Search Results (199)

The ecosystem services (ESs) of forests are the benefits that people derive from forest ecosystems. Their precise recognition is important for differentiating and determining the optimal principles of multifunctional forest management. The aim of this study is to identify some important ESs based on a site classification system at the lowest level—i.e., forest stands, at the forest owner level—as a tool for differentiated management. ESs were assessed within the Czech Republic and are expressed in units in accordance with the very sophisticated Forest Site Classification System. (1) Biomass production: The vertical differentiation of ecological conditions given by vegetation tiers, which reflect the influence of altitude, exposure, and climate, provides a basic overview of biomass production; the highest value is in the fourth vegetation tier, i.e., the Fageta abietis community. Forest stands are able to reach a stock of up to 900–1200 m³·ha⁻¹. The lowest production is found in the eighth vegetation tier, i.e., the Piceeta community, with a wood volume of 150–280 m³·ha⁻¹. (2) Soil conservation function: Geological bedrock, soil characteristics, and the geomorphological shape of the terrain determine which habitats serve a soil conservation function according to forest type sets. (3) The hydricity of the site, depending on the soil type, determines the hydric-water protection function of forest stands. Currently, protective forests occupy 53,629 ha in the Czech Republic; however, two subcategories of protective forests—exceptionally unfavorable locations and natural alpine spruce communities below the forest line—potentially account for 87,578 ha and 15,277 ha, respectively. Forests with an increased soil protection function—a subcategory of special-purpose forests—occupy 133,699 ha. The potential area of soil protection forests could be up to 188,997 ha. Water resource protection zones of the first degree—another subcategory of special-purpose forests—occupy 8092 ha, and there is potentially 289,973 ha of forests serving a water protection function (specifically, a water management function) in the Czech Republic. A separate subcategory of water protection with a bank protection function accounts for 80,529 ha. A completely new approach is presented for practical use by forest owners: based on the characteristics of the habitat, they can obtain information about the fulfillment of the habitat’s ecosystem services and, thus, have basic information for the determination of forest categories and the principles of differentiated management. Full article

The spatial distribution of ephemeral and perennial dryland plant species is increasingly modified and restricted by ever-changing climates and development expansion. At the interface of biodiversity conservation and developmental planning in desert landscapes is the growing need for adaptable tools in identifying and monitoring these ecologically fragile plant assemblages, habitats, and, often, heritage sites. This study evaluates usage of Sentinel-2 time series composite imagery to discriminate vegetation assemblages in a hyper-arid landscape. Spatial predictor spaces were compared to classify different vegetation communities: spectral components (PCs), vegetation indices (VIs), and their combination. Further, the uncertainty in discriminating field-verified vegetation assemblages is assessed using Shannon entropy and intensity analysis. Lastly, the intensity analysis helped to decipher and quantify class transitions between maps from different spatial predictors. We mapped plant assemblages in 2022 from combined PCs and VIs at an overall accuracy of 82.71% (95% CI: 81.08, 84.28). A high overall accuracy did not directly translate to high class prediction probabilities. Prediction by spectral components, with comparably lower accuracy (80.32, 95% CI: 78.60, 81.96), showed lower class uncertainty. Class disagreement or transition between classification models was mainly contributed by class exchange (a component of spatial allocation) and less so from quantity disagreement. Different artefacts of vegetation classes are associated with the predictor space—spectral components versus vegetation indices. This study contributes insights into using feature extraction (VIs) versus feature selection (PCs) for pixel-based classification of plant assemblages. Emphasising the ecologically sensitive vegetation in desert landscapes, the study contributes uncertainty considerations in translating optical satellite imagery to vegetation maps of arid landscapes. These are perceived to inform and support vegetation map creation and interpretation for operational management and conservation of plant biodiversity and habitats in such landscapes. Full article

