Search Results (4,182)

This study analyses long-term land cover changes in the Trnava District, Slovakia, with particular emphasis on urban expansion and agricultural land loss. CORINE Land Cover datasets from 1990, 2006 and 2018 were analysed in the TerrSet Land Change Modeler environment, and future land cover development up to 2064 was simulated using a CA–Markov modelling framework. The model incorporated four spatial explanatory variables: distance from built-up areas, distance from the road network, distance from wate courses, and elevation. Between 1990 and 2018, Non-irrigated arable land recorded the largest absolute net loss, decreasing by 962 ha (−1.9%). Urban and industrial categories expanded during the same period: Industrial or commercial units increased by 456 ha (+75.2%), Discontinuous urban fabric by 378 ha (+7.7%), and Continuous urban fabric by 70 ha (+175.0%). Broad-leaved forest increased by 796 ha (+6.9%), whereas Transitional woodland-shrub decreased by 487 ha (−76.7%), demonstrating that gross gains and losses within individual categories did not necessarily correspond to their net area changes. The scenario-based projection to 2064 indicates a continued reduction in arable land and further expansion of discontinuous urban fabric and industrial or commercial areas, particularly around existing settlements and transport corridors. The results demonstrate the simultaneous effects of urbanisation, agricultural land conversion and changes in semi-natural and forest categories. The study highlights the importance of distinguishing gross land cover turnover from net area change and provides spatially explicit information to support agricultural land protection, spatial planning and sustainable landscape management in rapidly urbanising regions. Full article

Groundwater nitrate contamination is delayed, spatially heterogeneous and difficult to screen with sparse monitoring, especially when exceedance probability and exposure concern must be considered together. We analysed 157 shallow groundwater samples from Handan City, China, collected in 2023, and 14 environmental predictors to map NO₃⁻ exceedance above 50 mg/L. Random forest, support vector machine (SVM) and XGBoost classifiers were evaluated by repeated stratified testing and spatial block cross-validation, with SHAP used to interpret model responses. Ordinary kriging and the USEPA non-carcinogenic risk framework estimated adult and child hazard quotients (HQs), and exceedance probability was cross-classified with child HQ in a probability–HQ matrix. Thirty-one samples (19.7%) exceeded the threshold. Although SVM achieved the highest ROC-AUC, XGBoost was retained for early-warning mapping because it better controlled missed exceedances. High-probability zones were concentrated in central-western and urban-fringe Handan. SHAP responses indicated that hydroclimatic, hydrogeological, land-use, terrain and soil factors jointly shaped model discrimination, without implying source attribution. Children had higher HQs than adults. At P = 0.5, general protection, exceedance-warning, concentration-verification and priority-intervention zones occupied 81.26%, 18.40%, 0.01% and 0.33% of the area, respectively. The framework supports targeted monitoring and drinking-water verification rather than fixed contamination boundaries. Full article

Large sensor arrays are an increasingly popular sampling method among ecologists. To last in the field, sensor housing needs to be resistant to damage from both weather and animals. The popular AudioMoth acoustic recorder does not have integral weather-resistant housing and is deployed by users in a wide variety of protective cases. One inexpensive way to protect AudioMoths is to deploy them in plastic bags, which offer moderate weather resistance but are susceptible to chewing damage from small mammals. In this study, we test the effectiveness of commercially available mammal deterrents in preventing such chewing damage. We deployed 115 treatment-control pairs across two grids in temperate forests in Pennsylvania. Bag treatments consisted of Liquid Fence, Bonide, and a cayenne and Vaseline mixture. For all deterrents, there was no statistically significant difference in the proportion or severity of mammal chewing damage between treatments and controls. Counter to expectations, for all three treatments, more of the bags treated with a deterrent were damaged by mammal chewing than the paired control bags. Our results strongly suggest that single applications of these three deterrents have no useful effect on preventing mammal chewing damage to sensor housing in the field. Full article

Climate change has significantly affected the geographical distribution and richness patterns of plant species worldwide. Pinus is a major component of many temperate and montane forests in the Northern Hemisphere and is important for carbon storage, climatic buffering and timber production. Here, we utilized the MaxEnt model and integrated global occurrence records of 113 Pinus species, predicting climatic envelopes and diversity distribution patterns under three emission scenarios for the 2050s and 2070s, while also identifying diversity hotspots, high-decline regions, and conservation gaps. As global overheating and linked climatic variations intensify, climatically suitable areas for Pinus are projected to shift poleward. Using projected climatic envelope contraction as a screening criterion, 7.96%–44.25% of species qualified as climate-vulnerable across scenarios. Additionally, Pinus demonstrates high species richness in North America, the Mediterranean region, and mid-latitude mountainous regions of East Asia. Our predictions reveal that species richness distribution will be notably influenced by climate change, with impacts gradually intensifying as climate change progresses. Fortunately, the current coverage rate of protected areas in diversity hotspots exceeds 92.31%, and the conservation gaps primarily occur in Mexico. It is anticipated that over 86.46% of hotspot areas will remain protected in the future. However, new conservation gaps may arise in eastern North America and southeastern Europe; these regions should be prioritized in future conservation planning. Our research enhances current understanding of how species might respond to the challenges of climate change while also providing practical guidance for priority conservation planning targeting both biodiversity hotspots and high-decline regions. Full article

