Search Results (955)

This study presents a comprehensive inventory of bryophytes in Serra dos Órgãos National Park (PARNASO), aiming to evaluate species richness, floristic composition and threatened taxa. Despite the state of Rio de Janeiro being one of the most extensively sampled regions for bryophytes in Brazil, detailed surveys of its conservation units remain scarce. Data were obtained through bibliographic review, herbarium specimen analysis, and new field collections. A total of 504 species were recorded, belonging to 202 genera and 76 families. The park harbors three locally endemic species, eight endemic to Rio de Janeiro, and sixty-nine species endemic to Brazil. Additionally, eleven species were identified as threatened, comprising seven Endangered (EN), two Critically Endangered (CR), and two Vulnerable (VU) according to the IUCN guidelines. PARNASO includes four distinct ecosystems along an altitudinal gradient: sub-montane forest (up to 500 m), montane forest (500–1500 m), upper-montane forest (1500–2000 m), and high-altitude fields (above 2000 m). Montane Forest showed the highest species richness, followed by high-altitude fields, upper-montane forest, and sub-montane forest. The findings highlight PARNASO’s importance in preserving bryophyte diversity in a highly diverse yet understudied region. This work contributes valuable baseline data to support conservation strategies and future ecological studies in Atlantic Forest remnants. Full article

Urban vegetation monitoring is essential for sustainable city planning but is often constrained by the high cost and limited frequency of field-based inventories. This study evaluates the use of the Normalized Difference Vegetation Index (NDVI), derived from Sino-Brazilian CBERS-4A satellite imagery, as a spatially explicit and low-cost proxy for urban tree census data. CBERS-4A provides medium-resolution multispectral data freely accessible across South America, yet remains underutilized in urban environmental applications. Focusing on Aracaju, a metropolitan region in northeastern Brazil, we compared NDVI-based classification results with official municipal tree census data from 2022. The analysis revealed a strong spatial correlation, supporting the use of NDVI as a reliable indicator of canopy presence at the urban block scale. In addition to mapping vegetation distribution, the NDVI results identified areas with insufficient canopy coverage, directly informing urban greening priorities. By validating remote sensing data against field inventories, this study demonstrates how CBERS-4A imagery and vegetation indices can support municipal tree management and serve as scalable tools for environmental planning and policy. Full article

Students, particularly those in the medical field, are exposed to various stressors that can affect their health-related behaviors, including smoking habits, with implications for oral health and quality of life. Background and Objectives: to analyze the relationship between smoking, oral health, perceived stress level, and self-assessed quality of life in a sample of dental students. Materials and Methods: The cross-sectional study included 338 students, who completed validated questionnaires and were clinically examined. Lifestyle was assessed using a smoking behavior questionnaire, stress levels were measured with the Student Stress Inventory (SSI), and quality of life was evaluated using the EQ-5D-5L instrument. The DMFT index was calculated to determine oral health status. Results: Among the 338 participating students, 53.8% were smokers. The lifestyle analysis revealed slightly higher average scores among non-smokers across all domains—social (3.26 vs. 3.09), attitudinal (2.75 vs. 2.97), and behavioral (3.82 vs. 3.49), but without statistically significant differences (p > 0.25). The mean DMFT score was 12.48, with no significant differences between smokers and non-smokers (p = 0.554). The SSI total score averaged 83.15, indicating a moderate level of perceived stress, again with no statistically significant differences between the groups (p > 0.05). However, slightly higher average stress scores among smokers may suggest the use of smoking as a coping mechanism. In contrast, quality of life as measured by EQ-5D-5L showed significantly worse outcomes for smokers across all five dimensions, including mobility (78.6% vs. 95.5%, p = 0.000) and self-care (93.4% vs. 100%, p = 0.001). Multivariable logistic regression identified smoking (OR = 1.935; p = 0.047) and moderate stress levels (OR = 0.258; p < 0.001) as independent predictors of oral health status. Conclusions: The results obtained suggest that smoking may function as a stress management strategy among students, supporting the relevance of integrating specific psychobehavioral interventions that address stress reduction and oral health promotion among student populations. Full article

