Conference Agenda

One of the factors that most limits utilization of EO by decision makers such as conservation managers and planners and those developing policy is the lack of “higher level” products. Because of cost and other factors, most space agencies do not routinely generate many products beyond Level 2 (e.g., Surface Radiance), however, applied biodiversity users need products such as the headline and other indicators of CBD’s Kunming-Montreal Global Biodiversity Framework. This gap between user needs and what is commonly available limits the impact that EO data should have. Bridging that gap is challenging but a federated system of systems approach may enable broader inclusion of organizations that can develop or generate needed products that help to fill that gap. Open, federated systems can grow organically based on well-defined interfaces that enable a range of organizations to participate based on their expertise and resources. This presentation will explore some of the pros and cons of such an approach.

Direct and indirect anthropogenetic activities are affecting global biodiversity, ecosystems functions and services as a whole and in an interconnected manner. Policies have set specific targets to be achieved and minimise these impacts. To measure progress towards these targets, a suite of indicators has been developed based on a combination of in situ data, predictions from models and remote sensing techniques (i.e. Earth Observations). Yet, collecting in situ data is still challenging, especially in remote areas (i.e. tropics, oceans) while predictive models can be uncertain and prone to errors, especially in data deficient areas. These limitations have led to an increased attention and use of satellite remote sensing biodiversity related products to support policy (through indicators) and science (through upscaling in situ observations). Because of the different communities using satellite data and different scales of processes involved, there is currently a disconnection between the different products for land-freshwater-marine ecosystems. Radiometric remote sensing measures the same physical properties across the domains, therefore providing a unifying perspective of the global ecosystem. This paper examines the commonalities across domains and identifies biodiverisity relevant products that offer potential for constructing global satellite derived datasets of biodiversity and environmental (abiotic) drivers through a critical literature review incorporating recent outcomes from projects under the Biodiversity+ Precursors/ESA Flagship action (e.g., EO4Diversity, BiCOME, and BIOMONDO) and ESA Ocean Health (BOOMS). These findings highlight key areas for future research and suggest that further efforts should be invested to enhance the understanding of the biosphere's response to multiple drivers. We highlight the need for global, climate relevant, satellite derived biodiversity and environmental (abiotic) drivers variables datasets across domains (i.e. datasets with long term ambition).

Earth System Science and Earth observation (EO) play a prominent role in the scientific understanding of ecosystems, ecological and biophysical processes. The next generation of biodiversity observing systems and science-based solutions will address the main causes and drivers of biodiversity loss, improving the conservation and restoration of vulnerable ecosystems. However, the massive amount of EO data poses a challenge for scientific applications dedicated to both research and operational use. The Committee on Earth Observation Satellites (CEOS) formed the Ecosystem Extent Task Team (EETT) in 2022 to investigate the use of Earth observation data to support the critical Biodiversity variable of Ecosystem Extent. One objective of this team is to develop data-cube based demonstrators to prepare future biodiversity monitoring systems.

The EETT of CEOS is currently exploring possibilities offered by the combination of cloud infrastructures and standardized protocols for producing essential biodiversity variables. A demonstrator based on free and open software is currently being developed to improve our capacity for ecosystem extent and condition mapping, forest biodiversity mapping and forest degradation and dieback monitoring based on Sentinel-2 time series processing over large scales.

We present results derived from Costa Rican forest ecosystems, leveraging Sentinel-2 data catalogs to generate advanced data cubes that incorporate spectral indices and spectral diversity metrics. The influence of cloud cover on spectral indices composites is analyzed to enhance temporal and spatial consistency. . The spectral diversity maps are subsequently compared with ground-based observations on forest types and species composition, as well as species distribution models.

Urban trees are essential for supporting biodiversity, as they provide habitats for various species and help regulate water storage and temperature, and sequester CO₂ in urban ecosystems.Urban forests have been proposed as a nature-based solution to fight climate change and provide ecosystem services to citizens. Mapping and monitoring urban trees is vital as it facilitates conservation strategies for both flora and fauna, early diagnosis of plant pathogens, and zoning and urban development. However, mapping trees has proved difficult for urban planners since they rely on in situ surveys or community-led projects that may not cover all areas; one such case is London, where the official survey only accounts for ~10% of the estimated 8 million trees in the city. Moreover, the geographic coordinates of trees are surprisingly unreliable due to a lack of precision of measuring devices (e.g. phones or commercial GPS).

We propose a method for calibrating urban tree locations using physical ground sensors as "anchors”. These sensors help reconcile spatial mismatches across various spatial datasets, including high-resolution satellite and aerial imagery and tree surveys collected by city councils or in open-data projects like OSM. These low-power sensors can also collect microclimate and other biodiversity-related data, such as passive acoustic animal activity monitoring, providing a richer picture of tree and urban ecosystem health and enabling high resolution maps not previously possible.

Our ultimate goal is to combine remote sensing information with ground-based measurements to support reliable data that can be used in geographic-based foundation models to help better urban planning strategies around trees that maximise their benefit to humans and nature.

Half of Germany’s land is used for agriculture, making it a crucial factor in the conservation and promotion of biodiversity. Land use, its intensity, and management practices shape biodiversity and consequently affect ecosystem functions and services. Farmland habitat status and quality can serve as proxies for assessing biodiversity, but developing reliable indicators, particularly for evaluating habitat quality, remains challenging. To advance the development of such indicators, a generalized workflow for a farmland habitat biodiversity indicator (FHBI) was proposed by the OECD. Here, agricultural landscape and management diversity is utilized as a proxy for biodiversity. Based on already available data, FHBI can be derived by assigning habitat quality scores of individual land cover classes based on their assumed influence on farmland biodiversity. The aggregated indicator aims at enabling the assessment of the status and trends of farmland habitat quality.

