bluuebunny/crossref_metadata_2025 huggingface dataset:
Correction: Bitsi et al. Supramolecular Triblock Copolymers through the Formation of Hydrogen Bonds: Synthesis, Characterization, Association Effects in Solvents of Different Polarity. Polymers 2020, 12, 468
Authors: Bitsi Spyridoula-Lida , Droulia Margarita , Pitsikalis Marinos
Published: August 2022

The authors wish to make the following corrections to the published paper [...]
A QbD Approach to Design and to Optimize the Self-Emulsifying Resveratrol–Phospholipid Complex to Enhance Drug Bioavailability through Lymphatic Transport
Authors: Gausuzzaman Syed Abul Layes , Saha Mithun , Dip Shahid Jaman , Alam Shaiful , Kumar Arup , Das Harinarayan , Sharker Shazid Md. , Rashid Md Abdur , Kazi Mohsin , Reza Hasan Mahmud
Published: August 2022

Objectives: Despite having profound therapeutic value, the clinical application of resveratrol is restrained due to its <1% bioavailability, arising from the extensive fast-pass effect along with enterohepatic recirculation. This study aimed to develop a self-emulsifying formulation capable of increasing the bioavailability of resveratrol via lymphatic transport. Methods: The resveratrol–phospholipid complex (RPC) was formed by the solvent evaporation method and characterized by FTIR, DSC, and XRD analyses. The RPC-loaded self-emulsifying drug delivery system (SEDDS) was designed, developed, and optimized using the QbD approach with an emphasis on resveratrol transport through the intestinal lymphatic pathway. The in vivo pharmacokinetic study was investigated in male Wister Albino rats. Results: The FTIR, DSC, and XRD analyses confirmed the RPC formation. The obtained design space provided robustness of prediction within the 95% prediction interval to meet the CQA specifications. An optimal formulation (desirability value of 7.24) provided Grade-A self-emulsion and exhibited a 48-fold bioavailability enhancement compared to the pure resveratrol. The cycloheximide-induced chylomicron flow blocking approach demonstrated that 91.14% of the systemically available resveratrol was transported through the intestinal lymphatic route. Conclusions: This study suggests that an optimal self-emulsifying formulation can significantly increase the bioavailability of resveratrol through lymphatic transport to achieve the desired pharmacological effects.
In Vivo Bone Tissue Engineering Strategies: Advances and Prospects
Authors: Tsiklin Ilya L. , Shabunin Aleksey V. , Kolsanov Alexandr V. , Volova Larisa T.
Published: August 2022

Reconstruction of critical-sized bone defects remains a tremendous challenge for surgeons worldwide. Despite the variety of surgical techniques, current clinical strategies for bone defect repair demonstrate significant limitations and drawbacks, including donor-site morbidity, poor anatomical match, insufficient bone volume, bone graft resorption, and rejection. Bone tissue engineering (BTE) has emerged as a novel approach to guided bone tissue regeneration. BTE focuses on in vitro manipulations with seed cells, growth factors and bioactive scaffolds using bioreactors. The successful clinical translation of BTE requires overcoming a number of significant challenges. Currently, insufficient vascularization is the critical limitation for viability of the bone tissue-engineered construct. Furthermore, efficacy and safety of the scaffolds cell-seeding and exogenous growth factors administration are still controversial. The in vivo bioreactor principle (IVB) is an exceptionally promising concept for the in vivo bone tissue regeneration in a predictable patient-specific manner. This concept is based on the self-regenerative capacity of the human body, and combines flap prefabrication and axial vascularization strategies. Multiple experimental studies on in vivo BTE strategies presented in this review demonstrate the efficacy of this approach. Routine clinical application of the in vivo bioreactor principle is the future direction of BTE; however, it requires further investigation for overcoming some significant limitations.
Fabrication, Property and Application of Calcium Alginate Fiber: A Review
Authors: Zhang Xiaolin , Wang Xinran , Fan Wei , Liu Yi , Wang Qi , Weng Lin
Published: August 2022

As a natural linear polysaccharide, alginate can be gelled into calcium alginate fiber and exploited for functional material applications. Owing to its high hygroscopicity, biocompatibility, nontoxicity and non-flammability, calcium alginate fiber has found a variety of potential applications. This article gives a comprehensive overview of research on calcium alginate fiber, starting from the fabrication technique of wet spinning and microfluidic spinning, followed by a detailed description of the moisture absorption ability, biocompatibility and intrinsic fire-resistant performance of calcium alginate fiber, and briefly introduces its corresponding applications in biomaterials, fire-retardant and other advanced materials that have been extensively studied over the past decade. This review assists in better design and preparation of the alginate bio-based fiber and puts forward new perspectives for further study on alginate fiber, which can benefit the future development of the booming eco-friendly marine biomass polysaccharide fiber.
Design and Preparation of Polyimide/TiO2@MoS2 Nanofibers by Hydrothermal Synthesis and Their Photocatalytic Performance
Authors: Chang Zhenjun , Sun Xiaoling , Liao Zhengzheng , Liu Qiang , Han Jie
Published: August 2022

