Mapping Posidonia meadow from high spatial resolution images in the Gulf of Oristano (Italy)

Matta, Erica; Aiello, Martina; Bresciani, Mariano; Gianinetto, Marco; Musanti, M.; Giardino, Claudia

doi:10.1109/igarss.2014.6947658

Cited by 9 publications

(8 citation statements)

References 18 publications

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“…Analysis of multidate spaceborne remote sensing data allows retrospective quantitative assessment of seagrass meadows (Dekker et al, 2006). Focusing on the Mediterranean seagrass ecosystems, several studies have utilized satellite imagery to map the distribution of the dominant P. oceanica (Borfecchia et al, 2013;Fornes et al, 2006;Matta et al, 2014;Pasqualini et al, 2005). The meadows of P. oceanica were studied in an IKONOS image to occupy depths of up to 15 m in the Magalluf embayment, SW Mallorca, Spain with an observed accuracy of 92% (Fornes et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

“…The meadows of P. oceanica were studied in an IKONOS image to occupy depths of up to 15 m in the Magalluf embayment, SW Mallorca, Spain with an observed accuracy of 92% (Fornes et al, 2006). Matta et al (2014) processed RapidEye imagery to map P. oceanica beds that spanned a depth range of 0-10 m in the Gulf of Oristano, W Sardegna, Italy, with an overall accuracy of 84%. Using two SPOT 5 images, Pasqualini et al (2005) mapped P. oceanica habitats up to 20 m of depth in the Laganas Bay, located in the Marine National Park of Zakynthos, Greece (eastern Mediterranean) with an accuracy between 73% and 96%.…”

Section: Introductionmentioning

confidence: 99%

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Mapping Mediterranean seagrasses with Sentinel-2 imagery

Traganos

Reinartz

2018

Marine Pollution Bulletin

149

113

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Mediterranean seagrasses have been hailed for their numerous ecosystem services, yet they are undergoing a decline in their coverage. The major complication with resolving this tendency is the sparsity of data on their overall distribution. This study addresses the suitability of the recently launched Sentinel-2 satellite for mapping the distribution of Mediterranean seagrass meadows. A comprehensive methodology is presented which applies atmospheric and analytical water column corrections and compares the performance of three different supervised classifiers. Remote sensing of the Thermaikos Gulf, northwestern Aegean Sea (Greece, eastern Mediterranean Sea) reveals that the utilization of Support Vector Machines on water column corrected reflectances yields best accuracies. Two Mediterranean seagrasses, Posidonia oceanica and Cymodocea nodosa, cover a total submerged area of 1.48km between depths of 1.4-16.5m. With its 10-m spatial resolution and 5-day revisit frequency, Sentinel-2 imagery can mitigate the Mediterranean seagrass distribution data gap and allow better management and conservation in the future in a retrospective, time- and cost-effective fashion.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Mapping Mediterranean seagrasses with Sentinel-2 imagery

Traganos

Reinartz

2018

Marine Pollution Bulletin

149

113

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“…As a matter of fact, P. oceanica meadows have declined rapidly in several areas of the Mediterranean basin, possibly also as a result of decreased habitat suitability in response to the localized effects of climate change, water quality degradation, coastal modification and other sources of human pressure (Chefaoui, Duarte, & Serrão, ; de los Santos et al, ; Marbà et al, ; Telesca et al, ). In this respect, airborne and satellite imagery could provide a dependable, deployable and cost‐effective tool to produce updated distribution maps for P. oceanica , as testified by the growing number of related applications (Borfecchia et al, ; Fornes et al, ; Matta et al, ; Pasqualini et al, ; Traganos et al, ). Although most of these studies refer to relatively small areas within the Mediterranean Sea, the most recent one (Traganos et al, ) proposes a workflow for regional‐scale mapping of seagrasses powered by remote sensing, machine learning and cloud‐based technologies that could be potentially scaled up to even larger (possibly global) spatial scales.…”