Plant diversity is often expected to enhance soil organic carbon (SOC) storage through greater and more heterogeneous plant inputs, but its relationships with functionally distinct SOC fractions in natural forests remain uncertain. This study compiled published SOC fraction data from 341 surface soil observations in natural forests across China and spatially matched these records with gridded plant alpha diversity, forest age, climate, topographic and soil properties datasets to evaluate biotic and abiotic associations with SOC, particulate organic carbon (POC), mineral-associated organic carbon (MAOC) and MAOC/SOC. Linear regression, multiple regression, piecewise structural equation modelling and stratified analyses were used to evaluate whether plant diversity was associated with the absolute accumulation and relative stabilization of SOC fractions. Plant alpha diversity was negatively associated with ln[SOC], ln[POC] and ln[MAOC] at the national scale, whereas its bivariate relationship with MAOC/SOC was weak. After accounting for forest age and environmental covariates, plant alpha diversity remained negatively related to the absolute contents of SOC fractions while showing a positive association with MAOC/SOC. Forest age was positively associated with ln[SOC], ln[POC] and ln[MAOC], and POC was more strongly related to plant diversity and forest age than MAOC. In contrast, MAOC and MAOC/SOC were more strongly associated with mineral protection potential, soil pH and precipitation background. Structural equation models indicated that mineral protection potential and mean annual precipitation were associated with greater MAOC accumulation and SOC allocation to the mineral-associated fraction, whereas temperature and topography were linked to MAOC partly through indirect associations with soil physicochemical conditions. Stratified analyses showed that plant diversity associations varied among forest types and climatic backgrounds. Additional interaction models showed that mineral protection potential significantly moderated the associations between plant alpha diversity and ln[SOC], ln[POC] and ln[MAOC], with negative diversity associations weakening under higher mineral protection potential. These findings indicate that plant diversity associations with SOC fractions in natural forests cannot be interpreted as universally positive input relationships. Instead, their direction and strength depend on hydroclimatic context and soil mineral protection, especially for the absolute accumulation of SOC fractions. Full article

This study aimed to investigate visitors’ perceptions of ecotourism landscapes in Thailand’s protected areas by integrating spatial, semantic, and affective information derived from social-sensing textual data. To this end, it examined the influence of ecosystem characteristics on thematic expressions and emotional tones across five national parks. Methodologically, a spatio-semantic, natural language processing (NLP) social-sensing framework was developed using geotagged Flickr tags and YouTube comments from international (English) and domestic (Thai) tourists. Textual data were preprocessed (cleaned, normalized, and tokenized) and analyzed using latent Dirichlet allocation (LDA), sentiment analysis, and diversity metrics. Concurrently, YouTube comments were similarly processed using LDA and rule-based sentiment analysis. Subsequently, a Diversity × Topic × Season matrix integrated Flickr-derived indicators with YouTube-derived sentiment and topic dominance. Binary logistic regression was applied to examine cross-platform relationships. The analysis identified three dominant themes, namely Nature & Landscapes, Travel & Activities, and Feelings & Experiences. Forest-mountain parks showed high semantic diversity and strong positive sentiment, whereas marine parks exhibited narrower but predominantly positive activity-driven discourses. Moreover, seasonal variation was evident, with summer and winter yielding the highest diversity and positivity. Building on these results, this study devised a spatio-semantic framework for analyzing variation in visitor expressions across parks and seasons. It captures eco-awareness, perceptions, and preferred activities in protected landscapes. From a practical perspective, semantic diversity and sentiment indicators can support ecotourism management through improved visitor monitoring and communication strategies. Full article

