bluuebunny/crossref_metadata_2025 huggingface dataset:
Effects of Coagent Functionalities on Properties of Ultrafine Fully Vulcanized Powdered Natural Rubber Prepared as Toughening Filler in Rigid PVC
Authors: Lin Yiting , Amornkitbamrung Lunjakorn , Mora Phattarin , Jubsilp Chanchira , Hemvichian Kasinee , Soottitantawat Apinan , Ekgasit Sanong , Rimdusit Sarawut
Published: January 2021

Ultrafine fully vulcanized powdered natural rubber (UFPNR) has a promising application as a renewable toughening modifier in polymer matrices. In this work, the effects of acrylate coagents, which had different amounts of functional groups, on properties of UFPNR produced by radiation vulcanization and spray-drying was systematically investigated for the first time. Dipropylene glycol diacrylate (DPGDA), trimethylol propane trimethaacrylate (TMPTMA), and ditrimethylol propane tetraacrylate (DTMPTA) were used as coagents with two, three, and four acrylate groups, respectively. The radiation in the range of 250 to 400 kGy and coagent contents of up to 11 phr were used in the production process. Physical, chemical, and thermal properties of the UFPNR were characterized by swelling analysis, scanning electron microscopy, infrared spectroscopy, thermogravimetric analysis, and differential scanning calorimetry. The properties of UFPNR produced by using different type and content of coagents were compared and discussed. The results revealed that UFPNR with the smallest particle size of 3.6 ± 1.1 μm and the highest thermal stability (Td5 = 349 °C) could be obtained by using DTMPTA, which had the highest amount of functional group. It was proposed that the coagent with the greater number of acrylate groups enhanced the crosslinking of natural rubber as it had more reactive groups. Finally, an application of UFPNR as a toughening filler in rigid PVC was demonstrated with 34% improvement of impact strength.
Thermomechanical Analysis of Isora Nanofibril Incorporated Polyethylene Nanocomposites
Authors: Jose Cintil , Chan Chin Han , Winie Tan , Joseph Blessy , Tharayil Abhimanyu , Maria Hanna J , Volova Tatiana , La Mantia Francesco Paolo , Rouxel Didier , Morreale Marco , Laroze David , Mathew Lovely , Thomas Sabu
Published: January 2021

The research on cellulose fiber-reinforced nanocomposites has increased by an unprecedented magnitude over the past few years due to its wide application range and low production cost. However, the incompatibility between cellulose and most thermoplastics has raised significant challenges in composite fabrication. This paper addresses the behavior of plasma-modified polyethylene (PE) reinforced with cellulose nanofibers extracted from isora plants (i.e., isora nanofibrils (INFs)). The crystallization kinetics of PE–INF composites were explained using the Avrami model. The effect of cellulose nanofillers on tuning the physiochemical properties of the nanocomposite was also explored in this work. The increase in mechanical properties was due to the uniform dispersion of fillers in the PE. The investigation on viscoelastic properties confirmed good filler–matrix interactions, facilitating the stress transfer.
Development of Biodegradable Films Loaded with Phages with Antilisterial Properties
Authors: López de Dicastillo Carol , Settier-Ramírez Laura , Gavara Rafael , Hernández-Muñoz Pilar , López Carballo Gracia
Published: January 2021

The inhibitory and bactericidal capacity of Listex P100 bacteriophage has been studied against different concentrations of Listeria monocytogenes in stationary and exponential phases. Three different matrices were employed to developed films incorporating Listex P100: (1) sodium caseinate, (2) sodium alginate mixed with gelatin, and (3) polyvinyl alcohol (PVOH). All the films were successfully developed by casting at room temperature. These active biodegradable films were optical, structural, and thermally characterized, and their antimicrobial capacities against L. monocytogenes were studied. The incorporation of phages did not affect the morphology, colour, opacity, and thermal stability of polymers. The antimicrobial analysis revealed the bacteriophage presented a high antimicrobial capacity against L. monocytogenes in the stationary phase (4.40 and 6.19 log reduction values or bactericide effect depending on the initial inoculum of the pathogen). Developed films showed antimicrobial capacity close to 1 log after 24 h of incubation at 30 °C. The effectiveness of PVOH films was greater under refrigeration conditions, reaching 2 log reduction after eight days of incubation. The use of these films as a coating in a food or as part of a packaging could improve food safety against the growth of pathogenic microorganisms such as Listeria monocytogenes.
Effects of Temperature and Extraction Time on Avocado Flesh (Persea americana) Total Phenolic Yields Using Subcritical Water Extraction
Authors: Mazyan Walid I. , O’Connor Ellen , Martin Elia , Vogt Anja , Charter Edward , Ahmadi Ali
Published: January 2021

