Search Results (8,606)

The detection and monitoring of asbestos–cement roofing remain a critical public health and environmental challenge, especially in urban and suburban areas where asbestos-containing materials are still widespread due to their extensive use in the 20th century. Although hyperspectral and high-resolution multispectral remote sensing have proven effective for mapping asbestos–cement roofs, many existing approaches rely on proprietary software, limiting transparency, reproducibility, and large-scale adoption. This study presents a fully reproducible, cost-free Python-based workflow for the detection and temporal monitoring of asbestos–cement roofing using high-resolution multispectral WorldView-3 imagery. The workflow integrates atmospheric correction (using the Py6S radiative transfer model), spatial preprocessing, supervised pixel-based classification, postprocessing, and building-level aggregation within an open framework. A Maximum Likelihood Classifier is applied to VNIR and SWIR data using empirically defined roof typologies to enhance class separability. Pixel-level results are aggregated to the building scale through adaptive thresholding enabling the translation of spectral classifications into meaningful building-level information. Tested over the city of Mantua (Italy), the approach achieved reliable classification performance and enabled multi-temporal comparison to identify changes potentially due to roof remediation. Evaluation metrics (precision, recall, and F1-score) highlight the importance of carefully choosing the building-level threshold. By relying exclusively on open-source tools, the workflow enhances transparency, reproducibility, and scalability for long-term monitoring. Full article

Mangrove represent vital coastal ecosystems that contribute to shoreline stabilization, ecological balance, and environmental management. Nevertheless, the precise delineation of mangrove regions using remote sensing data is often impeded by spectral similarities with intertidal mudflats and aquatic features, alongside the irregular spatial patterns and intricate margins of mangrove stands. This research utilizes high-resolution Gaofen-6 (GF-6) satellite observations as the foundational data to develop Triplet Axial High-Resolution Network (TAHRNet), a semantic segmentation architecture derived from the High-Resolution Network with Object-Contextual Representations (HRNet-OCR) framework for mangrove identification. The model integrates a Triplet Attention module to facilitate cross-dimensional feature dependencies and an improved Multi-Head Sequential Axial Attention mechanism to capture long-range spatial context while maintaining structural consistency. Based on evaluations using the test dataset, TAHRNet yielded a Mean Intersection over Union (MIoU) of 92.01% and a Overall Accuracy of 96.38%. Relative to U-Net and SegFormer, the proposed approach showed MIoU improvements of 5.25% and 1.88%, with corresponding Accuracy gains of 2.68% and 0.94%. Further application to coastal mapping in Zhanjiang produced results that align with manual visual interpretation. These findings suggest that TAHRNet is a viable tool for mangrove extraction and can provide technical support for coastal monitoring and ecological analysis. Full article

Laser fiber-based collimation straightness measurement can eliminate the intrinsic drift of the laser source while offering a simple configuration and simultaneous measurement of straightness in two orthogonal directions. As a high-precision optoelectronic sensing method, it has been widely used for the measurement of straightness, parallelism, perpendicularity, and multi-degree-of-freedom geometric errors. However, two common issues remain in practical applications. One is the nonlinear response of the four-quadrant detector, the core position-sensitive sensor, which is caused by detector nonuniformity and the quasi-Gaussian distribution of the spot. The other is the degradation of measurement performance by atmospheric inhomogeneity and air turbulence along the optical path, particularly in long-distance measurements. To address these issues, a two-dimensional planar calibration method is first proposed to replace conventional one-dimensional linear calibration. A polynomial surface-fitting model is introduced to correct the nonlinear response and inter-axis coupling errors of the four-quadrant photoelectric sensor. Simulation and experimental results show that the proposed method significantly reduces the standard deviation of calibration residuals and improves measurement accuracy. In addition, based on our previously developed common-path beam-drift digital compensation method, comparative experiments were carried out on double-pass common-path and single-pass optical configurations employing corner-cube retroreflectors, and theoretical simulations were performed to analyze the influence of air-turbulence disturbances on measurement stability. Both theoretical and experimental results show that the double-pass common-path configuration exhibits more pronounced temporal drift. Therefore, a real-time digital compensation method for beam drift in long-distance single-pass common-path measurements is proposed. Experimental results demonstrate that the proposed method effectively suppresses drift induced by environmental air turbulence and thereby improving the accuracy and stability of long-travel geometric-error and related straightness measurement for machine-tool linear axes. Full article

