Urban green spaces (UGS) provide important ecological, social, and climatic functions, but their effectiveness depends not only on their extent but also on their vertical vegetation structure. To this end, we present a high-resolution urban green space typology (UGST) for four Swiss cities (Basel, Bern, Geneva, and Zurich) derived from remotely sensed thematic (NDVI) and structural (vegetation height and VCI) data, comprising 33 classes. The structural heterogeneity of the vegetation cover was consistently bimodal, with low-complexity vegetation (grass) or tall, high-complexity wooded areas dominating, while intermediate structures were underrepresented. The UGST was used to assess species richness across five taxonomic groups and a multidiversity score (mean normalized richness across all groups) using GAMs under varying thematic resolutions. Results indicated taxon-specific, generally non-linear associations between normalized recorded species richness or multidiversity and the individual component scores or percentage cover of several UGST classes. The fine-grained joint models had higher in-sample deviance and explained more variance compared to highly aggregated-class and total-green-cover formulations, although the formulations differed in complexity and were not evaluated out of sample.

Spatio-temporal vegetation models are essential for simulating dynamics and shifts in species distributions over large areas, particularly under global change. One such model is the dynamic forest landscape model TreeMig. We have developed a framework that makes the model more user-friendly, improves its functionality, and expands its possibilities. The framework consists of the improved TreeMig model, the R package 'TreeMig-R' for pre-processing input data, model execution and visualization, a graphical user interface (GUI) for ease of use, and a set of sample input data. A suite of increasingly complex applications demonstrates its potential, including simulations of spatial forest dynamics under climate change in a highly fragmented landscape, the additional introduction and spread of an invasive tree species, and the coupling with an external model for the control of the invasive species.

Carbon sustains life, whereas mercury is a global toxin, yet their cycling in forests appears to be intimately linked. Here we show, using elemental stoichiometry, carbon and mercury isotopes and a global forest synthesis, that forests simultaneously couple and decouple mercury from carbon along contrasting ecosystem continua. Mercury/carbon ratios remain tightly conserved (0.5-0.9 × 10-6) along the aqueous-phase continuum, indicating proportional mercury transport with dissolved organic carbon. In comparison, mercury/carbon ratios increase by nearly three orders of magnitude from the atmosphere to soils (0.007-3.6 × 10-6) along the solid-phase continuum, reflecting progressive mercury enrichment during litter and soil organic matter decomposition. Standing litter acts concurrently as a net carbon source and mercury sink, whereas biomass regulates coupled carbon and mercury storage and litterfall deposition. These contrasting carbon-mercury trajectories reveal how forests both retain and redistribute atmospheric mercury and provide a conceptual framework for understanding terrestrial mercury cycling under environmental change.

Forest mortality is increasing globally under climate change, making detailed, large-scale monitoring essential for understanding ecosystem responses and guiding adaptive forest management. Here, we present a spatio-temporal assessment of standing deadwood in Switzerland from 2018 to 2023, derived from centimeter-scale high-resolution aerial imagery. We reveal a consistent upslope concentration of standing deadwood, with highest shares occurring around mid to high elevations (~1,500 m), despite declining forest cover, and relative increases of up to 43% in overlapping survey areas following the 2018 drought. Maximum temperature anomalies and conifer dominance were the strongest predictors of standing deadwood. The consistent accumulation of standing deadwood at higher elevations suggests increasing vulnerability of mountain forests, with implications for carbon storage, biodiversity, and disturbance susceptibility under ongoing climate change. These patterns highlight the need to address rising forest mortality as a key component of climate-adaptive forest management. Furthermore, our results demonstrate the potential of high-resolution remote sensing for large-scale forest mortality monitoring. Our methods offer a reproducible and transferable framework for identifying vulnerability hotspots and supporting climate-adapted forest management.

