Search Results (371)

The effect of afforestation in infertile mountainous areas is closely related to the soil ecological environment. Soil enzyme activities and the structure and functions of microbial communities are core indicators reflecting soil quality. Clarifying the response patterns of the two to Cyclobalanopsis gilva afforestation in infertile mountainous areas can provide a key scientific basis for targeted improvement of the cultivation efficiency of C. gilva plantations under different site conditions in the eastern subtropical region of China. In this study, 7-year-old C. gilva young forests in infertile mountainous areas and control woodland areas were selected in Shouchang Forest Farm, Jiande, Zhejiang Province, located in the subtropical region of China. Soil enzyme activities and microbial biomass in different soil layers, as well as metagenomes of rhizosphere and bulk soils, were determined to explore the effects and internal correlations of site conditions on soil enzyme activities and microbial community characteristics of C. gilva forests. The results showed that the activities of urease and catalase, as well as the content of microbial biomass nitrogen in the surface soil of infertile mountainous areas, were significantly lower than those in control woodland areas. The shared dominant phyla in the two types of sites included Proteobacteria and Acidobacteria, and the shared dominant genera included Bradyrhizobium. In addition, the relative abundances of three unclassified populations of Proteobacteria and functional genes related to cofactor and vitamin metabolism in the rhizosphere soil of infertile mountainous areas were significantly higher than those in control woodland areas. Meanwhile, the dominant microbial phyla in the rhizosphere soil of infertile mountainous areas had a closer correlation with soil enzyme activities and microbial biomass. This study clarified the ecological strategy of C. gilva young forests adapting to infertile mountainous areas: by increasing the relative abundances of functional genes related to cofactor and vitamin metabolism in rhizosphere microorganisms, promoting the enrichment of microorganisms associated with soil nitrogen cycling, and enhancing the correlations between dominant microbial phyla and soil enzyme activities and microbial biomass, the nitrogen resource limitation on soil microbial activity in infertile mountainous areas is balanced. This finding provides direct guidance for optimizing the afforestation and management techniques of C. gilva in infertile mountainous areas and has important practical value for promoting forest ecological restoration. Full article

Net ecosystem exchange (NEE) is a central metric for assessing carbon cycling, and its accurate quantification is critical for understanding terrestrial-atmosphere carbon exchange dynamics. However, in complex alpine regions, high-resolution NEE estimation remains challenging due to limited observations and heterogeneous surface processes. To address this, we developed a multimodal feature fusion model (Multimodal-CNN-Attention-RF, MMCA-RF) that integrates convolutional neural networks (CNN) and random forest (RF) for NEE estimation in the Babao River Basin on the northeastern Tibetan Plateau. The model incorporates a cross-modal attention mechanism to dynamically optimize feature interactions, thereby better capturing the spatially heterogeneous responses of vegetation to environmental drivers. Results demonstrate that MMCA-RF exhibits strong stability and generalization, with R² values of 0.89 (training) and 0.85 (testing). Based on model outputs, the Babao River Basin acted as a carbon sink during 2017–2023, with a mean annual NEE of −100.86 gC m⁻² yr⁻¹. Spatially, NEE showed pronounced heterogeneity, while seasonal variation followed a unimodal pattern. Among vegetation types, grasslands contributed the largest total carbon sink, whereas open woodlands showed the highest sequestration efficiency per unit area. Driver analysis identified temperature as the dominant control on NEE spatial variation, with interactions between temperature, precipitation, and topography further enhancing heterogeneity. This study provides a high-accuracy modeling approach for monitoring carbon cycling in alpine ecosystems and offers insights into the stability of regional carbon pools under climate change. Full article

