bluuebunny/crossref_metadata_2025 huggingface dataset:
Drying Kinetics, Physicochemical and Thermal Analysis of Onion Puree Dried Using a Refractance Window Dryer
Authors: Zalpouri Ruchika , Singh Manpreet , Kaur Preetinder , Kaur Amrit , Gaikwad Kirtiraj K. , Singh Ashutosh
Published: February 2023

Onions have a high moisture content, which makes them more susceptible to microbial growth. Drying is one of the postharvest preservation methods applied to decrease onion moisture content, thereby increasing its storage life. In this study, onions were peeled, washed, cut into quarters, hot water blanched, and pureed. The puree was further dried using two different drying methods: refractance window drying (RWD) (water temperature: 70 °C) and convective drying (CD) (50 °C). The puree was spread on prefabricated trays at varying thicknesses of 2 mm, 4 mm, and 6 mm. It was observed that, irrespective of the drying method, moisture ratio (MR) decreased and drying time and effective moisture diffusivity increased with respect to the thickness of the puree. In addition, the Lewis model and the Wang and Singh model showed the highest R2 and lowest SEE value for RWD and CD, respectively. Moreover, the MR of onion puree during RWD and CD was predicted using a multi-layer feed-forward (MLF) artificial neural network (ANN) with a back-propagation algorithm. The result showed that the ANN model with 12 and 18 neurons in the hidden layer could predict the MR, with a high R2 value for RWD and CD, respectively. The results also showed that the thickness of the puree and drying method significantly affected the physicochemical quality (color characteristics, pyruvic acid content, total phenolic content, total flavonoid content, antioxidant capacity, and hygroscopicity) of onion powder. It was concluded that RWD proved to be a better drying method than CD in terms of the quality of dried powder and reduced drying time. Irrespective of the drying method, 2 mm-thick puree dried yielded the best-dried onion powder in terms of physicochemical quality, as well yielding the lowest drying time. These samples were further analyzed for calculating the glass transition temperature.
County-Based PM2.5 Concentrations’ Prediction and Its Relationship with Urban Landscape Pattern
Authors: Yang Lijuan , Wang Shuai , Hu Xiujuan , Shi Tingting
Published: February 2023

Satellite top-of-atmosphere (TOA) reflectance has been validated as an effective index for estimating PM2.5 concentrations due to its high spatial coverage and relatively high spatial resolution (i.e., 1 km). For this paper, we developed an emsembled random forest (RF) model incorporating satellite top-of-atmosphere (TOA) reflectance with four categories of supplemental parameters to derive the PM2.5 concentrations in the region of the Yangtze River Delta-Fujian (i.e., YRD-FJ) located in east China. The landscape pattern indices at two levels (i.e., type level and overall level) retrieved from 3-year land classification imageries (i.e., 2016, 2018, and 2020) were used to discuss the correlation between county-based PM2.5 values and landscape pattern. We achieved a cross validation R2 of 0.91 (RMSE = 9.06 μg/m3), 0.89 (RMSE = 10.19 μg/m3), and 0.90 (RMSE = 8.02 μg/m3) between the estimated and observed PM2.5 concentrations in 2016, 2018, and 2020, respectively. The PM2.5 distribution retrieved from the RF model showed a trend of a year-on-year decrease with the pattern of “Jiangsu > Shanghai > Zhejiang > Fujian” in the YRD-FJ region. Our results also revealed that the landscape pattern of farmland, water bodies, and construction land exhibited a highly positive relationship with the county-based average PM2.5 values, as the r coefficients reached 0.74 while the forest land was negatively correlated with the county-based PM2.5 (r = 0.84). There was also a significant correlation between the county-based PM2.5 and shrubs (r = 0.53), grass land (r = 0.76), and bare land (r = 0.60) in the YRD-FJ region, respectively. Three landscape pattern indices at an overall level were positively correlated with county-based PM2.5 concentrations (r = 0.80), indicating that the large landscape fragmentation, edge density, and landscape diversity would raise the PM2.5 pollution in the study region.
Migrants and Mental Health: Equal Rights but Different Needs
Authors: Girardi Paolo , Modesti Martina Nicole , Del Casale Antonio
Published: February 2023

In 2020, the number of international migrants worldwide—people residing in a country other than their country of birth—reached almost 281 million [...]
Spanish Journal of Finance and Accounting (SJFA): Mapping of Knowledge over the Last 25 Years
Authors: Álvarez-García José , Durán-Sánchez Amador , Montalván-Burbano Néstor , del Río-Rama María de la Cruz
Published: February 2023

