bluuebunny/crossref_metadata_2025 huggingface dataset:
The Use of Recycled PET for the Synthesis of New Mechanically Improved PVP Composite Nanofibers
Authors: Gallardo-Sánchez Manuel A. , Chinchillas-Chinchillas Manuel J. , Gaxiola Alberto , Alvarado-Beltrán Clemente G. , Hurtado-Macías Abel , Orozco-Carmona Víctor M. , Almaral-Sánchez Jorge L. , Sepúlveda-Guzmán Selene , Castro-Beltrán Andrés
Published: July 2022

Polyethylene terephthalate (PET) waste has become a major challenge for the conservation of the environment due to difficult degradation. For this reason, it is important to develop new recycling strategies for reusing this waste. In this work, the electrospinning technique was used to synthesize composite nanofibers of polyvinylpyrrolidone (PVP), recycling PET (RPET) that was obtained from the chemical recycling of postconsumer PET with glycolysis and styrene (ST) as a crosslinking agent. The polymer solutions were analyzed by viscosity and frequency sweeping, while the composite nanofibers were characterized by scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), thermogravimetric analysis/differential scanning calorimetry (TGA/DSC), and nanoindentation to compare their properties. The PVP nanofibers presented an average diameter of 257 nm; the RPET/PVP and RPET/PVP/ST composite nanofibers had average diameters of 361 nm and 394 nm, respectively; and the modulus of elasticity and hardness of the RPET/PVP/ST composite nanofibers were 29 and 20 times larger, respectively, than those of the PVP nanofibers. With the synthesis of these composite nanofibers, a new approach to PET recycling is presented.
Study on Self-Leveling of Foamed Concrete for Long-Distance-Tunnel-Gas-Pipeline Backfill
Authors: Li Chunbao , Li Xiaotian , Li Shen , Guan Di , Xiao Chang , Lei Yun , Soloveva Valentina Y. , Dalerjon Hojiboev , Qin Pengju , Liu Xiaohui
Published: July 2022

In the foamed-concrete-backfilled-gas-pipeline project, the fluidity of foamed concrete has a great impact on the construction quality. This research studied the fluidity of foamed concrete through laboratory tests. By changing the water–cement ratio, admixtures, additives, foaming-agent content and other test parameters, foamed concrete with different fluidities was prepared, and the effects of the above parameters on the fluidity of foamed concrete were analyzed. At the same time, the construction equipment was improved in the three steps of transportation, production and pouring. The results show the factors affecting the fluidity of foamed concrete are, in order of importance, foaming-agent content > water–cement ratio > water-reducer content > admixture content. According to the orthogonal-test results, the control scheme meeting the fluidity requirements of the actual engineering project had gains as follows: the water–cement-ratio range from 0.5 to 0.6, the amount of admixture from 35% to 40%, the water-reducer content at 1% and the foaming-agent content from 3% to 3.5% so as to ensure the automatic leveling of foamed concrete under the best flow state.
Development of an Injection Molding Process for Long Glass Fiber-Reinforced Phenolic Resins
Authors: Maertens Robert , Liebig Wilfried V. , Weidenmann Kay A. , Elsner Peter
Published: July 2022

Glass fiber-reinforced phenolic resins are well suited to substitute aluminum die-cast materials. They meet the high thermomechanical and chemical demands that are typically found in combustion engine and electric drive train applications. An injection molding process development for further improving their mechanical properties by increasing the glass fiber length in the molded part was conducted. A novel screw mixing element was developed to improve the homogenization of the long fibers in the phenolic resin. The process operation with the mixing element is a balance between the desired mixing action, an undesired preliminary curing of the phenolic resin, and the reduction of the fiber length. The highest mixing energy input leads to a reduction of the initial fiber length L0 = 5000 μm to a weighted average fiber length of Lp = 571 μm in the molded part. This is an improvement over Lp = 285 μm for a short fiber-reinforced resin under comparable processing conditions. The mechanical characterization shows that for the long fiber-reinforced materials, the benefit of the increased homogeneity outweighs the disadvantages of the reduced fiber length. This is evident from the increase in tensile strength from σm = 21 MPa to σm = 57 MPa between the lowest and the highest mixing energy input parameter settings.
Mechanical Properties, Melting and Crystallization Behaviors, and Morphology of Carbon Nanotubes/Continuous Carbon Fiber Reinforced Polyethylene Terephthalate Composites
Authors: Qiao Liang , Yan Xu , Tan Hongsheng , Dong Shuhua , Ju Guannan , Shen Hongwang , Ren Zhaoying
Published: July 2022

