The European Space Agency (ESA) activity called Biodiversity+ Precursors is a contribution to the joint EC-ESA Earth System Science Initiative launched in February 2020 to jointly advance Earth System Science and its response to the global challenges that society is facing in the onset of this century. The ESA Biodiversity+ Precursors include three projects for different ecosystems; terrestrial, coastal and freshwater. BIOMONDO is focused on biodiversity in freshwater ecosystems, such as lakes, wetlands, rivers, flood plains and streams.
Based on an in-depth-analysis of the relevant sources for scientific and policy priorities, the main knowledge gaps and challenges in biodiversity monitoring, including capabilities of current and future Earth Observation (EO) systems, are identified. Requirements related to Essential Biodiversity Variables (EBVs), but also requirements to understand drivers for change and requirements for preserving ecosystem functions as conditions for favourable conditions for biodiversity are compared to (todays and future) possibilities and available data from EO.

These findings are then the basis for the development of innovative integrated earth science solutions that integrate EO based products, state-of-the-art biodiversity modelling and in situ data using advanced data science and information and communications technology.
Two models, GLOBIO-Aquatic (PBL, Netherlands) and Delft3D-ECO/ BLOOM (Deltares, Netherlands), will be included in the developments. The GLOBIO-Aquatic model describes the effects of human-induced changes to the environment and biodiversity of freshwater ecosystems. The biodiversity response is expressed as a biodiversity intactness index, and the driver-impact-relationships are based on meta-analyses of empirical data from literature. Delft3D-ECO/ BLOOM describes the processes and transport of phytoplankton biomass and Chlorophyll-a, associated nutrient concentrations (N, P and Si-based, inorganic and two organic fractions) as well as oxygen. In addition, BLOOM considers different phytoplankton groups such as diatoms, green algae, blue-green algae (Cyanobacteria), flagellates and dinoflagellates.

The combination of biogeochemical and physical data from Earth Observation, in-situ measurements and model outputs will be implemented in a Biodiversity Datacube. Analysis and processing functions for state-of-the-art methods, as well as visualisation interfaces and export functions will be available in virtual laboratory built on the datacube. Time series of the cube’d data will be analysed using ML technique and highly integrated Thematic Ecosystem Change Indices (TECI) will be deduced. The TECIs will inform about:

• LCLU and its change in the riparian zone and catchment area
• Water surface characterisation and evolution
• Water vegetation evolution
• Water quality and lake water temperature evolution
• Phenology (land)
• Phenology (water)
• Bottom characterisation (marcrophytes, sand)

The validation of the primary and integrated products and contrasting model with EO data are key tasks within BIOMONDO and beyond. Access and interoperability of this data will be eased significantly by the Lab.

A selection of the innovative integrated earth science solutions will be implemented and validated at two so called Earth System Science for Biodiversity Pilots (ESS Pilot) and will address critical issues of freshwater ecosystem, its services and threats. External Biodiversity Experts, Early Adopters (science and policy stakeholders) and the designated Advisory Board will get access to the novel EO products and models through the Lab, and support the validation and evaluation of impact and benefit of the developments. The Advisory Board consist of representatives from BiodivERsA+, TSU-IPBES, Freshwater BON, GEO BON Ecosystem Services, Ramsar Conventions Scientific and Technical Review Panel and other stakeholders. The Early Adopters will also use the produced results for the selected sites and explore their utility in relevant policy processes. To communicate about the initiative and to retrieve feedback from a larger community of stakeholders we will present the status and intermediate results at the symposium.

Changing environmental conditions, such as a change in temperature, precipitation, or land use, have significantly altered the phenology, spatial distribution, and abundances of species in both ‘green’ (terrestrial) and ‘blue’ (freshwater) ecosystems. In this study, we used remote sensing data to extract phenology metrics from lake chlorophyll estimates and terrestrial vegetation indices of 4264 lakes and their surrounding watersheds across a wide range of biomes and a period of 10-20 years. We investigated whether changes in the phenology of lake phytoplankton and terrestrial vegetation growth have occurred during this period, how these changes correlate with historical changes in environmental conditions, and whether the observed rates of change are similar for lakes and their surrounding watersheds.

