bluuebunny/crossref_metadata_2025 huggingface dataset:
Correction: Fanelli Kuczmarski, M.; et al. Aspects of Dietary Diversity Differ in Their Association with Atherosclerotic Cardiovascular Risk in a Racially Diverse US Adult Population. Nutrients 2019, 11, 1034
Authors: Fanelli Kuczmarski Marie , Brewer Benjamin C. , Rawal Rita , Pohlig Ryan T. , Zonderman Alan B. , Evans Michele K.
Published: October 2019

The authors wish to make a correction to Table 2 in the published version of their paper [...]
Advances in Legionella Control by a New Formulation of Hydrogen Peroxide and Silver Salts in a Hospital Hot Water Network
Authors: Girolamini Luna , Dormi Ada , Pellati Tiziana , Somaroli Paolo , Montanari Davide , Costa Andrea , Savelli Francesca , Martelli Andrea , Grottola Antonella , Fregni Serpini Giulia , Cristino Sandra
Published: October 2019

Legionella surveillance is an important issue in public health, linked to the severity of disease and the difficulty associated with eradicating this bacterium from the water environment. Different treatments are suggested to reduce Legionella risk, however long-term studies of their efficiency are lacking. This study focused on the activity of a new formulation of hydrogen peroxide and silver salts, WTP828, in the hospital hot water network (HWN) to contain Legionella contamination during two years of treatment. The effectiveness of WTP828 was tested measuring physical-chemical and microbiological parameters such as Legionella, Pseudomonas aeruginosa (P. aeruginosa), and a heterotopic plate count (HPC) at 36 °C. Legionella isolates were identified by serotyping and genotyping. WTP 828 induced a reduction in Legionella–positive sites (60% to 36%) and contamination levels (2.12 to 1.7 log10 CFU/L), with isolates belonging to L. pneumophila SG1 (ST1 and ST104), L. anisa and L. rubrilucens widely distributed in HWN. No relevant contamination was found for other parameters tested. The long-term effect of WTP828 on Legionella containment suggest the easy and safe application of this disinfectant, that combined with knowledge of building characteristics, an adequate environmental monitoring and risk assessment plan, become the key elements in preventing Legionella contamination and exposure.
Rotavirus Burden, Genetic Diversity and Impact of Vaccine in Children under Five in Tanzania
Authors: Malakalinga Joseph J. , Misinzo Gerald , Msalya George M. , Kazwala Rudovick R.
Published: October 2019

In Tanzania, rotavirus infections are responsible for 72% of diarrhea deaths in children under five. The Rotarix vaccine was introduced in early 2013 to mitigate rotavirus infections. Understanding the disease burden and virus genotype trends over time is important for assessing the impact of rotavirus vaccine in Tanzania. When assessing the data for this review, we found that deaths of children under five declined after vaccine introduction, from 8171/11,391 (72% of diarrhea deaths) in 2008 to 2552/7087 (36% of diarrhea deaths) in 2013. Prior to vaccination, the prevalence of rotavirus infections in children under five was 18.1–43.4%, 9.8–51%, and 29–41% in Dar es Salaam, Mwanza and Tanga, respectively, and after the introduction of vaccines, these percentages declined to 17.4–23.5%, 16–19%, and 10–29%, respectively. Rotaviruses in Tanzania are highly diverse, and include genotypes of animal origin in children under five. Of the genotypes, 10%, 28%, and 7% of the strains are untypable in Dar es Salaam, Tanga, and Zanzibar, respectively. Mixed rotavirus genotype infection accounts for 31%, 29%, and 12% of genotypes in Mwanza, Tanga and Zanzibar, respectively. The vaccine effectiveness ranges between 53% and 75% in Mwanza, Manyara and Zanzibar. Rotavirus vaccination has successfully reduced the rotavirus burden in Tanzania; however, further studies are needed to better understand the relationship between the wildtype strain and the vaccine strain as well as the zoonotic potential of rotavirus in the post-vaccine era.
Fighting Hypoxia to Improve PDT
Authors: Larue Ludivine , Myrzakhmetov Bauyrzhan , Ben-Mihoub Amina , Moussaron Albert , Thomas Noémie , Arnoux Philippe , Baros Francis , Vanderesse Régis , Acherar Samir , Frochot Céline
Published: October 2019

Photodynamic therapy (PDT) has drawn great interest in recent years mainly due to its low side effects and few drug resistances. Nevertheless, one of the issues of PDT is the need for oxygen to induce a photodynamic effect. Tumours often have low oxygen concentrations, related to the abnormal structure of the microvessels leading to an ineffective blood distribution. Moreover, PDT consumes O2. In order to improve the oxygenation of tumour or decrease hypoxia, different strategies are developed and are described in this review: (1) The use of O2 vehicle; (2) the modification of the tumour microenvironment (TME); (3) combining other therapies with PDT; (4) hypoxia-independent PDT; (5) hypoxia-dependent PDT and (6) fractional PDT.
Metformin-Encapsulated Liposome Delivery System: An Effective Treatment Approach against Breast Cancer
Authors: Shukla Snehal K. , Kulkarni Nishant S. , Chan Amanda , Parvathaneni Vineela , Farrales Pamela , Muth Aaron , Gupta Vivek
Published: October 2019

