Remote Sensing of Epibenthic Shellfish Using Synthetic Aperture Radar Satellite Imagery
On intertidal mudflats, reef-building shellfish, like the Pacific oyster and the blue mussel, provide a myriad of ecosystem services. Monitoring intertidal shellfish with high spatiotemporal resolution is important for fisheries, coastal management and ecosystem studies. Here, we explore the potenti...
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ftmdpi:oai:mdpi.com:/2072-4292/7/4/3710/ 2023-08-20T04:09:07+02:00 Remote Sensing of Epibenthic Shellfish Using Synthetic Aperture Radar Satellite Imagery Sil Nieuwhof Peter Herman Norbert Dankers Karin Troost Daphne Van der Wal agris 2015-03-27 application/pdf https://doi.org/10.3390/rs70403710 EN eng Multidisciplinary Digital Publishing Institute https://dx.doi.org/10.3390/rs70403710 https://creativecommons.org/licenses/by/4.0/ Remote Sensing; Volume 7; Issue 4; Pages: 3710-3734 SAR epibenthic shellfish oyster mussel mapping surface roughness Text 2015 ftmdpi https://doi.org/10.3390/rs70403710 2023-07-31T20:42:37Z On intertidal mudflats, reef-building shellfish, like the Pacific oyster and the blue mussel, provide a myriad of ecosystem services. Monitoring intertidal shellfish with high spatiotemporal resolution is important for fisheries, coastal management and ecosystem studies. Here, we explore the potential of X- (TerraSAR-X) and C-band (Radarsat-2) dual-polarized SAR data to map shellfish densities, species and coverage. We investigated two backscatter models (the integral equation model (IEM) and Oh’s model) for inversion possibilities. Surface roughness (vertical roughness RMSz and correlation length L) was measured of bare sediments and shellfish beds, which was then linked to shellfish density, presence and species. Oysters, mussels and bare sediments differed in RMSz, but because the backscatter saturates at relatively low RMSz values, it was not possible to retrieve shellfish density or species composition from X- and C-band SAR. Using a classification based on univariate and multivariate logistic regression of the field and SAR image data, we constructed maps of shellfish presence (Kappa statistics for calibration 0.56–0.74 for dual-polarized SAR), which were compared with independent field surveys of the contours of the beds (Kappa statistics of agreement 0.29–0.53 when using dual-polarized SAR). We conclude that spaceborne SAR allows one to monitor the contours of shellfish-beds (thus, distinguishing shellfish substrates from bare sediment and dispersed single shellfish), but not densities and species. Although spaceborne SAR cannot replace ground surveys entirely, it could very well offer a significant improvement in efficiency. Text Pacific oyster MDPI Open Access Publishing Pacific Remote Sensing 7 4 3710 3734 |
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Open Polar |
collection |
MDPI Open Access Publishing |
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ftmdpi |
language |
English |
topic |
SAR epibenthic shellfish oyster mussel mapping surface roughness |
spellingShingle |
SAR epibenthic shellfish oyster mussel mapping surface roughness Sil Nieuwhof Peter Herman Norbert Dankers Karin Troost Daphne Van der Wal Remote Sensing of Epibenthic Shellfish Using Synthetic Aperture Radar Satellite Imagery |
topic_facet |
SAR epibenthic shellfish oyster mussel mapping surface roughness |
description |
On intertidal mudflats, reef-building shellfish, like the Pacific oyster and the blue mussel, provide a myriad of ecosystem services. Monitoring intertidal shellfish with high spatiotemporal resolution is important for fisheries, coastal management and ecosystem studies. Here, we explore the potential of X- (TerraSAR-X) and C-band (Radarsat-2) dual-polarized SAR data to map shellfish densities, species and coverage. We investigated two backscatter models (the integral equation model (IEM) and Oh’s model) for inversion possibilities. Surface roughness (vertical roughness RMSz and correlation length L) was measured of bare sediments and shellfish beds, which was then linked to shellfish density, presence and species. Oysters, mussels and bare sediments differed in RMSz, but because the backscatter saturates at relatively low RMSz values, it was not possible to retrieve shellfish density or species composition from X- and C-band SAR. Using a classification based on univariate and multivariate logistic regression of the field and SAR image data, we constructed maps of shellfish presence (Kappa statistics for calibration 0.56–0.74 for dual-polarized SAR), which were compared with independent field surveys of the contours of the beds (Kappa statistics of agreement 0.29–0.53 when using dual-polarized SAR). We conclude that spaceborne SAR allows one to monitor the contours of shellfish-beds (thus, distinguishing shellfish substrates from bare sediment and dispersed single shellfish), but not densities and species. Although spaceborne SAR cannot replace ground surveys entirely, it could very well offer a significant improvement in efficiency. |
format |
Text |
author |
Sil Nieuwhof Peter Herman Norbert Dankers Karin Troost Daphne Van der Wal |
author_facet |
Sil Nieuwhof Peter Herman Norbert Dankers Karin Troost Daphne Van der Wal |
author_sort |
Sil Nieuwhof |
title |
Remote Sensing of Epibenthic Shellfish Using Synthetic Aperture Radar Satellite Imagery |
title_short |
Remote Sensing of Epibenthic Shellfish Using Synthetic Aperture Radar Satellite Imagery |
title_full |
Remote Sensing of Epibenthic Shellfish Using Synthetic Aperture Radar Satellite Imagery |
title_fullStr |
Remote Sensing of Epibenthic Shellfish Using Synthetic Aperture Radar Satellite Imagery |
title_full_unstemmed |
Remote Sensing of Epibenthic Shellfish Using Synthetic Aperture Radar Satellite Imagery |
title_sort |
remote sensing of epibenthic shellfish using synthetic aperture radar satellite imagery |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2015 |
url |
https://doi.org/10.3390/rs70403710 |
op_coverage |
agris |
geographic |
Pacific |
geographic_facet |
Pacific |
genre |
Pacific oyster |
genre_facet |
Pacific oyster |
op_source |
Remote Sensing; Volume 7; Issue 4; Pages: 3710-3734 |
op_relation |
https://dx.doi.org/10.3390/rs70403710 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/rs70403710 |
container_title |
Remote Sensing |
container_volume |
7 |
container_issue |
4 |
container_start_page |
3710 |
op_container_end_page |
3734 |
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