bluuebunny/crossref_metadata_2025 huggingface dataset:
Composite Cement Materials Based on β-Tricalcium Phosphate, Calcium Sulfate, and a Mixture of Polyvinyl Alcohol and Polyvinylpyrrolidone Intended for Osteanagenesis
Authors: Stepanova Kseniya , Lytkina Daria , Sadykov Rustam , Shalygina Kseniya , Khojazoda Toir , Mahmadbegov Rashidjon , Kurzina Irina
Published: January 2023

The primary purpose of the study, presented in this article, was to obtain a composite cement material intended for osteanagenesis. The β-tricalcium phosphate powder (β-TCP, β-Ca3(PO4)2) was obtained by the liquid-phase method. Setting and hardening of the cement system were achieved by adding calcium sulfate hemihydrate (CSH, CaSO4·1/2H2O). An aqueous solution of polyvinyl alcohol (PVA), polyvinylpyrrolidone (PVP), and a PVA/PVP mixture were used as a polymer component. The methods of capillary viscometry and Fourier-transform infrared spectroscopy (FTIR) revealed the formation of intermolecular hydrogen bonds between polymer components, which determines the good miscibility of polymers. The physicochemical properties of the synthesized materials were characterized by X-ray diffraction (XRD) and FTIR methods, and the added amount of polymers does not significantly influence the processes of phase formation and crystallization of the system. The size of crystallites CSD remained in the range of 32–36 nm, regardless of the ratio of polymer components. The influence of the composition of composites on their solubility was investigated. In view of the lower solubility of pure β-TCP, as compared to calcium sulfate dihydrate (CSD, CaSO4·2H2O), the solubility of composite materials is determined to a greater degree by the CSD solubility. Complexometric titration showed that the interaction between PVA and PVP impeded the diffusion of calcium ions, and at a ratio of PVA to PVP of 1/1, the smallest exit of calcium ions from the system is observed. The cytotoxicity analysis results allowed us to establish the fact that the viability of human macrophages in the presence of the samples varied from 80% to 125% as compared to the control.
Evaluation of Turbidity and Color Removal in Water Treatment: A Comparative Study between Opuntia ficus-indica Fruit Peel Mucilage and FeCl3
Authors: Otálora Maria Carolina , Wilches-Torres Andrea , Lara Carlos Rafael , Gómez Castaño Jovanny A. , Cifuentes Gabriel Ricardo
Published: January 2023

Natural coagulants derived from by-products have gained popularity as sustainable alternatives to inorganic coagulants in water/wastewater treatment due to their abundant availability, biodegradability, low cost, easy disposal and low sludge volumes. In this study, the mucilage obtained from the peel of Opuntia ficus-indica fruit was evaluated as a biocoagulant for treating synthetic turbid water and compared with a traditional chemical coagulant (FeCl3). The effects of coagulant dosage and pH on the turbidity and color-removal efficiency of synthetic turbid water were analyzed. To estimate the coagulation mechanism, the flocs produced under optimal values were characterized structurally (FTIR and zeta potential) and morphologically (SEM). The optimal condition for the removal of turbidity and color was a coagulant dose of 12 mg/L at pH 13. For the optimal values, the biocoagulant and the FeCl3 presented a maximum removal of 82.7 ± 3.28% and 94.63 ± 0.98% for turbidity and 71.82 ± 2.72% and 79.94 ± 1.77% for color, respectively. The structure and morphology of the flocs revealed that the coagulation mechanism of the mucilage was adsorption and bridging, whereas that of FeCl3 was charge neutralization. The results obtained showed that the mucilage could be used as an alternative coagulant to replace FeCl3.
Thermal Stabilities and Flame Retardancy of Polyamide 66 Prepared by In Situ Loading of Amino-Functionalized Polyphosphazene Microspheres
Authors: Lv Wenyan , Lv Jun , Zhu Cunbing , Zhang Ye , Cheng Yongli , Zeng Linghong , Wang Lu , Liao Changrong
Published: January 2023

The flame-retardant polyamide 66 composites (FR-PA66) were prepared by in situ loading of amino-functionalized polyphosphazene microspheres (HCNP), which were synthesized in the laboratory and confirmed by a Fourier transform infrared spectrometer (FTIR), scanning electron microscope (SEM), and transmission electron microscope (TEM). The thermal stabilities and flame retardancy of FR-PA66 were measured using thermogravimetric analysis (TGA), a thermogravimetric infrared instrument (TG-IR), the limiting oxygen index (LOI), the horizontal and vertical combustion method (UL-94), and a cone calorimeter. The results illustrate that the volatile matter of FR-PA66 mainly contains carbon dioxide, methane4, and water vapor under heating, accompanied by the char residue raising to 14.1 wt% at 600 °C and the value of the LOI and UL-94 rating reaching 30% and V-0, respectively. Moreover, the addition of HCNP decreases the peak of the heat release rate (pHRR), total heat release (THR), mass loss (ML), and total smoke release (TSR) of FR-PA66 to 373.7 kW/m2, 106.7 MJ/m2, 92.5 wt%, and 944.8 m2/m2, respectively, verifying a significant improvement in the flame retardancy of PA66.
Thermal Degradation and Carbonization Mechanism of Fe-Based Metal-Organic Frameworks onto Flame-Retardant Polyethylene Terephthalate
Authors: Ma Tianyi , Wang Wenqing , Wang Rui
Published: January 2023

