Search Results (1,748)

Urbanization is accompanied by an increased use of building materials. However, the lack of high-resolution building material stock (BMS) maps limits our understanding of the relationship between BMS and urban heat. To address this, we estimated BMS across eight typical Chinese megacities using multi-source geographic data and investigated the relationship between BMS and land surface temperature (LST). The results showed that (1) the total BMS for the eight megacities was 9175.07 Mt, with Beijing and Shanghai having the largest shares. While BMS correlated significantly with population, growth patterns varied across cities. (2) Spatial autocorrelation between BMS and LST was evident. Around 16% of urban areas exhibited High–High clustering between BMS and LST, decreasing to 10% during the daytime. The relationship between BMS and LST is nonlinear, and also prominent at night, especially in Beijing. (3) Diverse building forms, especially building height, contribute to a nonlinear relationship between BMS and LST. Full article

This study advances vegetation and forest mapping in temperate mixed forests by integrating airborne hyperspectral imagery (HSI) and light detection and ranging (LiDAR) data, overcoming the limitations of conventional multispectral imaging. Employing a Digital Canopy Height Model (DCHM) derived from LiDAR, our approach integrates these structural metrics with hyperspectral spectral information, alongside detailed remote sensing data extraction. Through machine learning-based clustering, which combines both structural and spectral features, we successfully classified eight specific tree species, community boundaries, identified dominant species, and quantified their abundance, contributing to precise vegetation and forest type mapping based on predominant species and detailed attributes such as diameter at breast height, age, and canopy density. Field validation indicated the methodology’s high mapping precision, achieving overall accuracies of approximately 98.0% for individual species identification and 93.1% for community-level mapping. Demonstrating robust performance compared to conventional methods, this novel approach offers a valuable foundation for National Forest Ecology Inventory development and significantly enhances ecological research and forest management practices by providing new insights for improving our understanding and management of forest ecosystems and various forestry applications. Full article

Low-altitude unmanned aerial vehicle (UAV) vision is critically hindered by the Sim-to-Real Gap, where models trained exclusively on simulation data degrade under real-world variations in lighting, texture, and weather. To address this problem, we propose RA3T (Region-Aligned 3D Transformer), a novel self-supervised framework that enables robust Sim-to-Real adaptation. Specifically, we first develop a dual-branch strategy for self-supervised feature learning, integrating Masked Autoencoders and contrastive learning. This approach extracts domain-invariant representations from unlabeled simulated imagery to enhance robustness against occlusion while reducing annotation dependency. Leveraging these learned features, we then introduce a 3D Transformer fusion module that unifies multi-view RGB and LiDAR point clouds through cross-modal attention. By explicitly modeling spatial layouts and height differentials, this component significantly improves recognition of small and occluded targets in complex low-altitude environments. To address persistent fine-grained domain shifts, we finally design region-level adversarial calibration that deploys local discriminators on partitioned feature maps. This mechanism directly aligns texture, shadow, and illumination discrepancies which challenge conventional global alignment methods. Extensive experiments on UAV benchmarks VisDrone and DOTA demonstrate the effectiveness of RA3T. The framework achieves +5.1% mAP on VisDrone and +7.4% mAP on DOTA over the 2D adversarial baseline, particularly on small objects and sparse occlusions, while maintaining real-time performance of 17 FPS at 1024 × 1024 resolution on an RTX 4080 GPU. Visual analysis confirms that the synergistic integration of 3D geometric encoding and local adversarial alignment effectively mitigates domain gaps caused by uneven illumination and perspective variations, establishing an efficient pathway for simulation-to-reality UAV perception. Full article

