bluuebunny/crossref_metadata_2025 huggingface dataset:
Use of Roselle Calyx Wastes for the Enrichment of Biscuits: An Approach to Improve Their Functionality
Authors: Hernández-Nava Rocio Guadalupe , Anaya-Tacuba José Daniel , Sánchez-Mundo María de la Luz , García-Barrientos Raquel , Flores-Castro Alejandra , Suárez-Rodríguez Carmen del Pilar , Espinosa-Solis Vicente
Published: January 2023

The objective of the present study was to evaluate the use of powder made out of Roselle Calyx Wastes (RCP) in developing a biscuit formulation with acceptable sensory value. Roselle calyxes were infused in water in a 1:10 ratio. The residual infused calyxes were dried at 50 °C for 16 h, grounded, sieved through a 50 mesh, and stored in plastic bags until used. The biscuit formulations were enriched with RCP at 0% (BC), 5% (BRCP5), 10% (BRCP10), and 15% (BRCP15). The amount of RCP added to the biscuit formulation did not change the protein content. However, the addition of RCP significantly affected the biscuit’s color; the lightness parameter (L*) decreased as the RP content increased from 69.66 to 49.04. The sensory evaluation showed that the control biscuit and the biscuit enriched with 5% of RP were the best accepted. As for the antiradical activity, the formulation with the highest activity was presented by the BRCP15 (587.43 µmol Trolox/100 g dwb). On the other hand, BRCP5 presented 189.96 µmol Trolox/100 g dwb. Therefore, the biscuit formulation with RCP at a 15% enrichment could be used to commercialize a functional product.
Special Issue “Recent Advances in Thermal Food Processing Technologies”
Authors: Hii Ching Lik , Tan Choon Hui , Woo Meng Wai
Published: January 2023

Historically, the application of thermal heating in food processing could be dated back to the invention by Nicholas Appert (1749–1841) which is nowadays known as canning and is able to preserve food products stored in glass bottles for an extended period [...]
Valorization of Cereal Byproducts with Supercritical Technology: The Case of Corn
Authors: Santana Ádina L. , Meireles Maria Angela A.
Published: January 2023

Ethanol and starch are the main products generated after the processing of corn via dry grinding and wet milling, respectively. Milling generates byproducts including stover, condensed distillers’ solubles, gluten meal, and the dried distillers’ grains with solubles (DDGS), which are sources of valuable compounds for industry including lignin, oil, protein, carotenoids, and phenolic compounds. This manuscript reviews the current research scenario on the valorization of corn milling byproducts with supercritical technology, as well as the processing strategies and the challenges of reaching economic feasibility. The main products recently studied were biodiesel, biogas, microcapsules, and extracts of enriched nutrients. The pretreatment of solid byproducts for further hydrolysis to produce sugar oligomers and bioactive peptides is another recent strategy offered by supercritical technology to process corn milling byproducts. The patents invented to transform corn milling byproducts include oil fractionation, extraction of undesirable flavors, and synthesis of structured lipids and fermentable sugars. Process intensification via the integration of milling with equipment that operates with supercritical fluids was suggested to reduce processing costs and to generate novel products.
Optimization of the Cultivation Conditions of the Green Algae Dunaliella salina by Using Simplex Method
Authors: Al-Mhanna Najah , Pistorius Michael , Al Sammarraie Lanah
Published: January 2023

The green algae Dunaliella salina offers great potential for the food industry due to its high β-carotene content. To guarantee the economic profitability of cultivation, growth conditions must be improved. Therefore, the effects of pH and salinity on the cultivation of the green alga D. salina were investigated and optimized. The simplex method was applied to find the optimum of these two parameters to maximize the biomass and the cell number of D. salina. The optimum pH was found at 7 and 8 at a salt content of 50 g/L, with a biomass content of 1.09 and 1.11 g/L, respectively. The highest biomass was found at a salinity of 50 g/L, with a final biomass of 1.11 g/L. However, by using the simplex method, an optimum product yield was found at a salinity of 64 g/L and an initial pH value of 7.2. Thus, a biomass of 1.23 mg/mL was achieved. In the single observation of both parameters, 14 experiments were conducted to obtain a satisfactory result, whereas eight runs only were required with the simplex method. This leads to the conclusion that using the simplex method is a useful way to drastically reduce the number of required experiments.
Prediction of Winter Wheat Harvest Based on Back Propagation Neural Network Algorithm and Multiple Remote Sensing Indices
Authors: Ji Hong , He Xun , Wang Wanzhang , Zhang Hongmei
Published: January 2023

