Mapping Mediterranean seagrasses with Sentinel-2 imagery

Traganos, Dimosthenis; Reinartz, Peter

doi:10.1016/j.marpolbul.2017.06.075

Cited by 149 publications

(129 citation statements)

References 40 publications

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“…On the other hand, previously published SDB results using Sentinel-2 in the validation site of the Thermaikos [31] with the original Traganos17 algorithm tuned in the same waters at depths between 0 and 20 m explained 1.76 times better the variation, with a 1.19-m smaller RMSE in comparison to the present Traganos17 results. In yet another SDB application in the Thermaikos site [34] with 5-m RapidEye imagery, the use of Dierssen03 empirical ratio exhibited a 0.49 lower R 2 with a decreased RMSE of 0.09 m in comparison to the herein validated Dierrsen03 model.…”

Section: Cloud-based Sdb and Model Performancecontrasting

confidence: 82%

“…The second and third selected approaches follow two modifications of the proposal by [30]-hereafter referred to as modified Stumpf03 and Traganos17 (after its first use in [31]), respectively-concerning the empirical relationship between the ratio of the log-transformed green band to the log-transformed blue band and water depth. The modified Stumpf03 is simply the multiplicative inverse of the original ratio; it employs the blue to green ratio instead of the original green to blue ratio:…”

Section: Empirical Satellite-derived Bathymetries (Sdb)mentioning

confidence: 99%

“…where m 1 and m 0 are the slope and y-intercept, set by the linear regression between the ratio and bathymetry, and n is a fixed constant (1000 in all experiments related to the approach in [31]) to assure the linear response of the logarithmic ratio with depth and that it will remain positive at all points. , respectively.…”

Section: Empirical Satellite-derived Bathymetries (Sdb)mentioning

confidence: 99%

“…Here, we developed a preprocessing workflow within GEE and a GIS environment which implements a plethora of widely used and well established algorithms in the remote sensing of optically shallow habitats (seagrasses, corals, algae etc.) prior to the calculation of four empirical satellite-derived bathymetries [28,[30][31][32] with Sentinel-2 along with their statistics and model residuals.…”

Section: Cloud-based Sdb and Model Performancementioning

confidence: 99%

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Estimating Satellite-Derived Bathymetry (SDB) with the Google Earth Engine and Sentinel-2

et al. 2018

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Bathymetry mapping forms the basis of understanding physical, economic, and ecological processes in the vastly biodiverse coastal fringes of our planet which are subjected to constant anthropogenic pressure. Here, we pair recent advances in cloud computing using the geospatial platform of the Google Earth Engine (GEE) with optical remote sensing technology using the open Sentinel-2 archive, obtaining low-cost in situ collected data to develop an empirical preprocessing workflow for estimating satellite-derived bathymetry (SDB). The workflow implements widely used and well-established algorithms, including cloud, atmospheric, and sun glint corrections, image composition and radiometric normalisation to address intra-and inter-image interferences before training, and validation of four SDB algorithms in three sites of the Aegean Sea in the Eastern Mediterranean. Best accuracy values for training and validation were R 2 = 0.79, RMSE = 1.39 m, and R 2 = 0.9, RMSE = 1.67 m, respectively. The increased accuracy highlights the importance of the radiometric normalisation given spatially independent calibration and validation datasets. Spatial error maps reveal over-prediction over low-reflectance and very shallow seabeds, and under-prediction over high-reflectance (<6 m) and optically deep bottoms (>17 m). We provide access to the developed code, allowing users to map bathymetry by customising the time range based on the field data acquisition dates and the optical conditions of their study area.

