bluuebunny/crossref_metadata_2025 huggingface dataset:
Porous Layer-by-Layer Films Assembled Using Polyelectrolyte Blend to Control Wetting Properties
Authors: Sung Choonghyun , Heo Yejin
Published: June 2021

Porous layer-by-layer (LbL) films have been employed for the implementation of superwetting surfaces, but they are limited to the LbL films consisting of only two oppositely charged polyelectrolytes. In this study, LbL films were assembled using a cationic polymer blend of branched poly(ethylene imine) (BPEI) and poly(allylamine hydrochloride) (PAH), and anionic poly(acrylic acid); they were then acid-treated at pH 1.8–2.0 to create a porous structure. The films of 100% BPEI exhibited a relatively smooth surface, whereas those of the 100% PAH exhibited porous surfaces. However, various surface morphologies were obtained when BPEI and PAH were blended. When coated with fluorinated silane, films with 50% and 100% PAH exhibited relatively higher water contact angles (WCAs). In particular, films with 50% PAH exhibited the highest WCA of 140–150° when treated at pH 1.8. These fluorinated films were further infused with lubricant oil to determine their feasibility as slippery surfaces. The water and oil sliding angles were in the range of 10–20° and 5–10°, respectively. Films prepared with the BPEI/PAH blend showed lower water slide angles than those prepared with 100% BPEI or PAH. Acid treatment of LbL films assembled using a polyelectrolyte blend can effectively control surface morphologies and can potentially be applied in superwetting.
New Solvent and Coagulating Agent for Development of Chitosan Fibers by Wet Spinning
Authors: Mohammadkhani Ghasem , Kumar Ramamoorthy Sunil , Adolfsson Karin , Mahboubi Amir , Hakkarainen Minna , Zamani Akram
Published: June 2021

Adipic acid was evaluated as a novel solvent for wet spinning of chitosan fibers. A solvent with two carboxyl groups could act as a physical crosslinker between the chitosan chains, resulting in improved properties of the fibers. The performance of adipic acid was compared with conventional solvents, i.e., lactic, citric, and acetic acids. Chitosan solutions were injected into a coagulation bath to form monofilaments. Sodium hydroxide (NaOH) and its mixture with ethanol (EtOH) were used as coagulation agents. Scanning electron microscopy confirmed the formation of uniform chitosan monofilaments with an even surface when using adipic acid as solvent. These monofilaments generally showed higher mechanical strength compared to that of monofilaments produced using conventional solvents. The highest Young’s modulus, 4.45 GPa, was recorded for adipic acid monofilaments coagulated in NaOH-EtOH. This monofilament also had a high tensile strength of 147.9 MPa. Furthermore, taking advantage of chitosan insolubility in sulfuric acid (H2SO4) at room temperature, chitosan fibers were successfully formed upon coagulation in H2SO4-EtOH. The dewatering of fibers using EtOH before drying resulted in a larger fiber diameter and lower mechanical strength. Adipic acid fibers made without dehydration illustrated 18% (for NaOH), 46% (for NaOH-EtOH), and 91% (for H2SO4-EtOH) higher tensile strength compared to those made with dehydration.
Electrospun Nanosystems Based on PHBV and ZnO for Ecological Food Packaging
Authors: Râpă Maria , Stefan Maria , Popa Paula Adriana , Toloman Dana , Leostean Cristian , Borodi Gheorghe , Vodnar Dan Cristian , Wrona Magdalena , Salafranca Jesús , Nerín Cristina , Barta Daniel Gabriel , Suciu Maria , Predescu Cristian , Matei Ecaterina
Published: June 2021

The electrospun nanosystems containing poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV) and 1 wt% Fe doped ZnO nanoparticles (NPs) (with the content of dopant in the range of 0–1 wt% Fe) deposited onto polylactic acid (PLA) film were prepared for food packaging application. They were investigated by scanning electron microscopy (SEM), energy dispersive X-ray (EDX), Fourier transform infrared spectroscopy (FT-IR), X-ray diffraction (XRD), antimicrobial analysis, and X-ray photoelectron spectrometry (XPS) techniques. Migration studies conducted in acetic acid 3% (wt/wt) and ethanol 10% (v/v) food simulants as well as by the use of treated ashes with 3% HNO3 solution reveal that the migration of Zn and Fe falls into the specific limits imposed by the legislation in force. Results indicated that the PLA/PHBV/ZnO:Fex electrospun nanosystems exhibit excellent antibacterial activity against the Pseudomonas aeruginosa (ATCC-27853) due to the generation of a larger amount of perhydroxyl (˙OOH) radicals as assessed using electron paramagnetic resonance (EPR) spectroscopy coupled with a spin trapping method.
Why POSS-Type Compounds Should Be Considered Nanomodifiers, Not Nanofillers—A Polypropylene Blends Case Study
Authors: Brząkalski Dariusz , Przekop Robert E. , Sztorch Bogna , Frydrych Miłosz , Pakuła Daria , Jałbrzykowski Marek , Markiewicz Grzegorz , Marciniec Bogdan
Published: June 2021

