Mapping air pollution across space remains challenging, even in countries with dense monitoring networks. In Germany, pollutant levels can change over short distances because of traffic, land use, and meteorological conditions, while national assessments often rely on unevenly distributed monitoring stations. This study examines how openly available satellite observations and reanalysis data can support annual modelling of NO2 and PM2.5 across Germany from 2019 to 2024. Sentinel-5P (NO2 and CO), MODIS Normalized Difference Vegetation Index (NDVI) and Multi-Angle Implementation of Atmospheric Correction (MAIAC) aerosol optical depth (AOD), and ERA5-Land meteorological variables were combined with EuroAirnet observations, and seven machine-learning algorithms were evaluated. Model performance was assessed using random cross-validation, an independent test set, and spatial cross-validation, while SHAP (Shapley Additive Explanations) values were used to interpret predictor contributions. For NO2, Random Forest achieved the highest accuracy (R2=0.68; RMSE=5.87μgm-3), with SHAP analysis identifying tropospheric NO2 and vegetation structure (NDVI) as the most influential predictors. PM2.5 proved more difficult to model at the annual scale: Gradient Boosting performed best (R2=0.50; RMSE=11.53μgm-3), with surface pressure, NDVI, and co-emitted gases emerging as key variables, while MAIAC AOD contributed little independent information when aggregated annually. A sensitivity analysis showed that including a static road-density layer improved NO2 estimates near monitoring sites but provided limited gains under spatial validation. The resulting concentration maps reproduce the main national patterns observed in the monitoring network, showing a decline in NO2 and more regionally variable behaviour for PM2.5. Although annual predictors cannot capture short-term variability or highly localised emission sources, the study provides a transparent and reproducible framework for national-scale air-quality assessment based entirely on open global datasets and highlights the potential to integrate additional Earth observation and climate reanalysis products in future research.

Intact tropical peatlands are globally important carbon stores, yet their hydrology remains poorly understood due to limited accessibility and sparse field measurements. In this study, we evaluate the potential of L-band Synthetic Aperture Radar (SAR) backscatter to monitor above-ground water level variation across diverse lowland peatland ecosystems in Colombia and Peru. Using vegetation structure metrics from GEDI with ancillary remote sensing data, we assess the sensitivity of L-band HH (L-HH) backscatter to water level changes. We observed significant linear correlations between water level and L-HH backscatter in white-sand ecosystems, palm swamp peatlands (open and forested) and seasonally flooded forests. Pole forest peatland water levels showed no correlation with L-HH backscatter. To predict these regressions, we developed ecosystem-specific multiple linear regression models using L-band HV backscatter, NDVI, and GEDI metrics, achieving strong predictive performance (R 2 = 0.8–0.94). We further tested the temporal robustness of these relationships by predicting water levels across different years. Our results demonstrate the potential of combining L-band SAR with vegetation metrics derived from spaceborne data for regional monitoring of peatland hydrology. This provides a methodological pathway for integrating tropical peatland dynamics into carbon cycle models. • L-band SAR HH backscatter correlates with water level in tropical peatland sites. • Vegetation structure controls L-band SAR sensitivity to hydrological changes. • Ecosystems with low vegetation density show strong monitoring potential. • Palm swamps and flooded forests exhibit good monitoring potential with L-band SAR. • Our approach supports the integration of peatland hydrology into carbon cycle models.

Comprehensive study on the performance optimization of hyperspectral unmixing algorithms: A focus on airborne hyperspectral data.

