bluuebunny/crossref_metadata_2025 huggingface dataset:
A Fatty Acid Mouth Rinse Decreases Self-Reported Hunger and Increases Self-Reported Fullness in Healthy Australian Adults: A Randomized Cross-Over Trial
Authors: Costanzo Andrew , Russell Catherine G. , Lewin Simone , Keast Russell
Published: March 2020

Fatty acid (FA) chemoreception in the oral cavity, known as fat taste, may trigger a satiety response that is homologous to FA chemoreception in the gastrointestinal tract. In addition, individuals with an impaired fat taste sensitivity are more likely to have an impaired satiety response. This study aimed to assess the effect of an FA mouth rinse on self-reported appetite, and to determine if the effect is modified by fat taste sensitivity. Thirty-one participants (age, 32.0 ± 8.4 y; body mass index (BMI), 26.1 ± 8.1 kg/m2) were studied on four separate days to evaluate the effect of a 20 mM oleic acid (OA) mouth rinse (in duplicate) compared to a control (in duplicate) on self-reported appetite by using a visual analogue scale (VAS) every 30 min for three hours following a standardized low-fat breakfast. The area under the curve ratings for fullness were greater (p = 0.003), and those for hunger were lower (p = 0.002) following the OA rinse compared to the control. The effect of the OA rinse was greater in individuals who were hypersensitive to fat taste compared to moderately sensitive and hyposensitive individuals for fullness (p < 0.010) and hunger (p < 0.010) ratings. In summary, an OA mouth rinse decreases self-reported hunger and increases self-reported fullness, particularly in those who are more sensitive to fat taste. FA receptors in the oral cavity may be potential targets to regulate appetite.
Oral Microbiota Profile Associates with Sugar Intake and Taste Preference Genes
Authors: Esberg Anders , Haworth Simon , Hasslöf Pamela , Lif Holgerson Pernilla , Johansson Ingegerd
Published: March 2020

Oral microbiota ecology is influenced by environmental and host conditions, but few studies have evaluated associations between untargeted measures of the entire oral microbiome and potentially relevant environmental and host factors. This study aimed to identify salivary microbiota cluster groups using hierarchical cluster analyses (Wards method) based on 16S rRNA gene amplicon sequencing, and identify lifestyle and host factors which were associated with these groups. Group members (n = 175) were distinctly separated by microbiota profiles and differed in reported sucrose intake and allelic variation in the taste-preference-associated genes TAS1R1 (rs731024) and GNAT3 (rs2074673). Groups with higher sucrose intake were either characterized by a wide panel of species or phylotypes with fewer aciduric species, or by a narrower profile that included documented aciduric- and caries-associated species. The inferred functional profiles of the latter type were dominated by metabolic pathways associated with the carbohydrate metabolism with enrichment of glycosidase functions. In conclusion, this study supported in vivo associations between sugar intake and oral microbiota ecology, but it also found evidence for a variable microbiota response to sugar, highlighting the importance of modifying host factors and microbes beyond the commonly targeted acidogenic and acid-tolerant species. The results should be confirmed under controlled settings with comprehensive phenotypic and genotypic data.
Oxidative Stress Biomarkers, Nut-Related Antioxidants, and Cardiovascular Disease
Authors: Lorenzon dos Santos Julia , Schaan de Quadros Alexandre , Weschenfelder Camila , Bueno Garofallo Silvia , Marcadenti Aline
Published: March 2020

Atherosclerosis is related to fat accumulation in the arterial walls and vascular stiffening, and results in acute coronary syndrome which is commonly associated with acute myocardial infarction. Oxidative stress participates in the pathogenesis of atherosclerosis. Thus, the inclusion of food sources of dietary antioxidants, such as different kinds of nuts, may improve biomarkers related to oxidative stress, contributing to a possible reduction in atherosclerosis progression. This article has briefly highlighted the interaction between oxidative stress, atherosclerosis, and cardiovascular disease, in addition to the effect of the consumption of different nuts and related dietary antioxidants—like polyphenols and vitamin E—on biomarkers of oxidative stress in primary and secondary cardiovascular prevention. Studies in vitro suggest that nuts may exert antioxidant effects by DNA repair mechanisms, lipid peroxidation prevention, modulation of the signaling pathways, and inhibition of the MAPK pathways through the suppression of NF-κB and activation of the Nrf2 pathways. Studies conducted in animal models showed the ability of dietary nuts in improving biomarkers of oxidative stress, such as oxLDL and GPx. However, clinical trials in humans have not been conclusive, especially with regards to the secondary prevention of cardiovascular disease.
Myrrh Oil in Vitro Inhibitory Growth on Bovine and Equine Piroplasm Parasites and Babesia microti of Mice
Authors: AbouLaila Mahmoud , El-Sayed Shimaa , Omar Mosaab , Al-Aboody Mohammad , Abdel Aziz Amer , Abdel-Daim Mohamed , Rizk Mohamed , Igarashi Ikuo
Published: March 2020