More specifically, we used daily lake chlorophyll estimates derived from Sentinel-3 data to extract phenology metrics (e.g. the onset and decline of peaks in chlorophyll concentration) for individual pixels within each of the 4264 lakes. Through a newly developed method, we determined the timing of blooms, i.e. clusters of peaks in different pixels occurring within the same lake during the same period of the year, and, subsequently, studied the change in the timing of those blooms across several years. Metrics of the phenology of vegetation growth (e.g. start of the growing season) were derived using a MODIS-based Enhanced Vegetation Index and changes in the timing of these metrics were studied for entire watersheds.

Our results suggest that the phenology of lake phytoplankton is more sensitive to environmental changes than the phenology of the vegetation on the surrounding watersheds. This has led to increasingly large mismatches between aquatic and terrestrial phenology across environmental gradients of which the direction and magnitude differ strongly between regions. A finding that is of particular importance because terrestrial vegetation and lake phytoplankton are, as ‘primary producers’, at the base of food webs, and because terrestrial and freshwater ecosystems are closely interlinked through biogeochemical cycles and species that inhabit both ecosystems.

The Convention on Biological Diversity and the UN Decade of the Ocean have set targets to reaching ocean sustainability by 2050. To assess if these targets have been met, each target is linked to a set of indicators measuring Essential Biodiversity Variables (EBV). Marine and coastal habitats are under threat through numerous anthropogenic stressors. At the same time, measuring indicators in the marine and coastal environment is costly, time consuming and unreliable due to weather conditions leading to a dearth of data in these areas. Satellite remote sensing is proposed as a tool to complement in-situ observations. It can measure some EBVs in a more consistent and reliable manner and increase the area covered as well as spatial and temporal resolution. However, due to the need for specific training and infrastructure to analyse raw remote sensing data, there is a need to understand the end users’ requirements to use such data for biodiversity monitoring. The European Space Agency funded Bi-COME project (Biodiversity of the Coastal Ocean: Monitoring with Earth Observation) aims to develop products that help measuring more EBVs more effectively and to involve stakeholders in the development process.
To this end, we are collecting the user requirements of seven case study partners using semi-structured interviews. We aim to compare their current approaches with new Earth Observation products by learning about their current methods to measure EBVs, ask what they would like to achieve by the use of improved Earth Observation products and how they are able to access such data. The case study partners consist of managers and data providers to local environmental managers of intertidal, subtidal and pelagic marine habitats. The case study sites consist of sandy intertidal habitat in France, seagrass habitats in Mozambique and pelagic floating vegetation in India and the Caribbean Sea. We plan to interview the case study partners after they have tested the products created so that they can help shape the development according to their needs. This presentation will discuss results from the first set of interviews.