Organic–inorganic nanocomposite fibers can avoid the agglomeration of single nanoparticles and reduce the cost (nanoparticles assembled on the surface of nanofibers), but also can produce new chemical, electrical, optical, and other properties, with a composite synergistic effect. Aromatic polyimide (PI) is a high-performance polymer with a rigid heterocyclic imide ring and an aromatic benzene ring in its macromolecular framework. Due to its excellent mechanical properties, thermal stability, and easy-to-adjust molecular structure, PI has been widely used in electronics, aerospace, automotive, and other industries related to many applications. Here, we report that TiO2 nanorods were grown on polyimide nanofibers by hydrothermal reaction, and MoS2 nanosheets were grown on TiO2 nanorods the same way. Based on theoretical analysis and experimental findings, the possible growth mechanism was determined in detail. Further experiments showed that MoS2 nanosheets were uniformly coated on the surface of TiO2 nanorods. The TiO2 nanorods have photocatalytic activity in the ultraviolet region, but the bandgap of organic/inorganic layered nanocomposites can redshift to visible light and improve their photocatalytic performance.
Cytotoxic and Apoptotic Effect of Rubus chingii Leaf Extract against Non-Small Cell Lung Carcinoma A549 Cells
Authors: Khafagy El-Sayed , Al Saqr Ahmed , Alotaibi Hadil Faris , Abu Lila Amr Selim
Published: August 2022

Rubus chingii is a traditional Chinese medicinal herbal that has been used since ancient times for its great dietary and medicinal values. Recent reports have underscored the promising cytotoxic effect of R. chingii extracts against a wide variety of cancer cells. Therefore, in the current study, we aim to explore the anticancer potential of the Rubus chingii ethanolic leaf extract (RcL-EtOH) against non-small cell lung cancer A549 cells. RcL-EtOH efficiently exerted a cytotoxic effect against A549 cells in a dose dependent manner, whilst, it exhibited non-significant toxic effects on normal murine macrophage cells, signifying its safety against normal cells. The reduced viability of A549 cells was reaffirmed by the acridine orange/ethidium bromide double staining, which confirmed the induction of apoptosis in RcL-EtOH-treated A549 cells. In addition, RcL-EtOH instigated the dissipation of mitochondrial membrane potential (ΔΨm) with mutual escalation in ROS generation in a dose-dependent manner. Furthermore, RcL-EtOH increased caspase-3, caspase-9 levels in A549 cells post-exposure to RcL-EtOH, which was concomitantly followed by altered mRNA expression of apoptotic (anti-apoptotic: Bcl-2, BclXL; pro-apoptotic: Bax, Bad). To sum up, the RcL-EtOH-instigated apoptotic cell death within A549 cells was assumed to be accomplished via targeting mitochondria, triggering increased ROS generation, with subsequent activation of caspase cascade and altering the expression of gene regulating apoptosis. Collectively, RcL-EtOH might represent a plausible therapeutic option for the management of lung cancer.
Whole Transcriptome-Based Study to Speculate upon the Silkworm Yellow Blood Inhibitor (I) Gene and Analyze the miRNA-Mediated Gene Regulatory Network
Authors: Ge Qi , Fan Yixuan , Xu Jia , Chen Liang , Ma Shangshang , Taha Rehab Hosny , Yao Qin , Yuan Yi , Chen Keping
Published: August 2022

White cocoon is developed and used as a natural fiber, and different silkworm strains have different cocoon colors. Natural-colored cocoons are preferred by people, however, the cocoon color mainly settles on sericin and it basically falls off after reeling. Currently, there are no varieties applied to production due to the formation mechanism of cocoon color is not clear. The formation of cocoon color involves multiple gene regulations. Previous studies have shown that the main genes regulating cocoon traits are the yellow blood (Y) gene, yellow blood inhibitor (I) gene, and yellow cocoon (C) gene. Among them, the products of the Y gene and C gene have been studied, but the I gene is still unclear. In this study, the midgut tissues of the yellow (NB) and the white (306) cocoon silkworm were analyzed by whole transcriptome sequencing. The results showed that there are 1639 DE-circRNAs, 70 DE-miRNAs, and 3225 DE-mRNAs, including 1785 up-regulated genes and 1440 down-regulated genes. GO and KEGG annotation results indicated that DE-mRNAs are mainly involved in intracellular transport, signal transduction, lipid transport, and metabolic processes. Two key genes, KWMTBOMO10339 and KWMTBOMO16553, were screened out according to the annotation results, which were involved in amino acid transport and ion exchange function, respectively. The interaction analysis between ncRNA and target genes showed that there were five miRNAs regulating these two genes. The qPCR analysis showed that the I gene was down-regulated, and the miRNA expression profiles were most up-regulated. Therefore, during the yellow and white cocoon formation, KWMTBOMO10339 and KWMTBOMO16553 may be regulated by miRNA, resulting in the non-expression of KWMTBOMO10339 and KWMTBOMO16553 in yellow cocoon silkworm, and the pigment molecules can enter hemolymph from the midgut to form yellow blood, then transport to the middle silk gland to finally form yellow cocoons.
Calcined Coal Gangue Fines as the Substitute for Slag in the Production of Alkali-Activated Cements and Its Mechanism
Authors: Liu Chenxu , Wang Changbai , Wu Jianyang , Gao Mengcheng
Published: August 2022