The present experimental study was conducted for the assessment of the efficacy of in vitro inhibition of myrrh oil on the propagation of Babesia bovis, B. divergens, B. bigemina, Theileria equi, and B. caballi and in vivo efficacy on B. microti in mice through fluorescence assay based on SYBR green I. The culture of B. divergens B. bovis and was used to evaluate the in vitro possible interaction between myrrh oil and other commercial compound, such as pyronaridine tetraphosphate (PYR), diminazene aceturate (DA), or luteolin. Nested-polymerase chain reaction protocol using primers of the small-subunit rRNA of B. microti was employed to detect any remnants of DNA for studied parasitic species either in blood or tissues. Results elucidated that; Myrrh oil significantly inhibit the growth at 1% of parasitic blood level for all bovine and equine piroplasm under the study. Parasitic regrowth was inhibited subsequently by viability test at 2 µg/mL for B. bigemina and B. bovis, and there was a significant improvement in the in vitro growth inhibition by myrrh oil when combined with DA, PYR, and luteolin. At the same time; mice treated with a combination of myrrh oil/DA showed a higher inhibition in emitted fluorescence signals than the group that challenged with 25 mg/kg of diminazene aceturate at 10 and 12 days post-infection. In conclusion, this study has recommended the myrrh oil to treat animal piroplasmosis, especially in combination with low doses of DA.
Examining the Effect of Host Recruitment Rates on the Transmission of Streptococcus suis in Nursery Swine Populations
Authors: Giang Elissa , Hetman Benjamin M. , Sargeant Jan M. , Poljak Zvonimir , Greer Amy L.
Published: March 2020

Streptococcus suis is a swine pathogen that is capable of causing severe outbreaks of disease in the nursery. Demographic parameters such as host recruitment rates can have profound effects on the transmission dynamics of infectious diseases and, thus, are critically important in high-turnover populations such as farmed swine. However, knowledge concerning the implications that such parameters have on S. suis disease control remains unknown. A stochastic mathematical model incorporating sub-clinically infected pigs was developed to capture the effects of changes in host recruitment rate on disease incidence. Compared to our base model scenario, our results show that monthly introduction of pigs into the nursery (instead of weekly introduction) reduced cumulative cases of S. suis by up to 59%, while increasing disease-removal rates alone averted up to 64% of cases. Sensitivity analysis demonstrated that the course of infection in sub-clinically infected pigs was highly influential and generated significant variability in the model outcomes. Our model findings suggest that modifications to host recruitment rates could be leveraged as a tool for S. suis disease control, however improving our understanding of additional factors that influence the risk of transmission would improve the precision of the model estimates.
Assessing Islamic Religious Education Curriculum in Flemish Public Secondary Schools
Authors: Lafrarchi Naïma
Published: March 2020

Islamic tradition promotes a holistic approach of personality development in which, we argue, three educational concepts take the centre stage: tarbiyah, ta’leem and ta’deeb. Looking through the lens of these concepts, we analyse two Islamic religious education (IRE) curricula: the 2001 and 2012 curricula for Flemish public secondary education provided by the Representative Body for IRE. We conduct a systematic thematic document analysis of the 2001 and 2012 curricula to map curricula elements that potentially contribute to Islamic personality development through IRE classes. Crucially, this article seeks to investigate whether the 2001 and 2012 curricula for Flemish public secondary education are in line with these central IRE concepts. We observe that the 2012 curriculum does contain relevant anchor points to work on tarbiyah, ta’leem and ta’deeb and to strengthen an Islamic personality in Muslim pupils. Hence, we argue that there is an urgent need for a new, adequate and sufficiently comprehensive IRE curriculum for Flemish public secondary education, developed by an expert committee—which should include Belgian-educated educational experts—in order to meet the expectations of all the stakeholders. Since in our view, this is the first step for a qualitative update of Flemish IRE. Further reflections on both curricula and recommendations for a new IRE curriculum are outlined in the discussion and conclusion sections.
Wildfire Smoke Particle Properties and Evolution, from Space-Based Multi-Angle Imaging
Authors: Junghenn Noyes Katherine , Kahn Ralph , Sedlacek Arthur , Kleinman Lawrence , Limbacher James , Li Zhanqing
Published: March 2020

Emitted smoke composition is determined by properties of the biomass burning source and ambient ecosystem. However, conditions that mediate the partitioning of black carbon (BC) and brown carbon (BrC) formation, as well as the spatial and temporal factors that drive particle evolution, are not understood adequately for many climate and air-quality related modeling applications. In situ observations provide considerable detail about aerosol microphysical and chemical properties, although sampling is extremely limited. Satellites offer the frequent global coverage that would allow for statistical characterization of emitted and evolved smoke, but generally lack microphysical detail. However, once properly validated, data from the National Aeronautics and Space Administration (NASA) Earth Observing System’s Multi-Angle Imaging Spectroradiometer (MISR) instrument can create at least a partial picture of smoke particle properties and plume evolution. We use in situ data from the Department of Energy’s Biomass Burning Observation Project (BBOP) field campaign to assess the strengths and limitations of smoke particle retrieval results from the MISR Research Aerosol (RA) retrieval algorithm. We then use MISR to characterize wildfire smoke particle properties and to identify the relevant aging factors in several cases, to the extent possible. The RA successfully maps qualitative changes in effective particle size, light absorption, and its spectral dependence, when compared to in situ observations. By observing the entire plume uniformly, the satellite data can be interpreted in terms of smoke plume evolution, including size-selective deposition, new-particle formation, and locations within the plume where BC or BrC dominates.
Drone-borne Differential SAR Interferometry
Authors: Luebeck Dieter , Wimmer Christian , F. Moreira Laila , Alcântara Marlon , Oré Gian , A. Góes Juliana , P. Oliveira Luciano , Teruel Bárbara , S. Bins Leonardo , H. Gabrielli Lucas , Hernandez-Figueroa Hugo E.
Published: March 2020