As foundation species, seagrass meadows are vital to coastal ecosystems. They provide important services such as carbon sequestration, coastal protection, and are a home for a wide array of animal biodiversity. Like many other coastal habitats worldwide, seagrass meadows are challenged by anthropic pressures such as dredging, eutrophication, and sea-level rise. Achieving a thorough understanding of the environmental drivers influencing seagrass dynamics is essential to implement efficient coastal zone management, in order to prevent seagrass meadows from further human impacts and to identify where conservation intervention is particularly required to sustain essential ecosystem services. Amongst a variety of environmental interactions, seagrass herbivory is of particular interest because grazing influence vegetation dynamics via top-down control, and because seagrass trajectories could subsequently influence herbivore status and population size via bottom-up interactions.
While there are numerous examples of the potential of Earth Observation to provide spatially and temporally resolved measurements of seagrass status worldwide, combined analyses of remotely sensed seagrass indicators and seagrass herbivores trajectories are still scarce. The objective of the present study was to analyze a long-term time-series of a Zostera noltei intertidal seagrass meadow in combination with the concurrent temporal variations in Brent goose (Branta bernicla bernicla) abundance at a wintering site along the Atlantic flyway. Brent goose is a migratory bird breeding in Siberia during summer and wintering along the North Sea and French Atlantic coasts. As Zostera seagrasses constitute about 95% of their diet, Brent goose migratory route and subsequent breeding success are highly dependent on the status of intertidal Z. noltei meadows in Europe.
In this work, seagrass density was measured using high spatial resolution satellite images in Bourgneuf Bay, an intertidal zone hosting one of the main seagrass meadow of the French Atlantic coast. A multi-mission Landsat, SPOT, and Sentinel2 time-series of seagrass normalized difference vegetation index (NDVI) was compiled and processed from 1985 - 2020 (Zoffoli et al., 2021). For each year, a summertime satellite image was selected during the period of maximal annual seagrass development. Seagrass percent cover was then computed from NDVI with an accuracy of about 15% (Zoffoli, et al., 2020), from which the meadow-averaged seagrass cover was estimated. The long-term dynamics of the seagrass indicators were then compared with concurrent time-series of Brent goose wintertime abundance. From 1984 - 2021, monthly records of Brent goose counts in Bourgneuf Bay were performed as part of the International Waterbird Census from September to April. Only years with coincident data of seagrass and goose counts were analyzed (N = 29). Seagrass and Brent goose datasets were normalized by their mean (x ̅) and standard deviation (σ), and covariance analysis (ANCOVA) was performed to assess the long-term trend over the past four decades. Spearman correlations were also performed between Brent goose maximal wintertime abundance and seagrass density for two situations: (i) with both seagrass and bird series corresponding to the same year; and (ii) with goose counts from the winter preceding seagrass summertime development. While the first situation indicated the influence of seagrass dynamics on Brent goose number (bottom-up interaction), the second situation was an indicator of seagrass top-down control by grazing birds.
Seagrass averaged density was significantly and positively correlated with Brent goose maximal wintertime abundance. Interestingly, the correlation was significant in the two types of configurations, i.e. when seagrass density and birds were evaluated during the same year (p-value < 0.05; R = 0.74) as well as when seagrass and birds were evaluated with a one year lag (p-value < 0.05; R = 0.64). In the first case, the positive correlation between seagrass and Brent geese was expected as a result of bottom-up interactions (i.e. the higher food supply in the seagrass pasture, the better for the birds). In the second case, the positive correlation was somehow counter intuitive, as a negative impact associated with overgrazing by geese has been previously observed in several seagrass meadows. On the other hand, the grazing effect might be reduced because Brent goose landings occur at the end of the seagrass growing season, thus causing little impact on seagrass development. Indeed, birds arrive at the meadow during seagrass senescent phase (in October), and they mostly depart before the start of seagrass growing phase (in March). Furthermore, previous field investigation showed that Brent geese could promote Z. noltei growth by seed propagation and sediment reworking. In complement to statistical correlation, seagrass density and Brent goose abundance presented positive temporal trends from 1985 to 2020. In particular, the slope of the long-term trend was similar for the normalized bird counts and meadow-averaged seagrass density (p-value < 0.05). Such a coincidence further demonstrates the strong link between Z. noltei meadow trajectories and Brent goose dynamics.
While the detailed ecological mechanisms underlying seagrass herbivory remain to be quantitatively evaluated through in situ experiments, our results confirmed that Brent geese - seagrass interactions go beyond trophic relationships, and suggest that Brent geese may positively influence seagrass meadow functioning and habitat structure. As migratory birds use coastal resources independent of any kind of migration policy, efficient ecosystem management calls for coordinated conservation partnerships at continental and global scales. Such international conservation actions are crucial to protect seagrass meadows and the many species they sustain.

