bluuebunny/crossref_metadata_2025 huggingface dataset:
Influence of Cultivar and Biocontrol Treatments on the Effect of Olive Stem Extracts on the Viability of Verticillium dahliae Conidia
Authors: López-Moral Ana , Agustí-Brisach Carlos , Leiva-Egea Francisco M. , Trapero Antonio
Published: February 2022

The effect of olive (Olea europaea) stem extract (OSE) on the viability of conidia of Verticillium dahliae, the causal agent of Verticillium wilt of olive (VWO), is not yet well understood. Thus, the aim of this study was to determine the influence of the olive genotype (cultivar resistance) and the interaction between olive cultivars and biocontrol treatments on the effect of OSE on conidial germination of V. dahliae by in vitro sensitivity tests. To this end, OSE from cultivars Frantoio, Arbequina, and Picual, respectively tolerant, moderately susceptible, and highly susceptible to V. dahliae, were tested alone or after treatments with biological control agents (BCAs) and commercial products efficient at reducing the progress of VWO. Aureobasidium pullulans strain AP08, Phoma sp. strain ColPat-375, and Bacillus amyloliquefaciens strain PAB-24 were considered as BCAs. Aluminium lignosulfonate (IDAI Brotaverd®), copper phosphite (Phoscuprico®), potassium phosphite (Naturfos®), and salicylic acid were selected as commercial products. Our results indicate that the influence of biological treatments against the pathogen depends on the genotype, since the higher the resistance of the cultivar, the lower the effect of the treatments on the ability of OSE to inhibit the germination of conidia. In ‘Picual’, the BCA B. amyloliquefaciens PAB024 and copper phosphite were the most effective treatments in inhibiting conidia germination by the OSE. This work represents a first approach to elucidate the role of cultivar and biological treatments in modifying the effect on the pathogen of the endosphere content of olive plants.
Suberin Biosynthesis, Assembly, and Regulation
Authors: Woolfson Kathlyn N. , Esfandiari Mina , Bernards Mark A.
Published: February 2022

Suberin is a specialized cell wall modifying polymer comprising both phenolic-derived and fatty acid-derived monomers, which is deposited in below-ground dermal tissues (epidermis, endodermis, periderm) and above-ground periderm (i.e., bark). Suberized cells are largely impermeable to water and provide a critical protective layer preventing water loss and pathogen infection. The deposition of suberin is part of the skin maturation process of important tuber crops such as potato and can affect storage longevity. Historically, the term “suberin” has been used to describe a polyester of largely aliphatic monomers (fatty acids, ω-hydroxy fatty acids, α,ω-dioic acids, 1-alkanols), hydroxycinnamic acids, and glycerol. However, exhaustive alkaline hydrolysis, which removes esterified aliphatics and phenolics from suberized tissue, reveals a core poly(phenolic) macromolecule, the depolymerization of which yields phenolics not found in the aliphatic polyester. Time course analysis of suberin deposition, at both the transcriptional and metabolite levels, supports a temporal regulation of suberin deposition, with phenolics being polymerized into a poly(phenolic) domain in advance of the bulk of the poly(aliphatics) that characterize suberized cells. In the present review, we summarize the literature describing suberin monomer biosynthesis and speculate on aspects of suberin assembly. In addition, we highlight recent advances in our understanding of how suberization may be regulated, including at the phytohormone, transcription factor, and protein scaffold levels.
Identification of the Potential Genes Regulating Seed Germination Speed in Maize
Authors: Zhang Huairen , Zang Jie , Huo Yanqing , Zhang Zhaogui , Chen Huabang , Chen Xunji , Liu Juan
Published: February 2022

Seed germination is the crucial stage in plant life cycle. Rapid and uniform germination plays an essential role in plant development and grain yield improvement. However, the molecular mechanism underlying seed germination speed is largely unknown due to the complexity of the dynamic process and the difficulty in phenotyping. Here, we conducted a time-series comparative transcriptome study of two elite maize inbred lines, 72-3 and F9721, with striking difference in seed germination speed, and identified a major locus underlying maize germination speed through genome-wide association analysis (GWAS) of an F2 segregation population. Comparative transcriptome study identified 12 h after imbibition (HAI) as the critical stage responsible for the variation in germination speed. The differentially expressed genes (DEGs) between 72-3 and F9721 were mainly enriched in metabolic pathways, biosynthesis of secondary metabolites, oxidoreductase activity pathways, hormone signal transduction, and amino acid transporter activity pathways. GWAS revealed that germination speed was controlled by a major locus on chromosome 1 with the leading SNP as AX-91332814, explaining 10.63% of phenotypic variation. A total of 87 proposed protein-coding genes surrounding the locus were integrated with DEGs. Combined with evidence from the gene expression database and gene synteny with other model species, we finally anchored three genes as the likely candidates regulating germination speed in maize. This study provides clues for the further exploration of genes controlling the maize seed germination speed, thus facilitating breeding of rapid germinated elite lines through marker assistant selection.
NMR Profiling of Reaction and Transport in Thin Layers: A Review
Authors: Nicasy Ruben , Huinink Henk , Erich Bart , Olaf Adan
Published: February 2022

