bluuebunny/crossref_metadata_2025 huggingface dataset:
The Effect of Fructose Feeding on Intestinal Triacylglycerol Production and De Novo Fatty Acid Synthesis in Humans
Authors: Steenson Simon , Shojaee-Moradie Fariba , B. Whyte Martin , G. Jackson Kim , Lovegrove Julie A. , A. Fielding Barbara , Umpleby A. Margot
Published: June 2020

A high fructose intake exacerbates postprandial plasma triacylglycerol (TAG) concentration, an independent risk factor for cardiovascular disease, although it is unclear whether this is due to increased production or impaired clearance of triacylglycerol (TAG)-rich lipoproteins. We determined the in vivo acute effect of fructose on postprandial intestinal and hepatic lipoprotein TAG kinetics and de novo lipogenesis (DNL). Five overweight men were studied twice, 4 weeks apart. They consumed hourly mixed-nutrient drinks that were high-fructose (30% energy) or low-fructose (<2% energy) for 11 h. Oral 2H2O was administered to measure fasting and postprandial DNL. Postprandial chylomicron (CM)-TAG and very low-density lipoprotein (VLDL)-TAG kinetics were measured with an intravenous bolus of [2H5]-glycerol. CM and VLDL were separated by their apolipoprotein B content using antibodies. Plasma TAG (p < 0.005) and VLDL-TAG (p = 0.003) were greater, and CM-TAG production rate (PR, p = 0.046) and CM-TAG fractional catabolic rate (FCR, p = 0.073) lower when high-fructose was consumed, with no differences in VLDL-TAG kinetics. Insulin was lower (p = 0.005) and apoB48 (p = 0.039), apoB100 (p = 0.013) and non-esterified fatty acids (NEFA) (p = 0.013) were higher after high-fructose. Postprandial hepatic fractional DNL was higher than intestinal fractional DNL with high-fructose (p = 0.043) and low-fructose (p = 0.043). Fructose consumption had no effect on the rate of intestinal or hepatic DNL. We provide the first measurement of the rate of intestinal DNL in humans. Lower CM-TAG PR and CM-TAG FCR with high-fructose consumption suggests lower clearance of CM, rather than elevated production, may contribute to elevated plasma TAG, possibly due to lower insulin-mediated stimulation of lipoprotein lipase.
Aqueous Fraction from Hibiscus sabdariffa Relaxes Mesenteric Arteries of Normotensive and Hypertensive Rats through Calcium Current Reduction and Possibly Potassium Channels Modulation
Authors: Alsayed Anas M.A. , Zhang Bei Li , Bredeloux Pierre , Boudesocque-Delaye Leslie , Yu Angèle , Peineau Nicolas , Enguehard-Gueiffier Cécile , Ahmed Elhadi M. , Pasqualin Côme , Maupoil Véronique
Published: June 2020

Background/Objectives: Hibiscus sabdariffa L. (H. sabdariffa (HS)) extract has a vascular relaxant effect on isolated rat thoracic aorta, but data on small resistance arteries, which play an important role on the development of hypertension, are still missing. The purposes of this study were (1) to assess the effect on isolated mesenteric arteries (MA) from normotensive (Wistar and Wistar-Kyoto (WKY)) and spontaneous hypertensive rats (SHR); (2) to elucidate the mechanism(s) of action underling the relaxant effect in light of bioactive components. Methods: Vascular effects of HS aqueous fraction (AF) on isolated MA rings, as well as its mechanisms of action, were assessed using the contractility and intracellular microelectrode technique. The patch clamp technique was used to evaluate the effect of HS AF on the L-type calcium current. Extraction and enrichment of AF were carried out using liquid–liquid extraction, and the yield was analyzed using HPLC. Results: The HS AF induced a concentration-dependent relaxant effect on MA rings of SHR (EC50 = 0.83 ± 0.08 mg/mL), WKY (EC50 = 0.46 ± 0.04 mg/mL), and Wistar rats (EC50 = 0.44 ± 0.08 mg/mL) pre-contracted with phenylephrine (10 µM). In Wistar rats, the HS AF maximum relaxant effect was not modified after endothelium removal or when a guanylate cyclase inhibitor (ODQ, 10 µM) and a selective β2-adrenergic receptor antagonist (ICI-118551, 1 µM) were incubated with the preparation. Otherwise, it was reduced by 34.57 ± 10.66% when vascular rings were pre-contracted with an 80 mM [K+] solution (p < 0.001), which suggests an effect on ionic channels. HS AF 2 mg/mL significantly decreased the peak of the L-type calcium current observed in cardiac myocytes by 24.4%. Moreover, though the vasorelaxant effect of HS, AF was reduced by 27% when the nonselective potassium channels blocker (tetraethylammonium (TEA) 20 mM) was added to the bath (p < 0.01). The extract did not induce a membrane hyperpolarization of smooth muscle cells, which might suggest an absence of a direct effect on background potassium current. Conclusion: These results highlight that the antihypertensive effect of HS probably involves a vasorelaxant effect on small resistance arteries, which is endothelium independent. L-type calcium current reduction contributes to this effect. The results could also provide a link between the vasorelaxant effect and the bioactive compounds, especially anthocyanins.
Identification of Essential Oils Including Garlic Oil and Black Pepper Oil with High Activity against Babesia duncani
Authors: Zhang Yumin , Bai Chunxiang , Shi Wanliang , Alvarez-Manzo Hector , Zhang Ying
Published: June 2020

