Search

The geometric positioning accuracy of synthetic aperture radar images is a key factor impacting their application, necessitating geometric calibration for improved accuracy. Traditional geometric calibration methods rely on ground calibration fields and supportive equipment such as corner reflectors, which often fall short in meeting both domestic and international geometric calibration demands. To enable convenient, swift, and large-scale or even global SAR calibration, we proposed a method that employs wind turbines as ground control points for SAR geometric calibration, and have constructed a wind turbine database for China. This database allows for precise positioning of wind turbine targets in SAR images. In this study, we applied the wind turbine geometric calibration model for the first time to four imaging modes of Gaofen-3 images. The results indicate that the positioning accuracy post-calibration ranges from 2.70 m to 8.72 m. The mean square error of positioning for the multi-scene calibration of 12 images spanning six provinces is less than 9 m, validating the method's applicability for large-scale calibration. Our proposed method presents a cost-effective solution for large-scale geometric calibration and can notably enhance the positioning accuracy of Gaofen-3 images, thereby increasing their application potential in remote sensing mapping, environmental monitoring, and resource management.

Glass fiber-reinforced polymer composites are important structural materials and are widely used in structure engineering. In this study, a new V-notch non-standard tensile specimen is proposed. All the in-plane stiffness coefficients of glass fiber-reinforced polymer composites could be obtained by the virtual fields method with only one uniaxial tensile test. First, the special virtual fields method for inversion of elastic constitutive parameters of orthotropic materials in the uniaxial tensile test was introduced. The optimization of the geometrical design of the specimen was conducted using finite element simulation experiments. Batch modeling calculation was conducted within the designed range of the geometric parameters of the specimen. The generated ideal strain field data were substituted into the virtual fields method to invert and identify the stiffness coefficients. The optimized geometry of the specimen was determined according to the objective function of minimum error. Through a tensile experiment on glass fiber composites, the influence of specimen deformation on the identification results was assessed, and the load level suitable for parameter identification was determined. Based on the results, it can be concluded that the inversion identification accuracy meets the requirements of engineering measurement.

Polarimetric synthetic aperture radar (PolSAR) images have been widely used in many fields due to its advantage in obtaining full polarization information, especially in land use classification. To evaluate the performance of airborne PolSAR images in land use classification, this paper systematically evaluated the potential value of PolSAR images in land use classification by using machine learning and deep learning algorithms, and improved the classification performance of airborne PolSAR images by constructing a multi-structural feature aware attention network (MSFA-Net). The classification performance of different algorithms was evaluated by the overall accuracy. The results showed that the machine learning algorithm Random Forest (RF) produced the lowest overall accuracy of 71.93%. Using RF as the baseline accuracy, the deep learning-based classification algorithm significantly improved the classification accuracy. Specifically, Wide Contextual Residual Network achieved an overall accuracy improvement of 8.87% to 80.80%, and Two-branch CNN generated an overall accuracy of 84.49%. Multi-source CNN produced the second highest overall accuracy of 87.25%, an improvement of 15.32%. The proposed MSFA-Net algorithm achieved the highest classification accuracy of 89.29% with an improvement of 17.36%, which further improves the land use mapping capability of airborne PolSAR images. A moderate reduction in resolution helps to improve classification accuracy, with 84.36% accuracy after 4-fold downsampling compared with the original resolution accuracy of 69.30%. On the other hand, noise, polarization error and motion error had a negative impact on the classification performance. This paper highlights the application of airborne SAR images in land use classification, and strengthens the understanding of deep learning and the influence of different factors on PolSAR images in land use classification.

