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.
Seagrasses are traversing the epoch of intense anthropogenic impacts that significantly decrease their coverage and invaluable ecosystem services, necessitating accurate and adaptable, global-scale mapping and monitoring solutions. Here, we combine the cloud computing power of Google Earth Engine with the freely available Copernicus Sentinel-2 multispectral image archive, image composition, and machine learning approaches to develop a methodological workflow for large-scale, high spatiotemporal mapping and monitoring of seagrass habitats. The present workflow can be easily tuned to space, time and data input; here, we show its potential, mapping 2510.1 km2 of P. oceanica seagrasses in an area of 40,951 km2 between 0 and 40 m of depth in the Aegean and Ionian Seas (Greek territorial waters) after applying support vector machines to a composite of 1045 Sentinel-2 tiles at 10-m resolution. The overall accuracy of P. oceanica seagrass habitats features an overall accuracy of 72% following validation by an independent field data set to reduce bias. We envision that the introduced flexible, time- and cost-efficient cloud-based chain will provide the crucial seasonal to interannual baseline mapping and monitoring of seagrass ecosystems in global scale, resolving gain and loss trends and assisting coastal conservation, management planning, and ultimately climate change mitigation.
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.
Coral reefs are among the most diverse and iconic ecosystems on Earth, but a range of anthropogenic pressures are threatening their persistence. Owing to their remoteness, broad spatial coverage and cross-jurisdictional locations, there are no high-resolution remotely sensed maps available at the global scale. Here we present a framework that is capable of mapping coral reef habitats from individual reefs (~200 km 2) to entire barrier reef systems (200 000 km 2) and across vast ocean extents (>6 000 000 km 2). This is the first time this has been demonstrated using a consistent and transparent remote sensing mapping framework. The ten maps that we present achieved good accuracy (78% mean overall accuracy) from multiple input image datasets and training data sources, and our framework was shown to be adaptable to either benthic or geomorphic reef features and across diverse coral reef environments. These new generation high-resolution map data will be useful for supporting ecosystem risk assessments, detecting change in ecosystem dynamics and targeting efforts to monitor local-scale changes in coral cover and reef health.
Coastal habitats provide a plethora of ecosystem services, yet they undergo continuous pressure and degradation due to the human-induced climate change. Conservation and management imply continuous monitoring and mapping of their spatial distribution at first. The present study explores the capabilities of the Copernicus Sentinel-2 mission and the contribution of its coastal aerosol band 1 (443 nm) for the mapping of the dominant Mediterranean coastal marine habitats and the bathymetry in three survey sites in the East Mediterranean. The selected sites have shallow to deep habitats and a high variability of oceanographic and seabed morphological conditions. The major findings of our study demonstrate the advantages of the downscaled Sentinel-2 coastal aerosol band 1 for both optically shallow habitat and satellite-derived bathymetry mapping due to its great water penetration. The use of Sentinel-2 band 1 allows detection of Posidonia oceanica seagrass beds down to 32.2 m of depth. Sentinel-2 constellation with its 10-m spatial resolution at most of the spectral bands, 5-day revisit frequency and open data policy can be an important tool to provide crucial missing information on the spatial distribution of coastal habitats and on their bathymetry distribution, especially in data-poor and/or remote areas with large gaps in a retrospective, rapid and non-intrusive manner. As such, it becomes a crucial ally for the conservation and management of coastal habitats globally.
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