Demand for mental health support has exerted unprecedented pressure on statutory services. Innovative solutions such as Green or Nature-Based Social Prescribing (NBSP) programmes may help address unmet need, improve access to personalised treatment, and support the sustainable delivery of primary services within a prevention model of population health. We piloted an innovative health economics-informed Social Return on Investment (SROI) analysis and forecast of a ‘Making Well’ therapeutic craft and horticulture programme for mental health between October 2021 and March 2022. Quantitative and qualitative outcome data were collected from participants with mild-to-moderate mental health conditions at baseline and nine-weeks follow-up using a range of validated measures, including the Short Warwick–Edinburgh Mental Well-being Scale, ICEpop CAPability measure for Adults (ICECAP-A), General Self-Efficacy Scale (GSES), and a bespoke Client Service Receipt Inventory (CSRI). The acceptability and feasibility of these measures were explored. Results indicate that the Making Well programme generated well-being-related social value in the range of British Pound Sterling (GBP) GBP 3.30 to GBP 4.70 for every GBP 1 invested. Our initial pilot forecast suggests that the programme has the potential to generate GBP 5.40 to GBP 7.70 for every GBP 1 invested as the programme is developed and delivered over a 12-month period. Despite the small sample size and lack of a control group, our results contribute to the evidence-base for the effectiveness and social return on investment of NBSP as a therapeutic intervention for improving health and well-being and provides an example of the use of health economic well-being outcome measures such as ICECAP-A and CSRIs in social value analysis. Combining SROI evaluation and forecast methodologies with validated quantitative outcome measures used in the field of health economics can provide valuable social cost–benefit evidence to decision-makers. Full article

The background for this study is the limitations of the conventional approach of using deforestation area multiplied by biomass densities or emission factors. We demonstrated how TanDEM-X and GEDI data can be combined to estimate forest Above Ground Biomass (AGB) change at the national scale for Tanzania. The results can be further recalculated to estimate CO₂ emissions and removals from the forest. We used repeated short wavelength, InSAR DEMs from TanDEM-X to derive changes in forest canopy height and combined this with GEDI data to convert such height changes to AGB changes. We estimated AGB change during 2012–2019 to be −2.96 ± 2.44 MT per year. This result cannot be validated, because the true value is unknown. However, we corroborated the results by comparing with other approaches, other datasets, and the results of other studies. In conclusion, TanDEM-X and GEDI can be combined to derive reliable temporal change in AGB at large scales such as a country. An important advantage of the method is that it is not required to have a representative field inventory plot network nor a full coverage DTM. A limitation for applying this method now is the lack of frequent and systematic InSAR elevation data. Full article

Accurate estimation of individual tree stem volume is essential for forest resource assessment and the implementation of sustainable forest management. In South Korea, traditional regression models based on non-destructive and easily measurable field variables such as diameter at breast height (DBH) and total height (TH) have been widely used to construct stem volume tables. However, these models often fail to adequately capture the nonlinear taper of tree stems. In this study, we evaluated and compared the predictive performance of traditional regression models and two machine learning algorithms—Random Forest (RF) and Extreme Gradient Boosting (XGBoost)—using stem profile data from 1000 destructively sampled Chamaecyparis obtusa trees collected across 318 sites nationwide. To ensure compatibility with existing national stem volume tables, all models used only DBH and TH as input variables. The results showed that all three models achieved high predictive accuracy (R² > 0.997), with XGBoost yielding the lowest RMSE (0.0164 m³) and MAE (0.0126 m³). Although differences in performance among the models were marginal, the machine learning approaches demonstrated flexible and generalizable alternatives to conventional models, providing a practical foundation for large-scale forest inventory and the advancement of digital forest management systems. Full article

