Conference Agenda

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.