Conference Agenda

Biodiversity is under pressure by anthropogenic and climate change, but it is difficult to measure, monitor and predict changes across the globe. We face large knowledge gaps in terms of the spatial distribution and temporal dynamics of biodiversity and related ecosystem functions. A new suite of current and upcoming remote sensing instruments is providing large-scale measurements of plant canopy structure, plant functional traits and diversity, and ecosystem functioning from space. For example, spaceborne lidar, such as from the GEDI instrument, can provide us with a new view on the three-dimensional plant canopy structure and its diversity at the landscape scale. I will present new results and challenges for mapping forest structural diversity in California and Central Africa using GEDI at scales from 1 to 25 km, which provides insights on a range of complex and diverse Mediterranean and tropical forest ecosystems. We found that GEDI’s RH98, Cover and FHD metrics were most effective to capture variation in forest canopy height, density and layering, and that GEDI captured the variation of canopy structure generally well in closed forests in flat terrain, while challenges emerged in open forests and in complex terrain. We found high structural diversity in mid-elevation and coastal forests in the US and in volcanic ranges and forest-savanna transitions in Africa. GEDI revealed spatial patterns of structural diversity that aligned with known ecological processes, including the influence of wildfire in the western US and topographic variation in central Africa. Besides ecosystem structure, we developed new methods using imaging spectroscopy to map the distribution of leaf biochemical and biophysical traits and derived patterns of plant functional diversity at the landscape scale. We developed and tested the methods using large-scale airborne imaging spectroscopy data acquired using AVIRIS Classic across a diverse elevation gradient in the California Sierra Nevada mountains to test the application to spaceborne instruments such as EnMAP, PRISMA or the future NASA SBG and ESA CHIME missions at 30 m spatial resolution. I will present results that give insights into mapping foliar traits at large spatial scale and the role of trait-trait relationships in mapping plant functional diversity. We found that there are at least three relevant functional axes of variation that should be represented in functional diversity analyses, and that the relationship among those axes and functional plant strategies is context dependent. We also found that patterns of functional diversity were related to elevation gradients and disturbance patterns, especially related to wildfire. Combining these new measurements with ground-based data will help to better understand biodiversity patterns and change over time. I will present examples of new analyses of remotely sensed patterns of plant functional and structural diversity, and their relationship to other dimensions of biodiversity and ecosystem functions, that demonstrate the value and potential of new remote sensing instruments and methods for biodiversity monitoring from space.

Plant-pollinator interactions are critical to terrestrial ecosystem functioning and global food production but are experiencing increasing pressures from land use and global environmental changes. Environmental conditions, such as climate and vegetation cover, influence both foraging resources and nesting habitat for pollinators. Yet, little is known about the role of vegetation structure and functional traits in determining the organisation of plant-pollinator networks, nor on methods to predict such networks at broad spatial scales. Here, we take a novel approach and evaluate how plant functional traits and vegetation structure influence plant-pollinator interaction patterns. Plant-pollinator network data analysed comprised a total of 209 networks from across the tropics, with vegetation structure and functional trait information extracted using spectral and LiDAR remote sensing datasets. We found that pollination network metrics responded to plant functional traits along a spectrum of plant resource use acquisition and conservation strategies, where networks were more modular with lower vegetation height and leaf nutrient content, while higher leaf photosynthetic capacity and nutrient contents were associated with higher levels of network connectance and complementary specialization. Additionally, networks were more nested with increasing trait variability. Our findings reveal that plant functional strategies, captured by remote sensing, play an important role in structuring biotic interactions such as those between plants and pollinators, paving the way to predict these interactions at scale.

Imaging spectroscopy missions like Earth Surface Mineral Dust Source Investigation and Surface Biology and Geology (SBG) provide valuable opportunities for assessing plant traits. Current empirical approaches, such as Partial Least Squares Regression (PLSR) and various machine learning methods, often lack interpretability and rigorous uncertainty quantification, and typically cannot transfer models across different sensors. To address these limitations, we propose a Bayesian framework to estimate multiple plant traits directly from spectra without requiring transformations like those used in PLSR.

Our Bayesian framework includes four models: a linear model (comparable to PLSR), a non-linear model (utilizing kernel transformation), a hierarchical model (accounting for trait variation across broadleaf and needleleaf trees), and a phenological model (allowing regression parameters to vary temporally). Additionally, we introduce a projection technique that reduces fitted trait models to submodels with fewer spectral bands while maintaining predictive accuracy. This technique identifies the optimal bands necessary for accurate trait estimation and enables flexible model adaptation across different spectral configurations.

We apply these models to predict leaf-level traits using a global dataset and extend this to the airborne scale using AVIRIS-NG data and trait measurements from the 2022 SBG High-Frequency Timeseries campaign. At both scales, the linear Bayesian model performs comparably or slightly better than PLSR, while the other Bayesian models show varying degrees of improvement depending on the specific trait. The reduced models identify between 6 and 30 essential bands.

To test the framework’s adaptability across sensor configurations, we resample AVIRIS-NG spectra to different resolutions and add synthetic errors. Our projection algorithm successfully adapts the AVIRIS-NG model to these simulated sensors without requiring spectral resampling. This approach demonstrates a robust, interpretable, and sensor-agnostic method for plant trait estimation, enabling consistent and reliable large-scale trait mapping across multiple missions.