This study aimed at developing metformin hydrochloride (Met) encapsulated liposomal vesicles for enhanced therapeutic outcomes at reduced doses against breast cancer. Liposomal Met was prepared using thin-film hydration through various loading methods; passive loading, active loading, and drug-loaded lipid film. The drug-loaded film method exhibited maximum entrapment efficiency (~65%) as compared to active loading (~25%) and passive loading (~5%) prepared Met-loaded liposomes. The therapeutic efficacy of these optimized liposomes was evaluated for cellular uptake, cytotoxicity, inhibition of metastatic activity, and apoptosis-inducing activity. Results demonstrated significantly superior activity of positively charged liposomes resulting in reduced IC50 values, minimal cell migration activity, reduced colony formation, and profound apoptosis-induced activity in breast cancer cells as compared to Met. The anti-tumor activity was investigated using a clinically relevant in vitro tumor simulation model, which confirmed enhanced anti-tumorigenic property of liposomal Met over Met itself. To the authors’ knowledge, this is the first report of Met-loaded liposomes for improving the efficacy and therapeutic effect of Met against breast cancer. With the results obtained, it can be speculated that liposomal encapsulation of metformin offers a potentially promising and convenient approach for enhanced efficacy and bioavailability in breast cancer treatment.
Integrating DNA Barcoding and Traditional Taxonomy for the Identification of Dipterocarps in Remnant Lowland Forests of Sumatra
Authors: Carneiro de Melo Moura Carina , Brambach Fabian , Jair Hernandez Bado Kevin , Krutovsky Konstantin V. , Kreft Holger , Tjitrosoedirdjo Sri Sudarmiyati , Siregar Iskandar Z. , Gailing Oliver
Published: October 2019

DNA barcoding has been used as a universal tool for phylogenetic inferences and diversity assessments, especially in poorly studied species and regions. The aim of this study was to contrast morphological taxonomy and DNA barcoding, using the three frequently used markers matK, rbcL, and trnL-F, to assess the efficiency of DNA barcoding in the identification of dipterocarps in Sumatra, Indonesia. The chloroplast gene matK was the most polymorphic among these three markers with an average interspecific genetic distance of 0.020. The results of the molecular data were mostly in agreement with the morphological identification for the clades of Anthoshorea, Hopea, Richetia, Parashorea, and Anisoptera, nonetheless these markers were inefficient to resolve the relationships within the Rubroshorea group. The maximum likelihood and Bayesian inference phylogenies identified Shorea as a paraphyletic genus, Anthoshorea appeared as sister to Hopea, and Richetia was sister to Parashorea. A better discriminatory power among dipterocarp species provided by matK and observed in our study suggests that this marker has a higher evolutionary rate than the other two markers tested. However, a combination of several different barcoding markers is essential for reliable identification of the species at a lower taxonomic level.
Growth and Nutritional Responses of Bean and Soybean Genotypes to Elevated CO2 in a Controlled Environment
Authors: Soares José , Deuchande Teresa , Valente Luísa M.P. , Pintado Manuela , Vasconcelos Marta W.
Published: October 2019

In the current situation of a constant increase in the atmospheric CO2 concentration, there is a potential risk of decreased nutritional value and food crop quality. Therefore, selecting strong-responsive varieties to elevated CO2 (eCO2) conditions in terms of yield and nutritional quality is an important decision for improving crop productivity under future CO2 conditions. Using bean and soybean varieties of contrasting responses to eCO2 and different origins, we assessed the effects of eCO2 (800 ppm) in a controlled environment on the yield performance and the concentration of protein, fat, and mineral elements in seeds. The range of seed yield responses to eCO2 was −11.0 to 32.7% (average change of 5%) in beans and −23.8 to 39.6% (average change of 7.1%) in soybeans. There was a significant correlation between seed yield enhancement and aboveground biomass, seed number, and pod number per plant. At maturity, eCO2 increased seed protein concentration in beans, while it did not affect soybean. Lipid concentration was not affected by eCO2 in either legume species. Compared with ambient CO2 (aCO2), the concentrations of manganese (Mn), iron (Fe), and potassium (K) decreased significantly, magnesium (Mg) increased, while zinc (Zn), phosphorus (P), and calcium (Ca) were not changed under eCO2 in bean seeds. However, in soybean, Mn and K concentrations decreased significantly, Ca increased, and Zn, Fe, P, and Mg concentrations were not significantly affected by eCO2 conditions. Our results suggest that intraspecific variation in seed yield improvement and reduced sensitivity to mineral losses might be suitable parameters for breeders to begin selecting lines that maximize yield and nutrition under eCO2.
Transport Properties of Thermoplastic R-BAPB Polyimide: Molecular Dynamics Simulations and Experiment
Authors: Volgin Igor V. , Andreeva Maria V. , Larin Sergey V. , Didenko Andrey L. , Vaganov Gleb V. , Borisov Ilya L. , Volkov Alexey V. , Klushin Leonid I. , Lyulin Sergey V.
Published: October 2019