Reaction and transport processes in thin layers of between 10 and 1000 µm are important factors in determining their performance, stability and degradation. In this review, we discuss the potential of high-gradient Nuclear Magnetic Resonance (NMR) as a tool to study both reactions and transport in these layers spatially and temporally resolved. As the NMR resolution depends on gradient strength, the high spatial resolution required in submillimeter layers can only be achieved with specially designed high-gradient setups. Three different high-gradient setups exist: STRAFI (STRay FIeld), GARField (Gradient-At-Right-angles-to-Field) and MOUSE (MObile Universal Surface Explorer). The aim of this review is to provide a detailed overview of the three techniques and their ability to visualize reactions and transport processes using physical observable properties such as hydrogen density, diffusion, T1- and T2-relaxation. Finally, different examples from literature will be presented to illustrate the wide variety of applications that can be studied and the corresponding value of the techniques.
Data-Driven Modelling of Polyethylene Recycling under High-Temperature Extrusion
Authors: Castéran Fanny , Delage Karim , Hascoët Nicolas , Ammar Amine , Chinesta Francisco , Cassagnau Philippe
Published: February 2022

Two main problems are studied in this article. The first one is the use of the extrusion process for controlled thermo-mechanical degradation of polyethylene for recycling applications. The second is the data-based modelling of such reactive extrusion processes. Polyethylenes (high density polyethylene (HDPE) and ultra-high molecular weight polyethylene (UHMWPE)) were extruded in a corotating twin-screw extruder under high temperatures (350 °C < T < 420 °C) for various process conditions (flow rate and screw rotation speed). These process conditions involved a decrease in the molecular weight due to degradation reactions. A numerical method based on the Carreau-Yasuda model was developed to predict the rheological behaviour (variation of the viscosity versus shear rate) from the in-line measurement of the die pressure. The results were successfully compared to the viscosity measured from offline measurement assuming the Cox-Merz law. Weight average molecular weights were estimated from the resulting zero-shear rate viscosity. Furthermore, the linear viscoelastic behaviours (Frequency dependence of the complex shear modulus) were also used to predict the molecular weight distributions of final products by an inverse rheological method. Size exclusion chromatography (SEC) was performed on five samples, and the resulting molecular weight distributions were compared to the values obtained with the two aforementioned techniques. The values of weight average molecular weights were similar for the three techniques. The complete molecular weight distributions obtained by inverse rheology were similar to the SEC ones for extruded HDPE samples, but some inaccuracies were observed for extruded UHMWPE samples. The Ludovic® (SC-Consultants, Saint-Etienne, France) corotating twin-screw extrusion simulation software was used as a classical process simulation. However, as the rheo-kinetic laws of this process were unknown, the software could not predict all the flow characteristics successfully. Finally, machine learning techniques, able to operate in the low-data limit, were tested to build predicting models of the process outputs and material characteristics. Support Vector Machine Regression (SVR) and sparsed Proper Generalized Decomposition (sPGD) techniques were chosen to predict the process outputs successfully. These methods were also applied to material characteristics data, and both were found to be effective in predicting molecular weights. More precisely, the sPGD gave better results than the SVR for the zero-shear viscosity prediction. Stochastic methods were also tested on some of the data and showed promising results.
NIPAm-Based Modification of Poly(L-lysine): A pH-Dependent LCST-Type Thermo-Responsive Biodegradable Polymer
Authors: Stamou Aggeliki , Iatrou Hermis , Tsitsilianis Constantinos
Published: February 2022

Polylysine is a biocompatible, biodegradable, water soluble polypeptide. Thanks to the pendant primary amines it bears, it is susceptible to modification reactions. In this work Poly(L-lysine) (PLL) was partially modified via the effortless free-catalysed aza-Michael addition reaction at room temperature by grafting N-isopropylacrylamide (NIPAm) moieties onto the amines. The resulting PLL-g-NIPAm exhibited LCST-type thermosensitivity. The LCST can be tuned by the NIPAm content incorporated in the macromolecules. Importantly, depending on the NIPAm content, LCST is highly dependent on pH and ionic strength due to ionization capability of the remaining free lysine residues. PLL-g-NIPAm constitutes a novel biodegradable LCST polymer that could be used as “smart” block in block copolymers and/or terpolymers, of any macromolecular architecture, to design pH/Temperature-responsive self-assemblies (nanocarriers and/or networks) for potential bio-applications.
Coating of Silicone Monofilaments with Elastic Carbon Black-Silver-Silicone Layers and Their Characterization Especially with Regard to the Change of the Electrical Resistance in Dependence on Strain
Authors: Klinkhammer Kristina , Nolden Ramona , Brendgen Rike , Niemeyer Manuela , Zöll Kerstin , Schwarz-Pfeiffer Anne
Published: February 2022