This paper investigates the optimum extraction temperature for enhanced total phenolic yields extracted from avocado fruit flesh (Persea americana) using subcritical water extraction, as well as the impact of fruit ripeness on phenol extraction efficiency. Additionally, extraction yield against extraction time was investigated for time intervals of 10 min over an overall extraction time of 30 min. The subcritical water conditions studied were 18 bar, 87 mL/min, and temperatures of 105 °C, 120 °C, and 140 °C. The total phenolic compounds content was compared for week one avocado flesh and ripe (week four) avocado flesh, with a four-week ripening period between the two samples. The results show that extracting with subcritical water at 105 °C provides the highest phenolic compounds yields of 0.11% and 0.26% by dried mass for week one and ripe fruit (week four), respectively. The experimental results also indicate that the implementation of lower extraction temperatures on week four avocado (i.e., following the selection of week one avocados and allowing them to ripen over a period of one month) enhances the phenolic compounds extraction yields by more than four times relative to the first week’s sample extract, specifically during the first 20 min of extraction.
Impacts of Low-Carbon Targets and Hydrogen Production Alternatives on Energy Supply System Transition: An Infrastructure-Based Optimization Approach and a Case Study of China
Authors: Li Tianxiao , Liu Pei , Li Zheng
Published: January 2021

Low-carbon transition pathways oriented from different transition targets would result in a huge variation of energy system deployment and transition costs. Hydrogen is widely considered as an imperative energy carrier to reach carbon neutral targets. However, hydrogen production, either from non-fossil power or fossil fuels with carbon capture, is closely linked with an energy supply system and has great impacts on its structure. Identifying an economically affordable transition pathway is attractive, and energy infrastructure is critical due to massive investment and long life-span. In this paper, a multi-regional, multi-period, and infrastructure-based model is proposed to quantify energy supply system transition costs with different low-carbon targets and hydrogen production alternatives, and China is taken as a case study. Results show that, fulfilling 2-degree and 1.5-degree temperature increase targets would result in 84% and 151% increases in system transition costs, 114% and 246% increases in infrastructure investment, and 211% and 339% increases in stranded investment, compared to fulfilling stated policy targets. Producing hydrogen from coal would be economical when carbon capture and sequestration cost is lower than 437 yuan per tonne, and reduce infrastructure investment and stranded coal investment by 16% and 35% respectively, than producing hydrogen from renewable power.
Oxygen-Blown Gasification of Pulp Mill Bark Residues for Synthetic Fuel Production
Authors: Weiland Fredrik , Lundström Sandra , Ögren Yngve
Published: January 2021

Synthetic fuel production via gasification of residual biomass streams from the pulp and paper industry can be an opportunity for the mills to enable improved resource utilization and at the same time reduce the production of excess heat. This paper summarizes initial oxygen-blown gasification experiments with two bark residues from a European pulp and paper mill, i.e., a softwood bark and a hardwood bark. The gasification process was characterized by measuring syngas yields and process efficiency to find optimum operating conditions. In addition, impurities in the syngas and ash behavior were characterized. Maximum yields of CO and H2 were obtained from softwood bark and amounted to approximately 29 and 15 mol/kg fuel, respectively. Optimum cold gas efficiency was achieved at an oxygen stoichiometric ratio of λ = 0.40 and was approximately 76% and 70% for softwood bark and hardwood bark, respectively. Increased λ had a reducing effect on pollutants in the syngas, e.g., higher hydrocarbons, NH3, HCl, and soot. The situation for sulfur species was more complex. Evaluation of the bark ashes indicated that slag formation could start already from 800 °C. Furthermore, a non-intrusive laser diagnostics technique gave rapid feedback on the millisecond scale. Measured syngas temperature and water content were in good agreement with the applied reference methods.
A Novel Mutual Information and Partial Least Squares Approach for Quality-Related and Quality-Unrelated Fault Detection
Authors: Aljunaid Majed , Tao Yang , Shi Hongbo
Published: January 2021

Partial least squares (PLS) and linear regression methods are widely utilized for quality-related fault detection in industrial processes. Standard PLS decomposes the process variables into principal and residual parts. However, as the principal part still contains many components unrelated to quality, if these components were not removed it could cause many false alarms. Besides, although these components do not affect product quality, they have a great impact on process safety and information about other faults. Removing and discarding these components will lead to a reduction in the detection rate of faults, unrelated to quality. To overcome the drawbacks of Standard PLS, a novel method, MI-PLS (mutual information PLS), is proposed in this paper. The proposed MI-PLS algorithm utilizes mutual information to divide the process variables into selected and residual components, and then uses singular value decomposition (SVD) to further decompose the selected part into quality-related and quality-unrelated components, subsequently constructing quality-related monitoring statistics. To ensure that there is no information loss and that the proposed MI-PLS can be used in quality-related and quality-unrelated fault detection, a principal component analysis (PCA) model is performed on the residual component to obtain its score matrix, which is combined with the quality-unrelated part to obtain the total quality-unrelated monitoring statistics. Finally, the proposed method is applied on a numerical example and Tennessee Eastman process. The proposed MI-PLS has a lower computational load and more robust performance compared with T-PLS and PCR.
Optimal Design of a Hydrolysis Sugar Membrane Purification System Using a Superstructure-Based Approach
Authors: Su Chien-Yuan , Ji Bo-Yan , Yu Pei-Jung , Wang Ming-Hua , Hung Wei-Chun , Chang Ying-Hsi , Lee Jui-Yuan
Published: January 2021