Nowadays, upcycling coal gangue into aggregates has become a popular method; nevertheless, the utilization of coal gangue fines (CGFs), a secondary waste generated during the production of coal gangue aggregates, is rarely concerned. This paper attempted to upcycle calcined CGFs at 800 °C as the partial substitution of slag to prepare alkali-activated cement (AAC). The variation in macroscopic compressive strength of AAC was studied, and the hydration mechanism of AAC was explored in depth by microstructure. AAC with CGFs shows tremendous advantages in compressive strength and hydration products. In the 10% calcined CGF content, the 3 d, 7 d, and 28 d compressive strengths of AAC displayed pronounced increases of 8%, 25%, and 13%, respectively. The study results showed that CGFs could replace the small amount of slag in AAC while providing a new theoretical guide and technical support for upcycling CGF into helpful material.
“Water Brought Us Together”: A Baptismal Ethic from Flint
Authors: Daley Mosier Kristen , Moore Daniel E. , Saddler Sharon , Timmons Greg , Villarreal Monica , Wymer Andrew
Published: August 2022

What lessons and insights can the broader Church learn from Christian faith leaders in Flint, Michigan, who have been in a sustained struggle for justice amidst the still unfolding impacts of environmental racism? How did the baptismal identities of faith leaders function as a resource in this struggle, and on a broader level how might Christian baptismal practices and theory be a resource for solidarity in the struggle for racial and environmental justice in and beyond Flint? In other words, might the waters of Christian baptism also “bring together” Christians to tangibly resist racism? This essay emerges from a qualitative research project examining baptismal practices amidst the water crisis in Flint, Michigan. Using liberationist methodology and employing conversation as a means of co-constructing theological knowledge, this article seeks to define a baptismal ethic born from the water crisis based upon the lived experiences of Christian leaders and their communities.
From Orthodox Christianity to “Jewish Law”: Unusual Conversion in the Russian Empire of the Early 19th Century
Authors: Khizhaya Tatiana
Published: August 2022

This article is a piece of microhistorical research of a court case investigating religious conversion in Russia in the 1820s. It presents the story of an Orthodox Christian girl who adopted ”Jewish law” and married a Jewish man. The article attempts to define the background and peculiarities of the conversion and clarify the context in which this was taking place. The work uses various methods: narrative, comparative, contextual analysis, text interpretation, etc. Analysis of the court case establishes that the girl’s change of faith was the result of: (1) close contacts with the Jews and lack of social ties within the Christian community; (2) poverty and extremely low social status; (3) lack of “religious capital”. Jewish social assistance practices, ways to legalize a new status, finding a job, and personal freedom turned out to be attractive to the serf woman. The novelty of this study involves the introduction of a previously unknown archival source representing a very rare phenomenon of conversion to Judaism in imperial Russia. In addition, the article presents the paradoxical case of an attempt at re-socialization by transitioning from the dominant confession to the faith of a religious minority and integration into a community whose rights in Russia were heavily curtailed.
The Impact of the COVID-19 Pandemic on the Psychological Well-Being of Catholic Priests in Canada
Authors: Kappler Stephan , Okozi Innocent , Diouf Francois , Hartinger Katharina
Published: August 2022

Among the general population, frontline workers have been identified to be at heightened risk for negative mental health consequences related to the COVID-19 Pandemic. Catholic priests, who minister to approximately 30% of Canadians, in their role as frontline workers, have been profoundly limited in the provision of pastoral care due to public health restrictions. However, little is known about the impact pandemic distress has on this largely understudied population. Four hundred and eleven Catholic priests across Canada participated in an online survey during May and June 2021. Multiple regression analysis examined how depression, anxiety, traumatic impact of events, loneliness, and religious coping style affect the psychological well-being, satisfaction as a priest, and priestly identity of participants. Results demonstrated that pandemic distress significantly impacts the psychological well-being of priest participants. Depression and loneliness surfaced as significant considerations associated with lowered psychological well-being. While neither anxiety nor traumatic distress reached a significance threshold, the religious coping style of participants emerged as an important factor in the psychological well-being of priests. Results of the study contribute to the understanding of how the pandemic has impacted a less visible group of frontline workers.
“Eliminating Social Distinctions” or “Preserving Social Relations”: Two Explanations of Datong in Modern China
Authors: Pi Mimi
Published: August 2022

A Confucian scholar, Kang Youwei, living in the late Qing period imagined a future utopian society called datong which eliminated all social distinctions. To illustrate it, he borrowed and developed the theory of the Three Ages, which first appeared in the Confucian classic Gongyang Commentary on the Spring and Autumn Annals, as well as in the theory of human nature in the Han dynasty. However, one of his students, Chen Huanzhang, made a new explanation of the “Liyun” chapter that greatly differed from his teacher. According to him, datong was a society committed to “preserving social relations”. The different understandings of datong reveals two different patterns of social relations in Confucianism. Besides the traditional wulun pattern, Kang Youwei offered another possible pattern. Although it proved to be a failure in practice, as a theory that discovered many hidden traditions in Confucianism, Kang Youwei’s datong theory is worthy of attention.
Singularity Avoidance for Cart-Mounted Hand-Guided Collaborative Robots: A Variational Approach
Authors: Salvato Erica , Vanzella Walter , Fenu Gianfranco , Pellegrino Felice Andrea
Published: August 2022