Carbon nanotube/continuous carbon fiber reinforced poly(ethylene terephthalate) (CNT/CCF/PET) composites are prepared by melt impregnating. The effects of CF and CNT content on the mechanical properties, melt and crystallization behaviors, and submicroscopic morphology of CNT/CCF/PET composites are studied. The tensile test results show that the increase of CF and the addition of appropriate amount of CNT improved the tensile strength and tensile modulus of the composites. When the content of CNT is 1.0 wt% and the content of CF is 56 wt%, the properties of the composites are the best, with tensile strength of 1728.7 MPa and tensile modulus of 25.1 GPa, which is much higher than that of traditional resin matrix composites. The results of dynamic mechanical analysis (DMA) show that the storage modulus of the composites increased with the increase of CF and CNT content. In particular, the addition of CNT greatly reduced the loss modulus of the composites. Morphological analysis show that the addition of CNT improved the fiber–matrix interface of the composite, which changes from fiber pull-out and fracture failure to fiber matrix fracture failure, and the fiber matrix interface is firmly bonded. In addition, there are polymer coated CNT protrusions on the surface of the fiber was observed. The results of differential scanning calorimetry (DSC) show that the melting temperature and crystallization temperature of the composites increased with the increase of CF content. The addition of CNT had little effect on the melting temperature of the composites, but it further improved the crystallization temperature of the composites. The effect of CNT content on the crystallization kinetics of the composites is studied. The non-isothermal crystallization kinetics of the composites is described by Jeziorny’s improved Avrami equation. The results show that CNT has a great influence on the crystallization type of the composites. As a nucleating agent, CNT has obvious heterogeneous nucleation effect in the composites, which improves the crystallization rate of PET.
Cadmium-Rich Plant Powder/PAN/PU Foams with Low Thermal Conductivity
Authors: Tang Wenying , Sun Jin , Tang Jie , Chen Zheng , Shi Yidong , Zhao Ruifang , Jiang Yuanzhang , Tan Lin
Published: July 2022

Treating and utilizing heavy metal enriched plants have become growing problems. In this work, a series of composite foams were made from the powder of Cadmium-rich plant, polyacrylonitrile (PAN) and polyurethane (PU). Test results indicated that the addition of plant powder can not only increase the specific surface area, but also improve the apparent density and thermal stability of the foams. Besides, compared with the foam without plant powder, the powder-added foams exhibited a decreasing trend for thermal conductivity, and the minimum was 0.048 w/(m·k), which indicated that the addition of plant powder can help to enhance the thermal insulation of composite foam. More importantly, the results of leaching experiment showed that the leaching rate of heavy metal cadmium in the composite foam with 50% plant powder content was as low as 0.14% after being immersed in the acidic (pH = 3) solution for 5 days, which implies that the foam materials are very safe. This study provides a new way to realize high value-added resource utilization of heavy metal-enriched plants.
Deposition Offset of Printed Foam Strands in Direct Bubble Writing
Authors: Rastogi Prasansha , Venner Cornelis H. , Visser Claas Willem
Published: July 2022

Direct Bubble Writing is a recent technique to print shape-stable 3-dimensional foams from streams of liquid bubbles. These bubbles are ejected from a core-shell nozzle, deposited on the build platform placed at a distance of approximately 10 cm below the nozzle, and photo-polymerized in situ. The bubbles are ejected diagonally, with a vertical velocity component equal to the ejection velocity and a horizontal velocity component equal to the motion of the printhead. Owing to the horizontal velocity component, a discrepancy exists between the nozzle trajectory and the location of the printed strand. This discrepancy can be substantial, as for high printhead velocities (500 mm/s) an offset of 8 mm (in radius) was measured. Here, we model and measure the deviation in bubble deposition location as a function of printhead velocity. The model is experimentally validated by the printing of foam patterns including a straight line, a circle, and sharp corners. The deposition offset is compensated by tuning the print path, enabling the printing of a circular path to the design specifications and printing of sharp corners with improved accuracy. These results are an essential step towards the Direct Bubble Writing of 3-dimensional polymer foam parts with high dimensional accuracy.
Corn Cob Char as Catalyst Support for Developing Carbon Nanotubes from Waste Polypropylene Plastics: Comparison of Activation Techniques
Authors: Modekwe Helen U. , Moothi Kapil , Daramola Michael O. , Mamo Messai A.
Published: July 2022

The future and continuity of nanomaterials are heavily dependent on their availability and affordability. This could be achieved when cheap materials are actively employed as starting materials for nanomaterials synthesis. In this study, waste corn cob char was used as support during the preparation of the NiMo catalyst, and the effect of different char-activating techniques on the microstructure, yield and quality of carbon nanotubes (CNTs) obtained from waste polypropylene (PP) plastics using the chemical vapor deposition (CVD) technique was investigated. Properties of the catalysts and obtained nanomaterials were evaluated by XRD, SEM, N2 physisorption experiment, FTIR, Raman spectroscopy and TEM. Results showed improved surface properties of the NiMo catalyst supported on chemically (NiMo/ACX) and physically activated char (NiMo/ACT) compared to the NiMo catalyst supported on non-activated char (NiMo/AC0). High-quality CNTs were deposited over NiMo/ACT compared to NiMo/ACX and NiMo/AC0. It was also observed that different activation methods resulted in the formation of CNTs of different microstructures and yield. Optimum yield (470.0 mg CNTs/g catalyst) was obtained with NiMo/AC0, while NiMo/ACT gave the least product yield (70.0 mg CNTs/g catalyst) of the as-produced nanomaterials. Based on the results of the analysis, it was concluded that utilizing a cheap pyrogenic product of waste corn cob as a catalyst support in a bimetallic NiMo catalyst could offer a promising approach to mass producing CNTs and as a low-cost alternative in CNTs production from waste plastics.
Multifunctional PEG Carrier by Chemoenzymatic Synthesis for Drug Delivery Systems: In Memory of Professor Andrzej Dworak
Authors: Puskas Judit E. , Shrikhande Gayatri , Krisch Eniko , Molnar Kristof
Published: July 2022