We present the initial results of a pilot study in which we calculate an FHBI for Germany for the period 2017-2023. The FHBI is based on a combination of different datasets derived from satellite data at an aggregation level of 100 ha hexagons. First, we derived pixel-level structural and functional crop diversity based on the frequency and duration of crop sequences and the share of cereal, leave, summer, and winter crops. Second, we derived grassland-use intensity as the number of mowing events detected from satellite time series. Third, we complemented these datasets with maps of small woody features and other perennial land use classes. Fourth, we assigned habitat quality scores ranging from one to five to each pixel in the combined land use (intensity) map. Finally, we calculated the area-weighted average of habitat quality values for each hexagon. Our FHBI map provides a first insight to detect large scale spatial patterns and differences in farmland habitats across Germany.

Soil biodiversity is essential for ecosystem health, driving multiple ecosystem functions and services. However, predicting multi-trophic soil biodiversity remains challenging due to complex environmental interactions and limited observation methods. Our research explores the potential of Earth Observation Foundation Models, using orthophotos with soil, climate, phenology, and landscape tabular data to enhance biodiversity predictions through machine learning (ML).

We collected relative abundance data for 53 trophic groups in the French Alps from the ORCHAMP observatory, which were pre-computed through eDNA metabarcoding and covered categories such as bacteria, collembola, fungi, insects, metazoans, oligochaetes, and protists. We gathered in-situ soil data, CHELSA climate data, COPERNICUS phenology, and THEIA OSO land cover. Additionally, image embeddings were extracted from 20cm resolution IGN Orthophotos using a state-of-the-art self-supervised Dinov2 model pre-trained on satellite imagery.

We built regression models for each trophic group, leveraging Light Gradient Boosting Machine (LGBM), Random Forest (RF), and Neural Networks (NN). The best R² values reached up to 0.82 for models based solely on tabular data, 0.73 using orthophoto embeddings, and 0.81 combining tabular data with embeddings. The latter often yielded results comparable or slightly lower than using tabular data alone. Principal Component Analysis (PCA) suggests that orthophoto embeddings capture similar, yet less comprehensive, information compared to tabular data. All this highlights the complexity of modeling soil biodiversity, where trophic group-specific characteristics pose distinct challenges.

While models based on tabular data performed best, image-based models offer a viable alternative when traditional data collection is impractical. Our dual-approach strategy - using either comprehensive tabular data or orthophoto embeddings - achieved comparable predictive performance for soil trophic group diversity.

This research demonstrates the potential of Earth Observation data and deep learning models as scalable tools for predicting soil biodiversity and improving our understanding of ecosystem structure, function, and resilience under changing environmental conditions.

Oceanic islands harbour high levels of species endemism and rarity, and are particularly vulnerable to habitat loss, introduction of invasive species, and climate change. Therefore, national, transnational, and global initiatives should explicitly include islands by considering essential biodiversity variables (EBVs) that capture their ecological uniqueness and tailoring protocols to the island context. In BioMonI, we endeavor to build a roadmap for long-term monitoring network adapted to the pressing needs of biodiversity conservation and monitoring on islands of the European Union and beyond. Our working group aims at scaling up island ecosystem monitoring, focusing on multiple EBVs. As such, we will integrate satellite remote sensing data from LIDAR, spectral, and hyperspectral sensors (such as MODIS, Landsat, Sentinel, GEDI and EnMAP) with in situ biodiversity observations.

In 2024, we collected terrestrial laser scans across five major habitats of Tenerife, Spain. We derived structural complexity indices to quantify the occupation of 3D space by the vegetation. Our results demonstrate that these indices can be used to characterize relevant aspects of the structure of different types of habitats, expanding the use of this technology beyond forest ecosystems. We will discuss the future directions of our methodologies to develop island biodiversity monitoring protocols and create an integrated dataset that incorporates measurements from field to space.

The Arctic and Antarctic are changing rapidly through amplified warming at the poles, impacting biodiversity and ecosystem functioning, with major feedbacks to the Earth system. In addition to climate, multiple other drivers of change of biodiversity become stronger, such as increasing industrial activity, tourism, wildfires, plastic pollution, and invasive species. In line with these rapid changes, the planning of the 5th International Polar Year in 2032-33 has started. The 5th IPY provides a unique platform for coordinated interdisciplinary research efforts in the Arctic and Antarctic, involving polar researchers, knowledge holders, rights holders and stakeholders. With this presentation, we aim at initiating discussions on how satellite observations and measurements on the ground could be coordinated and combined for biodiversity assessments in the Arctic and Antarctic. We highlight the need for coordinated measurements, data processing, synthesis and data management in order to close major knowledge gaps and feed results of the polar regions back to global biodiversity efforts and efficiently inform policy makers.

Ecologists suggest that the majority of land animals are either nocturnal or active across both the day and night. It has also been demonstrated that artificial illumination fundamentally impacts animal behavior. However, little information is available about artificial illumination potentially affecting these behavior at large scale factors. In our study, very high resolution multispectral night images from Jilin satellite are used to map artificial light in a rural area of Belgium. The geometric precision and the detection rates of light sources are estimated using a database of public lights and field visits at night. Other artificial light sources, mainly from residential and industrial areas, are also identified to derive “dark frame” connectivity in the study area. In addition, the discrimination of the type of light sources (light emitting diode or sodium vapour light) is measured because it has been hypothesized that the impact on animal behavior was linked with the color temperature. The critical analysis results demonstrate the huge value of multispectral night images for biodiversity studies, but also the need of higher temporal resolution.