Emerging evidence suggests that temperature increases due to climate change not only differ strongly between regions but also across seasons. As a rule of thumb, one could argue that colder seasons (e.g., winter) tend to warm up faster than warmer seasons, although there are notable exceptions to this rule (e.g., due to changes in the polar vortex). The implications of such seasonal differences in warming trends for plant phenology, i.e., the timing of key events during the plant seasonal cycle, however remain poorly understood. A gap in knowledge that arises, in part, because we lack a global overview of the period(s) of the year during which changing temperatures impact on the phenological cycle of plants the most.Here, we provide a global analysis of the interrelationships between seasonal temperature changes and global land surface phenology using satellite data from the period 2001-2019. More specifically, we determined the annual period of highest correlation between temperature fluctuations and the onset of different phenological stages within a 100km radius around 10.000 point locations. We found that, across most of the Northern Hemisphere’s mid and high latitudes, a wide range of these stages, i.e., from the onset of ‘greenup’ to ‘greendown’, correlate strongly with temperature fluctuations during roughly the same period of the year, i.e., up until a few weeks before or after the onset of greenup. We found that warming rates during this period were roughly 1.5-2.5 times faster than regional mean annual temperature increases, which, in turn, were roughly 1.5-2.0 times faster than the increase in global mean annual temperature (which includes air above the oceans).When assessing the impact of global mean annual temperature changes on global land surface phenology, it is thus crucial to consider seasonal differences in warming. These differences are likely to affect not only plant phenology but also many other key processes related to plant growth and development.

Changing environmental conditions caused by climate change, eutrophication, and other anthropogenic factors affect the timing, duration, and surface extent of lake algae blooms across the globe. It remains, however, challenging to quantify the relative impacts of different environmental changes on the timing and characteristics of lake algae blooms, and to detect phenological trends over time, as these blooms vary considerably from year to year. Global data sets that may allow us to study algae-bloom properties along a wide range of environmental conditions and years are needed to address these challenges. For this study, we developed such a data set using satellite remote sensing. We analyze the phytoplankton phenology of 2025 lakes across a wide range of climate zones over a period of approximately 20 years. More specifically, we used daily lake chlorophyll estimates derived from MERIS and OLCI data to extract phenology metrics (e.g. the onset and decline of peaks in chlorophyll concentration) for individual pixels within each of the 2025 lakes. Through a newly developed method, we determined the timing of blooms, i.e. clusters of peaks in different pixels occurring within the same lake during the same period of the year, and, subsequently, studied the change in the timing, duration, and size of those blooms across years. This will, ultimately, help us to get a better overview of the extent to which lake algae blooms have changed across the globe, to attribute those changes to anthropogenic drivers, and to develop effective environmental policies to combat those changes where needed.

Abstract Land‐use intensification in grassland ecosystems (i.e. increased mowing frequency, intensified grazing) has a strong negative effect on biodiversity and ecosystem services. However, accurate information on grassland‐use intensity is difficult to acquire and restricted to the local or regional level. Recent studies have shown that mowing events can be mapped for large areas using satellite image time series. The transferability of such approaches, especially to mountain areas, has been little explored, however, and the relevance for ecological applications in biodiversity and conservation has hardly been investigated. Here, we used a rule‐based algorithm to produce annual maps for 2018–2021 of grassland‐management events, that is, mowing and/or grazing, for Switzerland using Sentinel‐2 and Landsat 8 satellite data. We assessed the detection of management events based on independent reference data, which we acquired from daily time series of publicly available webcams that are widely distributed across Switzerland. We further examined the relationships between the generated grassland‐use intensity measures and plant species richness and ecological indicator values derived from a nationwide field survey. The webcam‐based verification for 2020 and 2021 revealed that most detected management events were actual mowing/grazing events (≥78%), but that a substantial number of events were not detected (up to 57%), particularly grazing events at higher elevations. We found lower plant species richness and higher mean ecological indicator values for nutrients and mowing tolerance with more frequent management events and those starting earlier in the year. A large proportion of the variance was explained by our use‐intensity measures. Our findings therefore highlight that remotely assessed management events can characterise land‐use intensity at fine spatial and temporal resolutions across broad scales and can explain plant biodiversity patterns in grasslands.