Accurate plant species identification in desert grasslands using hyperspectral data is a critical prerequisite for large-scale, high-precision grassland monitoring and management. However, due to prolonged overgrazing and the inherent ecological vulnerability of the environment, sericite–Artemisia desert grassland has experienced significant ecological degradation. Therefore, in this study, we obtained spectral images of the grassland in April 2022 using a Soc710 VP imaging spectrometer (Surface Optics Corporation, San Diego, CA, USA), which were classified into three levels (low, medium, and high) based on the level of participation of Seriphidium transiliense (Poljakov) Poljakov and Ceratocarpus arenarius L. in the community. The optimal index factor (OIF) was employed to synthesize feature band images, which were subsequently used as input for the DeepLabv3p, PSPNet, and UNet deep learning models in order to assess the influence of species participation on classification accuracy. The results indicated that species participation significantly impacted spectral information extraction and model classification performance. Higher participation enhanced the scattering of reflectivity in the canopy structure of S. transiliense, while the light saturation effect of C. arenarius was induced by its short stature. Band combinations—such as Blue, Red Edge, and NIR (BREN) and Red, Red Edge, and NIR (RREN)—exhibited strong capabilities in capturing structural vegetation information. The identification model performances were optimal, with a high level of S. transiliense participation and with DeepLabv3p, PSPNet, and UNet achieving an overall accuracy (OA) of 97.86%, 96.51%, and 98.20%. Among the tested models, UNet exhibited the highest classification accuracy and robustness with small sample datasets, effectively differentiating between S. transiliense, C. arenarius, and bare ground. However, when C. arenarius was the primary target species, the model’s performance declined as its participation levels increased, exhibiting significant omission errors for S. transiliense, whose producer’s accuracy (PA) decreased by 45.91%. The findings of this study provide effective technical means and theoretical support for the identification of plant species and ecological monitoring in sericite–Artemisia desert grasslands. Full article

Guadua amplexifolia J. Presl is a Neotropical bamboo native to southern Mexico through Central America to Colombia, where it thrives in riparian zones of the San Jorge River basin. Despite its ecological and socio-economic importance, its environmental dynamics and provision of ecosystem services remain poorly understood. This study (1) quantifies spatial and temporal land use/cover changes in the municipality of Montelíbano between 2002 and 2022 and (2) evaluates the ecosystem services that local communities derive from in 2002, 2012, and 2022, and they were classified in QGIS using G. amplexifolia. We applied a supervised classification of Landsat imagery (2002, 2012, 2022) in QGIS, achieving 85% overall accuracy and a Cohen’s Kappa of 0.82 (n = 45 reference points). For the social assessment, we held participatory workshops and conducted semi-structured interviews with artisans, fishers, authorities, and NGO representatives; responses were manually coded to extract key themes. The results show a 12% decline in total vegetated area from 2002 to 2012, followed by an 8% recovery by 2022, with bamboo-dominated stands following a similar pattern. Communities identified raw material provision (87% of mentions), climate regulation (82%), and cultural–recreational benefits (58%) as the most important services provided by G. amplexifolia. This is the first integrated assessment of G. amplexifolia’s landscape dynamics and community-valued services in the San Jorge basin, highlighting its dual function as a renewable resource and a natural safeguard against environmental risks. Our findings offer targeted recommendations for management practices and land use policies to support the species’ conservation and sustainable utilization. Full article

Monsoon evergreen broad-leaved forests (MEBFs) represent one of the most species-rich and structurally complex vegetation types, and one of the most widely distributed forests in Yunnan Province, Southwest China. However, they have yet to undergo a comprehensive analysis on their community diversity, spatial differentiation patterns, and underlying drivers across Yunnan. Based on extensive field surveys during 2021–2024 with 548 MEBF plots, this study employed the Unweighted Pair Group Method for forest community classification and Non-metric Multidimensional Scaling for ordination and interpretation of community–environment association. A total of 3517 vascular plant species were recorded in the plots, including 1137 tree species, 1161 shrubs, and 1219 herbs. Numerical classification divided the plots into 3 alliance groups and 24 alliances: (1) Castanopsis–Schima (Lithocarpus) Forest Alliance Group (16 alliances), predominantly distributed west of 102°E in central-south and southwest Yunnan; (2) Castanopsis–Machilus (Beilschmiedia) Forest Alliance Group (6 alliances), concentrated east of 101°E in southeast Yunnan with limited latitudinal range; (3) Castanopsis–Camellia Forest Alliance Group (2 alliances), restricted to higher-elevation mountainous areas within 103–104° E and 22.5–23° N. Climatic variation accounted for 81.1% of the species compositional variation among alliance groups, with contributions of 83.5%, 57.6%, and 62.1% to alliance-level differentiation within alliance groups 1, 2, and 3, respectively. Precipitation days in the driest quarter (PDDQ) and precipitation seasonality (PS) emerged as the strongest predictors of community differentiation at both alliance group and alliance levels. Topography and soil features significantly influenced alliance differentiation in Groups 2 and 3. Collectively, the interaction between the monsoon climate and topography dominate the spatial differentiation of MEBF communities in Yunnan. Full article