Sustainable management of ecosystem services (ESs) is critical for balancing human well-being with conservation goals in biosphere reserves. This study examined the spatial and socio-demographic variation in the use and perceived importance of provisioning, regulating, supporting, and cultural ESs across different vegetation types and land use zones in the Vhembe Biosphere Reserve (VBR), South Africa. Household surveys were administered to 447 randomly selected households in six rural communities. Descriptive statistics, Chi-square tests, Kruskal–Wallis tests, and Friedman mean ranking analysis were employed. Results revealed significant differences (p < 0.05) in ES distribution and value across vegetation types, land use categories, and household characteristics, including income, education, age, and gender. Provisioning services, particularly fuelwood, wild fruits, and wild vegetables, were most intensively utilized in Mountain Woodland Moist and Ironwood Forest areas due to accessibility and limited livelihood alternatives. Regulating and supporting services, including water purification, erosion control, and habitat provision, were associated with forested and traditionally protected areas. Cultural services reflected strong socio-cultural ties, especially in sacred and tourism-associated landscapes. Overall, the study highlights the multifunctional importance of forested and agroforestry systems in rural livelihoods, emphasizing the need for integrated, culturally informed, and ecologically sound land use planning to support sustainable development in the VBR. Full article

Extensive goat farming is the dominant livestock system in the Mediterranean region, where woody rangelands represent essential forage resources for goats. Understanding how goats move and select vegetation within these heterogeneous landscapes–and how these patterns are shaped by herding decisions-is critical for improving grazing management. This study investigated the spatio-temporal movement behavior of a goat flock in a complex woody rangeland using GPS tracking combined with GIS-based vegetation and land morphology mapping. The influence of seasonal changes in forage availability and the shepherd’s management on movement trajectories and vegetation selection was specifically examined over two consecutive years. Goat movement paths, activity ranges, and speed differed among seasons and years, reflecting changes in resource distribution, physiological stage, and herding decisions. Dense oak woodland and moderate shrubland were consistently the most selected vegetation types, confirming goats’ preference for woody species. The shepherd’s management—particularly decisions on grazing duration, route planning, and provision or withdrawal of supplementary feed—strongly affected movement characteristics and habitat use. Flexibility in adjusting grazing strategies under shifting economic conditions played a crucial role in shaping spatial behavior. The combined use of GPS devices, GIS software, vegetation maps, and direct observation proved to be an effective approach for assessing movement behavior, forage selection and grazing pressure. Such integration of technological and classical methods provides valuable insights into diet composition and resource use and offers strong potential for future applications in precision livestock management. Real-time monitoring and decision support tools based on this approach could help farmers optimize grazing strategies, improve forage utilization, and support sustainable rangeland management. Full article

The spatiotemporal evolution of ecosystem services has a profound influence on the fragile eco-environment in Inner Mongolia and the arid/semi-arid and the ecological barrier regions of Northern China; in particular, the small-scale and high-value land variables may lead to large eco-environment effects through altering the ecosystem services, which is still unclear in this vulnerable area. The differential driving mechanism of both human activities and natural factors on ecosystem services also needs to be revealed. To solve this scientific issue, the synergistic methodology of spatial analysis technology, the improved ecosystem service assessment method, flow gain/loss model, global/local Moran’s I approach, and the Geographically and Temporally Weighted Regression (GTWR) model were applied. Our main results are as follows: remote sensing monitoring showed that the land changes featured a persistent expansion of cropland and built-up areas, with a decline in grassland and wetland, along the east–west gradient from forests, grasslands, and unused-lands, to become the dominant cover type. According to our improved model, the ecosystem services considering the internal structure of build-up lands were first investigated in this ecologically fragile area of China, and the evaluated ecosystem service value (ESV) reduced from CNY 5515.316 billion to CNY 5425.188 billion, with an average annual decrease of CNY 3.004 billion from 1990 to 2020. Another finding was that the small-scale land variables with large ecological service impacts were quantified; namely, the proportion of grassland, woodland, wetland, and water body decreased from 62.71% to 61.34%, with only a relatively minor fluctuation of −1.37%, but this decline resulted in a large ESV loss of CNY 116.141 billion from 1990 to 2020. From the driving perspective, the temperature, digital elevation model (DEM), and slope exhibited negative effects on ESV changes, whereas a positive association was analyzed in terms of the precipitation and human footprint during the studied period. This study provides important support for optimizing land resource allocation, guiding the development of agriculture and animal husbandry, and protecting the ecological environment in arid/semi-arid and ecological barrier regions. Full article

