Europe's Mediterranean coastal zones face increasing pressures from urbanization, tourism, and climate change. To assess these dynamics, this study applies a remote sensing-based, transnational, pixel-level trend analysis over a ten-year period (2014–2023) within a standardized 10 km coastal buffer across Southern Europe. The analysis examines biophysical and human-activity-related indicators, including the Enhanced Vegetation Index (EVI), Land Surface Temperature (LST), Nighttime Light (NTL), and built-up cover using Mann–Kendall trend statistic. The results show that EVI decline remained localized, affecting about 5% of the study area, mainly in Croatia and Turkey. A widespread increasing trend in LST was observed, affecting 55% of the coastal zone, especially in the central Mediterranean region. Regionally, the Eastern Mediterranean showed the strongest increase in NTL intensity. Furthermore, despite the limited increase in built-up areas, the rise in NTL intensity suggests that many coastal areas are undergoing functional intensification rather than only physical urban expansion. The findings indicate that Mediterranean coastal zones are experiencing different combinations of human activity, land-use change, and thermal patterns. The study provides evidence-based insights to support integrated coastal zone management and long-term monitoring in Europe's Mediterranean coastal regions.

A quantum-inspired tensor network framework for wildfire susceptibility classification in the Gargano region is introduced, leveraging AlphaEarth embeddings and Matrix Product State models. The approach combines scalable geospatial representations with an interpretable quantum mask, enabling both binary and multiclass classification of wildfire susceptibility. Beyond predictive performance, the study reveals a pronounced grokking transition in the binary case and provides a detailed analysis of inter-class confusion in the multiclass setting. By introducing level-resolved mixedness diagnostics based on reduced density matrices, we show that the MPS classifier naturally encodes a hierarchy of class distinguishability, with non-adjacent categories becoming more separable than neighboring ones. These results demonstrate that tensor network models not only achieve competitive classification accuracy but also offer a physically grounded framework to quantify and interpret class separability in complex environmental datasets.

Increasing air temperatures and summer drought increasingly constrain tree growth across the Mediterranean region, but these responses occur within landscapes modified by urbanization, industrial infrastructure, and artificial land cover. How anthropogenic landscape context alters climate-growth relationships remains poorly understood, especially in planted stands and across spatial scales. Here, we investigated tree-ring growth of planted Pinus halepensis Mill. stands across 41 sites spanning contrasting levels of anthropogenic pressure in southern Italy. Using hierarchical mixed-effects models, we tested whether anthropogenic proxies act as additive drivers of radial growth or instead modify sensitivity to summer temperature and hydroclimatic variability.Summer mean temperature was the dominant climatic constraint on growth, whereas SPEI-12 showed a weaker but consistent additional effect. Anthropogenic variables had limited and inconsistent additive effects on ring width, but strongly modified climate sensitivity through interaction effects. Local artificial land cover, especially within 1 km, consistently amplified sensitivity to summer heat and was the strongest anthropogenic moderator of growth responses. By contrast, hydroclimatic sensitivity was spatially heterogeneous, with attenuated SPEI responses near the industrial core and stronger responses farther away. A final random-slope model confirmed that this amplification persisted after accounting for heterogeneity among sites and individual trees.Our results show that landscapes heavily impacted by human activities do not simply shift growth levels, but reshape climate-growth relationships in planted Mediterranean pine stands. Fine-scale human modification should therefore be considered when assessing planted-stand vulnerability to future warming.

Climate change reshapes forest biophysical effects, yet the impact direction and strength remain uncertain. Here we quantify the growing-season land surface temperature between forests and adjacent open land (triangle LSTgs) and show contrasting temporal trends in triangle LSTgs across the globe during 2001-2023. Rising vapour pressure deficit (VPD) has emerged as the primary driver of these contrasting trends, surpassing other common climatic factors. By contrast, plant anisohydricity-an indicator of stomatal regulation behaviour-is the most important forest trait that negatively modulates the strength of the triangle LSTgs response to VPD variability. At low latitudes, forests are more isohydric, and rising VPD has exceeded the hydraulic safety margin, resulting in weakened cooling. Conversely, high-latitude forests are more anisohydric; VPD remains below the safety margin, and rising VPD thus leads to enhanced cooling. These results highlight that the overall climate benefits of global forests may be undermined if global VPD continues to intensify in future.