Currently, the metal-organic framework (MOF) is a promising candidate for flame−retardant polymers. In this study, a Fe−based MOF, MIL-88B(Fe), was introduced to polyethylene terephthalate (PET) and 3−hydroxyphenylphosphinyl-propanoic acid copolymer (P−PET) to reduce the fire hazard involved in using PET. The limiting oxygen indexes (LOIs) of MIL−PET and MIL−P−PET improved by 27% and 30%, respectively. The UL−94 level achieved for MIL−P−PET was V−0 rating. The thermal degradation and carbonization mechanisms of MIL−PET and MIL−P−PET were systematically investigated through thermogravimetric analysis coupled with a Fourier transform infrared spectroscopy (TG−IR), pyrolysis-gas chromatography−mass spectrometry (Py−GC−MS), x−ray photoelectron spectroscopy (XPS), and Raman spectrum combined with quantum chemical molecular dynamics simulation. With the addition of MIL−88B(Fe), high graphitization and a hard flammability char residual were generated. Compared with neat PET, the ferric ions efficiently catalyzed the homolytic cleavage and dehydrogenation of PET to produce a large amount of CO2 and terephthalic acid for MIL−PET in gas phase. Rough and hierarchical char residual with ferric oxide was also generated when temperatures exceeded 600 °C. However, the carbonization process was inhibited due to the coordinated complex between phosphorus and ferric ions in MIL−P−PET, invaliding the decarboxylation and generating more benzoic acid and its precursor, which led to heavy smoke.
Prediction of Surface Subsidence of Deep Foundation Pit Based on Wavelet Analysis
Authors: Zhang Jindong , Cheng Zhangjianing
Published: January 2023

Predicting surface settlement in deep foundation pit engineering plays a central role in the safety of foundation pit construction. Recently, static or dynamic methods are usually applied to predict ground settlement in deep foundation pit projects. In this work, we propose a model combining wavelet noise reduction and radial basis neural network (XW-RBF) to reduce noise interference in monitoring data. The results show that the XW-RBF model predicts an average relative error of 0.77 and a root average square error of 0.13. The prediction performance is better than the original data prediction results with noise structure and has higher prediction accuracy. The noise data caused by the interference of construction and the surrounding environment in the original data can be removed via the wavelet noise reduction method, with the discreteness of the original data reducing by 30%. More importantly, our results show that the XW-RBF model can reflect the law of data change to predict the future data trend with high credibility. The findings of this study indicate that the XW-RBF model could optimize the deep foundation pit settlement prediction model for high accuracy during the prediction, which inspires the potential application in deep foundation pit engineering.
Mobile Mental Health Units in Heraklion Crete 2013–2022: Progress, Difficulties and Future Challenges
Authors: Paschalidou Anna , Anastasaki Maria , Zografaki Avgi , Krasanaki Christina Kalliopi , Daskalaki Maria , Chatziorfanos Vasilis , Giakovidou Anna , Basta Maria , Vgontzas Alexandros N.
Published: January 2023

Mobile mental health units (MMHUs) have been proposed as an effective model to serve the mental health needs of remote underserved areas. In 2013, the Department of Psychiatry, University of Crete, started a pilot MMHU to cover the needs of remote rural areas of the Heraklion Prefecture. The main objectives were early detection, prevention and therapy of patients with severe psychiatric disorders, without access to regular psychiatric care. In addition to regular visits to primary care health centers, a community support network was established. During this 10-year period, the MMHU has evaluated 3343 patients and performed 19,935 visits. The most frequent diagnoses are depression and anxiety (52.7%) whereas psychosis and bipolar disorders are the third (12.7%) and fifth most frequent diagnoses (4.7%). Half of the patients with depression/anxiety were older than 65 years and one third were living alone. Furthermore, the first visit with the MMHU of severely mentally ill was about 15 years later from the self-reported onset of psychotic symptoms. We discuss how to improve our ability to detect and intervene earlier in patients with severe mental illness, and how to address effectively, both pharmacologically and psychosocially, the depression and loneliness of older individuals living in small remote communities.
Release of PAH from Secondary Products Made from End-of-Life Tire Recyclates into 20% Aqueous Ethanol as Assessment of Human Dermal Absorption
Authors: Hoyer Stefan , Kroll Lothar , Fischer Benny , Pisulla Kai , Seidel Albrecht
Published: January 2023