Monitoring agricultural land in regions undergoing rapid change is essential for supporting management, policy development, and biodiversity conservation. Dam construction and associated irrigation systems drive land use change transitions from annual to permanent crops and intensify cultivation systems. Mapping crop types at the parcel level, particularly permanent crops, is therefore critical. The EU Crop Map 2018, the first attempt to map annual crops across the European Union using remote sensing and machine learning, aggregates permanent crops into the generic class “shrublands and woodlands”. This study refines the EU Crop Map classification by distinguishing permanent crop types using an automated machine learning model integrating Sentinel S1 and S2 imagery. The study area surrounds the Alqueva reservoir in southern Portugal, one of the Europe’s largest artificial lakes, where recent irrigation system expansion has driven rapid permanent crop adoption. The model achieved 91% overall accuracy, demonstrating strong performance in distinguishing permanent crops, forests, and other occupations. It effectively identified almond groves (F1 score = 0.90), and distinguished three major olive grove cultivation systems (F1-score ≥ 0.78), though performance was lower for vineyards (0.71) and other permanent crops (0.48). Comparison with the Portuguese official land use product COS 2018 showed strong overall spatial alignment, despite several inconsistencies, and lower F1 scores (0.60) in the direct comparison the new mapping produced. This study used a large reference dataset, enabling the assessment of the effect of training set size on classification accuracy. While overall accuracy remained above 83%, even with only 5% of the training data, underrepresented classes experienced significant performance degradation, highlighting the critical need to address class imbalance in agricultural land cover mapping. Full article

As a UNESCO World Heritage Site, the Dujiangyan Irrigation District is a key area for Chengdu’s rural revitalisation. However, as the plan progresses, issues have emerged, including loss of traditional features, cultural heritage, and landscape degradation. Within the framework of “landscape information collection—landscape information processing—landscape information output”, the study utilized literature review, field surveys, and remote sensing interpretation to collect data for the years 2000, 2010, and 2020 as time slices. A system of landscape characteristic elements was then built to identify the types of landscape characteristics. The types were determined, and a systematic analysis of the regional landscape’s evolution was conducted. The results indicated that the types of landscape characteristics were classified as follows: Urban Settlement Landscape (8.70–16.10%), Low-Hill Forest Landscape (1.82–3.47%), Village Woodland-Grove Landscape (15.89–44.23%), and Idyllic Agricultural Landscape (36.20–73.59%). Over the last two decades, there has been a steady increase in Urban Settlement Landscape, a slow overall growth trend in Low-Hill Forest Landscape, a rapid growth trend in Village Woodland-grove Landscape, and a rapid decline in Idyllic Agricultural Landscape. Among these, built-up land dominates Urban Settlement Landscape evolution; forest land shapes Low-Hill Forest Landscape; cultivated and built-up land influence Village Woodland-grove Landscape; and cultivated land drives Idyllic Agricultural Landscape changes. Based on the changes observed, the study explored the impact of relevant policies on the landscape characteristics of the study area. Policies for urban-rural integration have encouraged the networked growth of settlement landscapes, creating a system with several centres. Both ecological and economic gains have resulted from forestry practices. Policies that safeguard farmhouse forests have made multifunctional transformation easier. Large-scale farming and ecological agriculture are now linked in a zone established by agricultural modernisation strategies. The study offers scientific references for the protection of regional landscapes and the construction of rural living environments in the irrigation area. Full article