The interest in the blue carbon sequestration provided by seagrasses, mangroves and tidal flats has been increasing in the past few years due to the potential of these nature-based solutions in climate change mitigation within a conservation and restoration context. Despite only covering less than 0.2% of the global seafloor, seagrass meadows store 10% of the world’s atmospheric carbon deep in the soils. The extent of seagrass meadows has been declining at an alarming rate of 1.5%/year on average. Field data collection of the underwater habitats can be costly due to the environmental challenges. Our goal in the Biodiversity of the Coastal Ocean: Monitoring with Earth Observation (BiCOME) project is to develop analysis-ready products that can be used for Essential Biodiversity Variable (EBV) quantification and downstream impact.

This study highlights our efforts in mapping the seagrass extent and stored blue carbon across the entire Mozambican coastlines. We processed 9,089 100x100km2 Sentinel-2 image tiles across 34,193km2 of the coastal area in Mozambique acquired between 14 December 2018 and 20 April 2020 with our multitemporal processor to produce an image synthesis with minimized effects from the environmental noises such as waves, sunglint, cloud cover, and turbidity. By using in-situ bathymetry data, and both in-situ and self-annotated habitat data, we mapped the full extent of bathymetry and seagrass habitats in the optically shallow water areas of Mozambique. We mapped the bathymetry using the Clustering-based Lyzenga method, which helps detect the depth at which the seagrass meadows are located. The seagrass extent was mapped by employing the Random Forest machine learning image classification, resulting in 1,779.3 km2 of seagrass meadows, which covers 2.7% of the study area, located between depths of 1.6 and 9.2 m. We estimated the seagrass blue carbon stocks by multiplying the mapped nationwide seagrass extent with their corresponding Tier 1 and Tier 2 carbon stock estimates. Based on our Tier 1 carbon stock assessment, the national seagrass blue carbon stock was estimated at 1.78-147 million Mg and our Tier 2 carbon stock assessment results in 1.64-4.35 million Mg, which highlights a large overestimation of the Tier 1 globally averaged assessment.

These analysis-ready products we developed with multidisciplinary methodologies (extent, bathymetry, and carbon stock processors) can be further used to design and develop relevant EBVs for subtidal seagrass meadows. Moreover, the estimated seagrass habitat map can be useful to highlight the importance of seagrass restoration and conservation which can promote more cost-effective and targeted action for coastal biodiversity and blue carbon ecosystem services of seagrasses in tropical regions worldwide.

Invasive species encroachment is a top threat to biodiversity worldwide. In recent years, Nypa fruticans (Nypa Palm) have emerged as a concerning invasive species in the Niger Delta mangrove system - the largest in Africa and in the world. However, this region is currently experiencing loss due to oil exploration and urbanization, exacerbating the spread of Nypa fruticans.

Here, we use machine learning and Google Earth Engine to quantify and monitor the change in extent of Nypa from 2015-2020. With Landsat imagery and random forest classification, we quantify the extent of mangroves in the Niger Delta in 2019. The Nypa extent map was classified within areas of mangrove by testingSentinel 1 SAR, Sentinel 2 MSI, and ALOS PALSAR models. Random forest classifications using both SAR and Optical data were assessed for separating Nypa from native mangroves. A final model using Sentinel 1 SAR had the highest accuracy and was selected to monitor changes in Nypa extent. Forest height and complexity estimates from GEDI data were compared between areas of mangrove and of Nypa. The extent of Nypa grew by ~32% within the study period and accounted for ~45% of the study area by 2020. At its current rate of growth, Nypa will become the exclusive vegetation at the study area by 2033. Additionally, Nypa was skewed to lower estimates for all measures of structural variability, including top-of-canopy height, vegetation cover, plant area index, and foliage height diversity.

Invasion of Nypa has the potential for devastating impacts on the Niger Delta. Compared to Nypa, Mangroves store more carbon, while supporting of biodiverse ecosystems and livelihoods. Additionally, Nypa can disrupt travel routes by blocking waterways. Continued monitoring of Nypa in this region, as well as economic exploitation of this species presents an opportunity for reducing its encroachment. Future work should explore the use of LiDAR structural metrics to further improve the accuracy of Nypa classification.