Search Results (31,450)

Investigating vegetation dynamics and their drivers in ecologically vulnerable regions is essential for evaluating ecological restoration outcomes. This study examined the spatiotemporal evolution of the Normalized Difference Vegetation Index (NDVI) and its influencing factors in Fengning county, the Bashang region from 2001 to 2023 using land use transition matrix, trend analysis, and geographical detector methods. Key findings include the following: (1) Land use transition exhibited a clear phased pattern, shifting from cropland-to-grassland conversion (2001–2010) to grassland-to-forest conversion (2010–2023).(2) The annual mean NDVI increased significantly, showing a southeast–northwest spatial gradient consistent with landforms. The long-term trend followed a sequential “degradation–improvement–consolidation” trajectory. (3) Factor detection identified land use type as the primary driver of vegetation spatial heterogeneity (q = 0.297), highlighting the dominant influence of human activities. (4) Interaction detection demonstrated bivariate enhancement for all factor pairs, with the combination of land use type and precipitation yielding the highest explanatory power (q = 0.440). This underscores that vegetation dynamics are predominantly governed by nonlinear interactions between human-driven land use and climate. The research highlights the effectiveness of ecological restoration policies and offers valuable insights for guiding future ecosystem management in ecologically fragile areas under climate change. Full article

Farmland shelterbelts are crucial elements in safeguarding agricultural ecological security and sustainable development, with their precise extraction being vital for regional ecological monitoring and precision agriculture management. However, constrained by their narrow linear distribution, complex farmland backgrounds, and spectral confusion issues, traditional remote sensing methods encounter significant challenges in terms of accuracy and generalization capability. In this study, six representative deep learning semantic segmentation models—U-Net, Attention U-Net (AttU_Net), ResU-Net, U²-Net, SwinUNet, and TransUNet—were systematically evaluated for farmland shelterbelt extraction using high-resolution Gaofen-6 imagery. Model performance was assessed through four-fold cross-validation and independent test set validation. The results indicate that convolutional neural network (CNN)-based models show overall better performance than Transformer-based architectures; on the independent test set, the best-performing CNN model (U-Net) achieved a Dice Similarity Coefficient (DSC) of 91.45%, while the lowest DSC (88.86%) was obtained by the Transformer-based TransUNet model. Among the evaluated models, U-Net demonstrated a favorable balance between accuracy, stability, and computational efficiency. The trained U-Net was applied to large-scale farmland shelterbelt mapping in the study area (Alar City, Xinjiang), achieving a belt-level visual accuracy of 95.58% based on 385 manually interpreted samples. Qualitative demonstrations in Aksu City and Shaya County illustrated model transferability. This study provides empirical guidance for model selection in high-resolution agricultural remote sensing and offers a feasible technical solution for large-scale and precise farmland shelterbelt extraction. Full article

China’s Green Finance Reform and Innovation Pilot Zones (GFRIPZ) policy has emerged as a central instrument for promoting sustainable urban development and strengthening Urban Ecological and Environmental Resilience (UEER). However, systematic evidence on its actual effectiveness remains scarce. This study applies a difference-in-differences (DID) model to panel data for 279 Chinese cities from 2011 to 2022 to identify the causal impact of the GFRIPZ policy on UEER and to examine its transmission mechanisms and heterogeneity. Specifically, we incorporate green innovation efficiency and environmental regulation intensity to test the technological and regulatory channels through which green finance operates. The empirical results show that: (1) the GFRIPZ policy significantly improves UEER, and this finding is robust across a range of alternative specifications and robustness checks. (2) Green innovation efficiency and environmental regulation intensity serve as key mechanisms through which the policy enhances UEER. (3) The policy effect is stronger in eastern cities, megacities, small cities, and non-resource-based cities, while it is relatively weaker in central and western cities, medium-sized cities, and resource-based cities. These findings provide additional empirical evidence to inform the refinement and further advancement of the GFRIPZ policy and offer evidence-based implications for urban green development strategies. Full article

