We will discuss the utilisation of EO data for biodiversity monitoring in different biomes, using a multitude of metrics and indicators relevant for different reporting and understanding of biodiversity status. Proposed contributions can be from case studies exploring how EO could be used for biodiversity management, understanding ecological processes, and detecting responses and resilience in biodiversity. Speakers will share insights into calibrating EO data with in situ data, handling data to achieve standard quality requirements, uncertainty estimates and propagation, and working across different spatial and temporal scales. The session will also address how EO-derived indicators can support national reporting for the Kunming‑Montréal Global Biodiversity Framework and guide biodiversity management.
The session will be interactive. Following short oral presentations, an open forum will identify unresolved methodological challenges, such as detecting stress responses of different magnitudes or capturing below‑ground processes. By bringing together contributions from remote sensing, ecology, conservation and policy, the session will build a diverse team to ensure that the book provides comprehensive information on using EO data to monitor biodiversity change, address conservation targets, and inform management decisions. We will ensure and encourage diversity in geography, career stage, and discipline.
Long-term harmonized satellite time series - particularly those provided by the Sentinel missions - offer unprecedented opportunities to operationalize Essential Biodiversity Variables (EBVs) using remote sensing (RS). Within the Horizon Europe project OBSGESSION, we developed methods and pipelines to produce RS-enabled biodiversity products as well as upgrading them to EBV(-enabling) products by leveraging multitemporal data from Sentinel and Landsat archives to capture spatial and temporal dynamics at local to regional scales. We showcase this work for the Ecosystem structure EBV class by integrating consistent, high-resolution observations with advanced processing workflows, and demonstrate pathways towards scalable and reproducible EBV products that support biodiversity monitoring and policy frameworks.
Our approach combines data from multiple RS sensors. As such, historical coverage is extended to better trace biodiversity change over several decades and assess long-term ecosystem dynamics, while also anticipating the enhanced capabilities of upcoming missions such as those equipped with hyperspectral imaging (e.g., Hyperfield-1, CHIME, ...). These advances promise to improve the sensitivity of EBVs to subtle ecological changes and to broaden their applicability across diverse ecosystems and biomes.
We illustrate the potential of satellite-based EBVs through case studies that highlight how ecosystem structure indicators can be derived, applied and validated. Particular attention is given to methodological challenges, including sensor harmonization, spatial resolution trade-offs and the need for robust calibration and validation frameworks. We also discuss opportunities for linking satellite-based EBVs with in-situ ecological data, thereby strengthening their relevance for conservation practice, biodiversity management and national reporting obligations. In particular, we explore how Earth Observation (EO)-derived indicators can complement field-based monitoring to support the Kunming-Montréal Global Biodiversity Framework (K-M GBF), EU biodiversity strategies, and other international conventions.
The results underscore the capacity of satellite-based EBVs to bridge the gap between ecological theory, conservation practice, and global reporting needs. By advancing the operationalization of biodiversity indicators at scale, our work contributes to the development of harmonized monitoring systems that can inform conservation targets, address resilience and stress responses, and guide evidence-based management decisions. Ultimately, this research demonstrates how open satellite archives can be leveraged to create actionable biodiversity intelligence, supporting a nature-positive and climate-resilient future.
How to cite: Buchhorn, M., Vanuytrecht, E., Giagnacovo, L., Smets, B., Skidmore, A. K., Huesca Martinez, M., Abdullah, H., and Neinavaz, E.: Operationalizing Essential Biodiversity Variables (EBVs) through Remote Sensing, World Biodiversity Forum 2026, Davos, Switzerland, 14–19 Jun 2026, WBF2026-394, https://doi.org/10.5194/wbf2026-394, 2026.
Climate change is rapidly reshaping ecosystems, shifting species distributions, community composition, and the environmental conditions that sustain biodiversity. Understanding and predicting the impacts of this change depends on our capacity to combine biodiversity observations with environmental drivers across space and time. However, biodiversity data are intrinsically sparse, unevenly distributed, and taxonomically biased: most records are opportunistic, clustered in accessible places and periods, and reflect strong taxonomic and geographic biases. Without careful aggregation, these observations remain difficult to interpret, compare, or use for robust modelling and indicators.
By structuring occurrences into standardized, multidimensional, analysis-ready structures, data cubes transform fragmented observations into interoperable, policy-relevant evidence. This concept of biodiversity data cubes is developed in the Horizon Europe project B3 - Biodiversity Building Blocks for policy (ID No 101059592, b-cubed.eu). Built on GBIF’s open infrastructure, cubes make data provenance and bias transparent, support reproducible workflows, and scale from local monitoring to continental assessments. They can be generated in cloud environments and integrated with remote sensing products, producing digital, temporally explicit representations of ecosystems even when in-situ environmental histories are incomplete.
Cube-based workflows enable both cutting-edge research and policy reporting. The toolsets that are developed within the B3 project, provide a wide range of functionalities to calculate biodiversity indicators that are relevant for policy-makers. Moreover, by providing simulation methods for understanding biases in biodiversity observations, a deeper understanding of the trends in biodiversity observations can be gained. Moreover, cube-derived synthesized products underpin workflows supporting the Kunming–Montreal Global Biodiversity Framework by estimating the rate of invasive alien species establishment (Target 6) or calculating Phylogenetic Diversity (a complementary measure under Goal A and Goal B) with explicit uncertainty and compatibility with other environmental datasets.
Overall, data cubes unlock the full value of sparse biodiversity observations by providing a unifying backbone for monitoring biodiversity, modelling future scenarios, and open, policy-relevant indicators. In doing so, they transform fragmented records into integrated, actionable knowledge.
How to cite: Trekels, M., Breugelmans, L., MacFadyen, S., Estupinan Suarez, L., Beatriz Cortes Lobos, R., Rocchini, D., Rodrigues, A., and Groom, Q.: Turning Opportunistic Records into Evidence: Biodiversity Data Cubes for a Changing Planet, World Biodiversity Forum 2026, Davos, Switzerland, 14–19 Jun 2026, WBF2026-539, https://doi.org/10.5194/wbf2026-539, 2026.
Since the 1970s, conservation efforts have centered on legislation for protected areas, such as the UNESCO Man and the Biosphere Program and the EU Birds and Habitats Directives. The 2022 Kunming-Montreal Global Biodiversity Framework and the 2024 EU Nature Restoration Law establish measurable goals for restoring biodiversity, underscoring the necessity of extensive and effective monitoring strategies. High-quality monitoring is crucial for tracking these efforts.
