Shallow nearshore coastal waters provide a wealth of societal, economic, and ecosystem services, yet their topographic structure is poorly mapped due to a reliance upon expensive and time intensive methods. Space‐borne bathymetric mapping has helped address these issues, but has remained largely dependent upon in situ measurements. Here we fuse ICESat‐2 lidar data with Sentinel‐2 optical imagery, within the Google Earth Engine cloud platform, to create openly available spatially continuous high‐resolution bathymetric maps at regional‐to‐national scales in Florida, Crete and Bermuda. ICESat‐2 bathymetric classified photons are used to train three Satellite Derived Bathymetry (SDB) methods, including Lyzenga, Stumpf, and Support Vector Regression algorithms. For each study site the Lyzenga algorithm yielded the lowest RMSE (approx. 10%–15%) when compared with validation data. We demonstrate a means of using ICESat‐2 for both model calibration and validation, thus cementing a pathway for fully space‐borne estimates of nearshore bathymetry in shallow, clear water environments.
• Nearshore bathymetric depths can be retrieved using ICESat-2 lidar data • ICESat-2 bathymetric data can train Sentinel-2 Satellite Derived Bathymetry (SDB) models at shoreline to island nation scales • The fusion of ICESat-2 and Sentinel-2 data paves the way for openly available nearshore bathymetry mapping from space Accepted Article This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as
Seagrass ecosystems are globally significant hot spots of blue carbon storage, coastal biodiversity and coastal protection, rendering them a so-called natural climate solution. Their potential as a natural climate solution has been largely overlooked in national and international climate strategies and financing. This stems mainly from the lack of standardized, spatially explicit mapping and region-specific carbon inventories. Here, we introduce a novel seagrass ecosystem accounting framework that harnesses machine learning, big satellite data analytics and open region-specific reference data within the Google Earth Engine cloud computing platform. Leveraging a biennial percentile composite, assembled from 16 453 Sentinel-2 surface reflectance image tiles at 10-m spatial resolution, and 20 820 reference data points, we applied the cloud-native framework to produce the first national inventories of seagrass extent and total seagrass carbon stocks in Kenya, Tanzania, Mozambique and Madagascar. We estimated 4316 km 2 of regional seagrass extent (mean F1-score of 59.3% and overall accuracy of 84.3%) up to 23 m of depth. Pairing country-specific in situ carbon data and our spatially explicit seagrass extents, we calculated total regional seagrass blue carbon stocks between 11.2-40.2 million MgC, with the largest national carbon pool in Kenya (8-29.2 million MgC). We envisage that improvements in the remote sensing components of the framework guided by a necessary influx of region-specific data on seagrass stocks and fluxes could reduce uncertainties in our current spatially explicit ecosystem extent and carbon accounts, enhancing the incorporation of seagrasses into Multilateral Environmental Agreements for future resilient ecosystems, societies and economies.
Seagrasses are among the world’s most productive ecosystems due to their vast ‘blue’ carbon sequestration rates and stocks, yet have a largely untapped potential for climate change mitigation and national climate agendas like the Nationally Determined Contributions of the Paris Agreement. To account for the value of seagrasses for these agendas, spatially explicit high-confidence seagrass ecosystem assessments guided by nationally aggregated data are necessary. Modern Earth Observation advances could provide a scalable technological solution to assess the national extent and blue carbon service of seagrass ecosystems. Here, we developed and applied a scalable Earth Observation framework within the Google Earth Engine cloud computing platform to account the national extent, blue carbon stock and sequestration rate of seagrass ecosystems across the shallow waters of The Bahamas—113,037 km2. Our geospatial ecosystem extent accounting was based on big multi-temporal data analytics of over 18,000 10-m Sentinel-2 images acquired between 2017-2021, and deep feature engineering of multi-temporal spectral, color, object-based and textural metrics with Random Forests machine learning classification. The extent accounting was trained and validated using a nationwide reference data synthesis based on human-guided image annotation, recent space-borne benthic habitat maps, and field data collections. Bahamian seagrass carbon stocks and sequestration rates were quantified using region-specific in-situ seagrass blue carbon data. The mapped Bahamian seagrass extent covers an area up to 46,792 km2, translating into a carbon storage of 723 Mg C, and a sequestration rate of 123 Mt CO2 annually. This equals up to 68 times the amount of CO2 emitted by The Bahamas in 2018, potentially rendering the country carbon-neutral. The developed accounts fill a vast mapping blank in the global seagrass map—29% of the global seagrass extent—highlighting the necessity of including their blue carbon fluxes into national climate agendas and showcasing the need for more cost-effective conservation and restoration efforts for their meadows. We envisage that the synergy between our scalable Earth Observation technology and near-future nation-specific in-situ observations can and will support spatially-explicit seagrass and ocean ecosystem accounting, accelerating effective policy-making, blue carbon crediting, and relevant financial investments in and beyond The Bahamas.
The lack of clarity in turbid coastal waters interferes with light attenuation and hinders remotely sensed studies in aquatic ecology such as benthic habitat mapping and bathymetry estimation. Although turbid water column corrections can be applied on regions with seasonal turbidity by performing multi-temporal analysis, different approaches are needed in regions where the water is constantly turbid or only exhibits subtle turbidity variations through time. This study aims to detect these turbid zones (TZs) in optically shallow coastal waters using multi-temporal Sentinel-2 surface reflectance datasets to improve the aforementioned studies. The herein framework can be paired with other aquatic ecology remote sensing studies to establish the clear water focus area and can also be used by decision makers to identify rehabilitation areas. We selected the coastlines of Guinea-Bissau, Tunisia, and west Madagascar as our case studies which feature wide-ranging turbidity intensities across tropical, subtropical, and Mediterranean waters and applied three different methods for the TZ detection: Otsu’s method for bimodal thresholding, linear spectral unmixing, and Random Forest (RF) machine learning method on Google Earth Engine as an end-to-end process. Based on our experiments, the RF method yields good results in all study regions with overall accuracies ranging between 88 and 96% and F1-scores between 0.87 and 0.96. TZ detection is highly site-specific due to the inter-class variability that is mainly affected by the nature of the suspended materials and the environmental characteristics of the site.
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