The Spanish Journal of Finance and Accounting (SJFA) is a leading international journal in the field of accounting and finance, which was founded in 1972 with the aim of serving as a platform for dissemination at the service of academics, researchers and accounting and business professionals. The aim of this research is to examine the development of the SJFA journal from its creation to the present day, with the purpose of identifying the journal’s trend through published articles, authors, the most productive institutions and countries, and research topics, as well as citation patterns (most cited papers and typology of citing articles). The methodology used is bibliometric analysis and the data on which the bibliometric indicators are applied are taken from the Scopus database. This study also develops a graphical mapping of the bibliometric material by using the visualisation of similarities software. These graphs represent keyword co-occurrence, citation and co-citation analysis, and Journal Co-citation Analysis. The results show a significant growth of the journal over time, both in terms of the number and diversity of documents published and citations received, which has contributed to the positive evolution in terms of its impact and visibility. It is noteworthy that the journal is becoming more geographically diverse. The main topics covered in the journal are accounting and auditing management, and performance management. Papers dealing with topics related to management accounting, auditing, and management control, together with earnings management, accounting history and accounting regulation, stand out in terms of number of papers. Both topics are mediated by the subject of corporate governance.
Modern Western Thought and Islamic Reformism: Intellectual Challenges, Prior Discourse, and Future Prospects
Authors: Haq Zia Ul
Published: February 2023

Muslims were introduced to modern Western philosophy during the time of Western colonization, when they were not emotionally or mentally ready to absorb it and were generally skeptical of anything Western. This has caused an intellectual crisis, and some Muslims saw new ideas from the West as a direct threat to Islamic identity. The point here is why Muslim societies have always been skeptical of modern Western philosophy, even though Western societies accepted all new ideas without any trouble, and it does not stop the West from moving forward as a civilization. This study uses a comparative analytical method to look at how modern Western philosophy is received in Islamic societies, what it has caused, and where it might go in the future. It focuses on the issue of faith and reason as a talking point to show how Western and Islamic ways of knowing are different. Finally, the study makes important suggestions about how to deal with the effects of modern Western philosophy on Islamic societies.
Using Artificial Neural Networks to Assess Earthquake Vulnerability in Urban Blocks of Tehran
Authors: Afsari Rasoul , Nadizadeh Shorabeh Saman , Bakhshi Lomer Amir Reza , Homaee Mehdi , Arsanjani Jamal Jokar
Published: February 2023

The purpose of this study is to assess the vulnerability of urban blocks to earthquakes for Tehran as a city built on geological faults using an artificial neural network—multi-layer perceptron (ANN-MLP). Therefore, we first classified earthquake vulnerability evaluation criteria into three categories: exposure, sensitivity, and adaptability capacity attributed to a total of 16 spatial criteria, which were inputted into the neural network. To train the neural network and compute an earthquake vulnerability map, we used a combined Multi-Criteria Decision Analysis (MCDA) process with 167 vulnerable locations as training data, of which 70% (117 points) were used for training, and 30% (50 points) were used for testing and validation. The Mean Average Error (MAE) of the implemented neural network was 0.085, which proves the efficacy of the designed model. The results showed that 29% of Tehran’s total area is extremely vulnerable to earthquakes. Our factor importance analysis showed that factors such as proximity to fault lines, high population density, and environmental factors gained higher importance scores for earthquake vulnerability assessment of the given case study. This methodical approach and the choice of data and methods can provide insight into scaling up the study to other regions. In addition, the resultant outcomes can help decision makers and relevant stakeholders to mitigate risks through resilience building.
Impacts of Extreme Climates on Vegetation at Middle-to-High Latitudes in Asia
Authors: Wei Yuchen , Yu Miao , Wei Jiangfeng , Zhou Botao
Published: February 2023

In this study, we investigated the synchronous responses of vegetation to extreme temperatures and/or precipitation at middle-to-high latitudes in Asia using semi-monthly observations of the GIMMS and GLASS leaf area index (LAI) from 1982 to 2016. The extreme vegetation and climate states were specified using standard anomalies of the annual cycle with removed variables. The results show that the area with the maximum or minimum LAI increased or decreased in correspondence with global warming. Both the GIMMS and GLASS LAI mostly reached their maximum in spring and autumn. The GIMMS LAI mostly reached its minimum in summer, while the GLASS LAI mostly reached its minimum in late spring or late summer. The GIMMS and GLASS datasets were generally consistent regarding the vegetation responses to extreme temperatures and precipitation, especially in the areas covered by trees. Extreme cold and/or wet conditions inhibited forest growth in the area south of 60 °N, particularly from October to November. Extreme hot and/or dry conditions promoted forest growth, particularly in the central and northern parts of Siberia from August to September. However, in some arid areas of Central Asia and the Mongolian Highlands, which are mostly covered by sparse vegetation and grasses, low temperature extremes and/or strong precipitation promoted vegetation growth, while high temperature extremes and/or low precipitation had adverse effects on vegetation growth. This was more apparent in the GIMMS LAI than it was in the GLASS LAI, since the GIMMS dataset supplied more values representing extreme states of vegetation. The compound extreme of hot-and-dry and cold-and-wet climates were more frequent than the combination of cold and dry climates and hot-and-wet climates were. The overall positive response of the vegetation was superior to the negative response. The results of this study suggest that a continuous increase in vegetation density and coverage will occur over the boreal region in the future if the warming trend persists. The consequent climate feedback in this area on the regional and global scales should be afforded more attention.
A Reflection Symmetric Target Extraction Method Based on Hypothesis Testing for PolSAR Calibration
Authors: Chi Bowen , Zhang Jixian , Lu Lijun , Yang Shucheng , Huang Guoman , Gao Xu
Published: February 2023