Most collaborative robots (cobots) can be taught by hand guiding: essentially, by manually jogging the robot, an operator teaches some configurations to be employed as via points. Based on those via points, Cartesian end-effector trajectories such as straight lines, circular arcs or splines are then constructed. Such methods can, in principle, be employed for cart-mounted cobots (i.e., when the jogging involves one or two linear axes, besides the cobot axes). However, in some applications, the sole imposition of via points in Cartesian space is not sufficient. On the contrary, albeit the overall system is redundant, (i) the via points must be reached at the taught joint configurations, and (ii) the undesirable singularity (and near-singularity) conditions must be avoided. The naive approach, consisting of setting the cart trajectory beforehand (for instance, by imposing a linear-in-time motion law that crosses the taught cart configurations), satisfies the first need, but does not guarantee the satisfaction of the second. Here, we propose an approach consisting of (i) a novel strategy for decoupling the planning of the cart trajectory and that of the robot joints, and (ii) a novel variational technique for computing the former in a singularity-aware fashion, ensuring the avoidance of a class of workspace singularity and near-singularity configurations.
A Review of Earth Observation-Based Drought Studies in Southeast Asia
Authors: Ha Tuyen V. , Huth Juliane , Bachofer Felix , Kuenzer Claudia
Published: August 2022

Drought is a recurring natural climatic hazard event over terrestrial land; it poses devastating threats to human health, the economy, and the environment. Given the increasing climate crisis, it is likely that extreme drought phenomena will become more frequent, and their impacts will probably be more devastating. Drought observations from space, therefore, play a key role in dissimilating timely and accurate information to support early warning drought management and mitigation planning, particularly in sparse in-situ data regions. In this paper, we reviewed drought-related studies based on Earth observation (EO) products in Southeast Asia between 2000 and 2021. The results of this review indicated that drought publications in the region are on the increase, with a majority (70%) of the studies being undertaken in Vietnam, Thailand, Malaysia and Indonesia. These countries also accounted for nearly 97% of the economic losses due to drought extremes. Vegetation indices from multispectral optical remote sensing sensors remained a primary source of data for drought monitoring in the region. Many studies (~21%) did not provide accuracy assessment on drought mapping products, while precipitation was the main data source for validation. We observed a positive association between spatial extent and spatial resolution, suggesting that nearly 81% of the articles focused on the local and national scales. Although there was an increase in drought research interest in the region, challenges remain regarding large-area and long time-series drought measurements, the combined drought approach, machine learning-based drought prediction, and the integration of multi-sensor remote sensing products (e.g., Landsat and Sentinel-2). Satellite EO data could be a substantial part of the future efforts that are necessary for mitigating drought-related challenges, ensuring food security, establishing a more sustainable economy, and the preservation of the natural environment in the region.
Evaluation of CYGNSS Observations for Snow Properties, a Case Study in Tibetan Plateau, China
Authors: Ma Wenxiao , Huang Lingyong , Wu Xuerui , Jin Shuanggen , Bai Weihua , Li Xuanran
Published: August 2022

Snow plays an important role in the water cycle and global climate change, and the accurate monitoring of changes in snow depth is an important task. However, monitoring snow properties is still challenging and unclear, particularly in the Tibetan Plateau, which has rough land and uneven terrain. The traditional monitoring methods have some limitations in monitoring snow depth changes, and the Global Navigation Satellite System-Reflectometry (GNSS-R) provides a new opportunity for snow monitoring. This paper employed data from the Cyclone Global Navigation Satellite System (CYGNSS) to discover the effect of snow properties. Firstly, the observations of CYGNSS were used to find the sensitive to snow properties, and the relationships between signal to noise ratio (SNR), leading edge slope (LES), surface reflectivity (SR), and snow depth were studied and analyzed, respectively. It is found that the correlation between the first two parameters and snow depth is poor, while SR can indicate the changes in snow depth, and is proposed as an indicator of SR change, namely, surface reflectivity–difference ratio factor (SR–DR factor). Furthermore, the long-time series data in the Tibetan Plateau (2018–2019) are used to analyze its effects on the time series of the SR–DR factor, while the influences of the soil freeze/thaw (F/T) process and soil moisture are excluded during the analysis. The results indicate that the SR–DR factor can be a good indicator and discriminator for snow depth. Our work shows that space-borne GNSS-R has the potential for the monitoring of snow properties.
Estimating Large-Scale Interannual Dynamic Impervious Surface Percentages Based on Regional Divisions
Authors: Xu Tianyu , Li Erzhu , Samat Alim , Li Zhiqing , Liu Wei , Zhang Lianpeng
Published: August 2022

