bluuebunny/crossref_metadata_2025 huggingface dataset:
Enhanced Cellular Uptake in an Electrostatically Interacting Fucoidan–L-Arginine Fiber Complex
Authors: Arunagiri Vinothini , Tsai Hsieh-Chih , Darge Haile Fentahun , Hanurry Endiries Yibru , Lee Chang Yi , Lai Juin-Yih , Wu Szu-Yuan
Published: May 2021

Fucoidan is an abundant marine sulfated polysaccharide extracted from the cell wall of brown macroalgae (seaweed). Recently, fucoidan has been highly involved in various industrial applications, such as pharmaceuticals, biomedicals, cosmetics, and food. However, the presence of a sulfate group (negative surface charge) in the fucoidan structure limits its potential and biological activity for use in biomedical applications during cellular uptake. Thus, we aimed to improve the uptake of fucoidan by using an L-arginine uptake enhancer within an in vitro study. A Fucoidan–L-Arginine (Fuc-L-Arg) fiber complex was prepared via α-helical electrostatic interactions using a freeze-drying technique and confirmed using field-emission scanning electron microscopy, Fourier transform infrared spectroscopy, and nuclear magnetic resonance spectroscopy. In addition, fucoidan was conjugated with cyanine 3 (Cy3) dye to track its cellular uptake. Furthermore, the results of Fuc-L-Arg (1:1, 1:2.5) complexes revealed biocompatibility >80% at various concentrations (5, 10, 25, 50, 100 µg/mL). Owing to the higher internalization of the Fuc-L-Arg (1:5) complex, it exhibited <80% biocompatibility at higher concentrations (25, 50, 100 µg/mL) of the complex. In addition, improved cellular internalization of Fuc-L-Arg complexes (1:5) in HeLa cells have been proved via flow cytometry quantitative analysis. Hence, we highlight that the Fuc-L-Arg (1:5) fiber complex can act as an excellent biocomplex to exhibit potential bioactivities, such as targeting cancers, as fucoidan shows higher permeability in HeLa cells.
Native Spider Silk-Based Antimicrobial Hydrogels for Biomedical Applications
Authors: Withanage Sinith , Savin Artemii , Nikolaeva Valeria , Kiseleva Aleksandra , Dukhinova Marina , Krivoshapkin Pavel , Krivoshapkina Elena
Published: May 2021

Novel antimicrobial natural polymeric hybrid hydrogels based on hyaluronic acid (HA) and spider silk (Ss) were prepared using the chemical crosslinking method. The effects of the component ratios on the hydrogel characteristics were observed parallel to the primary physicochemical characterization of the hydrogels with scanning electron microscopic imaging, Fourier-transform infrared spectroscopy, and contact angle measurements, which confirmed the successful crosslinking, regular porous structure, exact composition, and hydrophilic properties of hyaluronic acid/spider silk-based hydrogels. Further characterizations of the hydrogels were performed with the swelling degree, enzymatic degradability, viscosity, conductivity, and shrinking ability tests. The hyaluronic acid/spider silk-based hydrogels do not show drastic cytotoxicity over human postnatal fibroblasts (HPF). Hydrogels show extraordinary antimicrobial ability on both gram-negative and gram-positive bacteria. These hydrogels could be an excellent alternative that aids in overcoming antimicrobial drug resistance, which is considered to be one of the major global problems in the biomedical industry. Hyaluronic acid/spider silk-based hydrogels are a promising material for collaborated antimicrobial and anti-inflammatory drug delivery systems for external use. The rheological properties of the hydrogels show shear-thinning properties, which suggest that the hydrogels could be applied in 3D printing, such as in the 3D printing of antimicrobial surgical meshes.
Development and Characterization of Bioactive Poly(butylene-succinate) Films Modified with Quercetin for Food Packaging Applications
Authors: Łopusiewicz Łukasz , Zdanowicz Magdalena , Macieja Szymon , Kowalczyk Krzysztof , Bartkowiak Artur
Published: May 2021

The preparation of biodegradable active packaging materials is still a major challenge. Here, we report the fabrication and characterization of poly(butylene succinate)-based (PBS) films enriched with a natural polyphenolic antioxidant—quercetin. The PBS-based films with various quercetin content (0.05; 0.10; 0.25 and 0.50 pph on PBS) were prepared via a solvent casting method. Physical (optical, mechanical, thermal, moisture and water sorption, water vapor and UV–vis barrier) and biofunctional (antioxidant and antibacterial against Escherichia coli and Staphylococcus aureus) film properties were tested. The migration of quercetin into model food liquid systems was determined. As a result of quercetin addition, significant changes in color, opacity and UV-blocking effect were observed. The presence of the active substance did not significantly affect the thermal properties of the PBS matrix. However, the mechanical properties of the films were slightly decreased. The films exhibited excellent free radicals (DPPH, ABTS, O2−) scavenging and some bactericidal activities. PBS-quercetin films with superior functional properties have many possibilities for active food packaging applications.
Ketone Number and Substitution Effect of Benzophenone Derivatives on the Free Radical Photopolymerization of Visible-Light Type-II Photoinitiators
Authors: Huang Tung-Liang , Chen Yung-Chung
Published: May 2021

