Search Results (12,258)

This study investigates the sustainable livelihoods of households in the coastal forest regions of Quang Tri Province, Vietnam, focusing on identifying the key factors that shape household resilience in the face of socio-economic and environmental challenges. Although the sustainable livelihoods approach is widely established in research, this study differentiates itself by applying a multivariate analysis to explore the relative impacts of various livelihood capitals—human, physical, financial, social, and environmental—specifically within the context of coastal forest ecosystems, a relatively under-researched area in Vietnam. The research identifies both factors affecting livelihood outcomes, emphasizing the role of community resources, seasonal fluctuations, and adaptation strategies. Additionally, the study highlights how environmental changes and natural resource constraints are more detrimental to livelihoods in these regions compared to other rural settings. Through these insights, this paper contributes to the growing body of literature by offering a nuanced understanding of how coastal forest communities can navigate the pressures of climate change, market volatility, and limited resources. The findings underscore the importance of enhancing adaptive capacity and crafting targeted policy interventions to support vulnerable households in the region. This study also highlights the limitations of existing research, emphasizing the need for future studies to integrate the complex interplay of environmental, social, and economic factors in coastal ecosystems. Full article

Mountain glaciers are the most direct and sensitive indicators of climate change. In the context of global warming, monitoring changes in glacier elevation has become a crucial issue in modern cryosphere research. The Global Ecosystem Dynamics Investigation (GEDI) is a full-waveform laser altimeter with a multi-beam that provides unprecedented measurements of the Earth’s surface. Many studies have investigated its applications in assessing the vertical structure of various forests. However, few studies have assessed GEDI’s performance in detecting variations in glacier elevation in land ice in high-mountain Asia. To address this limitation, we selected the Southeastern Tibetan Plateau (SETP), one of the most sensitive areas to climate change, as a test area to assess the feasibility of using GEDI to monitor glacier elevation changes by comparing it with ICESat-2 ATL06 and the reference TanDEM-X DEM products. Moreover, this study further analyzes the influence of environmental factors (e.g., terrain slope and aspect, and altitude distribution) and glacier attributes (e.g., glacier area and debris cover) on changes in glacier elevation. The results show the following: (1) Compared to ICESat-2, in most cases, GEDI overestimated glacier thinning (i.e., elevation reduction) to some extent from 2019 to 2021, with an average overestimation value of about −0.29 m, while the annual average rate of elevation change was relatively close, at −0.70 ± 0.12 m/yr versus −0.62 ± 0.08 m/yr, respectively. (2) In terms of time, GEDI reflected glacier elevation changes at interannual and seasonal scales, and the trend of change was consistent with that found with ICESat-2. The results indicate that glacier accumulation mainly occurred in spring and winter, while the melting rate accelerated in summer and autumn. (3) GEDI effectively monitored and revealed the characteristics and patterns of glacier elevation changes with different terrain features, glacier area grades, etc.; however, as the slope increased, the accuracy of the reported changes in glacier elevation gradually decreased. Nonetheless, GEDI still provided reasonable estimates for changes in mountain glacier elevation. (4) The spatial distribution of GEDI footprints was uneven, directly affecting the accuracy of the monitoring results. Thus, to improve analyses of changes in glacier elevation, terrain factors should be comprehensively considered in further research. Overall, these promising results have the potential to be used as a basic dataset for further investigations of glacier mass and global climate change research. Full article

This research objective is to overcome the shortcomings of the updated values added of the System of National Accounts 2025 (SNA 2025) in order to measure the spatialized total sustainable social income from forest ecosystems through an experimentally refined System of Environmental-Economic Accounting (rSEEA). Sustainable income measured at observed, imputed, and simulated market transaction prices is defined as the maximum potential consumption of products generated in the forest ecosystem without a real decline in the environmental asset and manufactured fixed capital at the closing of the current period, assuming idealized future conditions of stable real prices and dynamics of institutional and other autonomous processes. A key finding of this research is that sustainable income extends the SNA 2025 net value added by incorporating the omissions by the latter of environmental net operating surplus (or ecosystem service in the absence of environmental damage), ordinary changes in the environmental asset condition and manufactured fixed capital adjusted according to a less ordinary entry of manufactured fixed capital plus the manufactured consumption of fixed capital. Sustainable income was measured spatially for 15 individual products, the area units being the map tiles for Andalusia, Spain, Stone pine forest (Pinus pinea L.) canopy cover was predominant, covering an area of 243,559 hectares. In 2010, the SNA 2025 gross and net values added accounted for 24% and 27%, respectively, of the Stone pine forest sustainable income measured by the rSEEA. The ecosystem services omitted by the SNA 2025 made up 69% of the rSEEA sustainable income. Full article