Predicting the harvest time of wheat in large areas is important for guiding the scheduling of wheat combine harvesters and reducing losses during harvest. In this study, Zhumadian, Zhengzhou and Anyang, the main winter-wheat-producing areas in Henan province, were selected as the observation points, and the main producing areas were from south to north. Based on Landsat 8 satellite remote sensing images, the changes in NDVI (Normalized Difference Vegetation Index), EVI (Enhanced Vegetation Index), and NDWI (Normalized Difference Water Index) were analyzed at different growth stages of winter wheat in 2020. Multiple regression analysis and Back Propagation (BP) neural network machine learning methods were used to establish prediction models for the harvest time of winter wheat at different growth stages. The results showed that the prediction model based on a BP neural network had high accuracy. The RMSE, MAE and MAPE of the training set and the test set were 0.531 and 0.5947, 0.3001 and 0.3104, 0.0114% and 0.0119%, respectively. The prediction model of winter wheat harvest date based on BP neural network was verified in the main winter wheat producing areas of Henan province in 2020 and 2021. The average errors were 1.67 days and 2.13 days, which were less than 3 days, meeting the needs for winter wheat production and harvest. The grain water content of winter wheat at harvest time calculated by the prediction model reached the grain water standard of the wheat combine harvester. Therefore, the prediction of the winter wheat harvest time can be realized based on multiple remote sensing indicators.
Secularism and Ethnic Minorities: Comparative Case Studies on Ethnic, Religious, and Political Cognitions in Pakistani-Controlled Kashmir, Central Russia, Romania, and Northern Scandinavia
Authors: Csáji László Koppány
Published: January 2023

According to my study, “political secularism” means the separation of political power from religious institutions, while “social secularism” is a theory and endeavor to eliminate religiosity from not only public but also private life, considering it an obsolete way of thinking. I examine four case studies based on my ethnological fieldwork in Hunza (in the Pakistani-controlled Kashmir), the Middle Ural (Russia), Transylvania (Romania), and Sápmi (northern Scandinavia). I outline and compare ethnic minorities (Hunzakuts, Tatars, Szeklers, Samis) according to their historical background, contemporary social environment, relation to the majority, their political endeavors, and the role of religion(s) among them. Based on my fieldwork notes, interviews, and sociological data, I analyze the similarities and differences of ethnic complexity, terminological confusions, problems of “lived religion,” and the impact of social and political secularism. Since their religiosity differs from the majorities’ ones, I found that secularism has a complex role and reception. Political secularism is essential for defending these minorities from assimilation, but most of these minorities reject social secularism since religion is part of their multifunctional ethnic discourse space. Religiosity is part of their survival strategy. Notwithstanding, ethnic minorities’ religious institutions participate in political activity and propagate their claims for self-governance.
On the Value of Empathy to Inter-Religious Relations: A Case Study Based on the Thought of Charles Hartshorne
Authors: Wang Jiran
Published: January 2023

Introducing the results of psychology to the field of inter-religious relations, the value of empathy for the latter may seem equivocal. Based on a study of Hartshorne’s thought, this paper will clarify conceptually that, as a mechanism, empathy can promote integration and dialogue, but may also result in partiality due to the limitation of its scope, thus resulting in prejudice and even conflict. It will further argue that Hartshorne provides a view of ultimate reality that not only highlights the moral value of empathy, but also promotes the extension of its scope. This implies that a theological account of empathy can go beyond the framework constructed in psychology and transform it into something that has unequivocally positive value for inter-religious integration and dialogue.
How to Read the Quran in Religious Islamic Education: What Educators Can Learn from the Work of Mohammed Arkoun
Authors: Felsenthal Iddo , Agbaria Ayman
Published: January 2023

The study of the Quran is central to Religious Islamic Education (RIE). Exposed to different political and social pressures, teachers in RIE still struggle between traditional approaches concerning the divine nature of the Qur’an and more secular approaches emphasizing the historicity and critical analysis of the religious text. Mohammed Arkoun (d. 2010), an Algerian-born Sorbonne professor, offered a hermeneutical methodology for reading the Quran that was Western, scientific, and critical, and at the same time related to the Living Islamic Tradition, faith, and thought. The article analyzes Arkoun’s methodology for reading the Quran and its possible implications on RIE in a way that creates a space for creativity, criticism, and dialogue between worldviews and opens new possibilities for the faithful to teach and learn the Qur’an.
Analysis of UAS-LiDAR Ground Points Classification in Agricultural Fields Using Traditional Algorithms and PointCNN
Authors: Fareed Nadeem , Flores Joao Paulo , Das Anup Kumar
Published: January 2023

Classifying bare earth (ground) points from Light Detection and Ranging (LiDAR) point clouds is well-established research in the forestry, topography, and urban domains using point clouds acquired by Airborne LiDAR System (ALS) at average point densities (≈2 points per meter-square (pts/m2)). The paradigm of point cloud collection has shifted with the advent of unmanned aerial systems (UAS) onboard affordable laser scanners with commercial utility (e.g., DJI Zenmuse L1 sensor) and unprecedented repeatability of UAS-LiDAR surveys. Therefore, there is an immediate need to investigate the existing methods, and to develop new ground classification methods, using UAS-LiDAR. In this paper, for the first time, traditional ground classification algorithms and modern machine learning methods were investigated to filter ground from point clouds of high-density UAS-LiDAR data (≈900 pts/m2) over five agricultural fields in North Dakota, USA. To this end, we tested frequently used ground classification algorithms: Cloth Simulation Function (CSF), Progressive Morphological Filter (PMF), Multiscale Curvature Classification (MCC), and ArcGIS ground classification algorithms along with the PointCNN deep learning model were trained. We investigated two aspects of ground classification algorithms and PointCNN: (a) Classification accuracy of optimized ground classification algorithms (i.e., fine adjustment is user-defined parameters) and PointCNN over training site, and (b) transferability potential over four yet diverse test agricultural fields. The well-established evaluation metrics of omission error, commission error, and total error, along with kappa coefficients showed that deep learning outperforms the traditional ground classification algorithms in both aspects: (a) overall classification accuracy, and (b) transferability over diverse agricultural fields.
HDM-RRT: A Fast HD-Map-Guided Motion Planning Algorithm for Autonomous Driving in the Campus Environment
Authors: Guo Xiaomin , Cao Yongxing , Zhou Jian , Huang Yuanxian , Li Bijun
Published: January 2023