Understanding the dynamics of forests is crucial for ecology and climate change research.
Reliably estimating the extent and properties of existing forests on a global scale remains essential for planetary carbon balance investigations.
However, high local variability in forest structure, biomass, and productivity poses significant challenges.
These elements are influenced by successional states and disturbances, both natural and anthropogenic.
Remote sensing methods from orbital measurements can capture local features on a global scale.
Among large-coverage satellite missions, the TanDEM-X Radar offers high spatial resolution and temporal consistency.
Its bistatic backscattering methodology allows for interferometric analysis and detailed investigation of vertical forest structure.
We present different interferometric concepts to invert indicative forest properties such as vegetation canopy height and forest structure.
These techniques excel in providing extensive and consistent spatial and temporal coverage, enabling the monitoring of forests worldwide.
On the other hand, the individual-based forest model FORMIND offers detailed simulations of forest dynamics by accounting for individual tree growth, competition, and mortality.
This model captures the complexities of forest structure and successional stages with high ecological detail.
We integrated TanDEM-X X-band radar coherence measurements with coherence properties based on structure-specific simulations on top of the forest model FORMIND.
This synergy combines the broad reach and efficiency of remote sensing with the detailed, process-based understanding from ecological modeling.
We were able to improve local estimation precision while retaining large spatial coverage over the study sites of Barro Colorado Island (Panama) and Paracou (French Guyana).
Our approach enhances the accuracy of global carbon balance investigations and contributes to a better understanding of forest dynamics in the context of climate change.
By leveraging the strengths of both remote sensing and model-based methodologies, we demonstrate that their combined application provides a more comprehensive and precise assessment of forest ecosystems.
This integrated strategy is essential for effective climate change mitigation and the sustainable management of forest resources.

Estimating forest biodiversity is essential for effective conservation and ecosystem management. Traditional field surveys, while valuable, are often time-consuming and labor-intensive, challenging the collection of comprehensive and accurate biodiversity data. Over recent decades, various methods have emerged to assess forest structure and tree species diversity using remote sensing technologies. One notable indirect approach is the "Height Variation Hypothesis" (HVH). This hypothesis states that greater heterogeneity in tree height, as measured by LiDAR data, indicates higher complexity in forest structure and greater tree species diversity.

The HVH is based on the relationship between variations in canopy height and tree species diversity, using the forest's vertical structure as a biodiversity indicator. This hypothesis has garnered significant attention in recent literature, with numerous studies exploring its applications. Researchers have tested the HVH using airborne laser scanning LiDAR data and, more recently, GEDI LiDAR data, demonstrating how space-borne LiDAR can identify biodiversity patterns through variations in tree canopy height.

The approach has also been applied to forests affected by extreme wind events, which cleared entire areas, to investigate the role of tree height heterogeneity in forest stability and biodiversity. Beyond forest ecosystems, the HVH has been extended to agricultural landscapes, integrating LiDAR and photogrammetric data with ecological modelling to assess vertical heterogeneity at the landscape level. This integration has provided valuable insights into conserving avian and bee diversity in human-dominated landscapes.

In summary, the HVH presents a promising method for estimating biodiversity in different natural ecosystems, using LiDAR data. By synthesizing findings from recent studies, we highlight the potential of LiDAR technology to enhance our understanding of biodiversity patterns and support effective conservation and management strategies.

Soil accounts for up to a third of the total Amazonian forest carbon stocks ; however, uncertainties in soil organic carbon (SOC) stocks are very large compared to above-ground stocks. It is important that we learn more about SOC stocks and their management to learn about the functioning of the land carbon sink under continued climate and land use change. This study investigates the relationship between canopy structure and SOC in tropical forests, with the goal of improving SOC predictions across the landscape using satellite remote sensing.

We took soil samples in 142 locations up to a depth of 30 cm, with corresponding measurements of canopy structure using field hemispherical photography, airborne lidar and spaceborne lidar (within footprints of the Global Ecosystem Dynamics Investigation). These were analysed using open source software to ensure the methods are readily accessible. SOC in our study sites ranged from 0.34% to 9.04% and Plant Area Index between 2.28 and 9.59. We use statistical inference from Generalised Linear Models (GLMs) to develop understanding of mechanistic relationships between soil carbon concentrations and indicators of forest canopy structure (e.g. Plant Area Index, rumple index, vertical complexity index). These results inform modelling strategies for predicting soil carbon on landscape scales using spaceborne sensors such as GEDI and Landsat.

Our research offers a novel approach to refining landscape scale predictions of SOC in tropical ecosystems, providing further insights into the variation in carbon storage. This ultimately contributes to global efforts to understand terrestrial carbon dynamics and the land carbon sink under climate change conditions . Furthermore, our work demonstrates the value of openly available global data products, and methods that use this appropriately.

Vegetation plays a vital role in the ecological functioning of the Earth's ecosystems, and it is essential to quantify the response of plant biodiversity to climate change. Trait-based plant ecology links vegetation functioning to climate change drivers and quantify dimensions of functional diversity through field-based and remote sensing techniques. However, field-based and remote sensing approaches to depict landscape-based traits and diversity often exhibit methodological mismatches that must be addressed to deepen our understanding of how functional diversity varies across scales. We aim to identify conceptual similarities and dissimilarities between remote sensing and field ecology in the study of plant functional diversity. We conducted research weaving, a combination of bibliometric and systematic mapping, to identify key concepts and topics of plant trait diversity, knowledge gaps, and conceptual mismatches from the perspectives of both disciplines. We found evidence that trait-based research is strongly biased geographically, being dominated by countries in the northern hemisphere, and considerably more papers published in ecology than in remote sensing. Our topic model identified seven key concepts in the literature, reflecting the level of organization and the ecosystem of interest. We further identified large differences in spatial and biological resolution between disciplines, with field-based ecology sampling smaller areas (resolution and extent), and using leaf-level trait data to estimate well-defined functional diversity indices (e.g., functional dispersion, evenness, divergence, CWM), based on an extensive list of traits. In contrast, remote sensing assesses functional diversity with spatial resolutions on hundreds of square meters - pixel size - and proxies for functional diversity estimations rather than specific indices. These proxies are mainly related to a few traits (e.g. LMA, pigments, height, or nutrient content). We recommend a standardized approach in functional diversity research, as similar traits, spatial resolutions, and functional diversity indices, across both disciplines to improve comparability and integration between them.