Domestic demand for honey in Kenya consistently exceeds national production, resulting in periodic reliance on imports. Kenyan honey is typically branded and marketed according to its geographical origin, whereas information regarding botanical origin is rarely communicated. This study was undertaken in two phases: a systematic review of the literature on honey varieties in Kenya—with an emphasis on legislation, production, and processing—and an online survey assessing front-of-pack (FoP) labeling descriptions. Legislatively, Kenyan honey varieties are categorized based on (i) the bee species producing the honey (honeybee or stingless bee), (ii) the intended use (direct human consumption or industrial application), and (iii) the presence of added flavoring agents. The results from the FoP labeling survey indicated that all domestic honey samples (n = 24) failed to comply with labeling requirements, instead emphasizing descriptors such as “natural” and “pure.” Only 40% of imported honey brands (n = 10) declared the botanical origin and processing method. Mellisopalynological studies showed that honey produced in the Acacia woodlands of Baringo, West Pokot, and Kitui can legitimately be marketed as Acacia honey. In contrast, honey from the Eastern Mau forest can be characterized as monofloral Eucalyptus, Croton, Albizia, or Cordia spp. honeys, with numerous bifloral and multifloral combinations. Sisal and mangrove honeys were also identifiable in landscapes dominated by these plant species. The lack of legislative classification for Kenyan monofloral honeys appears to contribute to widespread non-compliance in industry labeling practices. Although Kenyan honey remains competitive, inadequate product differentiation and weak labeling hinder access to niche domestic and international markets. To strengthen competitiveness, Kenyan honey legislation should incorporate provisions for characterizing monofloral honey types, processing standards, and mellisopalynological authentication. Such measures will enhance producer awareness, promote adoption of good processing practices, strengthen compliance with trade regulations, and support the development of a robust national honey value chain. Full article

With the intensification of climate change and the urban heat island effect, there is growing awareness of the role of urban greening in improving the urban climate. The aim of this study is to explore how various characteristics of green spaces—including type, configuration (size and shape), location, and distance from the urban centre—affect their cooling effect. Landsat remote sensing land surface temperature data were analysed through Geographic Information Systems, using Sheffield as a case study. The results show that the cooling effect of woodland was significantly stronger than that of grassland and urban parks, with a cooling intensity reaching up to 2.93 °C, and a cooling extent that can reach up to 500 m beyond its boundary. When closer to the city centre, both the shape and size of green spaces show a positive correlation with their cooling effect, but this relationship becomes less evident as the distance from the city centre increases. The size of a woodland had a greater effect in terms of a reduction in land surface temperature than the shape of the woodland. The findings of this study can provide a better framework for landscape architects and urban planners to plan for climate change and propose stronger green strategies to mitigate the urban heat island effect. Full article

Water-extractable carbon is thought to originate from labile organic carbon pools and has been used as an active carbon indicator for soil evaluation in numerous studies. This study aims to explore the regional variation patterns of water-extractable organic carbon (WEOC) and the environmental impact factors associated with it. It examines the variability of WEOC under different climatic conditions and land use types, including grasslands and woodlands, thereby enhancing our understanding of WEOC. We measured the WEOC in the surface soil layers (0–10 cm) of woodlands and grasslands in arid and semi-arid regions. Additionally, we analyzed the effects of varying climatic conditions and land use types on WEOC based on data from literature research. WEOC distribution patterns diverged spatially from soil organic carbon (SOC). WEOC fractions decreased with increasing precipitation, and surface soil WEOC accumulation was observed in arid regions. This accumulation was more pronounced in forest-land, resulting in a more marked divergence in WEOC concentrations between woodlands and grasslands in arid regions. We inferred that the inconsistent correlation between WEOC and SOC across regions arises from their distinct distribution patterns along environmental humidity gradients. Owing to the climate sensitivity of WEOC, its surface soil accumulation in arid areas may increase the vulnerability of soil ecosystems, rendering them more susceptible to environmental disturbances. Such susceptibility could drive organic carbon loss and soil quality degradation. These findings hold promise for improving our understanding of WEOC dynamic, and will also give insight into refining soil carbon balance models and soil management strategies to address environmental changes. Full article