Three novel visible-light absorbing benzophenone-based hydrogen acceptors (BPD-D, BPDM-D and BPDP-D) were designed on the basis of a donor–benzophenone–donor structural backbone. Mono or diketone units and double diphenylamine electron-donating groups in para-or meta-positions were introduced to comprehend the electronic and structural effects on free radical photopolymerization (FRPP). Such a structural change leads not only to a red-shift of the absorption maxima but strongly enhances their molar extinction coefficients compared to the commercial phototinitiators such as benzophenone (BP) and 4,4′-bis(diethylamino) benzophenone (EMK). In addition, excellent melting points and thermal decomposition temperatures were achieved for those novel compounds. Further, the photochemical reaction behavior was studied by cyclic voltammograms (CV), photolysis and electron spin resonance (ESR) spectroscopy. Finally, benzophenone derivatives in combination with an amine (TEA, triethylamine) as a co-initiator were prepared and initiated the FRPP of trimethylolpropane trimethacrylate (TMPTMA) using a UV lamp as a light source. When used in stoichiometric amounts, the BPDP-D/TEA had the best double bond conversion efficiency among all the compounds studied, and were even superior to the reference compounds of BP/TEA and EMK/TEA. The results and conclusions could provide the fundamental rules applicable for the structural design of benzophenone derivative-based photoinitiators.
Effects of Different Winemaking Yeasts on the Composition of Aroma-Active Compounds and Flavor of the Fermented Jujube Wine
Authors: Zhao Yan , Yu Xiaobin , Zhu Fengtao , Liu Guangpeng , Chu Le , Yan Xinhuan , Ma Yinfei , He Fatao , Li Gen , Zhang Ying , Tan Mengnan , Lu Yao
Published: May 2021

For the winemaking bioprocess of jujube wine, the selection of optimal starter cultures is one of the major concerns before fermentation. In this study, we investigated the effects of different winemaking yeasts on the composition of aroma-active compounds in the fermented jujube wine and identified the principal components that determine the flavor quality. It showed that the starter winemaking yeasts produced a total of 43 aroma-active compounds, of which esters (e.g., ethyl caprylate, ethyl decanoate, ethyl hexanoate, and phenethyl acetate) contribute more to the wine quality attributes, especially for the improvement of the aroma. Moreover, the composition of aroma-active compounds, for example, the ratio of the content of esters and alcohols, exerts a great impact on the flavor quality of jujube wine. Different starter winemaking yeasts resulted in significant differences in the composition (both species and content) of aroma-active compounds, and thus formed different flavors in the jujube wine. Thus, we propose that screening of a desirable starter winemaking yeast is essential before the fermentation of jujube wine at a large scale, and more considerations should be taken into the resulting composition of aroma-active compounds.
Distributed Secondary Control of Islanded Microgrids for Fast Convergence Considering Arbitrary Sampling
Authors: Hong Yinqiu , Xie Jihua , Fang Fang
Published: May 2021

This paper proposes a novel distributed secondary control of MGs for fast convergence considering asynchronous sampling. With the employment of the algorithm, optimal power sharing and voltage restoration are implemented simultaneously. First, the hierarchical control objectives concerned with economic operation and voltage quality are introduced. Then, the execution process of the fast convergence algorithm is described for weighted average state estimation, with the illustration of corresponding features and the application in cooperative control. Further, the relevant stability issue is discussed based on large-signal dynamic modeling and a sufficient stability condition is derived based on the Lyapunov–Krasovskii theory. Our approach offers superior reliability, flexibility and robustness because of the loose implementation in terms of its performance concern, which is essential when the distributed consensus protocol is likely to yield toward deviation or even instability under arbitrary sampling and delays. The effectiveness of the proposed methodology is verified via simulations.
Ecumenical Convergences: Romanian Evangelicals Exploring Orthodoxy
Authors: Sonea Cristian
Published: May 2021

Historically, in Romania, the relations between the Romanian Orthodox Church and the evangelical communities have been characterized by tension and mutual distrust. That is why, unfortunately, there has been no official dialogue between the two communities so far. The present article investigates the theoretical possibility for such an ecumenical dialogue to occur by analysing the contributions of several evangelical theologians who published research studies on theological topics specific to Eastern orthodox theology. Their positions were analysed from the perspective of an inclusive theology which allowed us to identify some common themes for both traditions: the authority in interpreting the Scriptures, salvation as a process, and the Church understood through the application of a perichoretic model. All these convergent themes could constitute the basis for a future official ecumenical dialogue between the evangelicals and the orthodox from Romania.
A Novel Approach to Obtain Diurnal Variation of Bio-Optical Properties in Moving Water Parcel Using Integrated Drifting Buoy and GOCI Data: A Case Study in Yellow and East China Seas
Authors: Xu Yuying , Guan Weibing , Chen Jianyu , Cao Zhenyi , Qiao Feng
Published: May 2021