Some evidence indicated that human babesiosis caused by Babesia duncani has spread widely in North America. However, current therapeutic regimens (atovaquone + azithromycin) for human babesiosis are suboptimal with frequent recrudescence and side effects, and furthermore, there is no specific treatment for human babesiosis caused by B. duncani. Here, we screened 97 essential oils and identified 10 essential oils (garlic, black pepper, tarragon, palo santo, coconut, pine, meditation, cajeput, moringa, and stress relief) at a low concentration (0.001%; v/v) that showed good inhibitory activity against B. duncani in the hamster red blood cell culture model. Among them, garlic oil and black pepper oil performed best, as well as their potential active ingredients diallyl disulfide (DADS) and β-caryophyllene (BCP), respectively. Interestingly, further subculture study indicated that B. duncani could relapse after treatment with current therapeutic drugs atovaquone or azithromycin even at high concentrations. In contrast, the combination of garlic oil or DADS and azithromycin showed eradication of B. duncani at low concentrations without regrowth. These results are encouraging and suggest that the garlic-derived sulfur compound DADS and β-caryophyllene (BCP) may be promising drug candidates for evaluation of their ability to cure persistent B. duncani infections in the future.
Activating the DNA Damage Response and Suppressing Innate Immunity: Human Papillomaviruses Walk the Line
Authors: James Claire D. , Das Dipon , Bristol Molly L. , Morgan Iain M.
Published: June 2020

Activation of the DNA damage response (DDR) by external agents can result in DNA fragments entering the cytoplasm and activating innate immune signaling pathways, including the stimulator of interferon genes (STING) pathway. The consequences of this activation can result in alterations in the cell cycle including the induction of cellular senescence, as well as boost the adaptive immune response following interferon production. Human papillomaviruses (HPV) are the causative agents in a host of human cancers including cervical and oropharyngeal; HPV are responsible for around 5% of all cancers. During infection, HPV replication activates the DDR in order to promote the viral life cycle. A striking feature of HPV-infected cells is their ability to continue to proliferate in the presence of an active DDR. Simultaneously, HPV suppress the innate immune response using a number of different mechanisms. The activation of the DDR and suppression of the innate immune response are essential for the progression of the viral life cycle. Here, we describe the mechanisms HPV use to turn on the DDR, while simultaneously suppressing the innate immune response. Pushing HPV from this fine line and tipping the balance towards activation of the innate immune response would be therapeutically beneficial.
The Global Prevalence of Strongyloides stercoralis Infection
Authors: Buonfrate Dora , Bisanzio Donal , Giorli Giovanni , Odermatt Peter , Fürst Thomas , Greenaway Christina , French Michael , Reithinger Richard , Gobbi Federico , Montresor Antonio , Bisoffi Zeno
Published: June 2020

Strongyloidiasis is a common neglected tropical disease in tropical and sub-tropical climatic zones. At the worldwide level, there is high uncertainty about the strongyloidiasis burden. This uncertainty represents an important knowledge gap since it affects the planning of interventions to reduce the burden of strongyloidiasis in endemic countries. This study aimed to estimate the global strongyloidiasis prevalence. A literature review was performed to obtain prevalence data from endemic countries at a worldwide level from 1990 to 2016. For each study, the true population prevalence was calculated by accounting for the specificity and the sensitivity of testing and age of tested individuals. Prediction of strongyloidiasis prevalence for each country was performed using a spatiotemporal statistical modeling approach. The country prevalence obtained from the model was used to estimate the number of infected people per country. We estimate the global prevalence of strongyloidiasis in 2017 to be 8.1% (95% CI: 4.2–12.4%), corresponding to 613.9 (95% CI: 313.1–910.1) million people infected. The South-East Asia, African, and Western Pacific Regions accounted for 76.1% of the global infections. Our results could be used to identify those countries in which strongyloidiasis prevalence is highest and where mass drug administration (MDA) should be deployed for its prevention and control.
Adaptive Immunity to Dengue Virus: Slippery Slope or Solid Ground for Rational Vaccine Design?
Authors: Wilken Lucas , Rimmelzwaan Guus F.
Published: June 2020

The four serotypes of dengue virus are the most widespread causes of arboviral disease, currently placing half of the human population at risk of infection. Pre-existing immunity to one dengue virus serotype can predispose to severe disease following secondary infection with a different serotype. The phenomenon of immune enhancement has complicated vaccine development and likely explains the poor long-term safety profile of a recently licenced dengue vaccine. Therefore, alternative vaccine strategies should be considered. This review summarises studies dissecting the adaptive immune responses to dengue virus infection and (experimental) vaccination. In particular, we discuss the roles of (i) neutralising antibodies, (ii) antibodies to non-structural protein 1, and (iii) T cells in protection and pathogenesis. We also address how these findings could translate into next-generation vaccine approaches that mitigate the risk of enhanced dengue disease. Finally, we argue that the development of a safe and efficacious dengue vaccine is an attainable goal.
Development and Challenges in Animal Tuberculosis Vaccination
Authors: Balseiro Ana , Thomas Jobin , Gortázar Christian , Risalde María A.
Published: June 2020