As an alternative to gasoline, bioethanol can be produced from lignocellulosic biomass through hydrolysis using an ionic solution containing zinc chloride (ZnCl2). This method allows for a high yield of glucose from lignocellulose, but entails the removal of ZnCl2 from the hydrolysate using multiple nanofiltration membranes before the fermentation of glucose. This paper presents a mathematical technique for designing such a multistage membrane separation system. The optimization model for the synthesis of membrane networks is based on a superstructure with all feasible interconnections between the membrane units, and consists of mass balances, logical constraints and product specifications. A case study of the separation of a bagasse hydrolysis solution is used to demonstrate the application of the proposed model. Results show that using both types of nanofiltration membranes allows higher ZnCl2 removal ratios at each membrane unit, hence a decrease in the number of membrane units required and a reduction of about 35% in capital cost compared to the cases in which only one membrane type is used. Further analysis is performed to examine the effect of membrane performance on the economics of the separation system.
Hydrothermally Reduced Graphene Hydrogel Intercalated with Divalent Ions for Dye Adsorption Studies
Authors: Zheng Alvin Lim Teik , Boonyuen Supakorn , Ohno Teruhisa , Andou Yoshito
Published: January 2021

Fundamental studies involving divalent ion intercalated graphene-based hydrogel are still lacking in terms of their adsorption behavior towards dye pollutants. In this study, we prepared a self-assembled Mg2+ and Ca2+ intercalated reduced graphene hydrogel (rGH) using hydrothermal treatment to evaluate the intercalation impact on the adsorption capability towards cationic dyes, methylene blue and rhodamine B. The morphological, structural, thermal, and textural properties of the divalent ion intercalated reduced graphene hydrogels were studied using Fourier transform infrared spectrometer, thermogravimetric analysis, Raman spectroscopy, scanning electron microscope-energy dispersive spectroscopy, X-ray photoelectron spectroscopy, Brunauer–Emmett–Teller surface area analysis, and X-ray diffraction. The increased adsorption capacity of the divalent ion intercalated reduced graphene-based hydrogels towards the dye molecules resulted from the increase in the specific surface area and pore volume due to the Mg2+ and Ca2+ bridging that formed spaces between the graphene sheets framework. Adsorption kinetics and the equilibrium adsorption isotherm were fitted by a pseudo-second-order alongside intraparticle diffusion kinetic models and Langmuir isotherm respectively. In addition, the divalent ion intercalated reduced graphene hydrogel showed good generation after three cycles of simultaneous adsorption.
Selective Determination of Dopamine in Pharmaceuticals and Human Urine Using Carbon Quantum Dots as a Fluorescent Probe
Authors: Yang Xiupei , Tian Fangming , Wen Shaohua , Xu Hua , Zhang Lin , Zeng Jie
Published: January 2021

A cost-effective and environmentally friendly method was formulated for rapid dopamine (DA) detection that was based on the fluorescence (FL) quenching of carbon quantum dots (C-dots). Upon adding DA to the C-dots’ solution, we noticed a regular reduction in their fluorescence intensity. The effects of pH, amount of C-dots, reaction temperature and time on the determination of DA were investigated. Under the optimized experimental conditions, trace amounts of DA could be analyzed. Furthermore, dopamine hydrochloride injection and human urine samples with and without spiked DA were analyzed using the developed sensing system. The procedure was validated following the guidelines of the European Medicines Agency (EMA) in terms of the following: calibration range (0.3–100 μM), linearity (R2 = 0.9991), limit of detection (LOD) (93 nM). Recoveries of dopamine with spiked samples at three different levels were between 95.0 and 105.9%, and the relative standard deviations (RSDs) were within 2.68% (n = 6). This method is simple and suitable for the determination of dopamine in pharmaceuticals and human urine for clinical application. Compared with previous reports, the proposed method offers great advantages including ease of C-dot sensor preparation (one-pot synthesis), environmentally friendly sample preparation by using either water or phosphate buffer solution only, a short response time and selectivity.
Towards Autonomous Operation by Advanced Process Control—Process Analytical Technology for Continuous Biologics Antibody Manufacturing
Authors: Helgers Heribert , Schmidt Axel , Lohmann Lara Julia , Vetter Florian Lukas , Juckers Alex , Jensch Christoph , Mouellef Mourad , Zobel-Roos Steffen , Strube Jochen
Published: January 2021

Continuous manufacturing opens up new operation windows with improved product quality in contrast to documented lot deviations in batch or fed-batch operations. A more sophisticated process control strategy is needed to adjust operation parameters and keep product quality constant during long-term operations. In the present study, the applicability of a combination of spectroscopic methods was evaluated to enable Advanced Process Control (APC) in continuous manufacturing by Process Analytical Technology (PAT). In upstream processing (USP) and aqueous two-phase extraction (ATPE), Raman-, Fourier-transformed infrared (FTIR), fluorescence- and ultraviolet/visible- (UV/Vis) spectroscopy have been successfully applied for titer and purity prediction. Raman spectroscopy was the most versatile and robust method in USP, ATPE, and precipitation and is therefore recommended as primary PAT. In later process stages, the combination of UV/Vis and fluorescence spectroscopy was able to overcome difficulties in titer and purity prediction induced by overlapping side component spectra. Based on the developed spectroscopic predictions, dynamic control of unit operations was demonstrated in sophisticated simulation studies. A PAT development workflow for holistic process development was proposed.
Hemicellulosic Bioethanol Production from Fast-Growing Paulownia Biomass
Authors: Domínguez Elena , Río Pablo G. del , Romaní Aloia , Garrote Gil , Domingues Lucília
Published: January 2021

