Improving Seagrass Detection Through A Novel Method For Optically Deep Water Masking

Seagrasses 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

Development of a Semi-Analytical Model for Seagrass Mapping using Cloud-Based Computing and Open Sourced Optical Satellite Data

Seagrasses provide USD$2.28 trillion in annual ecosystem services, with US$169 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

A Cloud-based Coastal Earth Observation Framework for Regional Seagrass Environment Mapping Across The Eastern African Coastlines

Seagrasses play an important role in global coastal seascape configuration and extensive blue carbon sequestration through their connectivity with other seascape habitats. Unfortunately, their population keeps on dwindling down due to climate change and unsustainable human activities. Furthermore, the lack of seagrass distribution data and adequate level of protection hampers efforts to conserve these key ecosystems. Our study focuses on mapping seagrass distribution, bathymetry, and water quality along the East Africa coastline using Sentinel-2 satellite images on the Google Earth Engine cloud platform. We perform the seagrass mapping between the depth of 0-15 m in the country scales of Kenya, Tanzania, Mozambique, and Madagascar with a combination of large-scale in-situ and human-annotated data. The presented framework consists of big satellite data analysis, turbid zone masking, machine learning classification, and satellite-derived bathymetry (SDB) estimation. The overall accuracy of the seagrass mapping ranges between 73-89%. The SDB explains the variation in more than 60% of the validation data and features an error of less than 10% of the full mapped depth range. Our country-scale seagrass, bathymetry, and water quality inventories can support integrated science and management efforts pertaining to seascape connectivity, blue carbon spatial variability, resource conservation, and drivers of change in these optically complex natural architectures

Color Me Optically Shallow: A Simple And Adaptive Method For Standardized Analysis Ready Data For Coastal Ecosystem Assessments

Coastal aquatic remote sensing (RS) can help monitor the immensely valuable ecosystems of the global seascape, such as seagrasses and corals, by providing information on their extent, condition (e.g., water quality, bathymetry), ecosystem services (e.g., carbon sequestration, biodiversity maintenance), and trajectories. Unlike terrestrial RS, coastal aquatic RS applications require an additional consideration of the water column and its interactions with the light signal. This introduces new challenges as the water column attenuates light differently across the wavelengths, which has implications for signals from the benthic seabed where these subtidal ecosystems thrive. When the object(s) of interest is located on the benthic floor and not floating near the water surface, the additional depth increases the influence of the water column on light and affects the signals sensed by satellites at the top of the atmosphere. Besides these, other effects such as turbidity, waves, and sunglint introduce wide-ranging reflectance values as well. While these challenges have been traditionally handled through often complex methods in local computing environments, contemporary advances in cloud computing and big satellite data analytics offer highly scalable and effective solutions within the same context. The parallel processing of cloud platforms like the Google Earth Engine allows multitemporal composition of thousands of satellite images in a defined area over a defined time range through highly efficient statistical aggregations. As such, this approach yields Analysis Ready Data which are less redundant and more time efficient than the conventional laborious manual search for suitable single satellite image(s) which is often a yearlong assessment over cloud-dense coastal regions like the tropics. Regardless of the method, the pre-processing of the image and/or image composite remains a critical component of a successful coastal ecosystem assessment using RS. The impact of light attenuation changes the returning spectral signal, resulting in different signal profiles for the same seabed cover at different depths. In particular, at deeper depths, darker covers such as vegetated coastal beds (e.g., dense seagrass, microalgal mats) and optically deep water pixels are more likely to be confused and misclassified. A possible solution is to identify and remove these deep water pixels, where the water is too deep and thus no bottom signals are able to return to the sensor. By using a HSV-transformed B1-B2-B3 false-colour composite, namely the hue and saturation bands, of the Sentinel-2 image archive within the cloud computing platform of the Google Earth Engine, we are able to disentangle optically deep from optically shallow waters across four sites (Tanzania, the Bahamas, Caspian Sea (Kazakhstan) and Wadden Sea (Denmark and Germany)) with wide-ranging water qualities to improve the optically shallow benthic habitat classification. Furthermore, we compare our method with the three band ratios from a combination of the same three bands. While the band ratios may perform better in some sites, the specific band combination is site specific and thus might perform worse in others. In comparison, the hue and saturation bands show more consistent performance across all four sites. By using simple statistical reduction, the multitemporal composite is able to automatically mitigate common coastal aquatic RS showstoppers like clouds, cloud shadows or other temporal phenomena. However, there is also a need to remove images with explicitly no useful information, so that it does not affect the statistical approach. The use of metadata properties in the image archive is therefore additionally needed to filter out “bad” images, reducing the unnecessary computational costs of processing these low quality images. Case in point, this is a recommended procedure to filter for lower cloud covers prior to multitemporal composition in Google Earth Engine. We extend this approach further by integrating the various solar and viewing angles to estimate the presence of sunglint, on the basis that the spectral reflectance angle of the scene is a major factor to sunglint presence in satellite images. Finally, we draw comparisons with less pre-processed composites, showcasing methodological benefits for national coastal ecosystem assessments in the Bahamas, Seychelles, and East Africa