Wetland ecosystems, crucial for carbon sequestration and coastal hazard mitigation, have experienced tremendous losses in land surface area over the last century, primarily due to land reclamation. This has led to increased rates of land subsidence in regions with high levels of reclamation, causing heightened vulnerability in these areas under anticipated scenarios of climate induced sea-level rise. This study integrates multi-sensor satellite remote sensing (optical, thermal, and active microwave) with spatially explicit eddy covariance flux measurements to model gross primary productivity (GPP) in restored wetlands of California's Sacramento-San Joaquin Delta. GPP is a crucial process in this context, as it impacts the potential for wetlands to act as land carbon sinks and has been shown to reverse land subsidence in restored wetlands of previously reclaimed areas. Still, there remain gaps in understanding how vegetation vigor, wetland composition and structure, and environmental conditions individually and interactively impact carbon assimilation in these ecosystems. This research aims to understand how complementary remotely sensed signals from multiple satellite platforms across the electromagnetic spectrum combine to improve classical, optically based GPP models, while determining the relative importance of certain biotic and abiotic environmental conditions that regulate GPP. Using a Bayesian generalized additive modeling framework, we evaluated how vegetation vigor (NDVI), canopy structure and biomass density (microwave backscatter), and land surface temperature affect wetland GPP at 10-m spatial resolution over a five-year period. Our results reveal a strong hierarchical and complementary influence of these variables, with the highest GPP occurring in warm, well-watered, and densely vegetated conditions. The model explained on average 66% of GPP variability and provides a scalable, open-access framework for assessing carbon fluxes in wetland landscapes. These findings offer valuable insight into planning restoration, monitoring restoration outcomes, carbon accounting, and identifying coastal adaptation strategies for valuable blue carbon ecosystems. • We develop a wetland GPP model using eddy covariance and remote sensing data fusion. • Hierarchy of wetland GPP drivers revealed through remotely sensed observations. • Wetland GPP shows greatest sensitivity to interaction of drivers. • Hierarchical model enables informed upscaling of wetland GPP to landscape level.

Assessing the impacts of changing elephant densities on woody vegetation structure in private reserves within the Greater Kruger National Park, South Africa

Annual fires shape humid savanna dynamics, yet their interaction with heterogeneous vegetation structures remains poorly understood. This study examines how tree clumps influence vegetation dynamics in the fire-prone savanna of Lamto (Côte d’Ivoire), testing the hypothesis that these formations locally alter fire regimes, creating micro-habitats that facilitate species survival and forest tree establishment. We sampled fifty tree clumps (>9 trees each), comparing understory (internal) zones with adjacent external areas (5 m perimeter). Vegetation structure, fire behavior, tree health, and plant community composition were quantified. Results reveal a protective “umbrella effect” of tree clumps. Understory zones exhibited lower grass height and biomass, leading to significantly reduced flame heights compared to external areas. This fire mitigation enhanced species richness beneath tree clumps, supporting savanna species (Piliostigma thonningii, Crossopteryx febrifuga), along with juvenile regeneration of forest and exotic species (Elaeis guineensis, Ceiba pentandra, Azadirachta indica). External zones were dominated by pyrophilic grasses (Hyparrhenia sp. 88%), while understories featured less flammable assemblages. Tree health (Bridelia ferruginea) was superior under tree clumps, confirming their protective function. Tree clumps function as critical fire refuges, attenuating fire intensity and creating niches for fire-sensitive species within the savanna matrix. They serve as resilience nuclei and potential drivers of forest expansion. However, exotic species colonization within refuges raises concerns about invasion vulnerability. Preserving these keystone structures is essential for maintaining biodiversity and ecosystem processes in West African savannas under global change.

A growing number of emerging non-native species are expanding within their introduced ranges and may become increasingly problematic in the future. Among them, several Solanum taxa pose underappreciated ecological and economic risks. Solanum nitidibaccatum , a poorly studied annual native to South America and increasingly recorded across Europe, represents a suitable model for understanding the establishment of non-native ruderal and segetal Solanum species in a transformed landscape. We examined how the composition and functional structure of ruderal vegetation influence the occurrence of S. nitidibaccatum , hypothesizing that habitat filtering and high functional heterogeneity facilitate its establishment, whereas interspecific competition constrains it. Using community diversity metrics and functional structure indices, we related vegetation properties to the probability of this species occurrence. The probability of S. nitidibaccatum occurrence was higher in species-rich communities and in vegetation characterized by higher leaf dry matter content and greater functional dispersion. In contrast, it declined in assemblages with a stronger contribution of nitrogen-demanding species and taller vegetation with higher specific leaf area. Our findings indicate that the establishment of S. nitidibaccatum is shaped by the interplay between habitat filtering and functional heterogeneity in ruderal vegetation, which may create underutilized niche space that facilitates the spread of this emerging non-native species. Considering its potential capacity to colonize ecologically similar vegetation across a range of transformed ecosystems, S. nitidibaccatum should be regarded as a species with high potential to become a problematic invasive weed. Its continued spread, together with the emergence of other non-native Solanum species, underscores the need for further research to better anticipate ecological and economic consequences that are challenging to predict.