On campus, the complexity of the environment and the lack of regulatory constraints make it difficult to model the environment, resulting in less efficient motion planning algorithms. To solve this problem, HD-Map-guided sampling-based motion planning is a feasible research direction. We proposed a motion planning algorithm for autonomous vehicles on campus, called HD-Map-guided rapidly-exploring random tree (HDM-RRT). In our algorithm, A collision risk map (CR-Map) that quantifies the collision risk coefficient on the road is combined with the Gaussian distribution for sampling to improve the efficiency of algorithm. Then, the node optimization strategy of the algorithm is deeply optimized through the prior information of the CR-Map to improve the convergence rate and solve the problem of poor stability in campus environments. Three experiments were designed to verify the efficiency and stability of our approach. The results show that the sampling efficiency of our algorithm is four times higher than that of the Gaussian distribution method. The average convergence rate of the proposed algorithm outperforms the RRT* algorithm and DT-RRT* algorithm. In terms of algorithm efficiency, the average computation time of the proposed algorithm is only 15.98 ms, which is much better than that of the three compared algorithms.
Multi-Scale Feature Map Aggregation and Supervised Domain Adaptation of Fully Convolutional Networks for Urban Building Footprint Extraction
Authors: Aryal Jagannath , Neupane Bipul
Published: January 2023

Automated building footprint extraction requires the Deep Learning (DL)-based semantic segmentation of high-resolution Earth observation images. Fully convolutional networks (FCNs) such as U-Net and ResUNET are widely used for such segmentation. The evolving FCNs suffer from the inadequate use of multi-scale feature maps in their backbone of convolutional neural networks (CNNs). Furthermore, the DL methods are not robust in cross-domain settings due to domain-shift problems. Two scale-robust novel networks, namely MSA-UNET and MSA-ResUNET, are developed in this study by aggregating the multi-scale feature maps in U-Net and ResUNET with partial concepts of the feature pyramid network (FPN). Furthermore, supervised domain adaptation is investigated to minimise the effects of domain-shift between the two datasets. The datasets include the benchmark WHU Building dataset and a developed dataset with 5× fewer samples, 4× lower spatial resolution and complex high-rise buildings and skyscrapers. The newly developed networks are compared to six state-of-the-art FCNs using five metrics: pixel accuracy, adjusted accuracy, F1 score, intersection over union (IoU), and the Matthews Correlation Coefficient (MCC). The proposed networks outperform the FCNs in the majority of the accuracy measures in both datasets. Compared to the larger dataset, the network trained on the smaller one shows significantly higher robustness in terms of adjusted accuracy (by 18%), F1 score (by 31%), IoU (by 27%), and MCC (by 29%) during the cross-domain validation of MSA-UNET. MSA-ResUNET shows similar improvements, concluding that the proposed networks when trained using domain adaptation increase the robustness and minimise the domain-shift between the datasets of different complexity.
Application of Digital Twin in Medium-Voltage Overhead Distribution Network Inspection
Authors: Gauce Diana , Lektauers Arnis , Solovjova Irina , Grants Roberts , Kolosovs Deniss , Litvinenko Anna
Published: January 2023

The paper is devoted to the digital transformation of the core engineering maintenance processes involved in medium-voltage overhead distribution network infrastructure inspection. The study presents an analysis of digital twin utilization for inspection of the infrastructure of medium-voltage overhead distribution networks. At present, the infrastructure monitoring process is still manual, and its automatization is a challenging task due to the large distances between and small dimensions of the elements involved. The proposed digital twin is employed for 3D infrastructure modeling and complex analysis based on photogrammetry and aerial scanning data processing methods. The paper describes practical use cases for these data-driven methods for specific infrastructure management processes, e.g., scheduled inspection processes, including geometrical parameter measurements and visual infrastructure element defect identification, unplanned inspection processes (state of emergency, post-storm, etc.), and vegetation management processes. The proposed method allows operations to be performed remotely without physical presence in the field. At the same time, data-driven solutions provide objective results and potential automation via machine learning algorithms, which are more profitable economically. As the proposed method allows processes to be performed remotely, these data-driven solutions offer cost-effective results for automation. The digital twin concept is applied to the Latvian medium-voltage overhead distribution network with the support of the Latvian distribution system operator “Sadales tīkls” JSC.
A Study of the Method for Retrieving the Vegetation Index from FY-3D MERSI-II Data
Authors: Xiao Fengjin , Liu Qiufeng , Li Shuai , Qin Yun , Huang Dapeng , Wang Yanjiao , Wang Lei
Published: January 2023