Polarimetric calibration is indispensable to quantitatively apply and analyze the polarimetric synthetic aperture radar (PolSAR) image. At present, the polarimetric calibration methods relying on the assumption of reflection symmetry have been widely used, which need to extract the reference targets that meet the assumptions before calibration and then calculate the cross-pol channel imbalance and crosstalk errors. However, the distortion in the uncalibrated image will affect the calculation of polarization features, resulting in inaccurate target extraction results. Consequently, we proposed a reflection symmetric target extraction method that combines with spatial statistics information. The method first takes the initial extraction result based on the polarization power total Span and introduces the hypothesis testing to judge the homogeneous samples. Finally, we automatically calculate the threshold by the Otsu algorithm to achieve high-precision extraction of the reflection symmetric targets. Meanwhile, we carried out the polarimetric calibration experiments based on real C- and X-band airborne PolSAR data and conducted qualitative and quantitative evaluation and analysis of the experimental results. The studies demonstrated that, compared with classical approaches, the proposed approach further improved the accuracy of polarimetric calibration by extracting more accurate reference samples.
Water Body Extraction from Sentinel-2 Imagery with Deep Convolutional Networks and Pixelwise Category Transplantation
Authors: Billson Joshua , Islam MD Samiul , Sun Xinyao , Cheng Irene
Published: February 2023

A common task in land-cover classification is water body extraction, wherein each pixel in an image is labelled as either water or background. Water body detection is integral to the field of urban hydrology, with applications ranging from early flood warning to water resource management. Although traditional index-based methods such as the Normalized Difference Water Index (NDWI) and the Modified Normalized Difference Water Index (MNDWI) have been used to detect water bodies for decades, deep convolutional neural networks (DCNNs) have recently demonstrated promising results. However, training these networks requires access to large quantities of high-quality and accurately labelled data, which is often lacking in the field of remotely sensed imagery. Another challenge stems from the fact that the category of interest typically occupies only a small portion of an image and is thus grossly underrepresented in the data. We propose a novel approach to data augmentation—pixelwise category transplantation (PCT)—as a potential solution to both of these problems. Experimental results demonstrate PCT’s ability to improve performance on a variety of models and datasets, achieving an average improvement of 0.749 mean intersection over union (mIoU). Moreover, PCT enables us to outperform the previous high score achieved on the same dataset without introducing a new model architecture. We also explore the suitability of several state-of-the-art segmentation models and loss functions on the task of water body extraction. Finally, we address the shortcomings of previous works by assessing each model on RGB, NIR, and multispectral features to ascertain the relative advantages of each approach. In particular, we find a significant benefit to the inclusion of multispectral bands, with such methods outperforming visible-spectrum models by an average of 4.193 mIoU.
An Extended Robust Chance-Constrained Power Allocation Scheme for Multiple Target Localization of Digital Array Radar in Strong Clutter Environments
Authors: Xue Chenyan , Wang Ling , Zhu Daiyin
Published: February 2023

The traditional power allocation method for multi-target localization adopts the Robust Chance Constrained Power Allocation Scheme (RCC-PA), which does not consider the strong clutter characteristics in the current radar detection environment. However, how to reasonably allocate the power required for the radar to locate the target in the strong clutter is a fundamental challenge for improving the combat capability of the radar. Since Simultaneous Multi-beam Digital Array Radar (SM-DAR) can provide high-resolution information on targets in strong clutter, this paper extends the RCC-PA scheme to strong clutter and introduce an extended target model suitable for high-resolution SM-DAR. At the same time, the Gamma distribution is used to reflect the statistical characteristics of the Radar Cross Section (RCS) so that the extended RCC-PA scheme (ERCC-PA) can be suitable for all the scatterers whose RCS belong to Gamma distribution families. In the ERCC-PA scheme, the Strong Clutter Information Reduction Factor (SCIRF) of the extended target is first derived. Then, a Gamma Chance-constraint Programming Model (Γ-CCP model) is constructed to optimize the power allocation for locating multiple extended targets in strong clutter. The extended dichotomy method for power allocation is also given. Theoretical analysis shows that the power of multi-target localization in strong clutter can still be reasonably allocated under the target with multi-measurement characteristics. In addition, the power distribution of multi-target localization in the strong clutter is negatively correlated with the shape parameter of the Gamma distribution. Specifically, the larger the shape parameter characterizes the larger individual strong scatterer, resulting in the SM-DAR requiring less positioning power. The experimental results verify the theoretical analysis and show that the ERCC-PA scheme can improve the utilization of power compared with the benchmark and has the advantage of robustness to the fluctuation of the target RCS.
Novel Half-Spaces Based 3D Building Reconstruction Using Airborne LiDAR Data
Authors: Bizjak Marko , Mongus Domen , Žalik Borut , Lukač Niko
Published: February 2023

