Search Results (9,220)

In the current global energy sector where energy storage technology is highly regarded, the development of storage technology is crucial. Utilizing specific underground space for the storage of oil and gas and other energy sources is the direction of future development, and the space formed by deep-salt-mine water dissolution extraction has gradually become the preferred choice. However, in actual operation, multi-layer salt cavities are prone to collapse of interlayer and bending of pipes, seriously affecting the progress, quality, and safety of the entire energy storage space construction. Therefore, based on relevant principles, a targeted experimental platform was established, by taking photos and measurements of the falling process of specific falling objects, simulating the situation of falling objects in actual energy storage spaces and their impact on related components. In-depth research was conducted on the probability of falling objects hitting the inner pipe and the horizontal impact force under different conditions, and the experimental results were verified by rigorous numerical simulation analysis. The research results show that falling objects impacts can cause related components to bend, with the maximum impact probability reaching 5.1% and the maximum horizontal impact force reaching 24.6 N. In addition, the hydraulic fluctuations caused by the suction and drainage of the cavity pipe column have a relatively small impact on the falling object trajectory. The research findings can provide practical and effective guidance for the safe construction of specific energy storage facilities, ensuring that construction can be carried out safely and efficiently, and contribute to the steady development of the energy storage industry as a whole. Full article

Climate change is having a significant impact on groundwater storage, affecting water resources in many parts of the world. To characterize this impact, remote sensing and machine learning are essential tools to analyze the data accurately and efficiently. This study aims to predicting the variations of groundwater storage (GWS) using GRACE/GRACE-FO and multi-source remote sensing data, combined with machine learning techniques. The approach was applied over the Canadian Prairies region. The study area was classified into three zones of different aquifer potentials (low, medium, and high) using a combination of remote sensing data and the Classification and Regression Trees (CART) approach. The prediction model was developed using a machine-learning approach based on multiple linear regression to estimate GWS variations as a function of various environmental parameters. The results showed that the developed model was able to predict GWS variations with satisfactory accuracy (up to 95% of the explained variance) and good robustness (96% success rate). They also provided a better understanding of the variations in groundwater storage in the Canadian Prairies. Therefore, this work provides a promising method for predicting GWS, which could eventually be applied to other similar environmental conditions. Full article

The intertidal ecosystem serves as a critical transitional zone between terrestrial and marine environments, supporting diverse biodiversity and essential ecological functions. However, these systems are increasingly threatened by climate change, rising sea levels, and anthropogenic impacts. Accurately mapping intertidal ecosystems and differentiating mangroves, salt marshes, and tidal flats remains a challenge due to inconsistencies in classification frameworks. Here, we present a high-precision mapping approach for intertidal ecosystems using multi-source satellite data, including Sentinel-1, Sentinel-2, and Landsat 8/9, integrated with the Google Earth Engine (GEE) platform, to enable the detailed mapping of intertidal zones across China–ASEAN. Our findings indicate a total intertidal area of 73,461 km² in China–ASEAN, with an average width of 1.16 km. Analyses of patch area, abundance, and perimeter relationships reveal a power-law distribution with a scaling exponent of 1.52, suggesting self-organizing characteristics shaped by both natural and human pressures. Our findings offer foundational data to guide conservation and management strategies in the region’s intertidal zones and present a novel perspective to propel research on global coastal ecosystems. Full article

