Bahamian seagrass extent and blue carbon accounting using Earth Observation

Abstract

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.

Mapping the National Seagrass Extent in Seychelles Using PlanetScope NICFI Data

“…The processed image was then sampled with the cleaned reference data, which were then split into a training and test dataset in an 80:20 ratio. Following, the training dataset was normalised per class to remove the lowest 5-percentile and highest 10-percentile spectral values that might affect the classifier [21,52]. This would ensure that the trained classifier was not biased by any possible extreme or anomalous training data, which would allow it to be more suitable for transfer to other areas of the same geographic region.…”

“…The Random Forest function in GEE (ee.Classifier.smileRandomForest) was used for classification using the default parameters [16,[21][22][23], which includes a 50:50 random split of the training dataset for its training. The Random Forest model is a supervised machinelearning ensemble approach of many independent decision trees [60].…”

“…This has been further enhanced with the introduction of cloud computing, such as the Google Earth Engine (GEE) platform, which provides anyone with easy access to computational resources to map the Earth's surface across space and time [19,20]. Since then, seagrasses have been mapped in GEE on a national and regional level using Sentinel-2 [16,[21][22][23] and Landsat 8 [24]-both free and publicly available satellite archives that have a spatial resolution of 10 m and 30 m, respectively [2]. Traganos et al [16] mapped the Greek seagrass on a national level using a multitemporal approach, achieving an overall accuracy of 72%, and later extended it to the countries around the Mediterranean Sea with an average overall accuracy of 72% [22].…”

“…By using a soft probability classifier to map national seagrasses in Kenya, Tanzania, Mozambique, and Madagascar, overall mapping accuracies ranging from 73.2% to 89.6% were achieved in East Africa [23]. By including segmentation, texture features, as well as a Principal Component Analysis in conjunction with the variable selection and dataset normalisation, Blume et al [21] were able to increase the overall accuracy to 71.0% and 76.6%, depending on whether the minimum or maximum seagrass extent of the Bahamas was considered. Sebastian et al [25] mapped the seagrasses in Kalpeni Lagoon, India, over ten epochs between 2003 and 2020, achieving an R 2 value of 0.97 when validated with field survey data.…”

Potential Carbon Stock of Seagrass Biomass in Malang Regency

“…Most commonly, it refers to the role that salt marshes, soil and vegetation of mangroves IOP Publishing doi:10.1088/1755-1315/1328/1/012010 2 and seagrasses can play in carbon sequestration [3], [4]. The blue carbon working method refers to the ability of coastal ecosystems such as seagrass beds to capture carbon dioxide (CO2) from the atmosphere and store it in plant tissue and underwater sediments [5], [6]. This carbon storage reduces atmospheric CO 2 concentrations, which is one of the main greenhouse gases responsible for climate change.…”