Compliance with the legal limits set at the European level for the content of polycyclic aromatic hydrocarbons (PAH), which are harmful to human health and the environment, is of central importance for the recycling of rubber, particularly end-of-life tires (ELT), into secondary products, e.g., elastic fall protection mats or anti-slip mats for the transport sector. However, different regulations associated with different analytical methods apply to newly produced tires on the one hand and secondary products made from ELT on the other. Given these discrepancies, ELT can potentially contain levels of PAH, which can be problematic when reused in consumer products. The total PAH content, however, is not without doubt a reliable risk indicator, which should ultimately be based primarily on the transfer of the substances from the product into the skin of human beings or their release into the environment. Accordingly, additional studies are required to determine the extent to which migration-based measurements are more suitable for risk assessment and how the PAH content correlates with migration. Complementing the recently published results of PAH content in different types of ELT, this study investigates the release of PAH for a range of typical secondary materials composed entirely or partly of ELT. In the present study, migration tests with 20% aqueous ethanol were applied, a methodology that has been shown in previous investigations by others to reflect human skin exposure well, and the resulting migration rates of PAH are determined.
Inventory of Good Practices of Sustainable and Circular Phosphorus Management in the Visegrad Group (V4)
Authors: Smol Marzena , Marcinek Paulina , Šimková Zuzana , Bakalár Tomáš , Hemzal Milan , Klemeš Jiří Jaromír , Fan Yee Van , Lorencz Kinga , Koda Eugeniusz , Podlasek Anna
Published: January 2023

The most important raw material needed for food production is phosphorus (P), which cannot be replaced by other elements. P is listed as a Critical Raw Material (CRM) for the European Union (EU). It is an element essential for human nutrition and is used for fertiliser production. The key importance of P for human life is evidenced by the fact that if there were not enough P in fertilisers, we would only be able to feed 1/3 of the world’s population. Unfortunately, in Visegrad Group (V4) countries, Poland, Slovakia, Czech Republic, and Hungary, there is a lack of mineral deposits of phosphate rock. Therefore, there is a strong need to cover the demand for the P by importing from countries of varying stability, both economic and political, such as Russia, China, or Morocco. It is risky; if the borders for deliveries of goods are closed, it may be impossible to meet the needs of P. On the other hand, V4 countries have large secondary P resources in P-rich waste, which are lost due to P is not recovered on an industrial scale. The paper presents the importance of P raw materials in V4, the revision of primary and secondary P sources that can be used in agricultural systems, as well as the structure of import and export of P raw materials in these countries. In addition, examples of good phosphorus recovery practices in the V4 countries are presented. They include a list of initiatives dedicated to the sustainable management of P resources, and examples of P recovery projects. Implementation of P recovery for internal P-rich waste in V4 could ensure the safety of food production in this region. Such and similar initiatives may contribute to faster independence of the V4 countries from the import of P raw materials.
Blood Flow Restriction Training for Tendinopathy Rehabilitation: A Potential Alternative to Traditional Heavy-Load Resistance Training
Authors: Burton Ian
Published: January 2023

Tendinopathy is a chronic tendon disease which can cause significant pain and functional limitations for individuals, and which collectively places a tremendous burden on society. Resistance training has long been considered the treatment of choice in the rehabilitation of chronic tendinopathies, with both eccentric and heavy slow resistance training demonstrating positive clinical effects. The application of progressive tendon loads during rehabilitation is essential to not compromise tendon healing, with the precise dosage parameters of resistance training and external loading a critical consideration. Blood-flow restriction training (BFRT) has become an increasingly popular method of resistance training in recent years and has been shown to be an effective method for enhancing muscle strength and hypertrophy in healthy populations and in musculoskeletal rehabilitation. Traditional resistance training for tendinopathy requires the application of heavy training loads, whereas BFRT utilises significantly lower loads and training intensities, which may be more appropriate for certain clinical populations. Despite evidence confirming the positive muscular adaptations derived from BFRT and the clinical benefits found for other musculoskeletal conditions, BFRT has received a dearth of attention in tendon rehabilitation. Therefore, the purpose of this narrative review was threefold: firstly, to give an overview and analysis of the mechanisms and outcomes of BFRT in both healthy populations and in musculoskeletal rehabilitation. Secondly, to give an overview of the evidence to date on the effects of BFRT on healthy tendon properties and clinical outcomes when applied to tendon pathology. Finally, a discussion on the clinical utility of BFRT and its potential applications within tendinopathy rehabilitation, including as a compliment to traditional heavy-load training, is presented.
Mapping Tunneling-Induced Uneven Ground Subsidence Using Sentinel-1 SAR Interferometry: A Twin-Tunnel Case Study of Downtown Los Angeles, USA
Authors: Liu Linan , Zhou Wendy , Gutierrez Marte
Published: January 2023