This paper describes the synthesis and characterization of new bivalent folate-targeted PEGylated doxorubicin (FA2-dPEG-DOX2) made by modular chemo-enzymatic processes using Candida antarctica lipase B (CALB) as a biocatalyst. Unique features are the use of monodisperse PEG (dPEG) and the synthesis of thiol-functionalized folic acid yielding exclusive γ-conjugation of folic acid (FA) to dPEG. The polymer-based drug conjugate is built up by a series of transesterification and Michael addition reactions all catalyzed be CALB. In comparison with other methods in the literature, the modular approach with enzyme catalysis leads to selectivity, full conversion and high yield, and no transition metal catalyst residues. The intermediate product with four acrylate groups is an excellent platform for Michael-addition-type reactions for a wide variety of biologically active molecules. The chemical structures were confirmed by nuclear magnetic resonance spectroscopy (NMR). Flow cytometry analysis showed that, at 10 µM concentration, both free DOX and FA2-dPEG-DOX2 were taken up by 99.9% of triple-negative breast cancer cells in 2 h. Fluorescence was detected for 5 days after injecting compound IV into mice. Preliminary results showed that intra-tumoral injection seemed to delay tumor growth more than intravenous delivery.
An Analytical Model for Cure-Induced Deformation of Composite Laminates
Authors: Peng Xiaobo , Xu Jiang , Cheng Yong , Zhang Long , Yang Jie , Li Yinghui
Published: July 2022

Curing deformation prediction plays an important role in guiding the tools, curing process design, etc. Analytical methods can provide a rapid prediction and in-depth understanding of the curing deformation mechanism. In this paper, an analytical model is presented to study the cure-induced deformation of composite laminates. Based on the classical laminate theory, the thermal stress and deformation of composites during the curing process are calculated by considering the evolution of the mechanical properties of resin. Additionally, the coupling stiffness of the laminate is taken into consideration in the analytical model. An interface layer between the tool and the part is developed to simulate the variation of the tool–part interaction with the degree of resin cure. The maximum curing deformations and deformation profiles of different lay-up composite parts predicted by the proposed model are compared with the results of the finite element method and previous literature reports. Then, a comprehensive parametric study is carried out to investigate the influence of curing cycle, geometry, tool thermal expansion, and resin characteristics on the curing deformation of composite parts. The results reveal that geometry has a significant influence on the curing deformation of composite parts, but for dimensionally determined parts, curing deformation is mainly attributable to their own anisotropy in macro and micro aspects, as well as the stretching effect of the tool on the part. The percentage contribution of different factors to curing deformation composites with different lay-ups and geometries is also discussed.
Characterization and Antibacterial Properties of Polyetherketoneketone Coated with a Silver Nanoparticle-in-Epoxy Lining
Authors: Lee Wei-Fang , Wang Lu-Ying , Renn Ting-Yi , Yang Jen-Chang , Fang Lih-Sheng , Lee Yi-Huan , Peng Pei-Wen
Published: July 2022

Polyetherketoneketone (PEKK) is an alternative material for use in removable partial denture frameworks; these frameworks must exhibit antibacterial properties to reduce the risk of periodontal disease. In the present study, silver nanoparticles (AgNPs) were synthesized via the reduction of silver nitrate with sodium borohydride in a solution containing polyvinyl pyrrolidone (PVP). Transmission electron microscope images and dynamic light scattering confirmed that metallic nanoparticles had been created with an average size of 32 nm. Furthermore, the coating of the PEKK polymeric substrate with 0.5% AgNPs was carried out using an epoxy resin lining at room temperature. Fourier transform infrared (FTIR) spectra confirmed the successful transfer of the AgNP-in-resin lining onto the polymeric substrate. Scanning electron microscopy and atomic force microscopy confirmed that the AgNPs had been uniformly deposited onto the PEKK specimens. Finally, the antibacterial activity of the specimens was tested against Porphyromonas gingivalis. An inhibition zone of 22.5 mm and an antibacterial rate of 83.47% were found for the PEKK coated with 0.5% AgNPs (0.5% Ag-PEKK) compared to an untreated polyetheretherketone (PEEK) substrate, evidencing that 0.5% Ag-PEKK has potential antibacterial properties for implant applications.
Flame Retardant Coatings: Additives, Binders, and Fillers
Authors: Mohd Sabee Mohd Meer Saddiq , Itam Zarina , Beddu Salmia , Zahari Nazirul Mubin , Mohd Kamal Nur Liyana , Mohamad Daud , Zulkepli Norzeity Amalin , Shafiq Mohamad Danial , Abdul Hamid Zuratul Ain
Published: July 2022

This review provides an intensive overview of flame retardant coating systems. The occurrence of flame due to thermal degradation of the polymer substrate as a result of overheating is one of the major concerns. Hence, coating is the best solution to this problem as it prevents the substrate from igniting the flame. In this review, the descriptions of several classifications of coating and their relation to thermal degradation and flammability were discussed. The details of flame retardants and flame retardant coatings in terms of principles, types, mechanisms, and properties were explained as well. This overview imparted the importance of intumescent flame retardant coatings in preventing the spread of flame via the formation of a multicellular charred layer. Thus, the intended intumescence can reduce the risk of flame from inherently flammable materials used to maintain a high standard of living.
Hydrodynamic Predictions of the Ultralight Particle Dispersions in a Bubbling Fluidized Bed
Authors: Liu Hailang , Li Guohui , Liu Yang
Published: July 2022