Residual dry matter (RDM) is a term used in rangeland management to describe the non-photosynthetic plant material left on the soil surface at the end of the growing season. RDM measurements are used by agencies and conservation entities for managing grazing and fire fuels. Measuring the RDM using traditional methods is labor-intensive, costly, and subjective, making consistent sampling challenging. Previous studies have assessed the use of multispectral remote sensing to estimate the RDM, but with limited success across space and time. The existing approaches may be improved through the use of spectroscopic (hyperspectral) sensors, capable of capturing the cellulose and lignin present in dry grass, as well as Unmanned Aerial Vehicle (UAV)-mounted Light Detection and Ranging (LiDAR) sensors, capable of capturing centimeter-scale 3D vegetation structures. Here, we evaluate the relationships between the RDM and spectral and LiDAR data across the Jack and Laura Dangermond Preserve (Santa Barbara County, CA, USA), which uses grazing and prescribed fire for rangeland management. The spectral indices did not correlate with the RDM (R² < 0.1), likely due to complete areal coverage with dense grass. The LiDAR canopy height models performed better for all the samples (R² = 0.37), with much stronger performance (R² = 0.81) when using a stratified model to predict the RDM in plots with predominantly standing (as opposed to laying) vegetation. This study demonstrates the potential of UAV LiDAR for direct RDM quantification where vegetation is standing upright, which could help improve RDM mapping and management for rangelands in California and beyond. Full article

The Tomographic Synthetic Aperture Radar (TomoSAR) technique is widely used for monitoring urban infrastructures, as it enables the mapping of individual scatterers across additional dimensions such as height (3D), thermal dilation (4D), and deformation velocity (5D). Retrieving this information is crucial for building management and maintenance. Nevertheless, accurately extracting it from TomoSAR data poses several challenges, particularly the presence of outliers due to uneven and limited baseline distributions. One way to address these issues is through statistical detection approaches such as the Generalized Likelihood Ratio Test, which ensures a Constant False Alarm Rate. While effective, these methods face two primary limitations: high computational complexity and the off-grid problem caused by the discretization of the search space. To overcome these drawbacks, we propose an approach that combines a quick initialization process using Fast-Sup GLRT with local descent optimization. This method operates directly in the continuous domain, bypassing the limitations of grid-based search while significantly reducing computational costs. Experiments conducted on both simulated and real datasets acquired with the TerraSAR-X satellite over the Spanish city of Barcelona demonstrate the ability of the proposed approach to maintain computational efficiency while improving scatterer localization accuracy in the third and fourth dimensions. Full article

Background: Compression garments (CG) may influence countermovement jump (CMJ) performance by altering hip and knee biomechanics, but existing evidence remains controversial. This study aimed to compare the effects of compression tights (CTs), compression shorts (CSs), and control shorts (CCs) on CMJ performance and lower-limb biomechanics. Methods: Nine physically active men from a university were recruited to perform CMJ while wearing CTs, CSs, and CCs in a randomized sequence for a within-subjects repeated-measures design. A Vicon 3D motion capture system and an AMTI 3D force plate were used to collect biomechanical data. Visual3D software was used to calculate the joint angle, moment, and force of the lower limbs. Results: Statistical parametric mapping analysis with repeated measures analysis of variance (ANOVA) revealed that during the propulsion phase of the CMJ, wearing CSs significantly reduced the hip flexion angle compared to wearing CCs (25–36%); meanwhile, wearing CTs significantly reduced the knee extension and flexion moment (34–35%) and decreased the hip extension moment during the propulsion phase (36–37%). In addition, CTs significantly reduced the hip abduction angle during the flight phase (37–39%), and CSs significantly reduced the hip anterior force during the landing phase (59–60%). Conclusions: Compression legwear significantly affected the hip and knee biomechanics in propulsion, but these differences were not sufficient to improve the CMJ height. Due to the improvement in hip biomechanics in the flight and landing phases, there may be potential benefits for movement transitions and landing performance in CMJ. Full article

This study investigates the influence of root cohesion spatial heterogeneity on rainfall-induced landslide distribution across the Loess Plateau, addressing limitations in existing methods that oversimplify root reinforcement. Leveraging Landsat and GaoFen satellite images, we developed a regional root cohesion inversion model that quantifies spatial heterogeneity using tree height (derived from time series Landsat imagery) and above-ground biomass (from 30 m resolution satellite products). This approach, integrated with land use-specific hydrological parameters and an infinite slope stability model, significantly improves landslide susceptibility predictions compared to models ignoring root cohesion or using uniform assignments. High-resolution pre- and post-rainfall GaoFen satellite imagery validated landslide inventories, revealing dynamic susceptibility patterns: farmland exhibited the highest risk, followed by artificial and secondary forests, with susceptibility escalating post-rainfall. This study underscores the critical role of remote sensing-driven root cohesion mapping in landslide risk assessment, offering actionable insights for land use planning and disaster mitigation on the Loess Plateau. Full article

