1,720,999 research outputs found
Bathymetric isobaths from the Balearic Islands (0-40 m) [Dataset]
No full textProcessed bathymetric isobaths (0-40 m depth) available from the Balearic Islands and used to interpolate the following product: https://doi.org/10.6084/m9.figshare.26898310.v1
The coastline from CNIG (IGN) was used as the 0 m isobath.
Eivissa, Formentera and Menorca isobaths were accessed through MITECO. Cabrera isobaths were accessed through the National Park, and Mallorca's isobaths were retrieved from different projects across the island.MICIU/AEI/ 10.13039/501100011033 - CNS2023-143630; Spanish Ministry of Science, Innovation and Universities - FPU20/01294; European Union Next Generation EU/PRTR; European Union-Next Generation Program; University of Cadiz.Peer reviewe
Subtidal seagrass and blue carbon mapping at the regional scale: a cloud-native multi-temporal Earth Observation approach
Full text linkThe seagrass ecosystems are among the most important organic carbon sinks on Earth, having a key role as climate change buffers. Among all seagrasses, Posidonia oceanica, an endemic seagrass species in the Mediterranean Sea, has been observed to feature the highest carbon stock and sequestration rate among all seagrasses. We developed a satellite-based workflow to complement in situ seagrass monitoring efforts in the Balearic Islands (Western Mediterranean), reducing field expenses while covering regional spatial scales. Our synoptic tool uses Sentinel-2 A/B satellite imagery at 10 m spatial resolution to generate a multi-temporal composite (2016–2022) of the Balearic Islands’ coastal waters within the Google Earth Engine cloud computing platform, optimizing image processing and highlighting the importance of a high-resolution bathymetric dataset to increase seagrass mapping accuracies. Machine learning algorithms have been applied to perform seagrass detection, obtaining a seagrass cartography up to 30 m of depth, estimating 505.6 km2 of seagrass habitat extent. Using existing in situ soil carbon stock (Cstock) data, we estimated a mean Cstock value of 12.27 ± 2.1 million megagram (Mg) Corg, while mapping a total annual C fixation (Cfix) and C sequestration (Cseq) rates of P. oceanica of 1,116.3 Mg Corg and 227 Mg Corg, according to depth. Our methodology highlights the key role of using a large image archive to generate the multi-temporal optical composite and an optimized bathymetry dataset to better map and account blue carbon in seagrass ecosystems across depth, showing the importance to integrate this Earth Observation approach to ensure a seagrass ecosystem monitoring at regional scales. This information aims to support the development of blue carbon strategies with synoptic time- and cost-efficient seagrass monitoring in the Mediterranean Sea
Seagrass extent, carbon fixation and sequestration (Balearic Islands) [Dataset]
No full textSeagrass extent dataset derived from Sentinel-2 (0-30 m), as well as the estimated annual carbon fixation and sequestration rate for Posidonia oceanica in the Balearic Islands. According to Pergent-Martini et al (2021) in Posidonia oceanica:
Carbon fixation = -202.5 * ln (depth) + 724.6
Carbon sequestration = -40.5 * ln (depth) + 145.5MICIU/AEI/ 10.13039/501100011033 - CNS2023-143630; Spanish Ministry of Science, Innovation and Universities - FPU20/01294; European Union Next Generation EU/PRTR; European Union-Next Generation Program; University of Cadiz.Peer reviewe
Bathymetry Balearic Islands - 10 m raster [Dataset]
No full textReprocess of different high to medium resolution available bathymetric datasets in the Balearic Islands to a 10 m grid from 0 to 40 m depth using Inverse Distance Weighting (IDW). EPSG: 4326, WGS 1964.MICIU/AEI/ 10.13039/501100011033 - CNS2023-143630; Spanish Ministry of Science, Innovation and Universities - FPU20/01294; European Union Next Generation EU/PRTR; European Union-Next Generation Program; University of Cadiz.Peer reviewe
Subtidal seagrass and blue carbon mapping at the regional scale: a cloud-native multi-temporal Earth Observation approach [Dataset]
No full textThe seagrass ecosystems are among the most important organic carbon sinks on Earth, having a key role as climate change buffers. Among all seagrasses, Posidonia oceanica, an endemic seagrass species in the Mediterranean Sea, has been observed to feature the highest carbon stock and sequestration rate among all seagrasses. We developed a satellite-based workflow to complement in situ seagrass monitoring efforts in the Balearic Islands (Western Mediterranean), reducing field expenses while covering regional spatial scales. Our synoptic tool uses Sentinel-2 A/B satellite imagery at 10 m spatial resolution to generate a multi-temporal composite (2016–2022) of the Balearic Islands’ coastal waters within the Google Earth Engine cloud computing platform, optimizing image processing and highlighting the importance of a high-resolution bathymetric dataset to increase seagrass mapping accuracies. Machine learning