Differential synthetic aperture radar interferometry (DInSAR) has been widely applied since the pioneering space-borne experiment in 1989, and subsequently with the launch of the ERS-1 program in 1992. The DInSAR technique is well assessed in the case of space-borne SAR data, whereas in the case of data acquired from aerial platforms, such as airplanes, helicopters, and drones, the effective application of this technique is still a challenging task, mainly due to the limited accuracy of the information provided by the navigation systems mounted onboard the platforms. The first airborne DInSAR results for measuring ground displacement appeared in 2003 using L- and X-bands. DInSAR displacement results with long correlation time in P-band were published in 2011. This letter presents a SAR system and, to the best of our knowledge, the first accuracy assessment of the DInSAR technique using a drone-borne SAR in L-band. A deformation map is shown, and the accuracy and resolution of the methodology are presented and discussed. In particular, we have obtained an accuracy better than 1 cm for the measurement of the observed ground displacement. It is in the same order as that achieved with space-borne systems in C- and X-bands and the airborne systems in X-band. However, compared to these systems, we use here a much longer wavelength. Moreover, compared to the satellite experiments available in the literature and aimed at assessing the accuracy of the DInSAR technique, we use only two flight tracks with low time decorrelation effects and not a big data stack, which helps in reducing the atmospheric effects.
Parallel Regional Segmentation Method of High-Resolution Remote Sensing Image Based on Minimum Spanning Tree
Authors: Lin Wenjie , Li Yu
Published: March 2020

With finer spatial scale, high-resolution images provide complex, spatial, and massive information on the earth’s surface, which brings new challenges to remote sensing segmentation methods. In view of these challenges, finding a more effective segmentation model and parallel processing method is crucial to improve the segmentation accuracy and process efficiency of large-scale high-resolution images. To this end, this study proposed a minimum spanning tree (MST) model integrated into a regional-based parallel segmentation method. First, an image was decomposed into several blocks by regular tessellation. The corresponding homogeneous regions were obtained using the minimum heterogeneity rule (MHR) partitioning technique in a multicore parallel processing mode, and the initial segmentation results were obtained by the parallel block merging method. On this basis, a regionalized fuzzy c-means (FCM) method based on master-slave parallel mode was proposed to achieve fast and optimal segmentation. The proposed segmentation approach was tested on high-resolution images. The results from the qualitative assessment, quantitative evaluation, and parallel analysis verified the feasibility and validity of the proposed method.
Extended Feature Pyramid Network with Adaptive Scale Training Strategy and Anchors for Object Detection in Aerial Images
Authors: Guo Wei , Li Weihong , Gong Weiguo , Cui Jinkai
Published: March 2020

Multi-scale object detection is a basic challenge in computer vision. Although many advanced methods based on convolutional neural networks have succeeded in natural images, the progress in aerial images has been relatively slow mainly due to the considerably huge scale variations of objects and many densely distributed small objects. In this paper, considering that the semantic information of the small objects may be weakened or even disappear in the deeper layers of neural network, we propose a new detection framework called Extended Feature Pyramid Network (EFPN) for strengthening the information extraction ability of the neural network. In the EFPN, we first design the multi-branched dilated bottleneck (MBDB) module in the lateral connections to capture much more semantic information. Then, we further devise an attention pathway for better locating the objects. Finally, an augmented bottom-up pathway is conducted for making shallow layer information easier to spread and further improving performance. Moreover, we present an adaptive scale training strategy to enable the network to better recognize multi-scale objects. Meanwhile, we present a novel clustering method to achieve adaptive anchors and make the neural network better learn data features. Experiments on the public aerial datasets indicate that the presented method obtain state-of-the-art performance.
The Effect of Topographic Correction on Forest Tree Species Classification Accuracy
Authors: Dong Chao , Zhao Gengxing , Meng Yan , Li Baihong , Peng Bo
Published: March 2020

Topographic correction can reduce the influences of topographic factors and improve the accuracy of forest tree species classification when using remote-sensing data to investigate forest resources. In this study, the Mount Taishan forest farm is the research area. Based on Landsat 8 OLI data and field survey subcompartment data, four topographic correction models (cosine model, C model, solar-canopy-sensor (SCS)+C model and empirical rotation model) were used on the Google Earth Engine (GEE) platform to carry out algorithmic data correction. Then, the tree species in the study area were classified by the random forest method. Combined with the tree species classification process, the topographic correction effects were analyzed, and the effects, advantages and disadvantages of each correction model were evaluated. The results showed that the SCS+C model and empirical rotation model were the best models in terms of visual effect, reducing the band standard deviation and adjusting the reflectance distribution. When we used the SCS+C model to correct the remote-sensing image, the total accuracy increased by 4% when using the full-coverage training areas to classify tree species and by nearly 13% when using the shadowless training area. In the illumination condition interval of 0.4–0.6, the inconsistency rate decreased significantly; however, the inconsistency rate increased with increasing illumination condition values. Topographic correction can enhance reflectance information in shaded areas and can significantly improve the image quality. Topographic correction can be used as a pretreatment method for forest species classification when the study area’s dominant tree species are in a low light intensity area.
The Impact of Urban Renewal on Land Surface Temperature Changes: A Case Study in the Main City of Guangzhou, China
Authors: Qiao Zhi , Liu Luo , Qin Yuanwei , Xu Xinliang , Wang Binwu , Liu Zhenjie
Published: March 2020