In this work, a series of silsesquioxanes (SSQ) and spherosilicates (SS), comprising a group of cage siloxane (CS) compounds, was tested as functional additives for preparation of isotactic polypropylene (iPP)-based nanocomposites and discussed in the aspect of their rationale of applicability as such additives. For this purpose, the compounds were prepared by condensation and olefin hydrosilylation reactions. The effect of these cage siloxane products on properties of obtained CS/iPP nanocomposites was analyzed by means of mechanical, microscopic (scanning electron microscopy-energy dispersive spectroscopy), thermal (differential scanning calorimetry, thermogravimetry), thermomechanical (Vicat softening point) analyses. The results were compared with the previous findings on CS/polyolefin composites. The role of CS compounds was discussed in terms of plastic processing additives.
Supercritical Impregnation of PLA Filaments with Mango Leaf Extract to Manufacture Functionalized Biomedical Devices by 3D Printing
Authors: Rosales José María , Cejudo Cristina , Verano Lidia , Casas Lourdes , Mantell Casimiro , Martínez de la Ossa Enrique José
Published: June 2021

Polylactic Acid (PLA) filaments impregnated with ethanolic mango leaves extract (MLE) with pharmacological properties were obtained by supercritical impregnation. The effects of pressure, temperature and amount of extract on the response variables, i.e., swelling, extract loading and bioactivity of the PLA filaments, were determined. The analysis of the filaments biocapacities revealed that impregnated PLA filaments showed 11.07% antidenaturant capacity and 88.13% antioxidant activity, which after a 9-day incubation shifted to 30.10% and 9.90%, respectively. Subsequently, the same tests were conducted on printed samples. Before their incubation, the printed samples showed 79.09% antioxidant activity and no antidenaturant capacity was detected. However, after their incubation, the antioxidant activity went down to only 2.50%, while the antidenaturant capacity raised up to 23.50%. The persistence of the bioactive properties after printing opens the possibility of using the functionalized PLA filaments as the feed for a three-dimensional (3D) printer.
A New Approach to Solar Desalination Using a Humidification–Dehumidification Process for Remote Areas
Authors: Alsehli Mishal
Published: June 2021

Freshwater supply in remote areas has become a critical issue. This paper aims to introduce a new approach to a solar-powered humidification–dehumidification (HDH) desalination system. The design uses a solar collector, a thermal storage tank, and an HDH unit. The HDH works continuously by feeding water to absorb solar energy during the day and then relaying the desalination units with feed water at a top brine temperature (TBT) at night. The model predicts the amount of solar energy stored for the next day, and based on this, the amount of feed water that should be raised to the TBT is calculated. The system operation is carried out in two phases. During the day, the feed water absorbs the heat of solar energy, thereby increasing its temperature to TBT. This hot feed water is then kept in storage tanks. At night, the tank switches to discharging mode and starts feeding the HDH with the hot feed water. The system is designed so that the roles of the tank are rotated at sunset. To achieve the same TBT every day in response to changes in the available solar energy, the mass of the feed water is adjusted daily. The design is simulated using a dynamic model of the energy and mass balance resulting in an average daily production of 7.6 kg of fresh water per unit area of the solar collector. The daily average of the gain output and the recovery ratios are 0.3 and 0.09 respectively.
Systematic Design and Evaluation of a Citation Function Classification Scheme in Indonesian Journals
Authors: Yaniasih Yaniasih , Budi Indra
Published: June 2021

Classifying citations according to function has many benefits when it comes to information retrieval tasks, scholarly communication studies, and ranking metric developments. Many citation function classification schemes have been proposed, but most of them have not been systematically designed for an extensive literature-based compilation process. Many schemes were also not evaluated properly before being used for classification experiments utilizing large datasets. This paper aimed to build and evaluate new citation function categories based upon sufficient scientific evidence. A total of 2153 citation sentences were collected from Indonesian journal articles for our dataset. To identify the new categories, a literature survey was conducted, analyses and groupings of category meanings were carried out, and then categories were selected based on the dataset’s characteristics and the purpose of the classification. The evaluation used five criteria: coherence, ease, utility, balance, and coverage. Fleiss’ kappa and automatic classification metrics using machine learning and deep learning algorithms were used to assess the criteria. These methods resulted in five citation function categories. The scheme’s coherence and ease of use were quite good, as indicated by an inter-annotator agreement value of 0.659 and a Long Short-Term Memory (LSTM) F1-score of 0.93. According to the balance and coverage criteria, the scheme still needs to be improved. This research data was limited to journals in food science published in Indonesia. Future research will involve classifying the citation function using a massive dataset collected from various scientific fields and published from some representative countries, as well as applying improved annotation schemes and deep learning methods.
Nature, Spirit, and Spirituality in Husserl’s Phenomenology
Authors: Vecino María Celeste
Published: June 2021

This article deals with the relationship between Spirit (Geist) and Nature (Natur) in Husserl’s phenomenology and the potentially religious motifs involved in its treatment. I begin by outlining two different approaches that can be found in Husserl’s work regarding the dyad Nature-Spirit: firstly, a schematic opposition between the two, and secondly, the recognition of their fundamental intertwinement. I claim that, even in this second approach, there remains a sense of subordination of Nature to Spirit that is due to the transcendental character of Husserl’s phenomenology. I analyze this primacy in the context of Husserl’s monadological theory, bringing forward certain religious elements of his account in order to connect this notion of spirit to a more contemporary idea of spirituality.
Sacred and Profane: Tabooing in Russian Magical Manuscripts of the 17th–18th Centuries (Incantations and Herbals)
Authors: Ippolitova Aleksandra B.
Published: June 2021