Data analysis and machine learning have become essential cross-disciplinary skills for engineering students and professionals. Traditionally, these topics are taught through lectures or online courses using pre-existing datasets, which limits the opportunity to engage with the full cycle of data analysis and machine learning, including data collection, preparation, and contextualization of the application field. To address this, we designed and implemented a learning activity that involves students in every step of the learning process. This activity includes multiple stages where students conduct experiments to record their own electroencephalographic (EEG) signals and use these signals to learn data analysis and machine learning techniques. The purpose is to actively involve students, making them active participants in their learning process. This activity was implemented in six courses across four engineering careers during the 2023 and 2024 academic years. To validate its effectiveness, we measured improvements in grades and self-reported motivation using the MUSIC model inventory. The results indicate a positive development of competencies and high levels of motivation and appreciation among students for the concepts of data analysis and machine learning. Full article

Brain-derived neurotrophic factor (BDNF) is associated with nicotine use behaviours, the intensity of nicotine cravings, and the experience of withdrawal symptoms. Given the established influence of sex, brain-derived neurotrophic factor variants, personality traits and anxiety levels on nicotine use, this study aimed to conduct a comprehensive association analysis of these factors within a cohort of women who use nicotine. The study included 239 female participants: 112 cigarette users (mean age = 29.19, SD = 13.18) and 127 never-smokers (mean age = 28.1, SD =10.65). Study participants were examined using the NEO Five-Factor Inventory and the State–Trait Anxiety Inventory. Genotyping of rs6265, rs10767664, and rs2030323 was performed by real-time PCR using an oligonucleotide assay. We did not observe significant differences in the distribution of either genotype or allele of rs6265, rs10767664 and rs2030323 between groups. However, compared to the never-smokers, cigarette users scored significantly lower on the Agreeableness (5.446 vs. 6.315; p = 0.005767; d_Cohen’s = 0.363; η² = 0.032) and the Conscientiousness (5.571 vs. 6.882; p = 0.000012; d_Cohen’s = 0.591; η²= 0.08) scales. There was significant linkage disequilibrium between all three analysed polymorphic variants—between rs6265 and rs10767664 (D′ = 0.9994962; p < 2.2204 × 10⁻¹⁶), between rs6265 and rs2030323 (D′ = 0.9994935; p < 2.2204 × 10⁻¹⁶) and between rs10767664 and rs20330323 (D′ = 0.9838157; p < 2.2204 × 10⁻¹⁶), but the haplotype association analysis revealed no significant differences. While our study did not reveal an association between the investigated brain-derived neurotrophic factor polymorphisms (rs6265, rs10767664 and rs2030323) and nicotine use, it is essential to acknowledge that nicotine dependence is a complex, multifactorial phenotype. Our study expands the current knowledge of BDNF ’s potential role in addictive behaviours by exploring the understudied variants (rs10767664 and rs2030323), offering a novel contribution to the field and paving the way for future research into their functional relevance in addiction-related phenotypes. The lower Agreeableness and Conscientiousness scores observed in women who use nicotine compared to never-smokers suggest that personality traits play a significant role in nicotine use in women. The observed relationship between personality traits and nicotine use lends support to the self-medication hypothesis, suggesting that some women may initiate or maintain nicotine use as a coping mechanism for stress and negative affect. Public health initiatives targeting women should consider personality and psychological risk factors in addition to biological risks. Full article

Biogenic volatile organic compounds (BVOCs) are low-boiling-point compounds commonly synthesized by secondary metabolic pathways in plants. As key precursors of ozone (O₃) and secondary organic aerosols (SOA), BVOCs play a critical role in ecosystem-atmosphere interactions. However, their emission from both marine and terrestrial ecosystems, as well as their association with climate and the environment, remain poorly characterized. In light of recent advances in BVOC research, including the establishment of emission inventories, identification of driving factors, and evaluation of ecological and environmental impacts, this study reviews the latest advancements in the field. The findings underscore that the carbon losses via BVOC emission should not be overlooked when estimating the terrestrial carbon balance. Additionally, more work needs to be conducted to quantify the emission factors of specific tree species and to establish links between BVOC emission and climate or environment change. This study contributes to a deeper understanding of vegetation ecology and its environmental functions. Full article