Vaccination with Bacillus Calmette–Guérin (BCG) constituted a major advance in the prevention of human tuberculosis (TB) in the beginning of the past century. BCG has also a clear potential for use in animals and, in particular, in the main domestic species subjected to TB control programs, cattle. Nowadays, the use of BCG vaccination against TB in cattle is not permitted by European Union legislation because BCG can induce a cellular immune response producing diagnostic interference in the eradication programs based on tuberculin single and comparative intradermal tests imposed worldwide. In this review, we recall the history of TB vaccination as well as different vaccine trials and the response to vaccination in both domestic and wild animals. Promising potential inactivated vaccines are also reviewed. Research studies are mainly focused to improve vaccine efficacy, and at the same time to ensure its easy administration, safety and stability in the environment. Great challenges remain, particularly in terms of vaccine candidates and also in the acceptance of vaccination. Vaccination should be included in a strategic plan for integrated control of TB under a “one health” perspective, which also includes other measures such as improved biosafety on farms to avoid or decrease contact between domestic and wild animals or control of wildlife reservoirs to avoid overabundance that may favor infection maintenance.
A Novel Microalgae Harvesting Method Using Laser Micromachined Glass Fiber Reinforced Polymers
Authors: Tourlouki Konstantina , Tsavatopoulou Vasiliki , Alexandropoulos Dimitris , Manariotis Ioannis D. , Mazzucato Simone
Published: June 2020

Microalgae are an ideal source for next-generation biofuels due to their high photosynthetic rate. However, a key process limitation in microalgal biofuel production is harvesting of biomass and extraction of lipids in a cost-effective manner. The harvesting of the algal biomass amounts to approximately 20 to 30% of the total cost of the cultivation; hence, developing an efficient and universal harvesting method will make the commercialization of microalgal bio-cultures sustainable. In this study, we developed, demonstrated, and evaluated a novel harvesting method based on Glass Reinforced Fiber Polymer (GFRP) panels, suitable for industrial-scale installations. The proposed method was based on previous observations of preferential micro-algae development on glass surfaces, as well as in the assumption that the microalgae cells would prefer to attach to and grow on substrates with a similar size as them. At first, we developed a laser micromachining protocol for removing the resin and revealing the glass fibers of the GFRP, available for algal adhesion, thus acting as a microalgae biomass harvesting center. Surface micromachining was realized using a ns pulsed ultraviolet laser emitting at 355 nm. This laser ensured high machining quality of the GFRP, because of its selective material ablation, precise energy deposition, and narrow heat affected zone. A specially built open pond system was used for the cultivation of the microalgae species Scenedesmus rubescens, which was suitable for biofuel production. The cultivation was used for the experimental evaluation of the proposed harvesting method. The cultivation duration was set to 16 days in order for the culture to operate at the exponential growth phase. The biomass maximum recovery due to microalgae attachment on the GFRP surface was 13.54 g/m2, a yield comparable to other studies in the literature. Furthermore, the GFRP surfaces could be upscaled to industrial dimensions and positioned in any geometry dictated by the photobioreactor design. In this study, the glass fiber reinforced polymer used was suitable for the adhesion of Scenedesmus rubescens due to its fiber thickness. Other microalgae species could be cultivated, adhere, and harvested using GFRP of different fiber sizes and/or with a modified laser treatment. These very encouraging results validated GFRPs’ harvesting capabilities as an attachment substrate for microalgae. Additional studies with more algae species will further strengthen the method.
Kinetic Freeze-Out Properties from Transverse Momentum Spectra of Pions in High Energy Proton-Proton Collisions
Authors: Li Li-Li , Liu Fu-Hu
Published: June 2020

Transverse momentum spectra of negative and positive pions produced at mid-(pseudo)rapidity in inelastic or non-single-diffractive proton-proton collisions over a center-of-mass energy, s , range from a few GeV to above 10 TeV are analyzed by the blast-wave fit with Boltzmann (Tsallis) distribution. The blast-wave fit results are well fitting to the experimental data measured by several collaborations. In a particular superposition with Hagedorn function, both the excitation functions of kinetic freeze-out temperature ( T 0 ) of emission source and transverse flow velocity ( β T ) of produced particles obtained from a given selection in the blast-wave fit with Boltzmann distribution have a hill at s ≈ 10 GeV, a drop at dozens of GeV, and then an increase from dozens of GeV to above 10 TeV. However, both the excitation functions of T 0 and β T obtained in the blast-wave fit with Tsallis distribution do not show such a complex structure, but a very low hill. In another selection for the parameters or in the superposition with the usual step function, T 0 and β T increase generally quickly from a few GeV to about 10 GeV and then slightly at above 10 GeV, there is no such the complex structure, when also studying nucleus-nucleus collisions.
The Challenge of Chronotopicity: Female Co-Cremation in India Revisited in the Light of Time–Space Sensitive Ritual Criticism
Authors: Nugteren Albertina (Tineke)
Published: June 2020

