Abstract
Sandy coasts are often marked by an intense recession of the shoreline [1]. Recent advances in the radiometric, spatial, temporal, and spectral resolution of sensors have provided a valuable tool set for innovative coastal data processing methods. It has been demonstrated that satellite imagery, as well as new remote sensing methods, can provide more practical approaches to the mapping and monitoring of coastal environments.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Amrouni O, Hzami A, Heggy E (2019) Photogrammetric assessment of shoreline retreat in North Africa: anthropogenic and natural drivers. ISPRS J Photogramm Remote Sens 157:73–92. https://doi.org/10.1016/j.isprsjprs.2019.09.001
Bengoufa S, Niculescu S, Mihoubi MK, Belkessa R, Rami A, Rabehi W, Abbad K (2021) Machine learning and shoreline monitoring using optical satellite images: case study of the Mostaganem shoreline. Algeria J Appl Rem Sens 15. https://doi.org/10.1117/1.JRS.15.026509
Boumboulis V, Apostolopoulos D, Depountis N, Nikolakopoulos K (2021) The importance of geotechnical evaluation and shoreline evolution in coastal vulnerability index calculations. JMSE 9:423. https://doi.org/10.3390/jmse9040423
Himmelstoss EA, Henderson RE, Kratzmann MG, Farris AS (2018) Digital shoreline analysis system (DSAS) version 5.0 user guide (USGS numbered series no. 2018–1179), digital shoreline analysis system (DSAS) version 5.0 user guide, open-file report. U.S. Geological Survey, Reston, VA https://doi.org/10.3133/ofr20181179
Niculescu S, Xia J, Roberts D, Billey A (2020) Rotation forests and random forest classifiers for monitoring of vegetation in pays De Brest (France). Int Arch Photogramm Remote Sens Spatial Inf Sci XLIII-B3-2020:727–732. https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-727-2020
Sr SN, Billey A, Jr, H.T.-O.-A. (2018) Random forest classification using Sentinel-1 and Sentinel-2 series for vegetation monitoring in the Pays de Brest (France). In: Remote sensing for agriculture, ecosystems, and hydrology XX. Presented at the remote sensing for agriculture, ecosystems, and hydrology XX, SPIE, p. 1078305. https://doi.org/10.1117/12.2325546
Toure S, Diop O, Kpalma K, Maiga A (2019) Shoreline detection using optical remote sensing: a review. IJGI 8:75. https://doi.org/10.3390/ijgi8020075
Acknowledgments
The authors thank the ERASMUS+ program, the Jean Monnet Chair, and the European Spatial Studies of Sea and Coastal Zones -599967-EPP-1-2018-1-FR-EPPJMO-CHAIR.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Boussetta, A., Niculescu, S., Bengoufa, S., Zagrarni, M.F. (2023). Assessment of Shoreline Change of Jerba Island Based on Remote Sensing Data and GIS Using DSAS Tools. In: Niculescu, S. (eds) European Spatial Data for Coastal and Marine Remote Sensing. Springer, Cham. https://doi.org/10.1007/978-3-031-16213-8_13
Download citation
DOI: https://doi.org/10.1007/978-3-031-16213-8_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-16212-1
Online ISBN: 978-3-031-16213-8
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)