Smart textiles have properties that outperform the conventional protective and decorative function of textiles. By integrating special sensors into clothing, body functions and movements can be detected. Piezoresistive sensors measure a change in electrical resistance due to the application of force in the form of stretching, pressure or bending. In order to manufacture such sensors, conventional non-conductive textile materials need to be made conductive by finishing processes. Therefore, a non-conductive silicone monofilament was coated with a conductive carbon silicone and additional silver-containing components and investigated for its suitability as a strain sensor. The changes in electrical resistance and the gauge factor as a measure of the sensitivity of a sensor were measured and calculated. In this publication, the electrical properties of such a filament-based sensor in the context of particle composition and concentration are discussed. The electrical resistance was already significantly reduced in a first step by coating with conductive carbon silicone (145 kΩ). The addition of silver-containing components further reduced the electrical resistance in a second step. Thereby, flat flakes of silver proved to be much more effective than silver-containing particles (5 kΩ at 20% addition). The former was easier to integrate into the coating and formed contact surfaces with each other at higher concentrations. Stretching the samples increased the resistance by enlarging the distance between the conductive components. With 30% silver-coated glass flakes in the coating, the highest gauge factor of 0.33 was achieved. Consequently, the changes in electrical resistance during stretching can be exploited to detect motion and the gauge factor indicates that even small changes in strain can be detected, so the herein developed coated monofilaments are suggested for use as strain sensors. Future work includes matching the particle composition and concentration to the exact application and investigating the sensors in the field.
Sustainable Cellulose-Aluminum-Plastic Composites from Beverage Cartons Scraps and Recycled Polyethylene
Authors: Bonadies Irene , Capuano Roberta , Avolio Roberto , Castaldo Rachele , Cocca Mariacristina , Gentile Gennaro , Errico Maria
Published: February 2022

The sustainable management of multilayer paper/plastic waste is a technological challenge due to its composite nature. In this paper, a mechanical recycling approach for multilayer cartons (MC) is reported, illustrating the realization of thermoplastic composites based on recycled polyethylene and an amount of milled MC ranging from 20 to 90 wt%. The effect of composition of the composites on the morphology and on thermal, mechanical, and water absorption behavior was investigated and rationalized, demonstrating that above 80 wt% of MC, the fibrous nature of the filler dominates the overall properties of the materials. A maleated polyethylene was also used as a coupling agent and its effectiveness in improving mechanical parameters of composites up to 60 wt% of MC was highlighted.
Study on Bamboo Longitudinal Flattening Technology
Authors: Yuan Tiancheng , Zhang Tao , Huang Yaqian , Wu Yifei , Wang Xinzhou , Li Yanjun
Published: February 2022

In this paper, we introduced a bamboo longitudinal flattening technology and analyzed the effects of the softening–flattening process on the physical and mechanical properties of moso bamboo. This is a newer bamboo processing technology that can enhance the utilization and reduce pollution compared with traditional bamboo-based products. Results showed that the parenchyma cells distorted and compacted due to the flattening process. The hemicellulose and cellulose content decreased, while the content of lignin presented an increasing tendency. As expected, the dimensional stability of moso bamboo enhanced due to the decrement of hemicellulose. The softening–flattening process positively contributed to the micro-mechanical properties of treated bamboo specimens. For example, the hardness and modulus of elasticity of the untreated bamboo sample increased from 0.58 and 15.7 GPa to 0.8 and 17.5 GPa, respectively. In addition, the changes in cellulose crystallinity and mechanical properties were also investigated in this paper. The cellulose crystallinity increased from 37.5% to 43.2%, significantly. However, the modulus of rupture of the flattened bamboo board decreased from 9000 to 7500 MPa due to the grooves made by the flattening roller. The MOE of flattening bamboo board showed the same decreasing tendency.
Cu(II)/Guanidine Functionalized Disiloxane Complex of Supramolecular Structures for Visible Light-Driven Photocatalysis of Congo Red
Authors: Fortună Maria E. , Pricop Lucia , Zaltariov Mirela , Popovici Dumitru , Ignat Maria , Harabagiu Valeria , Simionescu Bogdan C.
Published: February 2022

The present study focuses on the synthesis of a new guanidine-functionalized disiloxane used as a ligand to obtain copper(II) complexes linked through hydrogen bonding into supramolecular structures. A two-step procedure was used to prepare the guanidine functionalized disiloxane ligand. Firstly, the hydrosilylation reaction between the siloxane precursor, namely 1,1,3,3-tetramethyldisiloxane (DS), and the allyl glycidyl ether (AGE) was performed in the presence of a platinum catalyst resulting in glycidoxypropyldisiloxane (DS-PMO) intermediary compound. In the second step, DS-PMO derivative was modified with 1,1,3,3-tetramethyl guanidine (TMGu) to obtain the guanidine-functionalized disiloxane ligand (bGu-DS) that was further used for the coordination of copper(II) acetate hydrate. The structures of the ligand and of its Cu(II) complex were confirmed by spectral methods (IR, UV-Vis, NMR, XRF) and correlated with theoretical calculations using semi-empirical PM6 and DFT methods. The copper(II) complex was found to exhibit low optical band gap energy (2.9 eV) and good photocatalytic activity under visible light irradiation in the decomposition of Congo Red (CR). A dye removal efficiency higher than 97% at the catalyst and CR concentrations of 1 and, respectively, 200 mg/L was obtained.
Flame Retardancy and Thermal Degradation Behaviors of Thiol-Ene Composites Containing a Novel Phosphorus and Silicon-Containing Flame Retardant
Authors: Wu Fangyi , Bao Xiaohui , Wang Jiangbo
Published: February 2022