Real-scene 3D digital campuses are essential for improving the accuracy and effectiveness of spatial data representation, facilitating informed decision-making for university administrators, optimizing resource management, and enriching user engagement for students and faculty. However, current approaches to constructing these digital environments face several challenges. They often rely on costly commercial platforms, struggle with integrating heterogeneous datasets, and require complex workflows to achieve both high precision and comprehensive campus coverage. This paper addresses these issues by proposing a systematic multi-source data fusion approach that employs open-source technologies to generate a real-scene 3D digital campus. A case study of Lanzhou Jiaotong University is presented to demonstrate the feasibility of this approach. Firstly, oblique photography based on unmanned aerial vehicles (UAVs) is used to capture large-scale, high-resolution images of the campus area, which are then processed using open-source software to generate an initial 3D model. Afterward, a high-resolution model of the campus buildings is then created by integrating the UAV data, while 3D Digital Elevation Model (DEM) and OpenStreetMap (OSM) building data provide a 3D overview of the surrounding campus area, resulting in a comprehensive 3D model for a real-scene digital campus. Finally, the 3D model is visualized on the web using Cesium, which enables functionalities such as real-time data loading, perspective switching, and spatial data querying. Results indicate that the proposed approach can effectively get rid of reliance on expensive proprietary systems, while rapidly and accurately reconstructing a real-scene digital campus. This framework not only streamlines data harmonization but also offers an open-source, practical, cost-effective solution for real-scene 3D digital campus construction, promoting further research and applications in twin city, Virtual Reality (VR), and Geographic Information Systems (GIS). Full article

Ensuring the ecological water requirements (EWR) suitable for wetlands are upheld is essential for maintaining the stability and health of their ecosystems, a challenge faced by wetlands globally. However, previous studies on EWRs estimation lack a comprehensive consideration of wetlands and still suffer from the problem of rough time scales. Prior studies have predominantly concentrated on its core and buffer zones, neglecting a comprehensive analysis of the wetland’s entirety and failing to account for the seasonal variations in EWRs. To fill this gap, we proposed a novel framework for estimating EWRs wetland’s entirety to guide the development of dynamic water replenishment strategies. The grey prediction model was used to project the wetland area under different scenarios and designed water replenishment strategies. We then applied this framework in a key wetland conservation area in China, the Momoge Wetland, which is currently facing issues of areal shrinkage and functional degradation due to insufficient EWRs. Our findings indicate that the maximum, optimal, and minimum EWRs for the Momoge Wetland are 24.14 × 10⁸ m³, 16.65 × 10⁸ m³, and 10.88 × 10⁸ m³, respectively. The EWRs during the overwintering, breeding, and flood periods are estimated at 1.92 × 10⁸ m³, 5.39 × 10⁸ m³, and 8.73 × 10⁸ m³, respectively. Based on the predicted wetland areas under different climatic conditions, the necessary water replenishment volumes for the Momoge Wetland under scenarios of dry-dry-dry, dry-dry-normal, dry-normal-dry, and normal-normal-normal are calculated to be 0.70 × 10⁸ m³, 0.49 × 10⁸ m³, 0.68 × 10⁸ m³, and 0.36 × 10⁸ m³, respectively. In years characterized by drought, the current water replenishment projects are inadequate to meet the wetland’s water needs, highlighting the urgent need for the implementation of multi-source water replenishment techniques to enhance the effectiveness of these interventions. The results of this study provide insights for annual and seasonal water replenishment planning and multi-source water management of wetlands with similar problems as the Momoge Wetland. With these new insights, our novel framework not only advances knowledge on the accuracy of wetland ecological water requirement assessment but also provides a scalable solution for global wetland water resource management, helping to improve the ecosystem’s adaptability to future climate changes. Full article

Timely monitoring of distribution and growth state of crops is crucial for agricultural management. Remote sensing (RS) techniques provide an effective tool to monitor crops. This study proposes a novel approach for the identification of typical crops, including rapeseed and wheat, using multisource remote sensing data and deep learning technology. By adopting an improved DeepLabV3+ network architecture that integrates a feature-enhanced module and an attention module, multiple features from both optical data and synthetic aperture radar (SAR) data are fully mined to take into account the spectral reflectance traits and polarimetric scattering straits of crops. The proposal can effectively address the limitations of using a single data source, alleviating the misclassification problem brought by the spectral similarity of crops in certain bands. Experimental results demonstrate that the proposed crop identification DeepLabV3+ (CI-DeepLabV3+) method outperforms traditional classification methods and the original DeepLabV3+ network, with an overall accuracy and F1 score of 94.54% and 94.55%, respectively. Experimental results also support the conclusion that using multiple features from multi-source data can indeed improve the performance of the network. Full article