Rituals are embedded in a particular time and space, and so are their objects and meanings. The ‘chronotope’ we focus on here is the occasional—partly self-chosen, partly societally forced—ritual death of Hindu widows along with their deceased husbands. Although never widely practiced, widow-burning caught the imagination of Europeans as illustrating both Hinduism’s ‘barbarity’ and its ‘high conjugal ideals’. Although satī had been outlawed since 1829, in 1987 a new case inflamed opposing sentiments. In 2002, in a passage called ‘Ritual Criticism and Widow Burning’, Ronald Grimes drew attention to it as a rite of passage that calls for normative comments and ritual criticism. Since then, in circles of ritual studies Hindu, widow-burning has occasionally been repeated as one of the ritual practices in need of condemnation. In order to put this rare practice, banned since almost 200 years ago, back into a proper time–place perspective, both its ritual details and its sociocultural contexts are revisited. Finally, we propose some case-specific factors that could serve as retrospective ritual criticism. We conclude with a plea for time–space sensitivity.
The Implications of M3C2 Projection Diameter on 3D Semi-Automated Rockfall Extraction from Sequential Terrestrial Laser Scanning Point Clouds
Authors: DiFrancesco Paul-Mark , Bonneau David , Hutchinson D. Jean
Published: June 2020

Rockfall inventories are essential to quantify a rockfall activity and characterize the hazard. Terrestrial laser scanning and advancements in processing algorithms have resulted in three-dimensional (3D) semi-automatic extraction of rockfall events, permitting detailed observations of evolving rock masses. Currently, multiscale model-to-model cloud comparison (M3C2) is the most widely used distance computation method used in the geosciences to evaluate 3D changing features, considering the time-sequential spatial information contained in point clouds. M3C2 operates by computing distances using points that are captured within a projected search cylinder, which is locally oriented. In this work, we evaluated the effect of M3C2 projection diameter on the extraction of 3D rockfalls and the resulting implications on rockfall volume and shape. Six rockfall inventories were developed for a highly active rock slope, each utilizing a different projection diameter which ranged from two to ten times the point spacing. The results indicate that the greatest amount of change is extracted using an M3C2 projection diameter equal to, or slightly larger than, the point spacing, depending on the variation in point spacing. When the M3C2 projection diameter becomes larger than the changing area on the rock slope, the change cannot be identified and extracted. Inventory summaries and illustrations depict the influence of spatial averaging on the semi-automated rockfall extraction, and suggestions are made for selecting the optimal projection diameter. Recommendations are made to improve the methods used to semi-automatically extract rockfall from sequential point clouds.
Reservoir Induced Deformation Analysis for Several Filling and Operational Scenarios at the Grand Ethiopian Renaissance Dam Impoundment
Authors: Madson Austin , Sheng Yongwei
Published: June 2020

Addressing seasonal water uncertainties and increased power generation demand has sparked a global rise in large-scale hydropower projects. To this end, the Blue Nile impoundment behind the Grand Ethiopian Renaissance Dam (GERD) will encompass an areal extent of ~1763.3 km2 and hold ~67.37 Gt (km3) of water with maximum seasonal load changes of ~27.93 (41% of total)—~36.46 Gt (54% of total) during projected operational scenarios. Five different digital surface models (DSMs) are compared to spatially overlapping spaceborne altimeter products and hydrologic loads for the GERD are derived from the DSM with the least absolute elevation difference. The elastic responses to several filling and operational strategies for the GERD are modeled using a spherically symmetric, non-rotating, elastic, and isotropic (SNREI) Earth model. The maximum vertical and horizontal flexural responses from the full GERD impoundment are estimated to be 11.99 and 1.99 cm, regardless of the full impoundment period length. The vertical and horizontal displacements from the highest amplitude seasonal reservoir operational scenarios are 38–55% and 34–48% of the full deformation, respectively. The timing and rate of reservoir inflow and outflow affects the hydrologic load density on the Earth’s surface, and, as such, affects not only the total elastic response but also the distance that the deformation extends from the reservoir’s body. The magnitudes of the hydrologic-induced deformation are directly related to the size and timing of reservoir fluxes, and an increased knowledge of the extent and magnitude of this deformation provides meaningful information to stakeholders to better understand the effects from many different impoundment and operational strategies.
Residual Dense Network Based on Channel-Spatial Attention for the Scene Classification of a High-Resolution Remote Sensing Image
Authors: Zhao Xiaolei , Zhang Jing , Tian Jimiao , Zhuo Li , Zhang Jie
Published: June 2020

The scene classification of a remote sensing image has been widely used in various fields as an important task of understanding the content of a remote sensing image. Specially, a high-resolution remote sensing scene contains rich information and complex content. Considering that the scene content in a remote sensing image is very tight to the spatial relationship characteristics, how to design an effective feature extraction network directly decides the quality of classification by fully mining the spatial information in a high-resolution remote sensing image. In recent years, convolutional neural networks (CNNs) have achieved excellent performance in remote sensing image classification, especially the residual dense network (RDN) as one of the representative networks of CNN, which shows a stronger feature learning ability as it fully utilizes all the convolutional layer information. Therefore, we design an RDN based on channel-spatial attention for scene classification of a high-resolution remote sensing image. First, multi-layer convolutional features are fused with residual dense blocks. Then, a channel-spatial attention module is added to obtain more effective feature representation. Finally, softmax classifier is applied to classify the scene after adopting data augmentation strategy for meeting the training requirements of the network parameters. Five experiments are conducted on the UC Merced Land-Use Dataset (UCM) and Aerial Image Dataset (AID), and the competitive results demonstrate that our method can extract more effective features and is more conducive to classifying a scene.
RTK GNSS-Assisted Terrestrial SfM Photogrammetry without GCP: Application to Coastal Morphodynamics Monitoring
Authors: Jaud Marion , Bertin Stéphane , Beauverger Mickaël , Augereau Emmanuel , Delacourt Christophe
Published: June 2020