We propose a time-synchronous, multiscale, remote sensing approach for biodiversity monitoring. The key goal is to provide decision-ready information for essential biodiversity variables (EBVs) derived from analysis-ready data (ARD) stemming from multispectral remote sensing. EBVs represent a quantitative yet theory-driven approach – in contrast to previously accepted data-driven approaches – that helps describe how the state of biodiversity changes over time and space. They can be seen as the connection between basic primary observations and advanced indicators. Priority lists of biodiversity metrics are available that are observable from space. However, standard procedures and protocols for deriving EBVs from various domain-specific field observations and providing these data as machine-readable ARD in digital format are lacking. To provide decision-ready information to stakeholders, ARD multimodal fusion from different domains and scales is essential. Validation of this information is also critical. Unmanned aerial systems (UAS) equipped with multispectral sensors operating in the same spectral range as those on Copernicus/Sentinel-2 provide a flexible, close-range remote sensing option for acquiring very high-resolution data at high frequencies. This helps to satisfy the need to capture data at specific times depending on the phenological state of vegetation, for example.
In the context of the ongoing EU Horizon Europe project BioMonitor4CAP (project number 101081964), we analyzed and compared selected EBVs derived from close-range and multispectral remote sensing data from three spatial scales: Copernicus/Sentinel-2 (10 m GSD), PlanetScope/SuperDove (3 m GSD), and UAS (5 cm GSD). We then compared these EBVs to in situ data, such as vegetation species distribution, acoustic monitoring, and eDNA soil sampling. In our contribution, we critically reflect on this interdisciplinary, holistic approach and discuss its advantages and challenges for multimodal data integration and analysis.
How to cite: Paulus, G., Ruess, S., Dalton, D. T., Kubanda, D., Anders, K.-H., and Scherling, U.: Time-synchronous, multiscale remote sensing and multimodal data fusion for biodiversity monitoring, World Biodiversity Forum 2026, Davos, Switzerland, 14–19 Jun 2026, WBF2026-806, https://doi.org/10.5194/wbf2026-806, 2026.
Near-term ecological forecasting offers a powerful early-warning system for ecological change, enabling proactive and adaptive management. Yet the ability to generate accurate, real-time forecasts remains limited. Although ecological monitoring and retrospective analyses have advanced rapidly, forecasting is still constrained by the scarcity of biodiversity datasets that provide frequent and standardized observations from the same locations.
Global biodiversity aggregators—such as the Global Biodiversity Information Facility and the Ocean Biodiversity Information System—and their sources provide invaluable broad-scale information, but their records are typically opportunistic and infrequently revisited, making them better suited to retrospective analyses than to near-term forecasting. Closing this gap requires collaboration with local data providers (e.g., national parks, protected areas, long-term monitoring networks) and the development of coordinated, open-access biodiversity monitoring infrastructures across larger spatial scales. Initiatives such as the Global Ecosystem Research Infrastructure, the Group on Earth Observations Biodiversity Observation Network and BioTime represent important but still underutilized foundations.
Earth observation (EO) data can help bridge several of these limitations by offering spatially synchronized measurements, high temporal resolution, and near-real-time environmental information. However, EO alone cannot generate ecological predictions. The most promising path forward lies in integrating frequently updated biodiversity observations with real-time EO indicators to build automated, transferable, and scalable forecasting pipelines. Cloud-based EO platforms (e.g., Google Earth Engine, WEkEO, CREODIAS) enable reproducible environmental data processing, while modern automation tools (e.g., GitHub Actions, Cron jobs) make continuous model updating feasible. Interactive, user-centred dashboards then provide accessible pathways for communicating forecasts.
Despite progress, challenges persist: biodiversity monitoring remains spatially uneven, temporal resolution is often insufficient, and forecasting systems lack standardized validation frameworks, robust uncertainty communication, and sustained software engineering support. Overcoming these barriers requires interoperable infrastructures that integrate biodiversity monitoring, EO processing, automated forecasting, and forecast dissemination.
Building such systems—grounded in INSPIRE and FAIR data principles—is essential for achieving the near-term ecological forecasting capacity needed to meet global policy commitments, including the Kunming–Montreal Global Biodiversity Framework and the Sustainable Development Goals. Strengthening the connections among local biodiversity monitoring, EO-derived indicators, and automated forecasting pipelines will substantially enhance our ability to anticipate ecological change and deliver timely, evidence-based guidance for conservation and resilience planning.
How to cite: Singh, P., Bektas, B., Evers, S., Fenollosa, E., Koren, G., Moorthy Parvathi, S. K., Pang, S., Paniw, M., Robert, D., Sialelli, G., Slingsby, J., Thornley, R., Underwood, E. L., Wegner, J. D., and Wu, W.: Opportunities and challenges for near-term ecological forecasting with biodiversity and EO data, World Biodiversity Forum 2026, Davos, Switzerland, 14–19 Jun 2026, WBF2026-672, https://doi.org/10.5194/wbf2026-672, 2026.
Genetic diversity within and among populations is essential for species persistence, yet its assessment across many species, at national and regional scales, remains challenging. Measuring genetic diversity with established DNA sequence-based and in situ measurements is time-consuming, expensive, and for some areas and species infeasible due to geographical or political reasons. Thus, conservationists, ecosystem managers, and Parties to the Convention on Biological Diversity (CBD) still require accessible tools for reliable and efficient monitoring at the multiple scales relevant for policy and decision-making.
We propose a new approach to support genetic diversity assessments with the available resources from space and describe how Earth Observation (EO) makes essential contributions to enable, accelerate, and improve genetic diversity monitoring. We introduce a stepwise workflow for integrating EO into existing genetic diversity monitoring strategies. Our key contribution is to make EO-based information - such as satellite data and higher-level products like Global Forest Watch or land cover data - accessible to support calculation of the genetic diversity indicators for the GBF monitoring framework and to inform management and monitoring decisions. The approach is based on the observation of Earth surface processes visible from space, although genetic diversity per se cannot be “seen” from space. Changes in land cover and land use or habitat condition can be used to estimate the health, size and suitability of a habitat for a certain population and the evolution over time (i.e., abrupt or long-term changes). By combining expert knowledge - such as species occurrence, preferred habitats, and species density - with land cover data, we can predict a population's development when analysing the evolution of their suitable habitats. Furthermore, the forthcoming generation of EO data (e.g., hyperspectral data) has high potential to support more direct assessments of habitat condition and even genetic diversity related traits from space by providing unprecedented spectral detail and temporal resolution.
How to cite: Röösli, C., Schuman, M. C., Mastretta-Yanes, A., Vernesi, C., Millette, K. L., Tobón-Niedfeldt, W., Albergel, C., Leigh, D. M., Hebden, S., Hoban, S. M., Lago, S. G., Schaepman, M. E., Laikre, L., and Asrar, G. R.: Making Earth Observation accessible for genetic diversity monitoring, World Biodiversity Forum 2026, Davos, Switzerland, 14–19 Jun 2026, WBF2026-682, https://doi.org/10.5194/wbf2026-682, 2026.