Ocean processes that can influence rapidly changing ocean color include water-mass movement and bio-optical property changes in the water parcel. Traditionally, diurnal variability of bio-optical properties relies on daily time series at fixed locations by satellite sensors or in situ observations. There is a lack of an effective way to observe diurnal variation of bio-optical properties in a moving water parcel on a large scale. In this paper, we propose a new method to acquire diurnal variation of bio-optical properties in a moving water parcel. The novel approach integrates drifting buoy data and GOCI data. The movement of surface current was tracked by a drifting buoy, and its spatiotemporally matching bio-optical properties were obtained via the GOCI data. The results in the Yellow and East China seas during the summers of 2012 and 2013 show that the variation of time series following the movement of water parcel was obviously different from that obtained at fixed locations. The hourly differences of the former are 15.7% and 16.3% smaller than those of the latter for Chl a and total suspended sediment (TSS), respectively. The value of ag440 was more stable within the moving water parcel than in the fixed location. Our approach provides a simple and feasible way for observing diurnal variability of bio-optical properties in a moving surface water parcel.
A New Faulty GNSS Measurement Detection and Exclusion Algorithm for Urban Vehicle Positioning
Authors: Cheng Qi , Chen Ping , Sun Rui , Wang Junhui , Mao Yi , Ochieng Washington Yotto
Published: May 2021

The performance requirements for Global Navigation Satellite Systems (GNSS) are becoming more demanding as the range of mission-critical vehicular applications, including the Unmanned Aerial Vehicle (UAV) and ground vehicle-based applications, increases. However, the accuracy and reliability of GNSS in some environments, such as in urban areas, are often affected by non-line-of-sight (NLOS) signals and multipath effects. It is therefore essential to develop an effective fault detection scheme that can be applied to GNSS observations so as to ensure that the vehicle positioning can be calculated with a high accuracy. In this paper, we propose an online dataset based faulty GNSS measurement detection and exclusion algorithm for vehicle positioning that takes account of the NLOS/multipath affected scenarios. The proposed algorithm enables a real-time online dataset based fault detection and exclusion scheme, which makes it possible to detect multiple faults in different satellites simultaneously and accurately, thereby allowing real-time quality control of GNSS measurements in dynamic urban positioning applications. The algorithm was tested with simulated/artificial step errors in various scenarios in the measured pseudoranges from a dataset acquired from a UAV in an open area. Furthermore, a real-world test was also conducted with a ground-vehicle driving in a dense urban environment to validate the practical efficiency of the proposed algorithm. The UAV based simulation exhibits a fault detection rate of 100% for both single and multi-satellite fault scenarios, with the horizontal positioning accuracy improved to about 1 metre from tens of metres after fault detection and exclusion. The ground vehicle-based real test shows an overall improvement of 26.1% in 3D positioning accuracy in an urban area compared to the traditional least square method.
An Optimal Image–Selection Algorithm for Large-Scale Stereoscopic Mapping of UAV Images
Authors: Lim Pyung-chae , Rhee Sooahm , Seo Junghoon , Kim Jae-In , Chi Junhwa , Lee Suk-bae , Kim Taejung
Published: May 2021

Recently, the mapping industry has been focusing on the possibility of large-scale mapping from unmanned aerial vehicles (UAVs) owing to advantages such as easy operation and cost reduction. In order to produce large-scale maps from UAV images, it is important to obtain precise orientation parameters as well as analyzing the sharpness of they themselves measured through image analysis. For this, various techniques have been developed and are included in most of the commercial UAV image processing software. For mapping, it is equally important to select images that can cover a region of interest (ROI) with the fewest possible images. Otherwise, to map the ROI, one may have to handle too many images, and commercial software does not provide information needed to select images, nor does it explicitly explain how to select images for mapping. For these reasons, stereo mapping of UAV images in particular is time consuming and costly. In order to solve these problems, this study proposes a method to select images intelligently. We can select a minimum number of image pairs to cover the ROI with the fewest possible images. We can also select optimal image pairs to cover the ROI with the most accurate stereo pairs. We group images by strips and generate the initial image pairs. We then apply an intelligent scheme to iteratively select optimal image pairs from the start to the end of an image strip. According to the results of the experiment, the number of images selected is greatly reduced by applying the proposed optimal image–composition algorithm. The selected image pairs produce a dense 3D point cloud over the ROI without any holes. For stereoscopic plotting, the selected image pairs were map the ROI successfully on a digital photogrammetric workstation (DPW) and a digital map covering the ROI is generated. The proposed method should contribute to time and cost reductions in UAV mapping.
Digital Ecosystems for Developing Digital Twins of the Earth: The Destination Earth Case
Authors: Nativi Stefano , Mazzetti Paolo , Craglia Max
Published: May 2021

