Natural forest expansion is constrained by multiple factors acting at different spatial and temporal scales. Broad-scale latitudinal and elevational gradients of primary vegetation successions are mostly attributed to global warming, whereas the abandonment of agricultural activities is one of the most important social-economic facilitators of local secondary successions. The interaction between climatic and human-related drivers make the analysis of forest expansion particularly multifaceted and complex. Although many local studies on this topic exist, most of the analyses have either a short temporal extent or a limited spatial scale. Historical geographical data such as vegetation, cadastral, landscape planning maps, and aerial photographs are geographical datasets that have been used globally to quantify changes and develop forecasting models. Nevertheless, standardization and homogenization are required. Therefore, in this session, we aim to bring together experts approaching the topic from different perspectives and focusing on various biomes worldwide. Particularly, we seek for spatially-explicit diachronic approaches—combining modern remote sensing with historical cartography and aerial photos, permanent plots, ecological modeling, and socioeconomic analysis— to track where, why and with what consequences forests expand.
Orals: Mon, 4 May, 08:30–10:15 | Room 2.23
Farmland abandonment is among the most widespread land-use change processes in Europe and is often assumed to promote natural afforestation through vegetation succession, particularly in temperate regions. However, empirical evidence quantifying the extent and controls of afforestation on abandoned farmlands remains limited. Using Lithuania as a case study of post-Soviet land-use transition, satellite Landsat, Sentinel-2 time-series were combined with spatial analysis to quantify farmland abandonment between 1990 and 2000 and subsequent land-cover trajectories through 2025. Further, it assessed the extent to which abandoned farmland reverted to forest and identified key biophysical and socio-economic determinants shaping these dynamics. The results indicated that approximately 25% of farmland was abandoned during the early post-Soviet period, yet only a portion of this land remained abandoned by 2025. Among the remaining abandoned areas, only a small fraction exhibited spectral convergence with adjacent natural forest, suggesting limited progression toward mature forest states. Reversion to forest was strongly conditioned by accessibility, socio-economic factors, seed dispersal potential, and biophysical constraints. In contrast, recultivation of abandoned farmland was promoted by favorable cultivation conditions, agricultural subsidies, and land-use interventions such as the designation of hunting grounds. Overall, the findings challenge the assumption of widespread, spontaneous forest recovery on abandoned farmland and demonstrate the value of long-term Earth observation data for disentangling land-use trajectories and their controlling factors, as well as the formation of novel scapes, neither representing agriculture nor newly established forests. The proposed analysis framework is expandable and transferable to other regions experiencing land-use transitions, thus helping better quantify natural and socio-economic potentials.
How to cite: Prishchepov, A. V.: Revisiting the progress of natural afforestation on abandoned farmlands using long-term satellite time observations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12047, https://doi.org/10.5194/egusphere-egu26-12047, 2026.
Alpine forest cover has increased in recent decades due to socioeconomic factors and climate change. This study focuses on the evolution of the ecosystem services offered by the forest in the Paneveggio region (Trentino, Central Eastern Alps, Italy) since 1954. Although the expansion of the forests could be viewed as a renaturalization, this trend also affected habitats diversity and landscape after the 1990s. The Vaia windstorm that hit the area in 2018 caused significant damage, underscoring the necessity of accurate ecosystem service quantification. In accordance with the Common International Classification for Ecosystem Services (CICES), this work aims to quantify some of the ecosystem services that the forest provides over time using a spatio-temporal approach, recognizing the complexities of ecological, social, and economic drivers shaping contemporary landscapes. We performed GIS spatial analysis using GRASS GIS, QGIS and a set of maps of forest coverage that were obtained from historical maps and aerial photos. Information on provisioning services, including wildlife trends, timber, and cattle numbers, was gathered from a variety of literature sources. Carbon stock and erosion protection were computed, the latter using the Revised Universal Soil Loss Equation (RUSLE). The sales of postcards showing the same landscape over time were used to gauge aesthetic preferences. Between the 1950s and 2018, the area covered by forests increased steadily, bringing with it all the benefits directly associated with trees, such as protection from erosion and carbon stock. However, biodiversity showed a more complicated pattern, with losses in open areas benefiting species that live in forests while harming priority habitats. Additionally, there was a fluctuating pattern in aesthetic preferences, indicating a preference for a well-balanced landscape of trees and grass. After 2018, some services were reduced, including protection and aesthetics, because of fallen and standing dead trees and the building of avalanche defence systems to cope with deforestation. To raise awareness of climate change, some of the fallen areas were turned into outdoor laboratories and opportunities for the development of multi-species forests, even attracting disaster tourism. Forest landscapes are constantly changing, requiring adaptive management strategies that address climate change while sustaining biodiversity, ecosystem services, and human value. Gathering information from several data sources is advocated by many authors as most appropriate to develop an evidence-based management strategy tailored to local situation. Developing long-term solutions to deal with a changing climate and society can be aided by an understanding of historical ecosystem dynamics. Our study contributes to a multidisciplinary understanding of past changes in Alpine environments and highlights the importance of ecosystem connectivity and restoration across spatial and temporal scales. This integrated perspective supports innovative mountain landscape planning and promotes biodiversity conservation amid growing pressures from anthropization and climate change. Forest management should integrate climate challenges into a broader landscape vision that balances ecological sustainability, forest production, and human–wildlife coexistence. This approach promotes resilience through mountain landscape diversification, supports ecosystem services, and provides a clear narrative of landscape change for the public and policymakers, encouraging inclusive, historically informed planning.
How to cite: Ciolli, M., Zatelli, P., Grilli, G., and Tattoni, C.: Dynamics of ecosystem services in response to land use and climate change: a case study in the eastern Italian Alps , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20456, https://doi.org/10.5194/egusphere-egu26-20456, 2026.
Alpine zones in northeastern North America (NNA) are rare and support diverse ecological communities distinct from lower elevation forests. Global change drivers can alter the composition and functioning of these plant communities. The forest-alpine ecotone (treeline) is also influenced by a myriad of environmental drivers and is anticipated to encroach into alpine systems. We sought to determine the extent and drivers of treeline advance in NNA using multiple methodologies, including aerial and satellite-based imagery and long-term in-situ observation. For our approach, we (1) compared current and historical high-resolution aerial imagery of two ranges to quantify the advance of treeline over the last four decades. Vegetation delineation of aerial images were coupled with in-situ surveys to ground-truth treeline classifications. We used mixed effects models to examine the importance of both topographic and climatic variables on treeline advance. (2) Greening trends (NDVI) were modeled at 35 alpine and treeline ecotone sites in the Adirondacks (New York), New England, and Quebec’s Gaspé Peninsula using Landsat 5-8 imagery (1984-2024). (3) Permanent alpine vegetation point-intercept transects (with associated environmental data) in the Adirondacks and White Mountains (New Hampshire) were periodically sampled to quantify changes in alpine species composition and monitored for increased tree abundance. NNA treelines have shifted upslope on average by 3 m/decade since the 1970’s. Diffuse treelines (low tree density) displayed significantly greater upslope shifts (5 m/decade) compared to denser treelines, suggesting that both warming and demography are important correlates of treeline shifts. Topographical features (slope, aspect) as well as climate (accumulated growing degree days, AGDD) explained significant variation in the magnitude of treeline advance (R2 = 0.32). Most sites (88% total alpine area) have experienced significant greening via annual NDVImax. Greening occurs in the alpine zone interior in addition to some ecotone positions, potentially revealing both increased alpine vegetation productivity and tree establishment. Greening trends are moderated by landscape position and vary among plant community type (herbaceous vs. shrub-dominated). Non-metric multidimensional scaling (NMDS) illustrates an increase in shrub and tree species abundance over the past four decades, with a decrease in arctic specialist species abundance. These changes were highly positively correlated with site-level temperature and negatively correlated with anthropogenic atmospheric nitrogen (N) loading. Tree encroachment (and shrubbification) of NNA alpine challenges the future character and functioning of these rare systems. Development of remote sensing-based monitoring programs for NNA alpine will provide methodologically-consistent regional-scale information on how such ecosystems respond to environmental change, better informing stewardship and management activities.