Automatic building reconstruction from laser-scanned data remains a challenging research topic due to buildings’ roof complexity and sparse data. A novel automatic building reconstruction methodology, based on half-spaces and a height jump analysis, is presented in this paper. The proposed methodology is performed in three stages. During the preprocessing stage, the classified input point cloud is clustered by position to obtain building point sets, which are then evaluated to obtain half-spaces and detect height jumps. Half-spaces represent the fundamental shape for generating building models, and their definition is obtained from the corresponding segment of points that describe an individual planar surface. The detection of height jumps is based on a DBSCAN search within a custom search space. During the second stage, the building point sets are divided into sub-buildings in such a way that their roofs do not contain height jumps. The concept of sub-buildings without height jumps is introduced to break down the complex building models with height jumps into smaller parts, where shaping with half-spaces can be applied accurately. Finally, the sub-buildings are reconstructed separately with the corresponding half-spaces and then joined back together to form a complete building model. In the experiments, the methodology’s performance was demonstrated on a large scale and validated on an ISPRS benchmark dataset, where an RMSE of 0.29 m was obtained in terms of the height difference.
Combining Multi-Dimensional SAR Parameters to Improve RVoG Model for Coniferous Forest Height Inversion Using ALOS-2 Data
Authors: Sa Rula , Nei Yonghui , Fan Wenyi
Published: February 2023

This paper considers extinction coefficient changes with height caused by the inhomogeneous distribution of scatterers in heterogeneous forests and uses the InSAR phase center height histogram and Gaussian function to fit the normalized extinction coefficient curve so as to reflect the vertical structure of the heterogeneous forest. Combining polarization decomposition based on the physical model and the PolInSAR parameter inversion method, the ground and volume coherence matrices can be separated based on the polarization characteristics and interference coherence diversity. By combining the new abovementioned parameters, the semi-empirical improved RVoG inversion model can be used to both quantify the effects of temporal decorrelation on coherence and phase errors and avoid the effects of small vertical wavenumbers on the large temporal baseline of spaceborne data. The model provided robust inversion for the height of the coniferous forest and enhanced the parameter estimation of the forest structure. This study addressed the influence of vertical structure differences on the extinction coefficient, though the coherence of the ground and volume in sparse vegetation areas could not be accurately estimated, and the oversensitivity of temporal decorrelation caused by inappropriate vertical wavenumbers. According to this method we used spaceborne L-band ALOS-2 PALSAR data on the Saihanba forest in Hebei Province acquired in 2020 for the purpose of height inversion, with a temporal baseline range of 14–70 days and the vertical wavenumber range of 0.01–0.03 rad/m. The results are further validated using sample data, with R2 reaching 0.67.
Up-Scaling Fuel Hazard Metrics Derived from Terrestrial Laser Scanning Using a Machine Learning Model
Authors: Taneja Ritu , Wallace Luke , Hillman Samuel , Reinke Karin , Hilton James , Jones Simon , Hally Bryan
Published: February 2023

The characterisation of fuel distribution across heterogeneous landscapes is important for wildfire mitigation, validating fuel models, and evaluating fuel treatment outcomes. However, efficient fuel mapping at a landscape scale is challenging. Fuel hazard metrics were obtained using Terrestrial Laser Scanning (TLS) and the current operational approach (visual fuel assessment) for seven sites across south-eastern Australia. These point-based metrics were then up-scaled to a continuous fuel map, an area relevant to fire management using random forest modelling, with predictor variables derived from Airborne Laser Scanning (ALS), Sentinel 2A images, and climate and soil data. The model trained and validated with TLS observations (R2 = 0.51 for near-surface fuel cover and 0.31 for elevated fuel cover) was found to have higher predictive power than the model trained with visual fuel assessments (R2 = −0.1 for the cover of both fuel layers). Models for height derived from TLS observations exhibited low-to-moderate performance for the near-surface (R2 = 0.23) and canopy layers (R2 = 0.25). The results from this study provide practical guidance for the selection of training data sources and can be utilised by fire managers to accurately generate fuel maps across an area relevant to operational fire management decisions.
Arctic Sea Ice Concentration Assimilation in an Operational Global 1/10° Ocean Forecast System
Authors: Shao Qiuli , Shu Qi , Xiao Bin , Zhang Lujun , Yin Xunqiang , Qiao Fangli
Published: February 2023

To understand the Arctic environment, which is closely related to sea ice and to reduce potential risks, reliable sea ice forecasts are indispensable. A practical, lightweight yet effective assimilation scheme of sea ice concentration based on Optimal Interpolation is designed and adopted in an operational global 1/10° surface wave-tide-circulation coupled ocean model (FIO-COM10) forecasting system to improve Arctic sea ice forecasting. Twin numerical experiments with and without data assimilation are designed for the simulation of the year 2019, and 5-day real-time forecasts for 2021 are implemented to study the sea ice forecast ability. The results show that the large biases in the simulation and forecast of sea ice concentration are remarkably reduced due to satellite observation uncertainty levels by data assimilation, indicating the high efficiency of the data assimilation scheme. The most significant improvement occurs in the marginal ice zones. The sea surface temperature bias averaged over the marginal ice zones is also reduced by 0.9 °C. Sea ice concentration assimilation has a profound effect on improving forecasting ability. The Root Mean Square Error and Integrated Ice-Edge Error are reduced to the level of the independent satellite observation at least for 24-h forecast, and sea ice forecast by FIO-COM10 has better performance than the persistence forecasts in summer and autumn.
Spatiotemporal Prediction of Radar Echoes Based on ConvLSTM and Multisource Data
Authors: Lu Mingyue , Li Yuchen , Yu Manzhu , Zhang Qian , Zhang Yadong , Liu Bin , Wang Menglong
Published: February 2023