Vitrimer composites with bond- exchange reactions offer significant advantages in terms of repair and recyclability. The mechanical properties of such composites can be designed by modifying the epoxy/anhydride ratio, thus allowing their interface properties to be customised. In this study, the mode-I interfacial fracture properties of vitrimer carbon fibre composites with different epoxy/anhydride ratios are investigated experimentally and numerically. The results show that composites with an epoxy/anhydride group ratio of 1:0.5 exhibit the highest fracture toughness, with a maximum value of 1.6 mJ/mm2. Meanwhile, the analysis of the composites’ microscopic fracture morphology indicates their good bonding performance with carbon fibres. Subsequently, a multiscale numerical approach is developed to simulate the mode-I fracture response of composite laminates and establish a trilinear cohesive model to simulate the macroscopic interfacial fracture behaviour. In this approach, a matrix elastic–plastic constitutive model and the J-integral method are applied to obtain the stress distribution in the fibre-bridging phase via a double cantilever beam fracture test. The macroscopic fracture behaviour obtained via simulation is consistent with the experimental results. Therefore, the proposed multiscale numerical simulation method, which correlates the matrix properties with macroscopic fracture parameters, allows the interlaminar properties of composites with novel matrices to be evaluated effectively.

We utilized a two-branch end-to-end network (MultiSenCNN) for land use and land cover (LULC) classification and flood event mapping using multispectral (MS), panchromatic (Pan) and synthetic aperture radar (SAR) images, where flooding was induced by typhoon Lekima in August 2019. Flood damages were assessed by considering both the LULC and flood maps. We defined three strategies to compare the MS + SAR and MS + Pan images to demonstrate the ability of the MultiSenCNN algorithm for LULC classification. The three strategies yielded an average overall accuracy of ∼98% and an average Kappa of ∼0.98 for LULC classification. The overall accuracy of the fused MS + SAR images is slightly higher than the MS + Pan images when using the same model training samples. The flood mapping shows an overall accuracy of 97.22% and a Kappa of 0.94, with a flood inundation area of 101 km2 that mainly inundated cropland and urban areas. Compared to other LULC types, the flooded cropland has caused more loss of ecosystem service values during typhoon Lekima, accounting for 81.19% of the total. Using SAR mages can well monitor the start/end states of flood events and the inundated areas, providing the flood status information to rescuers and governments for making timely decisions.

Megacities serve as crucial catalysts for national economic and social development, and Shanghai, one of China’s most prominent metropolitan areas, exemplifies this transformative urbanization. To study Shanghai’s urban expansion, we extracted urban land cover data from 1985 to 2020 using impervious area products and simulated urban expansion dynamics from 2021 to 2035 by employing the cellular automata model. Leveraging these data, we analyzed a 50-year period of urban expansion and investigated the drivers, including economic factors, population growth, and transportation infrastructure. Our findings indicate that the size of Shanghai’s urban area in 2035 will be nearly 13 times that of 1985. Over these five decades, Shanghai’s urban centroid shifted from the northeast to the southwest, with early urban expansion concentrated in the northeast and later expansion in the southwest. New urban patches primarily emerged at the edges of the initial urban area. As time progressed, areas with higher urban expansion intensity moved outward from the city center, mirroring the trend of urban expansion hotspots. Landscape indicators also demonstrated a trend of urban patches initially spreading and subsequently clustering. Overall, the development of Shanghai’s metropolitan area exhibits substantial spatiotemporal heterogeneity. By integrating correlation analysis and generalized additive models, we quantified the impact of urban expansion drivers. The results show that economic and population factors had high correlation coefficients (over 0.97) with urban area, and proximity to the city center and road network greatly contributed to urban expansion. Our research amalgamates various theories and methods to analyze the spatiotemporal dynamics of urban expansion in metropolitan areas. This work provides a valuable data foundation to aid policymakers in designing effective metropolitan development policies.