Pollen identification is critical for melissopalynology (honey authentication), ecological monitoring, and allergen tracking, yet manual microscopic analysis remains labor-intensive, subjective, and error-prone when multiple grains overlap in realistic samples. Existing automated approaches often fail to address multi-grain scenarios or lack systematic comparison across classical and deep learning paradigms, limiting their practical deployment. This study proposes a subinstance-based classification framework combining YOLOv12n object detection for grain isolation, independent classification via classical machine learning (ML), convolutional neural networks (CNNs), or Vision Transformers (ViTs), and majority voting aggregation. Five classical classifiers with systematic feature selection, three CNN architectures (ResNet50, EfficientNet-B0, ConvNeXt-Tiny), and three ViT variants (ViT-B/16, ViT-B/32, ViT-L/16) are evaluated on four datasets (full images vs. isolated grains; raw vs. CLAHE-preprocessed) for four berry pollen species (Ribes nigrum, Ribes uva-crispa, Lonicera caerulea, and Amelanchier alnifolia). Stratified image-level splits ensure no data leakage, and explainable AI techniques (SHAP, Grad-CAM++, and gradient saliency) validate biological interpretability across all paradigms. Results demonstrate that grain isolation substantially improves classical ML performance (F1 from 0.83 to 0.91 on full images to 0.96–0.99 on isolated grains, +8–13 percentage points), while deep learning excels on both levels (CNNs: F1 = 1.000 on full images with CLAHE; ViTs: F1 = 0.99). At the instance level, all paradigms converge to near-perfect discrimination (F1 ≥ 0.96), indicating sufficient capture of morphological information. Majority voting aggregation provides +3–5% gains for classical methods but only +0.3–4.8% for deep models already near saturation. Explainable AI analysis confirms that models rely on biologically meaningful cues: blue channel moments and texture features for classical ML (SHAP), grain boundaries and exine ornamentation for CNNs (Grad-CAM++), and distributed attention across grain structures for ViTs (gradient saliency). Qualitative validation on 211 mixed-pollen images confirms robust generalization to realistic multi-species samples. The proposed framework (YOLOv12n + SVC/ResNet50 + majority voting) is practical for deployment in honey authentication, ecological surveys, and fine-grained biological image analysis. Full article

Background/Objectives: Despite large-scale measles and rubella (MR) vaccination campaigns in West Africa, measles outbreaks persist, raising concerns about campaign effectiveness, coverage, and underlying determinants. This study assesses the impact of MR follow-up campaigns in 12 of 17 West African countries (2024–2025) and examines the factors contributing to post-campaign outbreaks. The main objective of this study is to evaluate the impact of MR campaigns on measles transmission, identify the characteristics of post-campaign outbreaks, and propose strategies to improve campaign effectiveness and accelerate progress toward measles elimination in West Africa. Methods: We conducted a cross-sectional and ecological analytical study to examine spatial and temporal variations based on measles surveillance data from 2024 to 2025, post-campaign coverage surveys (PCCS), district-level outbreak reports, and administrative coverage reports. Trends in measles cases before and after the MMR campaigns were assessed, along with demographic characteristics and spatial analyses of confirmed cases. Results: In 2024, 70.5% (12/17) of countries conducted measles vaccination campaigns, but measles outbreaks increased in 2025 (64 districts in 2024 versus 383 in 2025). Children under five remained the most affected (54%), with 85% of cases being either unvaccinated (57%) or of unknown status (28%). Administrative coverage exceeded 95% in most countries, but measles PCCS revealed gaps, with only Senegal (93%) and Guinea-Bissau (94%) achieving high verified coverage. No country achieved 95% national MPCC. Conclusions: Suboptimal campaign quality, gaps in immunity beyond target age groups, and unreliable administrative data contributed to the persistence of outbreaks. Recommendations include extending Measles vaccination campaigns to older children (5–14 years), improving preparedness by drawing on experiences from other programs such as polio, standardizing PCCS data survey and analysis methodologies across all countries, and integrating Measles vaccination campaigns with other services such as nutrition. Full article