Continuous sap-flow monitoring is a key approach for understanding water-use strategies of shelterbelt species in semi-arid sandy lands. This study investigated two tree species (Populus alba var. pyramidalis Bge. and Pinus sylvestris var. mongholica Litv.) and two shrub species (Salix psammophila C. Wang & C. Y. Yang and Atraphaxis bracteata Losinsk.) in the Zhanggutai region, southern Horqin Sandy Land. Sap flow, environmental variables, and precipitation were continuously measured during the 2019–2020 growing seasons. Stomatal conductance (g_s) and its sensitivity to vapor pressure deficit (VPD) were evaluated. Precipitation dropped from 302.3 mm in 2019 to 106.3 mm in 2020. Daily sap flow peaked mid-season and declined by 21.9%–29.2% during 2020. VPD and solar radiation were primary drivers with narrow response lags. Trees exhibited higher g_s and stronger stomatal sensitivity than shrubs, showing steeper declines as VPD rose, which indicates key drought adaptation and protective mechanisms. Conversely, shrubs maintained a moderate and more stable g_s profile throughout the growing season. In 2020, soil water deficits may force stomatal closure, causing a pronounced drop in baseline g_s and a subsequent decline in sensitivity across all species. This widespread environmental decoupling indicates a critical shift from an atmospheric demand-limited regime during the wet year to a supply-limited regime during the drought year. Overall, trees demonstrated more pronounced stand-scale hydroclimatic sensitivity than shrubs. Given the persistent water consumption by shrubs, our results suggest tree species may offer advantages in later forest restoration stages. Nevertheless, further studies integrating biomass growth and ecosystem water balance are needed to optimize sustainable shelterbelt management. Full article

Mangrove forests provide essential ecosystem services but are increasingly threatened by anthropogenic pressures and climate-related disturbances. Effective and accessible tools for assessing mangrove ecosystem condition are therefore needed to soundly support their conservation and management. This study adapted the “Coastal Health” framework originally proposed for assessing coastal ecosystems health to evaluate the health status of mangrove forests along the Caribbean coast of Colombia. Using freely available high-resolution imagery from Google Earth Pro 7.3, complemented by field observations, technical reports, and unpublished literature, 56 mangrove sites distributed across eight coastal departments were assessed according to their ecological integrity, hydrological connectivity, sediment dynamics, freshwater and marine inputs, mangrove species condition, and their potential for landward and seaward migration. The results showed that 54% of the evaluated sites were classified as being in “Good Health”, while 2% were categorized as “Health Warning”, 3% as “Surface Wounds”, 14% as “Minor Injury”, 25% as “Major Injury”, and 2% as “Deceased”. Mangroves in good condition were generally associated with protected areas, river mouths, estuaries, and relatively isolated coastal systems, whereas degraded ecosystems were affected by urban expansion, tourism infrastructure, hydrological alterations, coastal engineering works, and reduced freshwater inputs. The proposed methodology proved to be a simple, low-cost, and easily replicable tool for large-scale mangrove health assessment. It provides valuable information for prioritizing conservation and restoration actions and can be adapted to support mangrove monitoring and ecosystem-based coastal management in other regions worldwide. Full article

Long-term ecological monitoring is essential for understanding biodiversity dynamics and evaluating conservation effectiveness in protected areas. The Baishuihe National Nature Reserve, located in the southern Minshan Mountains, represents an important montane forest ecosystem within a global biodiversity hotspot. Using camera-trapping data collected from 2011 to 2020, we investigated long-term community dynamics and temporal activity patterns of mammals and ground-dwelling pheasants in the reserve. A total of 23 species, including 17 mammal and 6 pheasant species, were recorded during the 10-year survey. Carnivora and Artiodactyla were the most species-rich orders, while several ungulates and pheasants dominated detections throughout the monitoring period. Species richness increased with survey duration, while the rate of newly recorded species declined after 2016, and the observed assemblage became more consistent across years. Interannual community variation reflected both species turnover and nestedness-related processes, although dominant and common species remained consistently detected across years. Camera-trap data further revealed pronounced temporal niche differentiation among sympatric species. Carnivores, such as leopard cat (Prionailurus bengalensis) and masked palm civet (Paguma larvata), were primarily nocturnal, whereas golden snub-nosed monkey (Rhinopithecus roxellana), Temminck’s tragopan (Tragopan temminckii), and blood pheasant (Ithaginis cruentus) showed predominantly diurnal activity patterns. Several species exhibited broad temporal niches or seasonal shifts in activity rhythms, suggesting flexible behavioral adaptation to environmental conditions. Overall, the Baishuihe Reserve supported a taxonomically and functionally diverse camera-trap-detectable vertebrate assemblage, within which dominant and common species showed relatively persistent occurrence across years. This study highlights the importance of long-term camera-trap monitoring for understanding community stability, temporal niche organization, and biodiversity conservation in mountain ecosystems. Full article