Accurate and timely precipitation forecasts can help people and organizations make informed decisions, plan for potential weather-related disruptions, and protect lives and property. Instead of using physics-based numerical forecasts, which can be computationally prohibitive, there has been a growing interest in using deep learning techniques for precipitation prediction in recent years due to the success of these approaches in various other fields. These deep learning approaches generally use historical composite reflectivity (CR) at the surface level to predict future time steps. However, other relevant factors related to the potential motion and vertical structure of the storm have not been considered. To address this issue, this research proposes a multisource ConvLSTM (MS-ConvLSTM) model to improve the accuracy of precipitation forecasting by incorporating multiple data sources into the prediction process. The model was trained on a dataset of radar echo features, which includes not only composite reflectivity (CR), but also echo top (ET), vertically integrated liquid (VIL) water, and radar-retrieved wind field data at different elevations. Experiment results showed that the proposed model outperformed traditional methods in terms of various evaluation metrics, such as mean absolute error (MAE), mean squared error (MSE), probability of detection (POD), false alarm rate (FAR), and critical success index (CSI).
Sampling Survey Method of Wheat Ear Number Based on UAV Images and Density Map Regression Algorithm
Authors: Wu Wei , Zhong Xiaochun , Lei Chaokai , Zhao Yuanyuan , Liu Tao , Sun Chengming , Guo Wenshan , Sun Tan , Liu Shengping
Published: February 2023

The number of wheat ears is one of the most important factors in wheat yield composition. Rapid and accurate assessment of wheat ear number is of great importance for predicting grain yield and food security-related early warning signal generation. The current wheat ear counting methods rely on manual surveys, which are time-consuming, laborious, inefficient and inaccurate. Existing non-destructive wheat ear detection techniques are mostly applied to near-ground images and are difficult to apply to large-scale monitoring. In this study, we proposed a sampling survey method based on the unmanned aerial vehicle (UAV). Firstly, a small number of UAV images were acquired based on the five-point sampling mode. Secondly, an adaptive Gaussian kernel size was used to generate the ground truth density map. Thirdly, a density map regression network (DM-Net) was constructed and optimized. Finally, we designed an overlapping area of sub-images to solve the repeated counting caused by image segmentation. The MAE and MSE of the proposed model were 9.01 and 11.85, respectively. We compared the sampling survey method based on UAV images in this paper with the manual survey method. The results showed that the RMSE and MAPE of NM13 were 18.95 × 104/hm2 and 3.37%, respectively, and for YFM4, 13.65 × 104/hm2 and 2.94%, respectively. This study enables the investigation of the number of wheat ears in a large area, which can provide favorable support for wheat yield estimation.
Increasing Accuracy of the Soil-Agricultural Map by Sentinel-2 Images Analysis—Case Study of Maize Cultivation under Drought Conditions
Authors: Jędrejek Anna , Jadczyszyn Jan , Pudełko Rafał
Published: February 2023

The properties of soil constitute one of the most important features of the environment that determine the potential for food production in a given region. Knowledge of the soil texture and agroclimate allows for the proper selection of species and agrotechnics in plant production. However, in contrast to the agroclimate, the soil may show a large spatial variation of physical and chemical characteristics within the plot. In regions where the soil diversity is so high that the available soil maps are not sufficient, the only method that allows for precise mapping of the soil mosaic is remote sensing. This paper presents the concepts of using Sentinel-2 multispectral satellite images to detail the available soil-agriculture map at a scale of 1:25,000. In the presented work, the following research hypothesis has been formulated: spatial and temporal analysis of high-resolution satellite images can be used to improve the quality of a large-scale archival soil-agriculture map. It is possible due to the spatial differentiation of the spectral reflection from the field (canopy), which is influenced by soil conditions—especially the differentiation of physical properties (granulometric composition) in soil profiles which determine the possibility of water retention during drought conditions. The research carried out as a case study of maize remote sensing confirmed the hypothesis. It was based on the selection of the most appropriate term (maize development period: BBCH 79, 6-decade drought index: CBW = −206 mm) and the vegetation index (NDVI). This made it possible to make the scale of the map 10 times more detailed. The obtained results are the first step in developing a general model (based on remote sensing) for detailing the soil-agriculture map for Poland, which will significantly improve the accuracy of the drought monitoring system developed by the Institute of Soil Science and Plant Cultivation (Poland).
Icequakes and Large Shear Wave Velocity Drop in the Kuoqionggangri Glacier of Tibetan Plateau Observed with Fiber Optic Seismometer Array
Authors: Li Yanan , Huang Wenzhu , Li Guohui , Yang Wei , Zhang Xiaolong , Li Jiule , Zhang Wentao , Xu Baiqing
Published: February 2023