Spatial cognition refers to the mental processing, perception, and interpretation of spatial information. It is often operationalized through self-assessments like sense of direction and mental rotation ability or field-based real-world tasks like pointing to a specific building and wayfinding; however, the former and latter entail unclear ecological validity and high participant burdens, respectively. Since the advent of smartphones, this repertoire has been extended substantially through the use of sensors or apps. This study used a large longitudinal experience sampling method (ESM) in two different countries (Canada and Australia, N = 217) and analyzed spatial cognition both conventionally (i.e., sense of direction and speeded mental rotation test) and through new techniques like self-rated and objectively assessed daily Google Maps usage, movement patterns throughout the 14-day assessment phase (using H3 tiles for geolocation), and a Point North task. The Point North task objectively assessed deviation from the celestial direction, North, by using smartphone compass sensors. In both countries, spatial orientation was found to be associated only with the Point North task, while no significant associations were found for daily Google Maps usage (subjectively and objectively measured) and moving distance throughout the assessment phase. Although further validation is required, the Point North task shows promise as an objective, ecologically valid, and easily employable smartphone-based measure for assessing spatial cognition in real-world contexts. Full article

Mangrove ecosystems are extremely sensitive to compounded stress, as evidenced by the widespread degradation and mortality of the pioneer mangrove species Avicennia marina along the Guangxi coast in recent years. However, research on how mangrove ecosystems respond to compound biotic stressors remains limited. Therefore, the present study aimed to systematically examine the ecological response mechanisms of A. marina under dual threats from the burrowing isopod Sphaeroma terebrans and the defoliating moth Hyblaea puera. Two contrasting sites were selected: Guchengling (subject to chronic stem-boring and sudden defoliator outbreaks) and Tieshangang (free from compounded stress). Photosynthetic capacity, metabolic function, and root structural integrity were all compromised considerably by chronic boring stress. During insect outbreaks, 15.33 ha of mangroves were destroyed due to impairments that breached the ecological threshold. In contrast, the healthier Tieshangang community exhibited strong ecological resilience, with rapid green canopy regeneration following defoliation and notable recovery in the normalized difference vegetation index. To enable early identification and precise intervention in mangrove decline, a comprehensive health index model was developed that includes root–canopy coordination, root length, and boring density. Field validation results, showing 100% agreement with expert evaluations across 19 validation sites (Cohen’s κ = 1.0), confirmed the high accuracy of the model. This study highlights the importance of identifying sensitive zones and undertaking timely ecological restoration, thereby providing a scientific basis and a practical tool that could facilitate early warning and timely management of mangrove degradation events. Full article

Analyzing the composition and structure of the gut bacterial community in Antheraea pernyi is essential for improving its economic traits, as well as for understanding gut bacteria–host interactions in lepidopteran insects. This study utilized the Illumina MiSeq PE 300 platform to conduct 16S rRNA gene sequencing for a comparative analysis of gut bacterial community in laboratory-reared and field-released (spring and autumn) Antheraea pernyi larvae of the same strain. The study revealed the specific effects of rearing environment and seasonal variation on the structural and functional dynamics of the larval gut bacterial communities. The composition of the dominant gut bacteria varied significantly with rearing environment and season. Laboratory-reared and spring field-released groups exhibited similar bacterial community structures, whereas the autumn field-released group showed a significant trend toward specialization, characterized by enrichment of specific bacterial taxa. Linear discriminant analysis effect size identified statistically significant biomarkers across samples. Taxonomic analysis revealed that Actinomycetota, Actinobacteria, Mycobacteriales, Dietziaceae, and Dietzia were characteristic of the gut bacteria profile in spring field-released, Lactobacillales, Enterococcaceae, and Enterococcus were enriched in the autumn field-released group, and the laboratory-reared group exhibited a relative dominance of Alphaproteobacteria. Functional prediction indicated that gut bacterial community structure likely influences its metabolic potential, which may suggest an adaptive response of the Antheraea pernyi to distinct ecological environments. This study provides important insights into the highly complex nature of insect-microbe interactions. Full article