The Geographic Knowledge Graph (GeoKG) serves as an effective method for organizing geographic knowledge, playing a crucial role in facilitating semantic interoperability across heterogeneous data sources. However, existing GeoKGs are limited by a lack of hierarchical modeling and insufficient coverage of geographic knowledge (e.g., limited entity types, inadequate attributes, and insufficient spatial relationships), which hinders their effective use and representation of semantic content. This paper presents HGeoKG, a hierarchical geographic knowledge graph that comprehensively models hierarchical structures, attributes, and spatial relationships of multi-type geographic entities. Based on the concept and construction methods of HGeoKG, this paper developed a dataset named HGeoKG-MHT-670K. Statistical analysis reveals significant regional heterogeneity and long-tail distribution patterns in HGeoKG-MHT-670K. Furthermore, extensive geographic knowledge reasoning experiments on HGeoKG-MHT-670K show that most knowledge graph embedding (KGE) models fail to achieve satisfactory performance. This suggests the need to accommodate spatial heterogeneity across different regions and improve the embedding quality of long-tail geographic entities. HGeoKG serves as both a reference for GeoKG construction and a benchmark for geographic knowledge reasoning, driving the development of geographical artificial intelligence (GeoAI). Full article

Recent advancements in Earth Observation sensors, improved accessibility to imagery and the development of corresponding processing tools have significantly empowered researchers to extract insights from Multisource Remote Sensing. This study aims to use these technologies for mapping summer and winter Land Use/Land Cover features in Cuenca de la Laguna Merín, Uruguay, while comparing the performance of Random Forests, Support Vector Machines, and Gradient-Boosting Tree classifiers. The materials include Sentinel-2, Sentinel-1 and Shuttle Radar Topography Mission imagery, Google Earth Engine, training and validation datasets and quoted classifiers. The methods involve creating a multisource database, conducting feature importance analysis, developing models, supervised classification and performing accuracy assessments. Results indicate a low significance of microwave inputs relative to optical features. Short-wave infrared bands and transformations such as the Normalised Vegetation Index, Land Surface Water Index and Enhanced Vegetation Index demonstrate the highest importance. Accuracy assessments indicate that performance in mapping various classes is optimal, particularly for rice paddies, which play a vital role in the country’s economy and highlight significant environmental concerns. However, challenges persist in reducing confusion between classes, particularly regarding natural vegetation features versus seasonally flooded vegetation, as well as post-agricultural fields/bare land and herbaceous areas. Random Forests and Gradient-Boosting Trees exhibited superior performance compared to Support Vector Machines. Future research should explore approaches such as Deep Learning and pixel-based and object-based classification integration to address the identified challenges. These initiatives should consider various data combinations, including additional indices and texture metrics derived from the Grey-Level Co-Occurrence Matrix. Full article

Vegetation is a fundamental component of terrestrial ecosystems, and accurately assessing the effects of seasonal climate variations, extreme weather events, and land use changes on vegetation dynamics is crucial. The Ganjiang River Basin (GRB), a key region for water conservation and recharge in southeastern China, has experienced significant land use changes and variable climate in the past. However, comprehensive evaluations of how these changes have impacted vegetation remain limited. To address this gap, we used machine learning models (random forest and XGBoost) to assess the impact of seasonal and extreme climate variables, land cover, topography, soil properties, atmospheric CO₂, and night-time light intensity on vegetation dynamics. We found that the annual mean NDVI showed a slight increase from 1990 to 1999 but has decreased significantly over the last 8 years. XGBoost was better than the RF model in simulating the NDVI when using all five types of data source (R² = 0.85; RMSE = 0.04). The most critical factors influencing the NDVI were forest and cropland ratio, followed by soil organic carbon content, elevation, cation exchange capacity, night-time light intensity, and CO₂ concentration. Spring minimum temperature was the most important seasonal climate variable. Both linear and nonlinear relationships were identified between these variables and the NDVI, with most variables exhibiting threshold effects. These findings underscore the need to develop and implement effective land management strategies to enhance vegetation health and promote ecological balance in the region. Full article

