Conference Agenda

talk

Video Recording

Biodiversa+, a multinational partnership co-developed with the European Commission, aims to support biodiversity goals and harmonise monitoring methods across Europe. Globally, natural habitats are increasingly degraded, making new conservation and restoration actions a key priority in the EU's Biodiversity Strategy and Nature Restoration Law. However, inconsistent mapping and monitoring methods hinder effective assessments and conservation planning for valuable habitats. To address this, Biodiversa+ launched the Habitat Pilot.

Remote sensing (RS) offers a cost-effective solution for large-scale habitat monitoring but is underutilised, particularly for high-value habitats, such as those listed under the Habitats Directive. The pilot focuses on testing the applicability of RS methods in two European-wide, threatened habitat types: grasslands and wetlands.

The pilot includes four modules:

1. Review of existing habitat mapping and monitoring methods
2. Application of RS methods to map grasslands and wetlands
3. Use of RS to monitor habitat conditions
4. A synthesis and roadmap for future work

In its initial phase, the pilot involved 11 European countries and reviewed over 40 habitat mapping approaches, evaluating their strengths, weaknesses, and potential for integration into a standardised monitoring framework. Data availability among the partners was also assessed.

The review revealed regional differences in the use of RS technologies, with some areas more advanced and others still more reliant on traditional field methods. Despite these differences, a set of shared RS-based approaches was identified for testing in the subsequent pilot modules.

The pilot is linked with ongoing projects like EU Grassland Watch and integrates new modelling frameworks such as NaturaSat, alongside locally developed methods. The overarching aim is to support knowledge sharing, comparison, testing, and adaptation of methods to pave the way for transnational, harmonised RS-based biodiversity mapping and monitoring.

Satellite remote sensing is playing an increasingly important role for nature conservation agencies by providing spatially explicit and temporally dense data for monitoring and evaluating ecosystems and their use.

From the perspective of a national nature conservation agency, remote sensing methods offer important support in the following areas of application:

• Monitoring of biodiversity, landscapes, as well as the drivers of their change

• Fulfillment of reporting obligations from the local level to the EU, such as

• Enforcement of nature conservation laws

• Monitoring the effectiveness of nature conservation measures

On the basis of the fields of application mentioned, we show how remote sensing is already being used by nature conservation authorities in Germany. Secondly, we outline areas of development and the potentials for the future use of remote sensing for authorities in nature conservation such as for the upcoming activities under the Nature Restoration Law.

We also refer to the fact that remote sensing products are increasingly being used as a basis for ecosystem modeling and nature conservation planning. Therefore, we also aim to consider the future role of remote sensing products as continuous and spatially explicit input data for digital twins.

In addition to technical maturity, organizational and structural prerequisites also play a major role in whether remote sensing can be used successfully for official nature conservation purposes. We hence show which prerequisites should be in place so that remote sensing can support the work of nature conservation authorities in the future.

Overall, satellite remote sensing has great potential to increase efficiency and transparency in official nature conservation by promoting data-based decisions and strengthening accountability to the public.

PEOPLE-ECCO (Enhancing Ecosystems Conservation through Earth Observation Solutions, Capacity Development and Co-design) is a project funded by ESA under the Earth Observation Science for Society (EO4Society) programme. The project answers to critical needs identified by Civil Society Organizations (CSOs) and Non-Governmental Organizations (NGOs) striving to improve evidence-based ecosystem conservation. The project aims to develop and demonstrate innovative Earth Observation (EO)-integrated methods and tools to 1) monitor protected areas conditions and management effectiveness, and 2) identify high-priority areas to be protected. PEOPLE-ECCO follows a co-design and user-centred approach. This means we develop the tools together with conservation CSOs/NGOs and provide tailored capacity development enabling them to integrate these EO methodologies in their operational practices.

PEOPLE-ECCO commenced in October 2024 and will run for two years. In this presentation we will outline our overall approach which consists of two interacting parts: a user-focused part dedicated to user engagement, requirement consolidation and capacity development, and a technology-focused part focussing on EO-integrated methods and tools testing, development and demonstration. A central role is reserved for six NGOs/CSOs active in conservation actions with an interest in taking up EO solutions. These “Early Adopters” will jointly contribute to the development of actionable and relevant EO-integrated methods and tools. The Early Adopters in PEOPLE-ECCO (African Parks, Bulgarian Society for the Protection of Birds, Lebanon Reforestation Initiative, IUCN Vietnam, Prince Edwards Island Watershed Alliance and Reef Check Malaysia) are distributed over four continents, and the ecosystems they jointly manage cover a range of terrestrial and aquatic ecosystems.

Outputs of PEOPLE-ECCO aim to contribute to the EU Biodiversity Strategy for 2030 and the Kunming-Montreal Global Biodiversity Framework (GBF), especially to GBF Target 3 (Conserve 30 percent of land, water and seas) and Target 20 (Strengthen Capacity-Building, Technology Transfer, and Scientific and Technical Cooperation for Biodiversity).

The Horizon Europe project Nature FIRST, Forensic Intelligence and Remote Sensing Technologies for Nature Conservation, is generating different tools to support biodiversity monitoring and human-wildlife conflict (HWC) prevention.

Using Satellite Remote Sensing (SRS) technologies with a collaborative approach, Nature FIRST demonstrated the generation of a habitat mapping model in a given territory, which integrates the knowledge of their key actors. This results in semi-automatic, efficient, affordable and easy to update habitat distribution maps (EUNIS, Habitats of community interest), along with an automatic change detection system through available Copernicus data. The Habitat mapping model approach is intended to be applicable to protected area management, to monitor the conservation status of habitats and their dynamics. It also supports the establishment of conservation objectives, along with action and monitoring plans by the entities responsible for biodiversity in the territory.

In this context, the integration of data and information is key. The Nature FIRST system, based on the Sensing Clues platform, makes use of semantic knowledge graphs, which links species, habitat and Natura 2000 site data, together with SRS data. This framework supports additional applications for biodiversity management, such as predictive species movement, habitat suitability maps, and digital twins for monitoring and predicting HWC.

We showcase the practical outcomes of Nature FIRST, i) the creation of habitat mapping models on the territories of Bulgaria, Romania, Spain and Ukraine; ii) An associated habitat change detection system; iii) how the organisation of SRS and in situ data has allowed us to generate a predictive model of brown bear movements, their habitat suitability maps and a digital twin to monitor and predict conflicts, the Human-Bear Conflict Radar.

Maintaining functional ecosystems under anthropogenic pressures requires understanding cumulative impacts on habitat suitability and connectivity to support species conservation. We propose an integrative framework for identifying and preserving functionally connected habitats, utilizing computational tools that enhance conservation planning. This approach begins by modeling effective connectivity through three main steps: (1) estimating habitat permeability, (2) quantifying ecological distances, and (3) calculating effective connectivity for each species.

The approach then scales effective connectivity to the landscape level through the concept of “functional habitat,” linking niche suitability in environmental space with connectivity in geographic space to assess cumulative impacts across landscapes for conservation planning. The framework combines geographic information science, ecological niche modeling, and network science to model species movement across complex landscapes. Applied through scenario analysis to hydropower development impacts in Norway, this framework revealed extensive habitat loss due to fragmentation. The development of the ConScape library enable rapid, high-resolution assessment of connectivity and habitat functionality, facilitating data-driven conservation.

Finally, a sensitivity analysis developed within this framework identifies priority areas for conservation by examining the effects of local landscape changes. In Southern Norway, this analysis suggested that strategically placed wildlife overpasses could achieve a fourfold increase in connected habitat. Together, these methodologies support sustainable landscape management through scenario analysis, spatial prioritization, and mitigation strategies.