The present work evaluates the transport properties of thermoplastic R-BAPB polyimide based on 1,3-bis(3,3′,4,4′-dicarboxyphenoxy)benzene (dianhydride R) and 4,4′-bis(4-aminophenoxy)biphenyl (diamine BAPB). Both experimental studies and molecular dynamics simulations were applied to estimate the diffusion coefficients and solubilities of various gases, such as helium (He), oxygen (O2), nitrogen (N2), and methane (CH4). The validity of the results obtained was confirmed by studying the correlation of the experimental solubilities and diffusion coefficients of He, O2, and N2 in R-BAPB, with their critical temperatures and the effective sizes of the gas molecules, respectively. The solubilities obtained in the molecular dynamics simulations are in good quantitative agreement with the experimental data. A good qualitative relationship between the simulation results and the experimental data is also observed when comparing the diffusion coefficients of the gases. Analysis of the Robeson plots shows that R-BAPB has high selectivity for He, N2, and CO2 separation from CH4, which makes it a promising polymer for developing gas-separation membranes. From this point of view, the simulation models developed and validated in the present work may be put to effective use for further investigations into the transport properties of R-BAPB polyimide and nanocomposites based on it.
Highly Effective Flame-Retardant Rigid Polyurethane Foams: Fabrication and Applications in Inhibition of Coal Combustion
Authors: Wang Liancong , Tawiah Benjamin , Shi Yongqian , Cai Suncheng , Rao Xiaohui , Liu Chuan , Yang Ye , Yang Fuqiang , Yu Bin , Liang Yuntao , Fu Libi
Published: October 2019

The extemporaneous combustion of coal remains a major threat to safety in coal mines because such fire accidents result in casualties and significant property loss, as well as serious environmental pollution. This work proposed the fabrication of flame-retardant rigid polyurethane foam (RPUF) containing expandable graphite as char expander/sealant with melamine phosphosphate and 2-carboxyethyl (phenyl)phosphinic acid as char inducer and radical trapping agents. The as-prepared RPUF successfully inhibited coal combustion by forming thermally stable high graphitic content expandable intumescent char sealing over the coal. The RPUF achieved UL-94 V-0 rating in addition to significant reductions in peak heat release, total heat release, and CO and CO2 yields. The external and the internal residual char structure was studied by X-ray photoelectron spectra, Raman spectroscopy, and real-time Fourier transform infrared spectra techniques, and a flame-retardant mode of action has been proposed. This work provides important insight into a facile fabrication of highly efficient and economical flame-retardant RPUF to inhibit the spontaneous combustion of coal.
Crystalline Modification of Isotactic Polypropylene with a Rare Earth Nucleating Agent Based on Ultrasonic Vibration
Authors: Li Dan , Xin Yujun , Song Yan , Dong Ting , Ben Haoxi , Yu Renxia , Han Guangting , Zhang Yuanming
Published: October 2019

In this paper, the crystalline modification of isotactic polypropylene (PP) with a rare earth β nucleating agent (WBG) with different ultrasound conditions was investigated by scanning electron microscopy (SEM), wide-angle X-ray diffraction (XRD), and differential scanning calorimetry (DSC). The relationship between the ultrasound conditions and the crystalline structure, as well as the mechanism for the behavior, were revealed. SEM showed that the dispersion of the nucleating agent in the PP matrix was better at shorter ultrasound distances. In addition, the higher the water cooling temperature, the better the nucleating agent was dispersed in the PP matrix. The results of XRD and DSC showed that the crystallinity and the relative content of the β-crystal were increased with nearer ultrasound distance, as well as increased in higher water cooling temperatures. In particular, under the same conditions, the crystallinity and the relative content of the β-crystal after ultrasonic treatment were much higher than those without ultrasound.
Polycaprolactone-Based Mimetic Antimicrobial Peptide Copolymers Vesicles as an Effective Drug-Carrier for Cancer Therapy
Authors: Qian Yusheng , Zhou Xinyu , He Jing , Zhou Chuncai
Published: October 2019

