Detection of oyster habitat in tidal flats using multi-frequency polarimetric SAR data

Choe, Byung-Hun; Kim, Duk‐jin; Hwang, Ji-Hwan; Oh, Yisok; Moon, Wooil M.

doi:10.1016/j.ecss.2011.11.007

Cited by 44 publications

(32 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We have introduced the polarization coefficient, a dimensionless parameter that can be easily used to generate indicators for bivalve beds, particularly when basic statistical operations (mean and standard deviation) are performed within (running) spatial windows. Others (Choe et al 2012;Dehouck et al 2011;Deroin 2012) have already shown that the use of multi-polarization SAR imagery has some additional potential for the monitoring of intertidal flat surfaces using SAR sensors. In particular, the detection of mussel or oyster habitats seems easier when SAR data acquired at multiple polarization combinations are used (Choe et al 2012).…”

Section: Discussionmentioning

confidence: 99%

“…Others (Choe et al 2012;Dehouck et al 2011;Deroin 2012) have already shown that the use of multi-polarization SAR imagery has some additional potential for the monitoring of intertidal flat surfaces using SAR sensors. In particular, the detection of mussel or oyster habitats seems easier when SAR data acquired at multiple polarization combinations are used (Choe et al 2012). Not only do our results confirm these earlier findings, but they provide an easy method to improve the monitoring of exposed intertidal flats.…”

Section: Discussionmentioning

confidence: 99%

“…The use of polarimetric SAR data was also supported by Won (2009) and Lee et al (2012), the former comparing polarimetric SAR signatures of salt-marsh plants on the South Korean coast with ground-based radar measurements, the latter finding that the radar backscatter from wetlands is stronger at horizontal (HH) than at vertical (VV) polarization. In addition, Choe et al (2012), demonstrated that polarimetric SAR data can also be used to detect mussel beds. Those bivalves, sticking out of the sediment, increase the surface roughness locally, and this is why they are visible on SAR imagery (Dehouck et al 2011;Choe et al 2012;Gade et al 2014).…”

Section: Introductionmentioning

confidence: 99%

“…In addition, Choe et al (2012), demonstrated that polarimetric SAR data can also be used to detect mussel beds. Those bivalves, sticking out of the sediment, increase the surface roughness locally, and this is why they are visible on SAR imagery (Dehouck et al 2011;Choe et al 2012;Gade et al 2014). Pacific oysters, rapidly spreading over large parts of the German Wadden Sea, can thus be monitored using spaceborne SAR sensors, even when they are working at different radar bands.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Analyses of Multi-Year Synthetic Aperture Radar Imagery of Dry-Fallen Intertidal Flats

Gade

Melchionna

Kemme

2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

ABSTRACT:We analyzed a great deal of high-resolution Synthetic Aperture Radar (SAR) data of dry-fallen intertidal flats in the German Wadden Sea with respect to the imaging of sediments, macrophytes, and mussels. TerraSAR-X and Radarsat-2 images of five test areas along the German North Sea coast acquired between 2008 and 2013 form the basis for the present investigation and are used to demonstrate that pairs of SAR images, if combined through basic algebraic operations, can already provide useful indicators for morphological changes and for bivalve (oyster and mussel) beds. Depending on the type of sediment, but also on the water level and on environmental conditions (wind speed) exposed sediments may show up on SAR imagery as areas of enhanced, or reduced, radar backscattering. The (multi-temporal) analysis of series of such images allows for the detection of mussel beds, and our results show evidence that also single-acquisition, multi-polarization SAR imagery can be used for that purpose.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Analyses of Multi-Year Synthetic Aperture Radar Imagery of Dry-Fallen Intertidal Flats

Gade

Melchionna

Kemme

2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

show abstract

“…The length scale of shellfish shells is in the right order of magnitude (centimeters) to effectively affect backscatter of C-and X-band microwave signals, and some authors have shown that both C-and X-band microwaves are sensitive to surface roughness induced by epibenthic shellfish. Choe et al [15] showed that polarimetric descriptors (including Freeman-Durden target decomposition, cross-polarized ratio, co-polarized correlation and co-polarized phase difference) from fully-polarized Radarsat-2 (C-band) and ALOS PALSAR (L-band) data can be used to pick up the roughness signatures created by oysters. In their study, the influence of the incidence angle on the backscatter in oyster reefs is small, but the difference between oyster reefs and mudflats was most pronounced at larger incidence angles [15].…”

Section: Introductionmentioning

confidence: 99%

Remote Sensing of Epibenthic Shellfish Using Synthetic Aperture Radar Satellite Imagery

Nieuwhof

Herman

Dankers³

et al. 2015

Remote Sensing

View full text Add to dashboard Cite

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 OPEN ACCESSRemote Sens. 2015, 7 3711 spaceborne SAR cannot replace ground surveys entirely, it could very well offer a significant improvement in efficiency.

show abstract

Deep learning for coastal resource conservation: automating detection of shellfish reefs

Ridge

Gray

Windle

et al. 2020

Remote Sens Ecol Conserv

View full text Add to dashboard Cite

It is increasingly important to understand the extent and health of coastal natural resources in the face of anthropogenic and climate-driven changes. Coastal ecosystems are difficult to efficiently monitor due to the inability of existing remotely sensed data to capture complex spatial habitat patterns. To help managers and researchers avoid inefficient traditional mapping efforts, we developed a deep learning tool (OysterNet) that uses unoccupied aircraft systems (UAS) imagery to automatically detect and delineate oyster reefs, an ecosystem that has proven problematic to monitor remotely. OysterNet is a convolutional neural network (CNN) that assesses intertidal oyster reef extent, yielding a difference in total area between manual and automated delineations of just 8%, attributable in part to OysterNet's ability to detect oysters overlooked during manual demarcation. Further training of OysterNet could enable assessments of oyster reef heights and densities, and incorporation of more coastal habitat types. Future iterations will be applied to high-resolution satellite data for effective management at larger scales.

show abstract

Detection of oyster habitat in tidal flats using multi-frequency polarimetric SAR data

Cited by 44 publications

References 12 publications

Analyses of Multi-Year Synthetic Aperture Radar Imagery of Dry-Fallen Intertidal Flats

Analyses of Multi-Year Synthetic Aperture Radar Imagery of Dry-Fallen Intertidal Flats

Remote Sensing of Epibenthic Shellfish Using Synthetic Aperture Radar Satellite Imagery

Deep learning for coastal resource conservation: automating detection of shellfish reefs

Contact Info

Product

Resources

About