Heritage landscapes endure not through the preservation of fixed forms but through the capacity to adapt to changing social, political, economic, and environmental conditions. Conservation policies that privilege static ideals of authenticity risk undermining the very systems they aim to protect. This paper advances a model of shared stewardship that links conservation of heritage to support for livelihoods, functional flexibility, and community authority in decision-making. Using the Ifugao Rice Terraces of the Philippine Cordillera as a case study, we integrate archaeological, ethnographic, spatial, and agricultural economic evidence to examine the terraces as a dynamic socio-ecological system. Archaeological findings and oral histories show that wet-rice agriculture expanded in the 17th century, replacing earlier taro-based systems and incorporating swidden fields, managed forests, and ritual obligations. Contemporary changes such as the shift from heirloom tinawon rice to commercial crops, the impacts of labor migration, and climate variability reflect long-standing adaptive strategies rather than cultural decline. Comparative cases from other UNESCO and heritage sites demonstrate that economic viability, adaptability, and local governance are essential to sustaining long-inhabited agricultural landscapes. We thus argue that the Ifugao terraces, like their global counterparts, should be conserved as living systems whose cultural continuity depends on their ability to respond to present and future challenges. Full article

As the largest carbon reservoir in terrestrial ecosystems, soil organic carbon (SOC) plays a critical role in the global carbon cycle and climate change mitigation. A promising approach to swiftly procuring geographically dispersed SOC data is the amalgamation of UAV-based multispectral imagery at the local scale and Sentinel-2 satellite imagery at the regional scale. This integrated approach is particularly well-suited for precision agriculture and real-time monitoring. In this study, we evaluated the performance of UAVs and Sentinel-2 imagery in predicting SOC using four machine-learning models: Multiple Linear Regression (MLR), Support Vector Regression (SVR), Random Forest (RF), and Artificial Neural Networks (ANNs). UAV imagery outperformed Sentinel-2, achieving more accurate detection of local SOC variability thanks to its finer spatial resolution (5–10 cm versus 10–20 m). Among the models tested, the Random Forest algorithm achieved the highest accuracy, with an R² of up to 0.85 using UAV data and 0.65 using Sentinel-2 data, along with low RMSE values. All models confirmed the superiority of UAV imagery based on key error metrics (SSE, MSE, RMSE, and NSE). Although Sentinel-2 remains valuable for regional assessments, UAV imagery combined with Random Forest provides the most reliable SOC estimates at local scales. The spatial SOC maps generated from both UAV and Sentinel-2 imagery showed more nuanced spatial variability than standard interpolation techniques. While prediction accuracy using UAV-based models was slightly lower in some cases, UAV imagery provided greater spatial detail in SOC distribution. However, this is associated with higher acquisition and processing costs compared to freely available Sentinel-2 imagery. Given their respective advantages, we recommend using UAV imagery for detailed, site-specific SOC estimations and Sentinel-2 data for broader regional-to-global SOC mapping efforts. Full article

Information on the relationship between facial thermal responses and emotional state is valuable for sensing emotion. Yet, previous research has typically relied on linear methods of analysis based on regions of interest (ROIs), which may overlook nonlinear pixel-wise information across the face. To address this limitation, we investigated the use of machine learning (ML) for pixel-level analysis of facial thermal images to estimate dynamic emotional arousal ratings. We collected facial thermal data from 20 participants who viewed five emotion-eliciting films and assessed their dynamic emotional self-reports. Our ML models, including random forest regression, support vector regression, ResNet-18, and ResNet-34, consistently demonstrated superior estimation performance compared to traditional simple or multiple linear regression models for the ROIs. To interpret the nonlinear relationships between facial temperature changes and arousal, saliency maps and integrated gradients were used for the ResNet-34 model. The results show nonlinear associations of arousal ratings in nose = tip, forehead, and cheek temperature changes. These findings imply that ML-based analysis of facial thermal images can estimate emotional arousal more effectively, pointing to potential applications of non-invasive emotion sensing for mental health, education, and human–computer interaction. Full article