In order to exploit a fast-growing Paulownia hardwood as an energy crop, a xylose-enriched hydrolysate was obtained in this work to increase the ethanol concentration using the hemicellulosic fraction, besides the already widely studied cellulosic fraction. For that, Paulownia elongata x fortunei was submitted to autohydrolysis treatment (210 °C or S0 of 4.08) for the xylan solubilization, mainly as xylooligosaccharides. Afterwards, sequential stages of acid hydrolysis, concentration, and detoxification were evaluated to obtain fermentable sugars. Thus, detoxified and non-detoxified hydrolysates (diluted or not) were fermented for ethanol production using a natural xylose-consuming yeast, Scheffersomyces stipitis CECT 1922, and an industrial Saccharomyces cerevisiae MEC1133 strain, metabolic engineered strain with the xylose reductase/xylitol dehydrogenase pathway. Results from fermentation assays showed that the engineered S. cerevisiae strain produced up to 14.2 g/L of ethanol (corresponding to 0.33 g/g of ethanol yield) using the non-detoxified hydrolysate. Nevertheless, the yeast S. stipitis reached similar values of ethanol, but only in the detoxified hydrolysate. Hence, the fermentation data prove the suitability and robustness of the engineered strain to ferment non-detoxified liquor, and the appropriateness of detoxification of liquor for the use of less robust yeast. In addition, the success of hemicellulose-to-ethanol production obtained in this work shows the Paulownia biomass as a suitable renewable source for ethanol production following a suitable fractionation process within a biorefinery approach.
Novel Methods Using an Arthrobacter sp. to Create Anaerobic Conditions for Biobutanol Production from Sweet Sorghum Juice by Clostridium beijerinckii
Authors: Daengbussadee Chalida , Laopaiboon Lakkana , Kaewmaneewat Anuphon , Sirisantimethakom Likit , Laopaiboon Pattana
Published: January 2021

Biobutanol can be produced by Clostridia via an acetone–butanol–ethanol (ABE) fermentation under strictly anaerobic conditions. Oxygen-free nitrogen (OFN) gas is typically used to create anaerobic conditions for ABE fermentations. However, this method is not appropriate for large-scale fermentations as it is quite costly. The aim of this work was to study the feasibility of butanol production from sweet sorghum juice (SSJ) by Clostridium beijerinckii TISTR 1461 using various methods to create anaerobic conditions, i.e., growth of a strictly aerobic bacterium, an Arthrobacter sp., under different conditions and a chemical method using sodium dithionite (SDTN) to consume residual oxygen. SSJ containing 60 g/L of total sugar supplemented with 1.27 g/L of (NH4)2SO4 was used as a substrate for butanol production. The results showed that 0.25 mM SDTN could create anaerobic conditions, but in this case, C.beijerinckii TISTR 1461 could produce butanol at a concentration (PB) of only 8.51 g/L with a butanol productivity (QB) of 0.10 g/L·h. Arthrobacter sp. BCC 72131 could also be used to create anaerobic conditions. Mixed cultures of C.beijerinckii TISTR 1461 and Arthrobacter sp. BCC 72131 created anaerobic conditions by inoculating the C.beijerinckii 4 h after Arthrobacter. This gave a PB of 10.39 g/L with a QB of 0.20 g/L·h. Comparing butanol production with the control treatment (using OFN gas to create anaerobic conditions, yielding a PB of 9.88 g/L and QB of 0.21 g/L·h) indicated that using Arthrobacter sp. BCC 72131 was an appropriate procedure for creating anaerobic conditions for high levels of butanol production by C. beijerinckii TISTR 1461 from a SSJ medium.
Preparation of Polymer Bitumen Binder in the Presence of a Stabilizer
Authors: Imanbayev Yerzhan , Akkenzheyeva Anar , Bussurmanova Akkenzhe , Serikbayeva Akmaral , Boranbayeva Assiya
Published: January 2021

The article presents the results of research on the production of polymer-bitumen binder (PBB) based on mixtures of non-oxidized and oxidized petroleum products, namely high-viscosity tar, darkened vacuum distillate, and oxidized petroleum bitumen 70/100, obtained at technological installations of Limited Liability Partnership (LLP) “JV Caspi Bitum’’ and styrene-butadiene-styrene (SBS) block copolymer brand L 30-01A modifier in the presence of a stabilizer. The results obtained show that the introduction of the SBS modifier in the presence of a sulfur stabilizer improves the performance characteristics of PBB, such as elasticity, ductility, softening temperature, penetration, and brittleness temperature.
Model Predictive Control for the Process of MEA Absorption of CO2 Based on the Data Identification Model
Authors: Li Qianrong , Zhang Wenzhao , Qin Yuwei , An Aimin
Published: January 2021