This study investigates how different vegetation types influence the molecular structure and abundance of soil organic carbon (SOC), as well as their influence on microbial metabolic pathways and community composition. Soil samples were collected from four different sites: a woodland dominated by Drypetes perreticulata (DP), a woodland dominated by Horsfieldia hainanensis (HM), a Zea mays L. field (ZL), and a citrus reticulata orchard (CB). The molecular structure of soil organic carbon (SOC) was characterised using Fourier Transform Infrared (FTIR) spectroscopy, identifying aromatic carbon (ArC), polysaccharide carbon (PSC), alkyl carbon (AlkC), amine carbon (AmC), ether carbon (EtC), and olefin carbon (OleC). Our results indicated significant variations across vegetation types: DG exhibited a significantly higher ArC content, while maize fields showed lower PSC levels. To analyse the relationships between different samples, we employed principal component analysis (PCA), which revealed distinct organic carbon structures across vegetation types, with the forests (DG and HM) significantly differing from agricultural sites (ZL and CB). Additionally, the 16S V3_V4 region of soil bacteria was sequenced using high-throughput sequencing. We employed PICRUSt2 to predict microbial metabolic pathways, revealing consistent core metabolic functions across samples but significant variations in secondary metabolism, with HM samples exhibiting the most distinctive metabolic profiles. Redundancy analysis (RDA) further demonstrated that microbial metabolic pathway variation explained 55.66% of organic carbon structure variance. Key microbial taxa exhibited significant associations with specific carbon source types and functional pathways. These findings highlight the pivotal mechanisms by which different vegetation types regulate soil organic carbon structure and composition by driving changes in microbial metabolic traits and community assembly. This study provides a mechanistic basis for understanding the coupling between vegetation, microorganisms, and carbon cycling, offering significant guidance for optimising vegetation restoration strategies, enhancing soil carbon sequestration capacity, and advancing carbon management practices based on microbial regulation. Full article

Landscape Ecological Risk Assessment (LERA) is the basis for stable ecological functions in alpine meadow areas and is closely related to sustainable development. The research of LERA is complex, and there are problems with its identification scale and method selection. Existing LERA studies are relatively limited in focusing on alpine meadow areas. Therefore, we explored the characteristics of landscape ecological risk (LER) and its driving mechanisms in Gannan (GN) using the Landscape Ecological Risk Index (LERI) and GeoDetector at the grid scale based on the 2000–2020 CNLUCC data and other ancillary data. Results demonstrated the following: (1) From 2000 to 2020, grasslands and woodlands were the major landscape types in GN. Landscape changes mainly occurred between grasslands and woodlands. (2) During the time of the research, the overall environmental patch fragmentation and complexity of the study area increased. (3) LER in GN is dominated by medium-low and medium risk, while the overall trend of LER is decreasing. (4) The effect of natural factors on the evolution of the LER pattern in GN is greater than the effect of socio-economic factors. The elevation factor has the greatest impact among all factors. Additionally, the interaction of the factors on the evolution of LER was enhanced. Consequently, scientific artificial restoration works and maintaining a reasonable area of croplands are crucial for LER control. This study offers an important reference for fine-scale LERA research and provides a scientific basis for ecological management and sustainable development in the alpine meadow regions. Full article

Urban green hearts provide essential ecosystem services, including carbon sequestration, water purification, and hydrological regulation. The Green Heart Area of the Chang-Zhu-Tan Urban Agglomeration in Hunan Province, China, is the largest globally, and plays a critical role in regional water management. These functions are increasingly threatened by intensive land-use, while soil, as the foundational ecosystem component, mediates water retention, nutrient cycling, and erosion resistance. This study examined the effects of four land-use types—cropland, plantation, arbor woodland, and other woodland—on soil particle composition and key nutrients (organic carbon, total nitrogen, and total phosphorus). Statistical comparisons among land-use types were performed. Results indicated that silt was the dominant soil fraction across all land-uses (64–72%). Arbor woodland exhibited significantly higher sand content (29%) compared to cropland (19%; p < 0.05), suggesting improved water permeability and erosion resistance. Cropland showed elevated nutrient levels, with TN (1450.32 mg·kg⁻¹) and TP (718.86 mg·kg⁻¹) exceeding both national averages and those in arbor woodland. Coupled with acidic soil conditions (pH 5.23) and lower stoichiometric ratios (C/N: 10.82; C/P: 35.67; N/P: 3.29), these traits indicate an increased risk of nutrient leaching in croplands. In contrast, arbor woodland displayed more balanced C:N:P ratios (C/N: 12.21; C/P: 48.05; N/P: 3.84), supporting greater nutrient retention and aggregate stability. These findings underscore the significant influence of land-use type on soil ecological functions, including water infiltration, runoff reduction, and climate adaptability. The study highlights the importance of adopting conservation-oriented practices such as reduced tillage and targeted phosphorus management in croplands, alongside reforestation with native species, to improve soil structure and promote long-term ecological sustainability. Full article