Rapid identification of vessel height within the navigable space beneath bridges is crucial for ensuring bridge safety. To prevent bridge collisions caused by vessels exceeding their height limits, this article introduces a real-time warning framework for excessive vessel height based on video spatial transformation. The specific contributions include the following: (1) A spatial transformation-based method for locating vessel coordinates in the channel using buoys as control points, employing laser scanning to obtain their world coordinates from a broad channel range, and mapping the pixel coordinates of the buoys from side channel images to the world coordinates of the channel space, thus achieving pixel-level positioning of the vessel’s waterline intersection in the channel. (2) For video images, a key point recognition network for vessels based on attention mechanisms is developed to obtain pixel coordinates of the vessel’s waterline and top, and to capture the posture and position of multiple vessels in real time. (3) Analyzing the posture of vessels traveling in various directions within the channel, the method accounts for the pixel distance of spatial transformation control points and vessel height to determine vessel positioning coordinates, solve for the vessel’s height above water, and combine with real-time waterline height to enable over-height vessel collision warnings for downstream channel bridges. The method has been deployed in actual navigational scenarios beneath bridges, with the average error in vessel height estimation controlled within 10 cm and an error rate below 0.8%. The proposed approach enables real-time automatic estimation of vessel height in terms of computational speed, making it more suitable for practical engineering applications that demand both real-time performance and system stability. The system exhibits outstanding performance in terms of accuracy, stability, and engineering applicability, providing essential technical support for intelligent bridge safety management. Full article

Wheat stripe rust (Puccinia striiformis f. sp. tritici, Pst) resistance and agronomic traits are crucial determinants of wheat yield. Elucidating the quantitative trait loci (QTLs) associated with these essential traits can furnish valuable genetic resources for improving both the yield potential and disease resistance in wheat. Lantian 31 is an excellent Chinese winter wheat cultivar; multi-environment phenotyping across three ecological regions (2022–2024) confirmed stable adult-plant resistance (IT 1–2; DS < 30%) against predominant Chinese Pst races (CYR31–CYR34), alongside superior thousand-kernel weight (TKW) and kernel morphology. Here, we dissected the genetic architecture of these traits using a total of 234 recombinant inbred lines (RILs) derived from a cross between Lantian 31 and the susceptible cultivar Avocet S (AvS). Genotyping with a 15K SNP array, complemented by 660K SNP-derived KASP and SSR markers, identified four stable QTLs for stripe rust resistance (QYrlt.swust-1B, -1D, -2D, -6B) and eight QTLs governing plant height (PH), spike length (SL), and kernel traits. Notably, QYrlt.swust-1B (1BL; 29.9% phenotypic variance) likely represents the pleiotropic Yr29/Lr46 locus, while QYrlt.swust-1D (1DL; 22.9% variance) is the first reported APR locus on chromosome 1DL. A pleiotropic cluster on 1B (670.4–689.9 Mb) concurrently enhanced the TKW and the kernel width and area, demonstrating Lantian 31’s dual utility as a resistance and yield donor. The integrated genotyping pipeline—combining 15K SNP discovery, 660K SNP fine-mapping, and KASP validation—precisely delimited QYrlt.swust-1B to a 1.5 Mb interval, offering a cost-effective model for QTL resolution in common wheat. This work provides breeder-friendly markers and a genetic roadmap for pyramiding durable resistance and yield traits in wheat breeding programs. Full article

Pepper is a vital crop with extensive agricultural and industrial applications. Accurate phenotypic measurement, including plant height and stem diameter, is essential for assessing yield and quality, yet manual measurement is time-consuming and labor-intensive. This study proposes a deep learning-based phenotypic measurement method for peppers. A Pepper-mini dataset was constructed using offline augmentation. To address challenges in multi-plant growth environments, an improved YOLOX-tiny detection model incorporating a CA attention mechanism was developed, achieving a mAP of 95.16%. A detection box filtering method based on Euclidean distance was introduced to identify target plants. Further processing using HSV threshold segmentation, morphological operations, and connected component denoising enabled accurate region selection. Measurement algorithms were then applied, yielding high correlations with true values: $R^2$ = 0.973 for plant height and $R^2$ = 0.842 for stem diameter, with average errors of 0.443 cm and 0.0765 mm, respectively. This approach demonstrates a robust and efficient solution for automated phenotypic analysis in pepper cultivation. Full article