Low-cost air temperature and relative humidity sensors are increasingly deployed in dense urban monitoring networks for the characterization of urban heat islands and heat exposure. However, measurement accuracy strongly depends on the performance of the radiation shield protecting the sensor from solar heating. This study evaluates five low-cost radiation shield designs, including naturally ventilated, forced-ventilated, spherical, and chimney-type configurations, under tropical outdoor conditions on Reunion Island. Five calibrated SHT31 sensors were deployed simultaneously alongside a reference meteorological station over a five-week measurement campaign. Shield performance was assessed using standard metrological indicators, daytime–nighttime analyses, error distributions, and two-dimensional irradiance–wind diagnostics. Temperature RMSE values ranged from 0.68 to 1.18 °C, while relative humidity RMSE ranged from 2.65 to 7.39%. The forced-ventilated shield provided the best overall temperature performance, whereas the chimney-type design exhibited the largest errors. Combined irradiance–wind analyses showed that measurement errors were primarily governed by the balance between radiative forcing and convective cooling, with maximum temperature biases exceeding 2.5 °C under high-irradiance and low-wind-speed conditions. Based on these findings, several correction approaches were evaluated. A physically interpretable semi-empirical model reduced RMSE by 50%, while a Random Forest model achieved reductions of up to 66%. These results suggest that low-cost meteorological measurements can be substantially improved through appropriate shield design and meteorologically informed calibration procedures, particularly under tropical conditions characterized by strong solar radiation and limited precipitation. Full article

Tugai forests are intrazonal riparian ecosystems formed along river valleys in arid and semi-arid regions and characterized by high biodiversity and complex vegetation structure. The aim of this study was to assess the phytocenological structure, species composition, and ecological characteristics of tugai forests distributed within the Kura River basin and the Nakhchivan Autonomous Republic of Azerbaijan. The study was conducted using classical geobotanical and phytocenological methods based on the Braun—Blanquet approach. The results revealed clear ecological differentiation among tugai forest formations depending on hydrological regime, groundwater conditions, and soil salinity. High species diversity and a multi-layered vegetation structure were identified within the Populeta—Salicetum—Ulmosum, Ulmeta, Populeta, Saliceta—Alnueta—Populeta, and Tamariceta formation groups, which develop mainly on alluvial soils under conditions of elevated moisture. In the Nakhchivan region, tugai forests adapted to more arid environments are distributed mainly along the Araz River and are dominated by Tamarix ramosissima, Populus euphratica, Populus nigra, and Salix species. Anthropogenic impacts, including grazing, deforestation, and hydrological alterations, were identified as major factors contributing to ecosystem degradation and reduction in vegetation cover. The obtained results confirm the high ecological and conservation significance of tugai forests and emphasize the necessity of their protection, restoration, and sustainable management. Full article

Blasting is an essential operation in surface mining, but it can generate high particulate matter concentrations within a short period. Accurate prediction of blast-induced dust concentration is useful for air quality management, worker protection, and dust control planning. In this study, an interpretable machine learning framework was developed to predict particulate matter with an aerodynamic diameter less than 10 μm (PM₁₀) and total suspended particulate matter (TSP) concentrations induced by blasting in a large surface coal mine. The dataset was derived from published field monitoring records and included blasting design parameters, monitoring distance, material-related variables, and measured dust concentrations. A total of 148 valid samples were used for model development and evaluation. Six tree-based ensemble models, including Extra Trees Regression, Random Forest, Gradient Boosting Regression Trees, extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and categorical boosting (CatBoost), were established and compared. Bayesian optimization was used for hyperparameter tuning, and model performance was evaluated using the coefficient of determination (R²), root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). Under the adopted 80:20 hold-out validation scheme, CatBoost achieved the best test performance for PM₁₀ prediction, with a test R² of 0.9555, RMSE of 444.87 μg/m³, and MAE of 343.48 μg/m³. Extra Trees Regression performed best for TSP prediction, with a test R² of 0.8803, RMSE of 3899.04 μg/m³, and MAE of 3036.27 μg/m³. Residual analysis further indicated that the optimal models had no obvious systematic bias. Shapley additive explanations (SHAP) analysis, supported by within-model feature importance rankings, showed that explosive quantity, number of blastholes, and monitoring distance were the dominant variables affecting PM₁₀ and TSP predictions. The explosive quantity and number of blastholes mainly increased the predicted dust concentration, whereas the monitoring distance generally reduced it. The proposed framework may provide useful support for blast parameter optimization, monitoring point arrangement, and dust control decision-making in surface mines, but further validation using larger multi-site datasets is still needed. Full article

