Conference Agenda

Ecosystem extent information forms the foundation for numerous environmental analyses, serving as the baseline for understanding ecosystem condition, risks, trends, and the effectiveness of conservation and restoration efforts. Accurate data on ecosystem extent is essential for biodiversity assessments, climate change modeling, ecosystem services valuation, and land-use planning. It enables policymakers, scientists, and businesses to identify areas of ecological importance, track habitat loss, and prioritize interventions for protection and restoration.

The Global Ecosystems Atlas will provide this critical information through harmonized, high-resolution maps aligned with the IUCN Global Ecosystem Typology. The Atlas initiative aims to create a trusted, common map of the world’s ecosystems to facilitate consistent and coherent monitoring, reporting, and verification of conservation, sustainable management, restoration goals, and natural capital accounting. This will support users at national, regional, and global levels, including companies’ value chains and investors’ portfolios. At its core, the Atlas is a pioneering geospatial data product developed by integrating existing national and global data on ecosystem extent with new high-resolution Earth observation maps.

By offering a comprehensive and consistent view of global ecosystems distributions, the Atlas will allow users to perform more accurate analyses, inform decision-making processes, and meet reporting requirements under frameworks like the Global Biodiversity Framework (GBF) and the UN System for Environmental-Economic Accounting (SEEA EA).

The Atlas will:

• Integrate existing high-quality ecosystem maps, standardize and harmonize approaches to provide the best available spatial data on ecosystem extent, condition, and risks.
• Identify and fill knowledge gaps on ecosystem extent and condition using the latest Earth observation data, AI/ML technologies, and relevant ecological data.
• Provide tools to support global, regional, and national assessments, reporting, and accounting related to ecosystems.
• Enable businesses to develop coherent nature accounts and assess, report, and verify nature-related risks and key metrics with transparency and consistency.

The Atlas, currently available as a proof-of-concept, will continue to evolve as a collaborative resource for sustainability, risk management, and informed decision-making. It serves as a vital tool for achieving the goals and targets of the Kunming-Montreal Global Biodiversity Framework, ensuring that action is taken where it matters most.

Explore the proof-of-concept at globalecosystemsatlas.org

The Committee on Earth Observation Satellites (CEOS) was created 40 years ago as a way for the world’s civil space agencies to coordinate their activities and exchange ideas to support societal benefit and decision making. Areas of coordination include climate, disasters, capacity building, calibration/validation, and information systems as well as measurements for oceans, atmosphere and land surfaces. However, despite biodiversity’s importance to society and the critical role that Earth Observation (EO) plays in understanding, monitoring, and managing it, CEOS’s engagement with biodiversity has been minimal. To address this gap, CEOS is reaching out to a variety of biodiversity organizations including, among others, the CBD, IPBES, GEO BON and the GEO Ecosystem Atlas, and UNSEEA. The information gained is being used to create a path forward for CEOS and its agencies to better support biodiversity conservation and science and increase the societal impact of EO data.

As the coordinator for the world’s civil space agencies CEOS has tremendous potential to contribute to biodiversity conservation. This is particularly true in the context of numerous forthcoming missions, as sensor technology advances and gets into space, and as other technology such as AI and computing power move forward.

The Convention on Biological Diversity (CBD) calls upon member nations to report on national ecosystem conservation status using metrics on ecosystem extent for terrestrial, freshwater, and marine ecosystems. Similarly, the UN System for Environmental and Economic Accounting (UN SEEA) encourages nations to develop national ecosystem extent accounts. Several of the Sustainable Development Goals (SDGs) also include area-based conservation status metrics which require assessments of ecosystem extent. Both the CBD and UN SEEA processes encourage the use of the IUCN Global Ecosystem Typology (in particular the ecosystem functional groups from the third level of the hierarchy) as a reference classification. Ideally, these national ecosystem extent metrics would be produced by individual nations using a bottom-up, wall-to-wall, fine resolution ecosystem mapping approach. Notably, the GEO Global Ecosystems Atlas initiative has commenced an effort to produce a globally comprehensive ecosystem map as a synthesis and compendium of national ecosystems maps and other relevant ecosystem maps. Commendable progress has been made towards that goal, and a proof-of-concept characterization was presented recently at COP 16 in Cali, Colombia. The GEO Global Ecosystems Atlas initiative is recognized as a multi-year effort which includes a commitment to capacity building. It will be several years before a complete (globally comprehensive) bottom-up draft ecosystems map is available. In the meantime, the question of whether any of the existing top-down, standardized, globally comprehensive ecosystem maps have utility for national ecosystem conservation status reporting is often raised. In this context, the USGS/Esri World Terrestrial Ecosystems are discussed, exploring key dimensions such as mapping approach, source imagery derivation, compatibility with the IUCN GET, currency, spatial resolution, uncertainty, projected future distributions, etc.

For European habitat mapping with EO data and machine learning or deep learning techniques it is a prerequisite to obtain a large amount of in-situ habitat observations across Europe with a high precision and up-to-date. Up to now the training data for EUNIS habitats (level 3) is based on classified plot observations from the European Vegetation Archive (EVA). Although this database is huge in terms of number of vegetation plots (2,6 million) there are three important limitations: 1) Spatial limitation. Not all parts of Europe are evenly covered by plot data. Especially Scandinavia, Eastern Europe and the parts of Spain and Turkey are unrepresented in the database; 2) Temporal limitation. Especially for linking plot observations as ground truth to remotely sensed data, recent data is needed. Only half of the total number of plots (1.3 million) is recorded from the year 2000 and 0.72 million from the year 2010 onwards; and 3) Location uncertainty. The location uncertainty is a major issue in the EVA database. Apart from the fact that there are 343,000 classified plots without an indication of locational accuracy, there are only 183,000 plots with a location uncertainty of 10m or less. Taking only plots into account that have been recorded from the year 2000 the number drops to 115,000.