Section: Discussionmentioning

confidence: 99%

Spatial patterns and temporal variability of seagrass connectivity in the Mediterranean Sea

Mari

Melià

Fraschetti

et al. 2019

Diversity and Distributions

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Aim: The endemic seagrass Posidonia oceanica is a key component of the coastal seascapes of the Mediterranean Sea, where it provides crucial ecosystem services and promotes the assembly of diverse ecological communities. Although protection policies exist, P. oceanica meadows have been steadily declining in the recent past because of human activities and climate change. Here, we quantitatively analyse basin-wide patterns of seagrass connectivity over a 30-year-long period and identify connectivity hotspots that may serve as priority targets for conservation actions. Location: Mediterranean Sea. Time period: 1987-2016. Major taxa studied: The seagrass P. oceanica. Methods: A biophysical Lagrangian approach is used to simulate dispersal of seagrass fruits operated by marine currents. Connectivity metrics (self-retention, indegree and outdegree) are evaluated on top of Lagrangian simulations to identify the most ecologically connected areas. Time series of local connectivity scores are analysed to study temporal variability and possibly detect trends at different spatial scales. Results: Spatio-temporal variability is an important component of seagrass connectivity in the Mediterranean. Connectivity hotspots are unevenly distributed in all of its four main sub-basins, and along both European and African coastlines. Although statistically significant local trends in connectivity are generally quite infrequent across the whole basin, they appear to be relatively more prevalent in connectivity hotspots. The interannual variability of average connectivity scores seems to be at least partially linked to meteorological fluctuations.Main conclusions: The present study represents a step forward in the application of a quantitative, scalable and replicable methodological framework for the prioritization

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“…When taking into consideration the large areas and remote locations involved, satellite remote sensing method is both time and cost-effective (Knudby & Nordlund, 2011;Hossain, Bujang, Zakaria &Hashim, 2014;Traganos & Reinartz, 2018a). To this end, several studies have used satellite imagery to map P. oceanica distribution (Fornes et al, 2006;Matta et al, 2014;Pasqualini et al, 2005).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Posidonia oceanica Map Generated by Sentinel-2 Image: Gulbahce Bay Test Site

Yucel-Gier¹,

Kocak²,

Akçalı³

et al. 2020

Turk. J. Fish. Aquat. Sci.

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Remote Sensing offers an effective and low-cost solution for mapping the extent of seagrasses and temporally monitoring them routinely. In an in-situ well-explored Gülbahçe Bay test site, we used a high-resolution Sentinel-2 image to map the distribution of P. oceanica. Atmospheric and water column corrections were applied to the raw image. Thematic maps of the area were obtained by supervised classification and their accuracy was evaluated by cross-validation. The produced maps were also compared to a previously generated high accurate thematic map generated by the combination of sub-bottom profiler, side-scan sonar, and very high-resolution satellite image data. The classification achieved nearly 88% user accuracy at best. The assessment of the accuracy was also carried out in terms of water transparency. For this purpose, Secchi disk depths (SDDs) over the study area were inverted from the satellite-derived vertical attenuation coefficients by using a model. This model was retrieved by tuning the coefficients to 22 local SDD measurements. Sentinel-2 twinsatellite with its high spatial and temporal resolution data can contribute to mapping the P. oceanica cover in shallow waters. Their multispectral data can also be utilized for deriving the water transparency in order to determine the application limitations of the generated map.

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Mapping Posidonia meadow from high spatial resolution images in the Gulf of Oristano (Italy)

Cited by 9 publications

References 18 publications

Mapping Mediterranean seagrasses with Sentinel-2 imagery

Mapping Mediterranean seagrasses with Sentinel-2 imagery

Spatial patterns and temporal variability of seagrass connectivity in the Mediterranean Sea

Evaluation of Posidonia oceanica Map Generated by Sentinel-2 Image: Gulbahce Bay Test Site

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