To improve land use efficiency, urban renewal must also consider urban microclimates and heat islands. Existing research has depended on manual interpretation of high-resolution optical satellite imagery to resolve land surface temperature (LST) changes caused by urban renewal; however, the acquired ground time series data tend to be uneven and unique to specific frameworks. The objective of this study was to establish a more general framework to study LST changes caused by urban renewal using multi-source remote sensing data. Specifically, urban renewal areas during 2007–2017 were obtained by integrating Landsat and yearly Phased Array type L-band Synthetic Aperture Radar (PALSAR) images, and LST was retrieved from Landsat thermal infrared data using the generalized single-channel algorithm. Our results showed that urban renewal land (URL) area accounted for 1.88% of urban land area. Relative LST between URL and general urban land (GUL) of Liwan, Yuexiu, Haizhu, and Tianhe districts dropped by 0.88, 0.42, 0.43, and 0.10 K, respectively, whereas those of Baiyun, Huangpu, Panyu, and Luogang districts presented opposite characteristics, with a rise in the LST of 0.98, 1.03, 1.63, and 2.11 K, respectively. These results are attributable to population density, building density, and landscape pattern changes during the urban renewal process.
Mitigation of Short-Term Temporal Variations of Receiver Code Bias to Achieve Increased Success Rate of Ambiguity Resolution in PPP
Authors: Wang Jin , Huang Guanwen , Yang Yuanxi , Zhang Qin , Gao Yang , Zhou Peiyuan
Published: March 2020

Ambiguity resolution (AR) is critical for achieving a fast, high-precision solution in precise point positioning (PPP). In the standard uncombined PPP (S-UPPP) method, ionosphere-free code biases are superimposed by ambiguity and receiver clock offsets to be estimated. However, besides the time-constant part of the receiver code bias, the complex and time-varying term in receivers destroy the stability of ambiguities and degrade the performance of the UPPP AR. The variation of receiver code bias can be confirmed by the analysis in terms of ionospheric observables, code multipath (MP) of the Melbourne–Wübbena (MW) combination and the ionosphere-free combination. Therefore, the effect of receiver code biases should be rigorously mitigated. We introduce a modified UPPP (M-UPPP) method to reduce the effects of receiver code biases in ambiguities and to decouple the correlation between receiver clock parameters, code biases, and ambiguities parameters. An extra receiver code bias is set to isolate the code biases from ambiguities. The more stable ambiguities without code biases are expected to achieve a higher success rate of ambiguity resolution and a shortened convergence time. The variations of the receiver code biases, which are the unmodeled errors in measurement residuals of the S-UPPP method, can be estimated in the M-UPPP method. The maximum variation of the code biases is up to 16 ns within two-hour data. In the M-UPPP method, the averaged epoch residuals for code and phase measurements recover their zero-mean features. For the ambiguity-fixed solutions in the M-UPPP method, the convergence times are 14 and 43 min with 17.7% and 69.2% improvements compared to that in the S-UPPP method which are 17 and 90 min under the 68% and 95% confidence levels.
Developing Non-Negative Spatial Autoregressive Models for Better Exploring Relation Between Nighttime Light Images and Land Use Types
Authors: Zheng Honghan , Gui Zhipeng , Wu Huayi , Song Aihong
Published: March 2020

Exploring the relationship between nighttime light and land use is of great significance to understanding human nighttime activities and studying socioeconomic phenomena. Models have been studied to explain the relationships, but the existing studies seldom consider the spatial autocorrelation of night light data, which leads to large regression residuals and an inaccurate regression correlation between night light and land use. In this paper, two non-negative spatial autoregressive models are proposed for the spatial lag model and spatial error model, respectively, which use a spatial adjacency matrix to calculate the spatial autocorrelation effect of light in adjacent pixels on the central pixel. The application scenarios of the two models were analyzed, and the contribution of various land use types to nighttime light in different study areas are further discussed. Experiments in Berlin, Massachusetts and Shenzhen showed that the proposed methods have better correlations with the reference data compared with the non-negative least-squares method, better reflecting the luminous situation of different land use types at night. Furthermore, the proposed model and the obtained relationship between nighttime light and land use types can be utilized for other applications of nighttime light images in the population, GDP and carbon emissions for better exploring the relationship between nighttime remote sensing brightness and socioeconomic activities.
Measuring the Urban Land Surface Temperature Variations Under Zhengzhou City Expansion Using Landsat-Like Data
Authors: Yang Haibo , Xi Chaofan , Zhao Xincan , Mao Penglei , Wang Zongmin , Shi Yong , He Tian , Li Zhenhong
Published: March 2020