Urban blue–green space (UBGS) plays a critical role in mitigating the urban heat island (UHI) effect and reducing land surface temperatures (LSTs). However, existing research has not sufficiently explored the optimization of UBGS spatial configurations or their interactions with urban morphology. This study takes New York City as a case and systematically investigates small-scale urban cooling strategies by integrating multiple factors, including adjustments to the blue–green ratio, spatial layouts, vegetation composition, building density, building height, and layout typologies. We utilize multi-source geographic data, including LiDAR derived land cover, OpenStreetMap data, and building footprint data, together with LST data retrieved from Landsat imagery, to develop a prediction model based on generative adversarial networks (GANs). This model can rapidly generate visual LST predictions under various configuration scenarios. This study employs a combination of qualitative and quantitative metrics to evaluate the performance of different model stages, selecting the most accurate model as the final experimental framework. Furthermore, the experimental design strictly controls the study area and pixel allocation, combining manual and automated methods to ensure the comparability of different ratio configurations. The main findings indicate that a blue–green ratio of 3:7 maximizes cooling efficiency; a shrub-to-tree coverage ratio of 2:8 performs best, with tree-dominated configurations outperforming shrub-dominated ones; concentrated linear layouts achieve up to a 10.01% cooling effect; and taller buildings exhibit significantly stronger UBGS cooling performance, with super-tall areas achieving cooling effects approximately 31 percentage points higher than low-rise areas. Courtyard layouts enhance airflow and synergistic cooling effects, whereas compact designs limit the cooling potential of UBGS. This study proposes an innovative application of GANs to address a key research gap in the quantitative optimization of UBGS configurations and provides a methodological reference for sustainable microclimate planning at the neighborhood scale. Full article

Background: Digital dental photography is increasingly essential for documentation and smile design. This study aimed to compare the linear measurement accuracy of various smartphones and a Digital Single-Lens Reflex (DSLR) camera against digital models obtained by intraoral and desktop scanners. Methods: Tooth height and width from six different casts were measured and compared using images acquired with a Canon EOS 250D DSLR, six smartphone models (iPhone 13, iPhone 15, Samsung Galaxy S22 Ultra, Samsung Galaxy S23 Ultra, Samsung Galaxy S24, and Vivo T2), and digital scans obtained from the Helios 500 intraoral scanner and the Ceramill Map 600 desktop scanner. All image measurements were performed using ImageJ software (National Institutes of Health, Bethesda, MD, USA), and statistical analysis was conducted using one-way analysis of variance (ANOVA) with Tukey’s post hoc test (α = 0.05). Results: The results showed no significant differences in measurements across most imaging methods (p > 0.05), except for the Vivo T2, which showed a significant deviation (p < 0.05). The other smartphones produced measurements comparable to those of the DSLR, even at distances as close as 16 cm. Conclusions: These findings preliminary support the clinical use of smartphones for accurate dental documentation and two-dimensional smile design, including the posterior areas, and challenge the previously recommended 24 cm minimum distance for mobile dental photography (MDP). This provides clinicians with a simplified and accessible alternative for high-accuracy dental imaging, advancing the everyday use of MDP in clinical practice. Full article