NDVI data have been widely used to detect and monitor vegetation status at regional, continental, and global scales. FY-3D MERSI-II NDVI (FNDVI) is a critical operational product used in many studies monitoring ecosystems and agriculture and assessing climate change and its risks, including drought and fire. MERSI-II and MODIS have very similar spectral response functions in the red and near-infrared channels, making MERSI/NDVI an effective replacement for MODIS/NDVI (MNDVI). Therefore, it is critical to conduct a thorough evaluation of the product’s quality. In this study, the consistency characteristics of two normalized difference vegetation index (NDVI) products, FY-3D MERSI-II NDVI and MODIS NDVI, were compared and validated at national and regional scales in China from 2020 to 2021. To assess the consistency of these two NDVI datasets, the correlation coefficient, root-mean-square error, and mean bias error were used. The findings revealed that the spatial distribution patterns of FNDVI and MNDVI were highly consistent across the country at the monthly time scale. The correlation coefficients were greater than 0.9475 for the two years 2020–2021, while the average deviation was between 0.02 and 0.05, and the root-mean-square error was 0.11. Based on the difference in the time consistency between FNDVI and MNDVI, the changes in the monthly NDVI values of the two types of satellites are generally consistent across the country. Among the three typical experimental areas, the relative deviation of the regional time series for products was the highest in Xinjiang. The relative average deviation of FNDVI in other regions was low, and its change trend was consistent with that of MODIS.
Adaptive Voxel-Based Model for the Dynamic Determination of Tomographic Region
Authors: Ding Nan , Tan Xinglong , Liu Xin , He Zhifen , Zhang Yu , Wang Yuchen , Zhang Shubi , Holden Lucas , Zhang Kefei
Published: January 2023

Water vapor is a dominant greenhouse gas. It significantly impacts the atmosphere by trapping heat and infrared radiation. The greenhouse effect is essential for life on Earth but can also be harmful. Although the amount of water vapor in the atmosphere is not much during the water cycle, it is the most active element in rapid changes in both spatial and temporal domains. GNSS tomography’s ability to model the high-resolution 3D distribution of water vapor is a promising means of measuring and monitoring the spatial-temporal variation of water vapor. This study developed and tested a new GNSS tomographic model using adaptive voxel parameterization. It uses a 3D traversal algorithm to dynamically determine the position of voxels at each tomographic sampling epoch. It means that the new algorithm can exclude the voxels that no GNSS signals pass through, reducing the influence of such voxels in the construction of the tomographic model. This study provides a new approach to investigating the inversion of atmospheric water vapor. The experiment used one-month data from the Hong Kong network in September 2020, and the results were compared with the general system. The local radiosonde data is a reference for verification of the two approaches. The mean root-mean-square error (RMSE) and IQR of the water vapor profiles derived from AAR are decreased by 55% and 48% with respect to the GFR results, respectively. The results show that the accuracy of the new method outperforms the general approach in the result statistics. The successful implementation of the research has significant potential to drive the development of GNSS tomography in the study of weather and climate change.
Unraveling Segmentation Quality of Remotely Sensed Images on Plastic-Covered Greenhouses: A Rigorous Experimental Analysis from Supervised Evaluation Metrics
Authors: Senel Gizem , Aguilar Manuel A. , Aguilar Fernando J. , Nemmaoui Abderrahim , Goksel Cigdem
Published: January 2023

Plastic-covered greenhouse (PCG) segmentation represents a significant challenge for object-based PCG mapping studies due to the spectral characteristics of these singular structures. Therefore, the assessment of PCG segmentation quality by employing a multiresolution segmentation algorithm (MRS) was addressed in this study. The structure of this work is composed of two differentiated phases. The first phase aimed at testing the performance of eight widely applied supervised segmentation metrics in order to find out which was the best metric for evaluating image segmentation quality over PCG land cover. The second phase focused on examining the effect of several factors (reflectance storage scale, image spatial resolution, shape parameter of MRS, study area, and image acquisition season) and their interactions on PCG segmentation quality through a full factorial analysis of variance (ANOVA) design. The analysis considered two different study areas (Almeria (Spain) and Antalya (Turkey)), seasons (winter and summer), image spatial resolution (high resolution and medium resolution), and reflectance storage scale (Percent and 16Bit formats). Regarding the results of the first phase, the Modified Euclidean Distance 2 (MED2) was found to be the best metric to evaluate PCG segmentation quality. The results coming from the second phase revealed that the most critical factor that affects MRS accuracy was the interaction between reflectance storage scale and shape parameter. Our results suggest that the Percent reflectance storage scale, with digital values ranging from 0 to 100, performed significantly better than the 16Bit reflectance storage scale (0 to 10,000), both in the visual interpretation of PCG segmentation quality and in the quantitative assessment of segmentation accuracy.
Long-Term Tibetan Alpine Vegetation Responses to Elevation-Dependent Changes in Temperature and Precipitation in an Altered Regional Climate: A Case Study for the Three Rivers Headwaters Region, China
Authors: Wang Keyi , Zhou Yang , Han Jingcheng , Chen Chen , Li Tiejian
Published: January 2023