Forest canopy water storage capacity is a critical component of ecohydrological research. However, because most current studies focus on the plot or stand scale, upscaling these fine-scale measurements to regional spatial scales remains a major challenge. Taking the forest in southern Jiangxi province as a case study, we integrated water immersion experiments, Handheld Laser Scanning (HLS), Unmanned Aerial Vehicle LiDAR (UAV-LiDAR), and optical remote sensing data to construct a spatial upscaling model. This model aims to quantify regional canopy water storage capacity and delineate its spatial patterns. The results indicate that: (1) the water storage capacity of branches and leaves per unit surface area of coniferous trees was significantly higher than that of broad-leaved trees, and the water storage capacity of branches was 6.0–10.7 times that of leaves. The mean canopy water storage capacities of coniferous forests, mixed coniferous and broad-leaved forests, and broad-leaved forests were 1.41 ± 0.27 mm, 1.30 ± 0.45 mm, and 1.26 ± 0.36 mm, respectively. (2) The canopy water storage capacity was significantly positively correlated with canopy volume (VC) and average canopy area (AC) extracted from UAV-LiDAR data, and vegetation structure factors such as normalized difference vegetation index (NDVI) and vegetation cover (FVC) extracted from optical remote sensing, and significantly negatively correlated with altitude and slope. Among them, canopy closure (C), average canopy area (AC), and altitude were key factors affecting canopy water storage capacity. (3) The upscaling prediction models based on UAV-LiDAR data and optical remote sensing factors, respectively, show reliable prediction performance, with R2 values of 0.884 and 0.815, RMSE of 0.951 and 0.116 mm, respectively. (4) The canopy water storage in the study area ranged from 0 to 1.76 mm, with a prediction uncertainty ranging from 0.12 to 0.49 mm. Canopy water storage is higher in the continuous middle and low mountain and hill areas within the region, while it is relatively lower in the high elevation ridge areas along the western, eastern, and southern margins. The results provide baseline structural information for understanding the spatial patterns of regional forest canopy interception potential.

Riparian areas are the unique areas adjacent water body. These transitional zones play a vital role in maintaining water quality and regulating hydrological processes. The current study focuses on the downstream agro-ecosystem landscapes of the Bhadra River, aiming to develop a comprehensive checklist of riparian flora. It examines the tree species diversity, composition and richness of Riparian area from January to May 2025. The study also emphasis to assesses anthropogenic disturbances affecting riparian vegetation in the study area. Random plots were laid on both the sides of Agroecosystem riparian areas of downstream Bhadra River. A total of 48 tree species belonging to 24 families were recorded from the riparian agroecosystem zones of the Bhadra River dominated by the Family Fabaceae. IVI analysis reveals that Albizia saman, Pongemia pinnata, Leucaena leucocephala and Eucalyptus as dominant contributors to the vegetation structure with IVI values of 66.1,35.8 ,30.1 and 18.3 respectively. In contrast native riparian species such as Terminalia arjuna and Syzygium cumini were observed with very low IVI values. The dominance of Non riparian species in the riparian buffers of Bhadra indicates the possible Anthropogenic pressures which requires an immediate attention for the restoration of native riparian vegetation for the conservation of river morphology and associated aquatic habitats also for the maintenance of essential ecosystem services.