Satellite-based remote sensing technologies are utilized extensively to investigate urban thermal environment under rapid urban expansion. Current Moderate Resolution Imaging Spectroradiometer (MODIS) data are, however, unable to adequately represent the spatially detailed information because of its relatively coarser spatial resolution, while Landsat data cannot explore the temporally continued analysis due to the lower temporal resolution. Combining MODIS and Landsat data, “Landsat-like” data were generated by using the Flexible Spatiotemporal Data Fusion method (FSDAF) to measure land surface temperature (LST) variations, and Landsat-like data including Normalized Difference Vegetation Index (NDVI) and Normalized Difference Built Index (NDBI) were generated to analyze LST dynamic driving forces. Results show that (1) the estimated “Landsat-like” data are capable of measuring the LST variations; (2) with the urban expansion from 2013 to 2016, LST increases ranging from 1.80 °C to 3.92 °C were detected in areas where the impervious surface area (ISA) increased, while LST decreases ranging from −3.52 °C to −0.70 °C were detected in areas where ISA decreased; (3) LST has a significant negative correlation with the NDVI and a strong positive correlation with NDBI in summer. Our findings can provide information useful for mitigating undesirable thermal conditions and for long-term urban thermal environmental management.
SAR Image Segmentation Using Region Smoothing and Label Correction
Authors: Shang Ronghua , Lin Junkai , Jiao Licheng , Li Yangyang
Published: March 2020

The traditional unsupervised image segmentation methods are widely used in synthetic aperture radar (SAR) image segmentation due to the simple and convenient application process. In order to solve the time-consuming problem of the common methods, an SAR image segmentation method using region smoothing and label correction (RSLC) is proposed. In this algorithm, the image smoothing results are used to approximate the results of the spatial information polynomials of the image. Thus, the segmentation process can be realized quickly and effectively. Firstly, direction templates are used to detect the directions at different coordinates of the image, and smoothing templates are used to smooth the edge regions according to the directions. It achieves the smoothing of the edge regions and the retention of the edge information. Then the homogeneous regions are presented indirectly according to the difference of directions. The homogeneous regions are smoothed by using isotropic operators. Finally, the two regions are fused for K-means clustering. The majority voting algorithm is used to modify the clustering results, and the final segmentation results are obtained. Experimental results on simulated SAR images and real SAR images show that the proposed algorithm outperforms the other five state-of-the-art algorithms in segmentation speed and accuracy.
Impact of Satellite and In Situ Data Assimilation on Hydrological Predictions
Authors: Musuuza Jude Lubega , Gustafsson David , Pimentel Rafael , Crochemore Louise , Pechlivanidis Ilias
Published: March 2020

The assimilation of different satellite and in situ products generally improves the hydrological model predictive skill. Most studies have focused on assimilating a single product at a time with the ensemble size subjectively chosen by the modeller. In this study, we used the European-scale Hydrological Predictions for the Environment hydrological model in the Umeälven catchment in northern Sweden with the stream discharge and local reservoir inflow as target variables to objectively choose an ensemble size that optimised model performance when the ensemble Kalman filter method is used. We further assessed the effect of assimilating different satellite products; namely, snow water equivalent, fractional snow cover, and actual and potential evapotranspiration, as well as in situ measurements of river discharge and local reservoir inflows. We finally investigated the combinations of those products that improved model predictions of the target variables and how the model performance varied through the year for those combinations. We found that an ensemble size of 50 was sufficient for all products except the reservoir inflow, which required 100 members and that in situ products outperform satellite products when assimilated. In particular, potential evapotranspiration alone or as combinations with other products did not generally improve predictions of our target variables. However, assimilating combinations of the snow products, discharge and local reservoir without evapotranspiration products improved the model performance.
Object-Based Classification Approaches for Multitemporal Identification and Monitoring of Pastures in Agroforestry Regions using Multispectral Unmanned Aerial Vehicle Products
Authors: Vilar Pedro , Morais Tiago G. , Rodrigues Nuno R. , Gama Ivo , Monteiro Marta L. , Domingos Tiago , Teixeira Ricardo F. M.
Published: March 2020

Sown Biodiverse Pastures (SBP) are the basis of a high-yield grazing system tailored for Mediterranean ecosystems and widely implemented in Southern Portugal. The application of precision farming methods in SBP requires cost-effective monitoring using remote sensing (RS). The main hurdle for the remote monitoring of SBP is the fact that the bulk of the pastures are installed in open Montado agroforestry systems. Sparsely distributed trees cast shadows that hinder the identification of the underlaying pasture using Unmanned Aerial Vehicles (UAV) imagery. Image acquisition in the Spring is made difficult by the presence of flowers that mislead the classification algorithms. Here, we tested multiple procedures for the geographical, object-based image classification (GEOBIA) of SBP, aiming to reduce the effects of tree shadows and flowers in open Montado systems. We used remotely sensed data acquired between November 2017 and May 2018 in three Portuguese farms. We used three machine learning supervised classification algorithms: Random Forests (RF), Support Vector Machine (SVM) and Artificial Neural Networks (ANN). We classified SBP based on: (1) a single-period image for the maximum Normalized Difference Vegetation Index (NDVI) epoch in each of the three farms, and (2) multi-temporal image stacking. RF, SVM and ANN were trained using some visible (red, green and blue bands) and near-infrared (NIR) reflectance bands, plus NDVI and a Digital Surface Model (DSM). We obtained high overall accuracy and kappa index (higher than 79% and 0.60, respectively). The RF algorithm had the highest overall accuracy (more than 92%) for all farms. Multitemporal image classification increased the accuracy of the algorithms. as it helped to correctly identify as SBP the areas covered by tree shadows and flower patches, which would be misclassified using single image classification. This study thus established the first workflow for SBP monitoring based on remotely sensed data, suggesting an operational approach for SBP identification. The workflow can be applied to other types of pastures in agroforestry regions to reduce the effects of shadows and flowering in classification problems.
Validation of ASTER Emissivity Retrieval Using the Mako Airborne TIR Imaging Spectrometer at the Algodones Dune Field in Southern California, USA
Authors: Mushkin Amit , Gillespie Alan R. , Abbott Elsa A. , Batbaatar Jigjidsurengiin , Hulley Glynn , Tan Howard , Tratt David M. , N. Buckland Kerry
Published: March 2020