Particle and gas flow characteristics are numerically simulated by means of a proposed gas–particle second-order moment two-fluid model with particle kinetic–friction stress model in a bubbling fluidized bed. Anisotropic behaviors of gas–solid two-phase stresses and their interactions are fully considered by the two-phase Reynolds stress model and their closure correlations. The dispersion behaviors of the non-spherical expand graphite and spherical heavy particles are predicted by using the parameters of distributions of particle velocity, porosity, granular temperature, and dominant frequency. Compared to particles density 2700 kg/m3, ultralight particles exhibit the higher voidages with big bubbles and larger axial-averaged velocity of particles and stronger dispersion behaviors. Maximum granular temperature is approximately 3.0 times greater than that one, and dominant frequency for axial porosity fluctuations is 1.5 Hz that is 1/3 time as larger as that heavy particle.
Metabolomic Profile and Antibacterial Bioactivity of Akebia trifoliata (Thunb.) Koidz Pericarp Extract
Authors: Chen Jing , Sun Zhimin , Chen Jianhua , Luan Mingbao
Published: July 2022

Akebia trifoliata (A. trifoliata) is a significant medicinal and edible fruit crop and has some important bioactivities. However, there are few studies on the bacteriostatic activity of A. trifoliata, and the underlying mechanism of A. trifoliata for antibacterial activity is still unknown. Therefore, the bacteriostatic activity and antibacterial mechanism of A. trifoliata were investigated by a combination of chemical assays, using the UHPLC-TOF-MS/MS technique. The results indicated that alkaloids, triterpenoids, and flavonoids are the major secondary bioactive compounds in A. trifoliata that play a crucial role in antibacterial activity. We found that EEPA exhibited both bacteriostatic and bactericidal effects against all Gram-positive and Gram-negative bacteria tested, with IZDs ranging from 13.80 ± 0.79 to 17.00 ± 0.58 mm. Significant differences in terms of sensitivity between Gram-positive and Gram-negative bacteria were not observed. In contrast, both antibiotics (kanamycin sulfate and ampicillin sodium salt) exhibited much better antimicrobial activity against Gram-positive bacteria than Gram-negative bacteria. In addition, the primary antimicrobial mechanism was that EEPA increased cellular content leakage, altered the cell morphology, and destroyed the internal cell structure. Meanwhile, MA, UA, and OA, as the common triterpenoid components existing in plants, were used to analyze the relationships between the structures and the antimicrobial activities among homologous compounds, to determine the key functional group that plays an antibacterial role in MA, UA, and OA. As result, it was found that both the hydroxide and methyl groups present are important for their antibacterial activity. These findings suggested that EEPA exerted significant antimicrobial activity against S. aureus, E. coli, B. subtilis, and P. aeruginosa and might be a potential natural antibacterial.
Religion, Animals, and Indigenous Traditions
Authors: Weatherdon Meaghan S.
Published: July 2022

This article examines how the field of Indigenous studies can contribute to expanding the way religious studies scholars think through the question of the animal. It suggests that Indigenous intellectual traditions, which often position animals as persons, relatives, knowledge holders, and treaty makers, prompt further reflection on the fundamental questions of what it means to be a human animal and member of a pluralistic cosmology of beings. The article considers how Indigenous activists and scholars are actively re-centering animals in their decolonial pursuits and asks how a re-centering of animals might also contribute to decolonizing the study of religion.
Simplified Marsh Response Model (SMRM): A Methodological Approach to Quantify the Evolution of Salt Marshes in a Sea-Level Rise Context
Authors: Inácio Miguel , Freitas M. Conceição , Cunha Ana Graça , Antunes Carlos , Leira Manel , Lopes Vera , Andrade César , Silva Tiago Adrião
Published: July 2022

Salt marshes are highly valued coastal environments for different services: coastline protection, biodiversity, and blue carbon. They are vulnerable to climate changes, particularly to sea-level rise. For this reason, it is essential to project the evolution of marsh areas until the end of the century. This work presents a reduced complexity model to quantify salt marshes’ evolution in a sea-level rise (SLR) context through combining field and remote sensing data: SMRM (Simplified Marsh Response Model). SMRM is a two-dimensional rule-based model that requires four parameters: a digital terrain model (DTM), local tidal levels, a sea-level rise projection, and accretion rates. A MATLAB script completes the process, and the output is a GeoTIFF file. Two test areas were selected in Tróia sandspit (Setúbal, Portugal). Additionally, a sensitivity analysis for each parameter’s influence and a comparison with SLAMM (another rule-based model) were undertaken. The sensitivity analysis indicates that SLR is the most relevant parameter, followed by accretion rates. The comparison of SMRM with SLAMM shows quite similar results for both models. This new model application indicates that the studied salt marshes could be resilient to conservative sea-level rise scenarios but not to more severe sea-level rise projections.
Creating a Detailed Wetland Inventory with Sentinel-2 Time-Series Data and Google Earth Engine in the Prairie Pothole Region of Canada
Authors: DeLancey Evan R. , Czekajlo Agatha , Boychuk Lyle , Gregory Fiona , Amani Meisam , Brisco Brian , Kariyeva Jahan , Hird Jennifer N.
Published: July 2022