The present article describes a new and efficient method of Real Time Kinematic (RTK) Global Navigation Satellite System (GNSS) assisted terrestrial Structure-from-Motion (SfM) photogrammetry without the need for Ground Control Points (GCPs). The system only requires a simple frame that mechanically connects a RTK GNSS antenna to the camera. The system is low cost, easy to transport, and offers high autonomy. Furthermore, not requiring GCPs enables saving time during the in situ acquisition and during data processing. The method is tested for coastal cliff monitoring, using both a Reflex camera and a Smartphone camera. The quality of the reconstructions is assessed by comparison to a synchronous Terrestrial Laser Scanner (TLS) acquisition. The results are highly satisfying with a mean error of 0.3 cm and a standard deviation of 4.7 cm obtained with the Nikon D800 Reflex camera and, respectively, a mean error of 0.2 cm and a standard deviation of 3.8 cm obtained with the Huawei Y5 Smartphone camera. This method will be particularly interesting when simplicity, portability, and autonomy are desirable. In the future, it would be transposable to participatory science programs, while using an open RTK GNSS network.
Novel Ensemble Approaches of Machine Learning Techniques in Modeling the Gully Erosion Susceptibility
Authors: Arabameri Alireza , Asadi Nalivan Omid , Saha Sunil , Roy Jagabandhu , Pradhan Biswajeet , Tiefenbacher John P. , Thi Ngo Phuong Thao
Published: June 2020

Gully erosion has become one of the major environmental issues, due to the severity of its impact in many parts of the world. Gully erosion directly and indirectly affects agriculture and infrastructural development. The Golestan Dam basin, where soil erosion and degradation are very severe problems, was selected as the study area. This research maps gully erosion susceptibility (GES) by integrating four models: maximum entropy (MaxEnt), artificial neural network (ANN), support vector machine (SVM), and general linear model (GLM). Of 1042 gully locations, 729 (70%) and 313 (30%) gully locations were used for modeling and validation purposes, respectively. Fourteen effective gully erosion conditioning factors (GECFs) were selected for spatial gully erosion modeling. Tolerance and variance inflation factors (VIFs) were used to examine the collinearity among the GECFs. The random forest (RF) model was used to assess factors’ effectiveness and significance in gully erosion modeling. An ensemble of techniques can provide more accurate results than can single, standalone models. Therefore, we compared two-, three-, and four-model ensembles (ANN-SVM, GLM-ANN, GLM-MaxEnt, GLM-SVM, MaxEnt-ANN, MaxEnt-SVM, ANN-SVM-GLM, GLM-MaxEnt-ANN, GLM-MaxEnt-SVM, MaxEnt-ANN-SVM and GLM-ANN-SVM-MaxEnt) for GES modeling. The susceptibility zones of the GESMs were classified as very-low, low, medium, high, and very-high using Jenks’ natural break classification method (NBM). Subsequently, the receiver operating characteristics (ROC) curve and the seed cell area index (SCAI) methods measured the reliability of the models. The success rate curve (SRC) and predication rate curve (PRC) and their area under the curve (AUC) values were obtained from the GES maps. The results show that the ANN model combined with two and three models are more accurate than the other combinations, but the ANN-SVM model had the highest accuracy. The rank of the others from best to worst accuracy is GLM, MaxEnt, SVM, GLM-ANN, GLM-MaxEnt, GLM-SVM, MaxEnt-ANN, MaxEnt-SVM, GLM-ANN-SVM-MaxEnt, GLM-MaxEnt-ANN, GLM-MaxEnt-SVM and MaxEnt-ANN-SVM. The resulting gully erosion susceptibility models (GESMs) are efficient and powerful and could be used to improve soil and water conservation and management.
The Estimation of Surface Albedo from DSCOVR EPIC
Authors: Tian Qiuyue , Liu Qiang , Guang Jie , Yang Leiku , Zhang Hanwei , Fan Cheng , Che Yahui , Li Zhengqiang
Published: June 2020

Surface albedo is an important parameter in climate models. The main way to obtain continuous surface albedo for large areas is satellite remote sensing. However, the existing albedo products rarely meet daily-scale requirements, which has a large impact on climate change research and rapid dynamic changes of surface analysis. The Earth Polychromatic Imaging Camera (EPIC) on the Deep Space Climate Observatory (DSCOVR) platform, which was launched into the Sun–Earth’s first Lagrange Point (L1) orbit, can provide spectral images of the entire sunlit face of Earth with 10 narrow channels (from 317 to 780 nm). As EPIC can provide high-temporal resolution data, it is beneficial to explore the feasibility of EPIC to estimate high-temporal resolution surface albedo. In this study, hourly surface albedo was calculated based on EPIC observation data. Then, the estimated albedo results were validated by ground-based observations of different land cover types. The results show that the EPIC albedo is basically consistent with the trend of the ground-based observations in the whole time series variation. The diurnal variation of the surface albedo from the hourly EPIC albedo exhibits a “U” shape curve, which has the same trend as the ground-based observations. Therefore, EPIC is helpful to enhance the temporal resolution of surface albedo to diurnal. Surfaces with a three-dimensional structure that casts shadows display the hotspot effect, producing a reflectance peak in the retro-solar direction and lower reflectance at viewing angles away from the solar direction. DSCOVR observes the entire sunlit face of the Earth, which is helpful to make up for the deficiency in the observations of traditional satellites in the hotspot direction in bidirectional reflectance distribution function (BRDF) research, and can help to improve the underestimation of albedo in the direction of hotspot observation.
Data Reconstruction for Remotely Sensed Chlorophyll-a Concentration in the Ross Sea Using Ensemble-Based Machine Learning
Authors: Park Jinku , Kim Hyun-Cheol , Bae Dukwon , Jo Young-Heon
Published: June 2020