Credit card fraud remains a major cause of financial loss around the world. Traditional fraud detection methods that rely on supervised learning often struggle because fraudulent transactions are rare compared to legitimate ones, leading to imbalanced datasets. Additionally, the models must be retrained frequently, as fraud patterns change over time and require new labeled data for retraining. To address these challenges, this paper proposes an ensemble unsupervised learning approach for credit card fraud detection that combines Autoencoders (AEs), Self-Organizing Maps (SOMs), and Restricted Boltzmann Machines (RBMs), integrated with an Adaptive Reconstruction Threshold (ART) mechanism. The ART dynamically adjusts anomaly detection thresholds by leveraging the clustering properties of SOMs, effectively overcoming the limitations of static threshold approaches in machine learning and deep learning models. The proposed models, AE-ASOMs (Autoencoder—Adaptive Self-Organizing Maps) and RBM-ASOMs (Restricted Boltzmann Machines—Adaptive Self-Organizing Maps), were evaluated on the Kaggle Credit Card Fraud Detection and IEEE-CIS datasets. Our AE-ASOM model achieved an accuracy of 0.980 and an F1-score of 0.967, while the RBM-ASOM model achieved an accuracy of 0.975 and an F1-score of 0.955. Compared to models such as One-Class SVM and Isolation Forest, our approach demonstrates higher detection accuracy and significantly reduces false positive rates. In addition to its performance, the model offers considerable computational efficiency with a training time of 200.52 s and memory usage of 3.02 megabytes. Full article

Assessing ecological quality in mining areas is critical for environmental protection and sustainable resource management. However, most previous studies concentrate on large-scale analysis, overlooking fine-scale assessment in mining areas. To address this issue, this study proposed a novel analysis framework for mining areas by integrating high-resolution Landsat data, the Remote Sensing Ecological Index (RSEI), and the Random Forest regression method. Based on the framework, four decades of spatiotemporal dynamics and drivers of ecological quality were revealed in Youjiang River Valley. Results showed that from 1986 to 2024, ecological quality in Youjiang River Valley exhibited a fluctuating upward trend (slope = 0.004/year), with notable improvement concentrated in the most recent decade. Spatially, areas with a significant increasing trend in RSEI (48.71%) were mainly located in natural vegetation regions, whereas areas with a significant decreasing trend (9.11%) were concentrated in impervious surfaces and croplands in northern and central regions. Driver analysis indicates that anthropogenic factors played a crucial role in ecological quality changes. Specifically, land use intensity, precipitation, and sunshine duration were main determinants. These findings offer a comprehensive understanding of ecological quality evolution in subtropical karst mining areas and provide crucial insights for conservation and restoration efforts in Youjiang River Valley. Full article

Carbon sinks are of great significance for mitigating the greenhouse effect and climate change. However, only a few carbon sink measurement methods are suitable for small-scale research, such as at the city-region scale. Methods that can accurately distinguish the high–low gradients of forest carbon sinks within small-scale areas have not yet been established. To fill this gap, we used a tree allometric growth model—the i-Tree Eco model—and applied it to Tai’an, which is a National Forest City in China. By using indicator conversion methods, we innovatively combined the China Forest Resources Inventory Geographic Information Database with i-Tree Eco. The results showed that i-Tree Eco successfully estimated the carbon sinks provided by urban–rural forests (in 2019)—the total carbon storage in Tai’an forest was 5,828,165.90 t; the average carbon storage per hectare was 37.19 tC·ha⁻¹; the total carbon sequestration was 936,789.03 tC·yr⁻¹; and the annual carbon sequestration was, on average, 5.97 tC·ha⁻¹·yr⁻¹. Our method improved the spatial resolution of carbon sequestration and storage compared to the commonly used InVEST model, from about 350 m × 350 m to 195 m × 195 m. Compared to the traditional IPCC method, the i-Tree Eco model provided greater accuracy and timeliness in small-scale carbon sequestration measurements, eliminating the need to wait for the next forest inventory to be published. Our method yielded results that covered the entire city region and better reflected the spatial heterogeneity of carbon sinks. We conclude that the innovative application of the i-Tree Eco model to urban–rural-scale carbon sink measurements provides stronger technical support for urban green space planning, as well as data guidance, in relation to local carbon mitigation strategies. Full article