The German Federal Ministry for the Environment, Nature Conservation, Nuclear Safety and Consumer Protection has launched the Natural Climate Protection Action Program and commissioned the German Space Agency at DLR to implement measure 8.9 "Tapping the potential of remote sensing for natural climate protection". Started on 01.01.2025, the aim of the EO4ANK-project is to set up the EO4ANK-portal, including a modular EO toolbox, together with partners from science and industry and in close consultation with representatives of the German authorities, who will be the main users. The EO4ANK-Portal will support German authorities at federal, state and local level in implementing the measures from the action program and provide tools for an efficient environmental and nature conservation monitoring. Therefore, a total of 18 tools from the areas of peatlands, floodplains, forests, wilderness, soils and urban areas will be developed and made operationally available on the portal (e.g. heat islands in cities, determination of greenhouse gas emissions from peatlands and their reduction through rewetting, overflow areas etc.). The first tools should be operational by the end of 2026. It is important to provide the portal without any follow-up costs, which is why the toolbox is largely based on Copernicus data. In addition to the development and implementation of the necessary technical solutions, user training also plays a key role. The tools developed must be integrated into the operational working environment of the authorities and users must be trained accordingly, which is why numerous learning materials are produced and made available on the portal.

Habitat destruction, fragmentation, and degradation via human-induced land-cover and land-use change are the predominant drivers of biodiversity loss and are the most significant threats to chimpanzee survival. Conservation practitioners and decision-makers must understand and monitor the relative condition of chimpanzees and other forest and woodland habitats, the threats they face, and how this changes over time to plan and implement cost-effective conservation strategies and measure success. Recent developments in remote sensing and cloud computing such as NASA’s OPERA Land Surface Disturbance Alerts provide near-real-time access to vegetation cover loss intelligence from Harmonized Landsat and Sentinel-2 (HLS) scenes. It provides updates on vegetation cover, and disturbance, and estimates confidence every 2-4 days at 30-meter resolution across the globe. Decision-makers could potentially move from simply documenting the forests already lost toward faster action to stop illegal activities on the ground, slowing and preventing deforestation before it happens. However, to realize this potential, local decision-makers need easy-to-use, cost-effective, and practical solutions to connect and access relevant information and tools. There is an urgent need to find innovative ways to convert these near-real-time EO data into actionable information, meaningful and useful to support specific decision-making processes and build local capacities to access and use these products to drive action and impact. In this presentation, we will discuss the feasibility of OPERA data combined with Planetary Variables from Planet to support local communities and governments to monitor and manage chimpanzee habitats in private, village, district, and national protected areas in Tanzania and Uganda. We will then share ongoing efforts to integrate OPERA alerts into an existing Decision Support System to monitor habitats and threats and inform conservation strategies, and actions and measure success as part of national chimpanzee action plans in Tanzania and Uganda.

Sustained observations of plankton are critical to understand how environmental drivers and biological interactions shape trophic structure, food web dynamics, and ultimately the distribution and abundance of living marine resources. This study examined image-based marine plankton from surveys in south Florida coastal and shelf waters collected by the Southeast U.S. Marine Biodiversity Observation Network project. A goal is to characterize biogeographic distributions and phenology of phytoplankton and zooplankton. Ten field campaigns aboard the R/V Walton Smith (U. Miami) and R/V Hogarth (Florida Institute of Oceanography) were carried out every six weeks between December of 2022 and July of 2024. Plankton imagery were collected in depth profiles at ~70–90 stations with a Continuous Particle Imaging and Classification System (CPICS) mounted on a CTD rosette. Image segments (Regions of Interest; 11,424) were classified and quantified to estimate concentrations of diatoms, Trichodesmium spp, dinoflagellates, copepods, Rhizaria spp, polychaetes, pteropods, chaetognaths, ostracods, larvaceans, echinoderms, and gelatinous species as organisms per cubic meter. Plankton occurrences were matched to satellite-derived seascapes, which are dynamic biogeographic classifications of water masses based on multiple remote sensing data sources. Four seascape classes dominated the areas sampled: Tropical/Subtropical Transition (TST: 7%), Tropical Seas (TS: 26%), Warm, Blooms, High Nutrients (WBHN: 43%), and Hypersaline Eutrophic (HE: 15%). Results show differentiation in plankton distributions between seascape categories and seasonal variability in species composition within seascapes. Pteropods were notably higher in the oligotrophic TST class. Gelatinous species concentrations were typically lower in TST and other low nutrient, low plankton biomass classes. Copepod concentrations exhibited strong seasonality with abundances up to three orders of magnitude higher during summer months versus winter months. Satellite seascapes can provide a biogeographic framework to evaluate how plankton communities change over time and space, and drive ecosystem dynamics and marine living resources in the Florida Keys and shelf areas.

The Mediterranean marine ecosystems are tremendously impacted by climate change, leading to profound consequences on structure and functioning of living communities and biodiversity loss, starting from primary producers (i.e., phytoplankton). Using satellite-derived surface chlorophyll-a concentration (as a proxy for phytoplankton concentration), various studies have attempted to describe the general seasonal patterns of such organisms at the sea surface. However, the inter-annual variability of the resulting seascape has not been fully addressed, much less along the water column and in relation with climate. Within the ESA 4DMED-Sea project, we explored 26 years (1998-2023) of daily satellite-derived chlorophyll-a images at 4 km of spatial resolution and assess the interannual variability of the Mediterranean pelagic seascape based on the phenology of phytoplankton. By applying a clustering technique, we first confirmed the existence of seven major ecoregions in the Mediterranean Sea, though with different average chlorophyll seasonal cycles for coastal regions. The analysis also shows strong inter-annual variability among clusters, with some regions that are more stable than others, and a cluster with significantly reducing extension through years. We then applied the same clustering methodology up to 150m depth over 6 years (2016-2021) of daily 4D chlorophyll values at 4km resolution. The Mediterranean seascape becomes simpler with depth, revealing higher chlorophyll homogeneity. Finally, we investigated the drivers of observed changes in highest variability areas, focusing on their relationship with temperature trends, circulation patterns, and climatic indices.

Red tides, high-biomass phytoplankton blooms, are noteworthy phenomena and a major source of concern worldwide. Red tides can be harmful to marine fauna due to phycotoxins, mechanical damage, release of ammonia, and/or anoxia. During a red tide, phytoplankton biomass is orders of magnitude higher than during an open ocean bloom, and seawater optical variability is dominated by changes in phytoplankton abundance and composition. As the phytoplankton community is typically dominated by a single taxon, the absorption coefficient of a red tide sample can be merely approximated by the absorption coefficients of pure seawater and of the dominant phytoplankter. Identification of the causative species could therefore be feasible from remote sensing providing that the non-water absorption coefficient can be accurately inversed from the remote-sensing reflectance, and the information contained in the absorption spectrum unambiguously related to the bloom-forming taxon. Here, the objective was to explore the second, absorption-related issue. A unique dataset of 164 hyperspectral absorption measurements was obtained from monospecific culture data, compiling published and new measurements. Using spectral clustering techniques, we assessed the level of taxonomic information amenable to absorption-based analysis. The absorption-based clustering was consistent with phytoplankton taxonomical classes, thus demonstrating the potential of hyperspectral remote sensing to identify the red tide causative phytoplankter at class level, in the absence of field information. In particular, the ability to distinguish dinoflagellates from diatoms, prymnesiophytes, and raphidophytes was demonstrated. This is an important result because Dinophyceae are known to be notoriously challenging to discriminate from other phytoplankton classes. Moreover, several optical clusters were obtained for dinoflagellates, consistently with their pigment composition (e.g. peridin-bearing vs. fucoxanthin-bearing species). Using a single peridinin absorption type as an optical signature of dinoflagellates raises the risk of overlooking important HAB species when trying to identify phytoplankton types from optical observations.