algorithms have been applied to perform seagrass detection, obtaining a seagrass cartography up to 30 m of depth, estimating 505.6 km2 of seagrass habitat extent. Using existing in situ soil carbon stock (Cstock) data, we estimated a mean Cstock value of 12.27 ± 2.1 million megagram (Mg) Corg, while mapping a total annual C fixation (Cfix) and C sequestration (Cseq) rates of P. oceanica of 1,116.3 Mg Corg and 227 Mg Corg, according to depth. Our methodology highlights the key role of using a large image archive to generate the multi-temporal optical composite and an optimized bathymetry dataset to better map and account blue carbon in seagrass ecosystems across depth, showing the importance to integrate this Earth Observation approach to ensure a seagrass ecosystem monitoring at regional scales. This information aims to support the development of blue carbon strategies with synoptic time- and cost-efficient seagrass monitoring in the Mediterranean Sea.This research has been financially supported by the Grant CNS2023-143630 funded by MICIU/AEI/10.13039/501100011033 and by European Union Next Generation EU/PRTR; the OAPN under Grant Observatory TIAMAT, [2715/2021]; the Spanish Ministry of Science, Innovation and Universities under the Grant [FPU20/01294]; the University of Cadiz under the Grant “Estancias para la obtención de la Mención de Doctorado Internacional del Plan Propio de estímulo y apoyo a la Investigación y Transferencia – UCA 2022–2023”; the Banco Santander under Grant Fundación Universia; and DLR-DAAD Scholarship under Grant nº 57478193.Peer reviewe
Development of a Semi-Analytical Model for Seagrass Mapping using Cloud-Based Computing and Open Sourced Optical Satellite Data
Full text linkSeagrasses provide USD169 million arising solely from blue carbon sequestration, the absorption and storage of carbon emissions from these coastal vegetated ecosystems. Unfortunately, 51,000 km2 or 29% of the known global seagrasses were lost between 1879 and 2006. The best global seagrass map is an assemblage of known areas since the 1930s. With growing interests in blue carbon, a standardised approach to map seagrass is needed. Aquatic remote sensing introduces the water column as a second medium and other aquatic-specific challenges. Solutions include the computationally expensive physics-based or analytical approach, which is less data-dependent than the conventional statistical approach, or the hybrid semi-analytical approach which combines the strengths of both. Fortunately, the advent of cloud computing services such as the Google Earth Engine (GEE) brings easy access to computational power. This study aims to implement a semi-analytical approach on GEE to map seagrasses in Mozambique. A forward Hyperspectral Optimisation Process Exemplar (HOPE) model based on Sentinel-2 was implemented and supplemented by a bathymetry log-linear regression and published intrinsic optical properties of water (IOPs) values and/or equations. Support Vector Machine and Random Forest were used for classification. Support Vector Machine produced the best areal estimate of 3518.37 km2 with a seagrass producer’s accuracy of 51.02%, a seagrass user’s accuracy of 65.79% and an overall accuracy of 60.27%. The best bathymetry estimate featured an R2 of 0.68. Although there was no validation for IOPs, external validation showed that the total absorption had less than 25% difference from the Case-2 Regional / Coast Colour (C2RCC) processor. While requiring further improvements, this model has shown potential for seagrass mapping, especially in remote or understudied regions, and is a step towards a global seagrass map
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
A full cloud-native dive into regional-scale seagrass mapping in the Mediterranean using Sentinel-2 multitemporal data
Full text linkImproving Seagrass Detection Through A Novel Method For Optically Deep Water Masking
Full text linkSeagrasses provide many ecosystem services such as habitat provisioning, biodiversity maintenance, food security, coastal protection, and carbon sequestration. With the projected temperature extremes and sea level rise due to climate change, these important ecosystems are highly threatened. Conserving these important ecosystems requires accurate and efficient mapping of its distribution and trajectories of change. Unfortunately, the spectral similarities between the seagrass and optically deep water pixels in the satellite images, or dark pixel confusion, causes potential classification errors. Within the context of the Global Seagrass Watch project, funded by DLR and supported by the GEO-GEE program, we develop a novel open method within the Google Earth Engine platform to identify and mask out these optically deep water pixels on open Sentinel-2 satellite data. This method yields less confusion and results in a more accurate seagrass detection which could benefit scientists focused on seagrass-related climate science
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