How to cite: Tourville, J. and Chipman, J.: Tree encroachment in alpine environments of Northeastern North America: Evidence from multiple approaches, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4545, https://doi.org/10.5194/egusphere-egu26-4545, 2026.
Forests in south-western Europe are expanding spontaneously as a result of the abandonment of traditional land-uses. Expanding forests can recover aboveground biodiversity, biomass and structure within a few decades. Yet, the impacts of land-use belowground might not recover at the same pace, compromising long-term nutrient cycling, carbon (C) sequestration, resistance and resilience to drought and other extreme climatic events. Such ecosystem functions rely on the activity of soil dwelling organisms and in mid-latitude forests, ectomycorrhizal (ECM) fungi are arguably the most crucial player for maintaining nutrient, carbon and water cycling. Still, the recovery of ECM communities during forest expansion and its link to long term C storage remain underexplored. Here, we assess how land-use history changes the diversity and structure of ECM communities and how these changes have altered the C sink capacity. We selected forests that established after the second half of the XXth century, following the first massive rural exodus in Spain, and pre-existing forests. Our study encompasses three forest types with contrasting climatic conditions and functional attributes dominated by: Pinus uncinata (pre-alpine, evergreen needleleaved), Fagus sylvatica (temperate, deciduous broadleaved) and Quercus ilex (Mediterranean, evergreen broadleaved). In autumn 2025, we collected soil samples in these forests to analyse: (1) ECM fungal community composition and structure, using molecular techniques; (2) C storage and the ratio of labile vs. stable soil C; and (3) age of soil bulk C and of respired C, by measuring Δ14C. Analyses of preliminary data showed that the C:N ratio was higher in mature than in recently established forests, regardless of the dominant species, but the trends for total C and N content varied among forest types: total N content was higher in recently established F. sylvatica forests, and total C was higher in mature Q. ilex forest, whereas in P. uncinate forest, we did not find significant differences for total C or N. We suggest that a combination of differences in land-use history, and functional attributes could underlie these results . In turn, we expect that soil nutrient ratios will underlie functional soil attributes and in future analyses, we expect to find higher relative abundance of ECM types with long hyphae and longer C retention time a s the C:N ratio increases.
How to cite: Gimeno, T. E., Tyyskä, H., Vilà-Cabrera, A., Selwyn, M., Espelta, J. M., Fernández-Cacelo, P., and Muñoz, E.: Assessing the impact of land-use history on soil fungal diversity and carbon sequestration across Mediterranean and temperate forests, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12203, https://doi.org/10.5194/egusphere-egu26-12203, 2026.
Globally, alpine treelines are undergoing a spatially heterogeneous and frequently inconsistent and unpredicted altitudinal range expansion in response to accelerated climate warming. Previous findings in the Himalaya region reveal a significant decoupling between climatic control and treeline shift, suggesting that non-climatic factors are hindering the expected upward migration. We implemented a hierarchical sampling approach across krummholz and non-krummholz transects in Rolwalling and Langtang region extending from 3900 to 4300 m altitude. Soil was analysed for SOC, TN, microbial biomass to evaluate nutrient limitations and microbial stoichiometry. Allelochemical profiling was conducted for the analysis of secondary metabolites in leaf and root tissues of Rhododendron spp. Dendroecological climate sensitivity analysis was done through tree-rings study for drought response of the krummholz-forming R. campanulatum against the subalpine treeline species Abies spectabilis.
Our results reveal that Krummholz soils exhibit significantly higher acidity and elevated allelochemical concentrations profiling such as carboxylic acids, fatty acids, phenolics, and terpenoids as potential inhibitory metabolites in Rhododendron tissues. Krummholz site maintained a significantly higher soil C:N ratio (25:1) and an exceptionally low mean microbial quotient (qMIC = 0.17%), reflecting nitrogen immobilization and stagnant nutrient turnover. The lower dwarf shrub heath zone exhibited the highest mean MBC 1,170.8 µg g⁻¹ soil and MBN 111 µg g⁻¹ soil, while lower krummholz had the highest MBP mean 299.6 µg g⁻¹ soil. Furthermore, dendrochronological analysis showed that A. spectabilis is significantly more sensitive to drought severity than the resilient R. campanulatum. These findings suggest a 'Biotic Lock' mechanism: R. campanulatum not only modifies an edaphic niche through soil interference and nutrient dynamics but also exhibits greater physiological relaxation under climatic stress. This study identifies the krummholz forest as a critical biotic frontier that is inhibiting subalpine forest advance through complex edaphic interactions, allelopathic constraints, and higher resilience to moisture stress.
How to cite: Adhikari, R., Granacher, R., Kunz, L., Maharjan, A., Parajuli, M., Subedi, C. K., Gall, C., Seitz, S., Chaudhary, R. P., Oelmann, Y., Boehner, J., Schickhoff, U., and Scholten, T.: Krummholz at the Forefront of Treeline in the Himalaya: The Biotic Lock Decoupling Treeline Shift due to Climate Warming, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12186, https://doi.org/10.5194/egusphere-egu26-12186, 2026.
Biodiversity monitoring is important to support decision-making for managing landscapes sustainably and supporting national and international policy targets for nature conservation and restoration, including the Global Biodiversity Framework. While assessing the status, change and drivers of biodiversity remains challenging, we have new opportunities to support biodiversity monitoring from space with a growing suite of Earth observation satellites. Remote sensing is especially well suited to monitor ecosystems in terms of their vegetation structure and forest structural diversity with lidar and radar, as well as vegetation functions, foliar functional diversity and community composition with imaging spectroscopy and multispectral imaging. In this talk, we will provide examples for monitoring forest structural diversity using the spaceborne lidar GEDI. We evaluated and compared structural diversity across contrasting biomes in the Western US and Central Africa, and we found that general biogeographic patterns of higher horizontal structural diversity in areas with higher disturbance, higher topographic variation and lower aridity hold across continents and scales. For monitoring ecosystem functions, we will provide examples for monitoring plant traits and functional diversity using imaging spectroscopy along elevation gradients in California. We will provide insights into the role of trait-trait relationships and trait selection for mapping trait diversity patterns at the landscape scale. We found that diversity patterns vary by the type and number of functional traits included in the analyses, and that the interpretation is context dependent. And for monitoring composition, we will provide examples indicating how well we can distinguish different vegetation types, communities and species with spectroscopy, and how well we predict animal composition and niche space using a remote sensing-based biodiversity data cube, BioCube. With these examples, we will demonstrate new capabilities and avenues for monitoring different aspects of biodiversity change using remote sensing at the landscape scale, and we will provide important context for the interpretation of these results. Remote sensing can provide information about biological communities and habitats, ecosystems and biomes at different spatial scales and time steps that should be integrated with other biodiversity data, models and decision support tools to fully leverage its potential for biodiversity monitoring.