As a critical ecological security node in North China, the Baiyangdian Basin underpins regional water resources, biodiversity conservation, and environmental risk mitigation. Its ecological integrity is fundamental to the sustainable development of the Beijing–Tianjin–Hebei (BTH) megaregion. This study leveraged Google Earth Engine (GEE) to quantify spatiotemporal ecosystem dynamics within the Baiyangdian watershed from 1990 to 2023, utilizing the Remote Sensing Ecological Index (RSEI). The primary drivers influencing the watershed’s ecological and environmental quality were subsequently analyzed. The results show that the ecological quality of the Baiyangdian Basin showed fluctuating changes from 1990 to 2023. Overall, the northwestern part of the Baiyangdian Basin improved significantly, while the southeastern part was slightly degraded, and the intensity of the change between different RSEI grades was low, mainly fluctuating between poor, medium, and good grades. Both anthropogenic and natural factors have high explanatory power for the ecological quality of the Baiyangdian watershed, and the land use type in particular is the main driver of changes in the RSEI area. The explanatory power of these factors was significantly enhanced by the interaction between them, especially the interaction between the land use type and other drivers. Within the drivers of the land use type, the cropland area, woodland area, shrub area, and grassland area have a significant influence. In summary, the area change in different land use types is the main factor influencing the ecological quality of the Baiyangdian watershed. This study has demonstrative value and implications for large-scale shallow lakes and wetlands, ecological barriers in rapidly urbanizing regions, the integrated management of cross-administrative watersheds, and the use of the GEE platform for long time-series and large-scale ecological monitoring and assessment. Full article

Pine wilt disease endangers the ecological stability of China’s coniferous woodlands. In a specific region, the number of dead pine trees has exhibited a consistent year-on-year increase, highlighting the urgent need for efficient and sustainable monitoring strategies. However, UAV-based remote sensing methods currently face challenges in complex environments, including insufficient feature-capture capabilities, interference from visually similar objects, and limited localization accuracy. This study developed a remote sensing workflow leveraging high-resolution UAV imagery to oversee pine trees affected with pine wilt disease. An enhanced YOLOv5 detection model was employed to identify symptomatic trees. To strengthen feature extraction capabilities—particularly for color and texture traits indicative of infection—different types of attention mechanisms, for instance SE, CBAM, ECA, and CA, were integrated as part of the model. Furthermore, a BiFPN structure was incorporated to enhance the fusion of features across multiple scales, and the EIoU loss function was adopted to boost the accuracy of bounding box prediction, ultimately enhancing detection precision. Experimental results show that the enhanced SEBiE-YOLOv5 framework achieved a precision of 89.4%, with an AP of 86.1% and an F1-score of 83.1%. UAV-based monitoring conducted during the spring and autumn of 2023 identified 616 dead trees, with field verification accuracy ranging from 88.91% to 92.42% and localization errors within 1–10 m. These findings validate the method’s high accuracy and spatial precision in complex mountainous forest environments. By integrating attention mechanisms, BiFPN, and the EIoU loss function, the proposed SEBiE-YOLOv5 model substantially enhances the recognition accuracy of key features in infected trees as well as their localization performance, and offers a practical and computationally efficient approach for the long-term surveillance of pine wilt disease in challenging terrain. Full article