Recent studies offer more evidence that the rate of warming is amplified with elevation, indicating thereby that high-elevation ecosystems tend to be exposed to more accelerated changes in temperature than ecosystems at lower elevations. The phenomenon of elevation-dependent warming (EDW), as one of the regional climate-change impacts, has been observed across the Tibetan Plateau. Studies have often found large-scale greening trends, but the drivers of vegetation dynamics are still not fully understood in this region, such that the local implications of vegetation change have been infrequently discussed. This study was designed to quantify and characterize the seasonal changes in vegetation across the Three Rivers Headwaters Region (TRHR), where the land cradles the headwaters of the Yangtze, the Yellow, and the Lancang (Mekong). By mapping the normalized difference vegetation index (NDVI) over the growing season from 1982 to 2015, we were able to evaluate seasonal changes in vegetation cover over time. The results show a slightly increased tendency in green vegetation cover, which could possibly be attributed to sustained warming in this region over the past three decades, whereas a decline in the green-up rate with elevation was found, indicating an inconsistent trend of vegetation greening with EDW. The cause of the green-up rate decline at high elevations could be linked to the reduced soil water availability induced by the fast increase in warming rates associated with EDW. The findings of this study have important implications for devising adaptation strategies for alpine ecosystems in a changing climate.
Point Cloud Plane Segmentation-Based Robust Image Matching for Camera Pose Estimation
Authors: Bao Junqi , Yuan Xiaochen , Huang Guoheng , Lam Chan-Tong
Published: January 2023

The mainstream image matching method for recovering the motion of the camera is based on local feature matching, which faces the challenges of rotation, illumination, and the presence of dynamic objects. In addition, local feature matching relies on the distance between descriptors, which easily leads to lots of mismatches. In this paper, we propose a new robust image matching method for camera pose estimation, called IM_CPE. It is a novel descriptor matching method combined with 3-D point clouds for image matching. Specifically, we propose to extract feature points based on a pair of matched point cloud planes, which are generated and segmented based on depth images. Then, the feature points are matched based on the distance between their corresponding 3-D points on the point cloud planes and the distance between their descriptors. Moreover, the robustness of the matching can be guaranteed by the centroid distance of the matched point cloud planes. We evaluate the performance of IM_CPE using four well-known key point extraction algorithms, namely Scale-Invariant Feature Transform (SIFT), Speed Up Robust Feature (SURF), Features from Accelerated Segment Test (FAST), and Oriented FAST and Rotated Brief (ORB), with four sequences from the TUM RGBD dataset. According to the experimental results, compared to the original SIFT, SURF, FAST, and ORB algorithms, the NN_mAP performance of the four key point algorithms has been improved by 11.25%, 13.98%, 16.63%, and 10.53% on average, respectively, and the M.Score has also been improved by 25.15%, 23.05%, 22.28%, and 11.05% on average, respectively. The results show that the IM_CPE can be combined with the existing key points extraction algorithms and the IM_CPE can significantly improve the performance of these key points algorithms.
Retrieval of Atmospheric Water Vapor Content in the Environment from AHI/H8 Using Both Physical and Random Forest Methods—A Case Study for Typhoon Maria (201808)
Authors: Zhu Linyan , Zhou Ronglian , Di Di , Bai Wenguang , Liu Zijing
Published: January 2023

The advanced imagers onboard the new generation of geostationary satellites could provide multilayer atmospheric moisture information with unprecedented high spatial and temporal resolutions, while the physical retrieval algorithm (One-Dimensional Variational, 1DVAR) is performed for operational atmospheric water vapor products with reduced resolutions, which is due to the limited computational efficiency of the physical retrieval algorithm. In this study, a typical cost-efficient machine learning (Random Forecast, RF) algorithm is adopted and compared with the physical retrieval algorithm for retrieving the atmospheric moisture from the measurements of Advance Himawari Imager (AHI) onboard the Himawari-8 satellite during the typhoon Maria (201808). It is found that the accuracy of the RF-based algorithm has much high computational efficiency and provides moisture retrievals with accuracy 35–45% better than that of 1DVAR, which means the retrieval process can be conducted at full spatial resolution for potential operational application. Both the Global Forecast System (GFS) forecasts and the AHI measurements are necessary information for moisture retrievals; they provide added value for each other.
Temporal Evolution and Regional Properties of Aerosol over the South China Sea
Authors: Chen Jie , Zhu Wenyue , Liu Qiang , Qian Xianmei , Chen Xiaowei , Zheng Jianjie , Yang Tao , Xu Qiuyi , Yang Tengfei
Published: January 2023