Nearly three decades ago, the Spectral Variation Hypothesis (SVH) was brought to life, proposing that spatial variability in the reflectance (i.e., spectral diversity) of vegetated surfaces relates to plant species richness. Particularly, the accessibility and capability of multispectral satellite data have fueled enthusiasm for the SVH, given its potential to enable straightforward biodiversity estimation from space. However, recent studies have raised significant issues regarding the validity of the SVH on large spatial scales. Spectral differences observed between species at the leaf level do not easily translate to landscape-level assessments. This challenges the effectiveness of spectral diversity for large-scale biodiversity mapping and monitoring, suggesting it may be time to let the SVH rest in peace as a one-size-fits-all, easily applicable solution. Yet even as we bury the SVH, some useful ‘SVH zombies’ emerge, providing valuable insights on biodiversity in specific contexts. Drawing on our experience, we will present some of these 'SVH zombies' that, while moving away from the initial simplicity of the SVH, leverage tailored large scales spectral diversity implementations. Examples include the data fusion of different sensors, object-based approaches, the incorporation of temporal information, sub-pixel classification, uncertainty quantification, and the combination of spectral diversity with other biodiversity-relevant predictors.

The capacity of remote sensing to track radiation-related ecosystem functions has significantly improved in the last decade. Furthermore, remote sensing has more recently emerged as a potential biodiversity monitoring tool, thereby bringing new perspectives for studying biodiversity-ecosystem function relationships from space. To properly exploit these opportunities, we still need to improve our understanding of several methodological questions related to the capability of remote sensing to capture the different aspects of plant diversity, particularly functional diversity, and determine the best approaches to connect these estimates with the ecosystem functions to which remote sensing is sensitive to.

To explore these questions in a controlled environment, we have developed BOSSE, a “Biodiversity Observing System Simulation Experiment” that simulates dynamic vegetation scenes featuring multiple species sensitive to meteorological conditions. BOSSE simulates vegetation traits and radiation-related functions (photosynthesis, transpiration, etc…) together with spectral signals related both to the biophysical properties and the physiological state of vegetation (sun-induced chlorophyll fluorescence, land surface temperature, and photochemical reflectance index). Remote sensing imagery can be simulated for specific missions and at different spectral and temporal resolutions.

Using BOSSE, we explore the capability of remote sensing to disentangle biodiversity-ecosystem function relationships from plant diversity and ecosystem function estimates based only on remote sensing data, whereas the simulated vegetation properties and functions are used as a benchmark. We expect BOSSE to improve the interpretation of some pioneering studies, as well as increase the robustness of future analyses based on remote sensing imagery and eddy covariance data.

Biodiversity affects ecosystem functioning by regulating the biogeochemical exchange of carbon, water, energy, and nutrients within and between ecosystems. However, large-scale, systematic measurements of plant biodiversity are still lacking, and the effects of biodiversity on measured biogeochemical processes are understudied.

We leveraged fine-scale remote sensing data from Sentinel-2 to estimate biodiversity at 148 sites across the globe. At these sites, measured eddy covariance fluxes of carbon, water, and energy can be used to compute ecosystem functional properties. To assess the effect of biodiversity on the biogeochemical functioning of the ecosystems, we related remotely-sensed biodiversity (Rao Q) to the derived ecosystem functions, including ecosystem multifunctionality.

Rao Q computed from near-infrared reflectance of vegetation (NIRv) was a major predictor of single ecosystem functional properties and multifunctionality, highlighting the mostly positive effects of biodiversity on the functioning of ecosystems. Rao Q was generally more important than climate and comparable to the structural components of the ecosystem in predicting ecosystem functions and multifunctionality. In addition, Rao Q was more important than traditional biodiversity indices of taxonomic diversity measured at a subset of sites in North America where systematic plant species surveys were available. This reinforces the idea that structural and functional diversity, rather than species identity per se, are key aspects in the worldwide functioning of natural ecosystems.

In summary, we provide strong evidence for significant positive effects of a biodiversity-proxy derived from earth observations on single ecosystem functions and ecosystem multifunctionality. The positive biodiversity effects are robust to the inclusion of most major meteorological and structural parameters that might drive ecosystem functioning or confound the biodiversity-ecosystem functioning relationship. Considering recent and future advances in remote sensing of both diversity and ecosystem functions, our study paves the way to continuous spatiotemporal assessments of the biodiversity-ecosystem functioning relationship at the landscape, regional, and global scales.