The absorption process of CO2 by ethanolamine solution is essentially a dynamic system, which is greatly affected by the power plant startup and flue gas load changes. Hence, studying the optimal control of the CO2 chemical capture process has always been an important part in academic fields. Model predictive control (MPC) is a very effective control strategy used for such process, but the most intractable problem is the lack of accurate and effective model. In this work, Aspen Plus and Aspen Plus Dynamics are used to establish the process of monoethanolamine (MEA) absorption of CO2 related models based on subspace identification. The nonlinear distribution of the system under steady-state operation is analyzed. Dynamic tests were carried out to understand the dynamic characteristics of the system under variable operating conditions. Systematic subspace identification on open-loop experimental data was performed. We designed a model predictive controller based on the identified model combined with the state-space equation using Matlab/Simulink to analyze the changes of the system under two different disturbances. The simulation results show that the control performance of the MPC algorithm is significantly better than that of the traditional proportion integral differential (PID) system, with excellent setpoint tracking ability and robustness, which improve the stability and flexibility of the system.
Simulation of Flexible Fibre Particle Interaction with a Single Cylinder
Authors: Hamedi Naser , Westerberg Lars-Göran
Published: January 2021

In the present study, the flow of a fibre suspension in a channel containing a cylinder was numerically studied for a very low Reynolds number. Further, the model was validated against previous studies by observing the flexible fibres in the shear flow. The model was employed to simulate the rigid, semi-flexible, and fully flexible fibre particle in the flow past a single cylinder. Two different fibre lengths with various flexibilities were applied in the simulations, while the initial orientation angle to the flow direction was changed between 45° ≤ θ ≤ 75°. It was shown that the influence of the fibre orientation was more significant for the larger orientation angle. The results highlighted the influence of several factors affecting the fibre particle in the flow past the cylinder.
Racializing the Good Muslim: Muslim White Adjacency and Black Muslim Activism in South Africa
Authors: Rahman Rhea
Published: January 2021

Founded in Birmingham, England in 1984, Islamic Relief is today the world’s largest and most-recognized Western-based Islamically-inspired non-governmental organization. Framed by an analysis of processes of racialization, I argue that Islamic Relief operationalizes not a singular, but multiple Muslim humanitarianisms. I examine what I suggest are competing racial projects of distinct humanitarianisms with regards to HIV and AIDS, health, and wellness. I consider the racial implications of British state-based soft-power interventions that seek to de-radicalize Muslims towards appropriately ‘moderate’ perspectives on gender and sexuality. In South Africa, I argue that Black Muslim staff embrace grassroots efforts aimed towards addressing the material and social conditions of their community, with a focus on economic self-determination and self-sufficiency. I claim that the orientation of these Black Muslim grassroots initiatives denotes a humanitarianism of another kind that challenges the material and ethical implications of a humanitarianism framed within a logic of global white supremacy, and that is conditioned by racial capitalism.
Building the Nation: The Success and Crisis of Korean Civil Religion
Authors: Kim Andrew Eungi , Connolly Daniel
Published: January 2021

Civil religion refers to a country’s beliefs, symbols, and rituals that bolster national unity and strengthen its citizens’ sense of identity and belonging. However, the literature on civil religion is divided between those who attribute it to bottom-up cultural spontaneity and those who see it as an ideological top-down construction. Moreover, there has been a relative lack of scholarly attention to Korean civil religion. This paper addresses both issues by arguing that a strong civil religion indeed exists in the country and that it has been an important part of the “nation-building” process since the founding of the Republic of Korea in 1948. The paper highlights how a succession of authoritarian regimes (1948–1987) successfully mobilized a strong civil religion for political purposes. The resulting civil religion targeted economic growth as the national goal to overcome all social ills, focused on the country’s ethnic and cultural homogeneity to boost national confidence and pride, exalted its traditional religions, especially Confucianism, as repositories of Korean traditional culture, and rendered sacred meanings to national symbols such as the flag and national anthem. Even after democratization, Korean civil religion remains largely ideological, as the Korean government is heavily involved in framing, planning, sponsoring, and promoting the country’s civil religion. Nevertheless, the paper concludes by observing that this civil religion is entering a period of crisis due to political fragmentation among Korean elites and deeply rooted cultural and societal change.
Sequence Image Interpolation via Separable Convolution Network
Authors: Jin Xing , Tang Ping , Houet Thomas , Corpetti Thomas , Alvarez-Vanhard Emilien Gence , Zhang Zheng
Published: January 2021

Remote-sensing time-series data are significant for global environmental change research and a better understanding of the Earth. However, remote-sensing acquisitions often provide sparse time series due to sensor resolution limitations and environmental factors, such as cloud noise for optical data. Image interpolation is the method that is often used to deal with this issue. This paper considers the deep learning method to learn the complex mapping of an interpolated intermediate image from predecessor and successor images, called separable convolution network for sequence image interpolation. The separable convolution network uses a separable 1D convolution kernel instead of 2D kernels to capture the spatial characteristics of input sequence images and then is trained end-to-end using sequence images. Our experiments, which were performed with unmanned aerial vehicle (UAV) and Landsat-8 datasets, show that the method is effective to produce high-quality time-series interpolated images, and the data-driven deep model can better simulate complex and diverse nonlinear image data information.
Assessment of BRDF Impact on VIIRS DNB from Observed Top-of-Atmosphere Reflectance over Dome C in Nighttime
Authors: Li Jinjin , Qiu Shi , Zhang Yu , Yang Benyong , Gao Caixia , Qian Yonggang , Liu Yaokai , Zhao Yongguang
Published: January 2021