The COVID-19 pandemic constituted a global socioeconomic crisis with long-lasting consequences. In response to the rapid spread of the virus, governments implemented containment measures such as social distancing, mask mandates, and mobility restrictions, which also affected access to urban green spaces (UGS). This systematic review aimed to identify key lessons from the pandemic regarding the role of UGS during public health crises. The analysis included 155 studies published between 2020 and 2025, grouped into four main research domains: visitation patterns (n=88), planning and management of UGS (n=15), associations with viral transmission (n=11), and health benefits (n=41). The findings indicated substantial geographical disparities in scientific production and showed that the pandemic reshaped UGS visitation patterns, with proximity to residential areas becoming an important determinant of outdoor activities such as physical exercise and recreation. The literature also revealed persistent inequalities in access to urban nature, particularly among low-income communities, ethnic minorities, and other socially vulnerable groups. Drawing on different forms of evidence, five policy recommendations emerge: i) ensuring equitable access to UGS; ii) improving their quality and spatial distribution; iii) integrating UGS into emergency preparedness strategies; iv) implementing context-sensitive visitation and management protocols; and v) actively supporting health-promoting behaviors compatible with safe use of green environments. The applicability of these lessons to future crises will depend on pathogen characteristics, transmission routes, urban form, restriction policies, and governance capacity. The findings reinforce that equitable access to high-quality UGS should be recognized as a strategic priority for urban governance and public health resilience.

Wildfires in Southern Europe arise from interacting climatic, ecological, and socio-ecological mechanisms. Drought enhances fuel desiccation, land cover change reshapes vegetation flammability, and the expansion of the wildland–urban interface (WUI) increases ignition pressure and exposure. Understanding how these drivers overlap spatially and temporally is essential to identify where reinforcing or decoupled processes shape wildfire dynamics. This study develops a spatially explicit framework integrating drought variability (Standardized Precipitation–Evapotranspiration Index, SPEI), Landscape Flammability Classes (LFC), and Wildland–Urban Interface (WUI) expansion to detect statistically supported hotspots and coldspots of wildfire occurrence across biogeographical regions. We analysed a 20-year dataset (2001–2020) at 12-km resolution, combining number of fire and fire size with long-term trends in three macro-drivers (SPEI, LFC, WUI). Temporal changes in wildfire parameters were estimated using generalized linear models with False Discovery Rate (FDR) correction. Trends in SPEI and WUI were assessed through Mann–Kendall and Kendall tau tests supported by Theil–Sen slope estimation, while LFC change was derived from land-cover transitions between 2000 and 2018. Finally, a spatial co-occurrence analysis classified each grid cell as a hotspot, coldspot, or mismatch area based on the degree of alignment between macro-driver trajectories and wildfire trends. Significant and candidate hotspots were concentrated in the Anatolian, Continental, and Mediterranean bioregions, where over 30

Rapid urbanization exacerbates heat-related environmental issues. Yet urban expansion patterns and associated heat risks in developing countries remain poorly understood. We selected eight capital and eight secondary cities in Southeast Asia and characterized urban growth and heat hazards in their fringes using three spectral indices: built-up percentage of landscape (PLAND), Normalized Difference Vegetation Index (NDVI), and Land Surface Temperature (LST). From 2000 to 2020, PLAND and LST in the fringes increased by 11.93% and 1.39 degrees C, respectively. LST in the fringes of capital cities was 1.70 degrees C higher than in secondary cities. LST increases exceeded those in urban cores, particularly in secondary cities (+0.35 degrees C). Highly populated and wealthier cities showed marked PLAND increases and NDVI declines, but not necessarily elevated LST. We identify priority areas for land cover management and urban heat mitigation by city type and geographic location to inform sustainable regional planning.