Wetlands in the Prairie Pothole Region (PPR) of Canada and the United States represent a unique mapping challenge. They are dynamic both seasonally and year-to-year, are very small, and frequently altered by human activity. Many efforts have been made to estimate the loss of these important habitats but a high-quality inventory of pothole wetlands is needed for data-driven conservation and management of these resources. Typical landcover classifications using one or two image dates from optical or Synthetic Aperture Radar (SAR) Earth Observation (EO) systems often produce reasonable wetland inventories for less dynamic, forested landscapes, but will miss many of the temporary and seasonal wetlands in the PPR. Past studies have attempted to capture PPR wetland dynamics by using dense image stacks of optical or SAR data. We build upon previous work, using 2017–2020 Sentinel-2 imagery processed through the Google Earth Engine (GEE) cloud computing platform to capture seasonal flooding dynamics of wetlands in a prairie pothole wetland landscape in Alberta, Canada. Using 36 different image dates, wetland flood frequency (hydroperiod) was calculated by classifying water/flooding in each image date. This product along with the Global Ecosystem Dynamics Investigation (GEDI) Canopy Height Model (CHM) was then used to generate a seven-class wetland inventory with wetlands classified as areas with seasonal but not permanent water/flooding. Overall accuracies of the resulting inventory were between 95% and 96% based on comparisons with local photo-interpreted inventories at the Canadian Wetland Classification System class level, while wetlands themselves were classified with approximately 70% accuracy. The high overall accuracy is due, in part, to a dominance of uplands in the PPR. This relatively simple method of classifying water through time generates reliable wetland maps but is only applicable to ecosystems with open/non-complex wetland types and may be highly sensitive to the timing of cloud-free optical imagery that captures peak wetland flooding (usually post snow melt). Based on this work, we suggest that expensive field or photo-interpretation training data may not be needed to map wetlands in the PPR as self-labeling of flooded and non-flooded areas in a few Sentinel-2 images is sufficient to classify water through time. Our approach demonstrates a framework for the operational mapping of small, dynamic PPR wetlands that relies on open-access EO data and does not require costly, independent training data. It is an important step towards the effective conservation and management of PPR wetlands, providing an efficient method for baseline and ongoing mapping in these dynamic environments.
Advances in Land–Ocean Heat Fluxes Using Remote Sensing
Authors: Yao Yunjun , Zhang Xiaotong , Levy Gad , Jia Kun , Al-Quraishi Ayad M. Fadhil
Published: July 2022

Advanced remote sensing technology has provided spatially distributed variables for estimating land–ocean heat fluxes, allowing for practical applications in drought monitoring, water resources management, and climate assessment. This Special Issue includes several research studies using state-of-the-art algorithms for estimating downward longwave radiation, surface net radiation, latent heat flux, columnar atmospheric water vapor, fractional vegetation cover, and grassland aboveground biomass. This Special Issue intends to help scientists involved in global change research and practices better comprehend the strengths and disadvantages of the application of remote sensing for monitoring surface energy, water, and carbon budgets. The studies published in this Special Issue can be applied by natural resource management communities to enhance the characterization and assessment of land–ocean biophysical variables, as well as for more accurately partitioning heat flux into soil and vegetation based on the existing and forthcoming remote sensing data.
Investigating the Shallow-Water Bathymetric Capability of Zhuhai-1 Spaceborne Hyperspectral Images Based on ICESat-2 Data and Empirical Approaches: A Case Study in the South China Sea
Authors: Le Yuan , Hu Mengzhi , Chen Yifu , Yan Qian , Zhang Dongfang , Li Shuai , Zhang Xiaohan , Wang Lizhe
Published: July 2022

Accurate bathymetric and topographical information is crucial for coastal and marine applications. In the past decades, owing to its low cost and high efficiency, satellite-derived bathymetry has been widely used to estimate the depth of shallow water in coastal areas. However, insufficient spectral bands and availability of in situ water depths limit the application of satellite-derived bathymetry. Currently, the investigation about the bathymetric potential of hyperspectral imaging is relatively insufficient based on datasets of the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2). In this study, Zhuhai-1 hyperspectral images and ICESat-2 datasets were utilized to perform nearshore bathymetry and explore the bathymetric capability by selecting different bands based on classical empirical models (the band ratio model and the linear band model). Furthermore, experimental results achieved at the South China Sea indicate that the combination of blue (2 and 3 band) and green (9 band) bands and the combination of red (10 and 12 band) and near-infrared (29 band) bands are most suitable to achieve nearshore bathymetry. Correspondingly, the highest accuracy of bathymetry reached root mean square error values of 0.98 m and 1.19 m for different band combinations evaluated through bathymetric results of reference water depth. The bathymetric accuracy of Zhuhai-1 image is similar with that of Sentinel-2 when employing the blue and green bands. The combination of red and near-infrared bands has a higher bathymetric accuracy for Zhuhai-1 image than that for Sentinel-2 image.
Impacts of the Urmia Lake Drought on Soil Salinity and Degradation Risk: An Integrated Geoinformatics Analysis and Monitoring Approach
Authors: Feizizadeh Bakhtiar , Omarzadeh Davoud , Mohammadzadeh Alajujeh Keyvan , Blaschke Thomas , Makki Mohsen
Published: July 2022