Aerosol robotic network (AERONET) data from Dongsha Island (20.699N, 116.729E) and Taiping Island (35.90N, 3.03W) over the South China Sea (SCS) from January 2018 to December 2020 were used to analyze and discuss the temporal evolution properties of aerosols in the South China Sea. Surrounding AERONET stations (Hong Kong, NSPO, Nha Trang and Singapore) were also used to analyze regional characteristics. High aerosol loads over Dongsha were strongly associated with the anthropogenic fine particle transport from the southeastern coast of China and occasional advection of desert dust from Mongolian areas. The high fine aerosol loading in Taiping originates from the region between Singapore and Indonesia. Compared with other marine islands in the world, SCS was not a pure marine aerosol environment and was affected by terrestrial aerosols. In the Taiping area, aerosol optical depth τ (500 nm) was 0.17 ± 0.13 and the average Ångström exponent α (440–870 nm) was 0.96 ± 0.36. However, that of Dongsha shows the larger values of τ (0.26 ± 0.21) and α (1.1 ± 0.38), indicating that there are large fluctuations in aerosol concentration and size. Aerosol loads in different regions of the SCS due to uneven socioeconomic and complex meteorological systems, such as those of the coastal cities of China, Singapore, and the region between Singapore and Indonesia, contribute to the high optical depth. The special meteorological regime and aerosol source mechanism in the SCS leads to the obvious seasonal cycle of aerosol optical depth and Ångström index. Moreover, the loading variations of aerosols on Dongsha Island and Taiping Island were highly consistent with those of coastal cities around them, suggesting the significant effect of the aerosol in the SCS by the surrounding coastal cities, although the aerosol optical depth in these two places was much lower than that in the surrounding cities.
Fine Classification Method for Massive Microseismic Signals Based on Short-Time Fourier Transform and Deep Learning
Authors: Ma Chunchi , Ran Xuefeng , Xu Weihao , Yan Wenjin , Li Tianbin , Dai Kunkun , Wan Jiangjun , Lin Yu , Tong Ke
Published: January 2023

Numerous microseismic signals are produced by rock mass fracture during earthquakes, geological disasters, or underground excavations. Moreover, a large amount of noise signals are captured during microseismic signal monitoring. Specifically, some noise signals closely resemble microseismic signals, which severely impedes the rapid and accurate detection of the latter and the assessment of geological hazards. Therefore, we propose a precise model for identifying and classifying microseismic signals based on deep learning technology and short-time Fourier transform (STFT) technology. First, the STFT time–frequency analysis reveals the unique characteristics of noise, microseismic, and blasting signals, thereby allowing noise signals that are very similar to microseismic signals in the time domain to be finely distinguished. Second, the introduced attention mechanism focuses the classification on essential signal features. Finally, because tens of thousands of actual monitoring data points are considered, the deep neural network for microseismic classification is trained and tested under complex geological engineering conditions. The results demonstrate that the neural network model has good time–frequency feature extraction ability, and the well-trained model can satisfactorily complete daily classifications. Moreover, the model performs well when classifying similar noise and low-SNR microseismic signals. We believe that this type of signal-processing method, which considers multiple perspectives, can be extended to data processing in many other data-driven fields.
An Image-Aided Sparse Point Cloud Registration Strategy for Managing Stockpiles in Dome Storage Facilities
Authors: Liu Jidong , Hasheminasab Seyyed Meghdad , Zhou Tian , Manish Raja , Habib Ayman
Published: January 2023

Stockpile volume estimation plays a critical role in several industrial/commercial bulk material management applications. LiDAR systems are commonly used for this task. Thanks to Global Navigation Satellite System (GNSS) signal availability in outdoor environments, Uncrewed Aerial Vehicles (UAV) equipped with LiDAR are frequently adopted for the derivation of dense point clouds, which can be used for stockpile volume estimation. For indoor facilities, static LiDAR scanners are usually used for the acquisition of point clouds from multiple locations. Acquired point clouds are then registered to a common reference frame. Registration of such point clouds can be established through the deployment of registration targets, which is not practical for scalable implementation. For scans in facilities bounded by planar walls/roofs, features can be automatically extracted/matched and used for the registration process. However, monitoring stockpiles stored in dome facilities remains to be a challenging task. This study introduces an image-aided fine registration strategy of acquired sparse point clouds in dome facilities, where roof and roof stringers are extracted, matched, and modeled as quadratic surfaces and curves. These features are then used in a Least Squares Adjustment (LSA) procedure to derive well-aligned LiDAR point clouds. Planar features, if available, can also be used in the registration process. Registered point clouds can then be used for accurate volume estimation of stockpiles. The proposed approach is evaluated using datasets acquired by a recently developed camera-assisted LiDAR mapping platform—Stockpile Monitoring and Reporting Technology (SMART). Experimental results from three datasets indicate the capability of the proposed approach in producing well-aligned point clouds acquired inside dome facilities, with a feature fitting error in the 0.03–0.08 m range.
Added Value of Aerosol Observations of a Future AOS High Spectral Resolution Lidar with Respect to Classic Backscatter Spaceborne Lidar Measurements
Authors: Cornut Flavien , El Amraoui Laaziz , Cuesta Juan , Blanc Jérôme
Published: January 2023