Phytoplankton are dominant marine primary producers, and the structure of phytoplankton assemblages controls food web dynamics, and ecosystem resilience and services. Hence, understanding the environmental determinants that shape phytoplankton assemblage structure is imperative, especially in complex marine domains. This study aimed to assess spatial-temporal variability patterns of phytoplankton assemblages off southwestern Iberia, identify the underlying environmental drivers and predictors, and evaluate the performance of algorithms used to derive phytoplankton composition from space. Physico-chemical variables were acquired from different sources (e.g., satellite remote sensing, models, in situ observations), covering the mixed layer at three stations, along a coastal-offshore transect, over two years (July 2012-July 2014). Phytoplankton composition, derived from microscopic analysis, and specific diagnostic pigment composition (CHEMical TAXonomy analysis, CHEMTAX), was compared with satellite-based algorithms that retrieve phytoplankton size classes and/or specific taxa from abundance-based models or specific spectral features (Copernicus-GlobColour processor and based on inputs from the European Space Agency Climate Change Initiative). Higher mean photosynthetically available radiation in the mixed layer was observed during spring and early summer, whereas increased nutrient supply occurred during winter, and summer periods. The annual cycles of chlorophyll-a concentration ranged from bimodal (coastal) to unimodal further offshore. Phytoplankton abundance was dominated by pico-sized cyanobacteria, but for biomass, diatoms, prasinophyceans and dinoflagellates dominated. The assemblage structure differed between stations and seasons. Upwelling index, nitrite and suspended particulate matter concentrations emerged as the variables that best explained the variability derived from microscopy, whereas considering CHEMTAX-based results, only silicate concentration was identified as a relevant variable. The comparison of data derived from in situ observations and satellite-based algorithms identified the abundance-based model as the best performer for deriving nanophytoplankton, diatoms and dinoflagellates, for most performance metrics. Spectral-based algorithms performed better for retrieving pico- and microphytoplankton. Further calibration and validation are required to refine algorithms at regional scales.

Remote sensing techniques have been employed to elucidate phytoplankton community structure by analyzing spectral data from space, especially when coupled with in situ measurements of photosynthetic pigments. In this study, we introduce a novel ocean color algorithm designed to estimate the relative cell abundance of seven phytoplankton groups and their respective contributions to total chlorophyll a (Chl a) on a global scale. Leveraging machine learning, our algorithm utilizes remotely sensed parameters (including reflectance, backscattering, and attenuation coefficients at various wavelengths, as well as temperature and Chl a) in conjunction with an omics-based biomarker derived from Tara Oceans data. This biomarker targets a single-copy gene called psbO, encoding a component of the photosynthetic machinery present across all phytoplankton, spanning both prokaryotes and eukaryotes. This research delivers a comprehensive global dataset detailing the relative cell abundances of the seven phytoplankton groups and their impacts on total Chl a. These data types offer distinct insights: Chl a serves as a biomass proxy crucial for understanding energy and matter fluxes in ecological and biogeochemical processes, while cell abundance provides crucial information on community assembly processes. Moreover, our methodology allows comparisons with existing approaches, such as pigment-based methods. This integration underscores the potential of remote sensing observations as powerful tools for gathering Essential Biodiversity Variables (EBVs). By expanding our understanding of phytoplankton dynamics on a global scale, this study advances ecological research on the link between biodiversity and ecosystem functions.

Marine ecosystems are supported almost entirely by primary production provided by phytoplankton, which globally perform 50% of the photosynthesis on our planet. Phytoplankton also fix atmospheric carbon dioxide through photosynthesis and transport it to the ocean interior, which is essential for climate regulation. Their ability to capture carbon, however, depends on the availability of scarce nutrients like iron. Climate change and human activities are shifting the oceanic distribution and bioavailability of iron, yet the responses of different phytoplankton groups and overall ocean productivity to these changes remain poorly understood. In this study, we aimed to describe on a global scale the spatio-variability of phytoplankton’s iron nutritional status by integrating omics and satellite observations. First, we examined abundance and expression profiles of genes and transcripts linked to iron-responsive photosynthetic electron transport in metagenomes (n=690) and metatranscriptomes (n=709) from 127 Tara Oceans’ stations. Finally, we trained a random forest model with monthly 4-km satellite observations (Chla, SST, iPAR, Fluorescence in the red), 1-degree resolution monthly composites of biogeochemical model outputs (iron and copper), and WOA2018 compiled measurements (e.g., SiO2, PO43-, and NO3-), to predict the global distribution of these genes/transcripts ratios. The estimated values strongly correlated with in-situ values for metaG (r=0.76 for Fld/Fld+Fd; r=0.52 for PC/CytoC6+PC), and for metaT (r=0.66 for Fld/Fld+Fd; r=0.63 for PC/CytoC6+PC). The mean relative absolute error rates were relatively low for the metaG ratios (MRAE = 14–17%) when compared to the metaT ratios (MRAE = 30–56%). Although we highlight the need for increasing in-situ observations, our workflow provides the foundation for linking genomics and remote sensing to monitor phytoplankton iron nutritional status in the vast global ocean.

Fronts – the interface between water masses – are hotspots for rich and diverse marine life, influencing the foraging distribution of many megafauna. We have analysed a long time-series of Earth observation (EO) data using novel algorithms to characterise the distribution and dynamic of ocean fronts, and used these to investigate links to biodiversity hotspots and to explore key drivers for changes in fronts and these relationships.

FRONTWARD (Fronts for Marine Wildlife Assessment for Renewable Developments) aims to provide evidence to justify the inclusion of frontal locations in marine spatial planning, most pressingly for zones for offshore windfarms. Biodiversity hotspots are identified using a biodiversity index, created using an unprecedented collation of UK at-sea observations of seabirds, fish and cetaceans spanning several decades (1980s-2020s). Generalised additive models (GAMs) reveal the spatial influence of fronts on biodiversity, and provide predictions of biodiversity based on EO-detected front maps. The outcomes from this project will feed into the evidence base for marine conservation, and decisions on siting of future offshore renewable energy projects.

New tools such as optical satellite imagery analysis powered by advances in artificial intelligence, have potential to provide additional broad-scale and near real-time capacities for survey and monitoring marine mammals. While multiple studies demonstrated that large cetaceans are detectable in sub-meter satellite imagery, this work aimed to tackle multiple fundamental challenges of the application such as reaching high fidelity automated detection, performing broad-scale deployment in challenging ocean environments, and testing the shovel-readiness of satellite imagery for conservation monitoring needs with the use case of fishing gear entanglement risk in the California Dungeness commercial crab fishery. Statistical analysis of regional satellite imagery allowed the development of a deep-learning-based detection framework capable of optimizing learning from an originally small dataset. The best architecture generally achieved satisfying performance with an average balanced accuracy reaching up to 99.90% for gray whales. It was also demonstrated that gray-scale imagery can be used to perform detection with a high accuracy of 87.05%, opening a capability to monitor larger spatio-temporal ranges than previously thought. Broad-scale deployment of best-in class machine-learning models over an unprecedented amount of satellite imagery (> 650,000 km2), from December 2009 to March 2023, covering multiple times the entire California coast, resulted in the detection and construction of a satellite imagery database with over 3500 gray whales and 1500 humpback whales as well as opportunistic detections of blue and fin whales. It furthermore provided meaningful data points on the migration routes of gray whales within the Southern California Bight. Through a collaboration between UPC, The Nature Conservancy (TNC) and NOAA, the developed system is currently being deployed in the Channel Islands Sanctuary to inform a vessel speed reduction program and to potentially influence long-term shipping lane design. It is our hope that this approach can be replicated or adapted for other use cases around the world to support conservation policies.