Recent improvements in earth observation technologies and Geographical Information System (GIS) based spatial analysis methods require us to examine the efficiency of the different data-driven methods and decision rules for soil salinity monitoring and degradation mapping. The main objective of this study was to analyze the environmental impacts of the Lake Urmia drought on soil salinity and degradation risk in the plains surrounding the hyper-saline lake. We monitored the impacts of the lake drought on soil salinity by applying spatiotemporal indices to time-series satellite images (1990–2020) in Google Earth Engine environment. We also computed the soil salinity ratio to validate the results and determine the most efficient soil salinity monitoring techniques. We then mapped the soil degradation risk based on GIS spatial decision-making methods. Our results indicated that the Urmia Lake drought is leading to the formation of extensive salt lands, which impact the fertility of the farmlands. The land affected by soil salinity has increased from 2.86% in 1990 to 16.68% in 2020. The combined spectral response index, with a performance of 0.95, was the most efficient image processing method to assess soil salinity. The soil degradation risk map showed that 38.45% of the study area has a high or very high risk of degradation, which is a significant threat to food production. This study presents an integrated geoinformation approach for time-series soil salinity monitoring and degradation risk mapping that supports future studies by comparing the efficiency of different methods as state of the art. From a practical perspective, the results also provide key information for decision-makers, authorities, and local stakeholders in their efforts to mitigate the environmental impacts of lake drought and sustain the food production to sustain the 7.3 million residents.
High-Resolution Snow-Covered Area Mapping in Forested Mountain Ecosystems Using PlanetScope Imagery
Authors: John Aji , Cannistra Anthony F. , Yang Kehan , Tan Amanda , Shean David , Hille Ris Lambers Janneke , Cristea Nicoleta
Published: July 2022

Improving high-resolution (meter-scale) mapping of snow-covered areas in complex and forested terrains is critical to understanding the responses of species and water systems to climate change. Commercial high-resolution imagery from Planet Labs, Inc. (Planet, San Francisco, CA, USA) can be used in environmental science, as it has both high spatial (0.7–3.0 m) and temporal (1–2 day) resolution. Deriving snow-covered areas from Planet imagery using traditional radiometric techniques have limitations due to the lack of a shortwave infrared band that is needed to fully exploit the difference in reflectance to discriminate between snow and clouds. However, recent work demonstrated that snow cover area (SCA) can be successfully mapped using only the PlanetScope 4-band (Red, Green, Blue and NIR) reflectance products and a machine learning (ML) approach based on convolutional neural networks (CNN). To evaluate how additional features improve the existing model performance, we: (1) build on previous work to augment a CNN model with additional input data including vegetation metrics (Normalized Difference Vegetation Index) and DEM-derived metrics (elevation, slope and aspect) to improve SCA mapping in forested and open terrain, (2) evaluate the model performance at two geographically diverse sites (Gunnison, Colorado, USA and Engadin, Switzerland), and (3) evaluate the model performance over different land-cover types. The best augmented model used the Normalized Difference Vegetation Index (NDVI) along with visible (red, green, and blue) and NIR bands, with an F-score of 0.89 (Gunnison) and 0.93 (Engadin) and was found to be 4% and 2% better than when using canopy height- and terrain-derived measures at Gunnison, respectively. The NDVI-based model improves not only upon the original band-only model’s ability to detect snow in forests, but also across other various land-cover types (gaps and canopy edges). We examined the model’s performance in forested areas using three forest canopy quantification metrics and found that augmented models can better identify snow in canopy edges and open areas but still underpredict snow cover under forest canopies. While the new features improve model performance over band-only options, the models still have challenges identifying the snow under trees in dense forests, with performance varying as a function of the geographic area. The improved high-resolution snow maps in forested environments can support studies involving climate change effects on mountain ecosystems and evaluations of hydrological impacts in snow-dominated river basins.
Modeling Nutrition Quality and Storage of Forage Using Climate Data and Normalized-Difference Vegetation Index in Alpine Grasslands
Authors: Han Fusong , Fu Gang , Yu Chengqun , Wang Shaohua
Published: July 2022

Quantifying forage nutritional quality and pool at various spatial and temporal scales are major challenges in quantifying global nitrogen and phosphorus cycles, and the carrying capacity of grasslands. In this study, we modeled forage nutrition quality and storage using climate data under fencing conditions, and using climate data and a growing-season maximum normalized-difference vegetation index under grazing conditions based on four different methods (i.e., multiple linear regression, random-forest models, support-vector machines and recursive-regression trees) in the alpine grasslands of Tibet. Our results implied that random-forest models can have greater potential ability in modeling forage nutrition quality and storage than the other three methods. The relative biases between simulated nutritional quality using random-forest models and the observed nutritional quality, and between simulated nutrition storage using random-forest models and the observed nutrition storage, were lower than 2.00% and 6.00%, respectively. The RMSE between simulated nutrition quality using random-forest models and the observed nutrition quality, and between simulated nutrition storage using random-forest models and the observed nutrition storage, were no more than 0.99% and 4.50 g m−2, respectively. Therefore, random-forest models based on climate data and/or the normalized-difference vegetation index can be used to model forage nutrition quality and storage in the alpine grasslands of Tibet.
Satellite-Observed Thermal Anomalies and Deformation Patterns Associated to the 2021, Central Crete Seismic Sequence
Authors: Peleli Sofia , Kouli Maria , Vallianatos Filippos
Published: July 2022