Synthetic Aperture Radar (SAR) interferometry is a formidable technique to monitor surface deformation with a millimeter detection resolution. This study applies the Persistent Scatter-Interferometric Synthetic Aperture Radar (PSInSARTM) technique to measure ground subsidence related to a twin-tunnel excavation in downtown Los Angeles, USA. The PSInSARTM technique is suitable for urban settings because urban areas have strong reflectors. The twin tunnels in downtown Los Angeles were excavated beneath a densely urbanized area with variable overburden depths. In practice, tunneling-induced ground settlement is dominantly vertical. The vertical deformation rate in this study is derived by combining Line of Sight (LOS) deformation velocities obtained from SAR images from both ascending and descending satellite orbits. Local and uneven settlements up to approximately 12 mm/year along the tunnel alignment are observed within the allowable threshold. No severe damages to aboveground structures were reported. Furthermore, ground movements mapped one year before tunnel construction indicate that no concentrated ground settlements pre-existed. A Machine Learning (ML)-based permutation feature importance method is used for a parametric study to identify dominant factors associated with the twin-tunneling induced uneven ground subsidence. Six parameters are selected to conduct the parametric study, including overburden thickness, i.e., the thickness of artificial fill and alluvium soils above the tunnel springline, the distance between the two tunnel centerlines, the depth to the tunnel springline, building height, the distance to the tunnel, and groundwater level. Results of the parametric analysis indicate that overburden thickness, i.e., the thickness of artificial fill and alluvium soils above the tunnel springline, is the dominant contributing factor, followed by the distance between tunnel centerlines, depth to the tunnel springline, and building height. Two parameters, the distance to the tunnel, and the groundwater level, play lesser essential roles than others. In addition, the geological profile provides comprehension of unevenly distributed ground settlements, which are geologically sensitive and more concentrated in areas with thick artificial fill and alluvium soils, low tunnel depth, and high groundwater levels.
Ground Surface Freezing and Thawing Index Distribution in the Qinghai-Tibet Engineering Corridor and Factors Analysis Based on GeoDetector Technique
Authors: Ma Shen , Zhao Jingyi , Chen Ji , Zhang Shouhong , Dong Tianchun , Mei Qihang , Hou Xin , Liu Guojun
Published: January 2023

The land surface temperature obtained from remote sensing was widely used in the simulation of permafrost mapping instead of air temperature with the rapid development of remote sensing technology. The land surface freezing and thawing index (LFI and LTI), which is commonly regarded as the ground surface freezing and thawing index (GFI and GTI), can produce certain errors in the simulation of permafrost distribution on the Qinghai–Tibet Plateau. This paper improved the accuracy of the thermal condition of the surface soil in the Qinghai–Tibet Engineering Corridor (QTEC) by calculating the LFI (or LTI) and N-factors. The environmental factors affecting the spatial distribution of the GFI and GTI were detected by the GeoDetector model. Finally, the multiple linear relationships between the GFI (or GTI) and the environmental factors were established. The results from 25 monitoring sites in the QTEC show that the Nf (ratio of GFI to LFI) is 1.088, and the Nt (ratio of GTI to LTI) is 0.554. The explanatory power of the interaction between elevation and latitude for the GFI and GTI is 79.3% and 85.6%, respectively. The multiple linear regression model with six explanatory variables established by GFI (or GTI) has good accuracy. This study can provide relatively accurate upper boundary conditions for the simulation of permafrost distribution in the QTEC region.
Bouguer Anomaly Re-Reduction and Interpretative Remarks of the Phlegraean Fields Caldera Structures (Southern Italy)
Authors: De Ritis Riccardo , Cocchi Luca , Passaro Salvatore , Campagne Thomas , Gabriellini Gianluca
Published: January 2023

Phlegraean Fields is a large, active caldera located in the densely populated westernmost sector of Naples’s Bay (Southern Italy). Several Bouguer anomaly surveys are available for this area with different resolution and accuracy; gravity data derive from the integration of stations placed below and above the sea level as the caldera develops both onshore and offshore. The comparison of these maps with the Digital Elevation Model shows a still remaining Terrain Effect hiding the shallower and deep caldera structure’s signal. This effect has an impact on the modelling of the gravity source’s depth and geometry. In this research, we apply a geologically constrained terrain correction method to the higher resolution Free Air dataset available for the study area to enhance the complete Bouguer reduction. The correlation analysis between the residual and the topography allows us to assess the quality of the outcomes. The results represent an improvement in the anomalies’ isolation and clearly show a continuous circular-like clustering of maxima related to the geometry of the caldera rim. The minima are associated with volcano-tectonic depression filled with pyroclastic and sediment. Furthermore, features alignments overlap the fault systems, along which the volcanic activity occurred.
Capacity Estimation of Solar Farms Using Deep Learning on High-Resolution Satellite Imagery
Authors: Ravishankar Rashmi , AlMahmoud Elaf , Habib Abdulelah , de Weck Olivier L.
Published: January 2023

Global solar photovoltaic capacity has consistently doubled every 18 months over the last two decades, going from 0.3 GW in 2000 to 643 GW in 2019, and is forecast to reach 4240 GW by 2040. However, these numbers are uncertain, and virtually all reporting on deployments lacks a unified source of either information or validation. In this paper, we propose, optimize, and validate a deep learning framework to detect and map solar farms using a state-of-the-art semantic segmentation convolutional neural network applied to satellite imagery. As a final step in the pipeline, we propose a model to estimate the energy generation capacity of the detected solar energy facilities. Objectively, the deep learning model achieved highly competitive performance indicators, including a mean accuracy of 96.87%, and a Jaccard Index (intersection over union of classified pixels) score of 95.5%. Subjectively, it was found to detect spaces between panels producing a segmentation output at a sub-farm level that was better than human labeling. Finally, the detected areas and predicted generation capacities were validated against publicly available data to within an average error of 4.5% Deep learning applied specifically for the detection and mapping of solar farms is an active area of research, and this deep learning capacity evaluation pipeline is one of the first of its kind. We also share an original dataset of overhead solar farm satellite imagery comprising 23,000 images (256 × 256 pixels each), and the corresponding labels upon which the machine learning model was trained.
Automatic Segmentation of Bulk Material Heaps Using Color, Texture, and Topography from Aerial Data and Deep Learning-Based Computer Vision
Authors: Ellinger Andreas , Woerner Christian , Scherer Raimar
Published: January 2023