How to cite: Schneider, F. D., Zheng, T., Ferraz, A., Berman, L., Jakobsen, C. D., Revenga, J. C., Wu, Z., Ye, Z., Pavlick, R. P., Townsend, P. A., and Normand, S.: Monitoring Vegetation Structure, Function and Composition with Remote Sensing, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-333, https://doi.org/10.5194/egusphere-egu26-333, 2026.
Even though forest biodiversity is currently at the center of international attention in the context of ecosystem restoration and global conservation strategies, quantifying forest biodiversity across large spatial extents remains a central challenge in ecological monitoring and forest management. The effective management of biodiversity in both protected areas and managed forests has long been constrained by a lack of methodologically consistent, and spatially continuous data. RNew high-resolution remote sensing data—including Vegetation Height Models, phenology time series, and detailed forest area maps—combined with standardized inventories, can bridge existing data gaps and substantially enhance forest ecosystem monitoring and biodiversity management. These base datasets have enabled the generation of new value products such as tree species mapping, standing deadwood, biomass mapping and phenology anomalies opening a new level of forest monitoring. We present a multi-level showcase framework to demonstrate practical applications of these technologies in forest biodiversity and nature conservation: At the individual tree level, we explore the potential for delineating habitat tree priority areas by detecting indicators such as standing deadwood and crown dieback. These features serve as critical proxies for saproxylic insects, fungi, and cavity-nesting birds. While precise individual mapping remains a challenge, identifying these potential habitat backdrops enables a more targeted spatial approach to the conservation of rare or endangered tree species. At the stand level, structural heterogeneity and damages can be indicating biodiversity. A 26-class tree species map enables the assessment of compositional diversity and mixing degrees. Time-series of vegetation indices derived from sensors depict seasonal changes in forest phenology. This enables recognizing forest damages such as windthrow, bark beetle infestations, and even slow progressing tree pests. At the landscape and national levels, we utilize digital terrain models (DTM) and high-resolution vegetation height models to derive geodiversity and forest structure indices and identify micro-habitats. Landscape connectivity is addressed by mapping forest roads to measure fragmentation and planning ecological corridors between protected areas. Despite these opportunities, we address several critical limitations. While remote sensing offers scalability and objectivity, "ground truthing" - such as with the Austrian inventory data - remains an indispensable foundation for model validation. This necessitates a new profile of expertise: professionals who bridge deep ecological knowledge with data science. Only through interdisciplinary cooperation and a careful balance of technological gain versus energy consumption can digital models be meaningfully applied to protect our natural resources.
How to cite: Lapin, K., Schumacher, B., and Hoffmann, J. A.: Applying Remote Sensing for Improved Monitoring and Management of Forest Biodiversity: From Tree-Level Indicators to National Level , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10630, https://doi.org/10.5194/egusphere-egu26-10630, 2026.
Understanding plant functional diversity across scales requires integrating field-based ecology and remote sensing, yet these disciplines differ in how traits are measured, and upscaled. We synthesized three decades of research to evaluate conceptual and methodological convergence between these disciplines. Our results reveal that field-based ecology has undergone longer conceptual development and covers a broader range of traits, while remote sensing has experienced rapid growth driven by technological advances. Despite these differences, both disciplines are increasingly converging on similar concepts. However, major gaps in empirical coverage persist across biomes in both disciplines. While vegetation-dominated ecosystems have been extensively studied, extreme ecosystems remain comparatively undersampled. Trait analyses demonstrate a broad conceptual flexibility in defining "functional traits", yet both disciplines converge on a core set (e.g., plant height, leaf area, and leaf nitrogen content), reflecting their central role in plant strategies and spectral detectability. Remote sensing approaches differ in whether functional diversity is inferred from spatially aggregated mixtures at the pixel or community level or from resolved individual plants. Our synthesis underscores the potential for methodological synergy. Harmonizing trait definitions, scaling assumptions, and computational steps is essential to building a unified, multiscale framework for monitoring functional diversity in the context of global change.
How to cite: Cerda-Paredes, J., Craven, D., and Lopatin, J.: Bridging the gaps between field-based ecology and remote sensing to estimate plant functional diversity: a systematic review, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15525, https://doi.org/10.5194/egusphere-egu26-15525, 2026.
The Spectral Variation Hypothesis (SVH) proposes that spectral heterogeneity derived from remote sensing data can serve as a proxy for biodiversity. While spectral diversity metrics are increasingly applied in ecological studies, their reproducibility remains limited by methodological choices that are rarely evaluated systematically. In particular, the sensitivity of α-spectral diversity to clustering, spatial scale, masking strategies, and spectral input configuration within commonly used workflows such as BiodivMapR is poorly understood.
This study presents a systematic sensitivity analysis of α-spectral diversity derived from Sentinel-2 imagery using the BiodivMapR framework over a selected region in Estonia. The primary objective is to identify cluster size thresholds at which diversity values stabilize and to assess whether this stabilization depends on image extent. K-means clustering was evaluated across cluster sizes of 20, 40, 60, 100, 200, 500, and 1000, combined with image extents of 10, 15, 20, and 30 km. For all configurations, the same number of samples was used to derive clusters, while BiodivMapR’s default random initialization was retained to assess robustness.
The analysis was extended to examine ecosystem masking effects. Forest-only masking represents landscape-level diversity restricted to forest ecosystems, while an inward buffer (~15 m) was applied to exclude edge pixels influenced by roads and non-vegetated surfaces, isolating within-forest spectral heterogeneity. Additional experiments assessed the sensitivity of α-diversity to window size and spectral input choice (bands versus indices).
Results demonstrate that α-spectral diversity is highly sensitive to methodological configuration, with stabilization thresholds varying across spatial extents and masking strategies. Even within forest-only analyses, edge effects significantly influence diversity estimates. These findings highlight that spectral diversity metrics are strongly parameter-dependent and cannot be directly compared across studies without methodological harmonization.
How to cite: Biju, A., Borysenko, O., Virro, H., Féret, J.-B., Pisek, J., and Uuemaa, E.: Systematic Sensitivity Analysis of Spectral Variation Hypothesis Based Spectral Diversity Metrics from Sentinel-2 Using BiodivMapR , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4965, https://doi.org/10.5194/egusphere-egu26-4965, 2026.
Mountain ecosystems play a critical role in global biodiversity conservation, water regulation, and climate change adaptation. However, their pronounced topographic complexity poses major challenges for the large-scale estimation of plant functional traits using remote sensing, limiting our ability to characterize ecosystem functioning and vegetation responses to global warming. Variations in slope, aspect, and elevation strongly affect illumination conditions, viewing geometry, and canopy structure, introducing biases that are often overlooked in trait–reflectance relationships. Vegetation indices and empirical models are widely used to estimate plant traits from optical remote sensing data, yet their performance degrades in complex terrain due to topographic artifacts and limited field calibration data. Alternatively, radiative transfer models (RTMs) provide a physics-based framework for linking spectral reflectance to vegetation biophysical and biochemical properties. Despite their theoretical advantages, most commonly used RTMs assume flat or gently sloping terrain and are therefore poorly suited for mountainous landscapes, potentially compromising trait retrievals in these environments.