Synthetic aperture radar (SAR) image positioning is commonly affected by factors such as platform instability, aging of onboard instruments, and changing observation environments. Thus, geometric calibration is needed to improve the accuracy of image positioning before mapping and application. An improved geometric calibration model for SAR images was developed, which does not require long-delayed meteorological data and geometric calibration fields. In this method, the standard atmospheric and Saastamoinen models (SAM-S) are combined for atmospheric delay correction, and this integrated method named SAM-S was used for the first time in SAR image geometric calibration. The method was applied to China's Gaofen-3 satellite and a case study was conducted in the Yangtze River Delta, where the weather is cloudy and rainy. By selecting three images, four images, and five images from eight candidate images with a bandwidth of 60 MHz for calibration experiments, where the geometric positioning accuracy was increasingly stable with more SAR images included in the combination. The root mean square of the calibrated SAR images was about 6 m for different combinations and the maximum error was about 12 m for all images. Two selected areas showed that the geometric calibration has reduced the geometric distortion, showing shapes in the SAR images close to the ground targets. The positioning accuracy improvement of Gaofen-3 images can help improve their application potential in global remote sensing mapping, land-use change monitoring, and ground target detection. The proposed geometric calibration method can also be applied to other SAR missions such as ERS, RADARSAT, and TerraSAR-X.

Low-lying coastal cities are widely acknowledged as the most densely populated places of urban settlement; they are also more vulnerable to risks resulting from intensive land use and land cover change, human activities, global climate change, and the rising sea levels. This study aims to predict how urban growth is affected by sea level rise (SLR) in the Australian context. We develop an urban cellular automata model incorporating urban planning policies as potential drivers or constraints of urban growth under different SLR scenarios and adaption strategies. Drawing on data capturing the socioeconomic and environmental factors in South East Queensland, Australia, our model is positioned to address one core research question: how does SLR affect future urban growth and human resettlement? Results show that urban growth in coastal regions varies depending on the extent to which the sea level rises and is affected by a combination of factors relating to urban planning and human adaptation strategies. Our study demonstrates the complexity of urban growth in coastal regions and the nuanced outcomes under different adaptation strategies in the context of rising sea levels.

The picture frame test was applied to compare Kevlar neat and STF/Kevlar composite fabrics. The digital image correlation markers method was applied to measure the shear deformation behavior of the fabric in real-time under three loading rates: 100, 500, and 1000 mm/min. A theoretical model was applied to evaluate the effect of STF on the shear deformation stiffness of the fabric and cells and on the energy absorption during shear deformation. The results show that the STF/Kevlar composite fabric has a larger load-carrying capacity than the neat fabric in the picture frame test, and has obvious loading rate dependence. The yarn cell of the fabric undergoes slip deformation and reaches a shear-locked state; the shear modulus and the cell spring torsion coefficient of the STF/Kevlar composite fabric are significantly higher than those of neat fabric. The shear thickening behavior of STF occurs at higher loading rates, and the composite fabric has the highest shear deformation stiffness and shear energy absorption level.

Interferometric SAR (InSAR) is a practical technique to derive 3-D structure of the earth's surface. This article conducts a comparative study of digital elevation model (DEM) reconstruction using the single-baseline and multibaseline InSAR techniques. To improve the accuracy of the DEM generated from the single-baseline InSAR, we improve the weight definition approach according to the coherence magnitudes for the fusion of single-baseline InSAR DEMs. For multibaseline InSAR, we adopt a method based on the maximum-likelihood estimation (MLE) algorithm, which uses external DEM as a constraint. A TerraSAR-X dataset comprising four SAR images acquired over Barcelona is used for experimental investigation. The accuracies of four types of DEM are comparatively studied, including DEM by single-baseline InSAR, DEM by the fusion of single-baseline InSAR DEMs, DEM by unconstrained multibaseline InSAR, and DEM by constrained multibaseline InSAR. The results show that the proposed weighted fusion of single-baseline InSAR performed slightly better than the constrained multibaseline InSAR method, and both of them performed better than the single-baseline results, demonstrating that multiple observations can effectively improve the accuracy of the reconstructed DEM.

