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

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

Cubesats Allow High Spatiotemporal Estimates of Satellite-Derived Bathymetry

High spatial and temporal resolution satellite remote sensing estimates are the silver bullet for monitoring of coastal marine areas globally. From 2000, when the first commercial satellite platforms appeared, offering high spatial resolution data, the mapping of coastal habitats and the extraction of bathymetric information have been possible at local scales. Since then, several platforms have offered such data, although not at high temporal resolution, making the selection of suitable images challenging, especially in areas with high cloud coverage. PlanetScope CubeSats appear to cover this gap by providing their relevant imagery. The current study is the first that examines the suitability of them for the calculation of the Satellite-derived Bathymetry. The availability of daily data allows the selection of the most qualitatively suitable images within the desired timeframe. The application of an empirical method of spaceborne bathymetry estimation provides promising results, with depth errors that fit to the requirements of the international Hydrographic Organization at the Category Zone of Confidence for the inclusion of these data in navigation maps. While this is a pilot study in a small area, more studies in areas with diverse water types are required for solid conclusions on the requirements and limitations of such approaches in coastal bathymetry estimations

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

A cloud-based approach on remote sensing based uncertainty maps, in marine habitat mapping

The necessity of monitoring and expanding the existing Marine Protected Areas has led to vast and high-resolution map products which, even if they feature high accuracy, they lack information on the spatially explicit uncertainty of the habitat maps, a structural element in the agendas of policy makers and conservation managers for designation and field efforts.The target of this study is to fill the gaps in the visualization and quantification of the uncertainty of benthic habitat mapping by producing an end-to-end continuous layer using relevant training datasets. To be more accurate, by applying a semi-automated function in the Google Earth Engine’s cloud environment we were able to estimate the spatially explicit uncertainty of a supervised benthic habitat classification product. In this study we explore and map the aleatoric uncertainty of multi-temporal data driven, per-pixel classification in four different case studies in Mozambique, Madagascar, Bahamas, and Greece, which are regions known for their immense coastal ecological value. Aleatoric uncertainty, also known as data uncertainty, is part of the information theory that seeks for the data driven random and inevitable noise under the spectrum of bayesian statistics. We use the Sentinel 2 (S2) archive in order to investigate the adjustability and scalability of our uncertainty processor in the four aforementioned case studies. Specifically, we use biennial time series of S2 satellite images for each region of interest to produce a single, multi-band composite free of atmospheric and water column related influences. Our methodology revolves around the classification process of the mentioned composite. By calculating the marginal and conditional distribution’s divisions given the available training data, we can estimate the Expected Entropy, Mutual Information and Spatially Explicit Uncertainty of a maximum likelihood model outcome. Expected Conditional Entropy Predicts the overall data uncertainty of the distribution P(x,y), with x:training dataset and y:model outcome. Mutual Information Estimates in total and per classified class the level of independence and therefore the relation of y and x distributions. Spatially Explicit Uncertainty A per pixel estimation of the uncertainty of the classification. The aim by implementing the presented workflow is to quantitatively identify and minimize the spatial residuals in large-scale coastal ecosystem accounting. Our results indicate regions and classes with high and low uncertainty that can either be used for a better selection of the training dataset or to identify, in an automated fashion, areas and habitats that are expected to feature misclassifications not highlighted by existing qualitative accuracy assessments. By doing so,we can streamline more confident, cost-effective, and targeted benthic habitat accounting and ecosystem service conservation monitoring , resulting in strengthened research and policies, globally

Nationwide seagrass mapping using analysis-ready Sentinel-2 and PlanetScope data to support the Nationally Determined Contributions of Seychelles

There is a notable lack of spatially-explicit knowledge on seagrass meadows in many parts of the world, which hinders seagrass research, conservation, and carbon accounting efforts. With the recent introduction of the pan-tropical PlanetScope basemaps onto the Google Earth Engine (GEE) cloud platform through the Planet & Norway’s International Climate and Forests Initiative (NICFI), anyone can now freely access and process the entire pan-tropical archive of the PlanetScope between 2015 and today. In comparison to other public optical satellite archives available within GEE, like the Sentinel-2, Planet’s imagery has a shorter global revisit interval of 30.3 h, a better spatial resolution of 4.77m, but a worse spectral resolution of only the blue, green, red and near infrared bands. Despite the NICFI’s focus on terrestrial forest monitoring in the tropics, a vast pan-tropical area of optically shallow coastal waters is included in the cloud-native public archives. This paves the way for high-resolution seamless pan-tropical seagrass mapping with large time and cost efficiency. Here, we adapt our multitemporal composition approach on GEE, initially developed for Sentinel-2, to the six-year PlanetScope archive, to map the nationwide seagrass meadows in Seychelles. We compare the feasibility and performance of the PlanetScope data to Sentinel-2 in national seagrass mapping, leveraging the synergy of cloud computing, artificial intelligence, and open reference data. The development of this approach could and will provide a comprehensive blueprint seagrass mapping and monitoring system to quantify national seagrass blue carbon stocks for the Nationally Determined Contributions, both within and beyond Seychelles