Potato, a vital food and cash crop, necessitates precise identification and area estimation for effective planting planning, market regulation, and yield forecasting. However, extracting large-scale crop areas using satellite remote sensing is fraught with challenges, such as low spatial resolution, cloud interference, and revisit cycle limitations, impeding the creation of high-quality time–series datasets. In this study, we developed a high-resolution vegetation index time–series by calculating coordination coefficients and integrating reflectance data from Landsat-8, Landsat-9, and Sentinel-2 satellites. The vegetation index time–series were enhanced through using linear interpolation and Savitzky–Golay (S-G) filtering to reconstruct high-quality data. We employed the harmonic analysis of NDVI time–series (HANTS) method to extract features from the time–series and evaluated the classification accuracy across five feature sets: vegetation index time–series features, band means, vegetation index means, texture features, and color space features. The Random Forest (RF) model, utilizing the full feature set, emerged as the most accurate, achieving a precision rate of 0.97 and a kappa value of 0.94. We further refined the feature subset using the SHAP-SFS feature selection method, leading to the SHAP-SFS-RF classification approach for differentiating potato from non-potato crops. This approach enhanced accuracy by approximately 0.1 and kappa value by around 0.2 compared to the RF model, with the extracted areas closely aligning with statistical yearbook data. Our study successfully achieved the accurate extraction of potato planting areas at the county level, offering novel insights and methodologies for related research fields. Full article

Bayesian approaches to decision trees (DTs) using Markov Chain Monte Carlo (MCMC) samplers have recently demonstrated state-of-the-art accuracy performance when it comes to training DTs to solve classification problems. Despite the competitive classification accuracy, MCMC requires a potentially long runtime to converge. A widely used approach to reducing an algorithm’s runtime is to employ modern multi-core computer architectures, either with shared memory (SM) or distributed memory (DM), and use parallel computing to accelerate the algorithm. However, the inherent sequential nature of MCMC makes it unsuitable for parallel implementation unless the accuracy is sacrificed. This issue is particularly evident in DM architectures, which normally provide access to larger numbers of cores than SM. Sequential Monte Carlo (SMC) samplers are a parallel alternative to MCMC, which do not trade off accuracy for parallelism. However, the performance of SMC samplers in the context of DTs is underexplored, and the parallelization is complicated by the challenges in parallelizing its bottleneck, namely redistribution, especially on variable-size data types such as DTs. In this work, we study the problem of parallelizing SMC in the context of DTs both on SM and DM. On both memory architectures, we show that the proposed parallelization strategies achieve asymptotically optimal $O\left({log}_{2}N\right)$ time complexity. Numerical results are presented for a 32-core SM machine and a 256-core DM cluster. For both computer architectures, the experimental results show that our approach has comparable or better accuracy than MCMC but runs up to 51 times faster on SM and 640 times faster on DM. In this paper, we share the GitHub link to the source code. Full article

With the continuous advancement of deep learning algorithms and the rapid growth of computational resources, deep learning technology has undergone numerous milestone developments, evolving from simple BP neural networks into more complex and powerful network models such as CNNs, LSTMs, RNNs, and GANs. In recent years, the application of deep learning technology in ionospheric modeling has achieved breakthrough advancements, significantly impacting navigation, communication, and space weather forecasting. Nevertheless, due to limitations in observational networks and the dynamic complexity of the ionosphere, deep learning-based ionospheric models still face challenges in terms of accuracy, resolution, and interpretability. This paper systematically reviews the development of deep learning applications in ionospheric modeling, summarizing findings that demonstrate how integrating multi-source data and employing multi-model ensemble strategies has substantially improved the stability of spatiotemporal predictions, especially in handling complex space weather events. Additionally, this study explores the potential of deep learning in ionospheric modeling for the early warning of geological hazards such as earthquakes, volcanic eruptions, and tsunamis, offering new insights for constructing ionospheric-geological activity warning models. Looking ahead, research will focus on developing hybrid models that integrate physical modeling with deep learning, exploring adaptive learning algorithms and multi-modal data fusion techniques to enhance long-term predictive capabilities, particularly in addressing the impact of climate change on the ionosphere. Overall, deep learning provides a powerful tool for ionospheric modeling and indicates promising prospects for its application in early warning systems and future research. Full article