Linguistic taboos (euphemisms, omissions, and other) are an essential part of Slavic verbal and written culture. In this article, we analyze cryptography as a form of tabooing in the magical texts of the grassroots manuscript tradition of the 17th and 18th centuries (handwritten incantations and herbals). Our main objective is trying to see a system behind separate examples and define which kinds of texts are usually tabooed in incantations and herbals, their topics, and messages. We have managed to find out that the function of keeping secrecy is not relevant for the magical tradition; rather, encryption was used to emphasize the elements that are of special importance. In the book of incantations called the Olonets Codex, dating back to the 17th century, ciphering was used for the names and titles of sacred and demonological characters, antagonists, descriptions of certain rituals, closing phrases for the incantations (amen, “key”), etc. We hypothesize that the encryption is used in the Olonets Codex as a means of retaining the magical strength of all the texts in the manuscripts, protecting from hostile beings, sacralizing where necessary, tabooing what was considered sinful for religious reasons, accentuating the main meanings of the incantations, etc. In the herbals, cryptography is basically used for tabooing of “sinful” or trappy topics (love magic, magic used against courts and authorities, some contexts concerning sorcery, jinx, and “secret” knowledge), and in the texts that had to bear sacral meaning (incantations and prayers).
Parts-per-Object Count in Agricultural Images: Solving Phenotyping Problems via a Single Deep Neural Network
Authors: Khoroshevsky Faina , Khoroshevsky Stanislav , Bar-Hillel Aharon
Published: June 2021

Solving many phenotyping problems involves not only automatic detection of objects in an image, but also counting the number of parts per object. We propose a solution in the form of a single deep network, tested for three agricultural datasets pertaining to bananas-per-bunch, spikelets-per-wheat-spike, and berries-per-grape-cluster. The suggested network incorporates object detection, object resizing, and part counting as modules in a single deep network, with several variants tested. The detection module is based on a Retina-Net architecture, whereas for the counting modules, two different architectures are examined: the first based on direct regression of the predicted count, and the other on explicit parts detection and counting. The results are promising, with the mean relative deviation between estimated and visible part count in the range of 9.2% to 11.5%. Further inference of count-based yield related statistics is considered. For banana bunches, the actual banana count (including occluded bananas) is inferred from the count of visible bananas. For spikelets-per-wheat-spike, robust estimation methods are employed to get the average spikelet count across the field, which is an effective yield estimator.
Extracting Information on Rocky Desertification from Satellite Images: A Comparative Study
Authors: Pu Junwei , Zhao Xiaoqing , Dong Pinliang , Wang Qian , Yue Qifa
Published: June 2021

Rocky desertification occurs in many karst terrains of the world and poses major challenges for regional sustainable development. Remotely sensed data can provide important information on rocky desertification. In this study, three common open-access satellite image datasets (Sentinel-2B, Landsat-8, and Gaofen-6) were used for extracting information on rocky desertification in a typical karst region (Guangnan County, Yunnan) of southwest China, using three machine-learning algorithms implemented in the Python programming language: random forest (RF), bagged decision tree (BDT), and extremely randomized trees (ERT). Comparative analyses of the three data sources and three algorithms show that: (1) The Sentinel-2B image has the best capability for extracting rocky desertification information, with an overall accuracy (OA) of 85.21% using the ERT method. This can be attributed to the higher spatial resolution of the Sentinel-2B image than that of Landsat-8 and Gaofen-6 images and Gaofen-6’s lack of the shortwave infrared (SWIR) bands suitable for mapping carbonate rocks. (2) The ERT method has the best classification results of rocky desertification. Compared with the RF and BDT methods, the ERT method has stronger randomness in modeling and can effectively identify important feature factors for extracting information on rocky desertification. (3) The combination of the Sentinel-2B images and the ERT method provides an effective, efficient, and free approach to information extraction for mapping rocky desertification. The study can provide a useful reference for effective mapping of rocky desertification in similar karst environments of the world, in terms of both satellite image sources and classification algorithms. It also provides important information on the total area and spatial distribution of different levels of rocky desertification in the study area to support decision making by local governments for sustainable development.
An Approach to Accuracy Assessment of ASTER Derived Mineral Maps
Authors: Maghsoudi Moud Fardad , van Ruitenbeek Frank , Hewson Rob , van der Meijde Mark
Published: June 2021

