERDF)European Union (EU); national fund of Greece under the Cross-Border Cooperation Programme Greece-Cyprus; national fund of Cyprus under the Cross-Border Cooperation Programme Greece-Cyprusen_US
dc.description.sponsorshipThis work is supported by the Action entitled: "Cross-Border Cooperation for the development of Marine Spatial Planning" referred as THAL-CHOR is co-funded by the European Regional Development Fund (ERDF) by 80% and by national funds of Greece and Cyprus by 20%, under the Cross-Border Cooperation Programme Greece-Cyprus 2007-2013.en_US
dc.identifier.doi10.3390/ijgi5060075
dc.identifier.issn2220-9964
dc.identifier.issn2220-9964en_US
dc.identifier.issue6en_US
dc.identifier.urihttps://doi.org/10.3390/ijgi5060075
dc.identifier.urihttps://hdl.handle.net/11454/52733
dc.identifier.volume5en_US
dc.identifier.wosWOS:000379861200002en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.language.isoenen_US
dc.publisherMdpien_US
dc.relation.ispartofIsprs International Journal of Geo-Informationen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectUAV data acquisitionen_US
dc.subjectcoastal mappingen_US
dc.subjectstructure from motionen_US
dc.subjectGEOBIAen_US
dc.subject3D geovisualizationen_US
dc.subjectdigital surface modelen_US
dc.titleCoastline Zones Identification and 3D Coastal Mapping Using UAV Spatial Dataen_US
dc.typeArticleen_US

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