Urban green spaces with healthy vegetation play a key role in improving the quality of life in cities. However, urban soils, the basis of the urban greenery, are under strong anthropogenic influence and can considerably differ from natural soils. In this study, we observed short-term greening and browning of lawns during one vegetation period in urban parks of Geneva (Switzerland). We related the temporal trajectory of seasonal Normalized Difference Vegetation Index (NDVI) (8 days median) as a proxy of vegetation condition at different test sites to the physical soil properties (soil depth, coarse material, bulk density, conditioned air and water content) and Soil Organic Carbon (SOC). Strong drops of NDVI during dry periods in summer were related to shallow soil depths (<40 cm) and a high amount of coarse material (>10%) as well as lower SOC. Bulk density of the fine earth and the soil structure quality (expressed by air and water content of soil cores conditioned at a soil water potential of −100 hPa) had a significant influence on grass growth in spring but not in summer. Dense soils with conditioned air content closer to the trigger value of degraded soil structure resulted in lower NDVI values in spring. Our approach of using Earth Observation (EO) data for observing short-term greening and browning patterns, in this case the rise and decline of NDVI values, revealed that the role of the soil properties changed with the season. This approach may contribute to digital soil mapping and the assessment of soil ecosystem services in urban contexts. Urban planners are advised to save natural soils from over-building and keep them for green spaces. If soil has to be restored to create new green spaces, it should be deep and should not contain much coarse material, even for grassy vegetation.

Increasing urbanization degrades quantity, quality, and the functionality of spatial cohesion of natural areas essential to biodiversity and ecosystem functioning worldwide. The uncontrolled pace of building activity and the erosion of blue (i.e., aquatic) and green (i.e., terrestrial) landscape elements threaten existing habitat ranges and movability of wildlife. Local scale measures, such as nature-inspired engineered Blue-Green Infrastructure (BGI) are emerging mitigation solutions. Originally planned to promote sustainable stormwater management, adaptation to climate change and improved human livability in cities, such instruments offer interesting syn-ergies for biodiversity in support of existing ecological infrastructure. BGI are especially appealing for globally declining amphibians, a rich and diverse vertebrate assemblage sensitive to urbanization. We integrated bio-logical and highly resolved urban-rural land-cover data, ensemble models of habitat suitability, and connectivity models based on circuit theory to improve multi-scale and multi-species protection of core habitats and ecological corridors in the Swiss lowlands. Considering a broad spectrum of amphibian biodiversity, we iden-tified distributions of amphibian biodiversity hotspots and four landscape elements essential to amphibian movability at the regional scale, namely i) forest edges, ii) wet-forest habitats, iii) soils with variable moisture and iv) riparian zones. Our work shows that cities can make a substantial contribution (e.g., up to 15% of urban space in the study area) to wider landscape habitat connectivity. We highlight the importance of planning BGI locally in strategic locations across urban and peri-urban areas to promote the permeability and availability of 'stepping stone' habitats in densely populated landscapes, essential to the maintenance of regional habitat connectivity and thereby enhancing biodiversity and ecosystem functioning.

Central Europe has been experiencing unprecedented droughts during the last decades, stressing the decrease in tree water availability. However, the assessment of physiological drought stress is challenging, and feedback between soil and vegetation is often omitted because of scarce belowground data. Here we aimed to model Swiss forests' water availability during the 2015 and 2018 droughts by implementing the mechanistic soil-vegetation-atmosphere-transport (SVAT) model LWF-Brook90 taking advantage of regionalized depth-resolved soil information. We calibrated the model against soil matric potential data measured from 2014 to 2018 at 44 sites along a Swiss climatic and edaphic drought gradient. Swiss forest soils' storage capacity of plant-available water ranged from 53 mm to 341 mm, with a median of 137 +/- 42 mm down to the mean potential rooting depth of 1.2 m. Topsoil was the primary water source. However, trees switched to deeper soil water sources during drought. This effect was less pronounced for coniferous trees with a shallower rooting system than for deciduous trees, which resulted in a higher reduction of actual transpiration (transpiration deficit) in coniferous trees. Across Switzerland, forest trees reduced the transpiration by 23% (compared to potential transpiration) in 2015 and 2018, maintaining annual actual transpiration comparable to other years. Together with lower evaporative fluxes, the Swiss forests did not amplify the blue water deficit. The 2018 drought, characterized by a higher and more persistent transpiration deficit than in 2015, triggered widespread early wilting across Swiss forests that was better predicted by the SVAT-derived mean soil matric potential in the rooting zone than by climatic predictors. Such feedback-driven quantification of ecosystem water fluxes in the soil-plant-atmosphere continuum will be crucial to predicting physiological drought stress under future climate extremes.