We developed a kind of fiber optic seismometer array for a high mountain glacier and first tested it on the Kuoqionggangri Glacier in the Tibetan Plateau. The array clearly recorded substantial passive seismic source signals of various icequakes, including shallow, deep and hybrid events. These fracturing activities indicate that crevasses and/or fractures developed in the glacier. We further obtained the glacial thickness of about 40 m by analyzing the active seismic source after hitting the glacier surface with a hammer based on the seismic scattering method. Most importantly, we observed a low shear wave velocity layer with a large velocity drop of ~28% and thickness of about ~7 m in the lower glacier. It is inferred that the low-velocity layer may represent a temperate ice layer. Our experiment provides a kind of feasible seismic observation to study icequakes and the englacial structure of Tibetan glaciers, offering new insights for evaluating glacier change in the Tibetan Plateau.
A Multi-Scale Pseudo-Siamese Network with an Attention Mechanism for Classification of Hyperspectral and LiDAR Data
Authors: Song Dongmei , Gao Jiacheng , Wang Bin , Wang Mingyue
Published: February 2023

For the remote sensing classification task, the ability of a single data source to identify the ground objects remains limited due to the lack of feature diversity. As the typical remote sensing data sources, hyperspectral imagery (HSI) and light detection and ranging (LiDAR) data can provide complementary spectral features and elevation information, respectively. To enhance classification ability, a multi-scale Pseudo-Siamese Network with attention mechanism (MA-PSNet) is proposed by fusing HSI and LiDAR data. In the network, two sub-branch networks are designed for extracting the features from HSI and LiDAR, respectively, and the connection is further established between these two branches. Specifically, a multi-scale feature learning module is incorporated, enabling the image features to be fully extracted at different scales. Similarly, a convolutional attention module is also embedded to highlight the saliency information of the objects, which makes the network training can be more targeted, thereby eventually improving the model performance for classification. The evaluation experiments of the proposed model are carried out on an urban dataset from Houston, USA, and a rural dataset from Trento, Italy. The overall accuracy (OA) of the model can reach 95.03% on the Houston data and 99.16% on the Trento data. The experimental results fully demonstrate that the proposed model has competitive performance compared with several state-of-the-art methods.
Evaluation of Global Land Use–Land Cover Data Products in Guangxi, China
Authors: Hao Xuan , Qiu Yubao , Jia Guoqiang , Menenti Massimo , Ma Jiangming , Jiang Zhengxin
Published: February 2023

Land use–land cover (LULC) is an important feature for ecological environment research, land resource management and evaluation. Although global high-resolution LULC data sets are booming, their regional performances were still evaluated in limited regions. To demonstrate the local applicability of global LULC data products, six emerging LULC data products were evaluated and compared in Guangxi, China. The six products used are European Space Agency GlobCover (ESAGC), ESRI Land Use–Land Cover (ESRI–LULC), Finer Resolution Observation and Monitoring of Global Land Cover (FROM–GLC), the China Land Cover Dataset (CLCD), the Global Land Cover product with Fine Classification System at 30 m (GLC_FCS30) and GlobeLand30 (GLC30). Reference data were obtained from the local government statistical yearbook and high-resolution remote sensing images on Google Earth. The results showed that CLCD, ESRI–LULC and GLC30 were found to agree well with the forest reference data, with the highest correlation coefficient of 0.999. For the cropland areas, GLC30, CLCD and ESAGC agreed well with the reference data, and the highest correlation coefficient was 0.957. Combined with the comparison with the high-resolution images obtained by Google Earth, we finally concluded that ESAGC, CLCD and GLC30 can best represent the LULCs in Guangxi. Furthermore, the spatial consistency analysis showed that three or more products identified the same LULC type as high as 96.98% of the area. We suggest that majority voting might be applied to global LULC products to provide fused products with better performances on a regional or local scale to avoid the error caused by a single data product.
AI-TFNet: Active Inference Transfer Convolutional Fusion Network for Hyperspectral Image Classification
Authors: Wang Jianing , Li Linhao , Liu Yichen , Hu Jinyu , Xiao Xiao , Liu Bo
Published: February 2023

The realization of efficient classification with limited labeled samples is a critical task in hyperspectral image classification (HSIC). Convolutional neural networks (CNNs) have achieved remarkable advances while considering spectral–spatial features simultaneously, while conventional patch-wise-based CNNs usually lead to redundant computations. Therefore, in this paper, we established a novel active inference transfer convolutional fusion network (AI-TFNet) for HSI classification. First, in order to reveal and merge the local low-level and global high-level spectral–spatial contextual features at different stages of extraction, an end-to-end fully hybrid multi-stage transfer fusion network (TFNet) was designed to improve classification performance and efficiency. Meanwhile, an active inference (AI) pseudo-label propagation algorithm for spatially homogeneous samples was constructed using the homogeneous pre-segmentation of the proposed TFNet. In addition, a confidence-augmented pseudo-label loss (CapLoss) was proposed in order to define the confidence of a pseudo-label with an adaptive threshold in homogeneous regions for acquiring pseudo-label samples; this can adaptively infer a pseudo-label by actively augmenting the homogeneous training samples based on their spatial homogeneity and spectral continuity. Experiments on three real HSI datasets proved that the proposed method had competitive performance and efficiency compared to several related state-of-the-art methods.
Interannual Variation of Landfast Ice Using Ascending and Descending Sentinel-1 Images from 2019 to 2021: A Case Study of Cambridge Bay
Authors: Zhu Yikai , Zhou Chunxia , Zhu Dongyu , Wang Tao , Zhang Tengfei
Published: February 2023