Abstract Invasive alien plants can provide economic or cultural benefits to local communities, influencing perceptions and potentially affecting management decisions. Understanding these perceptions is crucial to avoiding inefficiencies, misunderstandings and conflicts in the management of invasive alien species. Our study explored community perceptions and interactions with the fleshy‐fruited invasive shrub Pyracantha angustifolia (Franch.) C. K. Schneid in South Africa's montane grasslands, using mainly in‐person questionnaires, as well as telephonic and online questionnaires, conducted between 2021 and 2022. Results showed that while communities occasionally consumed the fruits, which were mostly eaten by children, acting both as seed predators and dispersers, the fruits were not used for subsistence purposes. Residents generally did not perceive P. angustifolia as problematic, while farmers and conservation practitioners considered it detrimental because of its impact on grazing, recreation and vegetation structure. Fire was considered the least effective control method over mechanical and chemical control, while government‐supported initiatives, such as the Expanded Public Works Programme, were the most preferred form of assistance for management. Understanding how communities interact with and perceive invasive alien plants is essential for aligning ecological goals with social realities and reducing potential conflicts. Read the free Plain Language Summary for this article on the Journal blog.

The rupestrian vegetation on rocky hematite outcrops in the Brazilian Amazon has the smallest geographic distribution and highest endemism in the region. This study aimed to characterize the floristic composition and vegetation structure of these formations, locally known as Canga. The floristic inventory included both natural regeneration and the tree and shrub layers. Species richness and individual abundance were significantly lower in the rupestrian fields outside protected areas. Moreover, the dominant species differed markedly between protected and unprotected sites. Species typical of well-preserved habitats within protected areas were either absent or had drastically reduced abundance outside. Plant composition was entirely different between the two settings. The observed reduction in species richness, abundance, and changes in floristic composition is likely associated with human-induced disturbances.

Introduction Seasonal flood pulses in the Okavango Delta stimulate rapid vegetation growth that later dries during the dry season, creating extensive fuel loads that increase wildfire susceptibility in the Eastern Okavango Panhandle (EOP), Botswana. Despite this flood–fire interaction, quantitative and spatially explicit assessments of wildfire risk in the region remain limited. This study addresses this gap by developing a spatial Fire Risk Index (FRI) to identify high-risk areas and persistent wildfire hotspots. Methods Key biophysical variables (vegetation, land surface temperature, and topography) and anthropogenic factors (proximity to roads and settlements) were integrated within a Geographic Information Systems (GIS) framework to generate the FRI. Model performance was evaluated using Receiver Operating Characteristic (ROC) analysis with Moderate Resolution Imaging Spectroradiometer (MODIS) active fire data, producing an Area Under the Curve (AUC) value of 0.77. Spatial clustering of fire occurrences was further examined using Kernel Density Estimation and Getis-Ord Gi* hotspot analysis to identify statistically significant wildfire concentrations. Results and discussion The results indicate that approximately 30% of the landscape falls within moderate fire risk and 21.4% within high fire risk categories. High-risk zones are concentrated in remote, densely vegetated wildlife concessions with high fuel loads and limited accessibility. The NG13 concession emerged as a statistically significant wildfire hotspot (Getis-Ord Gi*, z-score > 2.58, p < 0.01), demonstrating how flood-driven fuel accumulation and human ignition sources interact to shape fire occurrence. By revealing that wildfire risk is concentrated in remote fuel-rich landscapes rather than only near settlements, the findings highlight an access paradox that challenges conventional fire management assumptions in savanna ecosystems. This study therefore provides the first high-resolution spatial framework for understanding wildfire dynamics in the Eastern Okavango Panhandle and supports a paradigm shift from reactive suppression toward proactive, evidence-based prevention through satellite monitoring, targeted fuel management, and strengthened landscape-level fire planning. These insights offer new understanding of how hydrological processes, vegetation structure, and human accessibility jointly shape fire regimes in flood-influenced savanna systems globally.