In this article, a novel phosphorus and silicon-containing flame retardant (DOPO-V-PA) was synthesized via condensation reaction and then added into thiol-ene (TE) to prepare a flame-retardant composite. The results of cone calorimeter measurement demonstrated that, compared with pure TE, 22.7% and 53.2% reduction of TE/DOPO-V-PA (thiol-ene/9,10-dihydro-9-oxa-10-phosphaphenanthrene-10-oxide-vinyltrimethoxysilane-phenyltrimethoxysilane-(3-aminopropyl)trimethoxysilane copolymer) was found for the peak heat release rate (PHRR) and total heat release (THR), respectively. The thermal degradation of TE composites was investigated by the TGA measurement under non-isothermal conditions, and kinetic parameters were both calculated by the Kissinger and Flynn-Wall-Ozawa methods. It was indicated that the activation energies of TE at conversions exceeding 50% were enhanced by the incorporation of DOPO-V-PA for the whole conversion range.
Characteristics Description of Shale Fracture Surface Morphology: A Case Study of Shale Samples from Barnett Shale
Authors: Qu Guanzheng , Shi Tao , Zhang Zheng , Su Jian , Wei Haitao , Guo Rui , Peng Jiao , Zhao Kairui
Published: February 2022

Shale reservoirs are the hot issue in unconventional resources. The key to the development of shale reservoirs lies in the complex fractures, which are the only path for fluid to migrate from the matrix to the wellbore in shale reservoirs. Therefore, the characteristics of shale fracture surface morphology directly affect fluid migration in shale reservoirs. However, there are few reports about the characteristics of shale fracture surface morphology as the parallel plate model was commonly used to characterize the fracture, neglecting its surface morphology characteristics and leading to great deviation. Thus, description methods were introduced to characterize shale fracture surface morphology with the aim to provide a foundation for the development of shale resources. Three shale samples were fractured by the Brazilian test, and the height distribution of the fracture surface was captured by a three-dimensional profilometer. Then, three-dimensional fracture surface morphology was regarded as a set of two-dimensional profiles, which converted three-dimensional information into two-dimensional data. Roughness, joint roughness coefficient, fractal dimension, tortuosity, and dip angle were employed to characterize shale fracture surface morphology, and their calculation methods were also, respectively, proposed. It was found that roughness, joint roughness coefficient, fractal dimension, tortuosity, and dip angle were all directional, and they varied greatly along with different directions. Roughness, joint roughness coefficient, fractal dimension, tortuosity, absolute dip angle, and overall trend dip angle were among 0.0834–0.2427 mm, 2.5715–10.9368, 2.1000–2.1364, 1.0732–1.1879, 17.7498°–24.5941°, and −3.7223°–13.3042°, respectively. Joint roughness coefficient, fractal dimension, tortuosity, and dip angle were all positively correlated with roughness.
Microbiological Control in Decontamination of Sludge from Wastewater Treatment Plant
Authors: Malcheva Boyka Z. , Petrov Petar G. , Stefanova Veneta V.
Published: February 2022

Dynamics of the microbiological indicators of fresh sludge from wastewater treatment plants with a concentration of CaO, 10% and 20%, and ash, −30% and 50%, and treated with quicklime, ash, and microbial fertilizer for a 50-day period of composting were studied. The influence of temperature, water content, and oxygen on the development of microbes was established in laboratory conditions. Microbiological analysis included the determination of non-pathogenic (non-spore-forming bacteria, bacilli, actinomycetes, micromycetes, bacteria digesting mineral nitrogen), and pathogenic (Salmonella, Listeria, Escherichia coli, Enterococcus, Clostridium perfringens) microorganisms. Of the beneficial microflora in the sludge before treating, the amount of non-spore-forming bacteria was the highest, followed by bacilli and micromycetes. It was found that actinomycetes were absent in the untreated sludge. Clostridium perfringens occupied a major share in the composition of the pathogenic microflora, followed by Escherichia coli, Enterococcus, and Listeria. The best results for decontamination of the sludge were obtained by adding 20% quicklime and 50% ash. Alkalization of the sludge after treatment led to the destruction of pathogenic microflora but also reduced the number of beneficial microorganisms. The decrease in pH during the study period determined the redevelopment of pathogenic microflora. Combined variants with lime or ash and microbial fertilizer showed better results for the development of non-pathogenic microflora and the destruction of pathogens.
Is the Number of Appointments for Complete Denture Fabrication Reduced with CAD-CAM? A Literature Review
Authors: Villias Aristeidis , Karkazis Hercules , Yannikakis Stavros , Artopoulou Ioli , Polyzois Gregory
Published: February 2022

One of the key arguments in favor of digitally produced complete dentures (CDs) is the requirement for less patient visits in comparison to the conventional workflow. However, it is not yet clear if this argument is accurate; nor, if indeed the insertion of the complete dentures is achieved in fewer appointments, how many are required. The purpose of this literature review was to investigate the reported number of required patient visits for the production of digitally fabricated CDs. An electronic search was performed in PubMed/MEDLINE using three groups of keywords: “complete dentures”, “CAD/CAM”, and “Appointments” with their alternative forms. Out of the initial 157 results, 36 articles were automatically selected utilizing exclusion keywords. After consensus between the two examiners, eight articles were finally analyzed and presented in a table. The majority (75%) of the reports came from institutions, and the average number of appointments up to complete denture insertion was 4.1, not always including try-in dentures. In this study, it can be concluded that, with a digital workflow, the insertion appointment is reached in fewer visits than the conventional five-visit procedure which is commonly taught in dental schools.
Ezekiel, Daniel, and Christian Diet Culture
Authors: Davis Stacy
Published: February 2022