Polar regions are too harsh to be continuously observed using ocean color (OC) sensors because of various limitations due to low solar elevations, ice effects, peculiar phytoplankton photosynthetic parameters, optical complexity of seawater and persistence of clouds and fog. Therefore, the OC data undergo a quality-control process, eventually accompanied by considerable data loss. We attempted to reconstruct these missing values for chlorophyll-a concentration (CHL) data using a machine-learning technique based on multiple datasets (satellite and reanalysis datasets) in the Ross Sea, Antarctica. This technique—based on an ensemble tree called random forest (RF)—was used for the reconstruction. The performance of the RF model was robust, and the reconstructed CHL data were consistent with satellite measurements. The reconstructed CHL data allowed a high intrinsic resolution of OC to be used without specific techniques (e.g., spatial average). Therefore, we believe that it is possible to study multiple characteristics of phytoplankton dynamics more quantitatively, such as bloom initiation/termination timings and peaks, as well as the variability in time scales of phytoplankton growth. In addition, because the reconstructed CHL showed relatively higher accuracy than satellite observations compared with the in situ data, our product may enable more accurate planktonic research.
Assessing Cloud Segmentation in the Chromacity Diagram of All-Sky Images
Authors: Krauz Lukáš , Janout Petr , Blažek Martin , Páta Petr
Published: June 2020

All-sky imaging systems are currently very popular. They are used in ground-based meteorological stations and as a crucial part of the weather monitors for autonomous robotic telescopes. Data from all-sky imaging cameras provide important information for controlling meteorological stations and telescopes, and they have specific characteristics different from widely-used imaging systems. A particularly promising and useful application of all-sky cameras is for remote sensing of cloud cover. Post-processing of the image data obtained from all-sky imaging cameras for automatic cloud detection and for cloud classification is a very demanding task. Accurate and rapid cloud detection can provide a good way to forecast weather events such as torrential rainfalls. However, the algorithms that are used must be specifically calibrated on data from the all-sky camera in order to set up an automatic cloud detection system. This paper presents an assessment of a modified k-means++ color-based segmentation algorithm specifically adjusted to the WILLIAM (WIde-field aLL-sky Image Analyzing Monitoring system) ground-based remote all-sky imaging system for cloud detection. The segmentation method is assessed in two different color-spaces (L*a*b and XYZ). Moreover, the proposed algorithm is tested on our public WMD database (WILLIAM Meteo Database) of annotated all-sky image data, which was created specifically for testing purposes. The WMD database is available for public use. In this paper, we present a comparison of selected color-spaces and assess their suitability for the cloud color segmentation based on all-sky images. In addition, we investigate the distribution of the segmented cloud phenomena present on the all-sky images based on the color-spaces channels. In the last part of this work, we propose and discuss the possible exploitation of the color-based k-means++ segmentation method as a preprocessing step towards cloud classification in all-sky images.
Stress Detection in New Zealand Kauri Canopies with WorldView-2 Satellite and LiDAR Data
Authors: Meiforth Jane J. , Buddenbaum Henning , Hill Joachim , Shepherd James D. , Dymond John R.
Published: June 2020

New Zealand kauri trees are threatened by the kauri dieback disease (Phytophthora agathidicida (PA)). In this study, we investigate the use of pan-sharpened WorldView-2 (WV2) satellite and Light Detection and Ranging (LiDAR) data for detecting stress symptoms in the canopy of kauri trees. A total of 1089 reference crowns were located in the Waitakere Ranges west of Auckland and assessed by fieldwork and the interpretation of aerial images. Canopy stress symptoms were graded based on five basic stress levels and further refined for the first symptom stages. The crown polygons were manually edited on a LiDAR crown height model. Crowns with a mean diameter smaller than 4 m caused most outliers with the 1.8 m pixel size of the WV2 multispectral bands, especially at the more advanced stress levels of dying and dead trees. The exclusion of crowns with a diameter smaller than 4 m increased the correlation in an object-based random forest regression from 0.85 to 0.89 with only WV2 attributes (root mean squared error (RMSE) of 0.48, mean absolute error (MAE) of 0.34). Additional LiDAR attributes increased the correlation to 0.92 (RMSE of 0.43, MAE of 0.31). A red/near-infrared (NIR) normalised difference vegetation index (NDVI) and a ratio of the red and green bands were the most important indices for an assessment of the full range of stress symptoms. For detection of the first stress symptoms, an NDVI on the red-edge and green bands increased the performance. This study is the first to analyse the use of spaceborne images for monitoring canopy stress symptoms in native New Zealand kauri forest. The method presented shows promising results for a cost-efficient stress monitoring of kauri crowns over large areas. It will be tested in a full processing chain with automatic kauri identification and crown segmentation.
Spatial Patterns of Structural Complexity in Differently Managed and Unmanaged Beech-Dominated Forests in Central Europe
Authors: Willim Katharina , Stiers Melissa , Annighöfer Peter , Ehbrecht Martin , Ammer Christian , Seidel Dominik
Published: June 2020