In October/November of 2023, the US National Aeronautics and Space Administration (NASA) conducted its first Biodiversity field and airborne campaign across terrestrial and aquatic environments in the South African Greater Cape Floristic Region (GCFR). From 4 airborne instruments (Airborne Visible-Infrared Imaging Spectrometer - Next Generation (AVIRIS-NG), Portable Remote Imaging SpectroMeter (PRISM), Hyperspectral Thermal Emission Spectrometer (HyTES), and Land, Vegetation, and Ice Sensor (LVIS)) the BioSCape Campaign’s remote sensing data products provides an unprecedented level of image spectroscopy from VSWIR to TIR wavelengths as well as full-waveform laser altimeter measurements. Airborne data are supplemented with a rich combination of contemporaneous biodiversity-relevant field observations toward an approach to measure and understand functional, phylogenetic, and taxonomic biological diversity as components of ecosystem function.

A majority of the BioSCape Campaign data will be archived through the NASA-funded Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC). The discipline-specific Center provides dataset content to NASA’s Earthdata Cloud which includes a standardized metadata called Common Metadata Repository (CMR), data discovery, and open access.

This hands-on demonstration will leverage a managed cloud environment to show programmatic discovery, access, and analysis of NASA BioSCape data/resources and concurrent orbital data. Included will be content and tutorials demonstrating derivation of estimates of biodiversity variables including:

• An overview of the BioSCape Campaign data acquisition

• NASA Earthdata Cloud: Search, Access, and Analysis Basics

• Explore BioSCape vegetation plot and image spectroscopy data

• Invasive species analysis from AVIRIS-NG and Vegetation Plot Data

• Vegetation Structural Diversity derived from LVIS and GEDI full waveform lidar data.

• Aquatic biodiversity estimates from PRISM, PACE, and EMIT

The demonstration aims to exhibit the ARIES (Artificial Intelligence for Environment and Sustainability) platform’s transparent, fast, and open-source approach to Environmental Accounting, highlighting how ARIES for SEEA supports these studies in countries with varying data availabilities and capacities. By presenting real-world applications, specifically focusing on a mapping ecosystem type, the workshop will illustrate the platform’s ability to provide state-of-the-art results under time and financial constraints.

Participants will learn how ARIES supports ecosystem condition assessment and ecosystem service flow estimation, ensuring reusability across scales and methodologies, and understand how ARIES technology facilitates scalable and interoperable ecosystem type mapping, adaptable to both data-scarce and data-rich contexts.

The demonstration will start with an overview of ARIES and its impact on advancing ecosystem accounting globally, after which the "ARIES for SEEA" application will be shown, demonstrating how Ecosystem Accounting can be accomplished in a simple and approachable way. It is also accessible to anyone (access is entirely free for non-commercial use, and datasets used are global and open) via a web browser (requires only an internet connection).

Finally, some case studies will be presented, showing practical applications done in the last years, specifically in Senegal, Colombia, and Germany, focusing on their unique challenges and achievements.

The EU Habitats Directive mandates the protection and monitoring of wetland habitats within Natura 2000 sites. However, comprehensive and timely assessment of wetland conservation status remains challenging. The reporting under article 17 of the Habitats directive is missing the detailed, spatially explicit information required for accurate assessment of wetland habitats conservation status, and in particular indicators of degradation.

This initiative, developed in collaboration with the European Commission's DG Environment (DG ENV) and the European Environment Agency (EEA), aims to design an operational geospatial information system to monitor critical wetlands, detect degradation, and assess conservation status within Natura 2000 sites.

Leveraging the Knowledge Centre on Earth Observation's (KCEO) policy-focused value chain and Deep Dive assessment methodology, we translate specific policy needs into technical requirements for Earth Observation (EO) products. We analyze the fitness-for-purpose of existing products and services, evaluating gaps, and provide recommendations to support the EU's commitment to biodiversity protection.

Our approach extends beyond assessment to prototype a Policy-driven Service for monitoring wetlands on selected areas. Ongoing and planned key activities include:

• Characterizing various European wetland habitats, their ecological functioning, and main pressures leading to degradation.

• Determining appropriate indicators for selected habitats and the relevant EO products, prioritizing wetland types based on current degradation levels (per Article 17 of the Habitats Directive), relevance beyond the Directive, and biodiversity value.

• Designing advanced spatial and temporal analysis tools for policymakers and conservation managers integrating cutting-edge EO technologies with ground-truth data and modelling.

This project will enhance our understanding of wetland dynamics and support more effective implementation of EU environmental policies, including the Biodiversity Strategy 2030 and the Nature Restoration Law. The insights and methodologies developed through this project will serve as the foundation for implementing a comprehensive web-based platform for monitoring all wetlands across the EU.

Wetlands are critical biodiversity hotspots, supporting 40% of the world’s plants and animals (https://doi.org/10.1017/S1464793105006950), and are important for storing water, reducing the impacts of droughts and flood events, recharging groundwater, improving water quality, and improving human well-being. Wetland ecosystems vary considerably across the globe, including vast boreal peatland complexes at higher latitudes, and seasonal prairie potholes in lower latitude grasslands. Detailed, reliable, up-to-date inventories of these wetlands is key to accurately monitoring and understanding changes due to natural or anthropogenic factors.

The Alberta Biodiversity Monitoring Institute (ABMI) brings together open-source Earth observation datasets from Sentinel-1/2, machine learning, and Google’s Earth Engine platform to support this crucial knowledge need. In 2021, we published a novel province-wide, temporally consistent, publicly accessible wetland inventory of Alberta bogs, fens, marshes, swamps, and open water. The dataset contains >3 million wetland polygons, producing overall accuracies of 80% or more. When combined with ABMI’s human footprint data, it reveals the dominant influences of agriculture, forestry, urban and industrial development on Alberta wetlands.

Recent, advanced mapping efforts in collaboration with Ducks Unlimited Canada and the Government of Alberta combined newer machine learning approaches, additional field and Earth observation datasets, and recent lidar acquisitions in two contrasting boreal areas, a parkland and a prairie pilot area. The new wetland inventories met provincial wetland mapping standards at the upland-wetland level, the class level (i.e. bog, fen, marsh, swamp and open water) and form level (i.e. open, shrubby, treed). These approaches are already used elsewhere to support groundwater dependent ecosystem mapping in Alberta’s northern oil sands region.

Complementary work at the ABMI is capturing lentic surface water dynamics. The goal is to deliver regularly updated hydroperiod information for long-term monitoring that reflects the state of Alberta’s wetland and freshwater shoreline habitats, which are sensitive to climate changes and human pressures.

Wetlands are the most threatened realm in South Africa, similar to the findings of the global assessment of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) in 2019. Wetlands in the predominantly temperate and arid climatic regions that dominate the South African landscape, are small, narrow and mostly palustrine (vegetated). Continuous work is underway to improve the representativity of wetlands, while monitoring of their integrity remains challenging. The availability of Sentinel-1 and -2 images have revolutionised the capability of mapping wetland biodiversity in South Africa, and tracking changes in their extent over time. Case studies will be presented with examples of both the lacustrine and palustrine wetland biomes, including: (a) biodiversity mapping and phenological variation in lacustrine wetlands; (b) tracking changes in the extent of estuarine and freshwater ecosystem functional groups; (c) the importance of the Africa land cover for assessing river ecosystem types and their ecological condition; and (d) monitoring of essential biodiversity variables such as above-ground biomass (i.a., for teal carbon), soil moisture as well as the hydrological regime and phenology metrics. These outputs have contributed to the capabilities of refined reporting to the Sustainable Development Goal 6.6.1a; the reporting of changes in ecosystems to target 1, 2 and 3 of the Kunming-Montreal Global Biodiversity Framework reporting in 2030, and also Red Listing of Ecosystems.

