Coastline change-detection method using remote sensing satellite observation data

• Autorzy: Markiewicz Ł. , Mazurek P. , Chybicki A.
• Języki publikacji: EN

Abstrakty

EN

Coastal zones are not only the fundaments for local economics based on trade, shipping and transport services, but also a source of food, energy, and resources. Apart from offering diverse opportunities for recreation and tourism, coastal zones provide protection against storms and other meteorological disturbances. Environmental information is also essential because of the direct influence on a country’s maritime zones, which are territorial sea and exclusive economic zones. Keeping local communities and ecosystems healthy requires monitoring and assessing of all the vital changes of territorial sea and its baseline. The paper presents a method and a concept of a system that provides an efficient means of automatic analysis of spatial data provided by satellite observation systems (optical Landsat 8 and SAR Sentinel 1) in order to monitor, and detect, changes in the coastline. The proposed methodology is based on a set of algorithms that enable one to trace and detect changes in coastline shape, and eventual damage to marine infrastructure, such as breakwaters and harbours, relying on high resolution satellite observational products.

Słowa kluczowe

EN

coastal monitoring; SAR; Sentinel; landsat; background subtraction; image processing

• Wydawca: Polskie Towarzystwo Akustyczne. Oddział Gdański
• Czasopismo: Hydroacoustics
• Rocznik: 2016
• Tom: Vol. 19
• Strony: 277—284
• Opis fizyczny: Bibliogr. 22 poz., rys.

Twórcy

autor

Markiewicz Ł.

• Department of Geoinformatics, Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology

autor

Mazurek P.

• BetterSolutions (BS Sp. z o. o. Sp. k.)

autor

Chybicki A.

• Department of Geoinformatics, Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology

Bibliografia

• [1] H. Greidanus, C. Santamaria, First analyses of Sentinel-1 images for maritime surveillance, European Commission's Joint Research Centre, ISBN: 978-92-79-44715- 0, 2015.
• [2] M. Moszyński, M. Kulawiak, A. Chybicki et al., Innovative Web-Based Geographic Information System for Municipal Areas and Coastal Zone Security and Threat Monitoring Using EO Satellite Data, Marine Geodesy, Vol. 38, Issue 3, 2015.
• [3] T. Lillesand, R. W. Kiefer, J. Chipman, Remote Sensing and Image Interpretation, John Wiley & Sons, ISBN 9781118343289, 2014.
• [4] J. Yang, P. Gong, R. Fu et al., The role of satellite remote sensing in climate change studies, Nature Climate Change Vol. 3 (875–883), 2013.
• [5] J.E. Patinoa, J.C. Duqueb, A review of regional science applications of satellite remote sensing in urban settings, Computers, Environment and Urban Systems, Vol. 37 (1–17), 2013.
• [6] M. Ford, Shoreline changes interpreted from multi-temporal aerial photographs and high resolution satellite images: Wotje Atoll, Marshall Islands, Remote Sensing of Environment, Vol. 135 (130–140), 2013
• [7] T. Kuleli, Quantitative analysis of shoreline changes at the Mediterranean Coast in Turkey, Environmental Monitoring and Assessment, Vol. 167, Issue 1, 2010.
• [8] R. Gens, Remote sensing of coastlines: detection, extraction and monitoring, International Journal of Remote Sensing, Vol. 31, Issue 7, 2010.
• [9] M. Piccardi, Background subtraction techniques: a review, IEEE International Conference on Systems, Man and Cybernetics, 2004.
• [10] Copernicus programme overview, available at: http://www.esa.int/Our_Activities/ Observing_the_Earth/Copernicus/Overview4 (accessed: 2016-05-20).
• [11] I. Manakos, S. Lavender, Remote Sensing in Support of the Geo-information in Europe, Land Use and Land Cover Mapping in Europe, Springer Netherlands, 3-10, 2014.
• [12] ESA S-1 SAR Technical Guide, available at: https://earth.esa.int/web/sentinel/iw-grdresolutions (accessed: 2016-06-03).
• [13] F. Qiu, J. Berglund, J.R. Jensen, P. Thakkar, D. Ren, Speckle Noise Reduction in SAR Imagery Using a Local Adaptive Median Filter, GIScience and Remote Sensing, Vol. 41, No. 3, 2004.
• [14] M. Mansourpour, M.A. Rajabi, J.A.R. Blais, Effect and performance of speckle noise reduction filter on active radar and SAR Images, ISPRS Vol. XXXVI, No. 1, 2006.
• [15] D. Roy, M. Wulder, T. Loveland et al., Landsat-8: Science and product vision for terrestrial global change research. Remote Sensing of Environment, Vol. 145 (154-172), 2014.
• [16] R. Cucchiara, C. Grana, M. Piccardi, A. Prati, Detecting Moving Objects, Ghosts and Shadows in Video Streams, IEEE Transactions On Pattern Analysis And Machine Intelligence, Vol. 25, No. 10, 2003.
• [17] F.Y.A. Rahman, A. Hussain, W.M.D. Zaki et al., Enhancement of Background Subtraction Techniques Using a Second Derivative in Gradient Direction Filter, Journal of Electrical and Computer Engineering, Vol. 2013, Article ID 598708, 2013.
• [18] A.R. Aldhaheri, E.A. Edirisinghe, Detection and Classification of a Moving Object in a Video Stream, Proc. of the Intl. Conf. on Advances in Computing and Information Technology - ACIT, 2014.
• [19] A. Elgammal, Background Subtraction: Theory and Practice, Synthesis Lectures on Computer Vision, Rutgers University, 2014.
• [20] A. Sobrala, A. Vacavantb, A comprehensive review of background subtraction algorithms evaluated with synthetic and real videos, Computer Vision and Image Understanding, Vol. 122 (4–21), 2014.
• [21] G. Kopsiaftis, K. Karantzalos, Vehicle detection and traffic density monitoring from very high resolution satellite video data, 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2015.
• [22] R. Dekker, H. Bouma, E.Breejen et al., Maritime Situation Awareness Capabilities from Satellite and Terrestrial Sensor Systems, Maritime Systems and Technology conference and exhibition, 2013.

Uwagi

PL

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017)