A Novel GIS-Based Approach for Automated Detection of Nearshore Sandbar Morphological Characteristics in Optical Satellite Imagery
Satellite remote sensing is a valuable tool for coastal management, enabling the possibility to repeatedly observe nearshore sandbars. However, a lack of methodological approaches for sandbar detection prevents the wider use of satellite data in sandbar studies. In this paper, a novel fully automate...
| Main Authors: | , , , , |
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| Format: | Article |
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MDPI AG
2021-06-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/13/11/2233 |
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| author | Rasa Janušaitė Laurynas Jukna Darius Jarmalavičius Donatas Pupienis Gintautas Žilinskas |
| author_facet | Rasa Janušaitė Laurynas Jukna Darius Jarmalavičius Donatas Pupienis Gintautas Žilinskas |
| author_sort | Rasa Janušaitė |
| collection | DOAJ |
| description | Satellite remote sensing is a valuable tool for coastal management, enabling the possibility to repeatedly observe nearshore sandbars. However, a lack of methodological approaches for sandbar detection prevents the wider use of satellite data in sandbar studies. In this paper, a novel fully automated approach to extract nearshore sandbars in high–medium-resolution satellite imagery using a GIS-based algorithm is proposed. The method is composed of a multi-step workflow providing a wide range of data with morphological nearshore characteristics, which include nearshore local relief, extracted sandbars, their crests and shoreline. The proposed processing chain involves a combination of spectral indices, ISODATA unsupervised classification, multi-scale Relative Bathymetric Position Index (RBPI), criteria-based selection operations, spatial statistics and filtering. The algorithm has been tested with 145 dates of PlanetScope and RapidEye imagery using a case study of the complex multiple sandbar system on the Curonian Spit coast, Baltic Sea. The comparison of results against 4 years of in situ bathymetric surveys shows a strong agreement between measured and derived sandbar crest positions (<i>R</i><sup>2</sup> = 0.999 and 0.997) with an average <i>RMSE</i> of 5.8 and 7 m for PlanetScope and RapidEye sensors, respectively. The accuracy of the proposed approach implies its feasibility to study inter-annual and seasonal sandbar behaviour and short-term changes related to high-impact events. Algorithm-provided outputs enable the possibility to evaluate a range of sandbar characteristics such as distance from shoreline, length, width, count or shape at a relevant spatiotemporal scale. The design of the method determines its compatibility with most sandbar morphologies and suitability to other sandy nearshores. Tests of the described technique with Sentinel-2 MSI and Landsat-8 OLI data show that it can be applied to publicly available medium resolution satellite imagery of other sensors. |
| first_indexed | 2024-03-10T10:38:22Z |
| format | Article |
| id | doaj.art-bc42939296b043008ce9aabe6601ccd7 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T10:38:22Z |
| publishDate | 2021-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-bc42939296b043008ce9aabe6601ccd72023-11-21T23:09:42ZengMDPI AGRemote Sensing2072-42922021-06-011311223310.3390/rs13112233A Novel GIS-Based Approach for Automated Detection of Nearshore Sandbar Morphological Characteristics in Optical Satellite ImageryRasa Janušaitė0Laurynas Jukna1Darius Jarmalavičius2Donatas Pupienis3Gintautas Žilinskas4Nature Research Centre, LT-08412 Vilnius, LithuaniaInstitute of Geosciences, Vilnius University, LT-03100 Vilnius, LithuaniaNature Research Centre, LT-08412 Vilnius, LithuaniaNature Research Centre, LT-08412 Vilnius, LithuaniaNature Research Centre, LT-08412 Vilnius, LithuaniaSatellite remote sensing is a valuable tool for coastal management, enabling the possibility to repeatedly observe nearshore sandbars. However, a lack of methodological approaches for sandbar detection prevents the wider use of satellite data in sandbar studies. In this paper, a novel fully automated approach to extract nearshore sandbars in high–medium-resolution satellite imagery using a GIS-based algorithm is proposed. The method is composed of a multi-step workflow providing a wide range of data with morphological nearshore characteristics, which include nearshore local relief, extracted sandbars, their crests and shoreline. The proposed processing chain involves a combination of spectral indices, ISODATA unsupervised classification, multi-scale Relative Bathymetric Position Index (RBPI), criteria-based selection operations, spatial statistics and filtering. The algorithm has been tested with 145 dates of PlanetScope and RapidEye imagery using a case study of the complex multiple sandbar system on the Curonian Spit coast, Baltic Sea. The comparison of results against 4 years of in situ bathymetric surveys shows a strong agreement between measured and derived sandbar crest positions (<i>R</i><sup>2</sup> = 0.999 and 0.997) with an average <i>RMSE</i> of 5.8 and 7 m for PlanetScope and RapidEye sensors, respectively. The accuracy of the proposed approach implies its feasibility to study inter-annual and seasonal sandbar behaviour and short-term changes related to high-impact events. Algorithm-provided outputs enable the possibility to evaluate a range of sandbar characteristics such as distance from shoreline, length, width, count or shape at a relevant spatiotemporal scale. The design of the method determines its compatibility with most sandbar morphologies and suitability to other sandy nearshores. Tests of the described technique with Sentinel-2 MSI and Landsat-8 OLI data show that it can be applied to publicly available medium resolution satellite imagery of other sensors.https://www.mdpi.com/2072-4292/13/11/2233sandbar crestnearshore morphologyautomated workflowrelative bathymetric position indexplanetscoperapideye |
| spellingShingle | Rasa Janušaitė Laurynas Jukna Darius Jarmalavičius Donatas Pupienis Gintautas Žilinskas A Novel GIS-Based Approach for Automated Detection of Nearshore Sandbar Morphological Characteristics in Optical Satellite Imagery Remote Sensing sandbar crest nearshore morphology automated workflow relative bathymetric position index planetscope rapideye |
| title | A Novel GIS-Based Approach for Automated Detection of Nearshore Sandbar Morphological Characteristics in Optical Satellite Imagery |
| title_full | A Novel GIS-Based Approach for Automated Detection of Nearshore Sandbar Morphological Characteristics in Optical Satellite Imagery |
| title_fullStr | A Novel GIS-Based Approach for Automated Detection of Nearshore Sandbar Morphological Characteristics in Optical Satellite Imagery |
| title_full_unstemmed | A Novel GIS-Based Approach for Automated Detection of Nearshore Sandbar Morphological Characteristics in Optical Satellite Imagery |
| title_short | A Novel GIS-Based Approach for Automated Detection of Nearshore Sandbar Morphological Characteristics in Optical Satellite Imagery |
| title_sort | novel gis based approach for automated detection of nearshore sandbar morphological characteristics in optical satellite imagery |
| topic | sandbar crest nearshore morphology automated workflow relative bathymetric position index planetscope rapideye |
| url | https://www.mdpi.com/2072-4292/13/11/2233 |
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