An accuracy assessment of a classified thematic map is critical for the success of a remote sensing project. The identification and quantification of accuracy sources for classified mineral maps derived from satellite images enable confident decisions to be made for further exploration operations. Nineteen rock samples were collected from the different lithological units of the hydrothermally altered Kuh Panj Cu porphyry occurrence within the south-eastern part of Iran. SPECIM hyperspectral imaging was applied to the rock samples, followed by X-ray diffraction (XRD) analysis to compare the SPECIM mineral maps. The SPECIM results were then interpreted for mineralogy and compositional mapping extracted from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images. The spectral angle mapper (SAM) and real value−area (RV−A) fractal methods were applied on SPECIM and ASTER images to interpret the mineralogy and derived classified map products. Two methods, including confusion matrix and one minus standard deviation over mean, were used to assess the accuracy of the classified SPECIM and ASTER derived mineral maps. Performing sensitivity studies were investigated, including the effect of spatially displacing ASTER mineral mapping and changing the SAM-derived threshold values on the accuracy of the ASTER mineral map. The sensitivity analyses demonstrated that selecting an optimum SAM-derived threshold value is more important than spatial displacement. Finally, accuracy sources were summarized in an accuracy budget table. The results demonstrated 89 and 88% accuracy for SPECIM and 88 and 92% accuracy for ASTER mineral maps via the confusion matrix and one minus standard deviation over mean methods, respectively. The accuracy budget helped to evaluate and compare all sources of accuracy with their accuracy values.
A Study of Two Impactful Heavy Rainfall Events in the Southern Appalachian Mountains during Early 2020, Part II; Regional Overview, Rainfall Evolution, and Satellite QPE Utility
Authors: Miller Douglas , Arulraj Malarvizhi , Ferraro Ralph , Grassotti Christopher , Kuligowski Bob , Liu Shuyan , Petkovic Veljko , Wu Shaorong , Xie Pingping
Published: June 2021

Two heavy rainfall events occurring in early 2020 brought flooding, flash flooding, strong winds, and tornadoes to the southern Appalachian Mountains. Part I of the study examined large-scale atmospheric contributions to the atmospheric river-influenced events and subsequent societal impacts. Contrary to expectations based on previous work in this region, the event having a lower event accumulation and shorter duration resulted in a greater number of triggered landslides and prolonged downstream flooding outside of the mountains. One purpose of this study (Part II) is to examine the local atmospheric conditions contributing to the rather unusual surface response to the shorter duration heavy rainfall event of 12–13 April 2020. A second purpose of this study is to investigate the utility of several spaced-based QPE and vertical atmospheric profile methods in illuminating some of the atmospheric conditions unique to the April event. The embedded mesoscale convective elements in the warm sector of the April event were larger and of longer duration than of the other event in February 2020, leading to sustained periods of convective rain rates. The environment of the April event was convectively unstable, and the resulting available potential energy was sustained by relatively dry airstreams at the 700 hPa level, continuously overriding the moist air stream at low levels attributed to an atmospheric river.
Seasonal Surface Subsidence and Frost Heave Detected by C-Band DInSAR in a High Arctic Environment, Cape Bounty, Melville Island, Nunavut, Canada
Authors: Robson Greg , Treitz Paul , Lamoureux Scott F. , Murnaghan Kevin , Brisco Brian
Published: June 2021

Differential interferometry of synthetic aperture radar (DInSAR) can be used to generate high-precision surface displacement maps in continuous permafrost environments, capturing isotropic surface subsidence and uplift associated with the seasonal freeze and thaw cycle. We generated seasonal displacement maps using DInSAR with ultrafine-beam Radarsat-2 data for the summers of 2013, 2015, and 2019 at Cape Bounty, Melville Island, and examined them in combination with a land-cover classification, meteorological data, topographic data, optical satellite imagery, and in situ measures of soil moisture, soil temperature, and depth to the frost table. Over the three years studied, displacement magnitudes (estimated uncertainty ± 1 cm) of up to 10 cm per 48-day DInSAR stack were detected. However, generally, the displacement was far smaller (up to 4 cm). Surface displacement was found to be most extensive and of the greatest magnitude in low-lying, wet, and steeply sloping areas. The few areas where large vertical displacements (>2.5 cm) were detected in multiple years were clustered in wet, low lying areas, on steep slopes or ridges, or close to the coast. DInSAR also captured the expansion of two medium-sized retrogressive thaw slumps (RTS), exhibiting widespread negative surface change in the slump floor.
WSGAN: An Improved Generative Adversarial Network for Remote Sensing Image Road Network Extraction by Weakly Supervised Processing
Authors: Hu Anna , Chen Siqiong , Wu Liang , Xie Zhong , Qiu Qinjun , Xu Yongyang
Published: June 2021

Road networks play an important role in navigation and city planning. However, current methods mainly adopt the supervised strategy that needs paired remote sensing images and segmentation images. These data requirements are difficult to achieve. The pair segmentation images are not easy to prepare. Thus, to alleviate the burden of acquiring large quantities of training images, this study designed an improved generative adversarial network to extract road networks through a weakly supervised process named WSGAN. The proposed method is divided into two steps: generating the mapping image and post-processing the binary image. During the generation of the mapping image, unlike other road extraction methods, this method overcomes the limitations of manually annotated segmentation images and uses mapping images that can be easily obtained from public data sets. The residual network block and Wasserstein generative adversarial network with gradient penalty loss were used in the mapping network to improve the retention of high-frequency information. In the binary image post-processing, this study used the dilation and erosion method to remove salt-and-pepper noise and obtain more accurate results. By comparing the generated road network results, the Intersection over Union scores reached 0.84, the detection accuracy of this method reached 97.83%, the precision reached 92.00%, and the recall rate reached 91.67%. The experiments used a public dataset from Google Earth screenshots. Benefiting from the powerful prediction ability of GAN, the experiments show that the proposed method performs well at extracting road networks from remote sensing images, even if the roads are covered by the shadows of buildings or trees.
Global Analysis of Coastal Gradients of Sea Surface Salinity
Authors: Dossa Alina N. , Alory Gaël , da Silva Alex Costa , Dahunsi Adeola M. , Bertrand Arnaud
Published: June 2021