The Day–Night Band (DNB) imaging sensor of the Visible Infrared Imaging Radiometer Suite (VIIRS) adds nighttime monitoring capability to the Suomi National Polar-Orbiting Partnership and National Oceanic and Atmospheric Administration 20 weather satellite launched in 2011 and 2017, respectively. Nighttime visible imagery has already found diverse applications, but image quality is often unsatisfactory. In this study, variations in observed top-of-atmosphere (TOA) reflectance were examined in terms of nighttime bidirectional effects. The Antarctica Dome C ground site was selected due to high uniformity. First, variation of reflectance was characterized in terms of viewing zenith angle, lunar zenith angle, and relative lunar azimuth angle, using DNB data from 2012 to 2020 and Miller–Turner 2009 simulations. Variations in reflectance were observed to be strongly anisotropic, suggesting the presence of bidirectional effects. Then, based on this finding, three popular bidirectional reflectance distribution function (BRDF) models were evaluated for effectiveness in correcting for these effects on the nighttime images. The observed radiance of VIIRS DNB was compared with the simulated radiance respectively based on the three BRDF models under the same geometry. Compared with the RossThick-LiSparseReciprocal (RossLi) BRDF model and Hudson model, the Warren model has a higher correlation coefficient (0.9899–0.9945) and a lower root-mean-square-error (0.0383–0.0487). Moreover, the RossLi BRDF model and Hudson model may have similar effects in the description of the nighttime TOA over Dome C. These findings are potentially useful to evaluate the radiometric calibration stability and consistency of nighttime satellite sensors.
A Single-Difference Multipath Hemispherical Map for Multipath Mitigation in BDS-2/BDS-3 Short Baseline Positioning
Authors: Liu Chao , Tao Yuan , Xin Haiqiang , Zhao Xingwang , Liu Chunyang , Hu Haojie , Zhou Tengfei
Published: January 2021

The BeiDou Navigation Satellite System (BDS) features a heterogeneous constellation so that it is difficult to mitigate the multipath in the coordinate-domain. Therefore, mitigating the multipath in the observation-domain becomes more important. Sidereal filtering is commonly used for multipath mitigation, which needs to calculate the orbit repeat time of each satellite. However, that poses a computational challenge and damages the integrity at the end of the multipath model. Therefore, this paper proposes a single-difference model based on the multipath hemispherical map (SD-MHM) to mitigate the BDS-2/BDS-3 multipath in a short baseline. The proposed method is converted from double-difference residuals to single-difference residuals, which is not restricted by the pivot satellite transformation. Moreover, it takes the elevation and the azimuth angles of the satellite as the independent variables of the multipath model. The SD-MHM overcomes the unequal observation time of some satellites and does not require specific hardware. The experimental results show that the SD-MHM reduces the root mean square of the positioning errors by 56.4%, 63.9%, and 67.4% in the east, north, and vertical directions; moreover, it contributes to an increase in the baseline accuracy from 1.97 to 0.84 mm. The proposed SD-MHM has significant advantages in multipath mitigation compared with the advanced sidereal filtering method. Besides, the SD-MHM also features an excellent multipath correction capability for observation data with a period of more than seven days. Therefore, the SD-MHM provides a universal strategy for BDS multipath mitigation.
Mangrove Phenology and Water Influences Measured with Digital Repeat Photography
Authors: Songsom Veeranun , Koedsin Werapong , Ritchie Raymond J. , Huete Alfredo
Published: January 2021

The intertidal habitat of mangroves is very complex due to the dynamic roles of land and sea drivers. Knowledge of mangrove phenology can help in understanding mangrove growth cycles and their responses to climate and environmental changes. Studies of phenology based on digital repeat photography, or phenocams, have been successful in many terrestrial forests and other ecosystems, however few phenocam studies in mangrove forests showing the influence and interactions of water color and tidal water levels have been performed in sub-tropical and equatorial environments. In this study, we investigated the diurnal and seasonal patterns of an equatorial mangrove forest area at an Andaman Sea site in Phuket province, Southern Thailand, using two phenocams placed at different elevations and with different view orientations, which continuously monitored vegetation and water dynamics from July 2015 to August 2016. The aims of this study were to investigate fine-resolution, in situ mangrove forest phenology and assess the influence and interactions of water color and tidal water levels on the mangrove–water canopy signal. Diurnal and seasonal patterns of red, green, and blue chromatic coordinate (RCC, GCC, and BCC) indices were analyzed over various mangrove forest and water regions of interest (ROI). GCC signals from the water background were found to positively track diurnal water levels, while RCC signals were negatively related with tidal water levels, hence lower water levels yielded higher RCC values, reflecting brownish water colors and increased soil and mud exposure. At seasonal scales, the GCC profiles of the mangrove forest peaked in the dry season and were negatively related with the water level, however the inclusion of the water background signal dampened this relationship. We also detected a strong lunar tidal water periodicity in seasonal GCC values that was not only present in the water background, but was also detected in the mangrove–water canopy and mangrove forest phenology profiles. This suggests significant interactions between mangrove forests and their water backgrounds (color and depth), which may need to be accounted for in upscaling and coupling with satellite-based mangrove monitoring.
Error Correction of Multi-Source Weighted-Ensemble Precipitation (MSWEP) over the Lancang-Mekong River Basin
Authors: Tang Xiongpeng , Zhang Jianyun , Wang Guoqing , Ruben Gebdang Biangbalbe , Bao Zhenxin , Liu Yanli , Liu Cuishan , Jin Junliang
Published: January 2021