The analysis of soil nematode communities provides information on their impact on soil quality and the health of different agricultural cropping systems and soil management practices, which is necessary to evaluate their sustainability. Here, we evaluated the status of nematode communities and trophic groups’ abundance in fifteen fields hosting different cropping systems and managed according to organic or conventional practices. The nematode population densities differed significantly across cropping systems and management types covering various European climatic zones (spanning 121 to 799 individuals per sample). Population density was affected by the duration of the cropping system, with the lowest value in the vegetable cropping system (on average about 300 individuals) and the highest in the long-term fruiting system (on average more than 500 individuals). The occurrence and abundance of the different trophic groups was partly dependent on the cropping system or the management method, particularly for the bacteria, fungal and plant feeders. The taxonomical classification of a subset of samples allowed us to identify 22 genera and one family (Dorylaimidae) within the five trophic groups. Few taxa were observed in all fields and samples (i.e., Rhabditis and Cephalobus), while Aphelenchoides or Pratylenchus were present in the majority of samples. Phosphorus content was the only soil chemical parameter showing a positive correlation with total nematode population and bacterial feeders’ absolute abundance. Based on the nematological ecological indices, all three cropping systems were characterized by disturbed soil conditions, conductive and dominated by bacterivorous nematodes. This knowledge could lead to a choice of soil management practices that sustain a transition toward healthy soils. Full article

Urban gardens, including family allotment gardens (FAGs) and community gardens (CGs), play an increasingly important role in urban resilience to climate change—particularly through the delivery of regulatory ecosystem services. They occupy as much as 2.6% of Warsaw’s land area and thus have a tangible impact on the entire metropolitan system. These gardens are used in different ways, and each use affects the magnitude of the provided ecosystem services. This preliminary study explores how different types of allotment garden uses affect biodiversity and ecosystem services, addressing a critical knowledge gap in the classification and ecological functioning of urban gardens. We surveyed 44 plots in Warsaw, categorizing them into five vegetation use types: turf, flower, vegetable, orchard, and abandoned. For each plot, we assessed the floristic diversity, vegetation structure (leaf area index, LAI), and six regulatory services: air and soil cooling, water retention, humidity regulation, PM 2.5 retention, and nectar provision. Flower gardens had the highest species diversity (Shannon index = 1.93), while turf gardens had the lowest (1.43) but the highest proportion of native species (92%). Abandoned plots stood out due to the densest vegetation (LAI = 4.93) and ecological distinctiveness. Principal component analysis showed that the selected ecosystem services explained 25% of the variation in vegetation types. We propose a use-based classification of urban gardens and highlight abandoned plots as a functionally unique and overlooked ecological category. Full article

High thematic resolution vegetation mapping is essential for monitoring wetland ecosystems, supporting conservation, and guiding water management. However, producing accurate, fine-scale vegetation maps in large, heterogeneous floodplain wetlands remains challenging due to complex hydrology, spectral similarity among vegetation types, and the high cost of extensive field surveys. This study addresses these challenges by developing a scalable vegetation classification framework that integrates cluster-guided sample selection, Random Forest modelling, and multi-source remote-sensing data. The approach combines multi-temporal Sentinel-1 SAR, Sentinel-2 optical imagery, and hydro-morphological predictors derived from LiDAR and hydrologically enforced SRTM DEMs. Applied to the Great Cumbung Swamp, a structurally and hydrologically complex terminal wetland in the lower Lachlan River floodplain of Australia, the framework produced vegetation maps at three hierarchical levels: formations (9 classes), functional groups (14 classes), and plant community types (PCTs; 23 classes). The PCT-level classification achieved an overall accuracy of 93.2%, a kappa coefficient of 0.91, and a Matthews correlation coefficient (MCC) of 0.89, with broader classification levels exceeding 95% accuracy. These results demonstrate that, through targeted sample selection and integration of spectral, structural, and terrain-derived data, high-accuracy, high-resolution wetland vegetation mapping is achievable with reduced field data requirements. The hierarchical structure further enables broader vegetation categories to be efficiently derived from detailed PCT outputs, providing a practical, transferable tool for wetland monitoring, habitat assessment, and conservation planning. Full article