Landfast ice has undergone a dramatic decline in recent decades, imposing potential effects on ice travel for coastal populations, habitats for marine biota, and ice use for industries. The mapping of landfast ice deformation and the investigation of corresponding causes of changes are urgent tasks that can provide substantial data to support the maintenance of the stability of the Arctic ecosystem and the development of human activities on ice. This work aims to investigate the time-series deformation characteristics of landfast ice at multi-year scales and the corresponding influence factors. For the landfast ice deformation monitoring technique, we first combined the small baseline subset approach with ascending and descending Sentinel-1 images to obtain the line-of-sight deformations for two flight directions, and then we derived the 2D deformation fields comprising the vertical and horizontal directions for the corresponding periods by introducing a transform model. The vertical deformation results were mostly within the interval [−65, 23] cm, while the horizontal displacement was largely within the range of [−26, 78] cm. Moreover, the magnitude of deformation observed in 2019 was evidently greater than those in 2020 and 2021. In accordance with the available data, we speculate that the westerly wind and eastward-flowing ocean currents are the dominant reasons for the variation in the horizontal direction in Cambridge Bay, while the factors causing spatial differences in the vertical direction are the sea-level tilt and ice growth. For the interannual variation, the leading cause is the difference in sea-level tilt. These results can assist in predicting the future deformation of landfast ice and provide a reference for on-ice activities.
Sensor-Aided Calibration of Relative Extrinsic Parameters for Outdoor Stereo Vision Systems
Authors: Wang Jing , Guan Banglei , Han Yongsheng , Su Zhilong , Yu Qifeng , Zhang Dongsheng
Published: February 2023

Calibration of the stereo vision systems is a crucial step for precise 3D measurements. Restricted by the outdoors’ large field of view (FOV), the conventional method based on precise calibration boards is not suitable since the calibration process is time consuming and the calibration accuracy is not guaranteed. In this paper, we propose a calibration method for estimating the extrinsic parameters of the stereo vision system aided by an inclinometer and a range sensor. Through the parameters given by the sensors, the initial rotation angle of the extrinsic parameters and the translation vector are pre-established by solving a set of linear equations. The metric scale of the translation vector is determined by the baseline length provided by the range sensor or GNSS signals. Finally, the optimal extrinsic parameters of the stereo vision systems are obtained by nonlinear optimization of inverse depth parameterization. The most significant advantage of this method is that it enhances the capability of the stereo vision measurement in the outdoor environment, and can achieve fast and accurate calibration results. Both simulation and outdoor experiments have verified the feasibility and correctness of this method, and the relative error in the outdoor large FOV was less than 0.3%. It shows that this calibration method is a feasible solution for outdoor measurements with a large FOV and long working distance.
A Comprehensive Evaluation of Flooding’s Effect on Crops Using Satellite Time Series Data
Authors: Miao Shuangxi , Zhao Yixuan , Huang Jianxi , Li Xuecao , Wu Ruohan , Su Wei , Zeng Yelu , Guan Haixiang , Abd Elbasit Mohamed A. M. , Zhang Junxiao
Published: February 2023

In July 2021, a flooding event, which attracted the attention of the whole country and even the world, broke out in Henan, resulting in dramatic losses across multiple fields (e.g., economic and agricultural). The basin at the junction of Hebi, Xinxiang, and Anyang was the most affected region, as the spread of water from the Wei river submerged surrounding agricultural land (e.g., corn-dominated). To comprehensively evaluate the flooding impacts, we proposed a framework to detect the flooding area and evaluated the degree of loss using satellite time series data. First, we proposed a double-Gaussian model to adaptively determine the threshold for flooding extraction using Synthetic Aperture Radar (SAR) data. Then, we evaluated the disaster levels of flooding with field survey samples and optical satellite images. Finally, given that crops vary in their resilience to flooding, we measured the vegetation index change before and after the flooding event using satellite time series data. We found the proposed double-Gaussian model could accurately extract the flooding area, showing great potential to support in-time flooding evaluation. We also showed that the multispectral satellite images could potentially support the classification of disaster levels (i.e., normal, slight, moderate, and severe), with an overall accuracy of 88%. Although these crops were temporarily affected by this flooding event, most recovered soon, especially for the slightly and moderately affected regions. Overall, the distribution of resilience of these affected crops was basically in line with the results of classified disaster levels. The proposed framework provides a comprehensive aspect to the retrospective study of the flooding process on crops with diverse disaster levels and resilience. It can provide rapid and timely flood damage assessment and support emergency management and disaster verification work.
Intensity Normalisation of GPR C-Scans
Authors: Luo Tess X. H. , Lai Wallace W. L. , Lei Zhanzhan
Published: February 2023