Advances in remote sensing now address some of the dimensions of the diversity of life on earth, Imagining specrtrometers can quantify plant functional diversity, detect features diagnostic of taxonomic identity and assess landscape patters with far more ecological resolution resolution than legacy sensors. Several high fedlity instruments are now on orbit and more are under development, meaning these data are potentially available anywhere and soon will be available everywhere as time series. Space-based spectroscopy compliments and are synergistic with in-situ data, while requiring in-situ data for calibration and validation of ecosystem-sensitive algorithms. These new space based data contribute to change detection, habitat analysis, ecosystem function and patterns of functional diversity. Imaging spectroscopy from space samples canopies, while in-situ date sample species and this disconnect is not only a methological challlenge, it shapes science and applications for which the image data are useful. Current global data sample many of the world's ecosystems and calibration data are available spanning the climatic and much of the phylogenetic variation for overstory vegetation. We plot extant field studies linked to remote sensing in climate and taxonomic space to show coverage and gaps in information for synergistic and calibration activity. Field data now available, and reasonably consistent span eco-climatic space, using the Olsen mapping approach, and cover most of the branches on the tree of life for vegetation in high, mid-and low-latitude ecosystems. A recent project in Panama adds coverage of tropical forests and a large number of families not previously sampled in conjunction with spectral data from air and space. We will discuss scaling challenges from the cm scale to the tens of meters sampled by space-borne imagers and the important role of aircraft or done intermediates. We will present results from a global survey of tropical ecosystems to demonstrate the potential of these new data through variation in trait space, scaling patterns and environmental variation and contrast these with upscaled in -situ approaches.
How to cite: schimel, D., Baresch, A., Czyz, E., and Townsend, P.: Global functional diversity from space: progress and challenges, World Biodiversity Forum 2026, Davos, Switzerland, 14–19 Jun 2026, WBF2026-882, https://doi.org/10.5194/wbf2026-882, 2026.
The rapid expansion of imaging spectroscopy missions has opened new opportunities for remote sensing based biodiversity assessment. However, the intrinsic trade-off between spatial resolution, spectral resolution, and signal-to-noise ratio necessitates synergistic approaches that combine multispectral and hyperspectral data in order to preserve both spatial detail and rich spectral information. Many biodiversity indices require the estimation of variation in a moving window manner, where the resulting metric is an aggregation of several (hundreds of) pixels, thus often the resulting biodiversity maps are an order of magnitude lower in resolution compared to the input dataset.
Current spaceborne imaging spectroscopy missions such as EnMAP, PRISMA, and the forthcoming CHIME provide imagery at 30m resolution, yet data fusion techniques that integrate these products with higher resolution multispectral sensors like Sentinel-2 (10 m) offer a pathway to enhance spatial detail while retaining the spectral properties of an image. These techniques can help us create biodiversity products that have high spatial resolution and at the same time capture spectral variation in a way not possible with existing multispectral sensors. Here, we present a case study showing the application of a fusion method based on spectral unmixing to derive 10m EnMAP-like product over the Bavarian Forest National Park in Germany. Using the fused products, we compute several functional diversity metrics - functional richness, divergence, and evenness - and compare outputs derived from multispectral data, hyperspectral data, and fused datasets. Leveraging the continuous coverage of the study area by EnMAP, we are able to span our analysis over four years (2022-2025) allowing us to account for uncertainties on interannual scale.
We further emphasize the critical role of rigorous data harmonization - including advanced cloud and cloud shadow masking, precise co-registration, and image alignment - which extends beyond standard Level-2A processing and is essential for the effective integration of multisensor data in biodiversity applications.
How to cite: Mileva, N.: Measuring biodiversity across spatial and spectral scales, World Biodiversity Forum 2026, Davos, Switzerland, 14–19 Jun 2026, WBF2026-642, https://doi.org/10.5194/wbf2026-642, 2026.
Central European forests are experiencing excess tree mortality since multiple years due to consecutive drought events. Increasing disturbance frequency and intensity have been reported leading to more planned and unplanned changes in forest structure. Novel forest management techniques are required to maintain future forest health by promoting biodiversity and multifunctionality through improved resilience towards disturbance. Enhancing the structural heterogeneity of forests has been identified as a key management technique to benefit biodiversity. We study experimental silvicultural treatments with increased variety of light conditions (selective thinning and gap felling) and deadwood features (downed deadwood, standing deadwood, habitat trees) that have been implemented in Central European broad-leaved forests to enhance forest structural heterogeneity. This unique large-scale forest manipulation experiment (234 patches in six regions of German forests) in the context of the interdisciplinary BETA-FOR project aims to provide novel insights on forest structure-biodiversity relationships with a direct context to forest management practices. In the present study, multi-source remote sensing analyses comprising in-situ (mobile and terrestrial laser scanning) and spaceborne data (Sentinel-1, Sentinel-2 time-series) were conducted to investigate enhanced forest structural heterogeneity in experimental silvicultural treatments. In addition, wall-to-wall forest structure data derived from a machine-learning modeling workflow combining GEDI, Sentinel-1 and Sentinel-2 data was considered for analyses. We found strong correlations (greater than 0.7) among in-situ and spaceborne data on forest structure. This finding demonstrates the potential of spatio-temporal continuous forest structure indicators derived from spaceborne sensors to complement local measurements on forest structure from in-situ platforms. In addition, multi-taxa biodiversity data (taxonomic diversity of bats, birds, gastropods, hoverflies, insects, moths, spiders, tree species) was analyzed to study forest structure-biodiversity relationships. Moderate correlations (greater than 0.4) for taxonomic diversity of birds, gastropods, hoverflies, insects, and tree species to spaceborne indicators of forest structure were identified. Our findings demonstrate the potential of multi-source remote sensing to monitor forest structure and biodiversity. Therefore, our study confirms the applicability of multi-sensor spaceborne forest structure indicators to monitor forest structure dynamics through novel forest management practices enhancing structural heterogeneity.
How to cite: Kacic, P., Köstler-Albert, L., Kümmet, S., Massó Estaje, C., Pierick, K., Rothacher, J., Stör, J.-L., Wild, C., Ammer, C., Claßen, A., Feldhaar, H., Müller, J., Steffan-Dewenter, I., and Kuenzer, C.: Experimental enhancement of forest structural heterogeneity can promote biodiversity in managed Central European forests - A remote sensing perspective, World Biodiversity Forum 2026, Davos, Switzerland, 14–19 Jun 2026, WBF2026-124, https://doi.org/10.5194/wbf2026-124, 2026.