Endemic plant species, with their restricted distribution, are vulnerable to extinction due to human activities and environmental change. Monitoring their ecological characteristics and habitat relationships is crucial for conservation. This study examined plant communities to prioritize populations for conserving the Korean endemic species, Taihyun’s abelia (Zabelia tyaihyonii (Nakai) Hisauti & H.Hara), and to identify threats and strategies for its protection. Vegetation surveys were conducted, classifying communities and analyzing species composition differences. Habitat quality and zeta diversity, assessed using the InVEST model, identified three community types: Quercus dentata–Thuja orientalis (Com. 1), Fraxinus rhynchophylla–Buxus koreana (Com. 2), and Quercus dentata–Carex humilis var. nana (Com. 3). Community classification was supported by a multi-response permutation procedure (p < 0.001) and non-metric multidimensional scaling (R² = 0.643). Species richness and soil calcium influenced species composition, and habitat quality was moderate (0.5562 ± 0.0294). Com. 1 and Com. 3 showed minimal zeta diversity decline, indicating strong habitat connectivity. However, fluctuations at zeta orders 8–12 suggested localized disturbances. Species turnover instability was linked to urbanization and disturbance. This study, using a diverse set of analytical tools, was able to pinpoint key features of habitat quality and composition associated with Z. tyaihyonii and the anthropogenic factors that will lead to its decline. Our work provides a road map for the conservation of other rare and endemic Korean plant species with similar conservation issues. Full article

Low-cost unmanned aerial vehicle (UAV) visible light remote sensing provides new opportunities for plant community monitoring, but its practical deployment in different ecosystems is still limited by the lack of standardized vegetation index (VI) optimization for multi-species coverage extraction. This study developed a universal method integrating four VIs—Excess Green Index (EXG), Visible Band Difference Vegetation Index (VDVI), Red-Green Ratio Index (RGRI), and Red-Green-Blue Vegetation Index (RGBVI)—to bridge UAV imagery with plant communities. By combining spectral separability analysis with machine learning (SVM), we established dynamic thresholds applicable to crops, trees, and shrubs, achieving cross-species compatibility without multispectral data. The results showed that all VIs achieved robust vegetation/non-vegetation discrimination (Kappa > 0.84), with VDVI being more suitable for distinguishing vegetation from non-vegetation. The overall classification accuracy for different vegetation types exceeded 92.68%, indicating that the accuracy is considerable. Crop coverage extraction showed a minimum segmentation error of 0.63, significantly lower than that of other vegetation types. These advances enable high-resolution vegetation monitoring, supporting biodiversity assessment and ecosystem service quantification. Our research findings track the impact of plant communities on the ecological environment and promote the application of UAVs in ecological restoration and precision agriculture. Full article

Seasonally dynamic plant communities present challenges for remote mapping, but estimating phenology can help identify periods of peak spectral distinction. While phenology is widely used in environmental and agricultural mapping, its broader ecological applications remain underexplored. Using a temperate wetland complex as a case study, we leveraged NDVI time series from Sentinel imagery to refine a wetland classification scheme by identifying periods of maximum plant community distinction. We estimated plant phenology with ground-reference points and mapped the study area using Random Forest (RF) with both Sentinel and PlanetScope imagery. Most plant communities showed distinct phenological variations between April–June (growing season) and September–October (transitional season). Merging phenologically similar communities improved classification accuracy, with April and September imagery yielding better results than the peak summer months. Combining both seasons achieved the highest classification accuracy (~77%), with key RF predictors including digital elevation, and near-infrared and tasseled cap indices. Despite its higher spatial resolution, PlanetScope underperformed compared to Sentinel, as spectral similarities between plant communities limited classification accuracy. While Sentinel provides valuable data, higher spectral resolution is needed for distinguishing similar plant communities. Integrating phenology into mapping frameworks can improve the detection of rare and ephemeral vegetation, aiding conservation efforts. Full article

Multi-driver interactions shape estuarine wetland evolution, yet the intricate evolution patterns and their controlling factors their spatiotemporal dynamics remain inadequately understood. This study employs high-resolution satellite data (1985–2020) and 3S technology (overall classification accuracy: 92.44%, Kappa coefficient: 0.9132) to reveal the development of tidal creeks and vegetation evolution patterns of the Dongtan wetland. Our findings indicate a transition in the development of tidal creeks and vegetation from a natural stage to an artificial intervention stage. Northern regions exhibited severe degradation of both vegetation and tidal creeks influenced by reclamation, contrasting with southern recovery post-restoration. This disparity highlights the varied responses to human activities across different areas of the Dongtan wetland. Notably, the introduction of the invasive species Spartina alterniflora has negatively impacted the habitat of native vegetation. The interaction mechanism between vegetation and tidal creeks manifest as: vegetation constrains tidal creek development through substrate stabilization, wave dissipation, and sediment retention, while tidal creeks modulate physicochemical properties of the substrate hydrological connectivity and seed dispersal, affecting vegetation zonation and community structures. Human activities exert dual modulation effects on the Dongtan wetland, driving its phase transition from natural to artificial landscapes, with artificial landscapes exhibiting the most dynamic landscape type through reclamation and ecological restoration projects. Our findings enhance the understanding of the mechanisms underlying estuarine wetland development and inform strategies for restoring healthy estuarine wetland ecosystems. Full article