Science for wetland conservation: diversity, vegetation structure, and health assessments of mangrove forests in eastern Marinduque, Philippines

Abstract Quantifying the distribution of Spanish moss ( Tillandsia usneoides L.) is challenging because it grows suspended from high tree branches, limiting manual sampling. Terrestrial laser scanning (TLS) provides a non-destructive means of capturing vegetation structure in three dimensions. However, no established methods exist for identifying Spanish moss from TLS data. We evaluated five classification methods for distinguishing Spanish moss in TLS-derived point cloud data: Graph, DBSCAN, Random Forest (RF), Kernel Point Convolution (KPConv), and PointNet++. PointNet++ achieved the highest accuracy (81%), followed by DBSCAN (70%), KPConv (61%), RF (54%), and Graph (52%). Unsupervised methods required minimal computational resources (2–3 min, 8–16 GB memory) without training. RF required 3 h for training, 8 for prediction with 1024 GB memory. Deep learning methods required substantially more: KPConv needed 60 h for training, 4 for prediction (256 GB), while PointNet++ required 48 h for training, 1 for prediction (128 GB). Agreement was lowest in the central and upper canopy due to occlusion. Surface variation, PCA1, and verticality contributed most to accurate predictions. These results demonstrate the feasibility of using TLS and advanced classification methods for non-destructive Spanish moss mapping and highlight the accurate classification ability of PointNet++ for future biomass estimation at landscape scales.

Abstract Stable carbon isotopes in Holocene soils provide key insights into past climate and ecosystems. This study presents high-resolution isotope analyses of pedogenic carbonates and organic carbon from modern loess-derived soils in northern Iran across a strong precipitation gradient (150–850 mm mean annual precipitation [MAP]). Eight soil profiles span five soil orders: Alfisols, Mollisols, Inceptisols, Aridisols, and Entisols. The mean δ 13 Cpc values show strong linear relationships with MAP (δ 13 Cpc = −0.0093 × MAP + 1.8878; R 2 = 0.98) and with the ratio of precipitation to potential evapotranspiration, P/PET (δ 13 Cpc = −8.6842 × P/PET + 1.608; R 2 = 0.99), indicating that δ 13 Cpc reliably reflects moisture availability during carbonate formation. Values range from −6.2‰ in wetter sites to −0.1‰ in dry areas, reflecting changes in soil respiration and CO₂ flux. δ 13 Coc values (−25.6‰ to −23.3‰) indicate dominant C₃ vegetation and exhibit a bimodal response to precipitation, increasing from arid to semiarid zones and decreasing in wetter forests. Oxygen isotopes in carbonate (δ 1 ⁸Opc = −7.9‰ to −6.6‰) show limited climate sensitivity, reflecting precipitation mainly from the Caspian Sea with minimal evaporative enrichment. Overall, δ 13 Cpc, δ 13 Coc, and δ 1 ⁸Opc provide robust proxies for soil moisture, vegetation structure, and water sources, supporting paleoenvironmental reconstruction in loess systems.

ABSTRACT Reforestation areas undergo rapid and continuous changes in vegetation structure, posing challenges for accurate and cost-effective monitoring. Here, we evaluate the accuracy of the geometric registration of orthomosaic images and digital elevation models using co-alignment, which does not rely on Ground Control Points or the Global Navigation Satellite System, to facilitate time-series analysis of the reforestation area. Three alignment methods – individual alignment (Ind), co-alignment among all epochs together (CA_All), and co-alignment between two epochs (CA_1on1) – were compared. The results show that both co-alignment methods significantly reduce horizontal and vertical relative errors compared to individually aligned images. This approach achieves geometric registration with horizontal accuracy within 0.05 m, whereas the vertical accuracy reaches approximately 0.1 m. When flight conditions and intervals differ markedly, the number of inter-epoch tie points decreases, occasionally leading to meter-scale misalignments. However, connecting more than two epochs reduces these errors and maintains accuracy sufficient for continuous monitoring. These findings suggest that co-alignment, combined with only one accurately georeferenced reference epoch, is highly effective for frequent and detailed forest monitoring. By leveraging a minimal subset of stable features (e.g. logging residues), multi-epoch unmanned aerial vehicle imagery can be seamlessly integrated, providing substantial cost and labor savings over traditional ground-control approaches. This paper presents an easy method for applying time-series analysis to early-stage forest management, with the potential to enable fine-scale detection of growth and changes in spatially complex and rapidly transforming landscapes in the future.