Global commitments under the Kunming-Montreal Global Biodiversity Framework demand scalable, robust methods for monitoring ecosystem restoration. Remote sensing (RS) technologies offer unprecedented spatial and temporal coverage, yet their capacity to track the complex dynamics of faunal reassembly remains a critical uncertainty. Tropical forest restoration presents a particularly urgent testbed: recovery trajectories vary widely among taxa, traits and landscapes, and reliable metrics must discriminate true ecological recovery from transient colonisation. Relying solely on RS-derived vegetation structure as a proxy for biodiversity risks overestimating ecological recovery if structural gains outpace the return of sensitive animal communities. Accurately calibrating RS signals with on-the-ground biodiversity data is therefore essential for developing effective monitoring frameworks.
We address this challenge through an integrated, multi-sensor monitoring approach across three clusters of large-scale restoration projects in Pará, Brazilian Amazon. We combine multi-scale RS data (LiDAR & RGB) with extensive in-situ data from high-throughput passive sensors. Acoustic monitors and camera traps were deployed at over 160 sampling points in a repeated-measures design (i.e., time-series), benchmarking restoration trajectories against old-growth forest (positive) and degraded pasture (negative) controls. To achieve scalability, we utilise cutting-edge Artificial Intelligence (AI) pipelines (e.g., BirdNET, SpeciesNET) for automated classification of birds and mammals, implementing rigorous validation protocols.
Our analysis evaluates the congruence between remotely sensed structural metrics and ground-based faunal recovery indicators, including community composition, functional diversity, and phylogenetic diversity. Demonstrating the sensitivity of this approach, our findings show that integrated acoustic and camera trap data are sensitive to early ecological changes, detecting measurable shifts in community composition towards reference conditions within the first years of restoration. We explore emerging patterns using hierarchical trajectory models and machine learning (random forest), uncovering how landscape context and RS-derived structural complexity predict multi-taxa recovery rates.
This research uncovers mechanisms of succession in early stages of recovery, while also demonstrating a scalable monitoring system that leverages the convergence of AI, in-situ sensors, and RS. By linking ground-truthed biodiversity data with advanced RS platforms, we provide an essential blueprint for aligning remote sensing technology with the ecological realities of biodiversity recovery and tracking progress towards global targets.
How to cite: Preston-Allen, R. and Banks-Leite, C.: Scaling biodiversity recovery monitoring: linking satellite-derived structure with multi-taxa acoustic and camera-trap data to inform robust indicators for Amazonian forest restoration, World Biodiversity Forum 2026, Davos, Switzerland, 14–19 Jun 2026, WBF2026-927, https://doi.org/10.5194/wbf2026-927, 2026.
Grasslands cover nearly 25% of Earth’s land surface, yet they remain among the most threatened ecosystems despite their ecological, economic, and cultural importance. The transformation of grasslands into other land-cover types and their widespread degradation have resulted in significant biodiversity loss and the erosion of ecosystem functions that underpin human well-being. Grasslands are also unique among the world’s biomes in that their structure, productivity, and biodiversity are maintained not by the absence of disturbance but by the recurrent action of disturbances such as prescribed fire and grazing. Although numerous studies have examined grassland biodiversity, most focus narrowly on a single dimension, such as plant taxonomic diversity, and are often limited to theoretical or small-scale experimental designs. Consequently, these studies frequently fall short of addressing critical knowledge gaps emerging at larger spatio-temporal scales. There is a need for a holistic approach to monitoring grassland biodiversity across scales while considering the role of real-world disturbances. To address these limitations, remote sensing technology has emerged as a viable option for developing a global biodiversity monitoring system. However, we argue that the full potential of remote sensing, particularly for monitoring biodiversity in grassy biomes, remains only partially realized.
We address this gap by leveraging spaceborne imaging spectroscopy (or hyperspectral imaging), which measures reflected light from the Earth's surface in many narrow, contiguous spectral bands, to investigate novel and important topics in grassland ecology. Specifically, we discuss two primary research advances in this talk: (1) using spaceborne imaging spectroscopy to capture both aboveground and belowground biodiversity and (2) integrating spaceborne imaging spectroscopy data with animal movement data to reveal the underlying drivers of grazer resource selection in grassy biomes. These advances are important for enhancing our understanding of grassland biodiversity across trophic levels, spatial scales, and time, including the mechanisms driving its change. Ultimately, our work informs the capability of current and next-generation spaceborne hyperspectral missions, including those from commercial platforms, to meet the requirements of a holistic and operational biodiversity monitoring system for grasslands and similar short-stature environments globally.
How to cite: Gholizadeh, H., Bachelot, B., Gamon, J., McMillan, N., Rakotoarivony, M. N. A., and Wang, R.: Remote sensing for a holistic and multi-scale grassland biodiversity monitoring system, World Biodiversity Forum 2026, Davos, Switzerland, 14–19 Jun 2026, WBF2026-422, https://doi.org/10.5194/wbf2026-422, 2026.
Earth observation (EO) products are increasingly used to derive biodiversity indicators and support national reporting, yet their ability to capture fine-scale ecological change in complex mountain landscapes remains uncertain. We present an ongoing comparison between Landsat-based land cover and habitat maps for Gran Paradiso National Park (Western Italian Alps) and a unique set of resurveys of historical vegetation plots collected 40 years apart.
Our EO analysis suggests that ~88% of grasslands have remained stable in terms of land-cover class over recent decades. However, field resurveys reveal marked changes in species composition and diversity, including the rapid transformation of snow-beds into grasslands. Structural changes, including woody encroachment, is well detected from both survey methods. These asynchronous signals of change raise critical questions about what is actually being measured, e.g. land cover, habitat state, or biodiversity status, when EO products are translated into indicators and impact metrics. Focusing on montane to alpine grasslands, we (i) quantify agreement between land-cover and habitat classes and in situ plot assignments, (ii) explore detectability thresholds for grassland-rock mosaics that are highly interspersed and prone to misclassification at high elevation, and (iii) identify where EO “stability” masks substantial compositional and functional change detected by botanists. We relate these patterns to class hierarchy, thematically aggregated versus detailed legends, and spatial resolution constraints, and discuss the consequences for derived indicators such as habitat extent, integrity and turnover.
By comparing the viewpoints of EO specialists and botanists, this contribution provides a concrete case study of the opportunities and limitations of merging satellite time series with vegetation resurveys. We outline practical recommendations for linking measurements to indicators, including the role of uncertainty analysis, cross-walking between land-cover and habitat typologies, and targeted field validation in areas of low detectability. Our results support the design of robust, scale-aware indicators for the Kunming-Montréal Global Biodiversity Framework and for adaptive management in protected mountain areas.
