SHORELINE EXTRACTION USING TIME SERIES OF SENTINEL-2 SATELLITE IMAGES BY GOOGLE EARTH ENGINE PLATFORM
In recent decades, global warming and sea level rise, population growth, and intensification of human activities, have directly affected the coasts and as such, their monitoring for the accretion and retreat are among the issues that are considered by the coastal countries. This study, compares two...
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2023-01-01
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| Series: | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/X-4-W1-2022/653/2023/isprs-annals-X-4-W1-2022-653-2023.pdf |
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| author | E. Rostami M. A. Sharifi M. Hasanlou |
| author_facet | E. Rostami M. A. Sharifi M. Hasanlou |
| author_sort | E. Rostami |
| collection | DOAJ |
| description | In recent decades, global warming and sea level rise, population growth, and intensification of human activities, have directly affected the coasts and as such, their monitoring for the accretion and retreat are among the issues that are considered by the coastal countries. This study, compares two supervised classification algorithms for classifying Sentinel-2 satellite imagery for shoreline extraction. Median monthly images from 2020/01 to 2021/12 are taken and classified by Random Forest (RF) and Support Vector Machine (SVM) algorithms. By validating the maps, it is found that the RF algorithm has better accuracy and as such by averaging the accuracy of all maps, the overall accuracy (OA) values of 97.18% and the kappa coefficient (KC) of 0.97, and the mean overall accuracy and kappa coefficient of maps from SVM algorithm of 85.15% and 0.79, respectively, is obtained. After extracting the shorelines, the Digital Shoreline Analysis System (DSAS) is used to calculate the displacement rate. By calculating the Linear Regression Rate (LRR) factor, it is found that in 91% of transects (166 transects) we see the shoreline retreat to land. In 54% of them, the average rate of the retreat is 5.42 meters per year and in only 9% (16 transects) we see the accretion towards the sea. |
| first_indexed | 2024-04-10T22:44:15Z |
| format | Article |
| id | doaj.art-b56d25bfb5a049e190ae8423b7b4aec7 |
| institution | Directory Open Access Journal |
| issn | 2194-9042 2194-9050 |
| language | English |
| last_indexed | 2024-04-10T22:44:15Z |
| publishDate | 2023-01-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spelling | doaj.art-b56d25bfb5a049e190ae8423b7b4aec72023-01-15T20:24:14ZengCopernicus PublicationsISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences2194-90422194-90502023-01-01X-4-W1-202265365910.5194/isprs-annals-X-4-W1-2022-653-2023SHORELINE EXTRACTION USING TIME SERIES OF SENTINEL-2 SATELLITE IMAGES BY GOOGLE EARTH ENGINE PLATFORME. Rostami0M. A. Sharifi1M. Hasanlou2School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, IranSchool of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, IranSchool of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, IranIn recent decades, global warming and sea level rise, population growth, and intensification of human activities, have directly affected the coasts and as such, their monitoring for the accretion and retreat are among the issues that are considered by the coastal countries. This study, compares two supervised classification algorithms for classifying Sentinel-2 satellite imagery for shoreline extraction. Median monthly images from 2020/01 to 2021/12 are taken and classified by Random Forest (RF) and Support Vector Machine (SVM) algorithms. By validating the maps, it is found that the RF algorithm has better accuracy and as such by averaging the accuracy of all maps, the overall accuracy (OA) values of 97.18% and the kappa coefficient (KC) of 0.97, and the mean overall accuracy and kappa coefficient of maps from SVM algorithm of 85.15% and 0.79, respectively, is obtained. After extracting the shorelines, the Digital Shoreline Analysis System (DSAS) is used to calculate the displacement rate. By calculating the Linear Regression Rate (LRR) factor, it is found that in 91% of transects (166 transects) we see the shoreline retreat to land. In 54% of them, the average rate of the retreat is 5.42 meters per year and in only 9% (16 transects) we see the accretion towards the sea.https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/X-4-W1-2022/653/2023/isprs-annals-X-4-W1-2022-653-2023.pdf |
| spellingShingle | E. Rostami M. A. Sharifi M. Hasanlou SHORELINE EXTRACTION USING TIME SERIES OF SENTINEL-2 SATELLITE IMAGES BY GOOGLE EARTH ENGINE PLATFORM ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| title | SHORELINE EXTRACTION USING TIME SERIES OF SENTINEL-2 SATELLITE IMAGES BY GOOGLE EARTH ENGINE PLATFORM |
| title_full | SHORELINE EXTRACTION USING TIME SERIES OF SENTINEL-2 SATELLITE IMAGES BY GOOGLE EARTH ENGINE PLATFORM |
| title_fullStr | SHORELINE EXTRACTION USING TIME SERIES OF SENTINEL-2 SATELLITE IMAGES BY GOOGLE EARTH ENGINE PLATFORM |
| title_full_unstemmed | SHORELINE EXTRACTION USING TIME SERIES OF SENTINEL-2 SATELLITE IMAGES BY GOOGLE EARTH ENGINE PLATFORM |
| title_short | SHORELINE EXTRACTION USING TIME SERIES OF SENTINEL-2 SATELLITE IMAGES BY GOOGLE EARTH ENGINE PLATFORM |
| title_sort | shoreline extraction using time series of sentinel 2 satellite images by google earth engine platform |
| url | https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/X-4-W1-2022/653/2023/isprs-annals-X-4-W1-2022-653-2023.pdf |
| work_keys_str_mv | AT erostami shorelineextractionusingtimeseriesofsentinel2satelliteimagesbygoogleearthengineplatform AT masharifi shorelineextractionusingtimeseriesofsentinel2satelliteimagesbygoogleearthengineplatform AT mhasanlou shorelineextractionusingtimeseriesofsentinel2satelliteimagesbygoogleearthengineplatform |