Tomato landraces possess distinct flavors, colors, textures and aromas, making them suitable for traditional cuisine. Tomato landraces contain a wide range of genes, including those involved in fruit quality, that can be isolated and used in local breeding programs. In regions recognized as centers of origin, domestication and diversification, traditional farmers play an important role in the preservation of tomato landraces adapted to local conditions and agricultural practices, on the whole maintaining high genetic diversity. This work aimed to evaluate the effects of the crop cycle (C), genotype (G) and C × G interactions on the contents of soluble solids, reducing sugars, lycopene, total polyphenols, flavonoids, and vitamin C, as well as the pH and antioxidant activity, in fifteen tomato landraces (genotypes) undergoing phenotypic selection and a commercial tomato variety (control). All the varieties were grown in two crop cycles under uniform greenhouse management using a randomized block design with four repetitions. Fruit composition was analyzed with AOAC and spectrophotometric methods. Significant differences (p ≤ 0.01) were detected in the soluble solid content, pH, flavor and maturity indices, polyphenol and flavonoid contents, and antioxidant activity between C, G and C × G interactions. In contrast, titratable acidity, reducing sugars, lycopene and vitamin C did not differ between cycles. Coefficients of phenotypic and genotypic variation and broad-sense heritability (H²) ranged from 4.3 to 33.7, 2.0 to 19.0, and 3.2 to 63.5%, respectively. H² for bioactive compounds ranged from moderate to slightly high (16.3–38.8%). These findings, supported by laboratory analyses, suggest that genotypes under agronomic selection have potential as parents to enhance fruit quality in current and future breeding programs. Full article

As rural revitalization advances, the age-friendliness of public spaces directly impacts the well-being of left-behind elderly populations. However, the spatial and social marginalization of these vulnerable groups in tourism-driven peri-urban villages remains critically underexplored. To bridge this gap, this study proposes a quantitative evaluation framework integrating space syntax and multiple linear regression to investigate the matching mechanism between physical spatial layout and elderly activity needs. Focusing on Shixia Village in Beijing, surveys and satisfaction assessments were conducted with 30 elderly residents (representing a rigorous 27.3% of the permanent population). Space syntax analysis revealed a distinct “core-periphery” spatial differentiation. Despite a moderate spatial intelligibility (0.586), the rapid decay of integration in peripheral clusters acts as the primary physical bottleneck restricting the elderly’s social radius. Furthermore, regression results indicate that public facility accessibility (β = 0.703) and residential environment quality (β = 0.779) are the core positive drivers of satisfaction (p < 0.001). Conversely, road connectivity exhibited an unexpected negative correlation (β = −0.308). This highlights a crucial “double-edged sword” effect: in traditional villages with tourism development, excessive spatial permeability diminishes the elderly’s territorial sense of security due to external traffic interference. Finally, targeted optimization strategies—including traffic-calming interventions and hierarchical node layouts—are proposed, providing an operational evaluation model and design reference for age-friendly environmental construction in similar peri-urban villages. Full article

This study evaluated the performance of terrestrial LiDAR (TLS) for post-fire forest inventory across two large wildfires in Spain as a function of burn severity. We analyzed tree-level diameter at breast height (DBH), plot-level above-ground biomass (AGB), and the influence of burn severity on return intensity. DBH of segmented trees was accurately retrieved across severities, with overall accuracies of 92.1%, 95.0%, and 94.4% and RMSE of 1.19, 0.94, and 0.93 cm in unburned, moderate, and severe plots, respectively (rRMSE = 7.97%, 6.46%, 6.94%). AGB showed lower agreement, with accuracies of 93%, 88%, and 74%. After adjusting by quadrant-level biomass consumption, mean post-fire AGB values were 76.29, 65.07, and 32.90 Mg ${\mathrm{h}\mathrm{a}}^{-1}$, with mean absolute errors of 4.55, 6.38, and 6.11 Mg ${\mathrm{h}\mathrm{a}}^{-1}$. Return intensity decreased with burn severity, reducing the number of returns by 14.9% in moderately burned and 54.3% in severely burned plots. These results support the use of TLS for post-fire forest inventory in low-to-moderate severity conditions. However, in high-severity plots, return intensity reduction limited tree segmentation and DBH extraction, introducing uncertainty in plot-level AGB estimation. Full article