One of the main goals of modern silviculture is to emulate the structural complexity of old-growth forests. In this context, it is of advantage to identify a target state of structural complexity at the stand level and to analyze the spatial characteristics that led to the desired complexity of forest structures in primary forest references. In this study, we used 3D forest scenes captured by terrestrial laser scanning (TLS) to identify spatial patterns of structural complexity of differently managed and unmanaged European forests dominated by beech (Fagus sylvatica L.). We scanned in managed even-aged and uneven-aged stands, as well as in formerly managed forests (National Parks) and primary forests. For three different forest strata, representing the understory, the midstory, and the overstory of a forest stand, we determined the structural complexity mathematically using fractal analysis. Beyond that, we analyzed the density, as well as the horizontal and vertical distribution of plant material. For all three forest strata, we observed differences in structural complexity between the different forest types. Within the lower and middle strata, the investigated primary forests showed a random to regular distribution of plant material, as well as a complex understory structure as a result of pronounced natural decay. Compared to the primary forests, the managed uneven-aged stands showed quite similar spatial patterns of distribution of plant material, but on average a higher space occupation in the lower and middle forest stratum. Our results suggest that single tree or group selection cutting is a useful management tool to imitate old-growth structures of undisturbed beech-dominated forests.
US EPA EnviroAtlas Meter-Scale Urban Land Cover (MULC): 1-m Pixel Land Cover Class Definitions and Guidance
Authors: Pilant Andrew , Endres Keith , Rosenbaum Daniel , Gundersen Gillian
Published: June 2020

This article defines the land cover classes used in Meter-Scale Urban Land Cover (MULC), a unique, high resolution (one meter2 per pixel) land cover dataset developed for 30 US communities for the United States Environmental Protection Agency (US EPA) EnviroAtlas. MULC data categorize the landscape into these land cover classes: impervious surface, tree, grass-herbaceous, shrub, soil-barren, water, wetland and agriculture. MULC data are used to calculate approximately 100 EnviroAtlas metrics that serve as indicators of nature’s benefits (ecosystem goods and services). MULC, a dataset for which development is ongoing, is produced by multiple classification methods using aerial photo and LiDAR datasets. The mean overall fuzzy accuracy across the EnviroAtlas communities is 88% and mean Kappa coefficient is 0.84. MULC is available in EnviroAtlas via web browser, web map service (WMS) in the user’s geographic information system (GIS), and as downloadable data at EPA Environmental Data Gateway. Fact sheets and metadata for each MULC community are available through EnviroAtlas. Some MULC applications include mapping green and grey infrastructure, connecting land cover with socioeconomic/demographic variables, street tree planting, urban heat island analysis, mosquito habitat risk mapping and bikeway planning. This article provides practical guidance for using MULC effectively and developing similar high resolution (HR) land cover data.
Population Spatialization in Beijing City Based on Machine Learning and Multisource Remote Sensing Data
Authors: He Miao , Xu Yongming , Li Ning
Published: June 2020

Remote sensing data have been widely used in research on population spatialization. Previous studies have generally divided study areas into several sub-areas with similar features by artificial or clustering algorithms and then developed models for these sub-areas separately using statistical methods. These approaches have drawbacks due to their subjectivity and uncertainty. In this paper, we present a study of population spatialization in Beijing City, China based on multisource remote sensing data and town-level population census data. Six predictive algorithms were compared for estimating population using the spatial variables derived from The National Polar-Orbiting Partnership/ Visible Infrared Imaging Radiometer Suite (NPP/VIIRS) night-time light and other remote sensing data. Random forest achieved the highest accuracy and therefore was employed for population spatialization. Feature selection was performed to determine the optimal variable combinations for population modeling by random forest. Cross-validation results indicated that the developed model achieved a mean absolute error (MAE) of 2129.52 people/km2 and a R2 of 0.63. The gridded population density in Beijing at a spatial resolution of 500 m produced by the random forest model was also adjusted to be consistent with the census population at the town scale. By comparison with Google Earth high-resolution images, the remotely-sensed population was qualitatively validated at the intra-town scale. Validation results indicated that remotely sensed results can effectively depict the spatial distribution of population within town-level districts. This study provides a valuable reference for urban planning, public health and disaster prevention in Beijing, and a reference for population mapping in other cities.
Soil Organic Carbon Mapping from Remote Sensing: The Effect of Crop Residues
Authors: Dvorakova Klara , Shi Pu , Limbourg Quentin , van Wesemael Bas
Published: June 2020