In this study, we quantify the influence of terrain complexity on the performance of both empirical and physically based models applied to hyperspectral data for estimating functional leaf traits in Andean forest ecosystems. Field data were collected in more than 120 plots distributed according to a fractal sampling design across strong gradients in elevation, slope, and aspect in the Mapocho River basin (central Chile). For each plot, we measured species abundance, leaf-level functional traits, and topographic variables, and linked these data with airborne hyperspectral reflectance. Our results show that model performance is highly sensitive to terrain conditions. Across traits and modelling approaches, explained variance ranged from near zero to approximately 50%, substantially lower than values typically reported in studies conducted in low-relief landscapes. Trait-specific responses were evident: some functional traits were better explained by spectral reflectance, while others were more strongly associated with topographic variables alone. Residual analyses further revealed systematic terrain-driven biases, indicating that both empirical models and RTMs struggle to disentangle spectral signals related to plant traits from those induced by complex topography.
These findings highlight a strong methodological and geographical bias in current remote sensing approaches for trait estimation, driven by the predominance of studies conducted in flat or gently undulating terrain. Because mountainous regions are essential for biodiversity, ecosystem services, and climate sensitivity, excluding or oversimplifying topographic effects limits the transferability and scalability of trait-based remote sensing models. Our study underscores the urgent need to develop terrain-aware modelling frameworks that explicitly integrate topography into hyperspectral trait estimation to improve ecological inference and support monitoring efforts in complex mountain systems.
How to cite: Lopatin, J.: Too much topography! Effects of topography on the estimation of plant functional traits using hyperspectral data, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15464, https://doi.org/10.5194/egusphere-egu26-15464, 2026.
Posters on site: Mon, 4 May, 14:00–15:45 | Hall X1
Mountain landscapes worldwide are experiencing rapid transformations, driven by land abandonment, climate change, shifting socio-economic paradigms, and evolving disturbance regimes. Despite their ecological importance and their role as climate-sensitive sentinels, no coordinated global framework exists to compile and harmonize long-term land-use change (LUC) data in mountain areas.
To address this urgent gap, we introduce GeoLUCA, the Geodatabase of Land-Use Change in Alpine and mountain environments, on behalf of all the colleagues participating to this effort. GeoLUCA envisions a global, open, and dynamic platform integrating historical aerial imagery, remote sensing products, and ecological and socio-environmental datasets to quantify landscape change across mountain systems.
GeoLUCA has already taken shape as a regional effort within the European Alps, bringing together ca. 20 interdisciplinary partner institutions spanning ecology, geography, environmental informatics, and land-system science. GeoLUCA is currently working on three complementary and parallel areas of research that embody the scope and some of the expected outcomes of the initiative: (i) reconstructing two centuries of forest dynamics in the European Alps, combining land-cover data from multiple sources over the last 200 years, (ii) developing a deep-learning workflow for classifying raw historical aerial images, enabling consistent land-cover mapping across decades and mountain ranges, (iii) analysing habitat change trajectories in the European Alps since the 1950s to evaluate ecological shifts and emerging hotspots.
GeoLUCA is now launching a global data-collection effort to gather original aerial photographs, historical maps, and satellite time-series from mountain regions worldwide. This includes developing standards for metadata and curating raw imageries and associated ecological data sources from contributors across the world. By building the first coordinated database of long-term LUC and land abandonment in mountains, GeoLUCA aims to support global change research, ecosystem modelling, evidence-based conservation, and policy design. We invite researchers to join the initiative and contribute data, expertise, and regional knowledge.
How to cite: Anselmetto, N. and Garbarino, M.: Introducing GeoLUCA: A Global Initiative to Map Land-Use Change and LandAbandonment in Mountain Ecosystems, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1307, https://doi.org/10.5194/egusphere-egu26-1307, 2026.
Mountain areas host several complex ecosystems that are not immune to biodiversity loss. In the European Alps, traditional agro-pastoral activities have shaped semi-natural cultural landscapes with unique ecological features, such as terraced landscapes and wood pastures. The peak in the abandonment of traditional practices in the last century led to intense natural forest expansion on montane slopes. This translated into changes in habitat structures resulting in the homogenisation of landscape features and the loss of open areas. These dynamics have rarely been investigated at appropriate ecological scales, as existing studies are constrained by trade-offs between spatial and temporal extent and resolution, and often rely on non-harmonised data.
The aim of this study is to analyse the trajectories and drivers of habitat changes across the Alps over the last 70 years, using three temporal steps, i.e., the 1950s, 1980s, and 2020s. We hypothesise that: i) natural forests expansion patterns are mostly associated with gap filling between the 1950s and the 1980s, but with treeline upshift in the last 40 years; ii) grasslands have decreased over the entire time period, but primary successions on unvegetated areas have recently occurred at higher elevations; iii) the relative importance and direction of effect of drivers differ between time periods and regional administrations, reflecting land-use legacies, history, and territorial policies.
A database of 393 historical aerial photographs from the 1950s and the 1980s, and satellite images from the 2020s was assembled for 138 alpine landscapes, each with an area of 9 km2, encompassing more than 1000 km2. Landscape areas were selected between 1000 and 3000 m a.s.l. to represent the topographic, climatic, and socio-economic diversity of the montane, subalpine, and alpine belts of the Alpine region. Images were obtained from multiple national and regional geoportals, orthorectified when needed, and harmonised at a spatial resolution of 1m. Deep-learning architectures were trained to classify the landscapes into 10 land-cover classes, including grasslands, croplands, forests, unvegetated or anthropic areas, and shadows. A semi-automatic post-processing procedure was implemented to ameliorate classification results. The spatiotemporal trajectories of habitat changes were assessed through landscape and class metrics, as well as morphological spatial pattern analysis. A machine-learning approach was adopted to quantify the importance and direction of effect of several topographic, socioeconomic, soil, climatic, and vegetation drivers.
Preliminary results confirmed the hypotheses regarding habitat transitions over time. Forests have increased everywhere, leading to a widespread landscape homogenisation dominated by closed-canopy habitats. By further investigating the evolution of spatial patterns of grasslands and open areas, we seek to better decipher landscape and habitat changes at different elevations. Moreover, the identification of the most relevant socio-ecological factors shaping semi-natural cultural landscapes can inform biodiversity conservation and rewilding agendas.
How to cite: Cavallo, S., Anselmetto, N., Mauri, L., Ferrero, S., Mainetti, A., and Garbarino, M.: From cultural landscapes to forest expansion: 70 years of habitat change in the European Alps, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2960, https://doi.org/10.5194/egusphere-egu26-2960, 2026.
The alpine tree line represents one of the most climate-sensitive ecological boundaries, wher multiple interacting factors determine vegetation distribution as its upper limit. This study investigates the spatio-temporal dynamics of the tree line in Senales Valley (South Tyrol, Italy) between 1985 and 2023, combining multi-temporal Landsat imagery, Random Forest (RF) classification and visual orthophoto interpretation performed by manually delineating the forest boundary to assess both spatial and elevational shifts. Climatic variables (temperature, precipitation, snow cover and growing season length) were analysed using linear model (LM) and generalized additive models (GAM) to identify long-term trends and potential drivers of tree line migration. The results reveal a consistent increase in forest cover in all 16 study areas, averaging +44%, with the largest expansion occuring on slopes facing W. Elevational advances were recorded in 15 of 16 areas, averaging +32 m for Landsat-derived data and +45 m for orthophotos. Elevated minimum temperatures during spring and autumn, alongside warmer summers and a significant rise in precipitation during the same season, created condition which maintained soil moisture and reduced water stress - factors known to facilitate tree line advancement. Wind exposure from the N-NW sector and associated föhn effects appeared to limit tree line expansion on S-SE facing slopes. Comparison between manual and RF-derived tree lines revealed overall high agreement, with deviation below one Landsat pixel (30 m) in most cases. This confirms that Landsat imagery combined with RF algorithms provides a robust, cost-effective method for assessing long-term tree line dynamics in heterogeneous alpine enviroments.