Remote sensing provides an efficient approach for monitoring ecosystem dynamics in the arid and semi-arid regions of Xinjiang, yet traditional forest-land extraction methods (e.g., spectral indices, threshold segmentation) show limited adaptability in complex environments affected by terrain shadows, cloud contamination, and spectral confusion with grassland or cropland. To overcome these limitations, this study used three convolutional neural network-based models (FCN, DeepLabV3+, and PSPNet) for accurate forest-land extraction. Four tri-band training datasets were constructed from Sentinel-2 imagery using combinations of visible, red-edge, near-infrared, and shortwave infrared bands. Results show that the FCN model trained with B4–B8–B12 achieves the best performance, with an mIoU of 89.45% and an mFscore of 94.23%. To further assess generalisation in arid landscapes, ESA WorldCover and Dynamic World products were introduced as benchmarks. Comparative analyses of spatial patterns and quantitative metrics demonstrate that the FCN model exhibits robustness and scalability across large areas, confirming its effectiveness for forest-land extraction in arid regions. This study innovatively combines band combination optimization strategies with multiple deep learning models, offering a novel approach to resolving spectral confusion between forest areas and similar vegetation types in heterogeneous arid ecosystems. Its practical significance lies in providing a robust data foundation and methodological support for forest monitoring, ecological restoration, and sustainable land management in Xinjiang and similar regions. Full article

Enhancing agricultural green total factor productivity (AGTFP) under ecological and environmental constraints is essential for advancing green agricultural development in the Beijing–Tianjin–Hebei (BTH) region. Using panel data from 13 prefecture-level cities from 2001 to 2022, this study applies a super-efficiency EBM model incorporating undesirable outputs together with the Malmquist–Luenberger index to measure AGTFP. Global and local Moran’s I indices as well as the spatial Durbin model are then employed to examine the temporal evolution, spatial disparities, and spatial interaction effects of AGTFP during 2001–2022. The findings indicate that: (1) From 2001 to 2022, the AGTFP in the BTH region grew at an average annual rate of 7.7%. This trend reflects a growth pattern primarily driven by green technological progress in agriculture, while substantial disparities in AGTFP persist across different subregions. (2) the global Moran’s I values show frequent shifts between positive and negative spatial autocorrelation, suggesting that a stable and effective regional coordination mechanism for green agricultural development has yet to be formed; (3) the determinants of AGTFP exhibit pronounced spatiotemporal heterogeneity, and the fundamental drivers of the region’s green agricultural transition increasingly rely on endogenous growth generated by technological innovation and rural human capital; (4) policy recommendations include strengthening benefit-sharing and policy coordination mechanisms, promoting cross-regional cooperation in agricultural science and technology, and implementing differentiated industrial layouts to support green agricultural development in the BTH region. These results provide valuable insights for promoting coordinated and sustainable green agricultural development across regions. Full article

Land consolidation requires reliable and objective land valuation to ensure transparency and fairness in the reallocation process. This study introduces a data-driven method for assessing agricultural site productivity based on vegetation indices derived from multispectral imagery, supported by Sentinel satellite data and validated using handheld chlorophyll meter measurements. Site productivity, defined as the land’s ability to generate yield and biological value, is determined by natural and environmental factors that directly influence economic worth. Vegetation indices (NDVI, SAVI) obtained from UAV imagery showed a strong correlation with chlorophyll content, confirming the reliability of this non-invasive assessment. The analysis, conducted in Poland and Slovakia, demonstrated the method’s applicability under two different land consolidation systems: a market-based model in Poland and an ecologically oriented approach in Slovakia. The proposed framework proved easy to implement and provided consistent results even without the use of ground control points. By reducing fieldwork time and costs while improving valuation accuracy, this method enhances the objectivity and transparency of land consolidation procedures. The findings confirm the potential of vegetation indices to support data-driven and environmentally informed land valuation across diverse consolidation contexts. Full article