Eco-Patterns is an Innovate UK project that aims to develop a standardised methodology to assess peatland condition remotely. Peatlands contain more carbon than all other UK vegetation combined, however, 80% of peatlands are degraded. Degraded peatlands actively release carbon and impact water quality and flood control. Eco-Patterns is led by Gentian Ltd in collaboration with the University of East London, BSG Ecology, and the IUCN UK Peatland Programme. Our approach combines high-resolution multispectral imagery (<1 m) with advanced deep learning models to identify and classify the spatial and spectral patterns that characterise peatland health. Rather than focusing solely on species, Eco-Patterns analyses habitat “fingerprints”—texture patterns created by species assemblages and structural features unique to these ecosystems. This method provides a comprehensive way to remotely assess peatland condition, offering the potential to underpin emerging market standards like the Peatland Code. Project validation partners include the West Midlands Combined Authority, the National Trust, Natural Resources Wales, NatureScot, and Northern Ireland Water, who are providing ground data and testing sites across the UK.

The European Space Agency (ESA) activity “Biodiversity+ Precursors” is a contribution to the joint EC-ESA Earth System Science Initiative to advance ESS and its response to the global challenges. The Precursor BIOMONDO was focused on biodiversity in freshwater ecosystems. Based on analysis of relevant sources for scientific and policy priorities, the main knowledge gaps and challenges in biodiversity monitoring were compared to possibilities from Earth Observation (EO). These findings were the basis for the development of innovative integrated earth science solutions (Pilots) that integrates EO based products, biodiversity modelling (GLOBIO and Delft3 model suites) and in situ data using advanced data science and information technology. The three pilots were focused on eutrophication, heat waves and river fragmentation, and its effect on biodiversity. The generated products were also implemented in a BIOMONDO Biodiversity data cube. In addition, time series of the cube’d data were analysed using Machine Learning (ML) technique and integrated Thematic Ecosystem Change Indices (TECI), e.g., water quality and lake water temperature evolution, were deduced and analysed. Validation of the integrated products was a key task within BIOMONDO, and several biodiversity and policy experts have been consulted. They were also provided access to the novel EO products in the cube via API or the implemented data viewer, a tool for visualisation and easy access to products and data.

Freshwater biodiversity faces challenges worldwide. One approach to describing its status is through the study of freshwater phenology, which is listed as an Environmental Biodiversity Variable (EBV). As phytoplankton is a central component of a lentic ecosystem, monitoring the phenology of it can be highly relevant in relation to freshwater biodiversity. While terrestrial phenology based on EO is advanced significantly, the study of phenology in lake ecosystems is in its early stages.

Phenological shifts of phytoplankton can be derived from chlorophyll-a concentrations, which are effectively measured using Earth observation techniques. Our method utilizes time series analysis to detect seasonal variations in phytoplankton blooms, identifying key characteristics such as the timing of bloom peaks, the duration of blooms, and their spatial distribution. Working with Copernicus satellite products, the data enables the observation of phytoplankton phenology across the whole waterbody, making it possible to detect the spatial distribution of individual bloom events and providing insights into these events at both spatial and temporal scales.

This method is currently being developed as part of the OBSGESSION EU Horizon Europe project and tested for the study sites in Sweden and Finland. We are aiming to scale our method to make it applicable to different lake types.

In the context of climate change and increasing water scarcity, lakes serve as water reservoirs and are supporting services, such as agricultural irrigation or maintaining discharge during low-flow periods. Their presence in a catchment impacts downstream ecosystem and biodiversity by altering water, sediment, nutrients and pollutants cycles. Moreover, increasing temperatures, declining water levels and nutrients fluxes are the principal drivers of eutrophication, threatening water quality and biodiversity in both lakes and downstream ecosystems. Monitoring these water bodies is essential for assessing eutrophication risk and informing management solutions, yet less than 1% of lakes in France are monitored by public authorities and most of the time with few data at the temporal scale.

In this study, we focused on thousands of reservoirs within the Adour-Garonne basin (South-Western France). We developed a methodology that combined machine-learning models to predict (1) nitrates and phosphorus inputs into lakes, and (2) lake chlorophyll-A dynamics and trends, from various environmental drivers such as meteorological data, land-use, land management data or lake characteristics. The training data for these basin-wide models were derived from field observations (nutrients) and Sentinel-2 images (Chlorophyll-A and turbidity). The Sentinel-2 images were analyzed for all reservoirs in the Adour-Garonne basin with surface areas exceeding 10,000 m² from 2018 to 2023, as part of the SCO XTREMQUALITY project. The first results indicate promising model performances, with good accuracy for chlorophyll-A prediction in lakes. Results help characterize eutrophication status and trends in thousands of various sized lakes and untangle relationships between eutrophication and driving factors, mainly land use and lake characteristics. Limitations and potential improvements in satellite image processing will also be discussed. These insights allow for the identification of priority lakes for enhanced monitoring or tailored management strategies, aiming to mitigate eutrophication impacts and preserve biodiversity in vulnerable aquatic ecosystems.

The hydrological cycle is critical for Earth system stability, involving intricate coupled processes and feedbacks tied closely to terrestrial ecosystems. Changes in key hydrological functions can have significant impact on both ecological and social systems, affecting biodiversity, crop yields, and ecosystem structure and function. Through the spatial connectivity of the water cycle, the effect of these changes may be felt from the local to the continental scale. Anthropogenic pressures, such as deforestation and land-use change, have led to a reduced capacity of ecosystems to recover from external perturbations, or resilience loss, in regions that are closely coupled to the water cycle, but the reciprocal impact of changes to terrestrial ecosystems on the resilience of hydrological functions remains an open question.

Here, we use remotely sensed data on soil moisture (SMOS), evapotranspiration (GLEAM), and precipitation (SSMI/S), and employ an early warning signal-based detection of the resilience of these key hydrological variables at the global scale. In doing so, we aim to present a first assessment of global-scale water resilience, and a characterisation of regions vulnerable to abrupt changes, or close to sensitive thresholds related to the stability of the hydro-climatic cycle. We compare our findings to assessments of resilience loss in terrestrial ecosystem variables, and assess the key driving variables to contribute to a holistic understanding of resilience in the terrestrial freshwater cycle.

Coastal marine areas form some of the densest biodiversity hotspots, with intertidal wetlands, such as seagrasses, mangroves and saltmarshes, covering vast portions of the intertidal area. Seagrass meadows directly and indirectly provide a wide range of ecosystem services (e.g. recreation; key forage, refuge and nursery habitats for fisheries species and non-targeted species; climate regulation; coastal stabilisation and water quality mediation). Unlike subtidal seagrasses, intertidal seagrass meadows directly provide services to both marine and terrestrial ecosystems, so monitoring their occurrence, extent, condition and diversity can be used to indicate the biodiversity and health of local ecosystems. The process of monitoring large intertidal areas is, however, resource intensive and unfeasible in many regions. Current global estimates of seagrass extent and recent comprehensive seagrass reviews either do not mention intertidal seagrasses and their seasonal variation, or combine them with subtidal seagrasses. Here, using cloud based composites of high-resolution satellite data acquired by the Sentinel-2 Multispectral Instrument (MSI) alongside a highly accurate neural network, we present the first full map of intertidal seagrasses in Europe. We found that cumulatively seagrasses cover an area similar to the land area of Luxembourg: 2110 ± 344 km². Although many Northern European countries have large intertidal seagrass total extents, the proportion of intertidal areas covered by intertidal seagrass decreased with latitude (from ~32 % at 58° to ~62 % at 35°). Furthermore, we showed clear latitudinal gradients in seagrass density, with high densities of seagrass being more prevalent in low latitudes and low densities being more prevelant in high latitudes. Finally, we showed a clear relationship between intertidal seagrass peak timing and latitude, going from 10 June at 58° to 27 November at 35°. This work has provided the first Europe wide intertidal seagrass map. Our seagrass map provides critical data for prioritising and developing policies, management and protection mechanisms across local, regional or international scales to safeguard these important ecosystems and the societies that dependent upon them.