Impervious surface information is an important indicator to describe urban development and environmental changes. The substantial increase in impervious surface area will have a significant impact on the regional landscape and environment. Therefore, the timely and accurate acquisition of large-scale impervious surface percentage (LISP) is of great significance for urban management and ecological assessment. However, previous LISP estimation methods often ignored the impact of regional geographic environment and climate differences on remote sensing information, resulting in low overall accuracy and obvious regional differences in the estimated results. Thus, in this study, based on the time-series characteristics of multi-temporal remote sensing images combined with the information on geographical environment and climate heterogeneity, a method of time-series remote sensing image fusion and LISP estimation based on regional divisions was proposed. Firstly, the entire region was divided into several regions according to the spatial differences of Köppen–Geiger climate data and MODIS NDVI time-series data. Subsequently, adaptive time-series image fusion methods and remote sensing feature construction methods were proposed for different regions. Finally, the proposed method was used to estimate the percentage of impervious surfaces in other years in Asia. The results indicate that the overall R2 of each region is better than 0.82, and the estimation models have a good ability to transfer across time and can directly estimate the impervious surface percentage in other years without using additional samples. In addition, compared with other existing impervious surface products, the proposed method has higher overall estimation accuracy and regional consistency.
Robust Multiple-Measurement Sparsity-Aware STAP with Bayesian Variational Autoencoder
Authors: Zhang Chenxi , Zhao Huiliang , Chen Wenchao , Chen Bo , Wang Penghui , Jia Changrui , Liu Hongwei
Published: August 2022

Due to the shortage of independent and identically distributed (i.i.d.) training samples, space−time adaptive processing (STAP) often suffers remarkable performance degradation in the heterogeneous clutter environment. Sparse recovery (SR) techniques have been introduced into STAP for the benefit of the drastically reduced training requirement, but they are incompletely robust for involving the tricky selection of hyper−parameters or the undesirable point estimation for parameters. Given this issue, we incorporate the Multiple−measurement Complex−valued Variational relevance vector machines (MCV) to model the space−time echoes and provide a Gibbs−sampling−based method to estimate posterior distributions of parameters accurately. However, the Gibbs sampler require quantities of iterations, as unattractive as traditional Bayesian type SR−STAP algorithms when the real−time processing is desired. To address this problem, we further develop the Bayesian Autoencoding MCV for STAP (BAMCV−STAP), which builds the generative model according to MCV and approximates posterior distributions of parameters with an inference network pre−trained off−line, to realize fast reconstruction of measurements. Experimental results on simulated and measured data demonstrate that BAMCV−STAP can achieve suboptimal clutter suppression in terms of the output signal to interference plus noise ratio (SINR) loss, as well as the attractive real−time processing property in terms of the convergence rate and computational loads.
Normalized Sand Index for Identification of Bare Sand Areas in Temperate Climates Using Landsat Images, Application to the South of Romania
Authors: Secu Cristian Vasilică , Stoleriu Cristian Constantin , Lesenciuc Cristian Dan , Ursu Adrian
Published: August 2022

The expansion of bare sand surfaces indicates a tendency towards desertfication in certain periods as a result of the improper agricultural use of sand soils and of the significant changes in the climate in the past 30 years. The Normalised Sand Index (NSI) is a new index used to identify bare sand areas and their spatio-temporal evolution in SW Romania. Landsat scenes (1988, 2001, 2019), spectral and soil texture analysis (36 samples), covariates (e.g., soil map), and field observations allowed for the validation of the results. The performance of the NSI was compared with indices from the sand index family (e.g., Normalized Differential Sand Areas Index) and supervised classifications (e.g., Maximum Likelihood Classification) based on 47 random control square areas for which the soil texture is known. A statistical analysis of the NSI showed 23.6% (27,310.14 hectares) of bare sands in 1988, followed by an accelerated increase to 47.2% (54,737.73 hectares) in 2001 because of economic and land-use changes, and a lower increase by 2019, which reached 52.5% (60,852.42 hectares) due to reforestation programs. Compared to the NSI, the bare sand areas obtained with the tested indicator were almost 20% higher. The traditional classification shows smaller areas of bare sands but uses a higher complexity of land use classes, while the producer accuracy values are lower than those of the NSI. The new index has achieved a correct spatial delimitation of soils in the interdune-dune and major riverbed-interfluvial areas, but it is limited to the transition Arenosols-Chernozems by humus content and agrotechnical works. The new spectral index favours bare sand monitoring and is a fast and inexpensive method of observing the desertification trend of temperate sandy agroecosystems in the context of climate change.
Time-Series Monitoring of Dust-Proof Nets Covering Urban Construction Waste by Multispectral Images in Zhengzhou, China
Authors: Li Zilu , Guo Huadong , Zhang Lu , Liang Dong , Zhu Qi , Liu Xvting , Zhou Heng
Published: August 2022