Remote sensing technologies continue to expand their role in environmental monitoring, providing invaluable advances in soil assessing and mapping. This study aimed to prove the need to apply spatial statistical models for processing data in remote sensing (RS), which appears to be an important source of spatial data at multiple scales. A crucial problem facing us is the fusion of multi-source spatial data of different natures and characteristics, among which there is the support size of measurement that unfortunately is little considered in RS. A data fusion approach of both sample (point) and grid (areal) data is proposed that explicitly takes into account spatial correlation and change of support in both increasing support (upscaling) and decreasing support (downscaling). The techniques of block cokriging and kriging downscaling were employed for the implementation of such an approach, respectively. The method is applied to soil sample data, jointly analysed with hyperspectral data measured in the laboratory, UAV, and satellite data (Planet and Sentinel 2) of an olive grove after filtering soil pixels. Each data type had its own support that was transformed to the same support as the soil sample data so that the data fusion approach could be applied. To demonstrate the statistical, as well as practical, effectiveness of such a method, it was compared by a cross-validation test with a univariate approach for predicting each soil property. The positive results obtained should stimulate advanced statistical techniques to be applied more and more widely to RS data. Full article

The surge in global population growth and the escalating demand for social and economic prosperity present formidable challenges in the 21st century. However, asserting the sustainability of some ecological impact reduction initiatives, such as recycling, requires a comprehensive evaluation within various domains, including performance, ecology, and economics, and contemporary advancements in integrating quantitative assessments of material and manufacturing properties, coupled with mathematical decision-making approaches, contribute to mitigating subjectivity in determining the efficiency of recycling. This paper implements a robust multi-criteria decision-making (MCDM) approach to address the complexities of recycling, validating its implementation and effectiveness through a case study. The focus is set on the application of bio-based polylactic acid (PLA) and petroleum-based polypropylene (PP) additively manufactured (AM) parts produced through Fused Filament Fabrication (an approach to ecology/performance domains). The work introduces a cost analysis focusing on calculating thermomechanical recycling within the economic domain. The well-known Analytical Hierarchical Process (AHP) provides a structured framework for decision-making (the ecological impact domain) with the focus being on application. The assessment or recycling viability, encompassing AHP calculations, preprocessing, and supplementary tools, is provided by developing an open-source software tool for practitioners in the field of material science and manufacturing. The results indicate a preference for industrial-scaled recycling over virgin or lab-recycled manufacturing, particularly for petroleum-based polypropylene. The versatility and simple utilization of the software tool allow seamless integration for diverse use cases involving different materials and processes. Full article

This study presents the design and fabrication of light retroreflectors utilizing surface plasmon resonance (SPR) in parallel-superimposed bi-grating structures. The bi-gratings were inscribed onto a thin azobenzene molecular glass film via photolithography and subsequently coated with a thin gold layer to support SPR. The two superimposed gratings operate in tandem, with one grating coupling incident light into the SPR mode and the other coupling it back out toward the light source, thereby achieving retroreflection. Monochromatic retroreflection is demonstrated for a target wavelength (785 nm) at angles from 5° to 10°, while multi-wavelength retroreflection is achieved for red, orange, and green wavelengths at corresponding angles. The findings highlight the potential of these bi-gratings for applications in optical sensing, communication, and advanced photonic systems, where compact, tunable, and angularly responsive designs are essential. Full article