This article proposes a novel approach to segment instances of bulk material heaps in aerial data using deep learning-based computer vision and transfer learning to automate material inventory procedures in the construction-, mining-, and material-handling industry. The proposed method uses information about color, texture, and surface topography as input features for a supervised computer vision algorithm. The approach neither relies on hand-crafted assumptions on the general shape of heaps, nor does it solely rely on surface material type recognition. Therefore, the method is able to (1) segment heaps with “atypical” shapes, (2) segment heaps that stand on a surface made of the same material as the heap itself, (3) segment individual heaps of the same material type that border each other, and (4) differentiate between artificial heaps and other objects of similar shape like natural hills. To utilize well-established segmentation algorithms for raster-grid-based data structures, this study proposes a pre-processing step to remove all overhanging occlusions from a 3D surface scan and convert it into a 2.5D raster format. Preliminary results demonstrate the general feasibility of the approach. The average F1 score computed on the test set was 0.70 regarding object detection and 0.90 regarding the pixelwise segmentation.
A Comparative Study of a Typical Glacial Lake in the Himalayas before and after Engineering Management
Authors: Zhou Zhaoye , Cheng Xiaoqiang , Shangguan Donghui , Li Wangping , Li Da , He Beibei , Wang Meixia , Ling Qing , Zhang Xiuxia , Wang Xiaoxian , Chen Lu , Liu Yadong , Chen Wei
Published: January 2023

One of the main glacier-related natural hazards that are common to alpine locations is the occurrence of glacial lake outburst floods (GLOFs), which can seriously harm downstream towns and infrastructure. GLOFs have increased in frequency in the central Himalayas in recent years as a result of global warming, and careful management of glacial lakes is a crucial step in catastrophe prevention. In this study, field surveys were conducted on 28 August 2020 and 1 August 2021 with the help of an unmanned aerial vehicle (UAV) and a boat bathymetric system on an unmanned surface vessel (USV), combined with 22 years of Landsat series imagery and Sentinel-2 MSI imagery data. Spatial analysis was then used to investigate changes in lake surface conditions, dam stability, and surrounding topography before and after an integrated project of the Jialong Co lake. The results show that: (1) from 2000 to 2020 (before engineering management), the area of the Jialong Co glacial lake increased from 0.2148 ± 0.0176 km2 to 0.5921 ± 0.0003 km2. The glacial lake expansion rate from 2000 to 2010 (0.0145 km2/a) was greater than the rate from 2011 to 2020 (6.92 × 10−6 km2/a). In 2021 (after engineering treatment), the glacial lake perimeter, area, and volume decreased by 0.6014 km, 0.1136 km2, and 1.90 × 107 m3, respectively. The amount of excavation during the project treatment was 8.13 million square meters, and the amount of filling was 1.24 million square meters. According to the results of the unmanned surface vessel (USV), the elevation of the lake surface dropped from 4331 m to 4281 m, and the water level dropped by 50 m (the designed safe water level line dropped by 30 m). (2) The results of the UAV topographic survey and geomorphological analysis showed that the engineered reinforcement of the outlet channel and surrounding dam effectively mitigated severe scouring of the foot of the final moraine at the outlet of the spillway, as well as the likelihood of glacial lake outbursts caused by ice avalanches and landslides. (3) The comprehensive engineering treatment of this typical glacial lake effectively lowered the water level and improved the stability of the moraine ridge and lake dam, providing a scientific foundation for other glacial lake outburst risk assessments and disaster mitigation and management measures. Thus, it is critical to evaluate the impact of comprehensive engineering management of key glacial lakes to support glacial lake management.
Impacts of Climate Change on European Grassland Phenology: A 20-Year Analysis of MODIS Satellite Data
Authors: Bellini Edoardo , Moriondo Marco , Dibari Camilla , Leolini Luisa , Staglianò Nicolina , Stendardi Laura , Filippa Gianluca , Galvagno Marta , Argenti Giovanni
Published: January 2023

The use of very long spatial datasets from satellites has opened up numerous opportunities, including the monitoring of vegetation phenology over the course of time. Considering the importance of grassland systems and the influence of climate change on their phenology, the specific objectives of this study are: (a) to identify a methodology for a reliable estimation of grassland phenological dates from a satellite vegetation index (i.e., kernel normalized difference vegetation index, kNDVI) and (b) to quantify the changes that have occurred over the period 2001–2021 in a representative dataset of European grasslands and assess the extent of climate change impacts. In order to identify the best methodological approach for estimating the start (SOS), peak (POS) and end (EOS) of the growing season from the satellite, we compared dates extracted from the MODIS-kNDVI annual trajectories with different combinations of fitting models (FMs) and extraction methods (EM), with those extracted from the gross primary productivity (GPP) measured from eddy covariance flux towers in specific grasslands. SOS and POS were effectively identified with various FM×EM approaches, whereas satellite-EOS did not obtain sufficiently reliable estimates and was excluded from the trend analysis. The methodological indications (i.e., FM×EM selection) were then used to calculate the SOS and POS for 31 grassland sites in Europe from MODIS-kNDVI during the period 2001–2021. SOS tended towards an anticipation at the majority of sites (83.9%), with an average advance at significant sites of 0.76 days year−1. For POS, the trend was also towards advancement, although the results are less homogeneous (67.7% of sites with advancement), and with a less marked advance at significant sites (0.56 days year−1). From the analyses carried out, the SOS and POS of several sites were influenced by the winter and spring temperatures, which recorded rises during the period 2001–2021. Contrasting results were recorded for the SOS-POS duration, which did not show a clear trend towards lengthening or shortening. Considering latitude and altitude, the results highlighted that the greatest changes in terms of SOS and POS anticipation were recorded for sites at higher latitudes and lower altitudes.
Enhanced Impact of Vegetation on Evapotranspiration in the Northern Drought-Prone Belt of China
Authors: Zeng Jian , Zhang Qiang , Zhang Yu , Yue Ping , Yang Zesu , Wang Sheng , Zhang Liang , Li Hongyu
Published: January 2023