The horizontal and vertical structure of forests, both in terms of canopy architecture and the distribution of traits, are indicators and drivers of the connections between biodiversity and ecosystem function. More structurally heterogeneous forests are often more productive, and they should generate more niche space for a wider array of organisms. Here we explore connections between forest 3-D structure, carbon uptake, and biodiversity at the University of Michigan Biological Station (UMBS). Over the past ~100 years, several large-scale disturbance experiments have taken place at UMBS, making it an ideal site for exploring connections between structural heterogeneity, biodiversity, and ecosystem function. In August 2019, the National Ecological Observatory Network’s Airborne Observation Platform (NEON AOP) collected hyperspectral imagery and lidar data over UMBS. Simultaneously, field data were collected to train the AOP data to generate site-wide maps of the vertical distribution of leaf area density (LAD, m2 m-3), top of canopy leaf traits (leaf mass per area (LMA, g m-2), leaf carbon (%), and leaf nitrogen (%)), and spectral diversity. We then used these maps, both individually and combined through an unsupervised clustering approach, to assess connections with other patterns of biodiversity and ecosystem function.

We found that the AOP-derived maps successfully identified different disturbance regimes across the landscape, though more subtle disturbances at smaller spatial scales were more difficult to detect. Correlations between remotely sensed metrics of 3D structure and disturbance intensity varied, but overall were able to explain variation in forest age with 87% accuracy. Overall, this work demonstrates the importance of both horizontal and vertical structure in understanding spatial ecosystem processes and connections between biodiversity and ecosystem function at the landscape scale.

Tropical forest canopies represent the biosphere’s most significant and concentrated atmospheric interface for carbon, water and energy. Here, we present a pantropical analysis that maps the diversity of tropical forest tree canopy functional traits and functional diversity at high spatial resolution. We combine field-collected data from more than 1800 vegetation plots and tree traits and merge these with satellite remote sensing, terrain, climate and soil data to predict variation across 13 tree morphological, structural and chemical functional traits, using these to compute and map the functional diversity of tropical forests. This reveals that the tropical Americas, Africa and Asia tend to occupy different portions of the total functional trait space available across tropical forests. The functional trait analysis across continents shows that tropical American forests have 40% greater functional richness than tropical African and Asian forests. Our predictions represent the first ground-based and remotely enabled global analysis of how tropical forest canopies vary across space.

The forests of the Congo Basin are a unique biodiversity hotspot. They provide multiple ecosystem services, from being a significant carbon sink to regulating the water cycle and regional climate. Additionally, they offer invaluable resources for subsistence, serve global economic demands, and possess cultural and recreational value. However, various environmental and anthropogenic drivers are exerting considerable pressure on these ecosystems, threatening the sustainability of these services. Beyond deforestation, these pressures may lead to dramatic changes in forest tree functional composition, with potential deleterious feedback on carbon and water cycles. Despite their importance, the forests of the Congo Basin remain largely understudied, and our understanding of such subtle compositional changes is modest at best.

The CoForFunc project, funded by the European Biodiversa+ program, aims to advance research towards biome-scale monitoring of the Congo Basin forest's functional composition. A primary focus is on characterizing tree phenology, interpreting these phenological behaviours mechanistically, and upscaling them to ecosystem functional properties (EFPs) at the scale of the Congo Basin. This effort relies on coordinated campaigns of ground measurements, drone surveys, and satellite remote sensing.

This presentation will discuss the strategies we have adopted concerning remote sensing, where cloud cover, atmospheric and directional effects present considerable challenges. We are exploring three different approaches: (1) increasing the availability of cloud-free imagery by leveraging the sub-daily revisit capacity of geostationary satellites to enhance Sentinel-2 BRDF corrections necessary for mapping subtle phenological shifts; (2) investigating the capacity of passive (SMOS) and active (Sentinel-1) microwave data, which are largely insensitive to cloud cover, to provide information on variations in canopy water content associated with phenology and drought response strategies; (3) exploring the use of sun-induced chlorophyll fluorescence (SIF) retrieved from TROPOMI on Sentinel-5P, which is less sensitive to clouds than traditional optical indices, to assess variations in structure and physiology.

A nature data revolution is unfolding, with unprecedented quantities of data available on many facets of global biodiversity. Spatial and temporal data gaps compromise trend change detection. New standards and protocols for monitoring mean that co-designed observing and information systems are needed to scale up our understanding of biodiversity change globally. Scientific and technical guidance is needed for organizations and agencies seeking to contribute to the planning, implementation and development of GBiOS.

In this workshop we will assess the requirements of GBiOS with a view to 2030. What are the data and information needs? what observations are needed that to detect, attribute and forecast biodiversity change? what measures of observing performance and capacity are needed to guide investment?

We see an opportunity to assemble a GBiOS designed to interact with the Global Ocean Observing System (GOOS), the Global Climate Observing System (GCOS) and the Global Terrestrial Observing System (GTOS) to support countries with the monitoring of their biodiversity goals and targets.

The first part of the workshop will be a “plenary” session describing the GBiOS concept, the major gaps and challenges it seeks to overcome and existing opportunities for collaboration. In the second, part we will have breakout groups focusing on key questions:

1. National and regional monitoring systems are the building block of GBiOS (BONs) – how can we link and coordinate them effectively to form a worldwide network of sites that is representative of current and expected trend change?

2. Can we improve understanding of Essential Biodiversity Variables and Essential Ecosystem Service Variables and their role in monitoring and indicators?

3. What data analysis systems are needed to monitor trend detection and attribution across a range of scales of space and time?

4. Can GBiOS support a global biodiversity modelling and forecasting service? Can workflows in platforms like BON-in-a-Box integrate remotely sensed and ground collected data to provide emergent understanding of trends?

5. How might we position GBiOS as a complement to existing global observing systems? Can we calculate the benefits (value) and avoided costs to society of this system?