This manuscript discusses the key characteristics of the Digital Ecosystems (DEs) model, which, we argue, is particularly appropriate for connecting and orchestrating the many heterogeneous and autonomous online systems, infrastructures, and platforms that constitute the bedrock of a digitally transformed society. Big Data and AI systems have enabled the implementation of the Digital Twin paradigm (introduced first in the manufacturing sector) in all the sectors of society. DEs promise to be a flexible and operative framework that allow the development of local, national, and international Digital Twins. In particular, the “Digital Twins of the Earth” may generate the actionable intelligence that is necessary to address global change challenges, facilitate the European Green transition, and contribute to realizing the UN Sustainable Development Goals (SDG) agenda. The case of the Destination Earth initiative and system is discussed in the manuscript as an example to address the broader DE concepts. In respect to the more traditional data and information infrastructural philosophy, DE solutions present important advantages as to flexibility and viability. However, designing and implementing an effective collaborative DE is far more difficult than a traditional digital system. DEs require the definition and the governance of a metasystemic level, which is not necessary for a traditional information system. The manuscript discusses the principles, patterns, and architectural viewpoints characterizing a thriving DE supporting the generation and operation of “Digital Twins of the Earth”. The conclusions present a set of conditions, best practices, and base capabilities for building a knowledge framework, which makes use of the Digital Twin paradigm and the DE approach to support decision makers with the SDG agenda implementation.
Simulating Imaging Spectroscopy in Tropical Forest with 3D Radiative Transfer Modeling
Authors: Ebengo Dav M. , de Boissieu Florian , Vincent Grégoire , Weber Christiane , Féret Jean-Baptiste
Published: May 2021

Optical remote sensing can contribute to biodiversity monitoring and species composition mapping in tropical forests. Inferring ecological information from canopy reflectance is complex and data availability suitable to such a task is limiting, which makes simulation tools particularly important in this context. We explored the capability of the 3D radiative transfer model DART (Discrete Anisotropic Radiative Transfer) to simulate top of canopy reflectance acquired with airborne imaging spectroscopy in a complex tropical forest, and to reproduce spectral dissimilarity within and among species, as well as species discrimination based on spectral information. We focused on two factors contributing to these canopy reflectance properties: the horizontal variability in leaf optical properties (LOP) and the fraction of non-photosynthetic vegetation (NPVf). The variability in LOP was induced by changes in leaf pigment content, and defined for each pixel based on a hybrid approach combining radiative transfer modeling and spectral indices. The influence of LOP variability on simulated reflectance was tested by considering variability at species, individual tree crown and pixel level. We incorporated NPVf into simulations following two approaches, either considering NPVf as a part of wood area density in each voxel or using leaf brown pigments. We validated the different scenarios by comparing simulated scenes with experimental airborne imaging spectroscopy using statistical metrics, spectral dissimilarity (within crowns, within species, and among species dissimilarity) and supervised classification for species discrimination. The simulation of NPVf based on leaf brown pigments resulted in the closest match between measured and simulated canopy reflectance. The definition of LOP at pixel level resulted in conservation of the spectral dissimilarity and expected performances for species discrimination. Therefore, we recommend future research on forest biodiversity using physical modeling of remote-sensing data to account for LOP variability within crowns and species. Our simulation framework could contribute to better understanding of performances of species discrimination and the relationship between spectral variations and taxonomic and functional dimensions of biodiversity. This work contributes to the improved integration of physical modeling tools for applications, focusing on remotely sensed monitoring of biodiversity in complex ecosystems, for current sensors, and for the preparation of future multispectral and hyperspectral satellite missions.
Hourly Ground-Level PM2.5 Estimation Using Geostationary Satellite and Reanalysis Data via Deep Learning
Authors: Lee Changsuk , Lee Kyunghwa , Kim Sangmin , Yu Jinhyeok , Jeong Seungtaek , Yeom Jongmin
Published: May 2021

This study proposes an improved approach for monitoring the spatial concentrations of hourly particulate matter less than 2.5 μm in diameter (PM2.5) via a deep neural network (DNN) using geostationary ocean color imager (GOCI) images and unified model (UM) reanalysis data over the Korean Peninsula. The DNN performance was optimized to determine the appropriate training model structures, incorporating hyperparameter tuning, regularization, early stopping, and input and output variable normalization to prevent training dataset overfitting. Near-surface atmospheric information from the UM was also used as an input variable to spatially generalize the DNN model. The retrieved PM2.5 from the DNN was compared with estimates from random forest, multiple linear regression, and the Community Multiscale Air Quality model. The DNN demonstrated the highest accuracy compared to that of the conventional methods for the hold-out validation (root mean square error (RMSE) = 7.042 μg/m3, mean bias error (MBE) = −0.340 μg/m3, and coefficient of determination (R2) = 0.698) and the cross-validation (RMSE = 9.166 μg/m3, MBE = 0.293 μg/m3, and R2 = 0.49). Although the R2 was low due to underestimated high PM2.5 concentration patterns, the RMSE and MBE demonstrated reliable accuracy values (<10 μg/m3 and 1 μg/m3, respectively) for the hold-out validation and cross-validation.
Impact of Atmospheric Optical Properties on Net Ecosystem Productivity of Peatland in Poland
Authors: Harenda Kamila M. , Samson Mateusz , Juszczak Radosław , Markowicz Krzysztof M. , Stachlewska Iwona S. , Kleniewska Małgorzata , MacArthur Alasdair , Schüttemeyer Dirk , Chojnicki Bogdan H.
Published: May 2021