Evapotranspiration (ET) is an essential component of the land–atmosphere water cycle. In this work, the trend of ET and its dominant factors during 1982 to 2011 are investigated in the northern drought-prone belt of China (NDPB) based on five datasets, including the gridded FLUXNET, using the Pearson correlation and linear regression methods. Specially, we focus on the increasing contribution of vegetation in the change of ET. During 1982–2011, summer ET significantly increased at the rate of 0.33 mm/year (p < 0.05) in the NDPB. However, similar to global-mean ET, the ET in NDPB also experienced a pronounced fluctuation during 1999 and 2002. The role of water supply differed remarkably before and after the fluctuation while the atmospheric demand maintained weak constraint on ET. Before the fluctuation (during 1982–2000), ET correlated significantly (p < 0.01) and positively with soil moisture, indicating ET was primarily limited by water supply. However, their correlation weakened remarkably after the fluctuation when soil moisture decreased to the lowest level for the past thirty years, indicating that neither moisture supply nor atmospheric demand dominated the ET during this period. In contrast, vegetation leaf area index (LAI) maintained consistent significant (p < 0.01) and positive correlation with ET before and after the fluctuation in the NDPB, and it reflected over 60% of the change in ET. Moreover, the LAI in NDPB increased by 19.6% which was more than double of the global-mean increase. The ET increase due to rising LAI offset the ET decrease due to reduction of soil moisture, and vegetation became the primary constraint on ET during 2001–2011. The expansion of vegetation may intensify the risk of drought and cause conflicting demands for water between the ecosystem and humans in the NDPB, especially in the case of weak summer monsoon.
Coseismic Deformation and Fault Inversion of the 2017 Jiuzhaigou Ms 7.0 Earthquake: Constraints from Steerable Pyramid and InSAR Observations
Authors: Peng Wenshu , Huang Xuri , Wang Zegen
Published: January 2023

The 8 August 2017 Ms 7.0 Jiuzhaigou earthquake was generated in the transition zone between the Tazang fault, Huya fault, and Minjiang fault, all being part of the East Kunlun fault system. In this study, two pairs of SAR (synthetic aperture radar) data from Sentinel-1 satellite were used to derive the surface displacement observations along the satellite line-of-sight (LOS) directions using the differential interferometric SAR (D-InSAR) method. A steerable pyramid filtering method (i.e., a method for a linear multiscale, multidirectional decomposition and filtering technology) was proposed to optimize and enhance the geological features from interferometric image and coseismic deformation field. The 3D deformation was derived under the constraint of the combined D-InSAR and MAI method. The small baseline subset InSAR (SBAS-InSAR) time series method was used to obtain the cumulative deformation across the fault system. Fault slip inversion results from interferogram of InSAR indicate that the 2017 Jiuzhaigou earthquake was dominated by left-lateral slip, the surface movement was dominated by horizontal deformation, the vertical deformation was small, and the coseismic deformation variable in the east–west direction was the largest, with a maximum deformation of 0.2 m to the east and 0.14 m to the west. The maximum slip is about 77 cm, which is located at a depth of 9 km. The moment magnitude obtained by inversion is Mw 6.6, and the seismic fault is the Huya fault.
Estimating Ground Elevation in Coastal Dunes from High-Resolution UAV-LIDAR Point Clouds and Photogrammetry
Authors: Pinton Daniele , Canestrelli Alberto , Moon Robert , Wilkinson Benjamin
Published: January 2023

Coastal dune environments play a critical role in protecting coastal areas from damage associated with flooding and excessive erosion. Therefore, monitoring the morphology of dunes is an important coastal management operation. Traditional ground-based survey methods are time-consuming, and data must be interpolated over large areas, thus limiting the ability to assess small-scale details. High-resolution uncrewed aerial vehicle (UAV) photogrammetry allows one to rapidly monitor coastal dune elevations at a fine scale and assess the vulnerability of coastal zones. However, photogrammetric methods are unable to map ground elevations beneath vegetation and only provide elevations for bare sand areas. This drawback is significant as vegetated areas play a key role in the development of dune morphology. To provide a complete digital terrain model for a coastal dune environment at Topsail Hill Preserve in Florida’s panhandle, we employed a UAV, equipped with a laser scanner and a high-resolution camera. Along with the UAV survey, we conducted a RTK–GNSS ground survey of 526 checkpoints within the survey area to serve as training/testing data for various machine-learning regression models to predict the ground elevation. Our results indicate that a UAV–LIDAR point cloud, coupled with a genetic algorithm provided the most accurate estimate for ground elevation (mean absolute error ± root mean square error, MAE ± RMSE = 7.64 ± 9.86 cm).
PrecipGradeNet: A New Paradigm and Model for Precipitation Retrieval with Grading of Precipitation Intensity
Authors: Zhang Danfeng , He Yuqing , Li Xiaoqing , Zhang Lu , Xu Na
Published: January 2023