Validation of emissivity (ε) retrievals from spaceborne thermal infrared (TIR) sensors typically requires spatial extrapolations over several orders of magnitude for a comparison between centimeter-scale laboratory ε measurements and the common decameter and lower resolution of spaceborne TIR data. In the case of NASA’s Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) temperature and ε separation algorithm (TES), this extrapolation becomes especially challenging because TES was originally designed for the geologic surface of Earth, which is typically heterogeneous even at centimeter and decameter scales. Here, we used the airborne TIR hyperspectral Mako sensor with its 2.2 m/pixel resolution, to bridge this scaling issue and robustly link between ASTER TES 90 m/pixel emissivity retrievals and laboratory ε measurements from the Algodones dune field in southern California, USA. The experimental setup included: (i) Laboratory XRD, grain size, and TIR spectral measurements; (ii) radiosonde launches at the time of the two Mako overpasses for atmospheric corrections; (iii) ground-based thermal measurements for calibration, and (iv) analyses of ASTER day and night ε retrievals from 21 different acquisitions. We show that while cavity radiation leads to a 2% to 4% decrease in the effective emissivity contrast of fully resolved scene elements (e.g., slipface slopes and interdune flats), spectral variability of the site when imaged at 90 m/pixel is below 1%, because at this scale the dune field becomes an effectively homogeneous mixture of the different dune elements. We also found that adsorption of atmospheric moisture to grain surfaces during the predawn hours increased the effective ε of the dune surface by up to 0.04. The accuracy of ASTER’s daytime emissivity retrievals using each of the three available atmospheric correction protocols was better than 0.01 and within the target performance of ASTER’s standard emissivity product. Nighttime emissivity retrievals had lower precision (<0.03) likely due to residual atmospheric effects. The water vapor scaling (WVS) atmospheric correction protocol was required to obtain accurate (<0.01) nighttime ASTER emissivity retrievals.
Statistical Methods for Thematic-Accuracy Quality Control Based on an Accurate Reference Sample
Authors: Alba-Fernández María V. , Ariza-López Francisco J. , Rodríguez-Avi José , García-Balboa José L.
Published: March 2020

The goal of this work is to present a set of statistical tests that offer a formal procedure to make a decision as to whether a set of thematic quality specifications of a product is fulfilled within the philosophy of a quality control process. The tests can be applied to classification data in thematic quality control, in order to check if they are compliant with a set of specifications for correctly classified elements (e.g., at least 90% classification correctness for category A) and maximum levels of poor quality for confused elements (e.g., at most 5% of confusion is allowed between categories A and B). To achieve this objective, an accurate reference is needed. This premise entails changes in the distributional hypothesis over the classification data from a statistical point of view. Four statistical tests based on the binomial, chi-square, and multinomial distributions are stated, to provide a range of tests for controlling the quality of product per class, both categorically and globally. The proposal is illustrated with a complete example. Finally, a guide is provided to clarify the use of each test, as well as their pros and cons.
A Quasigeoid-Derived Transformation Model Accounting for Land Subsidence in the Mekong Delta towards Height System Unification in Vietnam
Authors: Vu Dinh Toan , Bruinsma Sean , Bonvalot Sylvain , Remy Dominique , Vergos Georgios S.
Published: March 2020