Semi-arid areas have urban green spaces that can be classified as hotspots for biodiversity, but studies on flora and soil fungi are limited. A study was conducted using stratified quadrats to evaluate the diversity of plants and fungi in seven urban parks located in Hyderabad Cantonment, Sindh, Pakistan. Research methods included quadrat sampling to collect all specimens, culture-based methods for isolating fungi, and the determination of soil physicochemical characteristics to assess environmental conditions. The results showed that there are 105 different vascular plant species in the parks (40 species of trees, 26 species of shrubs, and 39 species of herbs). The most common vascular species include Azadirachta indica, Jatropha curcas, Cynodon dactylon, and Conocarpus erectus, with Rani Bagh (H' = 3.47 and 1-D = 0.93) and Zia Uddin Park having the greatest levels of biodiversity among the parks studied. This is primarily due to tree canopies that reduce human foot traffic over time. The results of the soil study showed that these urban locations were characterized by: (1) sandy soils that were slightly alkaline (pH 8.3–8.6), (2) low levels of organic matter (0.35–0.60%), and (3) variable salinity (EC = 0.37–9.52 dS m⁻¹). Across the seven parks, several genera of fungi were identified, with Aspergillus, Penicillium, Fusarium, and Trichoderma being the most dominant in areas that still had vegetative cover. Based on our analyses, the established pattern of vegetation structure and soil texture were more important factors in determining patterns of plant and fungi diversity than park size. Conservation efforts for urban resilience and health can be enhanced by establishing trees in urban areas, increasing soil nutrients, and planting native vegetation.

Elevation-driven influences on soil nutrient dynamics and vegetation structure in temperate subalpine forests of the Western Himalaya.

Vegetation structure has emerged as a key determinant of terrestrial biodiversity based on studies using randomly placed sampling grids. The resultant grid cells often contain substantial heterogeneity in ecological conditions that are highly relevant for the taxa of interest, potentially undermining our ability to detect relevant drivers of diversity. Here we use 12 structural metrics measured using a ground-based light detection and ranging (lidar) scanner to model mammalian diversity at 58 sampling locations across seven distinct tropical forest types in Indonesian Borneo. We conducted analyses at four spatial scales using over five years of camera trap data. Models predicting mammal diversity based on ecologically defined scales (i.e., forest type boundaries) outperformed models using a grid scale of comparable resolution. Our results highlight the importance of incorporating ecologically meaningful spatial scales in biodiversity studies and underscore the value of lidar in capturing forest structural metrics relevant to mammals.

• Small greenspaces can buffer extreme heat stress by up to 16 °C mPET on hot days • Vegetation structural complexity is a key driver of thermal comfort in small parks • Vegetation structure affects mPET directly and indirectly via sky view factor • Mature trees with tall canopies provide the strongest mPET buffering in small parks • Shading drives fine-scale thermal variability Urban greenspaces play an increasingly important role in urban planning and public health, particularly in providing thermal comfort during hot summer days. While the cooling potential of greenspaces generally increases with size, it also strongly depends on vegetation structure, particularly in small greenspaces. Optimizing these spaces for thermal comfort requires a clear understanding of how their vegetation structure shapes local cooling. Based on field measurements in 2024, we modeled the modified Physiologically Equivalent Temperature (mPET) across 12 structurally diverse small greenspaces (< 2 ha) in central Munich, Germany, and in their built surroundings. High-resolution vegetation structure derived from mobile terrestrial laser scanning was combined with sky view factor estimates from hemispherical photographs and micro-meteorological measurements. Using mixed-effects models and structural equation modeling, we assessed both direct and indirect pathways linking vegetation structure, sky openness, shading, and thermal comfort. Our results show that small greenspaces can reduce mPET by up to 16 °C compared to built surroundings. Vegetation structure emerged as a key determinant of this buffering capacity: greenspace plots with tall, multi-layered vegetation provided strong mPET buffering, whereas sparsely vegetated plots offered little to no thermal relief. Mean canopy height was the strongest predictor of mPET buffering, and vegetation structure influenced thermal buffering both directly and indirectly through reductions in sky view factor. At finer spatial scales, shading dominated local thermal variability, with shading effects being strongest in structurally complex plots. These findings highlight that small but structurally complex greenspaces can play a vital role in climate-adaptive urban planning.