Near-real-time precipitation retrieval plays an important role in the study of the evolutionary process of precipitation and the prevention of disasters caused by heavy precipitation. Compared with ground-based precipitation observations, the infrared precipitation estimations from geostationary satellites have great advantages in terms of geographical coverage and temporal resolution. However, precipitation retrieved from multispectral infrared data still faces challenges in terms of accuracy, especially in extreme cases. In this paper, we propose a new paradigm for satellite multispectral infrared data retrieval of precipitation and construct a new model called PrecipGradeNet. This model uses FY-4A L1 FDI data as the input, IMERG precipitation data as the training target, and improves the precipitation retrieval accuracy by grading the precipitation intensity through Res-UNet, a semantic segmentation network. To evaluate the precipitation retrieval of the model, we compare the retrieval results with the FY-4A L2 QPE operational product to the IMERG precipitation. IMERG is considered as the ground truth. We evaluate the precipitation retrieval from the precipitation fall area identification, the precipitation intensity interval discrimination, and the precipitation quantification. Experimental results show that PrecipGradeNet has better overall performance compared with the FY-4A QPE product in precipitation fall area identification with POD increased by 48% and CSI and HSS improved by 21% and 14%. PrecipGradeNet also has better performance in light precipitation with POD increased by 114% and CSI and HSS improved by 64% and 52%, and better overall precipitation quantification, with RMSE and CC improved by 16% and 15%. In addition, PrecipGradeNet avoids the overall bias in the low and extreme high precipitation cases. Therefore, the new paradigm proposed in this paper has the potential to improve the retrieval accuracy of satellite precipitation estimation products. This study suggests that the application of semantic segmentation methods may provide a new path to correct the intensity bias of the satellite-based precipitation products.
Application of the Fourier Series Expansion Method for the Inversion of Gravity Gradients using Gravity Anomalies
Authors: Liu Bei , Bian Shaofeng , Ji Bing , Wu Shuguang , Xian Pengfei , Chen Cheng , Zhang Ruichen
Published: January 2023

Accurate and highly precise gravity gradient data are an important component of, for example, gravity field modeling, seabed topography inversion, and resource exploration. However, high-precision gravity gradient data are difficult to obtain. To address this difficulty, this work introduces the Fourier series expansion method to the modeling of gravity gradient fields. Based on gravity anomalies, the analytic expressions of the gravity gradient tensors have been deduced, which provides a new mathematical method for obtaining gravity gradient data. The expression’s derivation and verification processes are as follows. First, these analytic expressions for inverting the gravity gradient based on gravity anomaly data are derived according to the Laplace equation, the boundary value conditions of spherical approximation, and the Fourier series expansion method. Then, global 1’ × 1’ gravity field data provided by UCSD are used to verify the accuracy of these formulas. Finally, the results are analyzed. The experimental results show that the results obtained based on this inversion formula can sufficiently show the details of gravity gradient changes. The formulas derived in this paper have good computational efficiency in the inversion of regional gravity gradients and provide a new mathematical method for gravity gradient data acquisition.
A Local–Global Framework for Semantic Segmentation of Multisource Remote Sensing Images
Authors: Qiu Luyi , Yu Dayu , Zhang Chenxiao , Zhang Xiaofeng
Published: January 2023

Recently, deep learning has been widely used in the segmentation tasks of remote sensing images. However, the existing deep learning method most focus on local contextual information and has limited field of perception, which makes it difficult to capture the long-range contextual feature of objects at large scales form very-high-resolution (VHR) images. In this paper, we present a novel Local–global Framework consisting of the dual-source fusion network and local–global transformer modules, which efficiently utilize features extracted from multiple sources and fully capture features of local and global regions. The dual-source fusion network is an encoder designed to extract features from multiple sources such as spectra, synthetic aperture radar, and elevations, which selective fuse features from multiple sources and reduce the interference of redundant features. The local–global transformer module is proposed to capture fine-grained local features and coarse-grained global features, which enables the framework to focus on recognizing multiple-scale objects from the local and global regions. Moreover, we propose a pixelwise contrastive loss, which could encourage that the prediction is pulled closer to the ground truth. The Local–global Framework achieves state-of-the-art performance with 90.45% mean f1 score on the ISPRS Vaihingen dataset and 93.20% mean f1 score on the ISPRS Potsdam dataset.
Bibliometric Analysis of the Permafrost Research: Developments, Impacts, and Trends
Authors: Du Qingsong , Li Guoyu , Chen Dun , Zhou Yu , Qi Shunshun , Wang Fei , Mao Yuncheng , Zhang Jun , Cao Yapeng , Gao Kai , Wu Gang , Li Chunqing , Wang Yapeng
Published: January 2023