Essential forest ecosystem services can be assessed by better understanding the diversity of vegetation, specifically those of Mediterranean region. A species level classification of maquis would be useful in understanding vegetation structure and dynamics, which would be an indicator of degradation or succession in the region. Although remote sensing was regularly used for classification in the region, maquis are simply represented as one to three categories based on density or height. To fill this gap, we test the capability of Sentinel-2 imagery, together with selected ancillary variables, for an accurate mapping of the dominant maquis formations. We applied Recursive Feature Selection procedure and used a Random Forest classifier. The algorithm is tested using ground truth collected from site and reached 78% and 93% overall accuracy at species level and physiognomic level, respectively. Our results suggest species level characterization of dominant maquis is possible with Sentinel-2 spatial resolution.

Countrywide winter and summer Sentinel-1 (S1) backscatter data, cloud-free summer Sentinel-2 (S2) images, an Airborne Laser Scanning (ALS)-based Digital Terrain Model (DTM) and a forest mask were used to model and subsequently map Dominant Leaf Type (DLT) with the thematic classes broadleaved and coniferous trees for the whole of Switzerland. A novel workflow was developed that is robust, cost-efficient and highly automated using reference data from aerial image interpretation. Two machine learning approaches based on Random Forest (RF) and deep learning (UNET) for the whole country with three sets of predictor variables were applied. 24 subareas based on aspect and slope categories were applied to explore effects of the complex mountainous topography on model performances. The reference data split into training, validation and test data sets was spatially stratified using a 25 km regular grid. Model accuracies of both RF and UNET were generally highest with Kappa (K) around 0.95 when predictors were included from both S1/S2 and the topographic variables aspect, elevation and slope from the DTM. While only slightly lower accuracies were obtained when using S2 and DTM data, lowest accuracies were obtained when only predictors from S1 and DTM were included, with RF performing worse than UNET. While on countrywide level RF and UNET performed overall similarly, substantial differences in model performances, i.e. higher variances and lower accuracies, were found in subareas with northwest to northeast orientations. The combined use of S1/S2 and DTM predictors mitigated these problems related to topography and shadows and was therefore superior to the single use of S1 and DTM or S2 and DTM data. The comparison with independent National Forest Inventory (NFI) plot data demonstrated precisions of K around 0.6 in the predictions of DLT and indicated a trend of increasing deviations in mixed forests. A comparison with the Copernicus High Resolution Layer (HRL) DLT 2018 revealed overall higher map accuracies with the exception of pure broadleaved forest. Although, spatial patterns of DTL were overall similar, UNET performed better than RF in areas with a distinct DLT on forest stand level, with the largest differences occurring when only S1 and DTM data was used. In contrast, predictions obtained from RF were more accurate in mixed stands. This study goes beyond the case study level and meets the requirements of countrywide data sets, in particular regarding repeatability, updating, costs and characteristics of training data sets. The 10 m countrywide DLT maps add complementary and spatially explicit information to the existing NFI estimates and are thus highly relevant for forestry practice and other related fields.

Abstract Livestock farmers rely on a high and stable grassland productivity for fodder production to sustain their livelihoods. Future drought events related to climate change, however, threaten grassland functionality in many regions across the globe. The introduction of sustainable grassland management could buffer these negative effects. According to the biodiversity–productivity hypothesis, productivity positively associates with local biodiversity. The biodiversity–insurance hypothesis states that higher biodiversity enhances the temporal stability of productivity. To date, these hypotheses have mostly been tested through experimental studies under restricted environmental conditions, hereby neglecting climatic variations at a landscape‐scale. Here, we provide a landscape‐scale assessment of the contribution of species richness, functional composition, temperature, and precipitation on grassland productivity. We found that the variation in grassland productivity during the growing season was best explained by functional trait composition. The community mean of plant preference for nutrients explained 24.8% of the variation in productivity and the community mean of specific leaf area explained 18.6%, while species richness explained only 2.4%. Temperature and precipitation explained an additional 22.1% of the variation in productivity. Our results indicate that functional trait composition is an important predictor of landscape‐scale grassland productivity.