The books of Ezekiel and Daniel, specifically Ezek 4.9 and Daniel 1, 3, and 6, are now being used to market healthy eating and diet plans to Christians, especially evangelical Christians, in ways that are the opposite of how the texts appear in their historical and literary contexts. Such usage is a potentially problematic example of prophetic reception history and its contemporary significance because the language in these plans is the same language found in secular diet plans with biblical prooftexts added to them. The addition may actually make the plans even more problematic by linking weight and fitness to religion and spirituality.
Semantic Segmentation (U-Net) of Archaeological Features in Airborne Laser Scanning—Example of the Białowieża Forest
Authors: Banasiak Paweł , Berezowski Piotr , Zapłata Rafał , Mielcarek Miłosz , Duraj Konrad , Stereńczak Krzysztof
Published: February 2022

Airborne Laser Scanning (ALS) technology can be used to identify features of terrain relief in forested areas, possibly leading to the discovery of previously unknown archaeological monuments. Spatial interpretation of numerous objects with various shapes and sizes is a difficult challenge for archaeologists. Mapping structures with multiple elements whose area can exceed dozens of hectares, such as ancient agricultural field systems, is very time-consuming. These archaeological sites are composed of a large number of embanked fields, which together form a recognizable spatial pattern. Image classification and segmentation, as well as object recognition, are the most important tasks for deep learning neural networks (DLNN) and therefore they can be used for automatic recognition of archaeological monuments. In this study, a U-Net neural network was implemented to perform semantic segmentation of the ALS-derived data including (1) archaeological, (2) natural and (3) modern features in the Polish part of the Białowieża Forest. The performance of the U-Net segmentation model was evaluated by measuring the pixel-wise similarity between ground truth and predicted segmentation masks. After 83 epochs, The Dice-Sorensen coefficient (F1 score) and the Intersect Over Union (IoU) metrics were 0.58 and 0.5, respectively. The IoU metric reached a value of 0.41, 0.62 and 0.62 for the ancient field system banks, ancient field system plots and burial mounds, respectively. The results of the U-Net deep learning model proved very useful in semantic segmentation of images derived from ALS data.
The Evaluation of Color Spaces for Large Woody Debris Detection in Rivers Using XGBoost Algorithm
Authors: Liang Min-Chih , Tfwala Samkele S. , Chen Su-Chin
Published: February 2022

Large woody debris (LWD) strongly influences river systems, especially in forested and mountainous catchments. In Taiwan, LWD are mainly from typhoons and extreme torrential events. To effectively manage the LWD, it is necessary to conduct regular surveys on river systems. Simple, low cost, and accurate tools are therefore necessary. The proposed methodology applies image processing and machine learning (XGBoost classifier) to quantify LWD distribution, location, and volume in river channels. XGBoost algorithm was selected due to its scalability and faster execution speeds. Nishueibei River, located in Taitung County, was used as the area of investigation. Unmanned aerial vehicles (UAVs) were used to capture the terrain and LWD. Structure from Motion (SfM) was used to build high-resolution orthophotos and digital elevation models (DEM), after which machine learning and different color spaces were used to recognize LWD. Finally, the volume of LWD in the river was estimated. The findings show that RGB color space as LWD recognition factor suffers serious collinearity problems, and it is easy to lose some LWD information; thus, it is not suitable for LWD recognition. On the contrary, the combination of different factors in different color spaces enhances the results, and most of the factors are related to the YCbCr color space. The CbCr factor in the YCbCr color space was best for identifying LWD. LWD volume was then estimated from the identified LWD using manual, field, and automatic measurements. The results indicate that the manual measurement method was the best (R2 = 0.88) to identify field LWD volume. Moreover, automatic measurement (R2 = 0.72) can also obtain LWD volume to save time and workforce.
Prospects for Reconstructing Daily Runoff from Individual Upstream Remotely-Sensed Climatic Variables
Authors: Fok Hok Sum , Chen Yutong , Zhou Linghao
Published: February 2022

Basin water supply, planning, and its allocation requires runoff measurements near an estuary mouth. However, insufficient financial budget results in no further runoff measurements at critical in situ stations. This has recently promoted the runoff reconstruction via regression between the runoff and nearby remotely-sensed variables on a monthly scale. Nonetheless, reconstructing daily runoff from individual basin-upstream remotely-sensed climatic variables is yet to be explored. This study investigates standardized data regression approach to reconstruct daily runoff from the individual remotely-sensed climatic variables at the Mekong Basin’s upstream. Compared to simple linear regression, the daily runoff reconstructed and forecasted from the presented approach were improved by at most 5% and 10%, respectively. Reconstructed runoffs using neural network models yielded ~0.5% further improvement. The improvement was largely a function of the reduced discrepancy during dry and wet seasons. The best forecasted runoff obtained from the basin-upstream standardized precipitation index, yielded the lowest normalized root-mean-square error of 0.093.
Extending the GOSAILT Model to Simulate Sparse Woodland Bi-Directional Reflectance with Soil Reflectance Anisotropy Consideration
Authors: Cheng Juan , Wen Jianguang , Xiao Qing , Wu Shengbiao , Hao Dalei , Liu Qinhuo
Published: February 2022