To fill the gaps in the synoptic observations of EUNIS habitats, the possibility is explored to use combinations of so-called opportunistic species observations. By using GBIF species observation data, extended with species data from EVA, the co-occurrence of species within grids cells of 10 by 10 meter and/or 100 by 100 meter can be used as a proxy for the presence of a EUNIS habitat type. To ease the process of finding optimal thresholds for each EUNIS habitat, an application (called the ‘Eunis Proxy Distribution Viewer’) has been developed, in which we analyse 52.2 million georeferenced plant species records in terms of co-existence of diagnostic, constant and dominant species at grid cells of 10 by 10 m or 100 by 100 m across Europe. The complete method of finding new potential locations of EUNIS habitats at level 3 for training or validation purposes is demonstrated, including the challenges. In the end all good habitat classifications depend on finding sufficient, up-to-date and well-distributed training data.

Earth Observation (EO) has the potential to enhance and accelerate ecosystem accounting within the SEEA EA framework, thereby offering the most economically efficient method for gathering extensive datasets in a standardized format, ensuring both spatial and temporal consistency. The European Space Agency (ESA) project “Pioneering Earth Observation Applications for the Environment – Ecosystem Accounting” (PEOPLE-EA) aimed to study and demonstrate the relevance of Earth Observation (EO) for ecosystem accounting in terrestrial and freshwater ecosystems. Ecosystem accounts are inherently spatial accounts, with the implication that they strongly depend on the availability of spatially explicit datasets. In particular, the development of extent accounts in selected test sites illustrated the changes in extent from one ecosystem type to another over an accounting period. To achieve this, we collected and integrated information from land cover, biodiversity, field surveys, ecological data, and other relevant factors to delineate and classify ecosystems based on their ecological characteristics, processes, and functions. Local, expert knowledge was also integrated in the data collection and validation phases and EUNIS Level 3 maps were generated through AI techniques as the final product. An approach to develop an independent change detection workflow provides a promising perspective, however further work is to be conducted to select the best deep learning network and training dataset to capture the expected transitions in ecosystems. The recently started World Ecosystem Extent Dynamics (WEED) ESA Project, will try make this further step on ecosystem extent mapping and accounting, by developing a global applicable open-source toolbox, leveraging existing datasets and tools while applying creative and novel methods to use EO, and enable users to generate comprehensive maps of the ecosystem extent and the distribution of terrestrial, freshwater and coastal ecosystem types and their temporal variations according to different ecosystem typologies. First approaches and outcomes are herein presented.

Mangroves are critical ecosystems, but data inconsistencies and lack of long-term monitoring hinder effective management in Tanzania. We present a comprehensive analysis of mangrove extent changes from 1990 to 2023, integrating historical and contemporary data sources.

We digitized and preserved unique paper maps from a 1989/1990 forest inventory—the first national-scale assessment of mangroves in mainland Tanzania—and combined these with Landsat, Sentinel-1 and -2 imagery, and training and validation points obtained from field validation using a custom mobile application and manual digitalization from Google Earth, updated with Planet NICFI monthly composites. Using Google Earth Engine (GEE) supervised Random Forest model and an online feedback tool with editable polygon capabilities, we integrated local expertise to iteratively improve the classification of mangrove extent and detect changes, achieving accuracies of 90% (1990) and 94% (2023).

Our integration of historical data, high-resolution imagery, robust machine learning models, and extensive validation addresses inconsistencies in previous estimates, providing an accurate, reproducible mangrove inventory. This foundation supports planning and conservation strategies, informing mangrove integration into the Tanzania National Forest Inventory. Uniquely, organizations from Tanzania, Germany, and the USA collaborated mainly remotely using online tools, integrating diverse expertise. Models and scripts are openly shared on GitHub, promoting transparency, reproducibility, and enabling future improvement.

Our findings close a fundamental data gap, informing the preparation of the national mangrove management strategy, action plan, and block management plans for mainland Tanzania and Zanzibar, ultimately supporting sustainable conservation of mangroves and the resilience of coastal ecosystems.

Biodiversity Net Gain (BNG) is a relatively new approach which seeks to deliver more sustainability-focused development, by creating or enhancing habitats to secure a net gain in biodiversity following construction. Demonstration of a net gain of 10% became mandatory for most proposed developments in England in February 2024. Land cover maps detailing the spatial distribution of UK Habitat (UKHab) classes are critical components of the baseline BNG assessments completed prior to development. Surveys to collect habitat data must be carried out by trained ecologists in-situ, requiring habitats to be classified according to type, condition and strategic significance. Such surveys are resource-intensive in terms of labour, cost and time, but recent advancements in both machine learning and remote sensing technologies may offer solutions to more rapidly assess BNG. However, existing methodologies for land cover classification may not capture the full complexity of natural habitats, especially for detailed biodiversity assessments. The development of vision-language models (VLMs) has the potential to improve land cover classification, as they enable the integration of visual and textual information. This information increases the understanding of the semantics required to identify and categorise different land cover types. However, few studies have assessed the application of this emerging technology in specific ecological contexts. Responding to this, our work shows that VLMs holds strong promise for automatic detection of land cover by interpreting visual features in the context of descriptive textual data, providing a comprehensive understanding of habitat characteristics. The presentation will show how VLMs can be used with Sentinel-2 and UK National LiDAR data to classify and track changes in UKHab classes. These results contribute to a better understanding of how advanced machine learning methods and open-source remote sensing data can be used to support sustainable development goals.