How to cite: Eusebio Bergò, S., Adamo, M., Siniscalco, C., and Richiardi, C.: How stable are “stable” grasslands? Confronting Landsat-based habitat maps with long-term vegetation resurveys in the Western Alps , World Biodiversity Forum 2026, Davos, Switzerland, 14–19 Jun 2026, WBF2026-502, https://doi.org/10.5194/wbf2026-502, 2026.
Earth observation data are widely used for mapping and assessing ecosystem extent, condition (abiotic, biotic and landscape characteristics), as well as their ecosystem services. However, earth observation data and products integration in biodiversity conservation is limited, especially when considering regional and local (large) scales, and is mainly depended on data availability at scales and/or thematic detail adequate for operational assessments. The Dynamic Habitat Indices are well established remotely sensed indices that have been explored for biodiversity assessments from global to national scales. Several studies have demonstrated the linkages of Dynamic Habitat Indices as indicators of aboveground vegetation productivity with species richness. Yet very few studies have explored their use at the local scale. In this communication, we present the first outcomes of local scale assessments, conducted under the LIFE IP 4 NATURA national project, where the sensitivity of the Dynamic Habitat Indices has been evaluated, utilizing multitemporal Sentinel-2 imagery, to explain species richness patterns at the local scale within Natura 2000 protected area network sites in Greece. Open access, spatial mapping data on habitat types, derived from the national Natura 2000 mapping and monitoring assessments for Special Areas for Conservation, as well as vegetation sampling plots (point data), and avifauna records, derived from recent field surveys, were used for interpreting the Dynamic Habitat Indices outcomes with real life conditions and simultaneously explored the strength of the relationships across different habitat types. The preliminary results indicate the great potential of Dynamic Habitat Indices for supporting biodiversity conservation and management at the local (large) scale. Our findings demonstrate how earth observation data and products can be combined with field data to produce a holistic framework for supporting scientific-based decision and policy making, acting as a crucial scientific tool that will “push” again biodiversity and nature conservation high on the political agenda, from the local to international scale.
How to cite: Kokkoris, I., Mallinis, G., Vatitsi, K., and Dimopoulos, P.: Earth observation data in the service of biodiversity protection: integrating Dynamic Habitat Indices in nature conservation in Greece , World Biodiversity Forum 2026, Davos, Switzerland, 14–19 Jun 2026, WBF2026-902, https://doi.org/10.5194/wbf2026-902, 2026.
Wetlands in North Central Nigeria support fisheries, flood regulation and smallholder agriculture, yet local observations point to a rapid decline in extent and persistence. For many inland wetlands in sub-Saharan Africa there are no long-term, consistent spatial products to quantify habitat change in ways that are meaningful for biodiversity management. Focusing on Ibaji (Kogi State), we present a cloud-based Earth observation framework in Google Earth Engine (GEE), co-designed with the National Space Research and Development Agency, (Nigeria) to reconstruct three decades of wetland dynamics and derive indicators relevant to the Kunming–Montréal Global Biodiversity Framework. The work is developed as a space-based solution addressing a water-challenge within the UNOOSA Space4Water programme, linking EO-based wetland monitoring to ecosystem services and community risk. The method adapts and simplifies a land-cover time-series approach originally developed for protected European mountain landscapes, replacing habitat-level mapping with a stakeholder-derived land cover reference. A manually interpreted training dataset for a single reference year, produced by local experts from Sentinel-2A and very-high spatial resolution imagery, is used to classify that year with a Random Forest model in GEE. The classifier uses multi-season Landsat 4-9 best-available-pixel composites, SRTM topography and TerraClimate variables. We then apply a Z-statistics approach to propagate this information through time. For each other year (1985–2024), multi-season predictor stacks are compared to the reference class signatures and only pixels close to the class-specific multivariate mean are retained as pure and stable training samples. These annually updated training sets drive year-specific Random Forest models that generate consistent land-cover maps with explicit water and wetland classes. These maps serve to derive indicators of wetland extent and trajectories of conversion to other land cover areas. Current work focuses on accuracy assessment with independent very-high resolution validation data, uncertainty estimation and spatially explicit change detection. This Space4Water “space-based solution” will support discussion on co-producing EO-based wetland and biodiversity indicators under sparse in situ data and discontinuous time series.
Disclaimer: The views expressed herein are those of the author(s) and do not necessarily reflect the views of the United Nations.
How to cite: Richiardi, C., Kickinger, N., Steinbach, S., Filipponi, F., Adamo, M., and Awe-Peter, H.: Disappearing wetlands in North Central Nigeria: a Google Earth Engine Z-statistics framework co-designed with local stakeholders, World Biodiversity Forum 2026, Davos, Switzerland, 14–19 Jun 2026, WBF2026-475, https://doi.org/10.5194/wbf2026-475, 2026.
Freshwater ecosystems sustain rich biodiversity and provide vital services, yet they are increasingly strained by human activities and shifting climate patterns. This study focuses on the Chaliyar River in Kerala’s Western Ghats biodiversity hotspot to identify seasonally responsive indicators of ecosystem health by integrating physicochemical, microbial, and biological observations.
Field sampling was conducted across three hydrological seasons of 2024, Pre-monsoon, Monsoon, and Post monsoon, was carried out at seven sites spanning forested headwaters to urban downstream zones. 23 water quality parameters and benthic macroinvertebrate assemblages were analysed to assess temporal and spatial variability. Significant differences (p < 0.05) in alkalinity, hardness, nitrate, COD, BOD, and microbial load highlighted monsoon driven dilution and nutrient enrichment. Benthic macroinvertebrate analysis (26 families, 8 orders; n = 1,267) showed notable variation in %EPT (H = 12.80, p = 0.0016), FBI (H = 8.83, p = 0.012), and functional groups such as scrapers (H = 8.38) and shredders (H = 9.95), confirming their sensitivity to monsoonal disturbance and post monsoon recovery.
In Multivariate analysis, significant seasonal variation (Kruskal–Wallis, p < 0.05) was observed for temperature (H = 9.88), alkalinity (H = 10.21), magnesium hardness (H = 13.44), nitrate (H = 12.88), COD (H = 10.15), and coliforms (H ≈ 11.24). Spearman correlation identified three gradients: ionic–alkalinity (EC–TDS ρ = 0.83), nutrient–organic enrichment (nitrate–phosphate ρ = 0.73), and redox–oxygen (Fe–DO ρ = –0.68). PCA explained 69.3% variance, separating premonsoon (ionic enrichment) and monsoon (organic loading). CCA showed BOD, nitrate, and coliforms controlling tolerant taxa, while DO favoured EPT assemblages (71.3% total inertia). NMDS (stress = 0.12, PERMANOVA p = 0.002) confirmed distinct seasonal clustering, establishing hydrological regulation as the key driver of biotic and abiotic patterns.