Rapid urbanization has produced a huge amount of construction waste. The operations and consequences of construction and demolition can lead to windblown dust problems, profoundly affecting the living environment of residents. Fortunately, dust-proof nets have been used in construction sites to reduce and prevent pollution by fine particles such as dust, so it is important to monitor and evaluate their efficacy. In this study, Earth observation techniques were used for the extraction and monitoring of solid waste and dust-proof nets. In order to fully perceive the validity and necessity of dust-proof nets for urban air health, we conducted a case study in Zhengzhou, China. We explored the potential of multispectral remote sensing available for monitoring urban construction waste and proposed a multi-layer classification method to identify construction waste and dust-proof nets based on Landsat-8 OLI and Sentinel-2 MSI data, with an average identification accuracy and Kappa coefficient of 96.27% and 0.93 for construction waste in the study area from 2015 to 2020, respectively. In addition, our study revealed the driving factors and impact of temporal variations in regional construction waste areas and dust-proof nets coverage. The results indicate the classification can track municipal solid waste management and changes in air pollutant concentrations and is useful for achieving SDG 11.6 (reduce the adverse per capita environmental impact of cities, including by paying special attention to air quality and municipal and other waste management). This study has the potential to monitor construction waste and dust-proof nets, paving the way for better urban environmental governance and surveillance actions in the future, especially involving big data.
Tree Species Classification of Backpack Laser Scanning Data Using the PointNet++ Point Cloud Deep Learning Method
Authors: Liu Bingjie , Chen Shuxin , Huang Huaguo , Tian Xin
Published: August 2022

To investigate forest resources, it is necessary to identify the tree species. However, it is a challenge to identify tree species using 3D point clouds of trees collected by light detection and ranging (LiDAR). PointNet++, a point cloud deep learning network, can effectively classify 3D objects. It is important to establish high-quality individual tree point cloud datasets when applying PointNet++ to identifying tree species. However, there are different data processing methods to produce sample datasets, and the processes are tedious. In this study, we suggest how to select the appropriate method by designing comparative experiments. We used the backpack laser scanning (BLS) system to collect point cloud data for a total of eight tree species in three regions. We explored the effect of tree height on the classification accuracy of tree species by using different point cloud normalization methods and analyzed the effect of leaf point clouds on classification accuracy by separating the leaves and wood of individual tree point clouds. Five downsampling methods were used: farthest point sampling (FPS), K-means, random, grid average sampling, and nonuniform grid sampling (NGS). Data with different sampling points were designed for the experiments. The results show that the tree height feature is unimportant when using point cloud deep learning methods for tree species classification. For data collected in a single season, the leaf point cloud has little effect on the classification accuracy. The two suitable point cloud downsampling methods we screened were FPS and NGS, and the deep learning network could provide the most accurate tree species classification when the number of individual tree point clouds was in the range of 2048–5120. Our study further illustrates that point-based end-to-end deep learning methods can be used to classify tree species and identify individual tree point clouds. Combined with the low-cost and high-efficiency BLS system, it can effectively improve the efficiency of forest resource surveys.
Monitoring Land Vegetation from Geostationary Satellite Advanced Himawari Imager (AHI)
Authors: Li Shengqi , Han Xiuzhen , Weng Fuzhong
Published: August 2022

For many years, the Advanced Very High-Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) instruments have been widely used to monitor the condition of surface vegetation. Since the polar-orbiting satellite provides limited daily samples on surface, a completed spatial coverage of land vegetation is often relied on over multiple days of observations. In this study, observations from the Japanese geostationary satellite imager Advanced Himawari Imagers (AHI) are used to derive the surface vegetation index. The AHI reflectance at visible and near-infrared bands are first corrected to the surface reflectance by using the 6S radiative transfer model. The AHI surface reflectance from various viewing angles and solar geometry is further normalized to form an angular-independent reflectance by using a BRDF model. Finally, the surface vegetation index is calculated and synthesized from the daytime AHI data. It is found that the high-frequency AHI observations can significantly reduce the impact of clouds on compositing land NDVI and require a shorter time for a complete coverage of surface conditions. Also, a single NDVI image from AHI exhibits spatial distribution similar to that from 16 days of MODIS data.
A Spatial Pattern Extraction and Recognition Toolbox Supporting Machine Learning Applications on Large Hydroclimatic Datasets
Authors: Wang Han , Xuan Yunqing
Published: August 2022

This paper presents the development and applications of a new, open-source toolbox that aims to provide automatic identification and classification of hydroclimatic patterns by their spatial features, i.e., location, size, orientation, and shape, as well as the physical features, i.e., the areal average, total volume, and spatial distribution. The highlights of this toolbox are: (1) incorporating an efficient algorithm for automatically identifying and classifying the spatial features that are linked to hydroclimatic extremes; (2) use as a frontend for supporting AI-based training in tracking and forecasting extremes; and (3) direct support for short-term nowcasting of extreme rainfall via tracking rainstorm centres and movement. The key design and implementation of the toolbox are discussed alongside three case studies demonstrating the application of the toolbox and its potential in helping build machine learning applications in hydroclimatic sciences. Finally, the availability of the toolbox and its source code is included.
Image-Based Obstacle Detection Methods for the Safe Navigation of Unmanned Vehicles: A Review
Authors: Badrloo Samira , Varshosaz Masood , Pirasteh Saied , Li Jonathan
Published: August 2022