The invasive perennial weed Ambrosia confertiflora (Burr ragweed) is widespread across various climatic regions in Israel and neighboring countries. This study examines how temperature affects the development of the plants’ aboveground and underground organs, as well as biomass allocation. We hypothesize that temperature influences how the plant distributes resources, thereby modifying its phenotypic morphology and contributing to its spread. Plants were grown in a phytotron under four seasonal temperature regimes (10–16 °C, 16–22 °C, 22–28 °C, 28–34 °C, N-D, 14 h light). We measured above- and belowground biomass, growth form, leaf size, and the interaction between temperature and apical dominance. Our results show that biomass allocation varies with temperature and developmental stage. During early growth, resources are primarily directed toward shoot development and leaf production. As plants matured, they shifted more resources to underground structures, eventually balancing allocation. At lower temperatures, plants invested more in underground growth while the shoot remained in the rosette form. In contrast, higher temperatures favored aboveground growth. Ambrosia confertiflora demonstrates significant phenotypic plasticity in response to temperature variation, affecting plant height, leaf morphology, and resource allocation in both shoot and underground tissues. Understanding how temperature drives these changes is critical to understanding the spread and ecological impact of this highly adaptable weed. Full article

This study examines how undergraduate design students imagined and critiqued biotechnological futures through speculative work with generative AI in a semester-long biodesign course. Using inductive qualitative coding and visual discourse analyses, we traced how students’ prompts, images, and reflections reveal an evolving grammar of speculation. Students shifted from crisis description to design-oriented possibility and socio-political reasoning about ecological, cultural, and ethical implications. Generative AI supported this shift by offering visual feedback that enabled students to recognize assumptions and critically examine speculative designs. Through repeated cycles of prompting and refinement, students advanced biodesign prototypes and developed a nuanced understanding of AI’s affordances and limits. Extending constructionism learning theories into speculative design with generative AI, this study examines how learners externalize discursive and imaginative thought through prompt-crafting. These findings articulate a grammar of speculation, showing how generative AI mediates critical AI literacy as a discursive and constructionist learning process. Full article

Environmental element monitoring is essential for assessing environmental quality, identifying pollution sources, evaluating ecological risks, and understanding long-term contamination trends. Modern monitoring campaigns routinely generate large volumes of complex data that require advanced analytical strategies. This study applied chemometric techniques to analyze elements and BVOCs (biogenic volatile organic compounds) measured from Posidonia oceanica and related environmental matrices (seawater, sediment, and rhizomes) during three sampling campaigns in the Tremiti Islands (Italy). Twenty-two trace elements were quantified, and BVOC profiles were obtained from the leaf samples. The dataset was analyzed using a combination of univariate visualizations, unsupervised and supervised multivariate techniques, and multi-way methods. PCA (Principal Component Analysis) and PLS-DA (Partial Least Squares-Discriminant Analysis) revealed distinct spatial (leaf section) and temporal (sampling period) trends, supported by consistent elemental markers. A low-level data fusion approach integrating BVOC and element data improved group discrimination and interpretability. PARAFAC (PARAllel FACtor analysis) applied to a three-way array successfully separated background trends from meaningful compositional changes, uncovering latent structures across chemical, spatial, and temporal dimensions. This work illustrates the usefulness of chemometrics in environmental monitoring and the effectiveness of combining multivariate tools and data fusion to improve the interpretability of complex environmental datasets. The methodology used in this study is fully generalizable and applicable to other environmental multi-way datasets. Full article

Measuring and simulating territorial space conflicts (TSCs) for the achievement of carbon neutrality is of critical significance for formulating regional sustainable utilization of territorial resources that are inherently green and low-carbon. This study develops a TSC evaluation framework: “conflict identification–scenario simulation–carbon effect assessment”. Focusing on Jiangsu Province, we clarify the evolutionary mechanism of TSCs under carbon neutrality goals, providing a scientific basis for high-quality regional development and low-carbon spatial governance. Results show that Jiangsu’s average TSC level was categorized as “strong conflict” (0.66) during 2005–2020. For 2030, four scenarios (natural development, economic priority, ecological protection, low-carbon development) project TSCs shifting from scattered to point-like distribution, concentrating in key core areas. Corresponding projected average carbon neutrality indices are 1.10, 1.11, 1.33, and 1.11, respectively. Under the low-carbon scenario, grid units with serious TSCs decreased by 4.53% compared to 2020—higher than natural development and economic priority scenarios, but lower than the ecological protection scenario (12.45%). Consequently, the low-carbon development scenario can optimally mitigate land use conflicts while maintaining carbon balance. This research provides robust data support for Jiangsu’s sustainable coordinated development and informs efficient land use and regional ecological security. Full article