How to cite: Menegaldo, I., Molbach Sforzini, V., Tognetti, R., and Torresani, M.: Tree line dynamics and forest densification in the European Alps revealed by Landsat images and machine learning: a case study in the Senales/Schanls Valley, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3085, https://doi.org/10.5194/egusphere-egu26-3085, 2026.
In many European countries, land-use changes driven by socio-economic contexts have induced widespread forest expansion during the 20th century, with still poorly explored implications for ecosystem dynamics. Previous research has shown that these “new forests” differ from “pre-existing forests” in terms of structure and productivity. However, beyond their current state, it remains unclear whether and how the long-term dynamics of new forests diverge from or converge towards those of pre-existing forests.
Here, we address this question using the framework of Ecological Dynamics Regimes (EDRs), which characterize ecosystem trajectories through time based on temporal changes in multiple state variables within a multidimensional state space. In this study, EDRs were defined using forest structural and compositional attributes. We assessed forest EDRs across two biogeographical regions of the Iberian Peninsula using data from 756 plots of the Spanish National Forest Inventory (1986–2023). Historical land-cover maps from 1956 were used to distinguish between pre-existing forests and new forests established after mid-20th-century land abandonment. Analyses focused on plots dominated by major Pinus and Quercus species in the region, from which we derived metrics describing the dispersion, length, and relative position of EDR trajectories.
Our results show that, for most species, new forests exhibit more dispersed (between 0 and 37% according to the species) and longer trajectories (11-75%), and are positioned further behind in state space (4-56%) compared to pre-existing forests. This indicates that new forests start from more heterogeneous initial conditions and experience faster or more pronounced structural and compositional changes, while nonetheless showing a tendency to converge towards the dynamics of pre-existing forests over time. These findings highlight the lasting influence of land-use legacies on forest dynamics and help to the understanding of forest responses under ongoing global change and increasing uncertainty.
How to cite: Canelles, Q., Sánchez-Pinillos, M., and Ameztegui, A.: Land-use legacies drive contrasting dynamics between new and pre-existing forests, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5097, https://doi.org/10.5194/egusphere-egu26-5097, 2026.
Across Alpine landscapes, a combination of land-use abandonment and climate change is driving forest expansion and promoting the upward migration of trees on grasslands. Yet, it remains unclear how rapidly the treeline will shift and how tree species composition of this ecotone will change, both in terms of species proportions and along elevational gradients.
Our aim is to investigate the future forest expansion in a landscape in the Eastern Alps under potential grassland abandonment, climate change, natural disturbances (wind and bark beetle), and forest management.
We used the iLand forest landscape model to simulate long-term dynamics (2020-2200) under different scenarios. We coupled model outputs with the concave hull algorithm to identify potential changes in the treeline, tracking tree species expansion and quantifying elevation and compositional shifts.
Under a potential abandonment of alpine grasslands, forest will likely expand rapidly within the 21st century regardless climate warming. This because the current treeline is mainly constrained by land use rather than climate. Our simulations also showed that ecotone shifts will be more pronounced on S-facing slopes, while climate change will affect more future tree species composition and forest stocking at higher elevations.
Our outcomes provide useful insights on future dynamics of the upper forest ecotone by using a forest landscape model and by integrating not only species migration and climate but also other factors such as disturbances and management. Our results could provide useful information for designing landscape management strategies in rapidly changing Alpine mountain valleys.
How to cite: Marzini, S., Tasser, E., Wellstein, C., Albrich, K., Rammer, W., and Mina, M.: Future expansion of the treeline under land-use and climate change in the Eastern Alps, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9249, https://doi.org/10.5194/egusphere-egu26-9249, 2026.
Expanding tree cover is widely seen as a nature-based solution for climate mitigation and biodiversity conservation. However, widespread increases in tree cover can unintentionally reduce habitat heterogeneity or replace open ecosystems, thereby posing risks to biodiversity. Here, we developed a framework integrating measures of tree cover, connectivity, and heterogeneity, and quantified the dynamics in these measures in Europe from 2001 to 2021. We also explored potential drivers of tree dynamics, including land management, natural disturbance, climate, and human activities. We found that 23% of the European land area experienced widespread increases in tree cover and connectivity, accompanied by a decline in heterogeneity. Importantly, former cropland areas were linked to considerable tree-cover expansion and improved tree connectivity. Our findings indicate widespread densification and homogenization of vegetation, which will compromise the capacity of European ecosystems to support biodiversity. Restoring natural disturbance processes, including herbivory and fire, is essential to fully capitalize on the biodiversity potential of nature recovery in Europe.
How to cite: Wu, W., Svenning, J.-C., Kuemmerle, T., Prishchepov, A. V., Baumann, M., and Buitenwerf, R.: Patterns, drivers and potential biodiversity consequences of widespread tree-cover expansion in Europe, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11310, https://doi.org/10.5194/egusphere-egu26-11310, 2026.
In Europe, alpine treelines are shifting upward under the combined influence of climate and land-use change. In the Mediterranean basin, historical human pressure has significantly lowered treeline elevations, and these legacy effects continue to shape their present-day trajectories. Such legacies are expected to contribute to the contrasting patterns observed between the Alps and the Apennines, the two main Italian mountain ranges. Because treeline ecotones are key for mountain biodiversity and ecosystem services, consistent monitoring is crucial. Satellite remote sensing—especially multi-decadal time series of vegetation indices (VIs)— offers a promising avenue to study forest dynamics and treeline shift. Here, we present a semi-automatic and reproducible method to delineate the uppermost forestlines and to identify significant hotspots of change. We then evaluate the main topographic, climatic, and anthropogenic factors predisposing to treeline dynamics according to the long-term increase of VIs. We integrated national and international open-source datasets within a semi-automatic workflow to detect uppermost forestlines based on vertical distances between forest pixels and their relative highest peaks. We assessed greening along a forestline buffer representing the treeline ecotone using a 40-year Landsat NDVI time series (1984–2023). Trend significance was tested with contextual Mann–Kendall statistics, while Theil–Sen slopes quantified the magnitude of change. Finally, we used Random Forest models to investigate the relative importance of predisposing factors. Highest forestline elevations occur in the Alps, where larger elevation ranges and the dominance of conifers appear to be associated with upward shifts. In contrast, Apennine treelines are mainly formed by European beech; its heavier seeds likely limit upslope encroachment, favouring gap infilling processes over treeline upward shift. Overall, this study contributes a standardized framework for mapping forestlines and analysing the predisposing factors of greening dynamics. The approach is transferable to other mountain regions, supporting comparisons across space and time.
How to cite: Baglioni, L., Vitali, A., Choler, P., Bayle, A., Garbarino, M., Morresi, D., Gennaretti, F., and Urbinati, C.: Predisposing factors to Landsat based greening trends in the Italian forestline ecotones , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13167, https://doi.org/10.5194/egusphere-egu26-13167, 2026.