The primary productivity of phytoplankton (PP_eu) is critical to the carbon cycle in aquatic ecosystems. However, in complex lakes covered by ice, the estimation of PP_eu using remote sensing techniques is constrained. To address this limitation, this study developed an estimation model for ice-covered PP_eu by incorporating optical parameters such as the ice surface refractive index and the extinction coefficient of the ice layer into the vertical generalized production model (VGPM). This approach overcomes the challenges associated with remote sensing-based estimation of PP_eu during ice-covered periods. The results indicate that the annual carbon sequestration of the WLSHL is 1.72 × 10⁴ t C, with an average annual PP_eu of 316.96 mg C·m⁻²·d⁻¹. In addition to the indicators that are directly involved in the estimation of PP_eu, the environmental factors that affect PP_eu include water temperature (WT), ice thickness (IT), snow, water depth (D), total dissolved solids (TDSs), salinity (S), ammonia nitrogen (NH₄⁺-N), nitrate nitrogen (NO₃⁻-N), and oxidation–reduction potential (ORP). The PP_eu in the ice period is found to be only 17% lower than that in the ice-free period. However, the PP_eu during the ice period is considerably higher than that during the ice + snow period. The findings indicate that the impact of freezing on PP_eu during the winter is relatively limited, whereas the influence of snowfall is more pronounced. In order to mitigate the elevated PP_eu and the occurrence of algal blooms during the summer, the intensity of underwater radiation can be regulated on a periodic basis. To optimize the function of the carbon sink in winter lakes, the PP_eu can be enhanced through initiatives such as water replenishment prior to freezing and snow removal following freezing. Full article

This study uses a bismuth ferrite (BiFeO₃) and MXene (Ti₃C₂Tx) to design a surface plasmon resonance (SPR) biosensor for the sensitivity enhancement at a 633 nm wavelength. Here, MXene serves as a biorecognition element (BRE) layer to ensure stable and reliable biomolecule adsorption. The MXene is a family of two-dimensional (2D) materials with metallic-like conductivity, a large surface area that can attach biomolecules, and improve biocompatibility. The addition of a conductive 2D MXene layer and a high-index BiFeO₃ dielectric layer greatly improves light–matter interaction and evanescent field penetration at the sensing interface. Strong plasmonic coupling is indicated by the reflectance analysis, which shows a distinct and consistent shift in the resonance angle as analyte RI increases. This study examined the sensitivity at optimized Ag and BiFeO₃ layer thickness. At an Ag of 39 nm and BiFeO₃ of 3 nm thickness, the maximal sensitivity of 340.68°/RIU with a remarkable figure of merit (FoM) of 47.38/RIU is obtained. The overall detection accuracy (DA) and FoM are significantly improved by the large sensitivity enhancement, despite a slight increase in full width at half maximum (FWHM). Furthermore, the penetration depth (PD) of 198.50 nm (at RI:1.330) and 199.52 nm (at RI:1.335) is attained with the proposed structure. Due to its high sensitivity, reusability, and reproducibility, the SPR biosensor has the potential to be used in biochemical, environmental, and medical detection. Full article

Structural health monitoring (SHM) plays an increasingly important role in managing aging, safety-critical infrastructure under growing environmental and operational demands. In recent years, fiber-optic sensors (FOSs) have attracted significant attention for SHM applications due to their immunity to electromagnetic interference, durability in harsh environments, multiplexing capability, and suitability for both localized and fully distributed measurements. In parallel, advances in machine learning (ML) have enabled new approaches for extracting actionable insights from large, high-dimensional sensing datasets. This paper presents a systematic review of FOS-based SHM systems integrated with ML across civil, transportation, energy, marine, and aerospace infrastructures. Following PRISMA 2020 guidelines, peer-reviewed studies were identified and synthesized to examine sensing principles, deployment configurations, data characteristics, and learning-based analytical strategies. Fiber optic technologies are categorized into point-based, quasi-distributed, and fully distributed systems, and their capabilities for capturing strain, temperature, and spatiotemporal structural responses are critically evaluated. ML approaches are examined from a task-oriented perspective, including damage detection, localization, severity assessment, environmental compensation, and prognosis, with emphasis on the alignment between sensing configurations and appropriate learning paradigms. Key challenges remain, particularly regarding large data volumes, environmental variability, limited labeled damage datasets, model generalization, and system-level integration. Emerging directions such as physics-informed and hybrid learning, transfer learning, uncertainty-aware modeling, and integration with digital twins are discussed as pathways toward more robust and scalable SHM systems. By jointly addressing sensing physics and data-driven intelligence, this review provides a structured reference and practical roadmap for advancing intelligent FOS-based SHM in next-generation infrastructure. Full article