Peatlands play an important role in the global carbon cycle due to the high carbon storage in the substrate. Ecosystem production depends, for example, on the solar energy amount that reaches the vegetation, however the diffuse component of this flux can substantially increase ecosystem net productivity. This phenomenon is observed in different ecosystems, but the study of the atmosphere optical properties on peatland production is lacking. In this paper, the presented methodology allowed us to disentangle the diffuse radiation impact on the net ecosystem production (NEP) of Rzecin peatland, Poland. It allowed us to assess the impact of the atmospheric scattering process determined by the aerosol presence in the air mass. An application of atmospheric radiation transfer (ART) and ecosystem production (EP) models showed that the increase of aerosol optical thickness from 0.09 to 0.17 caused NEP to rise by 3.4–5.7%. An increase of the diffusion index (DI) by 0.1 resulted in an NEP increase of 6.1–42.3%, while a DI decrease of 0.1 determined an NEP reduction of −49.0 to −10.5%. These results show that low peatland vegetation responds to changes in light scattering. This phenomenon should be taken into account when calculating the global CO2 uptake estimation of such ecosystems.
Unsupervised Identification of Targeted Spectra Applying Rank1-NMF and FCC Algorithms in Long-Wave Hyperspectral Infrared Imagery
Authors: Yousefi Bardia , Ibarra-Castanedo Clemente , Chamberland Martin , Maldague Xavier P. V. , Beaudoin Georges
Published: May 2021

Clustering methods unequivocally show considerable influence on many recent algorithms and play an important role in hyperspectral data analysis. Here, we challenge the clustering for mineral identification using two different strategies in hyperspectral long wave infrared (LWIR, 7.7–11.8 μm). For that, we compare two algorithms to perform the mineral identification in a unique dataset. The first algorithm uses spectral comparison techniques for all the pixel-spectra and creates RGB false color composites (FCC). Then, a color based clustering is used to group the regions (called FCC-clustering). The second algorithm clusters all the pixel-spectra to directly group the spectra. Then, the first rank of non-negative matrix factorization (NMF) extracts the representative of each cluster and compares results with the spectral library of JPL/NASA. These techniques give the comparison values as features which convert into RGB-FCC as the results (called clustering rank1-NMF). We applied K-means as clustering approach, which can be modified in any other similar clustering approach. The results of the clustering-rank1-NMF algorithm indicate significant computational efficiency (more than 20 times faster than the previous approach) and promising performance for mineral identification having up to 75.8% and 84.8% average accuracies for FCC-clustering and clustering-rank1 NMF algorithms (using spectral angle mapper (SAM)), respectively. Furthermore, several spectral comparison techniques are used also such as adaptive matched subspace detector (AMSD), orthogonal subspace projection (OSP) algorithm, principal component analysis (PCA), local matched filter (PLMF), SAM, and normalized cross correlation (NCC) for both algorithms and most of them show a similar range in accuracy. However, SAM and NCC are preferred due to their computational simplicity. Our algorithms strive to identify eleven different mineral grains (biotite, diopside, epidote, goethite, kyanite, scheelite, smithsonite, tourmaline, pyrope, olivine, and quartz).
Annually Urban Fractional Vegetation Cover Dynamic Mapping in Hefei, China (1999–2018)
Authors: Wang Yuliang , Li Mingshi
Published: May 2021

Vegetation measures are crucial for assessing changes in the ecological environment. Fractional vegetation cover (FVC) provides information on the growth status, distribution characteristics, and structural changes of vegetation. An in-depth understanding of the dynamic changes in urban FVC contributes to the sustainable development of ecological civilization in the urbanization process. However, dynamic change detection of urban FVC using multi-temporal remote sensing images is a complex process and challenge. This paper proposed an improved FVC estimation model by fusing the optimized dynamic range vegetation index (ODRVI) model. The ODRVI model improved sensitivity to the water content, roughness degree, and soil type by minimizing the influence of bare soil in areas of sparse vegetation cover. The ODRVI model enhanced the stability of FVC estimation in the near-infrared (NIR) band in areas of dense and sparse vegetation cover through introducing the vegetation canopy vertical porosity (VCVP) model. The verification results confirmed that the proposed model had better performance than typical vegetation index (VI) models for multi-temporal Landsat images. The coefficient of determination (R2) between the ODRVI model and the FVC was 0.9572, which was 7.4% higher than the average R2 of other typical VI models. Moreover, the annual urban FVC dynamics were mapped using the proposed improved FVC estimation model in Hefei, China (1999–2018). The total area of all grades FVC decreased by 33.08% during the past 20 years in Hefei, China. The areas of the extremely low, low, and medium grades FVC exhibited apparent inter-annual fluctuations. The maximum standard deviation of the area change of the medium grade FVC was 13.35%. For other grades of FVC, the order of standard deviation of the change ratio was extremely low FVC > low FVC > medium-high FVC > high FVC. The dynamic mapping of FVC revealed the influence intensity and direction of the urban sprawl on vegetation coverage, which contributes to the strategic development of sustainable urban management plans.
Assessment of Morphology Changes of the End Moraine of the Werenskiold Glacier (SW Spitsbergen) Using Active and Passive Remote Sensing Techniques
Authors: Głowacki Tadeusz , Kasza Damian
Published: May 2021