Sea surface salinity (SSS) is a key variable for ocean–atmosphere interactions and the water cycle. Due to its climatic importance, increasing efforts have been made for its global in situ observation, and dedicated satellite missions have been launched more recently to allow homogeneous coverage at higher resolution. Cross-shore SSS gradients can bear the signature of different coastal processes such as river plumes, upwelling or boundary currents, as we illustrate in a few regions. However, satellites performances are questionable in coastal regions. Here, we assess the skill of four gridded products derived from the Soil Moisture Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) satellites and the GLORYS global model reanalysis at capturing cross-shore SSS gradients in coastal bands up to 300 km wide. These products are compared with thermosalinography (TSG) measurements, which provide continuous data from the open ocean to the coast along ship tracks. The comparison shows various skills from one product to the other, decreasing as the coast gets closer. The bias in reproducing coastal SSS gradients is unrelated to how the SSS biases evolve with the distance to the coast. Despite limited skill, satellite products generally agree better with collocated TSG data than a global reanalysis and show a large range of coastal SSS gradients with different signs. Moreover, satellites reveal a global dominance of coastal freshening, primarily related to river runoff over shelves. This work shows a great potential of SSS remote sensing to monitor coastal processes, which would, however, require a jump in the resolution of future SSS satellite missions to be fully exploited.
Object Oriented Classification for Mapping Mixed and Pure Forest Stands Using Very-High Resolution Imagery
Authors: Oreti Loredana , Giuliarelli Diego , Tomao Antonio , Barbati Anna
Published: June 2021

The importance of mixed forests is increasingly recognized on a scientific level, due to their greater productivity and efficiency in resource use, compared to pure stands. However, a reliable quantification of the actual spatial extent of mixed stands on a fine spatial scale is still lacking. Indeed, classification and mapping of mixed populations, especially with semi-automatic procedures, has been a challenging issue up to date. The main objective of this study is to evaluate the potential of Object-Based Image Analysis (OBIA) and Very-High-Resolution imagery (VHR) to detect and map mixed forests of broadleaves and coniferous trees with a Minimum Mapping Unit (MMU) of 500 m2. This study evaluates segmentation-based classification paired with non-parametric method K- nearest-neighbors (K-NN), trained with a dataset independent from the validation one. The forest area mapped as mixed forest canopies in the study area amounts to 11%, with an overall accuracy being equal to 85% and K of 0.78. Better levels of user and producer accuracies (85–93%) are reached in conifer and broadleaved dominated stands. The study findings demonstrate that the very high resolution images (0.20 m of spatial resolutions) can be reliably used to detect the fine-grained pattern of rare mixed forests, thus supporting the monitoring and management of forest resources also on fine spatial scales.
Airborne Lidar Observations of a Spring Phytoplankton Bloom in the Western Arctic Ocean
Authors: Churnside James H. , Marchbanks Richard D. , Marshall Nathan
Published: June 2021

One of the most notable effects of climate change is the decrease in sea ice in the Arctic Ocean. This is expected to affect the distribution of phytoplankton as the ice retreats earlier. We were interested in the vertical and horizontal distribution of phytoplankton in the Chukchi Sea in May. Measurements were made with an airborne profiling lidar that allowed us to cover large areas. The lidar profiles showed a uniform distribution of attenuation and scattering from the surface to the limit of lidar penetration at a depth of about 30 m. Both parameters were greater in open water than under the ice. Depolarization of the lidar decreased as attenuation and scattering increased. A cluster analysis of the 2019 data revealed four distinct clusters based on depolarization and lidar ratio. One cluster was associated with open water, one with pack ice, one with the waters along the land-fast ice, and one that appeared to be scattered throughout the region. The first three were likely the result of different assemblages of phytoplankton, while the last may have been an artifact of thin fog in the atmosphere.
Forest Fire Risk Prediction: A Spatial Deep Neural Network-Based Framework
Authors: Naderpour Mohsen , Rizeei Hossein Mojaddadi , Ramezani Fahimeh
Published: June 2021