Forest fire regimes in Europe are evolving due to complex interactions between climate change, land use, and management strategies. Traditionally concentrated in Mediterranean countries during the summer, fire activity is increasingly affecting regions and periods not historically considered at risk. In this study, we analyzed 20 years (2001–2020) of satellite-derived weekly forest fire data across five European biogeographical regions—Mediterranean, Atlantic, Alpine, Continental, and Boreal—to detect shifts in fire frequency, size, and extent. Using wavelet analysis and multi-method breakpoint detection on weekly fire metrics (burned area, number, and size), we identified significant temporal and seasonal changes in several regions. Results show a decline in summer fire activity in the Mediterranean and Atlantic regions, likely reflecting improved fire suppression and prevention. In contrast, our results show that the Alpine region shows increasing fire activity, particularly outside the summer season, suggesting an expanding fire season linked to climatic and ecological changes. Smaller but notable changes were also detected in Boreal and Continental regions. Our findings highlight the growing relevance of fires occurring outside the summer season in shaping Europe’s fire regimes. These results underscore the need for adaptive, region-specific fire management strategies that account for shifting seasonality and emerging risks beyond the traditional summer fire window.

The increasing frequency and severity of natural hazards, such as floods, wildfires, land degradation, and ground displacement, pose significant challenges to the protection of urban areas worldwide. While traditional monitoring approaches based on a single-source satellite sensor have proved to be reliable, they often fail to provide a holistic representation of the complexity, scale, and rapid evolution of these phenomena. The recent advancement of artificial intelligence (AI), coupled with the unprecedented availability of multi-source satellite imagery, offers new perspectives for enhancing natural hazard monitoring and susceptibility mapping. In this study, we present a novel approach that leverages state-of-the-art Explainable AI (XAI) techniques, particularly SHAP (SHapley Additive exPlanations), to analyze multi-source satellite imagery for natural hazard monitoring and assessment in urban areas. The framework utilizes globally available, open-source satellite data (Sentinel-1/2, COSMO-SkyMed, SAOCOM) to ensure inherent scalability and transferability. XAI is chosen to move beyond black-box prediction, providing transparent attribution of susceptibility to underlying environmental and infrastructural parameters, which is essential for informed intervention. This interpretability is critical for building stakeholder trust and ensuring that automated predictions align with domain knowledge before deployment. Our approach was developed, applied, and validated in two distinct sites located in the Puglia region, southern Italy: the densely populated Bari Urban Region (BUR) and the diverse settlements and land uses within the Gargano Urban Region (GUR). We combined XAI-based models with optical imagery from Sentinel-2, SAR data from Sentinel-1, COSMO-SkyMed, and SAOCOM to extract the key features explaining the occurrence and magnitude of the following hazards: (1) sediment connectivity; (2) land displacement; (3) urban floods; and (4) urban wildfires. Our results demonstrate that the integration of multi-source satellite imagery through AI not only significantly enhances the accuracy and reliability of hazard detection (e.g., F1 scores consistently above 67.5% for three of the four hazards, and high Recall across all modules) but also enables the identification of subtle spatial patterns and crucial interrelationships.

The land sector has a crucial role in the global pathway towards carbon neutrality, being at the same time a significant contributor to global greenhouse gases (GHG) emissions and an active removal and storage of atmospheric carbon when sustainably managed. In this paper, we develop and apply a land-based approach to assess how sustainable land management solutions can pursue the dual aim of regenerating degraded rural areas and offsetting agricultural GHG emissions, thereby contributing to the achievement of carbon neutrality in rural districts. The proposed land-based approach integrates different methodologies, including Life Cycle Assessment, literature data and IPCC methods, with the objective to determine the agriculture-related GHG emissions and the potential for mitigation through sustainable land-based solutions implemented within the same rural district. The application to a case study in the Mediterranean region, i.e., southern Apulia region (Italy) affected since 2013 by the “olive quick decline syndrome” which causal agent is identified in the bacterium Xylella fastidiosa, showed that sustainable land-based solutions, besides restoring the degraded farming system, can lead to a carbon neutral rural district. Land use conversions, afforestation and sustainable agricultural practices would lead to a reduction and a complete offset of the agricultural GHG emissions in the area, even producing a net carbon removal (i.e., negative emissions) up to about 384 Gg CO2 year−1. As such, this study demonstrates that sustainable land-use options are key in contributing to climate change mitigation while improving the landscape and related co-benefits.