Wedel Jarlsberg Land in Svalbard is a region with a varied periglacial landscape. In the mountains and in the valleys, the climate is polar with permafrost. During the summer, the near-surface ground layer thaws. The Werenskiold Glacier, together with its end moraine, are located in the central part of this area. The rate of morphological changes observed within the moraine varies in time and space, and depends on the environmental conditions. This study investigates four periods of archival aerial photogrammetry measurements (1936, 1960, 1990, and 2011) performed for the end moraine of the glacier. The long-term analysis was also based on a direct GNSS RTK survey from 2015. Over a period of almost 80 years, more than 14 million m3 of rock and ice material disappeared from the end moraine of the glacier (an average of approximately 200 thousand m3/year). Analyses of the dynamic surface changes over one year (2015) were performed with the use of synthetic aperture radar interferometry (InSAR). The time interval between images was in this case 12 days and covered (simultaneously in each scene) the entire investigated area. In this case, the analysis demonstrated that over a period of only 4 months, the moraine lost 200 thousand m3 of material (approximately two thousand m3/day), which is equivalent to the entire annual mass loss of the moraine.
Disturbance Analysis in the Classification of Objects Obtained from Urban LiDAR Point Clouds with Convolutional Neural Networks
Authors: Balado Jesús , Arias Pedro , Lorenzo Henrique , Meijide-Rodríguez Adrián
Published: May 2021

Mobile Laser Scanning (MLS) systems have proven their usefulness in the rapid and accurate acquisition of the urban environment. From the generated point clouds, street furniture can be extracted and classified without manual intervention. However, this process of acquisition and classification is not error-free, caused mainly by disturbances. This paper analyses the effect of three disturbances (point density variation, ambient noise, and occlusions) on the classification of urban objects in point clouds. From point clouds acquired in real case studies, synthetic disturbances are generated and added. The point density reduction is generated by downsampling in a voxel-wise distribution. The ambient noise is generated as random points within the bounding box of the object, and the occlusion is generated by eliminating points contained in a sphere. Samples with disturbances are classified by a pre-trained Convolutional Neural Network (CNN). The results showed different behaviours for each disturbance: density reduction affected objects depending on the object shape and dimensions, ambient noise depending on the volume of the object, while occlusions depended on their size and location. Finally, the CNN was re-trained with a percentage of synthetic samples with disturbances. An improvement in the performance of 10–40% was reported except for occlusions with a radius larger than 1 m.
Synergistic Use of Multispectral Data and Crop Growth Modelling for Spatial and Temporal Evapotranspiration Estimations
Authors: Silvestro Paolo Cosmo , Casa Raffaele , Hanuš Jan , Koetz Benjamin , Rascher Uwe , Schuettemeyer Dirk , Siegmann Bastian , Skokovic Drazen , Sobrino José , Tudoroiu Marin
Published: May 2021

The aim of this research is to explore the analysis of methods allowing a synergetic use of information exchange between Earth Observation (EO) data and growth models in order to provide high spatial and temporal resolution actual evapotranspiration predictions. An assimilation method based on the Ensemble Kalman Filter algorithm allows for combining Sentinel-2 data with a new version of Simple Algorithm For Yield (SAFY_swb) that considers the effect of the water balance on yield and estimates the daily trend of evapotranspiration (ET). Our study is relevant in the context of demonstrating the effectiveness and necessity of satellite missions such as Land Surface Temperature Monitoring (LSTM), to provide high spatial and temporal resolution data for agriculture. The proposed method addresses the problem both from a spatial point of view, providing maps of the areas of interest of the main biophysical quantities of vegetation (LAI, biomass, yield and actual Evapotranspiration), and from a temporal point of view, providing a simulation on a daily basis of the aforementioned variables. The assimilation efficiency was initially evaluated with a synthetic, large and heterogeneous dataset, reaching values of 70% even for high measurement errors of the assimilated variable. Subsequently, the method was tested in a case study in central Italy, allowing estimates of the daily Actual Evapotranspiration with a relative RMSE of 18%. The novelty of this research is in proposing a solution that partially solves the main problems related to the synergistic use of EO data with crop growth models, such as the difficult calibration of initial parameters, the lack of frequent high-resolution data or the high computational cost of data assimilation methods. It opens the way to future developments, such as the use of simultaneous assimilation of multiple variables, to deeper investigations using more specific datasets and exploiting the advanced tools.
Extraction of Irrigation Signals by Using SMAP Soil Moisture Data
Authors: Zhu Liming , Zhu A-Xing
Published: May 2021