Stylistic neutrality is the basis for the stylistic evaluation of maps. Furthermore, the stylistic neutrality of a map as a cartographic text may be related to objectivity. However, what constitutes stylistic neutrality is not clearly stated in the field of cartography. The problem is complicated by the fact that the stylistically neutral image is a hypothetical image. The aim of this research is to investigate stylistic neutrality by exploring the peculiarities of cartographic language functioning in different fields of social activity. The research combines descriptive analysis, stylistic analysis, cartographic and interpretative methods. Firstly, the research reveals the concept of cartographic stylistic neutrality, in line with the cartographic linguistic paradigm. Secondly, an analysis of the characteristics of cartographic language in different fields of social activity from the point of view of stylistic neutrality is carried out. Thirdly, an example is developed to illustrate stylistic cartographic neutrality. Stylistic neutrality is characterised by the stylistic features of cartographic language: clarity, accuracy, conciseness, calmness, abstractness, temperance, neutrality and moderateness. The style of cartographic production for inventory and research activities is closest to stylistic neutrality, while the style of reflective activity is the most expressive and acts as a source of concreteness for stylistic neutrality. Full article

Morphological problems for distinguishing between glacier ice, glacier ice with a debris cover (debris-covered glaciers), and rock glaciers are outlined with respect to recognising and mapping these features. Decimal latitude–longitude [dLL] values are used for geolocation. One model for rock glacier formation and flow discusses the idea that they consist of ‘mountain permafrost’. However, signs of permafrost-derived ice, such as flow features, have not been identified in these landsystems; talus slopes in the neighbourhoods of glaciers and rock glaciers. An alternative view, whereby rock glaciers are derived from glacier ice rather than permafrost, is demonstrated with examples from various locations in the mountain domain, 𝔻𝕞. A Google Earth and field examination of many rock glaciers shows glacier ice exposed below a rock debris mantle. Ice exposure sites provide ground truth for observations and interpretations stating that rock glaciers are indeed formed from glacier ice. Exposure sites include bare ice at the headwalls of cirques and above debris-covered glaciers; additionally, ice cliffs on the sides of meltwater pools are visible at various locations along the lengths of rock glaciers. Inspection using Google Earth shows that these pools can be traced downslope and their sizes can be monitored between images. Meltwater pools occur in rock glaciers that have been previously identified in inventories as being indictive of permafrost in the mountain domain. Glaciers with a thick rock debris cover exhibit ‘hidden ice’ and are shown to be geomorphological units mapped as rock glaciers. Full article

Accurate biomass estimation is important for forest management and climate change mitigation. This study evaluates the potential of using LiDAR (Light Detection and Ranging) data, acquired through Unmanned Aerial Vehicles (UAVs), for estimating above-ground and total biomass in Eucalyptus globulus and Pinus pinaster stands in central and northern Portugal. The acquired LiDAR point clouds were processed to extract structural metrics such as canopy height, crown area, canopy density, and volume. A multistep variable selection procedure was applied to reduce collinearity and select the most informative predictors. Multiple linear regression (MLR) models were developed and validated using field inventory data. Random Forest (RF) models were also tested for E. globulus, enabling a comparative evaluation between parametric and machine learning regression models. The results show that the 25th height percentile, canopy cover density at two meters, and height variance demonstrated an accurate biomass estimation for E. globulus, with coefficients of determination (R²) varying between 0.86 for MLR and 0.90 for RF. Although RF demonstrated a similar predictive performance, MLR presented advantages in terms of interpretability and computational efficiency. For P. pinaster, only MLR was applied due to the limited number of field data, yet R² exceeded 0.80. Although absolute errors were higher for Pinus pinaster due to greater biomass variability, relative performance remained consistent across species. The results demonstrate the feasibility and efficiency of UAV LiDAR point cloud data for stand-level biomass estimation, providing simple and effective models for biomass estimation in these two species. Full article