These will then be discussed and synthesized collectively.

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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.

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.

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.

Precise data on ecosystem restoration projects can enhance scientific research on monitoring the long-term effectiveness of restoration efforts and the consecution of restoration objectives using remote sensing technologies. Through the combination of two FAO tools, (i) the Framework for Ecosystem Restoration Monitoring (FERM) that is used for compiling and publishing ecosystem restoration data and (ii) the System for Earth Observation Data Access, Processing and Analysis for Land Monitoring (SEPAL) that allows to produce sophisticated and relevant geospatial analyses we can monitor restoration actions on the ground.

Indicators and their corresponding metrics are the way to track the progress of restoration efforts. FERM provides the user the possibility to monitor ecosystem restoration through specific indicators. A good practice for restoration projects is having indicators measured on the ground by the project monitoring team. Furthermore, the use of earth observation technologies provides the possibility to determine scientific baselines as well as to monitor change over time through indicators, including after project completion.

With SEPAL, we will monitor two forest ecosystem restoration projects and indicators integrated into the FERM platform.

For monitoring forest restoration & agroforestry activities we will use SEPAL to create a yearly mosaic over the project location and calculate some indices (Normalized Difference Vegetation Index (NDVI), EVI) followed by a time series example of the project location using a built in CCDC algorithm. With the combination of all these approaches we will identify the gradual regrowth of vegetation in areas targeted for restoration.

For mangrove ecosystems, SEPAL will map aboveground biomass (AGB) using remote sensing techniques, by calculating the NDVI and Soil-Adjusted Vegetation Index (SAVI). This approach leverages vegetation indices as proxies to estimate AGB, providing essential insights into mangrove distribution, carbon storage potential, and ecosystem health.

The increasing frequency and intensity of droughts and heat waves driven by climate change have led to a significant increase in tree mortality worldwide. However, the lack of accurate and consistent data on the location, timing, species and structure of dead trees across vast geographical areas limits our understanding of climate-induced tree mortality. Furthermore, standing dead, dying, and habitat trees are crucial indicators of forest health and biodiversity but are often overlooked in existing forest resource mapping systems.

To address this, we present novel advancements in mapping individual tree mortality events using high-resolution (≤ 0.5 m) multi-temporal Earth Observation data, including both satellite and aerial imagery, combined with deep learning techniques. Our approach represents the first steps towards building an open large-scale database of individual tree mortality events across time. We have trained several U-Net-based deep learning models for detecting individual dead and dying trees from a wide array of imagery, enabling the creation of wall-to-wall datasets on tree mortality at national scales. We show results from the first nationwide individual tree mortality mapping, demonstrating the accuracy of sub-meter resolution satellite imagery in providing annual tree mortality data. We also discuss the challenges and limitations associated with detecting and characterizing detected dead trees across entire countries. We also show the accuracy of sub-meter resolution satellite imagery in providing annual tree mortality data using deep learning for several study areas.

We welcome scientists across the globe to contribute to creating a database on individual tree mortality events to support a wide range of tree mortality data needs in different scientific disciplines.

**"Integrating Indigenous Knowledge and Earth Observation for Carbon Monitoring in Amazon Reforestation"**

The Ecuadorian Amazon is at the forefront of reforestation efforts, driven by Indigenous communities working to restore and protect this critical ecosystem. Mario Vargas Shakaim, an Indigenous leader from the region, will present on the reforestation initiatives led by his community and the innovative approach of Project Shakaim. This project combines traditional ecological knowledge with Earth Observation (EO) data to quantify carbon sequestration in newly reforested areas.

Project Shakaim leverages satellite data alongside Indigenous land management practices to accurately measure the carbon stored in reforestation sites, providing crucial data for understanding carbon dynamics in tropical forests. By integrating these knowledge systems, the project enhances the precision of carbon monitoring while ensuring that local ecological insights guide reforestation efforts. This approach offers a replicable model for combining community-led initiatives with advanced monitoring technologies to address climate change.

This talk will illustrate how Indigenous perspectives enrich scientific approaches to ecosystem restoration and climate mitigation. Attendees will gain a deeper understanding of the role of Indigenous knowledge in improving carbon measurement accuracy and how such integrative approaches can inform global reforestation strategies.

The EU Nature Restoration Law came into force on August 18, 2024. With research project RestorEO we contribute to biodiversity and ecosystem restoration and conservation activities and reporting duties in Austria by developing and testing a wall-to-wall EO-based monitoring system for selected biodiversity indicators. We focus on three habitat areas (1) forests (2) cultural grassland, and (3) wetlands.

Forests are biologically diverse ecosystems that provide habitat for a multiplicity of plants, animals and micro-organism. For the forest use case, we develop methods that assess forest condition and detect standing and lying deadwood from both Sentinel-2 and LiDAR data. Additional product developments deal with forest fragmentation and connectivity based on GuidosToolbox by the EC Joint Research Center, and an estimation of organic carbon. The common forest bird index (Article 12(2)), an important indicator of biodiversity restoration, is closely linked to these parameters.

For grasslands, our developments focus on a Sentinel-2 based monitoring of the mowing intensity and yellowness of grassland plots. These are two parameters that indirectly reflect plant species diversity and that are used in butterfly habitat modeling. Better information on plant species diversity can serve as an indicator to detect changes related to the grassland butterfly index for areas where field surveys and butterfly counts are missing.