Nowadays, there has been a growing interest in understanding earthquake forerunners, i.e., anomalous variations that are possibly associated with the complex process of earthquake evolution. In this context, the Robust Satellite Technique was coupled with 10 years (2012–2021) of daily night-time MODIS-Land Surface Temperature remote sensing data to detect thermal anomalies likely related to the 27 September 2021, strong onshore earthquake of magnitude Mw6.0 occurring near the Arkalochori village in Central Crete, Greece. Eight intense (signal-to-noise ratio > 3) and infrequent, quite extensive, and temporally persistent thermal signal transients were detected and characterized as pre-seismic anomalies, while one thermal signal transient was identified as a co-seismic effect on the day of the main tectonic event. The thermal anomalies dataset was combined with tectonic parameters of Central Crete, such as active faults and fault density, seismogenic zones and ground displacement maps produced using Sentinel-1 satellite imagery and the Interferometric Synthetic Aperture Radar technique. Regarding the thermal anomaly of 27 September, its greatest portion was observed over the footwall part of the fault where a significant subsidence up to 20 cm exists. We suggest that the thermal anomalies are possibly connected with gas release which happens due to stress changes and is controlled by the existence of tectonic lines and the density of the faults, even if alternative explanations could not be excluded.
A Prior Level Fusion Approach for the Semantic Segmentation of 3D Point Clouds Using Deep Learning
Authors: Ballouch Zouhair , Hajji Rafika , Poux Florent , Kharroubi Abderrazzaq , Billen Roland
Published: July 2022

Three-dimensional digital models play a pivotal role in city planning, monitoring, and sustainable management of smart and Digital Twin Cities (DTCs). In this context, semantic segmentation of airborne 3D point clouds is crucial for modeling, simulating, and understanding large-scale urban environments. Previous research studies have demonstrated that the performance of 3D semantic segmentation can be improved by fusing 3D point clouds and other data sources. In this paper, a new prior-level fusion approach is proposed for semantic segmentation of large-scale urban areas using optical images and point clouds. The proposed approach uses image classification obtained by the Maximum Likelihood Classifier as the prior knowledge for 3D semantic segmentation. Afterwards, the raster values from classified images are assigned to Lidar point clouds at the data preparation step. Finally, an advanced Deep Learning model (RandLaNet) is adopted to perform the 3D semantic segmentation. The results show that the proposed approach provides good results in terms of both evaluation metrics and visual examination with a higher Intersection over Union (96%) on the created dataset, compared with (92%) for the non-fusion approach.
Mapping Asbestos-Cement Corrugated Roofing Tiles with Imagery Cube via Machine Learning in Taiwan
Authors: Yu Teng-To , Lin Yen-Chun , Lan Shyh-Chin , Yang Yu-En , Wu Pei-Yun , Lin Jo-Chi
Published: July 2022

Locating and calculating the number of asbestos-cement corrugated roofing tiles is the first step in the demolition process. In this work, archived image cubes of Taiwan served as the fundamental data source used via machine learning approach to identify the existence of asbestos-cement corrugated roofing tiles with more than 85% accuracy. An adequate quantity of ground-truth data covering all the types of roofs via aerial hyperspectral scan was the key to success for this study. Twenty randomly picked samples from the ground-truth group were examined by X-ray refraction detection to ensure correct identification of asbestos-cement corrugated roofing tiles with remote sensing. To improve the classifying accuracy ratio, two different machine learning algorithms were applied to gather the target layers individually using the same universal training model established from 400 ground-truth samples. The agreement portions within the overlapping layers of these two approaches were labeled as the potential targets, and the pixel growth technique was performed to detect the roofing boundary and create the polygon layer with size information. Exacting images from aerial photos within the chosen polygon were compared to up-to-date Sentinel-1 images to find the temporal disagreements and remove the mismatched buildings, identified as non-asbestos roofs, from the database to reflect the actual condition of present data. This automatic matching could be easily performed by machine learning to resolve the information lag while using archived data, which is an essential issue when detecting targets with non-simultaneous acquired images over a large area. To meet the 85% kappa accuracy requirement, the recurring processes were applied to find the optimal parameters of the machine learning model. Meanwhile, this study found that the support vector machine method was easier to handle, and the convolution neuro network method offered better accuracy in automatic classification with a universal training model for vast areas. This work demonstrated a feasible approach using low-cost and low-resolution archived images to automatically detect the existence of asbestos-cement corrugated roofing tiles over large regions. The entire work was completed within 16 months for an area of 36,000 km2, and the detected number of asbestos-cement corrugated roofing tiles was more than three times the initial estimation by statistics method from two small-area field surveys.
Design and Development of Array POS for Airborne Remote Sensing Motion Compensation
Authors: Qu Chunyu , Li Jianli , Bao Junfang , Zhu Zhuangsheng
Published: July 2022