Since the onset of agriculture, soils have lost their organic carbon to such an extent that the soil functions of many croplands are threatened. Hence, there is a strong demand for mapping and monitoring critical soil properties and in particular soil organic carbon (SOC). Pilot studies have demonstrated the potential for remote sensing techniques for SOC mapping in croplands. It has, however, been shown that the assessment of SOC may be hampered by the condition of the soil surface. While growing vegetation can be readily detected by means of the well-known Normalized Difference Vegetation Index (NDVI), the distinction between bare soil and crop residues is expressed in the shortwave infrared region (SWIR), which is only covered by two broad bands in Landsat or Sentinel-2 imagery. Here we tested the effect of thresholds for the Cellulose Absorption Index (CAI), on the performance of SOC prediction models for cropland soils. Airborne Prism Experiment (APEX) hyperspectral images covering an area of 240 km2 in the Belgian Loam Belt were used together with a local soil dataset. We used the partial least square regression (PLSR) model to estimate the SOC content based on 104 georeferenced calibration samples (NDVI < 0.26), firstly without setting a CAI threshold, and obtained a satisfactory result (coefficient of determination (R2) = 0.49, Ratio of Performance to Deviation (RPD) = 1.4 and Root Mean Square Error (RMSE) = 2.13 g kgC−1 for cross-validation). However, a cross comparison of the estimated SOC values to grid-based measurements of SOC content within three fields revealed a systematic overestimation for fields with high residue cover. We then tested different CAI thresholds in order to mask pixels with high residue cover. The best model was obtained for a CAI threshold of 0.75 (R2 = 0.59, RPD = 1.5 and RMSE = 1.75 g kgC−1 for cross-validation). These results reveal that the purity of the pixels needs to be assessed aforehand in order to produce reliable SOC maps. The Normalized Burn Ratio (NBR2) index based on the SWIR bands of the MSI Sentinel 2 sensor extracted from images collected nine days before the APEX flight campaign correlates well with the CAI index of the APEX imagery. However, the NBR2 index calculated from Sentinel 2 images under moist conditions is poorly correlated with residue cover. This can be explained by the sensitivity of the NBR2 index to both soil moisture and residues.
Sentinel-2 Data in an Evaluation of the Impact of the Disturbances on Forest Vegetation
Authors: Lastovicka Josef , Svec Pavel , Paluba Daniel , Kobliuk Natalia , Svoboda Jan , Hladky Radovan , Stych Premysl
Published: June 2020

In this article, we investigated the detection of forest vegetation changes during the period of 2017 to 2019 in the Low Tatras National Park (Slovakia) and the Sumava National Park (Czechia) using Sentinel-2 data. The evaluation was based on a time-series analysis using selected vegetation indices. The case studies represented five different areas according to the type of the forest vegetation degradation (one with bark beetle calamity, two areas with forest recovery mode after a bark beetle calamity, and two areas without significant disturbances). The values of the trajectories of the vegetation indices (normalized difference vegetation index (NDVI) and normalized difference moisture index (NDMI)) and the orthogonal indices (tasseled cap greenness (TCG) and tasseled cap wetness (TCW)) were analyzed and validated by in situ data and aerial photographs. The results confirm the abilities of the NDVI, the NDMI and the TCW to distinguish disturbed and undisturbed areas. The NDMI vegetation index was particularly useful for the detection of the disturbed forest and forest recovery after bark beetle outbreaks and provided relevant information regarding the health of the forest (the individual stages of the disturbances and recovery mode). On the contrary, the TCG index demonstrated only limited abilities. The TCG could distinguish healthy forest and the gray-attack disturbance phase; however, it was difficult to use this index for detecting different recovery phases and to distinguish recovery phases from healthy forest. The areas affected by the disturbances had lower values of NDVI and NDMI indices (NDVI quartile range Q2–Q3: 0.63–0.71; NDMI Q2–Q3: 0.10–0.19) and the TCW index had negative values (Q2–Q3: −0.06–−0.05)). The analysis was performed with a cloud-based tool—Sentinel Hub. Cloud-based technologies have brought a new dimension in the processing and analysis of satellite data and allowed satellite data to be brought to end-users in the forestry sector. The Copernicus program and its data from Sentinel missions have evoked new opportunities in the application of satellite data. The usage of Sentinel-2 data in the research of long-term forest vegetation changes has a high relevance and perspective due to the free availability, distribution, and well-designed spectral, temporal, and spatial resolution of the Sentinel-2 data for monitoring forest ecosystems.
The Infrared Absolute Radiance Interferometer (ARI) for CLARREO
Authors: Taylor Joe K. , Revercomb Henry E. , Best Fred A. , Tobin David C. , Gero P. Jonathan
Published: June 2020

The Absolute Radiance Interferometer (ARI) is an infrared spectrometer designed to serve as an on-orbit radiometric reference with the ultra-high accuracy (better than 0.1 K 3‑σ or k = 3 brightness temperature at scene brightness temperature) needed to optimize measurement of the long-term changes of Earth’s atmosphere and surface. If flown in an orbit that frequently crosses sun-synchronous orbits, ARI could be used to inter-calibrate the international fleet of infrared (IR) hyperspectral sounders to similar measurement accuracy, thereby establishing an observing system capable of achieving sampling biases on high-information-content spectral radiance products that are also < 0.1 K 3‑σ. It has been shown that such a climate observing system with <0.1 K 2‑σ overall accuracy would make it possible to realize times to detect subtle trends of temperature and water vapor distributions that closely match those of an ideal system, given the limit set by the natural variability of the atmosphere. This paper presents the ARI sensor's overall design, the new technologies developed to allow on-orbit verification and test of its accuracy, and the laboratory results that demonstrate its capability. In addition, we describe the techniques and uncertainty estimates for transferring ARI accuracy to operational sounders, providing economical global coverage. Societal challenges posed by climate change suggest that a Pathfinder ARI should be deployed as soon as possible.