For wetlands, our remote sensing approaches address the detection of drainage channels and shrub encroachment in ecologicaly important moors and heathlands to support Austrian biodiversity monitoring tasks.

With this contribution, we want to present some of the RestorEO mapping results for the different habitats and biodiveristy indicators and discuss the potential and limitations for operational integration of these remote sensing based parameters in national activities and reporting for ecosystem and biodiversity restoration.

To strengthen and operationalise the relationship between condition variables and Ecosystem Services (ES) as defined by the System of Environmental Economic Accounting Ecosystem Accounting framework (SEEA EA), is essential to integrate condition variables into models of ES potential, which is the capacity of the ecosystem to provide the service. This study focuses on the relationship between the ES flood control and key condition variables of the urban ecosystem measured with satellite remote sensing data, these are: imperviousness and tree cover density, which are an input in the ES model of flood control. The data sources are Copernicus High Resolution Layers for the EU. The model was adjusted to include Tree Cover Density making it responsive to an indicator of the Nature Restoration Regulation (NRR) for the urban ecosystem.
Additionally, the model uses CLC+ classes which enabled the analysis of the role of urban green spaces on flood control potential, urban green spaces is also an indicator of the NRR.
This study demonstrates that changes in condition variables, such as a decrease in tree cover density, can significantly impact the ES potential. The decrease in the ES potential is translated into a decrease of the ecosystem service flow. In conclusion, this study operationalizes SEEA EA condition variables and ecosystem service accounts, demonstrating the linkages between them and their potential to support policy objectives, particularly the Nature Restoration Regulation.

Climate adaptation in cities occurs at several levels including the conservation and restoration of green spaces. To support the implementing and monitoring large scale greening projects, we propose a multisource strategy that can be adapted to a variety of input images and data sources (VHR, Sentinel-2, airborne, Lidar, tri-stereo) with the aim to characterize ecological corridors in detail. We apply a first pass of a SegNet like model trained to segment canopy surfaces in RGB images (a version of this model has been adapted to grayscale images and can thus be used to go back in time). This canopy detection can be coupled with a detection of isolated trees, the latter making it possible to obtain the finest trees thanks to a RetinaNet type model specified for the detection of small, isolated trees. Each of these elements can be combined with digital height models, obtained by lidar or stereoscopic reconstruction, to refine the accuracy of the typology by assigning height strata classes. Finally, the use of Sentinel 2 time series, at the scale of the objects detected, makes it possible to refine the typology with phenological considerations. The methods developed can be adapted to images with resolutions ranging from 5cm to 50cm, with great robustness and invariance to acquisition conditions.

The Amazon rainforest is thought of as an important global carbon sink, but changes in local climate, extreme events, and human disturbance often result in it becoming a source of atmospheric carbon. Understanding shifting patterns in tree mortality is crucial to determining the carbon budget of the Amazon, but little is known about the extent, rate, and causes of mortality of large canopy trees, which contain most of the forest carbon and are hypothesised to be most at risk with climate change.

To address this data gap, we developed an algorithm that accurately detects canopy tree mortality across the Amazon Basin using a time series of Planet NICFI data from 2018 to 2024 at 4.77m pixel spacing. We detect mortality events by identifying changes in trend over time in the multispectral reflectances caused by either a decrease of photosynthetic activity or shadowing from adjacent trees.The same principle also allows us to categorize mortality events into standing dead trees and broken/uprooted trees. The end result is monthly predictions of mortality events with a detection rate of 75%. The probability of detection increases with tree crown size, to above 90% when the mortality event is larger than 150 square meters, which makes the algorithm particularly well suited to study large tree mortality.

Early results show an increase in mortality across the Amazon Basin during the 2023-24 El Niño event and the potential of this method to study widespread effects of climatic changes at short temporal scales. Being able to detect large tree mortality fills an important gap in our knowledge of vegetation turnover and vulnerability, which underpins our understanding of tipping points in the Amazon and its resilience to climate change.

The calculation of nature-related economic and financial risks should adhere to the conventional formula that characterises risk assessment. This requires the calculation of three key components: hazard, exposure and vulnerability. Earth observation (EO) represents a valuable source of critical information, capable of providing essential data for all of the aforementioned components. Specifically, on "hazard" by contributing to the mapping and assessment of relevant factors such as land cover, topography, proximity to waterways, weather patterns. On "exposure" by providing the geographical location of physical assets whose economic and financial value depends directly and/or indirectly on nature. On "vulnerability" by providing the critical variables to model where services from nature are needed but not provided. Such services refer in the short term to the provision of ecological inputs, the removal of pollution and the protection against physical and biological disasters; in the long term, they refer to overarching environmental targets such as climate change and halting biodiverosty loss. The presentation offers a conceptual framework for tracing the information flows required to assess "hazard", "exposure" and "vulnerability". It also identifies the specific contributions that Earth Observation (EO) can make at each stage of this process. The discussion is illustrated with a series of concrete examples, which provide a useful point of reference for further debate.

Financial institutions (FIs) are increasingly concerned about the nature-related financial risks of the companies they lend to and invest in. Before they can use earth observation and geospatial data to monitor and report on biodiversity impacts, FIs need data on where their clients are physically operating. This location-based approach is novel for most financial institutions but is embedded in the Taskforce for Nature-related Financial Disclosures' assessment and reporting framework. Asset location data – i.e. spatial data on the location and characteristics of capital-intensive assets like production facilities or plants - is, therefore, an essential building block for monitoring and reporting on biodiversity loss and risks. However, companies do not (consistently) disclose the specific locations and characteristics of their activities or associated nature-related impacts in those areas.