Regional carbon balance emerges from the complex interplay between land-use dynamics, spatial economic activities, and ecological processes as a socio-ecological system cannot be captured by any single analytical lens. This study develops an integrated assessment workflow that links three components: (i) coefficient-based carbon emission and sequestration accounting by land-use type; (ii) intra-provincial spatial-interaction analysis operationalized through two complementary tools—the Ecological Support Coefficient (ESC) and Economic Contribution Coefficient (ECC), which characterize the local economy–ecology relationship within each city, and a gravity-based emission-linkage model that uses GDP, population, emissions, and inter-city distance to characterize the network structure of inter-city emission attraction; and (iii) the intPLUS model, which combines random-forest-derived transition probabilities with patch-generation rules to simulate multi-scenario land-use trajectories. The framework is applied to Jiangsu Province, China, across 13 prefecture-level cities, using 1995–2020 historical data and three 2030 scenarios. Model performance is validated against observed 2020 land use, with an overall Kappa coefficient of 0.82. The results reveal a stable “high-south–low-north” gradient in emissions, a contrasting “high-ECC/low-ESC” versus “low-ECC/high-ESC” combining pattern across southern and northern Jiangsu, and a hierarchical core–periphery emission-linkage network anchored on the southern metropolitan cluster. Scenario projections for 2030 show clear divergence in provincial carbon budgets, with emissions of $\mathrm{12,326.59}\times {10}^4$ t, $\mathrm{11,745.26}\times {10}^4$ t, and $\mathrm{13,243.42}\times {10}^4$ t under natural development, ecological protection, and economic development, respectively, and corresponding sequestration of $-87.10\times {10}^4$ t, $-100.29\times {10}^4$ t, and $-85.61\times {10}^4$ t. Rather than treating carbon accounting and land-use simulation in isolation, this workflow bridges the analytical gap between physical land-use transitions and socioeconomic spatial emission spillovers, translating structural interactions into actionable spatial planning strategies. Relying on widely available data, the framework demonstrates strong methodological transferability for comparable subnational systems, provided that local parameters and sink coefficients are properly recalibrated. Full article

Understanding how landscape form influences inundation severity remains central to flood hazard assessment, yet many assumed relationships lack empirical scrutiny. We investigated whether five topographic attributes—elevation, slope, topographic wetness index, latitude, and longitude—could predict cumulative flood intensity across 541 hexagonal cells in Thailand’s Chi River floodplain. Using Random Forest regression and SHAP analysis, we identified three distinct asymmetries that challenge conventional assumptions. Elevation dominated predictions (58.5% importance) but operated through a sharp threshold near 150 m rather than a smooth gradient. Below 145 m, flood intensity was consistently high regardless of other factors; above 155 m, it was uniformly low. The flood-amplifying effect of low-lying terrain (+200 SHAP units) far outweighed the protective benefit of high ground (−100 SHAP units). More strikingly, the Topographic Wetness Index—a widely used theoretical measure of wetness potential—showed negligible correlation with observed flooding (r = 0.109) and contributed only 5.6% to predictive performance. Linear regression models captured barely 30% of the variance (R² ≈ 0.305), whereas Random Forest explained 77.6% (R² = 0.7765), a performance gap that quantifies the degree of non-linearity in the system. Spatial cross-validation confirmed generalizability (R² = 0.583). The elevation threshold offers a straightforward zoning framework: high-risk areas below 145 m, transitional zones from 145 to 155 m, and low-risk areas above 155 m. We conclude that theoretical indices require empirical validation and that combining machine learning with symmetry-based reasoning can expose hidden structures in environmental systems that linear approaches miss. Full article

Wildfires are an increasing threat in Central Europe and pose challenges for protective forests and areas at the wildland–urban interface (WUI). Understanding, describing and predicting fire behaviour is therefore becoming more relevant for fire management. This work aims to reconstruct the fire spread behaviour of past fire events occurred under differing environmental conditions with selected fire spread models. The three fire spread models Farsite, Simtable^TM and Prometheus were selected according to a list of predefined properties they were expected to fulfil. Subsequently, they were tested under different environmental conditions and evaluated against documented perimeter of past fire events. The focus of the analysis was on the spatial perimeter to quantify metrices such as over- and underestimated areas in percent, Sørensen–Dice coefficient and the Jaccard similarity coefficient. Farsite showed the best overall results in both regions. Simtable performed well in steep and complex terrain but produced underestimations in flat terrain. Prometheus lagged, likely due to inadequate parametrization of fuel data, which is a key input parameter in fire spread modelling. As Farsite is readily accessible, it has the greatest potential for further application and more in-depth research. For higher reliability, additional empirical data on fire behaviour are needed to develop custom fuel models or refine current adjustments used to simulate fire spread. Full article