Monitoring forest structure, diversity, and biomass in restoration areas is both expensive and time-consuming. Metrics derived from digital aerial photogrammetry (DAP) may offer a cost-effective and efficient alternative for monitoring forest restoration. The main objective of this study was to use metrics derived from digital aerial photogrammetry (DAP) point clouds obtained by remotely piloted aircraft (RPA) to estimate aboveground biomass (AGB), species diversity, and structural variables for monitoring restored secondary tropical forest areas. The study was conducted in three active and one passive forest restoration systems located in a secondary forest in Sergipe state, Brazil. A total of 2507 tree individuals from 36 plots (0.0625 ha each) were identified, and their total height (ht) and diameter at breast height (dbh) were measured in the field. Concomitantly with the field inventory, the plots were mapped using an RPA, and traditional height-based point cloud metrics and Fourier transform-derived metrics were extracted for each plot. Regression models were developed to calculate AGB, Shannon diversity index (H′), ht, dbh, and basal area (ba). Furthermore, multivariate statistical analyses were used to characterize AGB and H′ in the different restoration systems. All fitted models selected Fourier transform-based metrics. The AGB estimates showed satisfactory accuracy (R² = 0.88; RMSE = 31.2%). The models for H′ and ba also performed well, with R² values of 0.90 and 0.67 and RMSEs of 24.8% and 20.1%, respectively. Estimates of structural variables (dbh and ht) showed high accuracy, with RMSE values close to 10%. Metrics derived from the Fourier transform were essential for estimating AGB, species diversity, and forest structure. The DAP-RPA-derived metrics used in this study demonstrate potential for monitoring and characterizing AGB and species richness in restored tropical forest systems. Full article

Soil texture, defined by the proportions of sand, silt, and clay particles in the soil, is one of the most essential physical properties of soil. High-resolution soil texture data can provide critical parameter support for soil hydrological modeling, agricultural production management, and ecosystem assessment. In digital soil mapping, previous studies often predicted the sand, silt, and clay contents in soil and then indirectly calculated soil texture. Currently, approaches that directly map soil texture by classification modeling are gaining increasing attention due to the decreased error from data conversion, but few studies have systematically compared these two methods yet. In this study, we comprehensively assessed the performance of direct and indirect predicting soil texture using four machine learning algorithms (e.g., extreme gradient boosting, random forest, gradient boosting decision tree, and extremely randomized tree) with 190 covariates from the Digital Elevation Model, Sentinel-1/2 satellite images, and classification maps and generated a 10 m resolution soil texture map based on 405 topsoil (0–20 cm) sample data collected in Suichuan County, China. The results showed that compared with indirect predictions, direct predictions improved overall accuracy (OA) by 20.57–44.19% and the Kappa coefficient (Kappa) by 0.220–0.402. Among the models used, the XGB model achieved the highest accuracy (OA: 0.948; Kappa: 0.931) and the lowest uncertainty (confusion index: 0.052). The direct prediction map (nine classes recorded) exhibited more detailed and diverse spatial distribution patterns than the indirect prediction map (six classes recorded), aligning better with the actual environment. Based on accuracy validation and spatial distribution, the performance of the XGB model was best during direct prediction. The Shapley additive explanation from the XGB model revealed that the normalized height and stream power indices were the most significant factors driving the soil texture in the study area. Our results provide a reference for future studies on soil texture mapping using machine learning models. Full article

Woody plant encroachment (WPE) is transforming grasslands globally, yet accurately mapping this process remains challenging. State-funded, publicly available high-resolution aerial imagery offers a potential solution, including the USDA’s National Agriculture Imagery Program (NAIP) and NSF’s National Ecological Observatory Network (NEON) Aerial Observation Platform (AOP). We evaluated the accuracy of land cover classification using NAIP, NEON, and both sources combined. We compared two machine learning models—support vector machines and random forests—implemented in R using large training and evaluation data sets. Our study site, Konza Prairie Biological Station, is a long-term experiment in which variable fire and grazing have created mosaics of herbaceous plants, shrubs, deciduous trees, and evergreen trees (Juniperus virginiana). All models achieved high overall accuracy (>90%), with NEON slightly outperforming NAIP. NAIP underperformed in detecting evergreen trees (52–78% vs. 83–86% accuracy with NEON). NEON models relied on LiDAR-based canopy height data, whereas NAIP relied on multispectral bands. Combining data from both platforms yielded the best results, with 97.7% overall accuracy. Vegetation indices contributed little to model accuracy, including NDVI (normalized digital vegetation index) and EVI (enhanced vegetation index). Both machine learning methods achieved similar accuracy. Our results demonstrate that free, high-resolution imagery and open-source tools can enable accurate, high-resolution, landscape-scale WPE monitoring. Broader adoption of such approaches could substantially improve the monitoring and management of grassland biodiversity, ecosystem function, ecosystem services, and environmental resilience. Full article