The glomalin-related soil protein (GRSP), a class of stable glycoproteins produced by arbuscular mycorrhizal fungi, constitute an important microbial-derived carbon pool in terrestrial ecosystems. However, the response of GRSP accumulation to land-use change and quantitative contribution to soil organic carbon (SOC) pools, as well as the environmental and edaphic factors controlling GRSP dynamics in different land-use systems, require further elucidation. To address these knowledge gaps, we systematically collected surface soil samples (0–20 cm depth) from 72 plots across three land-use types—tea plantations (TP; n = 24), artificial forests (AF; n = 24), and natural forests (NF; n = 24) in China’s Huangshan Mountain region between July and August 2024. GRSP was extracted via autoclaving (121 °C, 20 min) in 20 mM citrate buffer (pH 8.0), fractionated into total GRSP (T-GRSP), and quantified using the Bradford assay. Results revealed distinct patterns in soil carbon storage, with NF exhibiting the highest concentrations of both SOC (33.2 ± 8.69 g kg⁻¹) and total GRSP (T-GRSP: 2.64 ± 0.34 g kg⁻¹), followed by AF (SOC: 14.9 ± 2.55 g kg⁻¹; T-GRSP: 1.42 ± 0.25 g kg⁻¹) and TP (SOC: 7.07 ± 1.72 g kg⁻¹; T-GRSP: 0.58 ± 0.11 g kg⁻¹). Although absolute GRSP concentrations were lowest in TP, its proportional contribution to SOC remained consistent across land uses (TP: 8.72 ± 2.84%; AF: 9.69 ± 1.81%; NF: 8.40 ± 2.79%). Statistical analyses identified dissolved organic carbon and microbial biomass carbon as primary drivers of GRSP accumulation. Structural equation modeling further demonstrated that land-use type influenced SOC through its effects on MBC and fine-root biomass, which subsequently enhanced GRSP production. These findings demonstrate that undisturbed forest ecosystems enhance GRSP-mediated soil carbon sequestration, emphasizing the critical role of natural forest conservation in ecological sustainability. Full article

The Amur tiger (Panthera tigris altaica) is a flagship and umbrella species of forest ecosystems in northeastern Asia. Climate change is profoundly and irreversibly affecting wildlife habitat suitability, especially for large mammals. To effectively protect the Amur tiger, it is necessary to understand the impact of climate change on the quality and the connectivity of its habitat. We used the species distribution models combined with the latest Shared Socioeconomic Pathway (SSP) climate scenarios to predict current and future changes in habitats and corridors. We found the following: (1) The total area of the Amur tiger’s suitable habitat currently amounts to approximately 4941.94 km², accounting for 27.64% of the study area represented by two adjacent national parks. Among these habitats, the highly suitable areas are mainly located on the both sides of the Sino-Russian border. The landscape connectivity remains relatively stable, and the degree of fragmentation in highly suitable habitats is low. (2) Although the suitable habitat of the Amur tiger shows an expansion trend under most climate scenarios (excluding SSP3-7.0), the area of suitable habitat within the entire study region does not increase significantly. Therefore, we should implement conservation measures to facilitate the continued expansion of suitable habitat for the Amur tiger. The quantity and length of landscape connectivity corridors are expected to vary in response to changes in core habitat patches, while the centroid of highly suitable habitats is also expected to shift to different extents. In such circumstances, new ecological corridors need to be constructed, while existing natural ecological corridors should be preserved. Full article

Land use change significantly affects regional carbon emissions and ecosystem service value (ESV). Under China’s Dual Carbon Goals, this study takes Beijing-Tianjin-Hebei, experiencing rapid land use change, as the study area and counties as the study unit. This study employs a combination of methods, including carbon emission coefficients, equivalent-factor methods, bivariate spatial autocorrelation, and a multinomial logit model. These were used to explore the spatial relationship between land use carbon emissions and ESV, and to identify their key driving factors. These insights are essential for promoting sustainable regional development. Results indicate the following: (1) Total land use carbon emissions increased from 2000 to 2015, then declined until 2020; emissions were high in municipal centers; carbon sinks were in northwestern ecological zones. Construction land was the primary contributor. (2) ESV declined from 2000 to 2010 but increased from 2010 to 2020, driven by forest land and water bodies. High-ESV clusters appeared in northwestern and eastern coastal zones. (3) A significant negative spatial correlation was found between carbon emissions and ESV, with dominant Low-High clustering in the north and Low-Low clustering in central and southern regions. Over time, clustering dispersed, suggesting improved spatial balance. (4) Population density and cultivated land reclamation rate were core drivers of carbon–ESV clustering patterns, while average precipitation, average temperature, NDVI, and per capita GDP showed varied effects. To promote low-carbon and ecological development, this study puts forward several policy recommendations. These include implementing differentiated land use governance and enhancing regional compensation mechanisms. In addition, optimizing demographic and industrial structures is essential to reduce emissions and improve ESV across the study area. Full article