Biodiversity is a vital component of natural capital that significantly influences ecosystem functions and provides essential services and benefits, ranging from food security to cultural heritage. However, species are currently disappearing at a rate 100 to 1,000 times higher than the natural extinction rate. Coastal ecosystems are particularly concerning: they are among the most vulnerable due to their exposure to cumulative anthropogenic pressures while biodiversity knowledge is lacking.

Supported by the French National Space Agency (CNES) and endorsed by the Space Climate Observatory (SCO), the BioEOS project aims to develop observation tools to characterize the spatiotemporal dynamics of coastal biodiversity. This initiative will map changes and produce operational indicators to assist in conservation and restoration efforts in the Marine Protected Area (MPA). The project primarily takes advantage of image time series from multispectral (Pleiades, Sentinel-2, Venus) and hyperspectral (EnMAP, PRISMA) satellite systems. A set of selected biodiversity proxy metrics are extracted using high SRL (Scientific Readiness Level) algorithms that have been widely used by the benthic scientific community. These algorithms encompass the inversion of radiative transfer models, machine learning-based scene segmentation, spectral unmixing, pansharpening, and the calculation of spectral indices. This approach enables to generate valuable information on bathymetry, bottom/habitat type abundances and distributions, as well as water column properties estimations. Coral reef and seagrasses of Southwestern Indian Ocean region (La Réunion, Mayotte, Glorieuses and Bassas da India) are the first targeted ecosystems for this experimentation.

We present the main advancements of a demonstrator providing key essential variables contributing to various end uses through four distinct use cases. Additionally, we will discuss the strengths and limitations of the satellite systems employed, in light of the initial objectives set forth.

As part of the ESA Coastal Blue Carbon project, our goal is to map and monitor caracteristics of blue carbon ecosystems (BCEs), such as extent, and subsequently estimate their biomass production and carbon storage potential using Earth observation data. To achieve this, we aim to ensure that the knowledge and techniques developed and tested at the local scale using very high spatial resolution imagery (<2m) remain reliable when scaled up with lower-resolution imagery such as Sentinel-2 (10m). To prepare for this scaling up, we propose an innovative approach designed to compare performance in terms of habitat mapping and vegetation biomass estimation across different image sources and remote sensing features. This approach also enables a rigorous benchmarking of various supervised classification methods (for habitat mapping) and multivariate regression methods (for biomass mapping).

We applied this approach to the salt marshes in the Arcachon Basin, France, in 2024, using a comprehensive experiment based on a time series of Sentinel-2 and Pléiades images. The method is structured around a fixed hexagonal grid, in this case with a 20-meter side length, which allows us to build reference data integrated into this area and usable at both resolutions. The advantage of this fixed grid is that it enables rigorous comparison of simple pixel-level classifiers (such as RandomForest) with more complex patch-level classifiers (such as CNNs and CNN-RNNs), which include neural networks tailored to analyze spatial information (e.g., textures and shapes) and temporal information (e.g., phenological trajectory patterns). This setup also allows for rigorous testing of architectures that combine Sentinel-2’s temporal information with Pléiades’ spatial resolution, offering a promising hybrid model. All predictions are then analyzed within these fixed grids, allowing for a precise interpretation and assessment of results, which in turn informs methodological decisions for scaling up.

Mangrove forests play a pivotal role in maintaining coastal biodiversity and supporting local livelihoods. They are among the most productive ecosystems on Earth, with a potential storage of organic carbon reaching 693 Mg C ha-1. Mapping and monitoring of mangrove carbon stocks over time represents a significant challenge for remote sensing studies. Indeed, greater consideration of the structural and functional diversity of mangrove stands is required to improve the accuracy of carbon maps.
As part of the ESA-funded Coastal Blue Carbon project (2024-2026), we have incorporated mangrove habitat diversity into a mapping model of aerial carbon stocks. Our approach uses extensive field data from forest inventories conducted in a diverse range of mangrove habitats since 1995. Subsequently, tree growth equations are employed to calculate the above-ground biomass (AGB) and carbon stocks of numerous forest stands at the time of acquisition of a large dataset of very high-resolution Pleiades satellite imagery (50 cm) over pilot sites in French Guiana, Amapá in Brazil, Suriname and Guyana.
The FOTO texture-based methodology (Fourier-based Textural Ordination algorithm, Proisy et al., 2007) was then applied to all Pleiades mangrove images. The objective was twofold: first, to map and label the diversity of mangrove habitats in terms of canopy properties; second, to predict the associated AGB at a 1-ha scale. The resulting AGB maps are transformed into carbon maps based on the total (soil, below- and above-ground) carbon storage model developed by Walcker et al. (2018) in French Guiana with the same field dataset.
Very high-resolution Earth Observation imagery for carbon stock assessment is critical for mapping and monitoring the blue carbon capacity of coastal ecosystems. The results support local decision-making for conservation and can inform global climate policy. However, these new results also highlight the need for new field data and new models of mangrove functioning.

Remote sensing is a key area of research that will strengthen the link between the different scientific disciplines involved in the Nature-based Solutions (NbS) framework. The present review covers three decades of remote sensing studies of healthy mangrove ecosystems in French Guiana. This effort is of great value in the context of the ESA-funded Coastal Blue Carbon project and two French national programmes (Solu-Biod and FairCarbon). We have identified three typical NbS themes associated with mangroves in French Guiana.

The first theme is the prediction of coastal dynamics and erosion based on operational mangrove spatial monitoring, modelled using time series of moderate-resolution satellite imagery. These predictions are then used to support coastal planning.

The second NbS theme concerns biodiversity, ecosystem functioning, resources and uses. We address this with the support of very high spatial resolution imagery, which is key for assessing mangrove habitats. We produce biomass and carbon maps, model fine-scale socio-economic surveys and describe the use of mangrove-derived resources to inform their management.

The third theme addresses the health of mangrove coasts, including ecosystem state and the associated potential risks for human health. Work on this theme uses the fine-scale characterization and monitoring of mangrove habitats to enable early detection of threats to the ecosystems, such as defoliation during caterpillar outbreaks. Furthermore, it permits investigating the hitherto largely unstudied risk posed by mosquitoes and culicoides in mangroves, which differ from vector communities found in urban areas.

It can be concluded from this research that remote sensing provides a strategic, operational and pioneering approach to anticipating coastal change in tropical regions. This allows for the rapid detection and public awareness of socio-environmental issues, as well as informing decision-making processes. Indeed, time series and remote sensing images facilitate understanding of global change and inform decisions about mangrove-dependent social-ecological systems.

Kelp forests (Order Laminariales) create incredibly complex and productive marine habitats which support marine biodiversity along 25% of global coastal shorelines. However, these critical ecosystems are in decline in many regions around the world. In order to enhance our understanding of kelp forest ecosystem dynamics, investigate drivers of change and assess conservation and restoration actions, spatial datasets and monitoring tools are needed. In British Columbia (BC), Canada, bull kelp (Nereocystis luetkeana) and giant kelp (Macrocystis pyrifera), are the two dominant kelp species and are located along a very complex coastal environment that presents unique mapping challenges. These challenges have led to the development of local, regional and coast-wide kelp mapping methods for a suite of remote sensing sensors and platforms (drones, aerial platforms and satellites) to map kelp forest extent and change through time. As part of a global kelp mapping community of practice, a time series of kelp extent data is being derived from the Landsat series of satellite sensors to create a coast-wide dataset from 1984 to present day in BC. In this work we describe how kelp forest extent data are derived from the Landsat imagery and how we are using local and regional spatial datasets to inform mapping accuracy and species-level considerations. This research informs ongoing work related to linking remote sensing data with available datasets for assessing kelp forest ecosystem productivity and biodiversity.