This study advances vegetation and forest mapping in temperate mixed forests by integrating airborne hyperspectral imagery (HSI) and light detection and ranging (LiDAR) data, overcoming the limitations of conventional multispectral imaging. Employing a Digital Canopy Height Model (DCHM) derived from LiDAR, our approach integrates these structural metrics with hyperspectral spectral information, alongside detailed remote sensing data extraction. Through machine learning-based clustering, which combines both structural and spectral features, we successfully classified eight specific tree species, community boundaries, identified dominant species, and quantified their abundance, contributing to precise vegetation and forest type mapping based on predominant species and detailed attributes such as diameter at breast height, age, and canopy density. Field validation indicated the methodology’s high mapping precision, achieving overall accuracies of approximately 98.0% for individual species identification and 93.1% for community-level mapping. Demonstrating robust performance compared to conventional methods, this novel approach offers a valuable foundation for National Forest Ecology Inventory development and significantly enhances ecological research and forest management practices by providing new insights for improving our understanding and management of forest ecosystems and various forestry applications. Full article

Agricultural greenhouse gas (GHG) emissions contribute significantly to climate change and underline the importance of reliable measurements and mitigation strategies. This life cycle assessment (LCA)-based study evaluates the environmental impacts of four key Mediterranean agricultural products, namely olives, sweet potatoes, corn, and grapes using GHG measurements at four pilot fields located in different regions of Greece. With the use of a cradle-to-gate approach six environmental impact categories, more specifically acidification potential (AP), eutrophication potential (EP), global warming potential (GWP), ozone depletion potential (ODP), photochemical ozone creation potential (POCP), and cumulative energy demand (CED) as energy-based indicator are assessed. The functional unit used is 1 ha of cultivated land. Any potential carbon offsets from mitigation practices are assessed through an integrated low-carbon certification framework and the use of innovative, site-specific technologies. In this context, the present study evaluates three life cycle inventory (LCI)-based scenarios: Baseline (BS), which represents a 3-year crop production period; Field-based (FS), which includes on-site CO₂ and CH₄ measurements to assess the effects of mitigation practices; and Inventoried (IS), which relies on comprehensive datasets. The adoption of carbon mitigation practices under the FS scenario resulted in considerable reductions in environmental impacts for all pilot fields assessed, with average improvements of 8% for olive, 5.7% for sweet potato, 4.5% for corn, and 6.5% for grape production compared to the BS scenario. The uncertainty analysis indicates that among the LCI-based scenarios evaluated, the IS scenario exhibits the lowest variability, with coefficient of variation (CV) values ranging from 0.5% to 7.3%. In contrast, the FS scenario shows slightly higher uncertainty, with CVs reaching up to 15.7% for AP and 14.7% for EP impact categories in corn production. The incorporation of on-site GHG measurements improves the precision of environmental performance and supports the development of site-specific LCI data. This benchmark study has a noticeable transferability potential and contributes to the adoption of sustainable practices in other regions with similar characteristics. Full article

In the case of forest inventory, heterogeneous areas are particularly challenging due to variability in vegetation structure. This is especially true in Galicia (northwest Spain), where land is highly fragmented, complicating the planning and management of single-species plantations such as Pinus radiata. This study proposes a cost-effective strategy using open-access tools and data to characterize and estimate wood volume in these plantations. Two stratification approaches—classical and cluster-based—were compared to a modeling method based on Principal Component Analysis (PCA). Data came from open-access national LiDAR point clouds, acquired using manned aerial vehicles under the Spanish National Aerial Orthophoto Plan (PNOA). Moreover, two volume estimation methods were applied: one from the Xunta de Galicia (XdG) and another from Spain’s central administration (4IFN). A Generalized Linear Model (GLM) was also fitted using PCA-derived variables with logarithmic transformation. The results show that although overall volume estimates are similar across methods, cluster-based stratification yielded significantly lower absolute errors per hectare (XdG: 28.04 m³/ha vs. 44.07 m³/ha; 4IFN: 25.64 m³/ha vs. 38.22 m³/ha), improving accuracy by 7% over classical stratification. Moreover, it does not require precise field parcel locations, unlike PCA modeling. Both official volume estimation methods tended to overestimate stock by about 10% compared to PCA. These results confirm that clustering offers a practical, low-cost alternative that improves estimation accuracy by up to 18 m³/ha in fragmented forest landscapes. Full article