Understanding the integration of newly recorded species into forest ecosystems is essential for evaluating their ecological impacts on native wildlife diversity. In this study, we examined the spatial and temporal niche dynamics of three sympatric squirrel species within the Labagoumen nature reserve, a temperate forest located in northern China. Particular emphasis was placed on the recently documented Eurasian red squirrel (Sciurus vulgaris) and its potential interactions with two native species: Père David’s rock squirrel (Sciurotamias davidianus) and the Siberian chipmunk (Tamias sibiricus). Using camera trapping data from 91 sites (2019–2024), we examined habitat use, activity rhythms, and niche overlap under contrasting levels of human disturbance. A total of 3419 independent effective photos of squirrels were recorded. S. vulgaris showed a broader spatial distribution and a higher relative abundance index (RAI) in the tourist area, while native species were more abundant in the non-tourist area. All three species showed similar annual activity patterns based on the monthly relative abundance index (MRAI), although native species exhibited an additional activity peak in June–July. Temporal niche overlap (C_ih) and the coefficient of overlap (Δ) between S. vulgaris and native species increased during the tourist season, suggesting synchronized activity under high disturbance. In contrast, lower overlap in the non-tourist season indicated stronger temporal partitioning. The daily activity rhythm of S. vulgaris remained stable, while native species displayed more variability, especially in non-tourist areas. S. vulgaris also exhibited a significantly broader spatial niche breadth (B_i), suggesting greater habitat exploitation and adaptability. Non-metric multidimensional scaling (NMDS) revealed no significant spatial segregation among the three species, indicating successful integration of S. vulgaris into the local community. Our findings emphasize the competitive advantage of S. vulgaris and demonstrate how human activities can restructure forest small mammal assemblages by altering spatiotemporal niche partitioning. We recommend long-term ecological monitoring to assess species diversity changes and guide adaptive conservation strategies. Full article

Platycerium bifurcatum is one of the most widely cultivated ornamental fern species worldwide and a valuable component of the biodiversity of pantropical forests. In addition to its photosynthetic function, the sporotrophophyll leaves of this species periodically develop a large, clearly demarcated sporangium at the leaf tips, enabling physiological and biochemical measurements both in the active sporulation part and in the non-sporulating leaf area. The aim of this study was to assess anatomical changes, determine thermal effects and the content of selected phytohormones, and analyze the spatial distribution of pigments in the sporophilic and trophophylic part of the same leaf during spore formation. The study utilized fluorescence microscopy, isothermal microcalorimetry, Raman mapping, and ultra-high-performance liquid chromatography coupled with a Triple Quad LC/MS analyzer. The results revealed significant physiological differences between the sporulating and non-sporulating leaf areas. For the first time, differences in thermogenesis within the two leaf regions accompanying sporulation and linked to the sporangium development stage have been demonstrated in ferns. Increases in gibberellins (GA3, GA4, and GA6), auxin (indole-3-butyric acid), (±)-cis, trans-abscisic acid, and abscisic acid glucose ester were observed in the sporophilic part of the leaf, as well as fluctuations in phytohormones in the trophophilic part, indicating internal metabolite relocation within the leaf. Raman analysis and 2D mapping revealed local lignin accumulation and fluctuations in carotenoid levels during spore maturation. The results of this study demonstrate physiological variation within a single leaf and the mechanisms accompanying sporulation, which provide a better understanding of fern adaptive strategies. Full article

Climate change poses significant threats to urban tree health and survival worldwide. This study evaluates climate suitability risks for 12 common tree species in Wuhan, a Chinese metropolis facing escalating climate challenges. We analyzed risk dynamics and interspecific variations across three periods, the baseline (1981–2022), near future (2023–2050), and distant future (2051–2100), quantifying climate risk as differences between local climate conditions and species’ climatic niches. We further examined how species’ geographic distribution and functional traits influence these climate risks. The results revealed significant warming trends in Wuhan during the baseline period (p < 0.05), with projected increases in temperature and precipitation under future scenarios (p < 0.05). The most prominent risk factors included the precipitation of the driest month (PDM), annual mean temperature (AMT), and maximum temperature of the warmest month (MTWM), indicating intensifying drought–heat stress in this region. Among the studied species, Cedrus deodara (Roxb.) G. Don, Platanus acerifolia (Aiton) Willd., Metasequoia glyptostroboides Hu & W.C.Cheng, and Ginkgo biloba L. faced significantly higher hydrothermal risks (p < 0.05), whereas Koelreuteria bipinnata Franch. and Osmanthus fragrans (Thunb.) Lour. exhibited lower current risks but notable future risk increases (p < 0.05). Regarding the factors driving these interspecific variation patterns, the latitude of species’ distribution centroids showed significant negative correlations with the risk values of the minimum temperature of the coldest month (MTCM) (p < 0.05). Among functional traits, the wood density (WD) and xylem vulnerability threshold (P50) were negatively correlated with precipitation-related risks (p < 0.05), while the leaf dry matter content (LDMC) and specific leaf area (SLA) were positively associated with temperature-related risks (p < 0.05). These findings provide scientific foundations for developing climate-adaptive species selection and management strategies that enhance urban forest resilience under climate change in central China. Full article