Coastal ecosystems can contribute significally to the carbon budget and climate change, particularly trough the concept of blue carbon. The Gross Primary Productivity (GPP) of mudflat is primarily due to the activity of microphytobenthos (MPB), a community of microscopic photosynthetic organisms that inhabit the upper layer of mudflats. Remote sensing of GPP contributes considerably in monitoring and upscaling the carbon fluxes for understanding their impact in climate change. From this perspective, this study conducted in estuarine environments in France, aims (1) to evaluate the spatio-temporal variation of GPP across different seasons and locations, as well as (2) to model GPP using hyperspectral indices coupled with environmental variables and direct carbon flux measurements. For this purpose, this research combines the hyperspectral remote sensing indices and environmental variables, including photosynthetically active radiation (PAR) and mudflat temperature with the CO2 chamber-based measurements of Net Ecosystem Exchange (NEE) and Respiration (R) to link direct measurements of GPP with remote sensing and environmental indices. The results show that the GPP measured values of MPB vary across seasons and locations, ranging from 144.26 mgC/m²/h to 289.08 mgC/m²/h. Remote sensing indices coupled with environmental variables capture these seasonal and spatial variations, allowing for reliable estimates of GPP.

Seagrasses are critical to coastal ecosystems, providing habitat, stabilizing sediments, and aiding carbon sequestration. Climate change has increased the frequency and intensity of heatwaves, potentially threatening seagrass health. This study investigates the impact of marine and atmospheric heatwaves on the pigment composition and reflectance of the intertidal seagrass Nanozostera noltei. We performed laboratory experiments, exposing N. noltei samples to controlled heatwave conditions and measured hyperspectral reflectance and pigment concentration to assess its impact over time. Results revealed that heatwaves induce significant declines in seagrass reflectance, particularly in the green and near-infrared regions, linked to (likely due to) pigment degradation. Key vegetation indices, such as the Normalized Difference Vegetation Index (NDVI) and Green Leaf Index (GLI), also displayed marked reductions under heatwave stress, with NDVI values decreasing by up to 34% and GLI by 57%. A novel Seagrass Darkening Index (SDI) was developed to identify seagrass darkening, showing a strong correlation with heatwave exposure. This research suggests that spectral monitoring can effectively track the early impacts of heatwaves on seagrasses, providing a valuable tool for remote sensing-based habitat assessment. Satellite observations confirmed these findings, showing widespread seagrass darkening during atmospheric and marine heatwave events in Quiberon, France. Darkened seagrasses observed after heatwaves were exposed more than 13.5 hours daily. This work highlights the need for continuous monitoring of seagrass meadows under the current climate regime, underscoring the potential of remote sensing in capturing rapid environmental changes in intertidal zones.

GBIF - the Global Biodiversity Information Facility - provides free and open access to over 3 billion species occurrence records to anyone with an account with the organisation. The use of this valuable data source is increasing year on year, with over 12000 peer-reviewed publications using GBIF-mediated data. t is a key data source for monitoring the state of biodiversity.

In this session, we aim to showcase the principles of good use of GBIF-mediated data and will address:

·       Data Sources and Standards - an introduction to GBIF data publication workflows and how the data is organised

·       Data Quality - key data quality issues that users should be aware of, and how to deal with such issues in data use

·       Data Access - the different mechanisms for accessing data including APIs, cloud computing and SQL downloads, and how to correctly cite data use

The session will be through a set of presentations, interspersed with guided navigation through GBIF resources on www.gbif.org, to support the effective use of GBIF-mediated data for all those who have used, or are planning to use, GBIF-mediated data.

UNBL is a free-to-use, open-source online platform that provides access to the best available global spatial data on biodiversity, climate, and sustainable development as well as analytic tools to enable governments and other stakeholders to support action that puts nature at the centre of sustainable development. UNBL combines the latest technology, the best available data, password-protected workspaces to upload user’s own data, and user-friendly analytics to enable users to better map ecosystems and biodiversity, calculate selected indicators, track changes over time and use integrated biodiversity-inclusive spatial planning.

UNBL strives to develop functionality that does not require GIS expertise, is available in English, French, Portuguese, Russian, and Spanish, and is overseen by a partnership between the CBD Secretariat, the UN Environment Programme World Conservation Monitoring Centre (UNEP-WCMC), the UN Development Programme (UNDP) and the UN Environment Programme (UNEP).

In this session, we will provide:

(i) an overview introduction to UNBL;

(ii) a data and functionality demo, and

(iii) ample time for questions, discussion and assisted hands-on experience.

After the session, attendees are expected to be able to:

(i) find relevant data in UNBL and visualize on maps for regions of interest;

(ii) find trends over time using relevant data or UNBL built-in analytics;

(iii) have learnt how to access data that can be used to support national capacity to plan, implement, monitor and report on biodiversity targets and sustainable development goals;

(iv) have learnt how data related to biodiversity, climate and sustainable development can be used to inform decision-making.

This demonstration provides a hands-on introduction to ConScape, an open-source library designed for high-resolution landscape connectivity modeling. ConScape offers a powerful solution for assessing connectivity across large, complex landscapes, allowing the leveraging of high-resolution satellite remote sensing (SRS) data for fragmented landscapes. Developed in the Julia programming language for optimal computational performance, ConScape allows for the rapid calculation of connectivity metrics, addressing critical challenges in biodiversity monitoring, conservation, and land-use planning.

Attendees will explore ConScape's diverse capabilities, from quantifying habitat connectivity and movement corridors to utilizing a randomized shortest paths framework for more nuanced connectivity analyses. Through practical exercises, participants will gain valuable insights into how ConScape can model connectivity in landscapes under pressure from habitat fragmentation and loss. Real-world case studies, such as the application of SRS data to track reindeer movement in Norway, will showcase the library’s potential for supporting conservation and restoration initiatives.

This session is ideal for ecologists, biodiversity scientists, and conservation planners seeking to integrate high-resolution spatial data into their workflows and leverage ConScape for data-driven decision-making in landscape connectivity, habitat conservation, and biodiversity restoration efforts.

The ambitions of the EU Green New Deal (e.g. ‘nature as a solution’, ‘building a bioeconomy’) as well as recent legislation (e.g. the Nature Restoration Regulation, the ecosystem accounting module under Regulation 691/2011) require much better data on biodiversity and ecosystems than currently available (in terms of spatial and thematic accuracy).

The ambitions of the EU Green New Deal (e.g. ‘nature as a solution’, ‘building a bioeconomy’) as well as recent legislation (e.g. the Nature Restoration Regulation, the ecosystem accounting module under Regulation 691/2011) require much better data on biodiversity and ecosystems than currently available (in terms of spatial and thematic accuracy).

The EU Copernicus program provides important data sets for monitoring the environment. Work on behalf of the European Environment Agency, the European Space Agency, in various (EU) research projects etc. has explored options for using satellite data in support of ecosystem and nature monitoring. However, converting research outcomes into operational Copernicus products for ecosystem monitoring is challenging and resource intensive.

This workshop reviews the key success factors for a successful operational implementation of ecosystem monitoring with satellite data. It has a particular focus on the components that need to be paired with modern satellite technology: habitat-level in situ data as well as stable operational infrastructure and expert capacity for developing and maintaining regular monitoring products.

The workshop will review current experience with developing ecosystem extent data sets in the European Union, present an overview of available and needed in situ data and engage participants in a discussion on how to overcome current bottlenecks and constraints in developing successful ecosystem monitoring products in an EU context.

Expected outcomes: The workshop outcomes include a better understanding of possibilities and limitations for using satellite data sets for ecosystem monitoring and a set of proposals for developing ecosystem monitoring products in an EU context.