Anisotropic canopy reflectance plays a crucial role in estimating vegetation biophysical parameters, whereas soil reflectance anisotropy affects canopy reflectance. However, woodland canopy bidirectional reflectance distribution function (BRDF) models considering soil anisotropy are far from universal, especially for the BRDF models of mountain forest. In this study, a mountain forest canopy model, named geometric-optical and mutual shadowing and scattering from arbitrarily inclined-leaves model coupled with topography (GOSAILT), was extended to consider the soil anisotropic reflectance characteristics by introducing the simple soil directional (SSD) reflectance model. The modified GOSAILT model (named GOSAILT-SSD) was evaluated using unmanned aerial vehicle (UAV) field observations and discrete anisotropic radiative transfer (DART) simulations. Then, the effects of Lambertian soil assumption on simulating the vi-directional reflectance factor (BRF) were evaluated across different fractions of vegetation cover (Cv), view zenith angles (VZA), solar zenith angles (SZA), and spectral bands with the GOSAILT-SSD model. The evaluation results, with the DART simulations, show that the performance of the GOSAILT-SSD model in simulating canopy BRF is significantly improved, with decreasing RMSE, from 0.027 to 0.017 for the red band and 0.051 to 0.037 for the near-infrared (NIR) band. Meanwhile, the GOSAILT-SSD simulations show high consistency with UAV multi-angular observations (R2 = 0.97). Besides, it is also found that the BRF simulation errors caused by Lambertian soil assumption are too large to be neglected, with a maximum relative bias of about 45% for the red band. This inappropriate assumption results in a remarkable BRF underestimation near the hot spot direction and an obvious BRF overestimation for large VZA in the solar principal plane (PP). Meanwhile, this simulation bias decreases with the increase of fraction of vegetation cover. This study provides an effective technique to improve the capability of the mountain forest canopy BRDF model by considering the soil anisotropic characteristics for advancing the modeling of radiative transfer (RT) processes over rugged terrain.
Assessing Storm Response of Multiple Intertidal Bars Using an Open-Source Automatic Processing Toolbox
Authors: Montreuil Anne-Lise , Moelans Robrecht , Houthuys Rik , Bogaert Patrick , Chen Margaret
Published: February 2022

Intertidal bars are common features of sandy beaches in meso- and macro-tidal environments, yet their behaviour under storm impact and subsequent recovery remain poorly documented. Intensive surveys provide valuable information; however, it takes time to process the vast amount of data. This study presents the morphological response of a multibarred macro-tidal beach along the Belgian coast after a severe storm that happened on 8–12 February 2020, and to develop and apply an oPen-source Raster prOcessing Toolbox for invEstigation Coast intertidal bar displacemenT (PROTECT) in Python for automated bar extraction. This toolbox was applied to the digital surface models of pre- and post-storm airborne LiDAR surveys of a multibarred intertidal beach. The PROTECT toolbox is capable of detecting the position and elevation of intertidal bars accurately. The uncertainty in the elevation characteristics of the bars induces an error in the elevation dimension of 0.10 m. Using the toolbox, the results showed that the intertidal bars changed in term of variations in bar number, dimensions and shape across the storm event. Overall, the storm significantly eroded the dune and the upper-beach zone with a sand loss equivalent elevation decrease of −0.14 m. This was followed by a continuous and full recovery after 9 months under fair weather conditions. In contrast, the sand budget in the intertidal zone did not change over the entire monitoring period although the bars showed significant morphological change. Applying the PROTECT toolbox on high-resolution 3D topographic datasets allows to increase the temporal mapping resolution of intertidal bars from long-term (years) to short (storm events) time scales. Similar assessments at locations worldwide would allow the improvement of our knowledge on the morphodynamical role of multibarred beaches and to forecast their evolution, thus contributing to manage future storm response and the progressively accelerating sea level rise.
A Cloud-Based Mapping Approach Using Deep Learning and Very-High Spatial Resolution Earth Observation Data to Facilitate the SDG 11.7.1 Indicator Computation
Authors: Verde Natalia , Patias Petros , Mallinis Giorgos
Published: February 2022

As urbanized areas continue to expand rapidly across all continents, the United Nations adopted in 2015 the Sustainable Development Goal (SDG) 11, aimed at shaping a sustainable future for city dwellers. Earth Observation (EO) satellite data can provide at a fine scale, essential urban land use information for computing SDG 11 indicators in order to complement or even replace inaccurate or invalid existing spatial datasets. This study proposes an EO-based approach for extracting large scale information regarding urban open spaces (UOS) and land allocated to streets (LAS) at the city level, for calculating SDG indicator 11.7.1. The research workflow was developed over the Athens metropolitan area in Greece using deep learning classification models for processing PlanetScope and Sentinel-1 imagery, employing freely-available cloud environments offered by Google. The LAS model exhibited satisfactory results while the best experiment performance for mapping UOS, considering both PlanetScope and Sentinel-1 data, yielded high commission errors, however, the cross-validation analysis with the UOS area of OpenStreetMap exhibited a total overlap of 67.38%, suggesting that our workflow is suitable for creating a “potential” UOS layer. The methodology developed herein can serve as a roadmap for the calculation of indicator 11.7.1 through national statistical offices when spatial data are absent or unreliable.
Assessing the Dynamics of Plant Species Invasion in Eastern-Mediterranean Coastal Dunes Using Cellular Automata Modeling and Satellite Time-Series Analyses
Authors: Kozhoridze Giorgi , Dor Eyal , Sternberg Marcelo
Published: February 2022