In the context of the Atmosphere Observing System (AOS) international program, a new-generation spaceborne lidar is expected to be in polar orbit for deriving new observations of aerosol and clouds. In this work, we analyze the added values of these new observations for characterizing aerosol vertical distribution. For this, synthetic observations are simulated using the BLISS lidar simulator in terms of the backscatter coefficient at 532 nm. We consider two types of lidar instruments, an elastic backscatter lidar instrument and a high spectral resolution lidar (HSRL). These simulations are performed with atmospheric profiles from a nature run (NR) modeled by the MOCAGE chemical transport model. In three case studies involving large events of different aerosol species, the added value of the HSRL channel (for measuring aerosol backscatter profiles with respect to simple backscatter measurements) is shown. Observations independent of an a priori lidar ratio assumption, as done typically for simple backscattering instruments, allow probing the vertical structures of aerosol layers without divergence, even in cases of intense episodes. A 5-day study in the case of desert dust completes the study of the added value of the HSRL channel with relative mean bias from the NR of the order of 1.5%. For low abundances, relative errors in the backscatter coefficient profiles may lay between +40% and −40%, with mean biases between +5% and −5%.
Real-Time and Continuous Tracking of Total Phosphorus Using a Ground-Based Hyperspectral Proximal Sensing System
Authors: Li Na , Zhang Yunlin , Shi Kun , Zhang Yibo , Sun Xiao , Wang Weijia , Qian Haiming , Yang Huayin , Niu Yongkang
Published: January 2023

Total phosphorus (TP) is the main limiting factor of eutrophication for most inland waters globally. However, the combination of the limited temporal-spatial ranges of traditional manual sampling, poor spectral resolutions, and weather-vulnerable satellite observations, have yielded great data gaps in TP dynamics in short-lived, extreme episodic, or unpredictable pollution. Hence, a novel ground-based hyperspectral proximal sensing system (GHPSs) with a maximum observation frequency of 20 s and a spectral resolution of 1 nm between 400 and 900 nm was developed for automatic, real-time and continuous observation of TP. Focusing on the GHRSs, a TP machine learning model was developed and validated with ideal accuracy (R2 = 0.97, RMSE = 0.017 mg·L−1, MAPE = 12.8%) using 377 pairs of in situ TP measurements collected from Fuchunjiang Reservoir (FR), Liangxi River (LR), and Lake Taihu (LT). Second-scale TP results showed a low-value stable period followed by a sharp change period in LT during 29–31 October and 1–3 November, respectively. The exponential increase (R2 = 0.65, p < 0.05) on 1 November and the two complete variations with peak values of 0.32 mg·L−1 and 0.42 mg·L−1 were recorded in LT on 2 and 3 November, respectively. Simultaneously, a significant decrease (R2 = 0.57, p < 0.05) over the observation days was observed in LR and no obvious change was observed in FR. High consistency between the GHPSs spectrum data standardized at 574 nm and the measured reflectance in different weather demonstrated the accuracy of the GHPSs spectrum data (R2 > 0.99, slop = 0.98). Short and rapid TP changes were observed within one day in LT and LR based on GHPSs minute scale monitoring, which highlighted the importance of high frequency observations of TP. Several advantages of real-time, high accuracy and wide applicability to complex weather were highlighted for the GHPSs for TP monitoring compared to traditional equipment. Therefore, there are potential applications of the GHPSs in the integrated space-air-ground TP monitoring, as well as emergency monitoring and early-warning systems in the future, and it can raise our awareness of the dynamics and driving mechanisms of water quality for inland waters.
Classification of Conservation Tillage Using Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model
Authors: Jiang Dapeng , Du Jia , Song Kaishan , Zhao Boyu , Zhang Yiwei , Zhang Weijian
Published: January 2023

In the remote sensing monitoring of conservation tillage, the acquisition of remote sensing data with high spatial and temporal resolution is critical. The current optical remote sensing images cannot realize both temporal and spatial resolution, especially under cloud and rain interference. Thus, this study employs the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) to obtain the normalized difference tillage index (NDTI) with both temporal and spatial resolution estimated by Sentinel−2 and MODIS using the Index−then−Blend (IB) and Blend−then−Index (BI) fusion schemes. After comparison, the IB scheme was better than the BI scheme in predicting results and prediction efficiency. The NDTI predicted by ESTARFM and Sentinel−2 on June 12, 2020 was compared. A coefficient of determination R2 of 0.73 and RMSE of 0.000117 was obtained, indicating a high prediction accuracy, which meets the prediction requirements. Based on the predicted ESTARFM NDTI of the study area on May 17, 2021, the maize residue cover (MRC) of the study area was estimated using the previously constructed MRC unary linear regression model. The MRC of the sampling points of the remote sensing images was estimated by verifying the predicted ESTARFM NDTI with the MRC of the sampling points taken in the field extracted by the maximum likelihood classifier, which has a coefficient of determination R2 of 0.78 and RMSE of 0.00676, signifying better prediction results. The proposed method provides considerable data sources for the remote sensing monitoring studies of conservation tillage.
Impact of Land Reclamation on Coastal Water in a Semi-Enclosed Bay
Authors: Tu Min-cheng , Huang Yu-chieh
Published: January 2023