Wildfires in the Mediterranean basin, particularly in Portugal, pose significant ecological challenges by altering landscapes and ecosystems. This study examines vegetation recovery in Serra do Açor seven years after the 2017 wildfires, using remote sensing and field data to analyze post-fire dynamics. The primary goal was to assess whether fire severity, measured via the dNBR index from Sentinel-2 imagery, impacts vegetation recovery or if site-specific factors and pre-fire floristic composition are more influential. Randomly assigned plots based on previous land use and fire severity were analyzed for floristic attributes. To quantify and classify cover changes, a supervised classification methodology based on the random forest algorithm was applied to Sentinel-2 data. The results showed no clear link between fire severity and recovery; instead, local factors like soil and topography, along with dominant pre-fire species, influenced recovery. Acacia and eucalyptus communities grew faster and increased the occupied area but exhibited lower diversity than native vegetation communities. Supervised classifications achieved high accuracy (Kappa > 0.90), showing increased shrubland areas and expansion of eucalyptus and acacia. The study highlights the methodology’s effectiveness and potential for broader applications in future research. Full article

Background and Clinical Significance: Men who engage in anal fisting may experience full rectal and colon thickness injury resulting in an endoscopic emergency. The endoscopist does not routinely question patients about their sexual habits, nor are patients compliant with counseling during the endoscopy procedure as indicated by the infectious disease clinician. Case Presentation: A 47-years-old HIV- and monkeypox virus (MPXV)-negative Caucasian gay man underwent colonoscopy because of changes in bowel habits with anal discomfort and rectal bleeding. The first colonoscopy showed a vegetative annular neoformation of the anal canal. There was a concentric stenosis of the lumen. The endoscopist suspected the diagnosis of anal squamous cell carcinoma and a histopathology investigation was requested. Biopsy histology excluded a frank neoplasm or anal intraepithelial neoplasia (AIN). Then, the patient was referred to a multidisciplinary team. With adequate counseling, the patient disclosed his habitual anal fisting. Laboratory identification of L1–L3 Chlamydia trachomatis (CT) genovars was positive for CT L1, L2, real-time PCR for Neisseria gonorrhoeae (NG), and Mycoplasma hominis. Human Papillomavirus (HPV)-DNA detection identified HPV type 70, 68, and 61. We illustrate this case with plenty of histology and immunohistochemistry. We also review the differential diagnosis of AIN according to the 5th edition (2019) WHO Classification of Digestive System Tumours. Conclusions: Our patient emphasizes two important aspects of endoscopy and pathology: first, the significance of understanding patients’ sexual behaviors in diagnosing rectal and colon injuries, as well as the need for sexually transmitted infections (STI) screening especially for CT; and second, the effectiveness of a multidisciplinary communication model that encourages private discussions to alleviate patients’ fears and improve prevention efforts. Full article

Wildfires represent an increasing threat to ecosystems and communities, driven by climate change, fuel dynamics, and human activities. In Ambato, Ecuador, a city in the Andean highlands, these risks are exacerbated by prolonged droughts, vegetation dryness, and urban expansion into fire-prone areas within the Wildland–Urban Interface (WUI). This study integrates climatic, ecological, and socio-economic data from 2017 to 2023 to assess wildfire risks, employing advanced geospatial tools, thematic mapping, and machine learning models, including Multinomial Logistic Regression (MLR), Random Forest, and XGBoost. By segmenting the study area into 1 km² grid cells, microscale risk variations were captured, enabling classification into five categories: ‘Very Low’, ‘Low’, ‘Moderate’, ‘High’, and ‘Very High’. Results indicate that temperature anomalies, reduced fuel moisture, and anthropogenic factors such as waste burning and unregulated land-use changes significantly increase fire susceptibility. Predictive models achieved accuracies of 76.04% (MLR), 77.6% (Random Forest), and 76.5% (XGBoost), effectively identifying high-risk zones. The highest-risk areas were found in Izamba, Pasa, and San Fernando, where over 884.9 ha were burned between 2017 and 2023. The year 2020 recorded the most severe wildfire season (1500 ha burned), coinciding with extended droughts and COVID-19 lockdowns. Findings emphasize the urgent need for enhanced land-use regulations, improved firefighting infrastructure, and community-driven prevention strategies. This research provides a replicable framework for wildfire risk assessment, applicable to other Andean regions and beyond. By integrating data-driven methodologies with policy recommendations, this study contributes to evidence-based wildfire mitigation and resilience planning in climate-sensitive environments. Full article