Monitoring vegetation dynamics is crucial for assessing the effects of global change on mountain ecosystems, particularly at treeline ecotones where tree recruitment success is highly variable and largely influenced by fine-scale site conditions. This study integrates ground-based surveys with UAV-based LiDAR and multispectral (MS) data to investigate recruitment patterns across two Alpine treelines in Italy (Mt. Genevris, Piedmont; Mt. Becco di Mezzodì, Veneto). During the 2024 and 2025 summer seasons, we conducted ground-based measurements along altitudinal transects spanning the treeline ecotone, from the upper ecotone limit down to the closed-canopy forest. We mapped trees and saplings using a GNSS rover, and we measured basal diameter and tree height. On the same slopes, we acquired and processed UAV LiDAR and MS imagery to derive high-resolution 3D point cloud. With the collected data, we derived canopy height model, microtopography, land-cover classification and MS metrics describing vegetation spectral variability. We used field-mapped individuals to train and validate machine-learning models for detecting individual trees and producing georeferenced point datasets across entire slopes. These individual-level spatial data enabled spatial point-pattern analyses to assess recruitment structure along the treeline, testing for facilitation versus competition among individuals in relation to key biotic elements (i.e. shrubs and patches). In addition, we examined establishment patterns in relation to microclimatic proxies derived from UAV-based topographic features, including indices of potential solar radiation, heat load and moisture availability. This integrated LiDAR–MS UAV framework, anchored to ground-truth data, enables individual mapping of treeline recruitment across entire slopes at spatial resolutions not achievable through ground surveys alone. By linking 3D structure, spectral information and spatial point-pattern analysis, this research approach improves the interpretation of micro-environmental controls on establishment niches and provides a transferable framework for scalable treeline monitoring under ongoing climate change.
How to cite: Vitali, A., Atzeni, F., Balestra, M., Fiorani, F., Garbarino, M., Urbinati, C., Gennaretti, F., and Lingua, E.: Mapping recruitment patterns at alpine treeline ecotones using UAV-based LiDAR and multispectral data, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21041, https://doi.org/10.5194/egusphere-egu26-21041, 2026.
Mountain forests provide essential ecosystem services, including protection against natural hazards, carbon storage, and biodiversity habitat. Climate change threatens the continuous provision of these services and is driving anticipated shifts in tree species ranges across elevation gradients. Understanding current regeneration dynamics is critical for predicting future forest composition and guiding adaptive management.
We analyzed two decades (2004-2023) of tree regeneration data from 2,377 plots across seven forest types spanning 1.3 million ha and an elevation gradient from 200 to 2,300 m asl in the Swiss Jura mountains and Alps. Using data from the Swiss National Forest Inventory and statistical models accounting for repeated measurements (generalized estimating equations), we assessed trends in presence for 20 tree species in two height classes: small saplings (40-129 cm height) and tall saplings (≥130 cm, DBH <12 cm).
Broadleaf species showed widespread expansion (35% of models), particularly among small saplings in low- to mid-montane forests (Beech, Fir-Beech, and Fir-Spruce types). Sycamore maple (Acer pseudoplatanus) and goat willow (Salix caprea) expanded most consistently across elevation gradients, establishing extensively in historically conifer-dominated forests. European beech (Fagus sylvatica) increased its presence in mixed-montane forests. Silver fir (Abies alba) exhibited a notable pattern in Fir-Beech forests: an increase in small saplings and a decrease in tall saplings, suggesting potential recruitment bottlenecks. European ash (Fraxinus excelsior) declined significantly across its range. At low elevations, oaks (Quercus spp.) failed to expand beyond their current forest types, while at high elevations, European larch (Larix decidua) saplings decreased in Stone Pine and Larch forests.
To identify potential drivers of these trends, we tested multiple environmental and stand variables. Basal area, temperature, and species richness emerged as the most important explanatory factors associated with observed shifts in regeneration patterns.
These results reveal substantial compositional shifts toward more broadleaf species in Central European mountain forests, with important implications for future ecosystem service provision, forest management strategies, and climate adaptation planning.
How to cite: Molteni, U., Abegg, M., Kupferschmid, A. D., Moser, B., Nikolova, P. S., Scherrer, D., and Wohlgemuth, T.: Hardwoods on the rise: regeneration trends in Central European mountain forests over two decades, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19803, https://doi.org/10.5194/egusphere-egu26-19803, 2026.
Studying Land Use and Land Cover Changes (LULCC) and their drivers is essential for understanding the past origins of current landscapes and anticipating their future evolution. Moreover, such analyses can help prioritize and plan conservation and restoration strategies.
From the Middle Age to the 19th century in Western Europe, humans - driven by population growth – have altered the natural vegetation succession, notably through forest clearing to establish cultivated fields and pastoral farming systems. Thanks to the industrial revolution and the resulting technical advances which resulted in agriculture mechanization and intensification, important phases of rural exodus happened, inducing waves of land abandonment used for agriculture or pastoralism. Mountainous areas were particularly affected, especially due to their difficult access and their low productivity. The aims of this study were (a) to characterize land-use transitions, with a particular focus on forests, and (b) to analyse the biophysical and socioeconomic drivers of these changes.
We focused on the northern part of the French Alps, which is divided into three eco-regions covering an area of 726 953 ha: Northern Prealps, Inner Northern Alps, External Northern Alps. We used historical maps and aerial photography to analyse LULCC across four periods from 1860 to 2023. Based on the literature, we selected a set of drivers including biophysical drivers (precipitations, temperature, topography, substrate type, avalanches), landscape configuration drivers (distance and percent cover of pre-existing forest, distance to pastoral units, distance to river) and socio-economic drivers (population density and change, distance to settlement, road density, tourism density, number and rate of change of pastoral units). This dataset allows us to analyse land-use changes over a long-time span (approximately 160 years) and at a large spatial scale.
To analyses changes between different dates, we used a grid of systematic points, with a density of one point per hectare, to generate several transition matrices. To assess the effect of drivers on LULCC we performed logistic regression models. Specifically, we fitted models of LULCC or forest recovery as a smooth or linear function of the different drivers. We also took into account potential biases in the results of the different models related to spatial autocorrelation of observations by integrating distance based on the sample variogram of the residuals obtained from a model without spatial dependence. Overall, we expect a global forest expansion, with positive effects of lower precipitations, steeper slopes, substrate type (hard), lower population density and pastoral decline, and negative effect of high tourism density and avalanches. Preliminary results will be presented at the conference.
How to cite: Mutillod, C., Delpouve, N., Rathgeber, C., Dupouey, J.-L., Leroy, N., Mollier, S., Nicoud, B., Bayle, A., Garbarino, M., Anselmetto, N., Eckert, N., Janssen, P., and Bergès, L.: Land Use Land Cover changes and their drivers since 1860 within the French northern Alps, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16873, https://doi.org/10.5194/egusphere-egu26-16873, 2026.
Spontaneously developed forests (SDFs) dominated by native tree species have expanded across vast areas of abandoned farmland in Central and Eastern Europe in the last decades. These forests hold considerable potential for contributing to the semi-natural forest restoration objectives of the European Union’s Nature Restoration Regulation. However, their effective management requires robust, comparative, and evidence-based research, while SDFs remain insufficiently studied in the Carpathian Basin. We aimed to assess how site locality, historical land use, and time since abandonment shape the species composition of spontaneously developed semi-natural forests across three forest landscape types in Hungary (riverine oak–ash–elm forests, mesic hornbeam–sessile oak forests, and thermophilous turkey oak–sessile oak forests). We surveyed the species composition of the canopy, shrub, and herb layers at 358 sampling points. Ordination analyses based on species cover data revealed that site locality, land-use history, and time since abandonment each contribute to deviations from general species-compositional patterns within forest layers and across landscape types. We found that the number of forest-generalist herbaceous species increased markedly following abandonment, reaching levels comparable to reference ancient forests already in 25–50-year-old stands across all habitat and land-use categories. In contrast, the number of forest-specialist species exhibited habitat-specific successional trajectories. These findings highlight the importance of management approaches that maintain the distinct species-compositional patterns of spontaneously developed forests on former farmland, thereby favouring close-to-nature and low-intensity forestry practices.