To allow extraction of irrigation signals from satellite-derived data on soil moisture, this study describes the development of an irrigation signal extraction method that takes into account multiple environmental factors in irrigation. Firstly, the fuzzy membership functions of irrigation relating to multiple environmental factors are constructed. Then, a model is built based on the fuzzy membership functions by using operation rules of fuzzy sets, which is used to infer the relevant degree of irrigation to nonirrigation. Finally, the irrigation signals in satellite-based soil moisture data are recognized according to the relevant degree. Taking Henan Province in the North China Plain as the study area, the proposed method is used to extract irrigation signals from the SMAP Level 3 Passive Soil Moisture Product. Extracted irrigation signals from two SMAP grids are validated using daily in situ soil moisture and precipitation data, with the results showing correct identification of most of the irrigation signals. By grading the membership degree of the extracted irrigation signals, irrigation frequency maps for the 2016–2017 winter crop growth season and the 2017 summer crop growth season are obtained for Henan Province. Compared to the irrigation frequency maps with data on the annual precipitation and the annual potential evapotranspiration, the irrigation frequency maps show a spatial pattern opposite that of the annual precipitation and a spatial pattern similar to that of the annual potential evapotranspiration. It is common sense that areas with low precipitation and high evapotranspiration need more irrigation frequency and irrigation water. Thus, the spatial patterns of irrigation frequency maps are reasonable in a sense. However, it should be noted that the observed irrigation data used in the qualitative assessments are rendered less convincing by the SMAP product’s coarse resolution. Quantitative validation of extracted irrigation signals remains a significant challenge, and small-scale irrigation cannot be captured by coarse-resolution satellite-based soil moisture products. Thus, a high-resolution soil moisture product should be used to extract irrigation signals in future.
3D Mesh Pre-Processing Method Based on Feature Point Classification and Anisotropic Vertex Denoising Considering Scene Structure Characteristics
Authors: Liu Yawen , Guo Bingxuan , Xiao Xiongwu , Qiu Wei
Published: May 2021

3D mesh denoising plays an important role in 3D model pre-processing and repair. A fundamental challenge in the mesh denoising process is to accurately extract features from the noise and to preserve and restore the scene structure features of the model. In this paper, we propose a novel feature-preserving mesh denoising method, which was based on robust guidance normal estimation, accurate feature point extraction and an anisotropic vertex denoising strategy. The methodology of the proposed approach is as follows: (1) The dual weight function that takes into account the angle characteristics is used to estimate the guidance normals of the surface, which improved the reliability of the joint bilateral filtering algorithm and avoids losing the corner structures; (2) The filtered facet normal is used to classify the feature points based on the normal voting tensor (NVT) method, which raised the accuracy and integrity of feature classification for the noisy model; (3) The anisotropic vertex update strategy is used in triangular mesh denoising: updating the non-feature points with isotropic neighborhood normals, which effectively suppressed the sharp edges from being smoothed; updating the feature points based on local geometric constraints, which preserved and restored the features while avoided sharp pseudo features. The detailed quantitative and qualitative analyses conducted on synthetic and real data show that our method can remove the noise of various mesh models and retain or restore the edge and corner features of the model without generating pseudo features.
Improving Parcel-Level Mapping of Smallholder Crops from VHSR Imagery: An Ensemble Machine-Learning-Based Framework
Authors: Zhang Peng , Hu Shougeng , Li Weidong , Zhang Chuanrong , Cheng Peikun
Published: May 2021

Explicit spatial information about crop types on smallholder farms is important for the development of local precision agriculture. However, due to highly fragmented and heterogeneous cropland landscapes, fine-scale mapping of smallholder crops, based on low- and medium-resolution satellite images and relying on a single machine learning (ML) classifier, generally fails to achieve satisfactory performance. This paper develops an ensemble ML-based framework to improve the accuracy of parcel-level smallholder crop mapping from very high spatial resolution (VHSR) images. A typical smallholder agricultural area in central China covered by WorldView-2 images is selected to demonstrate our approach. This approach involves the task of distinguishing eight crop-level agricultural land use types. To this end, six widely used individual ML classifiers are evaluated. We further improved their performance by independently implementing bagging and stacking ensemble learning (EL) techniques. The results show that the bagging models improved the performance of unstable classifiers, but these improvements are limited. In contrast, the stacking models perform better, and the Stacking #2 model (overall accuracy = 83.91%, kappa = 0.812), which integrates the three best-performing individual classifiers, performs the best of all of the built models and improves the classwise accuracy of almost all of the land use types. Since classification performance can be significantly improved without adding costly data collection, stacking-ensemble mapping approaches are valuable for the spatial management of complex agricultural areas. We also demonstrate that using geometric and textural features extracted from VHSR images can improve the accuracy of parcel-level smallholder crop mapping. The proposed framework shows the great potential of combining EL technology with VHSR imagery for accurate mapping of smallholder crops, which could facilitate the development of parcel-level crop identification systems in countries dominated by smallholder agriculture.
Forest Height Estimation from a Robust TomoSAR Method in the Case of Small Tomographic Aperture with Airborne Dataset at L-Band
Authors: Peng Xing , Li Xinwu , Du Yanan , Xie Qinghua
Published: May 2021

Forest height is an essential input parameter for forest biomass estimation, ecological modeling, and the carbon cycle. Tomographic synthetic aperture radar (TomoSAR), as a three-dimensional imaging technique, has already been successfully used in forest areas to retrieve the forest height. The nonparametric iterative adaptive approach (IAA) has been recently introduced in TomoSAR, achieving a good compromise between high resolution and computing efficiency. However, the performance of the IAA algorithm is significantly degraded in the case of a small tomographic aperture. To overcome this shortcoming, this paper proposes the robust IAA (RIAA) algorithm for SAR tomography. The proposed approach follows the framework of the IAA algorithm, but also considers the noise term in the covariance matrix estimation. By doing so, the condition number of the covariance matrix can be prevented from being too large, improving the robustness of the forest height estimation with the IAA algorithm. A set of simulated experiments was carried out, and the results validated the superiority of the RIAA estimator in the case of a small tomographic aperture. Moreover, a number of fully polarimetric L-band airborne tomographic SAR images acquired from the ESA BioSAR 2008 campaign over the Krycklan Catchment, Northern Sweden, were collected for test purposes. The results showed that the RIAA algorithm performed better in reconstructing the vertical structure of the forest than the IAA algorithm in areas with a small tomographic aperture. Finally, the forest height was estimated by both the RIAA and IAA TomoSAR methods, and the estimation accuracy of the RIAA algorithm was 2.01 m, which is more accurate than the IAA algorithm with 3.25 m.
A Remote Sensing Approach to Understanding Patterns of Secondary Succession in Tropical Forest
Authors: Chraibi Eric , Arnold Haley , Luque Sandra , Deacon Amy , Magurran Anne , Féret Jean-Baptiste
Published: May 2021

