Development of cloud-native and scalable algorithms to estimate seagrass composition and related carbon stocks in support of the Nationally Determined Contributions of the Paris Agreement

Seagrasses are one of the world’s most productive ecosystems, playing an important role in climate change mitigation and adaptation. They are vast natural carbon sinks which have important, yet largely overlooked and underestimated implications into national climate agendas like the Nationally Determined Contributions of the Paris Agreement. Precise knowledge of spatially-explicit seagrass distribution and country-specific in-situ blue carbon data is crucial for the ten countries which currently recognise this ecosystem within their Nationally Determined Contributions. This thesis combines open Sentinel-2 multi-temporal data with the open cloud computing platform Google Earth Engine to quantify country-scale seagrass extents and associated carbon stocks. The limited availability of reference data restricted the implementation of the created cloud-native mapping approach to only one country - The Bahamas. The mapped Bahamian seagrass covers an area between 11,779.44 and 27,629.32 km2, which can store 181,610,083.57 to 455,509,862.63 Mg carbon, and sequesters between 31.02 and 72.75 Mt CO2 per year. This equals 17 to 40 times the amount of CO2 emitted by The Bahamas in 2018, causing a carbon-neutral state and underlining the importance of the seagrass ecosystem for the Bahamian Nationally Determined Contributions. The generated data inventories could support interdisciplinary scientific research and management efforts within a regional and global climate action context

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

First Steps in Estimating the Spatial Uncertainty of Maximum Likelihood Tasks in a Cloud-based Environment in Context of Marine Remote Sensing

Recent developments in remote sensing technology including cloud computing and globally available optical satellite archives have allowed access to vast volumes of data, computation and scalability for mapping seagrasses and their environment. , Yet, beyond the traditional accuracy assessment, there is a broader lack of knowledge and methods for the per-pixel uncertainty of remotely sensed seagrass data.Spatially-explicit uncertainty is not only essential for more accurate remote sensing of seagrass extent, health and bathymetry, but could also aid more effective quantification of seagrasses’ ecosystem services like blue carbon stocks and coastal biodiversity maintenance. In this study, we utilise the open satellite image archives of Sentinel-2 and PlanetScope, through the Google Earth Engine (GEE) platform to develop per-pixel uncertainties of thematic benthic habitat mapping and continuous satellite based bathymetry data according to machine learning probabilistic principles.We present our uncertainty metrics and applications in two nationwide case studies in Bahamas and Belize. In contrast to traditional approaches that estimate uncertainty for the whole image/distribution, our approach, quantifies the uncertainty per pixel of both thematic and continuous remotely sensed data across large spatial scales and up to 5 m resolution. Our approach can improve the confidence and scalability of large-scale assessments of seagrass extent, condition and ecosystem services, supporting more effective policy uptake of seagrass ecosystems

Bahamas-wide Seagrass Blue Carbon Assessment leveraging Modern Earth Observation Advances

Seagrasses offer multiple ecosystem services and play an important role in carbon-related climate change mitigation and adaptation. Their carbon sequestration and storage potential can support a variety of Multilateral Environmental Agreements like the Nationally Determined Contributions of the Paris Agreement and the Sustainable Development Goals. Spatially explicit knowledge of seagrass distribution and site-specific in-situ carbon data are both crucial for the assessment of the potential of seagrass blue carbon and its policy uptake in national climate agendas and investments. Within the context of the Global Seagrass Watch project, we analysed open big Sentinel-2 satellite data within the open cloud computing platform of the Google Earth Engine to quantify the extent of Bahamian seagrass, and their associated carbon stocks (Tier 2 Assessment), and sequestration rates. Preliminary assessments indicate that seagrasses cover about 10 to 25% of the country's shallow water area and may store up to 456 million Mg of carbon in their soils. of. The mapped seagrass extent is estimated to sequester 17 to 40 times more CO2 than the annual emissions of The Bahamas in 2018. Our generated data inventories underline the importance of the seagrass ecosystem for The Bahamas and the necessity of recognizing their seagrass blue carbon into national climate agendas. In parallel, our preliminary assessment showcases the need for more cost-effective conservation and restoration efforts for seagrass meadows. Our remote sensing approach and data could support holistic efforts of scientists, managers, policy makers, and companies, from a national to a global climate action context

National Ecosystem Accounting of Seagrass Extent, Blue Carbon Stocks and Sequestration Potentials in The Bahamas harnessing contemporary Earth Observation advances

Seagrasses offer a wide range of ecosystem services, heralded as natural climate solutions, which are fundamental for sustaining the wellbeing and resilience of humans and the natural environment. These vegetated coastal foundation organisms are one of the world’s most productive ecosystems and play an important yet often overlooked and underassessed role in climate change mitigation and adaptation, biodiversity maintenance, and coastal protection from ext reme weather events. Their carbon sequestration and storage potential can support a variety of Multilateral Environmental Agreements like the Nationally Determined Contributions of the Paris Agreement, the EU Green Deal, and the Sustainable Development Goals. Standardized, comprehensive and spatially explicit knowledge of national seagrass extent and ecosystem services is crucial for meticulous seagrass ecosystem accounting. Within the Global Seagrass Watch project, funded by the German Aerospace Center (DLR) and supported by the Group on Earth Observations-Google Earth Engine program, we processed 18,881 single images to create a multi-temporal Sentinel-2 composite using the cloud computing platform Google Earth Engine to quantify the seagrass extent, and associated carbon stocks and sequestration rates for Bahamian waters. Preliminary results yield a seagrass extent larger than the land area of The Bahamas, which can store approximately 1,101 Mt CO2, and sequester 26 times more CO2 than annually emitted by the country. However, only about 11% of the Bahamian seagrass area lies within Marine Protected Areas. Our generated national data inventories underline the necessity of implicating seagrass blue carbon into national climate agendas and showcase the need for stronger and more cost-effective conservation and restoration efforts for seagrass meadows. Moreover, our data and technology can help to estimate the economic value of Bahamian seagrasses and their ecosystem services, demonstrating the importance of Earth Observation applications for ecosystem accounting frameworks like the System of Environmental-Economic Accounting (SEEA) - Ecosystem Accounting. We envisage that integrating Earth Observation into biophysical modelling could support holistic solutions for climate change mitigation, marine spatial planning, and biodiversity research within and beyond The Bahamas