How to cite: Demeter, L., Molnár, C., Bede-Fazekas, Á., Péter Molnár, Á., Zagyvai, G., and Molnár, Z.: Regular patterns and context-dependent deviations in successional trajectories of spontaneously developed semi-natural forests on abandoned farmland in Hungary, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13377, https://doi.org/10.5194/egusphere-egu26-13377, 2026.
Riparian zones are essential for the vitality of river systems and their adjacent environment while offering an abundance of ecosystem services. However, these ecosystems are increasingly exposed to various stressors with human activities like habitat fragmentation instigated by land use intensification, hydroclimatic changes, and environmental impacts, being the primary contributors. The growing availability of satellite datasets has enhanced the capacity and efficiency of monitoring these ecosystems. Based on these viewpoints, the objective of this work is to present a systematic methodology to examine the structure of vegetation in riparian forests using the spectral heterogeneity in their reflectance for the Delhi stretch of river Yamuna. The spectral variation hypothesis suggests that a higher spectral variability is positively related to plant species richness. Taking that into account, the primary objectives of the present study are: (1) determination of differentiable spectral clusters for riparian vegetation and deriving endmember spectra for each cluster from the scene using Vertex Component Analysis (VCA), (2) unmixing the vegetation spectra and determination of Shannon’s Index (SHDI) as an indicator of structural diversity in the study area. Landsat 7 ETM and Landsat 8 and 9 OLI scaled surface reflectance products have been used for the analysis. Pixels corresponding to the forested region are identified using land use landcover maps. The principal component analysis was first carried out to reduce the high correlation among the image bands. The clusters for deriving the endmembers were determined from the output principal components using K- means clustering. The optimal number of clusters (k) were obtained using a tolerance-based plateau detection for iterative k - value against its average mean centroid distance (AMCD) at each step prior to retracing the cluster identities in the reflectance space. The endmember spectra are identified using VCA for each cluster and the method of Non-Negative Least Squares (NNLS) is employed to optimize the endmember reflectance function. Since the endmembers are identified for multiple years, we have used Spectral Angle Mapping (SAM) to identify the classes of similar endmember types within each year and across multiple years before determining the SHDI values based on the proportion of the total area covered by the endmembers The results show a decreasing trend in SHDI which implies that there is a decline in structural diversity at 30 meter scale within the riparian zones and thereby the area is becoming dominated by fewer vegetation types over time. The study reveals that spectral unmixing-based SHDI serves as a remote, repeatable metric for assessing riparian vegetation structure. Monitoring alterations in diversity facilitates the identification of homogenization and habitat complexity loss, thereby aiding in early warning, restoration targeting, and assessment of management or land-use policy effects.
Keywords: Riparian zones, spectral endmembers, spectral unmixing, VCA, Shannon diversity index.
How to cite: Vyas, H. and Keshari, A. K.: Spectral Unmixing based Approach to Quantify Structural Vegetation Diversity in the Riparian Zones of River Yamuna: A Study for the Delhi Stretch, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2145, https://doi.org/10.5194/egusphere-egu26-2145, 2026.
Timely forest health monitoring depends on fast and accurate methods that identify tree mortality due to climate change driven phenomena such as bark beetle attacks. Aerial imagery coupled with deep learning is an efficient tool for detecting standing dead trees compared to field work but requires reliable and quickly accessible applications for forest owners and decision makers. Current challenges are related to laborious training data acquisition that models require in order to generalize for large areas. Few studies address how locally trained dead tree models can be transferred to new sites with minimal manual delineation of calibration data.
This presentation introduces three binary CNN segmentation models for detecting standing dead trees during the Finnish leaf-on season, with training and testing applied on aerial images of Koli 2017, Koli 2022 and Lapinjärvi 2022 study sites. These models were trained using 300–543 tiled aerial image samples and then transferred to images of Koli and Lapinjärvi taken in 2025 where only a small calibration set of n=12 samples are manually delineated for both images. To expand this calibration data, various geometric augmentations are applied to the samples. This dataset allows for transferability tests between eastern and southern Finland as well as across 8 years of aerial image data with varying imaging conditions.
Pixel-wise F1 scoring of all models ranged from 0.69 to 0.82 while the calibration improved transferred model F1 scores by 13–123% depending on site and year. This presentation will also provide a clear explanation of the used models, as well as the used aerial images with their inherent characteristics, for example spectral variations that affect calibration efficiency. Furthermore, standing dead tree mortality maps are shown to visualize the tree mortality extent in Koli and Lapinjärvi study areas.
Augmentation can efficiently generalize standing dead tree detection models as well as enable effortless calibration to new sites. Therefore, this approach can be extended to other tasks as well, such as forest fire mapping.
How to cite: Rönkkö, J., Waga, K., Kukkonen, M., and Rana, P.: Deep learning and model transferability for standing dead tree mapping, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2150, https://doi.org/10.5194/egusphere-egu26-2150, 2026.
Recent advances in remote sensing of biodiversity and biodiversity-related products have significantly enhanced our capacity to monitor and understand biodiversity. Typical remote sensing products directly related to biodiversity are spectral features and plant traits, and their diversity in space, i.e., spectral diversity and functional diversity. Hence, remote sensing of biodiversity involves measuring biophysical quantities from signals recorded by a sensor in response to radiation reflected from the Earth’s surface. As for any other measurements, the biodiversity quantities estimated via remote sensing are inherently uncertain. Starting from the digital numbers recorded by the detector, the processing to obtain surface reflectance products, to the final biodiversity output, various sources of uncertainty can arise. Failing to account for such uncertainties may lead to over- or underestimates of diversity, with downstream repercussions on management strategies and policy making. Nevertheless, uncertainties are rarely quantified in remotely sensed biodiversity products, limiting our understanding of biodiversity processes and their detection. Sparse quantification of uncertainties is further exacerbated by the confusion arising from the inconsistent and improper use of uncertainty terms. Here, we clarify the concept of uncertainty by defining what it is and what it is not, outlining its typologies, highlighting sources of uncertainty and providing examples of uncertainty estimation and propagation. Our examples are based on spaceborne imaging spectroscopy data to propagate surface reflectance uncertainties into vegetation indices, principal components, plant traits, and spectral diversity metrics. By raising awareness of the magnitude and implications of uncertainty, establishing a shared terminology, and proposing a practical framework for uncertainty estimation, we contribute toward more transparent, interpretable, and ultimately more reliable remotely sensed biodiversity products.
How to cite: Rossi, C., Hueni, A., Koch, T. L., Karaman, K., and Santos, M. J.: Uncertainties in Remote Sensing of Biodiversity, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6668, https://doi.org/10.5194/egusphere-egu26-6668, 2026.
Remote sensing pursues the estimation of vegetation functional diversity. For that, both measurements of spectral variables and plant functional traits must be performed. However, these measurements encompass the intrinsic component of uncertainty (i.e., any measurement is composed of the measured value and the associated uncertainty). Uncertainty can take different forms: systematic biases with respect to the true value, or random variations that may or may not be correlated with it. Different measurands can be sampled independently (e.g., foliar and structural traits) or simultaneously (e.g., spectral radiance at different bands), and their uncertainties may exhibit different degrees of correlation. The different uncertainties propagate from the measurands to the derived variables of interest. Whereas remote sensing has mostly focused on the propagation of uncertainties to measurands representing the averaged value of an observation (e.g., the pixel reflectance factor), the effect on estimates of their diversity (i.e., functional diversity metrics), while different, remains unclear.