Forest fire is one of the foremost environmental disasters that threatens the Australian community. Recognition of the occurrence patterns of fires and the identification of fire risk is beneficial to mitigate probable fire threats. Machine learning techniques are recognized as well-known approaches to solving non-linearity problems such as forest fire risk. However, assessing such environmental multivariate disasters has always been challenging as modelling may be biased from multiple uncertainty sources such as the quality and quantity of input parameters, training processes, and a default setup for hyper-parameters. In this study, we propose a spatial framework to quantify the forest fire risk in the Northern Beaches area of Sydney. Thirty-six significant key indicators contributing to forest fire risk were selected and spatially mapped from different contexts such as topography, morphology, climate, human-induced, social, and physical perspectives as input to our model. Optimized deep neural networks were developed to maximize the capability of the multilayer perceptron for forest fire susceptibility assessment. The results show high precision of developed model against accuracy assessment metrics of ROC = 95.1%, PRC = 93.8%, and k coefficient = 94.3%. The proposed framework follows a stepwise procedure to run multiple scenarios to calculate the probability of forest risk with new input contributing parameters. This model improves adaptability and decision-making as it can be adapted to different regions of Australia with a minor localization adoption requirement of the weighting procedure.
An Improved Particle Swarm Optimization Based on Total Variation Regularization and Projection Constraint with Applications in Ground-Penetrating Radar Inversion: A Model Simulation Study
Authors: Dai Qianwei , Zhang Hao , Zhang Bin
Published: June 2021

The chaos oscillation particle swarm optimization (COPSO) algorithm is prone to binge trapped in the local optima when dealing with certain complex models in ground-penetrating radar (GPR) data inversion, because it inherently suffers from premature convergence, high computational costs, and extremely slow convergence times, especially in the middle and later periods of iterative inversion. Considering that the bilateral connections between different particle positions can improve both the algorithmic searching efficiency and the convergence performance, we first develop a fast single-trace-based approach to construct an initial model for 2-D PSO inversion and then propose a TV-regularization-based improved PSO (TVIPSO) algorithm that employs total variation (TV) regularization as a constraint technique to adaptively update the positions of particles. B by adding the new velocity variations and optimal step size matrices, the search range of the random particles in the solution space can be significantly reduced, meaning blindness in the search process can be avoided. By introducing constraint-oriented regularization to allow the optimization search to move out of the inaccurate region, the premature convergence and blurring problems can be mitigated to further guarantee the inversion accuracy and efficiency. We report on three inversion experiments involving multilayered, fluctuated terrain models and a typical complicated inner-interface model to demonstrate the performance of the proposed algorithm. The results of the fluctuated terrain model show that compared with the COPSO algorithm, the fitness error (MAE) of the TVIPSO algorithm is reduced from 2.3715 to 1.0921, while for the complicated inner-interface model the fitness error (MARE) of the TVIPSO algorithm is reduced from 1.9539 to 1.5674.
Prospecting the UNESCO World Heritage Site of Müstair (Switzerland)
Authors: Schlegel Jona , Verhoeven Geert J. , Cassitti Patrick , Hinterleitner Alois , Löcker Klaus , Schiel Hannes , Walser Christoph , Reitmaier Thomas , Neubauer Wolfgang
Published: June 2021

The Benedictine Convent of Saint John at Müstair is a UNESCO World Heritage Site located in the eastern part of Switzerland close to South Tyrol’s border (Italy). Known as a well-preserved Carolingian building complex housing Carolingian and Romanesque frescoes, the convent has received much academic attention. However, all research activities so far have been concentrated on the area enclosed by the convent’s walls, even though the neighbouring fields to the east and south are also part of the convent’s property. This paper reports on the archaeological magnetic and ground-penetrating radar surveys of these areas, executed as part of a pilot project exploring the convent’s immediate environment. At present, these fields are used for agriculture and located on a massive alluvial fan of the mountain stream Valgarola. Dense geophysical sampling revealed an intricate network of distributary channels with stream and mudflow deposits, constituting a natural border of the convent’s territory. In addition to different field systems, a newly discovered broad pathway appears to be an original Roman road. Numerous structural elements, mapped within the convent’s walls, could be attributed to specific building phases. Over 40 large and deep burial shafts, arranged in three rows, were discovered outside the convent’s burial ground. Their specific design and arrangement are characteristic of early medieval burials, such as those of the 6th century Lombards on the edge of the eastern Alps.
Multispectral LiDAR Point Cloud Classification Using SE-PointNet++
Authors: Jing Zhuangwei , Guan Haiyan , Zhao Peiran , Li Dilong , Yu Yongtao , Zang Yufu , Wang Hanyun , Li Jonathan
Published: June 2021

A multispectral light detection and ranging (LiDAR) system, which simultaneously collects spatial geometric data and multi-wavelength intensity information, opens the door to three-dimensional (3-D) point cloud classification and object recognition. Because of the irregular distribution property of point clouds and the massive data volume, point cloud classification directly from multispectral LiDAR data is still challengeable and questionable. In this paper, a point-wise multispectral LiDAR point cloud classification architecture termed as SE-PointNet++ is proposed via integrating a Squeeze-and-Excitation (SE) block with an improved PointNet++ semantic segmentation network. PointNet++ extracts local features from unevenly sampled points and represents local geometrical relationships among the points through multi-scale grouping. The SE block is embedded into PointNet++ to strengthen important channels to increase feature saliency for better point cloud classification. Our SE-PointNet++ architecture has been evaluated on the Titan multispectral LiDAR test datasets and achieved an overall accuracy, a mean Intersection over Union (mIoU), an F1-score, and a Kappa coefficient of 91.16%, 60.15%, 73.14%, and 0.86, respectively. Comparative studies with five established deep learning models confirmed that our proposed SE-PointNet++ achieves promising performance in multispectral LiDAR point cloud classification tasks.
Response of Multi-Incidence Angle Polarimetric RADARSAT-2 Data to Herbaceous Vegetation Features in the Lower Paraná River Floodplain, Argentina
Authors: Morandeira Natalia Soledad , Barber Matías Ernesto , Grings Francisco Matías , Ahern Frank , Kandus Patricia , Brisco Brian
Published: June 2021