At the Oxford Sustainable Finance Group's Spatial Finance Initiative, we have been working with asset location data for nearly 10 years. We want to collaborate with the EO biodiversity data community and present two areas of our work:

1. An overview of data sources and methodologies for identifying the location and characteristics of companies’ assets. This includes (a) machine learning models to identify the location of cement and steel plants in China using EO data and (b) using EO-derived measurements to model the plant capacity and capital investments of Chinese cement and steel plants and of meatpacking plants in the US.
2. Case studies on how financial institutions then combine this asset location data with EO and other geospatial data layers to support different biodiversity finance applications

Ultimately, we want to engage the audience in a discussion on how to increase the usability of EO-derived biodiversity data and insights to support nature-friendly financial decision-making.

Co-authors: Changenet, Alexandre; Schoefield, Paul; Pellet, Cameron; Wood, Anna; Creer, Simon; Bush, Alex

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Ecosystem conservation and restoration actions requires structured means for reliable monitoring in order to ensure the credibility needed to quantify their success in and facilitate their financing. Here we present a framework for Monitoring, Reporting and Verification of Biodiversity and Ecosystem Services (MRV-BES) which would enable outcome-based payments, fostering efficient conservation and restoration. The framework builds upon previous experiences in MRV of carbon credits, making use of previous good practices and avoiding shortcomings, thus extending MRV systems so that payments for carbon removals would be just one ES among many others, providing a multidimensional consideration of BES in MRV. In our framework, additionally is proven through the construction of a reference model for the restoration action, which is compared against a business as usual model which we use as counterfactual. The difference between the reference model and the counterfactual can be used as reference levels of relative success in the restoration goals that can be employed as a common ’BES currency’ which, in comparison with an existing market (e.g. carbon credits) can be employed to give monetary values to all the BES involved. The amount of effort employed in the monitoring needs to be accounted for in the valuing of BES credits, so that dedicating resources in reliable monitoring would bring a monetary revenue that encourages its investment. For this reason, the framework is based in the principle of conservativeness in MRV, for which payments are to be granted on the basis of the most conservative evidence available. This principle of conservativeness ensures that intensive monitoring reducing the uncertainty in estimates of BES indicators can pay off for its own investment. In the context of the multidimensionality of BES credits this is of particular importance because increasing the number of ES under consideration also increases the confidence in the success of the restoration action, and thus our MRV-BES framework also encourages the multidimensional character of BES to be monitored and accounted for. Our MRV-BES framework also allows to take into consideration synergies and trade-offs among diverse ES. In SUPERB project we apply the MRV-BES framework to 12 demo areas across with diverse ecosystem restoration actions in Europe. These projects include a large number of restoration goals and ecosystem services involved, with monitoring methods including remote sensing techniques involving LiDAR and multispectral drones plus mobile laser scanning, DNA metabarcoding of airborne and soil arthropods plus soil fungi, bioacoustics of bats and birds, and citizen-science assessments of ground vegetation. The MRV-BES framework provided common means for reporting the success at these 12 demo areas, given this diversity of goals and techniques involved. We advocate for payment for outcome schemes, and for that reason these MRV-BES systems need to be underpinned by bundle agreements with defined spatio-temporal bounds. Our MRV-BES framework is in principle meant for the scale of individual conservation and restoration actions under voluntary markets. Nonetheless, public sector regulation and monitoring of a network of reference and counterfactual sites could enable scaling of restoration efforts, which would enable this MRV-BES framework to also be used to prove progress toward restoration and conservation policy target in national reporting.

Oil palm plantations (OPP) are a threat to biodiversity: at least 53 mammal, 50 bird and 23 amphibian species might be threatened by OPP globally according to the Red List of the International Union for the Conservation of Nature (IUCN). However, this number is likely underestimated, since the IUCN Red List does not code threats specifically for OPP, and there are no global spatial analyses assessing how exposed is biodiversity to this threat. Using a recently published OPP map based on remote sensing data, we provide the first global spatially explicit analysis of how much species are exposed to OPP. We also analyse how much protected areas, countries and ecoregions are exposed to OPP. For each feature of interest (species, protected areas, countries or ecoregions), we calculate exposure as the percentage of the feature’s range overlapping with OPP, and further distinguish exposure from industrial and small holders plantations. By highlighting which species, countries and ecoregions are the most exposed to OPP, our work contributes to identify the species or areas for which conservation actions should be prioritized to limit the impact of OPP on biodiversity. We also stress out how much of OPP exposure occurs within species, countries or ecoregions’ protected range, and consequently discuss the role of protected areas in mitigating threats from OPP. While our analyses depict a worrying situation regarding the exposure of biodiversity to OPP, the impact of alternative oil production scenarios on biodiversity still need to be explored.

BirdWatch, funded under the Horizon Europe Program, focuses on improving the state of biodiversity of the EU's agricultural landscape, in line with the EU Green Deal, the EU Biodiversity Strategy for 2030, and the Farm to Fork Strategy.

Leveraging Copernicus satellite data, the project assesses agricultural areas to identify their suitability for farmland birds and strategises ways to enhance ecological conditions. As indicator species, birds offer insights into overall biodiversity health, contributing to a broader understanding of ecosystem well-being.

The project employs species distribution modeling to link bird occurrence data with habitat requirements, establishing models that gauge habitat suitability and the likelihood of an area being suitable for specific bird species. Satellite data are used to quantify essential environmental descriptors such as structural variability, land cover type, crop type, mowing intensity and soil moisture. These parameters are then fed into the habitat models to assess landscape suitability.