Large-scale smart agriculture requires reliable and energy-efficient wireless connectivity to support distributed environmental sensing across wide rural areas. However, existing low-power wide-area network (LPWAN) technologies often face limitations in scalability, reliability, or infrastructure dependency when deployed in large agricultural fields. This study presents the design and experimental evaluation of a hierarchical sensor network architecture that integrates LoRaMESH for multi-hop sensing communication and Wi-Fi HaLow as a sub-GHz backhaul for data aggregation and cloud connectivity. In the proposed system, LoRaMESH forms intra-cluster sensor networks using a lightweight controlled flooding protocol, while Wi-Fi HaLow provides long-range IP-based connectivity between cluster gateways and a central access point. A real-world deployment covering approximately $2.5\mathrm{km}\times 1\mathrm{km}$ of agricultural area was implemented to evaluate the performance of the proposed architecture. Experimental results show that the LoRaMESH network achieves packet delivery ratios above $90\%$ across one to three hops, with average end-to-end delays between $10.6$ s and $13.3$ s. The Wi-Fi HaLow backhaul demonstrates high reliability within short to medium distances, reaching $99.5\%$ packet delivery ratio at 50 m and $89.68\%$ at 200 m. Energy measurements further indicate that the sensor nodes consume only $21.19\mathsf{\mu }\mathrm{A}$ in sleep mode, enabling long-term battery-powered operation suitable for agricultural monitoring applications. These results indicate that the proposed hierarchical architecture is a feasible connectivity option for the tested large-scale agricultural sensing scenario. Because no side-by-side LoRaWAN or NB-IoT benchmark was conducted on the same testbed, the results should be interpreted as a field validation of the proposed architecture rather than as a direct experimental demonstration of superiority over alternative LPWAN systems. Full article

Accurate estimation of curvature radius from interference fringes is critical in optical metrology and precision manufacturing. Conventional interferogram analytical approaches often require manual intervention and are sensitive to fringe variations related to noise and environmental vibrations. To address these limitations, we combine an improved Twyman–Green interferometer with different artificial intelligence (AI) deep learning models and utilize a self-developed MATLAB analysis program to propose a non-destructive and rapid measurement system for optical coating substrates. The proposed AI-assisted Twyman–Green interferometric system differs fundamentally from conventional wavefront sensing techniques in both principle and implementation. This paper utilizes the Twyman–Green interferometer to generate interference fringe datasets on B270 glass and sapphire substrates, and employs convolutional neural network (CNN), ResNet-18, and VGG-16 models for training and evaluation. The proposed method integrates image enhancement, fringe pattern clustering, and analysis and validation based on fast Fourier transform (FFT). Experimental results show that ResNet-18 outperforms other models, with a mean absolute percentage error of 5.44% on sapphire substrates and 3.40% on B270 glass substrates. These findings highlight the effectiveness and robustness of deep learning models, especially residual networks, in automatic ROC prediction for optical measurement applications. Full article

Background: Human health is closely interconnected to our ecosystem. Several studies found evidence that nature-based interventions improve mental health. Very recently, these approaches have started including mindfulness practices. Nature-based mindfulness interventions (NBMIs) combine contemplative practices with exposure to natural environments and are increasingly recognised as promising tools for supporting mental health and resilience within a One Health perspective, fostering physio-psychological wellbeing whilst promoting nature awareness and a sense of connection with our planet—“biofilia”, as defined by American biologist Edward Wilson. Given the growing psychological impacts of climate-related stressors, NBMIs may offer particular value for regions with high climate-risk and ecological vulnerability. Methods: A narrative literature review was conducted following established principles for high-quality non-systematic reviews. A non-systematic but structured search of PubMed, Scopus, Web of Science and Cochrane Library (January 2018–November 2025), complemented by grey literature, identified studies involving adolescents and adults participating in interventions integrating mindfulness practices with natural environments. Extracted data included study context, participant characteristics, intervention type, mental health and resilience outcomes. Results: Across heterogeneous designs, NBMIs consistently reduced stress, anxiety, depressive symptoms and rumination, while improving sleep, vitality, attention and self-regulation. Most studies reported enhanced nature connectedness—an important mediator of wellbeing and pro-environmental behaviour. Programmes delivered to disaster-affected populations showed reductions in distress. Conclusions: NBMIs are feasible, low-cost and adaptable interventions with dual benefits for mental health and ecological awareness. They offer promising One Health-aligned strategies for strengthening psychological resilience in climate-vulnerable regions, warranting further research and context-specific adaptation. Full article