Incorporating spatial nonstationarity in urban models is essential to accurately capture its spatiotemporal dynamics. Spatially-varying coefficient methods, e.g. geographically weighted regression (GWR) and the Bayesian spatially-varying coefficient (BSVC) model, can reflect spatial nonstationarity. However, GWR possess weak ability eliminating the negative effects of non-constant variance because the method is sensitive to data outliers and bandwidth selection. We proposed a new cellular automata (CA) approach based on BSVC for multi-temporal urban sprawl simulation. With case studies in Hefei and Qingdao of China, we calibrated and validated two CA models, i.e. CABSVC and CAGWR, to compare their performance in simulating urban sprawl from 2008 to 2018. Our results demonstrate that CABSVC outperformed CAGWR in terms of FOM by ~2.1% in Hefei and ~3.6% in Qingdao during the calibration stage, and showed more accuracy improvement during the validation stage. The CABSVC model simulated urban sprawl more accurately than the CAGWR model in regions having similar proximity to the existing built-up areas, especially in less developed regions. We applied CABSVC to predict urban sprawl at Hefei and Qingdao out to 2028, and the urban scenarios suggest that the proposed model shows better performance and reduced bias in reproducing urban sprawl patterns, and extends urban simulation methods by accounting for spatial nonstationarity.

The phenomenon of inhomogeneous microstructure evolution and mechanical properties of rotary friction welded (RFWed) AA2024 joints was investigated. The inhomogeneity was explored by examining the mechanical properties of samples sliced from the joint into four parts along the radius (R): Center, 0.25R, 0.5R, 0.75R. The maximum tensile strength of 433 MPa and elongation of 9.25% were obtained in 0.25R position. Thermo-mechanically affected zone (TMAZ) was divided into two regions: zone with distorted grains (TMAZ-I) and zone with broken grains (TMAZ-II). The softened zone on rotary side was much narrower than that on stationary side, which was more likely to result in the fracture in TMAZ-I on rotary side. Dynamic recrystallized zone (DRZ) exhibited the maximum recrystallization fraction and the highest hardness but the worst plasticity. TMAZ-I mainly experienced the recovery which extremely enhanced the plasticity but deteriorated the hardness and strength, moreover the width of TMAZ-I gradually increased from Center to 0.75R position. The mechanical properties of TMAZ-II fell in between DRZ and TMAZ-I owing to recovery and recrystallization. Furthermore, the existence of TMAZ-II improved the tensile strength in 0.25R position, while the increment of the width of TMAZ-I deteriorated the strength in 0.5R and 0.75R positions. The uneven distribution of DRZ, TMAZ-I and TMAZ-II resulted in the inhomogeneity of AA2024 aluminum alloy RFWed joints.

Urban road network information is an important part of modern spatial information infrastructure and is crucial for high-precision navigation map production and unmanned driving. Synthetic aperture radar (SAR) is a widely used remote-sensing data source, but the complex structure of road networks and the noises in images make it very difficult to extract road information through SAR images. We developed a new method of extracting road network information from SAR images by considering angular (A) and texture (T) features in the sliding windows and points of interest (POIs, or P), and we named this method ATP-ROAD. ATP-ROAD is a sliding window-based semi-automatic approach that uses the grayscale mean, grayscale variance, and binary segmentation information of SAR images as texture features in each sliding window. Since POIs have much-duplicated information, this study also eliminates duplicated POIs considering distance and then selects a combination of POI linkages by discerning the direction of these POIs to initially determine the road direction. The ATP-ROAD method was applied to three experimental areas in Shanghai to extract the road network using China’s Gaofen-3 imagery. The experimental results show that the extracted road network information is relatively complete and matches the actual road conditions, and the result accuracy is high in the three different regions, i.e., 89.57% for Area-I, 96.88% for Area-II, and 92.65% for Area-III. Our method together with our extraction software can be applied to extract information about road networks from SAR images, providing an alternative for enriching the variety of road information.