By combining in situ biological monitoring with detailed physicochemical assessment, this study establishes a good framework for evaluating biodiversity in tropical rivers. The findings aid in developing standardized, scalable metrics for freshwater biodiversity that align with global biodiversity observation systems. They also point to the potential for linking field-based indicators with new eDNA and remote-sensing methods for thorough ecological monitoring.
How to cite: Arayilakath, S. and Anoop Das, K. S.: Modelling Seasonal Dynamics and Bioindicator Responses in a Tropical Monsoon River: Insights from the Chaliyar River, Western Ghats, India, World Biodiversity Forum 2026, Davos, Switzerland, 14–19 Jun 2026, WBF2026-163, https://doi.org/10.5194/wbf2026-163, 2026.
Despite substantial progress in biodiversity research, assessing freshwater ecosystems at large spatial scales remains limited by sparse in situ observations, inconsistent monitoring approaches, and the high temporal variability of aquatic environments. Freshwater biodiversity is declining faster than for any other ecosystem type, but consistent, spatially detailed tools to monitor these changes and link them to climate drivers are still lacking. Integrating satellite Earth Observation (EO) with ecological knowledge provides a way to address this gap and improve largescale assessments of freshwater ecosystem dynamics.
The CIBER project is a part of the ESA Biodiversity-Climate Studies Activities and addresses these challenges by integrating satellite EO based information with ecological modelling. Building on the ESA Climate Change Initiative (CCI), CIBER uses the Essential Climate Variable (ECV) datasets together with complementary EO products to improve understanding of how climate driven and environmental processes shape freshwater biodiversity. A key part of this project involves developing literature-informed habitat template models for inland fish species across Europe and Central Asia. These templates combine knowledge on environmental tolerances, functional requirements, and species environment relationships, forming a basis for linking ecological information with spatially explicit EO variables.
The complementary information generated in CIBER corresponds to spatially resolved habitat-relevant information from satellite based measurements of lake temperature regimes, trophic state, chlorophyll concentration, transparency, and turbidity. These variables can be mapped consistently across space and time, providing an opportunity to identify ecological gradients and detect shifts that local surveys may not capture. The project also develops model based reconstructions of vertical temperature and oxygen profiles in lakes, which are key factors of fish habitat availability and community organisation.
By combining EO datasets, ecological theory, and modelling tools, CIBER advances the development of scalable indicators for freshwater biodiversity assessment. This integrated framework improves the capacity to evaluate climate related impacts, informs conservation and management strategies, and contributes to a more consistent and data driven understanding of freshwater ecosystem dynamics across large geographic regions.
How to cite: Philipson, P., Borchardt, D., Brockmann, C., Gesing, M., Khaliq, I., Narwani, A., Odermatt, D., Ouattara, B., Rinke, K., Scheliga, A., Scholze, J., Stelzer, K., and Thulin, S.: CIBER - Climate Impacts on Freshwater Biodiversity, Ecosystems and Resources, World Biodiversity Forum 2026, Davos, Switzerland, 14–19 Jun 2026, WBF2026-821, https://doi.org/10.5194/wbf2026-821, 2026.
Coastal zones host some of the planet’s most biodiverse and productive ecosystems, including mangroves, seagrasses, and salt marshes, “blue carbon” habitats that provide essential ecosystem services such as shoreline protection, fisheries support, and substantial carbon sequestration. Yet these ecosystems are under increasing pressure from climate change, sea-level rise, pollution, and habitat degradation. Effective biodiversity monitoring and management therefore require approaches that integrate ecological processes with the complex physical dynamics of coastal environments.
The COASTS (Coastal Observation Advances Leveraging Space Technology Services) project addresses this challenge by combining multi-scale Earth observation (EO) data, advanced modelling, and process-based morphodynamic simulations to map, model, and monitor coastal biodiversity and blue carbon ecosystem dynamics. COASTS brings together European partners to close knowledge gaps through the integration of EO, ecological surveying, and digital analysis tools. Working closely with local stakeholders at three pilot sites in Germany, Jersey/Chausey, and the Maldives, the project co-designs fit-for-purpose solutions tailored to regional management needs.
A central goal of COASTS is the development of a downstream service that leverages Copernicus Marine data and other EO initiatives to provide decision-ready environmental information. This includes spatial-temporal mapping of blue carbon habitats, assessments of ecosystem services, and quantification of coastal resilience functions such as erosion protection. The project further examines how EO-derived indicators can support identify priority areas for restoration and conservation.
A key component of COASTS is its nested hydro-morphodynamic modelling framework, which captures coastal processes across scales relevant to biodiversity and ecosystem functioning. The workflow downscales Copernicus Marine global or regional data to local, high-resolution simulations through a sequence of nested models. Delft3D Flexible Mesh (FM) provides detailed hydrodynamics and wave–flow interactions, incorporating bathymetry, seabed roughness, wind forcing and sediment transport. These outputs drive site-specific XBeach models that resolve nearshore morphodynamics, sediment exchange and storm-driven change at meter-scale resolution. Using satellite-derived bathymetry, seabed classification, ERA5 winds and CMEMS ocean products, this modelling chain reconstructs past coastal behaviour and generates future scenarios relevant for habitat stability, erosion risk and blue carbon ecosystem resilience.
The project will deliver a web-based decision-support tool enabling stakeholders to visualise, analyse, and report on coastal ecosystem dynamics.
How to cite: Reithmeier, M., Ressl, R., Mahapatra, P., Lobelle, D., Delandmeter, P., Dobbelaere, T., Spinewine, B., and Matheen, N.: Integrating Earth Observation and Modelling for Blue Carbon and Coastal Biodiversity Monitoring, World Biodiversity Forum 2026, Davos, Switzerland, 14–19 Jun 2026, WBF2026-838, https://doi.org/10.5194/wbf2026-838, 2026.
Emerging remote sensing technologies are revolutionizing our ability to monitor biodiversity and ecosystem function, particularly across fire-prone and drought-stressed landscapes. Leveraging 12 years of GPS telemetry from over 3,000 individuals across three mammalian trophic groups—herbivorous mule deer (Odocoileus hemionus), omnivorous black bears (Ursus americanus), and carnivorous cougars (Puma concolor)—and integrating satellite-derived vegetation indices, burn severity maps, and habitat suitability models, we quantify how fire and drought drive changes in the spatial distribution, quality, and demographic outcomes of large mammal habitats in the American West.