Land reclamation has a profound impact on coastal environments. On the Chinese coast, the new Xiang’an International Airport has been built on newly reclaimed land. The impact of the massive land reclamation project (finished in 2018) on water quality and coast conditions in a nearby semi-enclosed bay is investigated using remotely sensed data. Factors affecting surface water quality and coast conditions are further analyzed using multiple regression. All water quality and coast condition indices show no long-term trend from 2005 to 2021. The suspended solid concentration (with a maximum value of 96.11 mg/L) is much lower than the threshold of 188 mg/L. When considering variations in sediment concentration, the probability that the concentration reaches the threshold is less than 1×10−6%; therefore, suspended solids have little threat to the local oyster-growing industry. The trend of dissolved inorganic nitrogen concentration is steady, implying little alteration to nutrient circulation in the semi-enclosed bay. Within the observation timeframe of 2005–2021, a recent sedimentation trend (surrogated by the normalized difference water index) appears after 2018 but it needs to be confirmed by a longer observation. Statistical models based on multiple regression highlight the following links: (1) the sediment source is outside the bay, (2) the overland runoff from newly claimed land dilutes nutrient concentrations, and (3) the coast conditions are mainly affected by tides and rainfall. Neither actively reclaimed or cumulative reclaimed areas form a direct causal relationship to water quality or coast conditions in the semi-enclosed bay.
A New Drought Monitoring Index on the Tibetan Plateau Based on Multisource Data and Machine Learning Methods
Authors: Cheng Meilin , Zhong Lei , Ma Yaoming , Wang Xian , Li Peizhen , Wang Zixin , Qi Yuting
Published: January 2023

Drought is a major disaster over the Tibetan Plateau (TP) that exerts great impacts on natural ecosystems and agricultural production. Furthermore, most drought indices are only useful for assessing drought conditions on a coarse temporal scale. Drought indices that describe drought evolution at a fine temporal scale are still scarce. In this study, four machine learning methods, including random forest regression (RFR), k-nearest neighbor regression (KNNR), support vector regression (SVR), and extreme gradient boosting regression (XGBR), were used to construct daily drought indices based on multisource remote sensing and reanalysis data. Through comparison with in situ soil moisture (SM) over the TP, our results indicate that the drought index based on the XGBR model outperforms other models (R2 = 0.76, RMSE = 0.11, MAE = 0.08), followed by RFR (R2 = 0.74, RMSE = 0.11, MAE = 0.08), KNNR (R2 = 0.73, RMSE = 0.11, MAE = 0.08) and SVR (R2 = 0.66, RMSE = 0.12, MAE = 0.1). A new daily drought index, the standardized integrated drought index (SIDI), was developed by the XGBR model for monitoring agricultural drought. A comparison with ERA5-Land SM and widely used indices such as SPI-6 and SPEI-6 indicated that the SIDI depicted the dry and wet change characteristics of the plateau well. Furthermore, the SIDI was applied to analyze a typical drought event and reasonably characterize the spatiotemporal patterns of drought evolution, demonstrating its capability and superiority for drought monitoring over the TP. In addition, soil properties accounted for 59.5% of the model output, followed by meteorological conditions (35.8%) and topographic environment (4.7%).
Status of Earth Observation and Remote Sensing Applications in Svalbard
Authors: Jawak Shridhar D. , Pohjola Veijo , Kääb Andreas , Andersen Bo N. , Błaszczyk Małgorzata , Salzano Roberto , Luks Bartłomiej , Enomoto Hiroyuki , Høgda Kjell Arild , Moholdt Geir , Dinessen Frode , Fjæraa Ann Mari
Published: January 2023

Remarkable developments in the fields of earth observation (EO) satellites and remote sensing (RS) technology over the past four decades have substantially contributed to spatial, spectral, and temporal sampling [...]
The Effect of Negative Samples on the Accuracy of Water Body Extraction Using Deep Learning Networks
Authors: Song Jia , Yan Xiangbing
Published: January 2023

Water resources are important strategic resources related to human survival and development. Water body extraction from remote sensing images is a very important research topic for the monitoring of global and regional surface water changes. Deep learning networks are one of the most effective approaches and training data is indispensable for ensuring the network accurately extracts water bodies. The training data for water body extraction includes water body samples and non-water negative samples. Cloud shadows are essential negative samples due to the high similarity between water bodies and cloud shadows, but few studies quantitatively evaluate the impact of cloud shadow samples on the accuracy of water body extraction. Therefore, the training datasets with different proportions of cloud shadows were produced, and each of them includes two types of cloud shadow samples: the manually-labeled cloud shadows and unlabeled cloud shadows. The training datasets are applied on a novel transformer-based water body extraction network to investigate how the negative samples affect the accuracy of the water body extraction network. The evaluation results of Overall Accuracy (OA) of 0.9973, mean Intersection over Union (mIoU) of 0.9753, and Kappa of 0.9747 were obtained, and it was found that when the training dataset contains a certain proportion of cloud shadows, the trained network can handle the misclassification of cloud shadows well and more accurately extract water bodies.