The Global Seagrass Watch: Spatially-explicit seagrass ecosystem accounting enabled by contemporary remote sensing advances

Seagrasses provide globally significant yet underestimated ecosystem services to humans, economies and ecosystems, the so-called natural climate solutions. Climate change, eutrophication, coastal development and uneven protection levels are impacting the health and services of seagrasses. Contemporary approaches are needed to reverse this loss of our coastal natural capital. Ecosystem Accounting (EA) presents a new holistic approach to streamline physical and monetary evaluation for natural ecosystems. The conceptualization of the System of Environmental-Economic Accounting (SEEA) EA by the United Nations signals a new era of comprehensive solutions for the conservation, restoration and protection of ecosystems like seagrasses. Here we present the Global Seagrass Watch coastal EA framework. Our framework harnesses powerful cloud computing, globally aggregated public satellite and reference datasets, and AI-guided big data analytics to map the ecosystem extent, condition, and services of seagrasses, including their accuracies and uncertainties. We showcase recent national seagrass EA applications across largely-uncharted underwater biomes in more than 30 countries and 300,000 km2. We targeted these coastal biomes due to their vast blue carbon and coastal biodiversity stocks, lack of spatially explicit information, and uneven uptake in global funding and policy strategies. Our introduced EA system yet concerns only baseline mapping of seagrass biophysical stocks. In the next phase, we aim to integrate ecological and economic modelling, and spaceborne change detection into our scalable framework. This amalgamation of legacy and modern mapping, modelling, and variables could support spatially-explicit blue carbon accounting, policy making, financing, and, ultimately, coastal resilience within and beyond the 21st century

Spatially-Explicit Uncertainty of Remote Sensing Coastal Biodiversity Products using a scalable cloud-based framework in the Google Earth Engine

Recent advances in remote sensing have enabled the global monitoring of Earth's biodiversity. These developments are providing global information on the extent, structure, function and services of different ecosystem types, and their benefits to the environment and humans. In contradiction with the advances, relevant uncertainty methods and information are missing the understanding of the product biases. In our study, we present a uncertainty quantification framework, developed entirely within the Google Earth Engine, which assesses both thematic (e.g., ecosystem presence/absence) and continuous products (e.g., satellite-derived bathymetry) related to coastal biodiversity using multi-temporal and cloud-free 10-m Sentinel-2, field data collections, and human-annotated data points. By exploiting the cloud-native machine learning classifier and its outputs, we estimate the uncertainty of the procedure per pixel. With that information, our model is able to re-train itself in a data driven way and produce better results. There are three areas of interest in this study. The first is the Archipelago of Bahamas, where we assess a four-class benthic habitat classification product. Our second and third study area is the national scale of Belize and the Quirimbas Archipelago (Mozambique), respectively, in which we generate a satellite-derived bathymetry map. In the case of classification, our model achieved a better overall accuracy in comparison with the initial classification while the producer and user accuracy of the habitat class that we are interested in, seagrass, rose by 13% and 7% respectively. On the regression results, our framework highlights the areas with most uncertainty given the byproducts of the maximum likelihood regression that took place. While still in its alpha version, we think that further developments of the framework could allow better quantification of the data and model uncertainty. By reducing the uncertainties in the coastal biodiversity monitoring, more effective policy making efforts can be achieved and thus, better conservation

Earth Observation for Seagrass Blue Carbon Assessment in East Africa

The plan to mitigate the rise in global temperature includes nature-based solutions such as the blue carbon stocks of coastal ecosystems. Seagrass meadows rank among the most efficient organic carbon sinks on Earth. However, due to the high costs of the required equipment and enormous environmental challenges, field data collection of underwater habitats are expensive. In order to fill the knowledge gap, we utilized openly available Earth Observation data and algorithms which enabled us to remotely assess the blue carbon stocks here. In this study, we opted for the Sentinel-2 optical satellite images to map the national extent of seagrass meadows across the East African coastline. We developed and applied our mapping framework in the geospatial cloud platform Google Earth Engine of which processors are capable of processing 16,453 images available from 2018-2020 at 10-m spatial resolution. We estimated a regional extent of 4,316 km^2 of shallow water seagrass meadows along the coastlines of Kenya, Tanzania, Mozambique, and Madagascar with the overall accuracy of 84.3%, up to 23 m of depth. By pairing the country-specific in situ soil carbon data, we estimated regional seagrass blue carbon stocks between 11.2-40.2 million MgC in East Africa. Our regional seagrass map and its resulting blue carbon stock estimates are useful to highlight the importance of seagrass restoration and conservation for coastal biodiversity and blue carbon ecosystem services worldwide