Urban forests are primary nature-based solutions (NBS) against the Urban Heat Island (UHI) effect, yet the specific cooling contributions of three-dimensional (3D) canopy structure versus simple two-dimensional (2D) coverage remain under-explored. This study decouples the thermal regulation effects of vertical (e.g., height, foliage diversity) and horizontal (e.g., cover, plant area index) canopy metrics in Beijing using Global Ecosystem Dynamics Investigation (GEDI) LiDAR and Landsat data. Using Structural Equation Modeling (SEM), we found that vertical structure (Relative Height 95th Percentile, RH95) is the dominant cooling factor, reducing Land Surface Temperature (LST) by approximately 1.13 degrees C per 2.75 m increase in height, independent of vegetation greenness (Normalized Difference Vegetation Index, NDVI). While horizontal density enhances cooling indirectly by boosting NDVI, the surrounding built environment (Normalized Difference Built-up Index, NDBI) directly counteracts these benefits. Crucially, using Local Climate Zone (LCZ)-based stratified analysis, and incorporating NDVI as a background gradient, we systematically investigated the regulatory effects of canopy structural metrics on land surface temperature under diverse urban morphological contexts. Combined with our model- validated against ground-based LiDAR (R2=0.69) - demonstrates that in high-density urban zones, maximizing canopy height and vertical complexity is more effective for heat mitigation than merely increasing canopy cover. These findings advocate for a shift in urban forestry planning from 'greening coverage' to 'structural complexity'.

Climatic and anthropogenic disturbances have led to intense small-scale tree cover loss in global forests. However, it remains unclear when forest attributes at a large scale (e.g., 0.05° resolution) will decline in response to such sub-grid (e.g., 30-m) tree cover losses within forest ecosystems. Utilizing global maps of forest attribute proxies, we discover that vegetation greenness, canopy structure, composition, and photosynthesis function can all increase under limited tree cover loss, indicating a widely existing safety margin in global forests that is primarily buffered by a positive edge effect of landscape fragmentation within forest ecosystems. The safety margin varies across biomes (tropical: 7.7%; temperate: 3.7%; boreal: 1.0%) and is often positively correlated with ecosystem resistance. In addition, about 35.7% of the remaining global forests have exceeded the safety margin. Our finding contrasts with the conventional perception that sub-grid tree cover losses are inevitably associated with declines in forest attributes and functions. It provides quantitative information for mitigating forest degradation and has strong implications for sustainable forest management practices.

Nature-based Solutions (NbS) are increasingly used to address interconnected urban socio-environmental challenges, yet their implementation remains largely project-based and fragmented. We introduce Nature-based Policy (NbP) as a cross-sectoral framework that incorporates nature into organisational decision-making, investment priorities and governance practices. By enabling Nature-based Solutions to contribute to lasting organisational change, NbP provides an institutional pathway towards urban sustainability transitions.

In this perspective editorial, we analyzed publication trends and author-provided keywords across ten leading ecology journals from 2012 to 2025 to synthesize recent developments in ecological research. We identified six major trends that have shaped the field: (1) the shift from descriptive to mechanistic investigations, (2) the relevance of climate change and ecosystem feedbacks, (3) the growing integration of the human dimension into ecological research, (4) the application of advanced technologies and analytical methods, (5) the increasingly diverse contributions from the global scientific community, and (6) the rise of issue-focused research. Building on this synoptic overview, we discuss emerging frontiers likely to drive future scientific advances and influence journal development. Future ecological research will increasingly focus on understanding and predicting ecosystem responses to global environmental and societal change, strengthening the integration of human and natural systems, improving ecological forecasting, and addressing ecosystem complexity, resilience, and adaptive capacity. Continued progress will depend on the effective adoption of emerging technologies, data-intensive approaches, and advanced analytical methods. At the same time, ecology faces challenges in an era characterized by rapid advances in knowledge and technology, accelerating globalization, climate change, evolving societal needs, and shifting scientific culture. Balancing investments in fundamental ecological research with increasing demands for applied, policy-relevant, and solution-oriented science will remain a central challenge and opportunity.