Mobile robots lack a driver or a pilot and, thus, should be able to detect obstacles autonomously. This paper reviews various image-based obstacle detection techniques employed by unmanned vehicles such as Unmanned Surface Vehicles (USVs), Unmanned Aerial Vehicles (UAVs), and Micro Aerial Vehicles (MAVs). More than 110 papers from 23 high-impact computer science journals, which were published over the past 20 years, were reviewed. The techniques were divided into monocular and stereo. The former uses a single camera, while the latter makes use of images taken by two synchronised cameras. Monocular obstacle detection methods are discussed in appearance-based, motion-based, depth-based, and expansion-based categories. Monocular obstacle detection approaches have simple, fast, and straightforward computations. Thus, they are more suited for robots like MAVs and compact UAVs, which usually are small and have limited processing power. On the other hand, stereo-based methods use pair(s) of synchronised cameras to generate a real-time 3D map from the surrounding objects to locate the obstacles. Stereo-based approaches have been classified into Inverse Perspective Mapping (IPM)-based and disparity histogram-based methods. Whether aerial or terrestrial, disparity histogram-based methods suffer from common problems: computational complexity, sensitivity to illumination changes, and the need for accurate camera calibration, especially when implemented on small robots. In addition, until recently, both monocular and stereo methods relied on conventional image processing techniques and, thus, did not meet the requirements of real-time applications. Therefore, deep learning networks have been the centre of focus in recent years to develop fast and reliable obstacle detection solutions. However, we observed that despite significant progress, deep learning techniques also face difficulties in complex and unknown environments where objects of varying types and shapes are present. The review suggests that detecting narrow and small, moving obstacles and fast obstacle detection are the most challenging problem to focus on in future studies.
Scanning Inside Volcanoes with Synthetic Aperture Radar Echography Tomographic Doppler Imaging
Authors: Biondi Filippo
Published: August 2022

A problem with synthetic aperture radars (SARs) is that due to the poor penetrating action of electromagnetic waves within solid bodies, the ability to see through distributed targets is precluded. In this context, indeed, imaging is only possible for targets distributed on the scene surface. This work describes an imaging method based on the analysis of micro-motions present in volcanoes and generated by the Earth’s underground heat. Processing the coherent vibrational information embedded in a single SAR image, in the single-look-complex configuration, the sound information is exploited, penetrating tomographic imaging over a depth of about 3 km from the Earth’s surface. Measurement results are calculated by processing a single-look-complex image from the COSMO-SkyMed Second Generation satellite constellation of Vesuvius. Tomographic maps reveal the presence of the magma chamber, together with the main and the secondary volcanic conduits. This technique certainly paves the way for completely new exploitation of SAR images to scan inside the Earth’s surface.
UAV-Based Hyperspectral Imagery for Detection of Root, Butt, and Stem Rot in Norway Spruce
Authors: Allen Benjamin , Dalponte Michele , Ørka Hans Ole , Næsset Erik , Puliti Stefano , Astrup Rasmus , Gobakken Terje
Published: August 2022

Numerous species of pathogenic wood decay fungi, including members of the genera Heterobasidion and Armillaria, exist in forests in the northern hemisphere. Detection of these fungi through field surveys is often difficult due to a lack of visual symptoms and is cost-prohibitive for most applications. Remotely sensed data can offer a lower-cost alternative for collecting information about vegetation health. This study used hyperspectral imagery collected from unmanned aerial vehicles (UAVs) to detect the presence of wood decay in Norway spruce (Picea abies L. Karst) at two sites in Norway. UAV-based sensors were tested as they offer flexibility and potential cost advantages for small landowners. Ground reference data regarding pathogenic wood decay were collected by harvest machine operators and field crews after harvest. Support vector machines were used to classify the presence of root, butt, and stem rot infection. Classification accuracies as high as 76% with a kappa value of 0.24 were obtained with 490-band hyperspectral imagery, while 29-band imagery provided a lower classification accuracy (~60%, kappa = 0.13).
Estimating Fractional Vegetation Cover Changes in Desert Regions Using RGB Data
Authors: Xie Lu , Meng Xiang , Zhao Xiaodi , Fu Liyong , Sharma Ram P. , Sun Hua
Published: August 2022

Fractional vegetation cover (FVC) is an important indicator of ecosystem changes. Both satellite remote sensing and ground measurements are common methods for estimating FVC. However, desert vegetation grows sparsely and scantly and spreads widely in desert regions, making it challenging to accurately estimate its vegetation cover using satellite data. In this study, we used RGB images from two periods: images from 2006 captured with a small, light manned aircraft with a resolution of 0.1 m and images from 2019 captured with an unmanned aerial vehicle (UAV) with a resolution of 0.02 m. Three pixel-based machine learning algorithms, namely gradient enhancement decision tree (GBDT), k-nearest neighbor (KNN) and random forest (RF), were used to classify the main vegetation (woody and grass species) and calculate the coverage. An independent data set was used to evaluate the accuracy of the algorithms. Overall accuracies of GBDT, KNN and RF for 2006 image classification were 0.9140, 0.9190 and 0.9478, respectively, with RF achieving the best classification results. Overall accuracies of GBDT, KNN and RF for 2019 images were 0.8466, 0.8627 and 0.8569, respectively, with the KNN algorithm achieving the best results for vegetation cover classification. The vegetation coverage in the study area changed significantly from 2006 to 2019, with an increase in grass coverage from 15.47 ± 1.49% to 27.90 ± 2.79%. The results show that RGB images are suitable for mapping FVC. Determining the best spatial resolution for different vegetation features may make estimation of desert vegetation coverage more accurate. Vegetation cover changes are also important in terms of understanding the evolution of desert ecosystems.
Evaluation of Methods for Estimating Lake Surface Water Temperature Using Landsat 8
Authors: Dyba Krzysztof , Ermida Sofia , Ptak Mariusz , Piekarczyk Jan , Sojka Mariusz
Published: August 2022