Our first study explores the role of wildland fires, including both high-severity wildfires and prescribed fires, in shaping large mammal habitat quality and selection. We integrated GPS and remote sensing data to quantify how fire characteristics (extent, severity, age, vegetation recovery) influence resource use, showing substantial habitat overlap with burned landscapes. Herbivores and omnivores showed improved or neutral habitat quality in low-severity or small burns, driven by increased forage and cover, but consistently avoided large, severe wildfires, which diminished habitat quality for up to 30 years post-fire. Machine learning algorithms helped distinguish the nuanced effects of fire at broad scales, revealing pathways to optimize fire management for biodiversity outcomes.
Our second analysis addresses the mounting threat of extreme drought. We examined how annual drought conditions, quantified with high-resolution remote sensing of vegetation productivity and biomass, cause widespread contractions in highly suitable habitats for all three species. Severe droughts reduced suitable habitat by 10–18%, with the greatest impacts observed for upper trophic levels. Critically, we linked broad-scale predictions to demographic shifts, finding that mule deer fawn recruitment dropped by over 34% during severe drought years. These findings underscore the necessity of integrating multi-source remote sensing products, ecological theory, and in-situ fitness data to scale biodiversity monitoring from individual movement to population viability, thereby informing adaptive management under the Kunming-Montreal Biodiversity Framework and SDG targets.
Our work demonstrates that next-generation Earth observation combined with advanced ecological modeling is essential to anticipate, monitor, and mitigate the synergistic impacts of fire and drought on wildlife and ecosystem resilience at landscape scales.
How to cite: Carter, N., Stoner, D., Sexton, J., Ditmer, M., Leclerc, M., Mills, K., and Wang, P.: Remote Sensing, Fire, and Drought: Scaling Biodiversity Insights for Large Mammals in the American West, World Biodiversity Forum 2026, Davos, Switzerland, 14–19 Jun 2026, WBF2026-520, https://doi.org/10.5194/wbf2026-520, 2026.
Net primary production (NPP) is a key measure of ecosystem functioning and an important Essential Biodiversity Variable (EBV) class, reflecting the biomass produced by plants after accounting for autotrophic respiration. It serves as a vital indicator of carbon balance, ecosystem resilience, and overall forest health. Climate change, particularly the growing frequency and intensity of heatwaves, strongly influences NPP by altering temperature, precipitation, and soil moisture patterns. Understanding these impacts is critical for predicting ecosystem responses to extreme events and guiding sustainable forest management.
Mediterranean forests are especially significant due to their high biodiversity, unique species composition, carbon sequestration capacity, soil protection, and provision of essential ecosystem services, including climate regulation, water retention, and habitat for numerous species. Although these forests are naturally adapted to seasonal droughts, they remain vulnerable to compound climate extremes, such as simultaneous heat and dry conditions. This makes them ideal systems for studying the interactions between climate stressors and forest productivity, as well as the resilience conferred by functional and species diversity.
This study aims to quantify the effects of heatwaves on NPP in European Mediterranean forests during 2016–2025. Satellite-derived land surface temperature is used as an Essential Climate Variable (ECV) to capture heat extremes, while the Standardized Precipitation-Evapotranspiration Index accounts for concurrent drought conditions. NPP will be estimated using a light-use efficiency model that integrates remote sensing and meteorological data, and validated against Integrated Carbon Observation System (ICOS) flux tower measurements.
By identifying heatwave hotspots and evaluating their impacts on forest productivity, this study leverages EBV and ECV indicators to enhance understanding of ecosystem vulnerability and resilience. It provides a framework for monitoring Mediterranean forest responses to climate extremes, informs conservation and management strategies, and supports the sustainable maintenance of biodiversity, carbon storage, and other critical ecosystem services. These insights are essential for preparing Mediterranean forests for future climate variability and for promoting forest management practices that maximize ecosystem stability and resilience in the face of increasing environmental stressors.
How to cite: Neinavaz, E., Farzana, R., Abdullah, H., and Nyktas, P.: Assessing Heatwave Impacts on Mediterranean Forest NPP Using Satellite LST: Linking Ecosystem Productivity to EBVs and ECVs, World Biodiversity Forum 2026, Davos, Switzerland, 14–19 Jun 2026, WBF2026-406, https://doi.org/10.5194/wbf2026-406, 2026.
Understanding ecosystem functioning is central to assessing how biodiversity responds to accelerating climate change. Remote sensing has greatly advanced this field by tracking vegetation traits linked to primary metabolism, such as photosynthetic activity, nutrient status, and productivity. These metrics are essential for monitoring vegetation dynamics, yet they capture only part of the physiological processes that underpin ecosystem health and resilience. Plant secondary metabolic pathways offer an additional and often overlooked source of information. These compounds mediate defence, stress tolerance, species interactions, and many aspects of ecosystem stability. Because secondary metabolic responses often change rapidly under environmental stress, they can serve as early indicators of physiological decline, well before changes appear in traditional vegetation indices or structural metrics. This makes them valuable for identifying emerging stress conditions in ecosystems already vulnerable or exposed to climate extremes. Despite their ecological importance, secondary metabolic traits remain rarely incorporated into large-scale biodiversity assessments, largely due to the difficulty of detecting them remotely. Their concentrations are relatively low, and the link between leaf chemistry and canopy reflectance becomes increasingly complex with canopy structure and landscape heterogeneity. Recent advances in field spectroscopy, airborne and satellite hyperspectral systems, and high-resolution multispectral time series now offer new opportunities to explore these biochemical dimensions of vegetation function. Combining these observations with ecological understanding can help reveal how secondary metabolic traits vary across environmental gradients, how they respond to climatic extremes such as heatwaves and drought, and how they relate to broader ecosystem processes such as productivity and long-term resilience. Incorporating secondary metabolic information into biodiversity monitoring can greatly expand our ability to detect early stress, quantify ecosystem vulnerability, and understand how vegetation is adjusting to rapid climatic change. As climate pressures intensify, such biochemical indicators may become essential for anticipating ecological responses and supporting more informed conservation and adaptation strategies across ecosystems.
How to cite: Sun, Q., Darvishzadeh, R., Neinavaz, E., and Dou, Y.: Advancing Remote Sensing of Ecosystem Functioning and Health Under Climate Change, World Biodiversity Forum 2026, Davos, Switzerland, 14–19 Jun 2026, WBF2026-508, https://doi.org/10.5194/wbf2026-508, 2026.
Land-use change and the expansion of socio-ecological barrier networks are major drivers of biodiversity loss in agricultural landscapes. Because these processes operate across spatial scales, biodiversity monitoring requires methods capable of integrating indicators across scales. This study aimed to fill scales gaps in biodiversity monitoring using Unmanned Aerial Vehicles (UAVs), which quantitatively provides a pathway to map and model vegetation ecophysiological traits as well as to assess habitat structure, corridors, and barriers, thereby supporting ecosystem integrity and functioning, offering insights into the complex interactions shaping agricultural landscapes.