Wetland ecosystems play a key role in hydrological and biogeochemical cycles. In emergent vegetation targets, the occurrence of double-bounce scatter is indicative of the presence of water and can be valuable for hydrological monitoring. Double-bounce scatter would lead to an increase of σ0HH over σ0VV and a non-zero co-polarized phase difference (CPD). In the Lower Paraná River floodplain, a total of 11 full polarimetric RADARSAT-2 scenes from a wide range of incidence angles were acquired during a month. Flooded targets dominated by two herbaceous species were sampled: Schoenoplectus californicus (four sites, Bulrush marshes) and Ludwigia peruviana (three sites, Broadleaf marshes). As a general trend, σ0HH was higher than σ0VV, especially at the steeper incidence angles. By modeling CPD with maximum likelihood estimations, we found results consistent with double-bounce scatter in two Ludwigia plots, at certain scene incidence angles. Incidence angle accounted for most of the variation on σ0HH, whereas emergent green biomass was the main feature influencing σ0HV. Multivariate models explaining backscattering variation included the incidence angle and at least two of these variables: emergent plant height, stem diameter, number of green stems, and emergent green biomass. This study provides an example of using CPD to decide on the contribution of double-bounce scatter and highlights the influence of vegetation biomass on radar response. Even with the presence of water below vegetation, the contribution of double-bounce scatter to C-band backscattering depends on scene incidence angles and may be negligible in dense herbaceous targets.
Hyperspectral LiDAR-Based Plant Spectral Profiles Acquisition: Performance Assessment and Results Analysis
Authors: Jia Jianxin , Jiang Changhui , Li Wei , Wu Haohao , Chen Yuwei , Hu Peilun , Shao Hui , Wang Shaowei , Yang Fan , Puttonen Eetu , Hyyppä Juha
Published: June 2021

In precision agriculture, efficient fertilization is one of the most important pursued goals. Vegetation spectral profiles and the corresponding spectral parameters are usually employed for vegetation growth status indication, i.e., vegetation classification, bio-chemical content mapping, and efficient fertilization guiding. In view of the fact that the spectrometer works by relying on ambient lighting condition, hyperspectral/multi-spectral LiDAR (HSL/MSL) was invented to collect the spectral profiles actively. However, most of the HSL/MSL works with the wavelength specially selected for specific applications. For precision agriculture applications, a more feasible HSL capable of collecting spectral profiles at wide-range spectral wavelength is necessary to extract various spectral parameters. Inspired by this, in this paper, we developed a hyperspectral LiDAR (HSL) with 10 nm spectral resolution covering 500~1000 nm. Different vegetation leaf samples were scanned by the HSL, and it was comprehensively assessed for wide-range wavelength spectral profiles acquirement, spectral parameters extraction, vegetation classification, and the laser incident angle effect. Specifically, three experiments were carried out: (1) spectral profiles results were compared with that from a SVC spectrometer (HR-1024, Spectra Vista Corporation); (2) the extracted spectral parameters from the HSL were assessed, and they were employed as the input features of a support vector machine (SVM) classifier with multiple labels to classify the vegetation; (3) in view of the influence of the laser incident angle on the HSL reflected laser intensities, we analyzed the laser incident angle effect on the spectral parameters values. The experimental results demonstrated the developed HSL was more feasible for acquiring spectral profiles with wide-range wavelength, and spectral parameters and vegetation classification results also indicated its great potentials in precision agriculture application.
An Unmanned Lighter-Than-Air Platform for Large Scale Land Monitoring
Authors: Gili Piero , Civera Marco , Roy Rinto , Surace Cecilia
Published: June 2021

The concept and preliminary design of an unmanned lighter-than-air (LTA) platform instrumented with different remote sensing technologies is presented. The aim is to assess the feasibility of using a remotely controlled airship for the land monitoring of medium sized (up to 107 m2) urban or rural areas at relatively low altitudes (below 1000 m) and its potential convenience with respect to other standard remote and in-situ sensing systems. The proposal includes equipment for high-definition visual, thermal, and hyperspectral imaging as well as LiDAR scanning. The data collected from these different sources can be then combined to obtain geo-referenced products such as land use land cover (LULC), soil water content (SWC), land surface temperature (LSC), and leaf area index (LAI) maps, among others. The potential uses for diffuse structural health monitoring over built-up areas are discussed as well. Several mission typologies are considered.
Deep Learning-Based Point Upsampling for Edge Enhancement of 3D-Scanned Data and Its Application to Transparent Visualization
Authors: Li Weite , Hasegawa Kyoko , Li Liang , Tsukamoto Akihiro , Tanaka Satoshi
Published: June 2021