Changes in lake water temperature, observed with the greatest intensity during the last two decades, may significantly affect the functioning of these unique ecosystems. Currently, in situ studies in Poland are conducted only for 38 lakes using the single-point method. The aim of this study was to develop a method for remote sensing monitoring of lake water temperature in a spatio-temporal context based on Landsat 8 imagery. For this purpose, using data obtained for 28 lakes from the period 2013–2020, linear regression (LM) and random forest (RF) models were developed to estimate surface water temperature. In addition, analysis of Landsat Level-2 Surface Temperature Science Product (LST-L2) data provided by United States Geological Survey (USGS) and the National Aeronautics and Space Administration (NASA) was performed. The remaining 10 lakes not previously used in the model development stage were used to validate model performance. The results showed that the most accurate estimation is possible using the RF method for which RMSE = 1.83 °C and R2 = 0.89, while RMSE = 3.68 °C and R2 = 0.8 for the LST-L2 method. We found that LST-L2 contains a systematic error in the coastal zone, which can be corrected and eventually improve the quality of estimation. The satellite-based method makes it possible to determine water temperature for all lakes in Poland at different times and to understand the influence of climatic factors affecting temperature at the regional scale. On the other hand, spatial presentation of thermics within individual lakes enables understanding the influence of local factors and morphometric conditions.
Orographic Construction of a Numerical Weather Prediction Spectral Model Based on ASTER Data and Its Application to Simulation of the Henan 20·7 Extreme Rainfall Event
Authors: Wang Yingjie , Wu Jianping , Yang Xiangrong , Peng Jun , Pan Xiaotian
Published: August 2022

Numerical weather prediction (NWP) has become an important method of predicting extreme weather events, but orography is one of the key factors affecting the performance of NWPs. In this paper, based on Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) elevation data, a method for constructing a global orographic dataset suitable for NWP spectral models is investigated. The Yin-He global spectrum model (YHGSM) is used to simulate the early and peak periods of the extreme rainfall event on 20 July 2021 in Henan Province, China, and the heavy rain in Beijing in order to verify the effectiveness and superiority of the proposed orographic construction method. It is demonstrated that in a few cases the direct two-dimensional filter can sometimes simulate more intense rainfall, but in general, the bidirectional one-dimensional filter is better than the direct two-dimensional filter in orographic processing, and the bidirectional one-dimensional filter can filter out more of the small-scale orographic information. The effect of the higher orographic resolution before conversion to spectral space is not very obvious, but it is demonstrated that the simulation results are better for the heavy-rainfall level. In conclusion, in most cases, the simulations conducted using the new global orographic dataset based on ASTER data are better than those obtained using the model’s original orography, especially for torrential and extreme rainfall. These conclusions provide a reference for future predictions of and research on extreme rainfall events.
Crop Mapping Using the Historical Crop Data Layer and Deep Neural Networks: A Case Study in Jilin Province, China
Authors: Jiang Deyang , Chen Shengbo , Useya Juliana , Cao Lisai , Lu Tianqi
Published: August 2022

Machine learning combined with satellite image time series can quickly, and reliably be implemented to map crop distribution and growth monitoring necessary for food security. However, obtaining a large number of field survey samples for classifier training is often time-consuming and costly, which results in the very slow production of crop distribution maps. To overcome this challenge, we propose an ensemble learning approach from the existing historical crop data layer (CDL) to automatically create multitudes of samples according to the rules of spatiotemporal sample selection. Sentinel-2 monthly composite images from 2017 to 2019 for crop distribution mapping in Jilin Province were mosaicked and classified. Classification accuracies of four machine learning algorithms for a single-month and multi-month time series were compared. The results show that deep neural network (DNN) performed the best, followed by random forest (RF), then decision tree (DT), and support vector machine (SVM) the least. Compared with other months, July and August have higher classification accuracy, and the kappa coefficients of 0.78 and 0.79, respectively. Compared with a single phase, the kappa coefficient gradually increases with the growth of the time series, reaching 0.94 in August at the earliest, and then the increase is not obvious, and the highest in the whole growth cycle is 0.95. During the mapping process, time series of different lengths produced different classification results. Wetland types were misclassified as rice. In such cases, authors combined time series of two lengths to correct the misclassified rice types. By comparing with existing products and field points, rice has the highest consistency, followed by corn, whereas soybeans have the least consistency. This shows that the generated sample data set and trained model in this research can meet the crop mapping accuracy and simultaneously reduce the cost of field surveys. For further research, more years and types of crops should be considered for mapping and validation.