A vertical offset model for Vietnam and its surrounding areas was determined based on the differences between height anomalies derived from 779 Global Navigation Satellite System (GNSS)/levelling points and those derived from a dedicated high-resolution gravimetric-only quasigeoid model called GEOID_LSC. First, the deterministic transformation model to effectively fit the differences between the quasigeoid and GNSS/levelling heights was based on a third-order polynomial model. Second, the residual height anomalies have been interpolated to a grid employing Least-Squares Collocation. Finally, the distortions were restored to the residual grid. This model can be used for combination with a gravimetric quasigeoid model in GNSS levelling. The quality of GNSS/levelling data in Vietnam was analyzed and evaluated in this study. The annual subsidence rate from ALOS-1 was also used to analyze the effects of subsidence on the quality of GNSS/levelling data in the Mekong Delta. From this we made corrections to improve the accuracy of GNSS/levelling data in this region. The offset model was evaluated using cross-validation technique by comparing with GNSS/levelling data. Results indicate that the offset model has a standard deviation of 5.9 cm in the absolute sense. Based on this offset model, GNSS levelling can be carried out in most of Vietnam’s territory complying third-order levelling requirements, while the accuracy requirements for fourth-order levelling networks is met for the entire country. This model in combination with the developed gravimetric quasigeoid model should also contribute to the modernization of Vietnam’s height system. We also used high-quality GNSS/levelling data and the determined quasigeoid model to determine the geopotential value W0 for the Vietnam Local Vertical Datum. The gravity potential of the Vietnam Local Vertical Datum is estimated equal to W 0 LVD = 62,636,846.81 ± 0.70 m2s−2 with the global equipotential surface realized by the conventional value W0 = 62,636,853.4 m2s−2.
The VIIRS-Based RST-FLARE Configuration: The Val d’Agri Oil Center Gas Flaring Investigation in Between 2015–2019
Authors: Faruolo Mariapia , Lacava Teodosio , Pergola Nicola , Tramutoli Valerio
Published: March 2020

The RST (Robust Satellite Techniques)-FLARE algorithm is a satellite-based method using a multitemporal statistical analysis of nighttime infrared signals strictly related to industrial hotspots, such as gas flares. The algorithm was designed for both identifying and characterizing gas flares in terms of radiant/emissive power. The Val d’Agri Oil Center (COVA) is a gas and oil pre-treatment plant operating for about two decades within an anthropized area of Basilicata region (southern Italy) where it represents a significant potential source of social and environmental impacts. RST-FLARE, developed to study and monitor the gas flaring activity of this site by means of MODIS (Moderate Resolution Imaging Spectroradiometer) data, has exported VIIRS (Visible Infrared Imaging Radiometer Suite) records by exploiting the improved spatial and spectral properties offered by this sensor. In this paper, the VIIRS-based configuration of RST-FLARE is presented and its application on the recent (2015-2019) gas flaring activity at COVA is analyzed and discussed. Its performance in gas flaring characterization is in good agreement with VIIRS Nightfire outputs to which RST-FLARE seems to provide some add-ons. The great consistency of radiant heat estimates computed with both RST-FLARE developed configurations allows proposing a multi-sensor RST-FLARE strategy for a more accurate multi-year analysis of gas flaring.
Using NDVI to Differentiate Wheat Genotypes Productivity Under Dryland and Irrigated Conditions
Authors: Naser Mohammed , Khosla Raj , Longchamps Louis , Dahal Subash
Published: March 2020

Crop breeders are looking for tools to facilitate the screening of genotypes in field trials. Remote sensing-based indices such as normalized difference vegetative index (NDVI) are sensitive to biomass and nitrogen (N) variability in crop canopies. The objectives of this study were (i) to determine if proximal sensor-based NDVI readings can differentiate the yield of winter wheat (Triticum aestivum L.) genotypes and (ii) to determine if NDVI readings can be used to classify wheat genotypes into grain yield productivity classes. This study was conducted in northeastern Colorado in 2010 and 2011. The NDVI readings were acquired weekly from March to June, during 2010 and 2011. The correlation between NDVI and grain yield was determined using Pearson’s product-moment correlation coefficient (r). The k-means clustering method was used to classify mean NDVI and mean grain yield into three classes. The overall accuracy between NDVI and yield classes was reported. The findings of this study show that, under dryland conditions, there is a reliable correlation between grain yield and NDVI at the early growing season, at the anthesis growth stage, and the mid-grain filling growth stage, as well as a poor association under irrigated conditions. Our results suggest that when the sensor is not saturated, i.e., NDVI < 0.9, NDVI could assess grain yield with fair accuracy. This study demonstrated the potential of using NDVI readings as a tool to differentiate and identify superior wheat genotypes.
Primary Production, an Index of Climate Change in the Ocean: Satellite-Based Estimates over Two Decades
Authors: Kulk Gemma , Platt Trevor , Dingle James , Jackson Thomas , Jönsson Bror , Bouman Heather , Babin Marcel , Brewin Robert , Doblin Martina , Estrada Marta , Figueiras Francisco , Furuya Ken , González-Benítez Natalia , Gudfinnsson Hafsteinn , Gudmundsson Kristinn , Huang Bangqin , Isada Tomonori , Kovač Žarko , Lutz Vivian , Marañón Emilio , Raman Mini , Richardson Katherine , Rozema Patrick , Poll Willem , Segura Valeria , Tilstone Gavin , Uitz Julia , Dongen-Vogels Virginie , Yoshikawa Takashi , Sathyendranath Shubha
Published: March 2020