Objectives of the workshop:

- Raise attention for existing EU investment gap in making satellite approaches effective

- Highlight the critical data gap on biodiversity in situ data

- Discuss EU policy priorities for biodiversity and ecosystem monitoring

- Review need for increasing institutional capacity for regular application ready data sets

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Civil Society Organizations (CSOs) and Non-Governmental Organizations (NGOs) are key actors in achieving an effective conservation and restoration of ecosystems, which are crucial to halt global biodiversity loss and to mitigate the effects of global climate change. In a consultation process initiated by the European Space Agency (ESA), CSOs and NGOs raised the importance to (i) develop tools to monitor ecosystems under conservation and restoration actions and (ii) to develop clear processes for identifying high-priority sites for conservation and restoration actions. While they acknowledged the value of earth observation (EO) to achieve these goals, NGO/CSO participants in the consultation process also highlighted a knowledge gap inhibiting the exploitation of the full potential of EO within their activities. In response, ESA funded the PEOPLE-ECCO (Enhancing Ecosystems Conservation through Earth Observation Solutions, Capacity Development and Co-design) project which has as goals to develop EO-supported tools for assessing conservation action effectiveness (A) and identification of high-priority areas for conservation (B), and to develop EO capacity within CSOs/NGOs.

In this workshop we first present user requirements gathered from the CSO/NGO community and invite workshop participants to share their requirements for EO-supported tools and to express their needs for EO capacity development. In the second part of the workshop, participants will identify and co-develop the tools to be further elaborated during the PEOPLE-ECCO project. Both parts of the workshop will include presentations of CSO/NGO participants of the PEOPLE-ECCO project, interactive online feedback, and breakout group discussions.

Expected outcome: The outcomes of the workshop will help consolidate the user requirements, raise awareness of the project, identify opportunities for CSO/NGO engagement and capacity development, and guide the development of user-oriented tools and methods, which will maximise the impact of the PEOPLE-ECCO project activities.

· Grasslands and savannahs are key landscapes globally, whether as hay meadows, grazing marshes, open rangelands or woody clearings. They maintain biodiversity and food production, but also influence ecological processes including pollination, water supply, carbon sequestration, and climate regulation. They cover a significant part of the EU and 70 % of the world's agricultural land, resulting in grasslands that are both diverse and extensive habitats.

· These important habitats are currently facing numerous threats, agriculture conversion, tree plantations, intensification and abandonment, and may be considered to have been undervalued in conservation and restoration policies. However, European legislation (under the Habitats Directive) actively protects natural grasslands and requires the European Union Member States to take steps to avoid degradation in their protected sites with the Natura 2000 network, and reports on their actual conservation status. They highlight the urgent need for effective monitoring although until recently there have been some limitations to monitor their actual extent and ecosystem dynamics using remote sensing techniques. However, in recent years interest has increased, and new technologies have been used for monitoring different features related to degradation or sustainable land use.

· The aim of this workshop is to provide a forum to present and exchange information on novel grassland research, operational user requirements, monitoring approaches for biodiversity and land management practices. The workshop focuses on the advances in Earth Observation solutions to address grassland characteristics and properties, including

· Essential Biodiversity Variables, ecosystem extent and connectivity, biophysical parameters, species distribution, climate change impacts and ecosystem services. The final outcome will be recommendations and onward collaborations to support research and services to conserve and restore grasslands and savannahs worldwide.

· The workshop is organised by the Leibniz Centre for Agricultural Landscape Research (ZALF), the Global Grasslands and Savannahs Dialogue Platform organized by WWF and the EU Grassland Watch team.

Essential Ocean Variables (EOV) and Essential Biodiversity Variables (EBV) are complementary frameworks that enable standardized metrics to inform policy and planning conservation efforts and make progress towards biological diversity targets. They are fundamental for reporting on national biodiversity priorities and international agreements (i.e. Convention on Biological Diversity Kunming-Montreal Global Biodiversity Framework, Sustainable Development Goals, CCAMLR, IPCC, IPBES, Marine Strategy Framework Directive, Maritime Spatial Planning Framework Directive, etc.). The variables are curated by the Global Ocean Observing System - GOOS Biology and Ecosystems panel of experts and the GEO BON’s Marine Biodiversity Observation Network, and popularized by researcher networks including the Marine Life 2030 and OBON, SUPREME, and other Ocean Decade Programmes. Some of the physics, biogeochemistry, and biology and ecosystems EOVs are also as Essential Climate Variables (ECV). An important goal is to improve modelling and forecasting of marine life and ecological scenarios. This is especially challenging but critical for gathering meaningful environmental knowledge and data at temporal and spatial scales of complex biological, biogeochemical and physical processes to inform ecosystem-based approaches to biodiversity conservation and manage for sustainable ocean development. The remote sensing community has initiated an approach to estimate Essential Variables. The aim of the workshop is to help advance consensus among the private, government, and academic communities on the mapping of EOV, EBV and other products based on remote sensing. It seeks to identify limitations of satellite Earth Observation (SEO) for deriving accurate ocean EBV at the required spatial and temporal scales. To do so we aim to focus on these aspects:

1. Prioritize remote sensing observations and products needed for local, national, and international biodiversity monitoring and management in the EBV framework.

2. Highlight scientific, monitoring gaps, and policy options that may be addressed by defining specifications for future satellite remote sensing missions.

3. Identify in situ observations to calibrate, validate, and complement remote sensing data. Methods of interest include the use of eDNA, passive and active acoustics, autonomous systems and approaches, imaging and other optics observing in the context of remote sensing advances for Essential Variables.

4. Advances in modelling that combine remote sensing and in situ biodiversity EOV to generate EBVs, indicators; examples of practical management and other operational applications are especially welcome.

Workshop methodology: The workshop is open to any BIOSPACE paricipant. After introducing the key perspective of the workshop, participants will be guided through group discussion to (1) identify metrics, indicators, and related remote sensing products, and their potential application

for specific monitoring and policy needs, and (2) identify platforms, limitations, and requirements for metrics retrieval

Expected Outcomes: The workshop will elaborate a roadmap of products and services that are available for answering policy needs, define a list of products and indicators that can be produced in the future, and identify limitations and challenges deriving from methodological challenges and data gaps. The workshop will provide a summary of present platforms, limitations, and requirements for metrics retrieval. An output for space agencies will be requirements for future satellite earth observation to address marine biodiversity challenges.

We invite stakeholders from government, private, and academic groups to move these goals forward by actively participating to this workshop.

The accelerating biodiversity crisis underscores the urgent need for effective strategies to conserve and restore critical ecosystems, especially wetland., Both inland and coastal wetlands support diverse species and vital ecological functions, provide a wide range of ecosystem services, offering increased resilience to global change for local communities.

This workshop explores how satellite-based technologies can play a pivotal role in supporting wetland mapping and restoration prioritisation efforts and addressing the biodiversity crisis by improving our understanding of wetland habitats. Leveraging high-resolution imagery and advanced analytical techniques, earth observation (EO) and geotechnologies offer unique capacities to monitor wetlands and provide information supporting biodiversity monitoring and enabling conservationists, policymakers, and land managers to make informed, timely decisions.

This workshop seeks to foster open dialogue among scientists and practitioners, exploring current practices, identifying remaining challenges, and highlighting research opportunities to better harness satellite technology for wetland monitoring locally and globally.

The workshop is divided into two sessions.

· The first session focusses on key global conventions and policy instruments for protecting and restoring wetland ecosystems. It will highlight the role of EO tools in assessing and restoring wetlands, emphasizing national wetland inventories and regional and global mapping initiatives, including links to the Global Biodiversity Framework, National Biodiversity Strategies and Action Plans, and the Sustainable Development Goals.

· The second session addresses the policy context in Europe with special attention to the EU Biodiversity Strategy 2030 and the Nature Restoration Regulation. Based on case studies, the participants will discuss EO applications to support habitat restoration, mitigate habitat loss, and strengthen EU biodiversity policy frameworks.

The expected outcomes include:

· Workshop summary reporting on priority topics identified by the workshop participants

· Short paper on the identified priorities and recommendation for future direction on developing EO technologies in support of policy-relevant information, with open contribution from all workshop participants