The three-dimensional (3D) ground-penetrating radar (GPR) has been widely applied in subsurface surveys and imaging, and the quality of the resulting C-scan images is determined by the spatial resolution and visualisation contrast. Previous studies have standardised the suitable spatial resolution of GPR C-scans; however, their measurement normalisation remains arbitrary. Human bias is inevitable in C-scan interpretation because different visualisation algorithms lead to different interpretation results. Therefore, an objective scheme for mapping GPR signals after standard processing to the visualisation contrast should be established. Focusing on two typical scenarios, a reinforced concrete structure and an urban underground, this study illustrated that the essential parameters were greyscale thresholding and transformation mapping. By quantifying the normalisation performance with the integration of image segmentation and structural similarity index measure, a greyscale threshold was developed in which the normalised standard deviation of the unit intensity of any surveyed object was two. A transformation function named “bipolar” was also shown to balance the maintenance of real reflections at the target objects. By providing academia/industry with an object-based approach, this study contributes to solving the final unresolved issue of 3D GPR imaging (i.e., image contrast) to better eliminate the interfering noise and better mitigate human bias for any one-off/touch-based imaging and temporal change detection.
Classification of Arctic Sea Ice Type in CFOSAT Scatterometer Measurements Using a Random Forest Classifier
Authors: Zhai Xiaochun , Xu Rui , Wang Zhixiong , Zheng Zhaojun , Shou Yixuan , Tian Shengrong , Tian Lin , Hu Xiuqing , Chen Lin , Xu Na
Published: February 2023

The Ku-band scatterometer called CSCAT onboard the Chinese–French Oceanography Satellite (CFOSAT) is the first spaceborne rotating fan-beam scatterometer (RFSCAT). A new algorithm for classification of Arctic sea ice types on CSCAT measurement data using a random forest classifier is presented. The random forest classifier is trained on the National Snow and Ice Data Center (NSIDC) weekly sea ice age and sea ice concentration product. Five feature parameters, including the mean value of horizontal and vertical polarization backscatter coefficient, the standard deviation of horizontal and vertical polarization backscatter coefficient and the copol ratio, are innovatively extracted from orbital measurement for the first time to distinguish water, first-year ice (FYI) and multi-year ice (MYI). The overall accuracy and kappa coefficient of sea ice type model are 93.35% and 88.53%, respectively, and the precisions of water, FYI, and MYI are 99.67%, 86.60%, and 79.74%, respectively. Multi-source datasets, including daily sea ice type from the EUMETSAT Ocean and Sea Ice Satellite Application Facility (OSI SAF), NSIDC weekly sea ice age, multi-year ice concentration (MYIC) provided by the University of Bremen, and SAR-based sea ice type released by Copernicus Marine Environment Monitoring Service (CMEMS) have been used for comparison and validation. It is shown that the most obvious difference in the distribution of sea ice types between the CSCAT results and OSI SAF sea ice type are mainly concentrated in the marginal zones of FYI and MYI. Furthermore, compared with OSI SAF sea ice type, the area of MYI derived from CSCAT is more homogeneous with less noise, especially in the case of younger multiyear ice. In the East Greenland region, CSCAT identifies more pixels as MYI with lower MYIC values, showing better accuracy in the identification of areas with obvious mobility of MYI. In conclusion, this research verifies the capability of CSCAT in monitoring Arctic sea ice classification, especially in the spatial homogeneity and detectable duration of sea ice classification. Given the high accuracy and processing speed, the random forest-based algorithm can offer good guidance for sea ice classification with FY-3E/RFSCAT, i.e., a dual-frequency (Ku and C band) scatterometer called WindRAD.
Unsupervised Image Dedusting via a Cycle-Consistent Generative Adversarial Network
Authors: Gao Guxue , Lai Huicheng , Jia Zhenhong
Published: February 2023

In sand–dust weather, the quality of the image is seriously degraded, which affects the ability of advanced applications to image using remote sensing. To improve the image quality and enhance the performance of image dedusting, we propose an end-to-end cyclic generative adversarial network (D-CycleGAN) for image dedusting, which does not require pairs of sand–dust images and corresponding ground truth images for training. In other words, we train the network in an unpaired way. Specifically, we designed a jointly optimized guided module (JOGM), comprised of the sandy guided synthesis module (SGSM) and the clean guided synthesis module (CGSM), which aim to jointly guide the generator through corresponding discriminator adversarials to reduce the color distortion and artifacts. JOGM can significantly improve image quality. We propose a network hidden layer adversarial branch to perform adversarials from inside the network, which better supervises the hidden layer to further improve the quality of the generated images. In addition, we improved the original CycleGAN loss function and propose a dual-scale semantic perception loss in feature space and a color identity-preserving loss in pixel space to constrain the network. Extensive experiments demonstrate that our proposed network model effectively removes sand dust, has better clarity and image quality, and outperforms the state-of-the-art techniques. In addition, the proposed method can help the target detection algorithm to improve its detection accuracy and capability, and our method generalizes well to the enhancement of underwater images and hazy images.