Developing a methodological framework grounded in landscape ecology principles, this study integrates multiple spatial scales into biodiversity monitoring. As part of this framework, a multi-scale UAV-based approach (RGB and multispectral imagery) was applied to link fine-scale vegetation dynamics with landscape-level biodiversity patterns in an extensively managed farmland landscape in the East Havel region (Brandenburg, Germany). UAV imagery was processed to derive ecological indicators including vegetation indices (NDVI, NDSI), canopy height model (CHM), Shannon diversity index (SHDI) , patch density, patch connectivity, texture metrics, and habitat maps across nested spatial scales (6m, 50m, and 120m UAV flight altitudes) to assess vegetation structure, habitat heterogeneity, and ecological complexity vary with scale.
UAV-derived metrics revealed clear scale-dependent patterns. At 6m altitude, individual vegetation species were identified, while at 50m and 120m, structural metrics were quantified to link habitat features to vegetation composition. Fine-scale analyses captured small elements, such as flowering patches and micro-topography, important for insects, whereas landscape-scale metrics highlighted fragmentation and semi-natural elements as biodiversity reservoirs. Some indicators, like CHM and NDVI variability, were consistent across scales, while others, such as patch density and texture entropy, shifted with resolution.
These multi-scale relationships demonstrate how UAV-based remote sensing can fill critical gaps in understanding vegetation and wildlife interactions. Overall, this multi-scale approach bridges theoretical principles of landscape ecology with practical biodiversity assessment. By uncovering structural linkages between vegetation traits, habitat configuration, and scale-dependent ecological patterns, the framework provides a transferable tool for landscape ecologists and conservation planners. Our methodological framework can also help to identify thresholds between land-sharing and land-sparing approaches in the landscape planning.
How to cite: Darvishi, A. and Schirrmann, M.: A Multi-Resolution Approach to Bridge Scales in Biodiversity Monitoring using Unmanned Aerial Vehicles (UAVs), World Biodiversity Forum 2026, Davos, Switzerland, 14–19 Jun 2026, WBF2026-458, https://doi.org/10.5194/wbf2026-458, 2026.
Understanding vegetation phenology at landscape to continental scales is essential for tracking ecosystem responses to climate change, improving biodiversity assessments, and strengthening land-surface models. While satellite remote sensing provides broad spatial coverage, it often lacks the temporal and structural detail necessary to resolve fine-scale phenological dynamics. In particular, satellite phenology products struggle to resolve species-specific responses to climatic stress. In the past decade, PhenoCam networks have been developed to address such blind spots of satellite remote sensing. Here, we build on these efforts and introduce a new Switzerland-scale phenocam dataset covering both individual and canopy-level phenological signals. We curate a large dataset of high-frequency, high-resolution webcam imagery and use it to monitor expert-annotated regions of interest (ROI) of both individual trees with known species and tree canopies.
The first iteration of our dataset is based on webcam imagery captured in 32 sites across Switzerland and spanning the different elevational ranges of its territory. The first images date back to 2010 for some sites and cover the entire period up to the present day. On average, each site has a history of 6 years, amounting to a total of 175 site-years. The dataset currently includes over 5,000 tree-years of observations for individual trees, providing a view of changes in phenology at the species level. For approximately 1,000 trees, expert-annotated phenophase dates are available, offering a unique benchmark for calibration and validation. Additionally, for all individuals and canopy-level ROIs, we employed automated greenness-based methods to extract green-up and green-down dates and study species-specific trends along Switzerland’s different climatic zones, strongly shaped by elevational belts. We also quantify the agreement between our phenological transition dates and those obtained from common satellite remote sensing platforms.
By making high-frequency phenological observations accessible at the country scale, this dataset provides an unprecedented resource for phenology monitoring, and supports the development of methods for ecological forecasting, and climate-change research. We invite the community to utilize this dataset to advance understanding of vegetation dynamics in a rapidly changing world.
How to cite: Sainte Fare Garnot, V., Lever, J., Vitasse, Y., Gessler, A., and Wegner, J. D.: The SwissPhenoCam dataset: Country-scale phenology monitoring at the individual tree level , World Biodiversity Forum 2026, Davos, Switzerland, 14–19 Jun 2026, WBF2026-434, https://doi.org/10.5194/wbf2026-434, 2026.
Accelerating climate change and biodiversity loss are driving rapid ecosystem transformations, making it increasingly important to track resilience – the capacity of ecosystems to resist and recover from disturbance. Traditional monitoring approaches often focus on gradual changes in biodiversity and ecosystem properties but overlook the dynamic processes that provide early warning signals of resilience loss and critical transitions (e.g., desertification). Advances in Earth observation (EO) now enable spatially comprehensive and temporally consistent assessments of ecosystem resilience. To effectively integrate EO data into monitoring frameworks, we need to reconcile existing approaches to measuring resilience, understand which aspects of resilience different EO-derived indicators capture, and evaluate how these indicators relate within and across ecosystem types. Here, we synthesize multiple EO-derived indicators that capture complementary aspects of resilience – resistance and recovery. Our findings highlight the multidimensional nature of resilience and show how the relationship between these indicators varies across biomes, underscoring differences in underlying ecological processes. Together, these metrics provide a more robust framework to predict emergent patterns in resilience across large spatial gradients. Using this approach, we find distinct differences in resilience across the bioclimatic envelopes of the world’s functional forest biomes. Notably, there are signs of lower resilience at the hot and dry edges of sparse woodlands in warm temperate, Mediterranean and dry tropical regions. In contrast, cold temperate and tropical moist forests generally show reduced resilience at both the hot and cold edges of their bioclimatic envelope. Supporting classic metabolic theory and emergent patterns from research on boreal vegetation trends, we find signals of increased resilience on the intermediate-to-cold edges of the boreal forests. These findings demonstrate the value of integrating multiple EO-based resilience indicators to capture the complexity of resilience and underscore the need for biome-specific interpretation. Our framework advances our understanding of global resilience patterns, complements biodiversity monitoring, and supports evidence-based strategies to anticipate and mitigate the risks of abrupt ecosystem change.
How to cite: Runge, K., Berdugo, M., Jimenez, Y. G., Tucker, M., Lauber, T., Fournier de Lauriere, C., Guirado, E., Crowther, T. W., and Bialic-Murphy, L.: Diminished resilience toward bioclimatic limits of the world’s forests revealed by Earth observation, World Biodiversity Forum 2026, Davos, Switzerland, 14–19 Jun 2026, WBF2026-31, https://doi.org/10.5194/wbf2026-31, 2026.