Biological invasion is a major contributor to local and global biodiversity loss, in particular in dune ecosystems. In this study we evaluated current and future cover expansion of the invasive plant species, Heterotheca subaxillaris, and Acacia saligna, in the Mediterranean coastal plain of Israel. This is the first effort to quantify current surface cover of the focal species in this area. We reconstructed plant cover for 1990–2020 using Landsat time series and modeled future potential expansion using cellular automata (CA) modeling. The overall accuracy of the results varied in the range 85–95% and the simulated plant growth using CA varied between 74% and 84%, for A. saligna and H. subaxillaris, respectively. The surface area covered by H. subaxillaris in 2020, 45 years since its introduction, was approximately 81 km2. Acacia saligna covered an area of 74.6 km2, while the vacant area available for potential spread of these two species was 630 km2. Heterotheca subaxillaris showed a mean expansion rate of 107% per decade from 2000 to 2020, while the mean expansion rate of A. saligna was lower, ranging between 48% and 54% within the same time period. Furthermore, based on the plant expansion model simulation we estimated that A. saligna and H. subaxillaris will continue to spread by 60% per decade, on average, from 2020 to 2070, with a maximum growth rate of 80% per decade during 2040–2050. According to future expansion projections, the species will cover all open vacant areas by 2070 (95% of the total vacant area) and most areas will be shared by both species.
Quantification of Foraging Areas for the Northern Bald Ibis (Geronticus eremita) in the Northern Alpine Foothills: A Random Forest Model Fitted with Optical and Actively Sensed Earth Observation Data
Authors: Wehner Helena , Huchler Katharina , Fritz Johannes
Published: February 2022

The Northern Bald Ibis (Geronticus eremita, NBI) is an endangered migratory species, which went extinct in Europe in the 17th century. Currently, a translocation project in the frame of the European LIFE program is carried out, to reintroduce a migratory population with breeding colonies in the northern and southern Alpine foothills and a common wintering area in southern Tuscany. The population meanwhile consists of about 200 individuals, with about 90% of them carrying a GPS device on their back. We used biologging data from 2021 to model the habitat suitability for the species in the northern Alpine foothills. To set up a species distribution model, indices describing environmental conditions were calculated from satellite images of Landsat-8, and in addition to the well-proven use of optical remote sensing data, we also included Sentinel-1 actively sensed observation data, as well as climate and urbanization data. A random forest model was fitted on NBI GPS positions, which we used to identify regions with high predicted foraging suitability within the northern Alpine foothills. The model resulted in 84.5% overall accuracy. Elevation and slope had the highest predictive power, followed by grass cover and VV intensity of Sentinel-1 radar data. The map resulting from the model predicts the highest foraging suitability for valley floors, especially of Inn, Rhine, and Salzach-Valley as well as flatlands, like the Swiss Plateau and the agricultural areas surrounding Lake Constance. Areas with a high suitability index largely overlap with known historic breeding sites. This is particularly noteworthy because the model only refers to foraging habitats without considering the availability of suitable breeding cliffs. Detailed analyses identify the transition zone from extensive grassland management to intensive arable farming as the northern range limit. The modeling outcome allows for defining suitable areas for further translocation and management measures in the frame of the European NBI reintroduction program. Although required in the international IUCN translocation guidelines, the use of models in the context of translocation projects is still not common and in the case of the Northern Bald Ibis not considered in the present Single Species Action Plan of the African-Eurasian Migratory Water bird Agreement. Our species distribution model represents a contemporary snapshot, but sustainability is essential for conservation planning, especially in times of climate change. In this regard, a further model could be optimized by investigating sustainable land use, temporal dynamics, and climate change scenarios.
Coseismic Deformation Field Extraction and Fault Slip Inversion of the 2021 Yangbi MW 6.1 Earthquake, Yunnan Province, Based on Time-Series InSAR
Authors: Li Xue , Wang Chisheng , Zhu Chuanhua , Wang Shuying , Li Weidong , Wang Leyang , Zhu Wu
Published: February 2022

An earthquake of moderate magnitude (MW 6.1) occurred in Yangbi County, Dali, Yunnan Province, China, on 21 May 2021. Compared to strong earthquakes, the measurement of the deformation fields of moderate earthquakes is more susceptible to errors associated with atmospheric, orbital, and topographic features. We adopted a new time-series InSAR method to process preseismic and postseismic Sentinel-1A SAR time-series images and separated the coseismic deformation signals from various error signals. This method uses preseismic time-series interferograms to estimate the spatially correlated look angle error induced by the digital elevation model and the atmospheric and orbital errors in the master image. The preseismic and postseismic time-series interferograms were then segmented for spatio-temporal filtering to provide a precise estimate of the atmospheric and orbital errors in slave images. Such time-series processing accurately separates various errors from the coseismic deformation signal and prevents the coseismic deformation signal from being included as noise in the error estimation during filtering. Based on this approach, we effectively eliminated the masking of the deformation signal by the errors and extracted coseismic deformation field of the Yangbi MW 6.1 earthquake with high precision. The maximum LOS displacement in the ascending and descending tracks were determined to be −74 and −62 mm, respectively. Subsequently, we used the Geodetic Bayesian Inversion Software to invert the fault geometric parameters of this earthquake, and based on this inverted the rupture slip distribution using the least-squares method. The results showed that the fault orientation is 133.43°, dip angle is 76.98°, source depth is 5.5 km, fault sliding mode is right-lateral strike-slip. The moment magnitude (MW) was calculated to be 6.07.
Lite-YOLOv5: A Lightweight Deep Learning Detector for On-Board Ship Detection in Large-Scene Sentinel-1 SAR Images
Authors: Xu Xiaowo , Zhang Xiaoling , Zhang Tianwen
Published: February 2022