A Simple Cloud-Native Spectral Transformation Method to Disentangle Optically Shallow and Deep Waters in Sentinel-2 Images.

This study presents a novel method to identify optically deep water using purely spectral approaches. Optically deep waters, where the seabed is too deep for a bottom reflectance signal to be returned, is uninformative for seabed mapping. Furthermore, owing to the attenuation of light in the water column, submerged vegetation at deeper depths is easily confused with optically deep waters, thereby inducing misclassifications that reduce the accuracy of these seabed maps. While bathymetry data could mask out deeper areas, they are not always available or of sufficient spatial resolution for use. Without bathymetry data and based on the coastal aerosol blue green (1,2,3) bands of the Sentinel 2 imagery, this study investigates the use of band ratios and a false colour HSV transformation of both L1C and L2A images to separate optically deep and shallow waters across varying water quality over four tropical and temperate submerged sites: Tanzania, the Bahamas, the Caspian Sea (Kazakhstan) and the Wadden Sea (Denmark and Germany). Two supervised thresholds based on annotated reference data and an unsupervised Otsu threshold were applied. The band ratio group usually featured the best overall accuracies (OA), F1 scores and Matthews correlation coefficients, although the individual band combination might not perform consistently across different sites. Meanwhile, the saturation and hue band yielded close to best performance for the L1C and L2A images, featuring OA of up to 0.93 and 0.98, respectively, and a more consistent behaviour than the individual band ratios. Nonetheless, all these spectral methods are still susceptible to sunglint, the Sentinel 2 parallax effect, turbidity and water colour. Both supervised approaches performed similarly and were superior to the unsupervised Otsus method: the supervised methods featuring OA were usually over 0.70, while the unsupervised OA were usually under 0.80. In the absence of bathymetry data, this method could effectively remove optically deep water pixels in Sentinel 2 imagery and reduce the issue of dark pixel misclassification, thereby improving the benthic mapping of optically shallow waters and their seascapes

A Full Cloud-Native Dive into Bioregional-Scale Seagrass Mapping in the Mediterranean using Sentinel-2 Multitemporal Data

The seagrass Posidonia oceanica is the main habitat-forming species of the coastal Mediterranean, providing millennia-scale ecosystem services including habitat provisioning, biodiversity maintenance, food security, coastal protection, and carbon sequestration. Yet, projected temperature extremes and sea level rise due to climate change, the current knowledge gaps in its basin-wide extent, and its slow growth increase the risk of reduction and loss of these wide-scope services. As a result, accurate and efficient mapping of its distribution and trajectories of change is needed. Here, we leverage recent advances in Earth Observation—cloud computing, open satellite data, and machine learning—and field observations through a cloud-native geoprocessing framework to estimate the pan-Mediterranean extent of P. oceanica species. Employing 279,186 Sentinel-2 images taken between 2015 and 2019, and a human-labeled training dataset of 62,928 pixels, we map 19,020 km2 of P. oceanica meadows up to 25 m of depth in 22 Mediterranean countries, across a total seabed area of 56,783 km2. Using 2,480 independent, field-based points, we observe an overall accuracy of 72%. Given suitable reference data, our highly-scalable cloud-native framework can provide effective and data-driven seagrass mapping products to timely support pertinent Multilateral Environmental Agreements—from national to continental and global scale

Bahamian seagrass extent and blue carbon accounting using Earth observation

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