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Section: Cloud-based Sdb and Model Performancecontrasting

confidence: 82%

Section: Empirical Satellite-derived Bathymetries (Sdb)mentioning

confidence: 99%

Section: Empirical Satellite-derived Bathymetries (Sdb)mentioning

confidence: 99%

Section: Cloud-based Sdb and Model Performancementioning

confidence: 99%

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Estimating Satellite-Derived Bathymetry (SDB) with the Google Earth Engine and Sentinel-2

et al. 2018

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“…Further trials showed that close to the coastline the remote sensing reflectance of B3 (i.e., R rs (B3) green band) is greater than the equivalent reflectance of B2 (blue band) and B4 (red band). This fact revealed the existence of subsurface meadows at depths of approximately 0-8 m, which mainly consist of species such as Posidonia oceanica and Cymodocea nodosa, the latter being characteristic species of the marine flora in the study area [22]. An unsupervised classification methodology was then adopted in order to model bathymetry over various seabed compositions and by implementing as criteria the main IOPs and AOPs (i.e., the total backscattering coefficient, the total absorption coefficient, and the diffuse attenuation coefficient (K d )).…”

Section: Band Ratio Methodologymentioning

confidence: 87%

A Hybrid Bio-Optical Transformation for Satellite Bathymetry Modeling Using Sentinel-2 Imagery

et al. 2019

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The article presents a new hybrid bio-optical transformation (HBT) method for the rapid modelling of bathymetry in coastal areas. The proposed approach exploits free-of-charge multispectral images and their processing by applying limited manpower and resources. The testbed area is a strait between two Greek Islands in the Aegean Sea with many small islets and complex seabed relief. The HBT methodology implements semi-analytical and empirical steps to model sea-water inherent optical properties (IOPs) and apparent optical properties (AOPs) observed by the Sentinel-2A multispectral satellite. The relationships of the calculated IOPs and AOPs are investigated and utilized to classify the study area into sub-regions with similar water optical characteristics, where no environmental observations have previously been collected. The bathymetry model is configured using very few field data (training depths) chosen from existing official nautical charts. The assessment of the HBT indicates the potential for obtaining satellite derived bathymetry with a satisfactory accuracy for depths down to 30 m.

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One hundred priority questions for advancing seagrass conservation in Europe

Nordlund,

Unsworth,

Wallner‐Hahn

et al. 2024

Plants People Planet

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Societal Impact StatementSeagrass ecosystems are of fundamental importance to our planet and wellbeing. Seagrasses are marine flowering plants, which engineer ecosystems that provide a multitude of ecosystem services, for example, blue foods and carbon sequestration. Seagrass ecosystems have largely been degraded across much of their global range. There is now increasing interest in the conservation and restoration of these systems, particularly in the context of the climate emergency and the biodiversity crisis. The collation of 100 questions from experts across Europe could, if answered, improve our ability to conserve and restore these systems by facilitating a fundamental shift in the success of such work.SummarySeagrass meadows provide numerous ecosystem services including biodiversity, coastal protection, and carbon sequestration. In Europe, seagrasses can be found in shallow sheltered waters along coastlines, in estuaries & lagoons, and around islands, but their distribution has declined. Factors such as poor water quality, coastal modification, mechanical damage, overfishing, land‐sea interactions, climate change and disease have reduced the coverage of Europe’s seagrasses necessitating their recovery. Research, monitoring and conservation efforts on seagrass ecosystems in Europe are mostly uncoordinated and biased towards certain species and regions, resulting in inadequate delivery of critical information for their management. Here, we aim to identify the 100 priority questions, that if addressed would strongly advance seagrass monitoring, research and conservation in Europe. Using a Delphi method, researchers, practitioners, and policymakers with seagrass experience from across Europe and with diverse seagrass expertise participated in the process that involved the formulation of research questions, a voting process and an online workshop to identify the final list of the 100 questions. The final list of questions covers areas across nine themes: Biodiversity & Ecology; Ecosystem services; Blue carbon; Fishery support; Drivers, Threats, Resilience & Response; Monitoring & Assessment; Conservation & Restoration; Governance, Policy & Management; and Communication. Answering these questions will fill current knowledge gaps and place European seagrass onto a positive trajectory of recovery.

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Mapping Mediterranean seagrasses with Sentinel-2 imagery

Cited by 149 publications

References 40 publications

Estimating Satellite-Derived Bathymetry (SDB) with the Google Earth Engine and Sentinel-2

Estimating Satellite-Derived Bathymetry (SDB) with the Google Earth Engine and Sentinel-2

A Hybrid Bio-Optical Transformation for Satellite Bathymetry Modeling Using Sentinel-2 Imagery

One hundred priority questions for advancing seagrass conservation in Europe

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