Permafrost is a significant part of the cryosphere, which has gained increasing attention from scientists, policy-makers, and the general public due to global warming, environmental degradation, water shortages, and intense human activities. Although many permafrost research review articles have been published, these studies were predominantly limited to either one subject or one field, while systematic studies about permafrost based on bibliometric analysis methods remain limited. We aim to fill this gap by conducting a bibliometric analysis of 13,697 articles in the field of permafrost research from 1942 to 2021, collected from the Web of Science core collection database. The results indicate that permafrost research is a typically multi-author, multi-country, and multi-institution cooperative field, involved in many research fields. The cumulative number of publications has presented an exponential increase over the past 80 years, with an average annual growth rate of 10.40%. Since 2000, China has seen a rapid growth in the number of publications per year, surpassing the USA in 2016 and leading in the years since then. In addition, the authors from China have great contributions in publications, and there is good room for permafrost development in the future according to the authors’ M-index ranking. After the analysis of authors’ keywords, we found that, compared to the conventional methods, machine learning and interferometric synthetic aperture radar (InSAR) are new technological approaches introduced in recent years, and the Qinghai–Tibet Plateau has become a popular study area. The results presented here can help related researchers, scholars, and students in the field to better understand the past developments, current status, and future trends of permafrost research. Furthermore, this paper presents and expands the general process of the bibliometric method used in permafrost studies, which can provide researchers with new inspirations and improve discipline research approach.
Remote Sensing for Agricultural Water Management in Jordan
Authors: Al-Bakri Jawad T. , D’Urso Guido , Calera Alfonso , Abdalhaq Eman , Altarawneh Maha , Margane Armin
Published: January 2023

This study shows how remote sensing methods are used to support and provide means for improving agricultural water management (AWM) in Jordan through detailed mapping of irrigated areas and irrigation water consumption (IWC). Digital processing and classification methods were applied on multi-temporal data of Landsat 8 and Sentinel-2 to derive maps of irrigated areas for the period 2017–2019. Different relationships were developed between the normalized difference vegetation index (NDVI) and the crop coefficient (Kc) to map evapotranspiration (ET). Using ground data, ET maps were transferred to IWC for the whole country. Spatial analysis was then used to delineate hotspots where shifts between ET and groundwater abstraction were observed. Results showed that the applied remote sensing methods provided accurate maps of irrigated areas. The NDVI-Kc relationships were significant, with coefficients of determination (R2) ranging from 0.89 to 0.93. Subsequently, the ET estimates from the NDVI-Kc relationships were in agreement with remotely sensed ET modeled by SEBAL (NSE = 0.89). In the context of Jordan, results showed that irrigated areas in the country reached 98 thousand ha in 2019, with 64% of this area located in the highlands. The main irrigated crops were vegetables (55%) and fruit trees and olives (40%). The total IWC reached 702 MCM in 2019, constituting 56% of the total water consumption in Jordan, with 375 MCM of this amount being pumped from groundwater, while reported abstraction was only 235 MCM. The study identified the hotspots where illegal abstraction or incorrect metering of groundwater existed. Furthermore, it emphasized the roles of remote sensing in AWM, as it provided updated figures on groundwater abstraction and forecasts for future IWC, which would reach 986 MCM in 2050. Therefore, the approach of ET and IWC mapping would be highly recommended to map ET and to provide estimates of present and future IWC.
XANet: An Efficient Remote Sensing Image Segmentation Model Using Element-Wise Attention Enhancement and Multi-Scale Attention Fusion
Authors: Liang Chenbin , Xiao Baihua , Cheng Bo , Dong Yunyun
Published: January 2023

Massive and diverse remote sensing data provide opportunities for data-driven tasks in the real world, but also present challenges in terms of data processing and analysis, especially pixel-level image interpretation. However, the existing shallow-learning and deep-learning segmentation methods, bounded by their technical bottlenecks, cannot properly balance accuracy and efficiency, and are thus hardly scalable to the practice scenarios of remote sensing in a successful way. Instead of following the time-consuming deep stacks of local operations as most state-of-the-art segmentation networks, we propose a novel segmentation model with the encoder–decoder structure, dubbed XANet, which leverages the more computationally economical attention mechanism to boost performance. Two novel attention modules in XANet are proposed to strengthen the encoder and decoder, respectively, namely the Attention Recalibration Module (ARM) and Attention Fusion Module (AFM). Unlike current attention modules, which only focus on elevating the feature representation power, and regard the spatial and channel enhancement of a feature map as two independent steps, ARM gathers element-wise semantic descriptors coupling spatial and channel information to directly generate a 3D attention map for feature enhancement, and AFM innovatively utilizes the cross-attention mechanism for the sufficient spatial and channel fusion of multi-scale features. Extensive experiments were conducted on ISPRS and GID datasets to comprehensively analyze XANet and explore the effects of ARM and AFM. Furthermore, the results demonstrate that XANet surpasses other state-of-the-art segmentation methods in both model performance and efficiency, as ARM yields a superior improvement versus existing attention modules with a competitive computational overhead, and AFM achieves the complementary advantages of multi-level features under the sufficient consideration of efficiency.