To fill this gap, we simulate synthetic measurements and introduce different types and magnitudes of uncertainty, evaluating their impact on various functional diversity metrics. While abstract, this exercise allows us to understand the role of each uncertainty type across different metric formulations via a Monte Carlo approach. For a clearer understanding of practical cases, we further use the Biodiversity System Simulation Experiment (BOSSE) to perform this assessment under synthetic landscapes.
Preliminary results suggest that the impact of uncertainty depends on the formulation of the functional diversity metric, but that standardization and principal component analysis applied to the spectral or plant functional traits attenuate some of the sources of uncertainty.
How to cite: Pacheco-Labrador, J. and Rossi, C.: Assessing the impact of measurement uncertainty on functional diversity metrics, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12923, https://doi.org/10.5194/egusphere-egu26-12923, 2026.
Globally, accelerated human activities and climate change have driven a continuous decline in natural ecosystems, resulting in habitat fragmentation and overall biodiversity loss. In response to this crisis, the international community adopted the Global Biodiversity Framework (GBF) at the 15th Conference of the Parties to the Convention on Biological Diversity (CBD COP15) in 2022, highlighting spatial planning–based biodiversity management as a central strategy. Consequently, there is a growing demand for spatial datasets with global consistency and high accuracy to enable quantitative assessment of ecosystem-related indicators. The Global Ecosystem Typology (GET) proposed by the International Union for Conservation of Nature (IUCN) provides a standardized framework and spatial information for consistent ecosystem classification at the global scale. However, the existing GET spatial datasets are produced at a global resolution, which limits their applicability at the national level due to spatial resolution mismatches and reduced classification accuracy for ecosystem types. In this study, we developed an IUCN GET ecosystem map for the Republic of Korea using time-series Landsat satellite imagery. Ecosystem classification was conducted for the period (2020–2024) using machine learning and deep learning approaches, resulting in ecosystem maps comprising 36 classes specific to Korea. The modeling results achieved an overall classification accuracy of approximately 85%, with several ecosystem classes exceeding 90% accuracy. The results of this study enable rapid and efficient detection of long-term and large-scale ecosystem changes. Furthermore, the enhanced precision and accuracy of ecosystem type classification support detailed ecosystem area analysis and provide a foundation for biodiversity conservation–oriented spatial planning.
How to cite: Park, S., Koh, M., Baek, H., Cho, M., Kim, B., Boukhers, Z., Park, G., Beilschmidt, C., and Drönner, J.: AI-BioDynamics: Artificial Intelligence for Biodiversity Mapping and Conservation Decision-Making, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16158, https://doi.org/10.5194/egusphere-egu26-16158, 2026.
Posters virtual: Tue, 5 May, 14:00–18:00 | vPoster spot 2
EGU26-21572 | ECS | Posters virtual | VPS5
Revealing nested archetypes of cropland abandonment based on social-ecological system theoryTue, 05 May, 14:33–14:36 (CEST) vPoster spot 2
Cropland abandonment is a key land-use change process within the human-environment system, shaped by diverse environmental and socio-economic determinants. However, many studies overlook the complex interrelationships among these determinants, which may result in the reconfiguration of agricultural landscapes. Here, we developed an analytic framework based on social-ecological system theory to map cropland abandonment archetypes in Sichuan Province, Southwest China, using a combination of biophysical conditions, proximity characteristics, socio-economic determinants, and the extent, cumulative proportion, and spatial configuration of abandoned croplands. We implemented self-organizing feature maps using a nested clustering approach, which resulted in 25 sub-archetypes and 6 meta-archetypes. We used random forest regressions to quantify the relative importance of explanatory determinants influencing archetype geographies. Our results revealed diverse cropland abandonment archetypes, with meta-archetype area shares ranging from 4.4 % to 48.4 %. The most widespread archetype was characterized by favorable terrain, low cropland per capita, and low cumulative proportions of abandonment. Determinants of meta-archetypes varied in their importance but consistently highlighted the role of environmental determinants (i.e., topography, temperature), as well as productivity-related and socio-economic determinants (i.e., employee wages, pension insurance, high-value crops) as the most important determinants. Our findings argue against one-size-fits-all solutions and are highly relevant to nuance existing regional land-use policies addressing cropland abandonment. They further allow targeting key determinants of cropland abandonment and considering regional and local socio-ecological contexts in decision-making processes.
How to cite: Hong, C.: Revealing nested archetypes of cropland abandonment based on social-ecological system theory, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21572, https://doi.org/10.5194/egusphere-egu26-21572, 2026.
EGU26-3212 | ECS | Posters virtual | VPS5
Bamboo Expansion Drives Divergence in Productivity and Spectral Diversity in Wuyishan National Park over Nearly Four DecadesTue, 05 May, 14:36–14:39 (CEST) vPoster spot 2
Bamboo expansion constitutes a significant process altering forest ecosystem structure and function. However, quantitative research remains scarce on how its long-term evolution influences the critical relationship between ecosystem productivity and biodiversity. This study aims to evaluate the long-term ecological effects of bamboo expansion in Wuyishan National Park (a biodiversity hotspot in China).
By analysing Landsat time-series imagery (1986–2020) and existing land cover dynamic classification maps, seven land cover types—including bamboo forests and broad-leaved forests—were identified. Analysis of surface classification results revealed an overall upward trend in bamboo forest area, with expansion primarily occurring at the expense of broad-leaved forests. Notably, 48% of the newly added bamboo forest area resulted from the conversion of broad-leaved forests.
To assess ecosystem responses to bamboo expansion, spectral diversity was quantified using Rao's Q index (functional diversity) and Shannon index (species diversity), calculated from NDVI and NDMVI. Ecosystem productivity was characterised via habitat indices (DHIs) derived from NDVI time series. Results indicate that regional ecosystem productivity has steadily increased, whereas spectral diversity has markedly declined, with both Rao's Q and Shannon indices showing significant downward trends. Specifically, bamboo forest patches exhibited higher Cumulative DHI (8.8 ± 0.65) than broadleaf forests, yet lower Rao's Q indices (0.010 ± 0.004), whereas broadleaf forests recorded (8.7 ± 0.69) and (0.011 ± 0.003), respectively. Moreover, farmland and tea plantations exhibited abnormally high Rao's Q values, likely attributable to fragmentation and edge effects (small patches embedded within forest backgrounds) rather than genuine species richness.
The study employed Theil–Sen trend estimation and Mann–Kendall significance testing to investigate correlations between bamboo forest area changes and biodiversity. Results revealed a significant negative correlation between bamboo forest expansion and spectral diversity indices (R²≈−0.36), suggesting bamboo encroachment may diminish biodiversity.
The observed trend of increasing productivity coupled with declining spectral diversity warrants further analysis to elucidate underlying drivers. Future research should integrate additional vegetation indices and morphological parameters for diversity calculations. Furthermore, long-term assessments of animal habitat suitability and ecosystem stability require combined ground-truthing and modelling approaches.
How to cite: Shi, W., Zhang, Q., and Qiu, F.: Bamboo Expansion Drives Divergence in Productivity and Spectral Diversity in Wuyishan National Park over Nearly Four Decades, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3212, https://doi.org/10.5194/egusphere-egu26-3212, 2026.