Large-scale 3D-scanned point clouds enable the accurate and easy recording of complex 3D objects in the real world. The acquired point clouds often describe both the surficial and internal 3D structure of the scanned objects. The recently proposed edge-highlighted transparent visualization method is effective for recognizing the whole 3D structure of such point clouds. This visualization utilizes the degree of opacity for highlighting edges of the 3D-scanned objects, and it realizes clear transparent viewing of the entire 3D structures. However, for 3D-scanned point clouds, the quality of any edge-highlighting visualization depends on the distribution of the extracted edge points. Insufficient density, sparseness, or partial defects in the edge points can lead to unclear edge visualization. Therefore, in this paper, we propose a deep learning-based upsampling method focusing on the edge regions of 3D-scanned point clouds to generate more edge points during the 3D-edge upsampling task. The proposed upsampling network dramatically improves the point-distributional density, uniformity, and connectivity in the edge regions. The results on synthetic and scanned edge data show that our method can improve the percentage of edge points more than 15% compared to the existing point cloud upsampling network. Our upsampling network works well for both sharp and soft edges. A combined use with a noise-eliminating filter also works well. We demonstrate the effectiveness of our upsampling network by applying it to various real 3D-scanned point clouds. We also prove that the improved edge point distribution can improve the visibility of the edge-highlighted transparent visualization of complex 3D-scanned objects.
Application of Ground-Based Microwave Radiometer in Retrieving Meteorological Characteristics of Tibet Plateau
Authors: Wei Jiahua , Shi Yang , Ren Yan , Li Qiong , Qiao Zhen , Cao Jiongwei , Ayantobo Olusola O. , Yin Jianguo , Wang Guangqian
Published: June 2021

The characteristics of plateau precipitation and atmosphere, once accurately and comprehensively understood, can be used to inform sound air–water resource development practices. In this study, atmospheric exploration of the Tibet Plateau (TP) was conducted using ground-based microwave radiometer (MWR) data collected during the East Asian summer monsoon. Atmospheric temperature, pressure, humidity, and other variables were gathered under clear-sky, cloudy-sky, and rainy-sky conditions. Statistical characteristics of the air parcel height and stability/convection indices such as convective available potential energy (CAPE) and convective inhibition (CIN) were investigated, with a special focus on the rainy-sky condition. Two retrieval applications for characterizing precipitation, namely short-term precipitation forecast and quantitative precipitation estimation were presented. Results showed that CAPE values in the Darlag region reached extremes around 18:00–20:00 (UTC+8) for cloudy-sky and rainy-sky conditions with corresponding peaks of about 1046.56 J/kg and 703.02 J/kg, respectively. When stratiform or convective–mixed precipitation occurs, the precipitable water vapor (PWV) and CAPE values were generally greater than 1.7 cm and 1000 J/kg, respectively. CAPE values are likely to decrease before the occurrence of precipitation due to the release of the latent heat in the atmosphere.
Dynamics and Drivers of Vegetation Phenology in Three-River Headwaters Region Based on the Google Earth Engine
Authors: Wang Jiyan , Sun Huaizhang , Xiong Junnan , He Dong , Cheng Weiming , Ye Chongchong , Yong Zhiwei , Huang Xianglin
Published: June 2021

Phenology shifts over time are known as the canary in the mine when studying the response of terrestrial ecosystems to climate change. Plant phenology is a key factor controlling the productivity of terrestrial vegetation under climate change. Over the past several decades, the vegetation in the three-river headwaters region (TRHR) has been reported to have changed greatly owing to the warming climate and human activities. However, uncertainties related to the potential mechanism and influence of climatic and soil factors on the plant phenology of the TRHR are poorly understood. In this study, we used harmonic analysis of time series and the relative and absolute change rate on Google Earth Engine to calculate the start (SOS), end (EOS), and length (LOS) of the growing season based on MOD09A1 datasets; the results were verified by the observational data from phenological stations. Then, the spatiotemporal patterns of plant phenology for different types of terrain and basins were explored. Finally, the potential mechanism involved in the influence of climatic and soil factors on the phenology of plants in the TRHR were explored based on the structural equation model and Pearson’s correlation coefficients. The results show the remotely sensed monitoring data of SOS (R2 = 0.84, p < 0.01), EOS (R2 = 0.72, p < 0.01), and LOS (R2 = 0.86, p < 0.01) were very similar to the observational data from phenological stations. The SOS and LOS of plants possessed significant trends toward becoming advanced (Slope < 0) and extended (Slope > 0), respectively, from 2001 to 2018. The SOS was the earliest and the LOS was the longest in the Lancang River Basin, while the EOS was the latest in the Yangtze River Basin owing to the impact of climate change and soil factors. Meanwhile, the spatial patterns of SOS, EOS, and LOS have strong spatial heterogeneity at different elevations, slopes, and aspects. In addition, the results show that the drivers of plant phenology have basin-wide and stage differences. Specifically, the influence of soil factors on plant phenology in the Yangtze River Basin was greater than that of climatic factors, but climatic factors were key functional indicators of LOS in the Yellow and Lancang river basins, which directly or indirectly affect plant LOS through soil factors. This study will be helpful for understanding the relationship between the plant phenology of the alpine wetland ecosystem and climate change and improving the level of environmental management.