Primary production by marine phytoplankton is one of the largest fluxes of carbon on our planet. In the past few decades, considerable progress has been made in estimating global primary production at high spatial and temporal scales by combining in situ measurements of primary production with remote-sensing observations of phytoplankton biomass. One of the major challenges in this approach lies in the assignment of the appropriate model parameters that define the photosynthetic response of phytoplankton to the light field. In the present study, a global database of in situ measurements of photosynthesis versus irradiance (P-I) parameters and a 20-year record of climate quality satellite observations were used to assess global primary production and its variability with seasons and locations as well as between years. In addition, the sensitivity of the computed primary production to potential changes in the photosynthetic response of phytoplankton cells under changing environmental conditions was investigated. Global annual primary production varied from 38.8 to 42.1 Gt C yr − 1 over the period of 1998–2018. Inter-annual changes in global primary production did not follow a linear trend, and regional differences in the magnitude and direction of change in primary production were observed. Trends in primary production followed directly from changes in chlorophyll-a and were related to changes in the physico-chemical conditions of the water column due to inter-annual and multidecadal climate oscillations. Moreover, the sensitivity analysis in which P-I parameters were adjusted by ±1 standard deviation showed the importance of accurately assigning photosynthetic parameters in global and regional calculations of primary production. The assimilation number of the P-I curve showed strong relationships with environmental variables such as temperature and had a practically one-to-one relationship with the magnitude of change in primary production. In the future, such empirical relationships could potentially be used for a more dynamic assignment of photosynthetic rates in the estimation of global primary production. Relationships between the initial slope of the P-I curve and environmental variables were more elusive.
Dual-Axis Metasurface Strain Sensor Based on Polarization–Phase-Deformation Relationship
Authors: Sun Zhiwei , Liu Run , Cao Hailin , Gong Heling , Du Mingzhu , Li Shirun
Published: March 2020

Herein, we propose an approach for sensitivity improvement of dual-axis strain sensing using the property of a metasurface (MS) that the phase response shifts sharply with the MS deformation. A feasible approach for phase measurement is first demonstrated by calculating multi-polarized reception when the incident electromagnetic (EM) wave has anisotropic phase values. A flexible MS consisting of periodically arranged lantern-shaped elements is designed and fabricated for dual-axis strain sensing and evaluation based on the proposed method. The simulation and measurement results demonstrated a high sensitivity of the proposed MS for strain sensing in the microwave band. The method can be used potentially in both pressure and tensile sensing. Moreover, the operational frequency can be extended to the THz range and even to the optical band.
A BeiDou Signal Acquisition Approach Using Variable Length Data Accumulation Based on Signal Delay and Multiplication
Authors: Yang Menghuan , Wu Hong , Wang Qiqi , Zhao Yingxin , Liu Zhiyang
Published: March 2020

The secondary modulation with the Neumann-Hoffman code increases the possibility of bit sign transition. Unlike other GNSS signals, there is no pilot component for synchronization in BeiDou B1/B3 signals, which increases the complexity in acquisition. A previous study has shown that the delay and multiplication (DAM) method is able to eliminate the bit sign transition problem, but it only applies to pretty strong signals. In this paper, a DAM-based BeiDou signal acquisition approach, called variable length data accumulation (VLDA), is proposed to acquire weak satellite signals. Firstly, the performance of DAM method versus the different delays is analyzed. The DAM operation not only eliminates bit sign transition, but it also increases noise power. Secondly, long-term signal is periodically accumulated to improve signal intensity in order to acquire weak signals. While considering the Doppler frequency shift of ranging codes, the signal length must be compensated before accumulating long-term signal. Finally, the fast-Fourier-transform based parallel code phase algorithm are used for acquisition. The simulation results indicate that the proposed VLDA method has better acquisition sensitivity than traditional non-coherent integration method under the same calculation amount. The VLDA method only requires approximately 27.5% of calculations to achieve the same acquisition sensitivity (35 dBHz). What is more, the actual experimental results verify the feasibility of the VLDA method. It can be concluded that the proposed approach is an effective and feasible method for solving the bit sign transition problem.
Machine Learning-Based Gully Erosion Susceptibility Mapping: A Case Study of Eastern India
Authors: Saha Sunil , Roy Jagabandhu , Arabameri Alireza , Blaschke Thomas , Tien Bui Dieu
Published: March 2020

Gully erosion is a form of natural disaster and one of the land loss mechanisms causing severe problems worldwide. This study aims to delineate the areas with the most severe gully erosion susceptibility (GES) using the machine learning techniques Random Forest (RF), Gradient Boosted Regression Tree (GBRT), Naïve Bayes Tree (NBT), and Tree Ensemble (TE). The gully inventory map (GIM) consists of 120 gullies. Of the 120 gullies, 84 gullies (70%) were used for training and 36 gullies (30%) were used to validate the models. Fourteen gully conditioning factors (GCFs) were used for GES modeling and the relationships between the GCFs and gully erosion was assessed using the weight-of-evidence (WofE) model. The GES maps were prepared using RF, GBRT, NBT, and TE and were validated using area under the receiver operating characteristic (AUROC) curve, the seed cell area index (SCAI) and five statistical measures including precision (PPV), false discovery rate (FDR), accuracy, mean absolute error (MAE), and root mean squared error (RMSE). Nearly 7% of the basin has high to very high susceptibility for gully erosion. Validation results proved the excellent ability of these models to predict the GES. Of the analyzed models, the RF (AUROC = 0.96, PPV = 1.00, FDR = 0.00, accuracy = 0.87, MAE = 0.11, RMSE = 0.19 for validation dataset) is accurate enough for modeling and better suited for GES modeling than the other models. Therefore, the RF model can be used to model the GES areas not only in this river basin but also in other areas with the same geo-environmental conditions.