Synthetic aperture radar (SAR) satellites can provide microwave remote sensing images without weather and light constraints, so they are widely applied in the maritime monitoring field. Current SAR ship detection methods based on deep learning (DL) are difficult to deploy on satellites, because these methods usually have complex models and huge calculations. To solve this problem, based on the You Only Look Once version 5 (YOLOv5) algorithm, we propose a lightweight on-board SAR ship detector called Lite-YOLOv5, which (1) reduces the model volume; (2) decreases the floating-point operations (FLOPs); and (3) realizes the on-board ship detection without sacrificing accuracy. First, in order to obtain a lightweight network, we design a lightweight cross stage partial (L-CSP) module to reduce the amount of calculation and we apply network pruning for a more compact detector. Then, in order to ensure the excellent detection performance, we integrate a histogram-based pure backgrounds classification (HPBC) module, a shape distance clustering (SDC) module, a channel and spatial attention (CSA) module, and a hybrid spatial pyramid pooling (H-SPP) module to improve detection performance. To evaluate the on-board SAR ship detection ability of Lite-YOLOv5, we also transplant it to the embedded platform NVIDIA Jetson TX2. Experimental results on the Large-Scale SAR Ship Detection Dataset-v1.0 (LS-SSDD-v1.0) show that Lite-YOLOv5 can realize lightweight architecture with a 2.38 M model volume (14.18% of model size of YOLOv5), on-board ship detection with a low computation cost (26.59% of FLOPs of YOLOv5), and superior detection accuracy (1.51% F1 improvement compared with YOLOv5).
Considerations and Multi-Criteria Decision Analysis for the Installation of Collocated Permanent GNSS and SAR Infrastructures for Continuous Space-Based Monitoring of Natural Hazards
Authors: Kakoullis Dimitris , Fotiou Kyriaki , Melillos George , Danezis Chris
Published: February 2022

Over the past few decades, the global population and the built environment’s vulnerability to natural hazards have risen dramatically. As a result, decisive actions, such as the SENDAI framework, have emerged to foster a global culture of successful disaster risk reduction policies, including actions to mitigate the social and economic impact of geohazards. The effective study of natural disasters requires meticulous and precise monitoring of their triggering factors, with ground- and space-based techniques. The integration of GNSS and SAR observations through the establishment of permanent infrastructures, i.e., Continuously Operating Reference Stations (CORS) networks and arrays of Corner Reflectors (CRs), may form a seamless ground displacement monitoring system. The current research literature provides fragmented guidelines, regarding the co-location of SAR and GNSS permanent infrastructures. Furthermore, there exist no guidelines for the determination of the most suitable locations using a holistic approach, in terms of criteria and required data. The purpose of this paper is to present a semi-automatic multicriteria site suitability analysis and evaluation of candidate sites for the installation of a permanent CORS and two CRs; one for each pass, taking into account various parameters and criteria. The first results demonstrate that the collocation of SAR and GNSS permanent infrastructures, utilizing a holistic criteria-based approach, is successful and complies with all the literature’s requirements.
The River–Sea Interaction off the Amazon Estuary
Authors: Yu Di , Liu Shidong , Li Guangxue , Zhong Yi , Liang Jun , Shi Jinghao , Liu Xue , Wang Xiangdong
Published: February 2022

The Amazon River has the highest discharge in the world. Nevertheless, there is still a lack of the research on the interaction between river-diluted water and the ocean. This study used the remote sensing data (2008–2017) of the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Aqua satellite, and data of the currents, wind fields, sea surface temperature, and water depth. The river–sea interaction off the Amazon estuary was studied by analyzing the diffusion of river-diluted water and the distribution of surface suspended particulate matter (SPM). The results revealed that the Amazon estuary has a “filter effect,” whereby the distribution of the surface SPM exhibited significant spatial characteristics of being high in the nearshore area and low in the offshore area. Most of the SPM accumulated within the estuary in a fan shape, although some was distributed in the shallow water region of the continental shelf along the coasts on both sides of the estuary. The currents were found to limit the diffusion range of SPM. The flow direction and velocity of the North Brazil Current and the North Equatorial Countercurrent, which are largely driven by the magnitude of the trade wind stress, are the main forces controlling the long-distance diffusion of diluted water, thus forming unique river–sea interaction patterns in the Amazon estuary. This research provides a supplement and reference for the study of the diffusion process of SPM and river-diluted water, and on the estuarine river–sea interactions of other large rivers worldwide.