Monitoring biodiversity on a global scale is a major challenge for biodiversity conservation. Field assessments commonly used to assess patterns of biodiversity and habitat condition are costly, challenging, and restricted to small spatial scales. As ecosystems face increasing anthropogenic pressures, it is important that we find ways to assess patterns of biodiversity more efficiently. Remote sensing has the potential to support understanding of landscape-level ecological processes. In this study, we considered cacao agroforests at different stages of secondary succession, and primary forest in the Northern Range of Trinidad, West Indies. We assessed changes in tree biodiversity over succession using both field data, and data derived from remote sensing. We then evaluated the strengths and limitations of each method, exploring the potential for expanding field data by using remote sensing techniques to investigate landscape-level patterns of forest condition and regeneration. Remote sensing and field data provided different insights into tree species compositional changes, and patterns of alpha- and beta-diversity. The results highlight the potential of remote sensing for detecting patterns of compositional change in forests, and for expanding on field data in order to better understand landscape-level patterns of forest diversity.
Shallow Bathymetry from Multiple Sentinel 2 Images via the Joint Estimation of Wave Celerity and Wavelength
Authors: de Michele Marcello , Raucoules Daniel , Idier Deborah , Smai Farid , Foumelis Michael
Published: May 2021

In this study, we present a new method called BathySent to retrieve shallow bathymetry from space that is based on the joint measurement of ocean wave celerity (c) and wavelength (λ). We developed the method to work with Sentinel 2 data, exploiting the time lag between two Sentinel 2 spectral bands, acquired quasi-simultaneously, from a single satellite dataset. Our method was based on the linear dispersion law, which related water depth to wave celerity and wavelength: when the water depth was less than about half the dominant wavelength, the wave celerity and wavelength decreased due to decreasing water depth (h) as the waves propagated towards the coast. Instead of using a best weighted (c,λ) fit with the linear dispersion relation to retrieve h, we proposed solving the linear dispersion relation for each (c,λ) pair to find multiple h-values within the same resolution cell. Then, we calculated the weighted averaged h-value for each resolution cell. To improve the precision of the final bathymetric map, we stacked the bathymetry values from N-different datasets acquired from the same study area on different dates. We first tested the algorithm on a set of images representing simulated ocean waves, then we applied it to a real set of Sentinel 2 data obtained of our study area, Gâvres peninsula (France, 47°,67 lat.; −3°35 lon.). A comparison with in situ bathymetry yielded good results from the synthetic images (r2 = 0.9) and promising results with the Sentinel 2 images (r2 = 0.7) in the 0–16 m depth zone.
How Much Can We See from a UAV-Mounted Regular Camera? Remote Sensing-Based Estimation of Forest Attributes in South American Native Forests
Authors: Miranda Alejandro , Catalán Germán , Altamirano Adison , Zamorano-Elgueta Carlos , Cavieres Manuel , Guerra Javier , Mola-Yudego Blas
Published: May 2021

Data collection from large areas of native forests poses a challenge. The present study aims at assessing the use of UAV for forest inventory on native forests in Southern Chile, and seeks to retrieve both stand and tree level attributes from forest canopy data. Data were collected from 14 plots (45 × 45 m) established at four locations representing unmanaged Chilean temperate forests: seven plots on secondary forests and seven plots on old-growth forests, including a total of 17 different native species. The imagery was captured using a fixed-wing airframe equipped with a regular RGB camera. We used the structure from motion and digital aerial photogrammetry techniques for data processing and combined machine learning methods based on boosted regression trees and mixed models. In total, 2136 trees were measured on the ground, from which 858 trees were visualized from the UAV imagery of the canopy, ranging from 26% to 88% of the measured trees in the field (mean = 45.7%, SD = 17.3), which represented between 70.6% and 96% of the total basal area of the plots (mean = 80.28%, SD = 7.7). Individual-tree diameter models based on remote sensing data were constructed with R2 = 0.85 and R2 = 0.66 based on BRT and mixed models, respectively. We found a strong relationship between canopy and ground data; however, we suggest that the best alternative was combining the use of both field-based and remotely sensed methods to achieve high accuracy estimations, particularly in complex structure forests (e.g., old-growth forests). Field inventories and UAV surveys provide accurate information at local scales and allow validation of large-scale applications of satellite imagery. Finally, in the future, increasing the accuracy of aerial surveys and monitoring is necessary to advance the development of local and regional allometric crown and DBH equations at the species level.