Interlaminar fracture toughness is an important parameter to characterize the delamination performance of carbon fiber-reinforced epoxy laminates. In this study, the mode-I fracture toughness of interlaminar interfaces with different local ply angles were studied by experiments and numerical simulations. A set of double cantilever beam specimens of T800 carbon fiber/EH104 epoxy were prepared according to the ASTM-D5528–01 standard, where four specimens had straight ply interfaces (0°//0°, 15°//−15°, 30°//−30°, and 45°//−45°) and two specimens had curved ply interfaces (curvature radius: 300 and 500 mm). The mode-I fracture toughness was characterized using a data reduction scheme based on the modified beam theory. The results revealed that the mode-I fracture toughness values for the specimens with a curved ply interface were significantly higher than those for specimens with a straight ply interface.

The COVID-19 pandemic is currently spreading widely around the world, causing huge threats to public safety and global society. This study analyzes the spatiotemporal pattern of the COVID-19 pandemic in China, reveals China's epicenters of the pandemic through spatial clustering, and delineates the substantial effect of distance to Wuhan on the pandemic spread. The results show that the daily new COVID-19 cases mostly occurred in and around Wuhan before March 6, and then moved to the Grand Bay Area (Shenzhen, Hong Kong and Macau). The total COVID-19 cases in China were mainly distributed in the east of the Huhuanyong Line, where the epicenters accounted for more than 60% of the country's total in/on 24 January and 7 February, half in/on 31 January, and more than 70% from 14 February. The total cases finally stabilized at approximately 84,000, and the inflection point for Wuhan was on 14 February, one week later than those of Hubei (outside Wuhan) and China (outside Hubei). The generalized additive model-based analysis shows that population density and distance to provincial cities were significantly associated with the total number of the cases, while distances to prefecture cities and intercity traffic stations, and population inflow from Wuhan after 24 January, had no strong relationships with the total number of cases. The results and findings should provide valuable insights for understanding the changes in the COVID-19 transmission as well as implications for controlling the global COVID-19 pandemic spread.

Urbanization has led to the continuous expansion of built-up areas and the ever-growing urban population, threatening the quantity and quality of urban green space (UGS). Exploring the spatiotemporal variations of UGS is substantially conducive to the formulation of land-use policies to protect the ecosystems. As one of the largest megacities all around the world, Shanghai has experienced rapid urbanization in the past three decades. Insights into how UGS changes in response to urbanization and greening policies are essential for guiding sustainable urban development. This paper employed integrated approaches to characterize the changing patterns and intensities of green space in Shanghai, China from 1990 to 2015. The spatiotemporal dynamics of the UGS pattern were derived through four main methods: green space ratio, dynamic change degree (DCD), transition matrix and landscape metrics. The results showed that Shanghai’s green space decreased from 84.8% in 1990 to 61.9% in 2015 while the built-up areas increased from 15.0% to 36.5%. Among the green space sub-types, farmland was largely encroached and fragmented by urban sprawl, especially in the Outer Ring Expressway and Suburban Ring Expressway belts of the city. About 1522 km2 of the green space has transferred into built-up areas, followed by farmland, waterbody, forest, and grassland in descending order. The 2000–2010 period witnessed the strong urban expansion and dramatic changes in UGS, but then the change around 2015 turned down and stable. The landscape pattern metrics showed that the entire green space in Shanghai was growingly fragmented and isolated during the past 25 years. Combined with the green space-related planning and policies issued in 1990–2015, the results revealed that both rapid urbanization and greening policies accounted for the spatiotemporal dynamics of UGS. Based on the results, some implicants to new urban planning and policies of Shanghai were highlighted.

The aim of this work was to study the buckling behavior and failure mode of the double-sided laser-welded Al–Li alloy panel structure under the effect of axial compression via experimental and numerical simulation methods. In the test, multi-frequency fringe projection profilometry was used to monitor the out-of-plane displacement of the laser-welded panel structure during the axial compression load. In addition, the in-plane deformation was precisely monitored via strain gauge and strain rosette. The basic principles of fringe projection profilometry were introduced, and how to use fringe projection profilometry to obtain out-of-plane displacement was also presented. Numerical simulations were performed using the finite element method (FEM) to predict the failure load and buckling modes of the laser-welded panel structure under axial compression, and the obtained results were compared with those of the experiment. It was found that the fringe projection profilometry method for monitoring the buckling deformation of the laser-welded structure was verified to be effective in terms of a measurement accuracy of sub-millimeter level. The structural failure was caused by local buckling of the skin. The observed failure modes such as local buckling of the skin, bending deformation of the stringers, continuous fracture of several welds, and failure of local strength and stiffness were attributed to the deformed laser-welded panel structure under the axial compression. The predicted failure load in the numerical simulation was slightly smaller than that of the experimental test, and the error of the simulation result relative to the test result was −2.7%. The difference between them might be due to the fact that the boundary and loading conditions used in the FEM model could not be completely consistent with those used in the actual experiment.