Leaf age structure strongly regulates canopy photosynthesis in Amazon rainforests yet its large-scale patterns and dynamics remain poorly understood. Here we map the fraction of leaf area of photosynthetically efficient young leaves (fyoung) using remote sensing data and assess its spatiotemporal variability from 2001 to 2023. We find that fyoung varies markedly with elevation and canopy height: tall or mountain forests (canopy ≥32 m or elevation ≥300 m) exhibit higher fyoung than short or lowland forests, reflecting higher leaf turnover driven by stronger radiation, greater atmospheric dryness and longer dry seasons. Across the basin, fyoung increased significantly in 85.2% of forests during 2001-2023, linked to decreasing precipitation, rising sunlight, intensifying atmospheric dryness and lengthening dry seasons. This widespread trend towards more juvenile leaves is projected to persist under future climate change. Our findings reveal a fundamental shift in Amazon leaf age structure and highlight its importance for predicting future photosynthetic responses in a warmer, drier climate.

Abstract Urbanization is accelerating vertical urban development, yet the complex relationship between building height (BH) and urban heat island (UHI) intensity remains underexplored. Using 30‐m high‐resolution urban building data from 2005 to 2020, we quantify the impact of BH on UHI across 11,290 urban grid cells in 331 Chinese prefecture‐level cities and assess the potential for UHI mitigation through BH composition reorganization. Our analysis reveals a critical BH threshold (BH crit ) that marks a shift in the thermal effect of BH. Below BH crit , increasing BH intensifies UHI effects; beyond this threshold, further height increases result in cooling—a finding that challenges the conventional assumption that taller buildings consistently exacerbate urban heat. We further project that, in principle, China's urban grid cells could reduce UHI intensity through BH reorganization, with a national average mitigation potential of 0.31 ± 0.004°C. These findings underscore the importance of optimizing vertical urban composition as a proactive UHI mitigation strategy and provide valuable insights for sustainable urban densification from a three‐dimensional perspective.

Land-use change contributes significantly to climate change mitigation through biophysical changes (albedo, alpha) and biogeochemical (greenhouse gases, GHG) emissions (here refers to methane, CH4, and nitrous oxide, N2O). While the impact of grassland-cropland conversion on global warming potential (GWP) is well-documented globally, research remains scarce in the saline-alkaline agropastoral transition zone (APTZ) of the western Songnen Plain, Northeast China, an ecotone uniquely characterized by soil-crusting and seasonal inundation. We conducted in situ bi-weekly measurements of N2O and CH4 fluxes (June-September) to acquire growing season GWP(N2O) and GWP(CH4), alongside alpha. The study compared an undisturbed fenced meadow (FMD) with three adjacent land-use types, clipped meadow (CMD), saline-alkaline meadow (SAL), and paddy rice field (PDY), converted from FMD from 2018 to 2022. Annual alpha-induced GWP (GWP Delta alpha) was positive across all converted sites (CMD, SAL, and PDY), indicating a warming effect due to lower alpha compared to FMD. The PDY exhibited the highest CH4 emission (5.04 kg CO2 m(-2) yr(-1)), exceeding other land uses by three orders of magnitude (p < 0.05). Conversely, N2O emissions remained consistently minimal and stable across all sites. When integrating the net ecosystem exchange of CO2 (NEE), the PDY functioned as a net warming source. In contrast, the warming effects of alpha and non-CO2 GHGs were effectively offset by the NEE in other land uses. Machine learning identified soil water content (SWC) as the dominant predictor of alpha across all land uses in growing season. However, a mechanistic divergence was observed, i.e., alpha in low saline-alkali ecosystems (FMD, CMD and PDY) was shaped by coupled biotic and soil moisture controls, whereas in the degraded SAL ecosystem, alpha is almost exclusively abiotic-driven. These findings demonstrate that land-use conversion in the Songnen Plain governs complex land-surface feedbacks through distinct pathways. This study provides a quantitative framework for integrating biophysical and biogeochemical impacts to optimize land management for climate resilience in saline-alkaline agropastoral ecotones.