Mapping and monitoring agricultural land-use intensity (LUI) changes are essential for understanding their effects on biodiversity. Current land-use models provide a rather coarse spatial resolution, while in-situ measurements of LUI cover only a limited extent and are time-consuming and expensive. The purpose of this study is to evaluate the feasibility of using habitat type, topo-climatic, economic output, and remote-sensing data to map LUI at a high spatial resolution. To accomplish this, we first rated the habitat types across the agricultural landscape in terms of the amount and frequency of fertiliser input, pesticide input, ploughing, grazing, mowing, harvesting, and biomass output. We consolidated these ratings into one LUI index per habitat type that we then related to topo-climatic, economic output, and remote-sensing predictors. The results showed that the LUI index was strongly related to plant indicator values for mowing tolerance and soil nutrient content and to aerial nitrogen deposition, and thus, is an adequate index. Topo-climatic, and, to a smaller extent, economic output and remote-sensing predictors, proved suitable for mapping LUI. Large- to medium-scale patterns are explained by topo-climatic predictors, while economic output predictors explain medium-scale patterns and remote-sensing predictors explain local-scale patterns. With the fine-scale LUI map produced from this study, it is now possible to estimate within unvarying land-use classes, the effect on agrobiodiversity of an increase in LUI on fertile and accessible lands and of a decrease of LUI by the abandonment of marginal agricultural lands, and thus, provide a valuable base for understanding the effects of LUI on biodiversity. Due to the worldwide availability of remote-sensing and climate data, our methodology can be easily applied to other countries where habitat-type data are available. Given their low explanatory power, economic output variables may be omitted if not available.

The combination of drought and heat affects forest ecosystems by deteriorating the health of trees, which can lead to large‐scale die‐offs with consequences on biodiversity, the carbon cycle, and wood production. It is thus crucial to understand how drought events affect tree health and which factors determine forest susceptibility and resilience. We analyze the response of Central European forests to the 2018 summer drought with 10 × 10 m satellite observations. By associating time‐series statistics of the Normalized Difference Vegetation Index (NDVI) with visually classified observations of early wilting, we show that the drought led to early leaf‐shedding across 21,500 ± 2,800 km2, in particular in central and eastern Germany and in the Czech Republic. High temperatures and low precipitation, especially in August, mostly explained these large‐scale patterns, with small‐ to medium‐sized trees, steep slopes, and shallow soils being important regional risk factors. Early wilting revealed a lasting impact on forest productivity, with affected trees showing reduced greenness in the following spring. Our approach reliably detects early wilting at the resolution of large individual crowns and links it to key environmental drivers. It provides a sound basis to monitor and forecast early‐wilting responses that may follow the droughts of the coming decades.

Pressures on natural resources are increasing and a number of challenges need to be overcome to meet the needs of a growing population in a period of environmental variability. The key to sustainable development is achieving a balance between the exploitation of natural resources for socioeconomic development and maintaining ecosystem services that are critical to human’s wellbeing and livelihoods. Some of these environmental issues can be monitored using remotely sensed Earth Observations (EO) data that are increasingly available from freely and openly accessible repositories. Hereafter, we present the Swiss Data Cube, a unique Analysis Ready Data archive of satellite imagery and some use cases to monitor Sustainable Development Goals.

Early detection of bark beetle infestations by remote sensing: what is feasible today? Infestation by the Norway spruce (Picea abies) bark beetle (Ips typographus) in uniform forest stands of the high montane and subalpine stage is a major challenge for management. It is impossible to identify in time all susceptible or already infested spruces in the often steep terrain solely by terrestrial observations and to prevent the proliferation of the beetle. A time-saving, cost-effective and effective method for finding these spruces is necessary and remote sensing techniques appear promising. Therefore, we investigated the potential of hyperspectral remote sensing data for the early detection of stressed or infested spruces using a case study in the experimental forest of the Swiss Federal Institute of Technology Zurich (ETHZ) in Sedrun. The approach that we developed is based on a combination of field surveys, hyperspectral data, vegetation indices calculated from these and their classification into the three classes “dead”, “stressed” and “healthy” using Random Forests, a machine-learning approach. We demonstrate that stressed spruces can be identified with this approach, but it is not yet ready for operational use. In particular, a slope-specific calibration of the method is necessary, which makes practical application impossible.

Methods and workflows for creating area-wide products using remote sensing methods have been developed for various reasons. For the sample-based estimates in the Swiss National Forest Inventory (NFI), area-wide data sets are used for the two-phase estimations, which have substantially lower estimation errors than one-phase inventories. Using area-wide data sets, it is possible to skip dense manual interpretation of the stereo-images and thus save resources. Further, many functions of the forest, such as biodiversity and protection against natural hazards, can be described and quantified better with area-wide spatial data than with field plot data.