The demand for accurate long-term precipitation data is increasing, especially in the Lancang-Mekong River Basin (LMRB), where ground-based data are mostly unavailable and inaccessible in a timely manner. Remote sensing and reanalysis quantitative precipitation products provide unprecedented observations to support water-related research, but these products are inevitably subject to errors. In this study, we propose a novel error correction framework that combines products from various institutions. The NASA Modern-Era Retrospective Analysis for Research and Applications (AgMERRA), the Asian Precipitation Highly-Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE), the Climate Hazards group InfraRed Precipitation with Stations (CHIRPS), the Multi-Source Weighted-Ensemble Precipitation Version 1.0 (MSWEP), and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Records (PERSIANN) were used. Ground-based precipitation data from 1998 to 2007 were used to select precipitation products for correction, and the remaining 1979–1997 and 2008–2014 observe data were used for validation. The resulting precipitation products MSWEP-QM derived from quantile mapping (QM) and MSWEP-LS derived from linear scaling (LS) are evaluated by statistical indicators and hydrological simulation across the LMRB. Results show that the MSWEP-QM and MSWEP-LS can better capture major annual precipitation centers, have excellent simulation results, and reduce the mean BIAS and mean absolute BIAS at most gauges across the LMRB. The two corrected products presented in this study constitute improved climatological precipitation data sources, both time and space, outperforming the five raw gridded precipitation products. Among the two corrected products, in terms of mean BIAS, MSWEP-LS was slightly better than MSWEP-QM at grid-scale, point scale, and regional scale, and it also had better simulation results at all stations except Strung Treng. During the validation period, the average absolute value BIAS of MSWEP-LS and MSWEP-QM decreased by 3.51% and 3.4%, respectively. Therefore, we recommend that MSWEP-LS be used for water-related scientific research in the LMRB.
A Continental-Scale Assessment of Density, Size, Distribution and Historical Trends of Farm Dams Using Deep Learning Convolutional Neural Networks
Authors: Malerba Martino E. , Wright Nicholas , Macreadie Peter I.
Published: January 2021

Farm dams are a ubiquitous limnological feature of agricultural landscapes worldwide. While their primary function is to capture and store water, they also have disproportionally large effects on biodiversity and biogeochemical cycling, with important relevance to several Sustainable Development Goals (SDGs). However, the abundance and distribution of farm dams is unknown in most parts of the world. Therefore, we used artificial intelligence and remote sensing data to address this critical global information gap. Specifically, we trained a deep learning convolutional neural network (CNN) on high-definition satellite images to detect farm dams and carry out the first continental-scale assessment on density, distribution and historical trends. We found that in Australia there are 1.765 million farm dams that occupy an area larger than Rhode Island (4678 km2) and store over 20 times more water than Sydney Harbour (10,990 GL). The State of New South Wales recorded the highest number of farm dams (654,983; 37% of the total) and Victoria the highest overall density (1.73 dams km−2). We also estimated that 202,119 farm dams (11.5%) remain omitted from any maps, especially in South Australia, Western Australia and the Northern Territory. Three decades of historical records revealed an ongoing decrease in the construction rate of farm dams, from >3% per annum before 2000, to ~1% after 2000, to <0.05% after 2010—except in the Australian Capital Territory where rates have remained relatively high. We also found systematic trends in construction design: farm dams built in 2015 are on average 50% larger in surface area and contain 66% more water than those built in 1989. To facilitate sharing information on sustainable farm dam management with authorities, scientists, managers and local communities, we developed AusDams.org—a free interactive portal to visualise and generate statistics on the physical, environmental and ecological impacts of farm dams.
New Method for Automated Quantification of Vertical Spatio-Temporal Changes within Gully Cross-Sections Based on Very-High-Resolution Models
Authors: Šiljeg Ante , Domazetović Fran , Marić Ivan , Lončar Nina , Panđa Lovre
Published: January 2021