Multi-antenna airborne remote sensing systems have received more attention recently because they can realize high-resolution three-dimensional (3-D) imaging, such as array Synthetic Aperture Radar (SAR). Their high-precision imaging needs multi-antenna motion and relative motion between antennas. However, the existing facility and technology hardly meet the motion measurement precision demand of array SAR. To solve this problem, an array Position and Orientation System (POS) for airborne remote sensing motion compensation is designed and developed. It is composed of a high-precision POS, several small-size Inertial Measurement Units (IMU), and a 6-D deformation measurement system based on Fiber Bragg Grating (FBG) sensors. Firstly, the transfer alignment method based on 6-D deformation is used to measure the relative motion between array POS. Then, the motion conversion method from array POS to array SAR is presented to obtain the multi-antenna motion and relative motion between antennas. Finally, the ground experiment results identify that the accuracies of multi-antenna position, multi-antenna attitude, and flexible baseline length between antennas are superior to 3 cm, 0.01°, and 0.1 mm, respectively, which can meet the motion measurement precision demand of array SAR.
Weakly Supervised Learning for Transmission Line Detection Using Unpaired Image-to-Image Translation
Authors: Choi Jiho , Lee Sang Jun
Published: July 2022

To achieve full autonomy of unmanned aerial vehicles (UAVs), obstacle detection and avoidance are indispensable parts of visual recognition systems. In particular, detecting transmission lines is an important topic due to the potential risk of accidents while operating at low altitude. Even though many studies have been conducted to detect transmission lines, there still remains many challenges due to their thin shapes in diverse backgrounds. Moreover, most previous methods require a significant level of human involvement to generate pixel-level ground truth data. In this paper, we propose a transmission line detection algorithm based on weakly supervised learning and unpaired image-to-image translation. The proposed algorithm only requires image-level labels, and a novel attention module, which is called parallel dilated attention (PDA), improves the detection accuracy by recalibrating channel importance based on the information from various receptive fields. Finally, we construct a refinement network based on unpaired image-to-image translation in order that the prediction map is guided to detect line-shaped objects. The proposed algorithm outperforms the state-of-the-art method by 2.74% in terms of F1-score, and experimental results demonstrate that the proposed method is effective for detecting transmission lines in both quantitative and qualitative aspects.
Aerosol Mineralogical Study Using Laboratory and IASI Measurements: Application to East Asian Deserts
Authors: Alalam Perla , Deschutter Lise , Al Choueiry Antoine , Petitprez Denis , Herbin Hervé
Published: July 2022

East Asia is the second-largest mineral dust source in the world, after the Sahara. When dispersed in the atmosphere, mineral dust can alter the Earth’s radiation budget by changing the atmosphere’s absorption and scattering properties. Therefore, the mineralogical composition of dust is key to understanding the impact of mineral dust on the atmosphere. This paper presents new information on mineralogical dust during East Asian dust events that were obtained from laboratory dust measurements combined with satellite remote sensing dust detections from the Infrared Atmospheric Sounding Interferometer (IASI). However, the mineral dust in this region is lifted above the continent in the lower troposphere, posing constraints due to the large variability in the Land Surface Emissivity (LSE). First, a new methodology was developed to correct the LSE from a mean monthly emissivity dataset. The results show an adjustment in the IASI spectra by acquiring aerosol information. Then, the experimental extinction coefficients of pure minerals were linearly combined to reproduce a Gobi dust spectrum, which allowed for the determination of the mineralogical mass weights. In addition, from the IASI radiances, a spectral dust optical thickness was calculated, displaying features identical to the optical thickness of the Gobi dust measured in the laboratory. The linear combination of pure minerals spectra was also applied to the IASI optical thickness, providing mineralogical mass weights. Finally, the method was applied after LSE optimization, and mineralogical evolution maps were obtained for two dust events in two different seasons and years, May 2017 and March 2021. The mean dust weights originating from the Gobi Desert, Taklamakan Desert, and Horqin Sandy Land are close to the mass weights in the literature. In addition, the spatial variability was linked to possible dust sources, and it was examined with a backward trajectory model. Moreover, a comparison between two IASI instruments on METOP-A and -B proved the method’s applicability to different METOP platforms. Due to all of the above, the applied method is a powerful tool for exploiting dust mineralogy and dust sources using both laboratory optical properties and IASI detections.
Generative Adversarial Networks Based on Transformer Encoder and Convolution Block for Hyperspectral Image Classification
Authors: Bai Jing , Lu Jiawei , Xiao Zhu , Chen Zheng , Jiao Licheng
Published: July 2022

Nowadays, HSI classification can reach a high classification accuracy when given sufficient labeled samples as training set. However, the performances of existing methods decrease sharply when trained on few labeled samples. Existing methods in few-shot problems usually require another dataset in order to improve the classification accuracy. However, the cross-domain problem exists in these methods because of the significant spectral shift between target domain and source domain. Considering above issues, we propose a new method without requiring external dataset through combining a Generative Adversarial Network, Transformer Encoder and convolution block in a unified framework. The proposed method has both a global receptive field provided by Transformer Encoder and a local receptive field provided by convolution block. Experiments conducted on Indian Pines, PaviaU and KSC datasets demonstrate that our method exceeds the results of existing deep learning methods for hyperspectral image classification in the few-shot learning problem.