Object detection in optical remote sensing imagery has emerged as a crucial task in computer vision, with applications ranging between environmental monitoring to disaster management, precision agriculture, and urban planning. This review systematically examines current methodologies, categorising them into four principal approaches: (1) template matching-based methods, which leverage predefined patterns for object identification; (2) knowledge-based methods, which incorporate geometric and contextual information to enhance detection accuracy; (3) object-based image analysis (OBIA), which segments images into meaningful objects using spectral and spatial properties; (4) machine learning-based methods, particularly deep convolutional neural networks (CNNs), which have revolutionised the field through automatic feature learning. Each methodology’s performance characteristics, computational requirements, and suitability for different remote sensing applications are analysed. Our systematic review, following PRISMA guidelines, analysed 189 studies published from 2010 to 2025, of which 73 provided quantitative results on standard benchmarks. The three most critical challenges identified are as follows: (1) annotation bottleneck, as dense bounding box labelling of remote sensing imagery remains highly labour-intensive for deep learning approaches, (2) extreme scale variation spanning 2–3 orders of magnitude within single scenes, and (3) domain adaptation failures when models encounter new geographic regions or sensor characteristics. This review identifies critical research gaps and proposes prioritised future directions, emphasising foundation models for zero-shot detection, efficient architectures for resource-constrained deployment, and standardised benchmarks with size-specific metrics. The analysis provides practitioners with evidence-based decision frameworks for method selection and researchers with a roadmap for advancing object detection in remote sensing applications. Full article

Evaluating whether zoning-based management is associated with measurable ecosystem function benefits is crucial for China’s national park system reform, yet most existing assessments emphasize greening or productivity alone. Here, we evaluate zoning-associated patterns in the Three-River Headwaters Region by combining MODIS-derived carbon use efficiency (CUE = NPP/GPP; 2001–2024), a boundary–buffer comparison with environmental matching, and an explainable machine learning attribution framework. NPP increased across all zones, whereas CUE remained stable to slightly declining, indicating a productivity–efficiency decoupling in the remote sensing record. Core and Buffer zones maintained higher long-term median CUE than the Outside zone, but matched boundary contrasts were heterogeneous, and the Experimental–Outside CUE contrast, although robust in sign, was small in magnitude. Zone–year attribution (2002–2020) suggests that interannual CUE variability is dominated by climate and land surface structure/change, while human pressure shows a smaller negative association; these grouped SHAP contributions should be interpreted as indicative rather than precise estimates. Post-2020 climate baseline residuals show persistent negative CUE anomalies in Buffer and Experimental zones, suggesting additional non-climatic influences but not demonstrating causality. Given the temperature-sensitive structure of MOD17 and the representativeness limits of QC-filtered 500 m observations, we interpret these results as management-consistent patterns rather than stand-alone causal proof. The findings support incorporating carbon use efficiency into zonal monitoring and may inform differentiated, efficiency-oriented management review. Full article

Continuous, non-invasive cardiopulmonary monitoring is receiving increasing attention as population aging and chronic diseases rise. Acoustic sensing provides diagnostically relevant information with relatively simple hardware. Yet, physiological body sounds span heterogeneous and partially overlapping spectra and are highly susceptible to environmental noise and motion artifacts, which limit conventional stethoscopes and fixed-frequency sensors. Frequency-Tunable Acoustic Sensors (FTAS) offer a promising route toward frequency-selective amplification and adaptive interference suppression by matching their resonance to target signals, thereby potentially supporting multi-site monitoring and personalized diagnostics on a single platform. This review starts with an overview of physiological sound generation and the evolution of auscultation, then surveys mainstream medical acoustic transducers (piezoelectric, capacitive microelectromechanical systems (MEMS), piezoresistive and triboelectric) and their limitations in frequency selectivity. Resonance-tuning strategies are classified into three paradigms: electrical tuning, material-based tuning, and geometric reconfiguration, and their tuning ranges, response characteristics, and representative implementations are comparatively discussed. Finally, this review discusses the potential translational value of FTAS in physiological acoustic signal monitoring, particularly in cardiovascular and respiratory assessment, and emphasizes the remaining challenges, including the trade-off between sensitivity and selectivity, as well as long-term biocompatibility. At the same time, this review highlights their development prospects in customizable acoustic sensing platforms. Full article