Gully erosion is one of the most prominent natural denudation processes of the Mediterranean. It causes significant soil degradation and sediment yield. Most traditional field methods for measurement of erosion-induced spatio-temporal changes are time and labor consuming, while their accuracy and precision are highly influenced by various factors. The main research question of this study was how the measurement approach of traditional field sampling methods can be automated and upgraded, while satisfying the required measurement accuracy. The VERTICAL method was developed as a fully automated raster-based method for detection and quantification of vertical spatio-temporal changes within a large number of gully cross-sections (GCs). The developed method was tested on the example of gully Santiš, located at Pag Island, Croatia. Repeat unmanned aerial vehicle (UAV) photogrammetry was used, as a cost-effective and practical method for the creation of very-high-resolution (VHR) digital surface models (DSMs) of the chosen gully site. A repeat aerophotogrammetric system (RAPS) was successfully assembled and integrated into one functional operating system. RAPS was successfully applied for derivation of interval (the two-year research period) DSMs (1.9 cm/pix) of gully Santiš with the accuracy of ±5 cm. VERTICAL generated and measured 2379 GCs, along the 110 m long thalweg of gully Santiš, within which 749 052 height points were sampled in total. VERTICAL proved to be a fast and reliable method for automated detection and calculation of spatio-temporal changes in a large number of GCs, which solved some significant shortcomings of traditional field methods. The versatility and adaptability of VERTICAL allow its application for other, similar scientific purposes, where multitemporal accurate measurement of spatio-temporal changes in GCs is required (e.g., river material dynamics, ice mass dynamics, tufa sedimentation and erosion).
Seasonal Variations of Daytime Land Surface Temperature and Their Underlying Drivers over Wuhan, China
Authors: Chen Liang , Wang Xuelei , Cai Xiaobin , Yang Chao , Lu Xiaorong
Published: January 2021

Rapid urbanization greatly alters land surface vegetation cover and heat distribution, leading to the development of the urban heat island (UHI) effect and seriously affecting the healthy development of cities and the comfort of living. As an indicator of urban health and livability, monitoring the distribution of land surface temperature (LST) and discovering its main impacting factors are receiving increasing attention in the effort to develop cities more sustainably. In this study, we analyzed the spatial distribution patterns of LST of the city of Wuhan, China, from 2013 to 2019. We detected hot and cold poles in four seasons through clustering and outlier analysis (based on Anselin local Moran’s I) of LST. Furthermore, we introduced the geographical detector model to quantify the impact of six physical and socio-economic factors, including the digital elevation model (DEM), index-based built-up index (IBI), modified normalized difference water index (MNDWI), normalized difference vegetation index (NDVI), population, and Gross Domestic Product (GDP) on the LST distribution of Wuhan. Finally, to identify the influence of land cover on temperature, the LST of croplands, woodlands, grasslands, and built-up areas was analyzed. The results showed that low temperatures are mainly distributed over water and woodland areas, followed by grasslands; high temperatures are mainly concentrated over built-up areas. The maximum temperature difference between land covers occurs in spring and summer, while this difference can be ignored in winter. MNDWI, IBI, and NDVI are the key driving factors of the thermal values change in Wuhan, especially of their interaction. We found that the temperature of water area and urban green space (woodlands and grasslands) tends to be 5.4 °C and 2.6 °C lower than that of built-up areas. Our research results can contribute to the urban planning and urban greening of Wuhan and promote the healthy and sustainable development of the city.
Triple-Attention-Based Parallel Network for Hyperspectral Image Classification
Authors: Qu Lei , Zhu Xingliang , Zheng Jiannan , Zou Liang
Published: January 2021

Convolutional neural networks have been highly successful in hyperspectral image classification owing to their unique feature expression ability. However, the traditional data partitioning strategy in tandem with patch-wise classification may lead to information leakage and result in overoptimistic experimental insights. In this paper, we propose a novel data partitioning scheme and a triple-attention parallel network (TAP-Net) to enhance the performance of HSI classification without information leakage. The dataset partitioning strategy is simple yet effective to avoid overfitting, and allows fair comparison of various algorithms, particularly in the case of limited annotated data. In contrast to classical encoder–decoder models, the proposed TAP-Net utilizes parallel subnetworks with the same spatial resolution and repeatedly reuses high-level feature maps of preceding subnetworks to refine the segmentation map. In addition, a channel–spectral–spatial-attention module is proposed to optimize the information transmission between different subnetworks. Experiments were conducted on three benchmark hyperspectral datasets, and the results demonstrate that the proposed method outperforms state-of-the-art methods with the overall accuracy of 90.31%, 91.64%, and 81.35% and the average accuracy of 93.18%, 87.45%, and 78.85% over Salinas Valley, Pavia University and Indian Pines dataset, respectively. It illustrates that the proposed TAP-Net is able to effectively exploit the spatial–spectral information to ensure high performance.
Preliminary Archeological Site Survey by UAV-Borne Lidar: A Case Study
Authors: Balsi Marco , Esposito Salvatore , Fallavollita Paolo , Melis Maria Grazia , Milanese Marco
Published: January 2021

Preliminary analysis of an archaeological site requires the acquisition of information by several diverse diagnostic techniques. Remote sensing plays an important role especially in spatially extended and not easily accessible sites for the purposes of preventive and rescue archaeology, landscape archaeology, and intervention planning. In this paper, we present a case study of a detailed topographic survey based on a light detection and ranging (LiDAR) sensor carried by an unmanned aerial vehicle (UAV; also known as drone). The high-resolution digital terrain model, obtained from the cloud of points automatically labeled as ground, was searched exhaustively by an expert operator looking for entrances to prehistoric hypogea. The study documents the usefulness of such a technique to reveal anthropogenic structures hidden by vegetation and perform fast topographic documentation of the ground surface.