Yellow sweetclover (Melilotus officinalis (L.) Lam.; MEOF) is an invasive forb pervasive across the Northern Great Plains in the United States, often linked to traits such as wide adaptability, strong stress tolerance, and high productivity. Despite MEOF's prevalent ecological-economic impacts and importance, knowledge of its spatial distribution and temporal evolution is extremely limited. Here, we aim to develop a spatial database of annual MEOF abundance (2016-2023) across western South Dakota (SD) at 10 m spatial resolution by applying a generalized prediction model on Sentinel-2 imagery. We collected in situ quadrat-based total vegetation cover with MEOF percent cover estimates across western SD from 2021 through 2023 and synthesized with other available percent cover estimates (2016-2022) of several federal, state, and non-governmental sources. We conducted drone overflights at 14 sites across Butte County, SD in 2023 to develop very high spatial resolution (4-6 cm) and accurate MEOF cover maps by applying a random forest (RF) classification model. The field-measured and uncrewed aerial system (UAS) derived MEOF percent cover estimates were used to train, test, and validate a RF regression model. The predicted MEOF percent cover dataset was validated with UAS-derived percent cover in 2023 across four sites (out of 14 sites). We found that the variation in the Normalized Difference Moisture Index and Distance to roads were among the top predicting variables in predicting MEOF abundance. Our predictive model yielded greater accuracies with an R2 of 0.76, RMSE of 15.11 %, MAE of 10.95 %, and MAPE of 1.06 %. We further validated our 2023 predicted maps using the 3 m resolution PlanetScope imagery for regions where field samples could not be collected in 2023. The database of MEOF abundance showed consecutive years of average or above-average precipitation yielded a higher MEOF abundance across the study region. The database could assist local land managers and government officials pinpoint locations requiring timely land management to control the rapid spread of MEOF in the Northern Great Plains. The developed invasive MEOF percent cover datasets are freely available at the figshare repository (10.6084/m9.figshare.29270759.v1, Saraf et al., 2025).

Biological invasions are a major driver of biodiversity change, particularly in disturbed urban forests where propagule pressure favors opportunistic species. Here, we document the demographic expansion and biomass contribution of the invasive Australian palm Archontophoenix cunninghamiana H.Wendl. & Drude in an urban Atlantic Forest remnant in São Paulo, a subtropical megacity in southeastern Brazil. Using one hectare of permanent plots monitored between 2017 and 2022, we quantified palm density and assessed changes following management actions implemented by municipal authorities. We also compiled official data on removed individuals in 2021 and estimated aboveground biomass using the allometric model of Hughes et al., (1999). Prior to management, density reached 391 individuals ha⁻¹, decreasing to 138 individuals ha⁻¹ after removal. Official records indicated the removal of 738 individuals, storing 3.3 tons of aboveground biomass, with medium-to-large individuals contributing most to biomass stock. Despite its carbon storage, the high density and regenerative success of A. cunninghamiana relative to native palms may intensify biotic homogenization and threaten recruitment dynamics in urban forest remnants and adjacent Atlantic Forest landscapes. Our findings highlight the need for coordinated prevention and management to reduce propagule pressure from ornamental plantings and limit invasion spread across metropolitan forest fragments.

The urban green spaces (UGS) structure are important attributes affecting a wide range of ecosystem services in cities, especially the potential cooling effects of trees and vegetation. However, the coarse resolution of most satellite images limits the ability to detect and assess the structural characteristics of UGS and their effects on the thermal environment. In this direction, drones provide an ideal tool to overcome this issue. In this study, we employed Random Forest algorithms and Python tools modelling to produce detailed land use patterns for two typical UGS in China (i.e., urban parks) to explore the effect of UGS structural factors on the thermal environment and its cooling capacity. We used two UGS in Baoding as case studies: Jingxiu Park with diverse and complex spatial structures, and Military Academy Square characterized by single and simple structures. Our results suggest that Jingxiu Park (complex struct patterns) provides a higher cooling effect (0.551 degrees C lower) and temperature regulation than Military Academy Square (simple struct patterns). Additionally, land surface temperature comparisons of impervious surfaces with and without shaded areas demonstrate that shading has an enhanced cooling effect and varies across habitat types.