Knowing the state of habitat suitability and the habitat requirements, BirdWatch identifies which of the agroecological schemes under the EU’s Common Agricultural Policy (CAP), have to be applied to improve the farmland conditions. The agri-environmental schemes are selected in such a way to ensure that they are not in conflict with any spatial or ecological requirements.

Here, BirdWatch uses spatial optimisation, taking into account both the ecological requirements and the economic and operational constraints of the farmers who need to implement the agri-environmental measures as part of their obligations under the CAP.

Benefiting from Copernicus program's high temporal resolution, BirdWatch evaluates the success of agri-environmental measures and makes adjustments as needed.

Upon project completion, the service will be accessible through a web-based GIS application in the project regions of Flanders, Germany, Lithuania, and South Tyrol.

Closing the global biodiversity finance gap requires innovative financial mechanisms that value the preservation and restoration of healthy ecosystems. We introduce our Landler.io platform that enables asset-grade nature investment portfolios; along with “Biodiversity Units” -- a conservation focused, scalable and accessible monitoring framework designed to quantify ecological integrity across diverse ecosystems.

Methodology: By integrating a top-down remote sensing approach with bottom-up observations of species occurrences, our method achieves a balance between scientific rigor and practical accessibility. Key elements are habitat intactness, connectivity and species presence, which are scored annually and serve as the biophysical underlying for all investments.

Habitat intactness quantifies anthropogenic pressures, such as deforestation, infrastructure or cropland development and is monitored using remote sensing. Connectivity is included to value the ecological contribution in the regional context. The habitat perspective is complemented by in-situ monitoring of selected indicator species as proxies for ecosystem functioning. We utilize camera trap and acoustics surveys, as well as direct species observations. With this, our framework allows for rigorous monitoring that is feasible for large actors but keeps the entry bar low enough to enable the participation of community projects, too.

Platform: Our platform provides the connecting interface between investors and land stewards. It displays monitoring outcomes in an accessible, transparent and auditable form. Moreover, it establishes the market where investors can fund the protection and restoration of high-integrity ecosystems and land-stewards receive financial incentives for maintaining or enhancing the ecological integrity of their properties – hence, enabling conservation as an economically viable from of land-use.

Here, we discuss our work with major conservation actors from small-scale restoration areas to large-scale national parks; all of which generated first successful transactions. With our platform, we are hoping to foster the uptake of biodiversity finance approaches with a particular focus on catalysing private sector investment.

We argue that nature-related risks, particularly exposure to biodiversity loss, constitute a systemic financial risk. To assess these risks, we utilise Earth observation (EO) data for biodiversity risk assessments of financial issuers impacted by nature-related risks. Unlike ratings-based assessments, our approach is forward-looking, objective, non-manipulable, and independent of potentially biased, self-reported disclosures from financial issuers. We introduce the Biodiversity Geospatial Risk Impact Framework (BGRIF), a methodology for assessing geographic-based biodiversity-induced financial systemic risk using satellite imagery. Our method builds on the System of Environmental Economic Accounts Ecosystem Accounting (SEEA EA) framework, proposing indicators to evaluate the condition of ecosystem services in specific geographic locations linked to the activities of financial issuers. By employing the cascade model from ecology, we connect industrial activities with their dependence on ecosystem services using the Exploring Natural Capital Opportunities, Risks, and Exposure (ENCORE) database, estimating biodiversity risk exposure at the industry level. Furthermore, we analyse the interdependencies and systemic risks between industries operating within the European NUTS2 regions, linking them through inter-regional trade flows, which act as mechanisms for transferring biodiversity risks. Using a core-periphery model, we examine how these trade connections shape the distribution of biodiversity risk across European regions. While our primary focus is on assessing biodiversity risk at the regional level, the methodology is adaptable to corporate issuers by aligning risk assessments with the geographic locations of their assets and supply chains.

The EU's Biodiversity Strategy for 2030 is an ambitious initiative aimed at restoring ecosystems and reversing biodiversity loss, in line with the European Green Deal. It seeks to build resilience against threats like climate change, wildfires, and food insecurity. Achieving these objectives requires robust biodiversity and ecosystem data, supported by recent legislation such as the Nature Restoration Regulation and the Marine Ecosystem Protection Action Plan. Globally, the strategy aligns with the UN Convention on Biological Diversity (CBD) and the Kunming-Montreal Global Biodiversity Framework, both emphasizing the importance of accessible data to drive biodiversity action.

The Copernicus Earth Observation program, launched in 2014, provides essential data for environmental monitoring across Europe and globally. Its six services deliver critical datasets for monitoring land and marine environments, supporting biodiversity conservation efforts.

This workshop will explore Copernicus’s contributions to biodiversity and ecosystem monitoring in response to conservation needs. It will present various Copernicus services and products designed to monitor biodiversity and ecosystem health, including climate change impacts, recognized as a primary driver of biodiversity loss.

The workshop will engage EU Member States and global organizations like GEOBON to discuss user needs, identify knowledge gaps, and explore new Earth Observation (EO) opportunities. Participants will address the limitations of satellite data for ecosystem monitoring and propose areas for further research.

Workshop outcomes are expected to enhance user engagement with Copernicus products, expand the service portfolio, and develop biodiversity-focused tools to better meet ecosystem monitoring needs. This aligns Copernicus with EU and global biodiversity goals, providing a robust foundation for ongoing conservation efforts.