Land surface temperature (LST) is a fundamental Earth parameter, on both regional and global scales. We used seven Landsat images to derive LST at Suzhou City, in spring and summer 1996, 2004, and 2016, and examined the spatial factors that influence the LST patterns. Candidate spatial factors include (1) land coverage indices, such as the normalized difference built-up index (NDBI), the normalized difference vegetation index (NDVI), and the normalized difference water index (NDWI), (2) proximity factors such as the distances to the city center, town centers, and major roads, and (3) the LST location. Our results showed that the intensity of the surface urban heat island (SUHI) has continuously increased, over time, and the spatial distribution of SUHI was different between the two seasons. The SUHIs in Suzhou were mainly distributed in the city center, in 1996, but expanded to near suburban, in 2004 and 2016, with a substantial expansion at the highest level of SUHIs. Our buffer-zone-based gradient analysis showed that the LST decays logarithmically, or decreases linearly, with the distance to the Suzhou city center. As inferred by the generalized additive models (GAMs), strong relationships exist between the LST and the candidate factors, where the dominant factor was NDBI, followed by NDWI and NDVI. While the land coverage indices were the LST dominant factors, the spatial proximity and location also substantially influenced the LST and the SUHIs. This work improved our understanding of the SUHIs and their impacts in Suzhou, and should be helpful for policymakers to formulate counter-measures for mitigating SUHI effects.

Cellular automata (CA) is a spatially explicit modeling tool that has been shown to be effective in simulating urban growth dynamics and in projecting future scenarios across scales. At the core of urban CA models are transition rules that define land transformation from non-urban to urban. Our objective is to compare the urban growth simulation and prediction abilities of different metaheuristics included in the R package optimx. We applied five metaheuristics in optimx to near-optimally parameterize CA transition rules and construct CA models for urban simulation. One advantage of metaheuristics is their ability to optimize complexly constrained computational problems, yielding objective parameterization with strong predictive power. From these five models, we selected conjugate gradient-based CA (CG-CA) and spectral projected gradient-based CA (SPG-CA) to simulate the 2005–2015 urban growth and to project future scenarios to 2035 with four strategies for Su-Xi-Chang Agglomeration in China. The two CA models produced about 86% overall accuracy with standard Kappa coefficient above 69%, indicating their good ability to capture urban growth dynamics. Four alternative scenarios out to the year 2035 were constructed considering the overall effect of all candidate influencing factors and the enhanced effects of county centers, road networks and population density. These scenarios can provide insight into future urban patterns resulting from today’s urban planning and infrastructure, and can inform future development strategies for sustainable cities. Our proposed metaheuristic CA models are also applicable in modeling land-use and urban growth in other rapidly developing areas.

Recent advances of interfacial micromechanics in fiber reinforced composites using micro-Raman spectroscopy are given. The faced mechanical problems for interface design in fibrous composites are elaborated from three optimization ways: material, interface, and computation. Some reasons are depicted that the interfacial evaluation methods are difficult to guarantee the integrity, repeatability, and consistency. Micro-Raman study on the fiber interface failure behavior and the main interface mechanical problems in fibrous composites are summarized, including interfacial stress transfer, strength criterion of interface debonding and failure, fiber bridging, frictional slip, slip transition, and friction reloading. The theoretical models of above interface mechanical problems are given.