A novel series of amphiphilic mimicking antimicrobial peptide copolymers PCL16-b-Kn can assemble in water to form uniform vesicles. Transmission electron microscopy was used to observe the vesicular structure of the nanoparticles, and dynamic light scattering revealed their uniform size and narrow dispersion. Critical vesiculation concentrations were also tested, revealing that these vesicles can exist at low concentrations. Furthermore, in vitro and intracellular drug release of doxorubicin(DOX)-vesicles were conducted. These vesicles could encapsulate DOX and achieve efficient intracellular drug release. Overall, these copolymer vesicles exhibit potential application value as multifunctional drug-carrier systems with antibacterial capability in cancer therapy.
Credit Risk Migration and Economic Cycles
Authors: Ferretti Camilla , Gabbi Giampaolo , Ganugi Piero , Sist Federica , Vozzella Pietro
Published: October 2019

The misestimation of rating transition probabilities may lead banks to lend money incoherently with borrowers’ default trajectory, causing both a deterioration in asset quality and higher system distress. Applying a Mover-Stayer model to determine the migration risk of small and medium enterprises, we find that banks are over-estimating their credit risk resulting in excessive regulatory capital. This has important macroeconomic implications due to the fact that holding a large capital buffer is costly for banks and this in turn influences their ability to lend in the wider economy. This conclusion is particularly true during economic downturns with the consequence of exacerbating the cyclicality in risk capital that therefore acts to aggravate economic conditions further. We also explain part of the misevaluation of borrowers and the actual relevant weight of non-performing loans within banking portfolios: some of the prudential requirements, at least as regards EMS credit portfolios, cannot be considered effective as envisaged by the regulators who developed the “new” regulation in response to the most recent crisis. The Mover-Stayers approach helps to reduce calculation inaccuracy when analyzing the historical movements of borrowers’ ratings and consequently, improves the efficacy of the resource allocation process and banking industry stability.
Spatiotemporal Comparison and Validation of Three Global-Scale Fractional Vegetation Cover Products
Authors: Liu Duanyang , Jia Kun , Wei Xiangqin , Xia Mu , Zhang Xiwang , Yao Yunjun , Zhang Xiaotong , Wang Bing
Published: October 2019

Fractional vegetation cover (FVC) is an important parameter for many environmental and ecological models. Large-scale and long-term FVC products are critical for various applications. Currently, several global-scale FVC products have been generated with remote sensing data, such as VGT bioGEOphysical product Version 2 (GEOV2), PROBA-V bioGEOphysical product Version 3 (GEOV3) and Global LAnd Surface Satellite (GLASS) FVC products. However, studies comparing and validating these global-scale FVC products are rare. Therefore, in this study, the performances of three global-scale time series FVC products, including the GEOV2, GEOV3, and GLASS FVC products, are investigated to assess their spatial and temporal consistencies. Furthermore, reference FVC data generated from high-spatial-resolution data are used to directly evaluate the accuracy of these FVC products. The results show that these three FVC products achieve general agreements in terms of spatiotemporal consistencies over most regions. In addition, the GLASS and GEOV2 FVC products have reliable spatial and temporal completeness, whereas the GEOV3 FVC product contains much missing data over high-latitude regions, especially during wintertime. Furthermore, the GEOV3 FVC product presents higher FVC values than GEOV2 and GLASS FVC products over the equator. The main differences between the GEOV2 and GLASS FVC products occur over deciduous forests, for which the GLASS product presents slightly higher FVC values than the GEOV2 product during wintertime. Finally, temporal profiles of the GEOV2 and GLASS FVC products show better consistency than the GEOV3 FVC product, and the GLASS FVC product presents more reliable accuracy (R2 = 0.7878, RMSE = 0.1212) compared with the GEOV2 (R2 = 0.5798, RMSE = 0.1921) and GEOV3 (R2 = 0.7744, RMSE = 0.2224) FVC products over these reference FVC data.
Climatic Drivers of Greening Trends in the Alps
Authors: Filippa Gianluca , Cremonese Edoardo , Galvagno Marta , Isabellon Michel , Bayle Arthur , Choler Philippe , Carlson Bradley Z. , Gabellani Simone , Morra di Cella Umberto , Migliavacca Mirco
Published: October 2019

Since the 1980s, vegetated lands have experienced widespread greening at the global scale. Numerous studies have focused on spatial patterns and mechanisms of this phenomenon, especially in the Arctic and sub-Arctic regions. Greening trends in the European Alps have received less attention, although this region has experienced strong climate and land-use changes during recent decades. We studied the rates and spatial patterns of greening in an inner-alpine region of the Western Alps. We used MODIS-derived normalized difference vegetation index (NDVI) at 8-day temporal and 250 m spatial resolution, for the period 2000–2018, and removed areas with disturbances in order to consider the trends of undisturbed vegetation. The objectives of this study were to (i) quantify trends of greening in a representative area of the Western Alps; and (ii) examine mechanisms and causes of spatial patterns of greening across different plant types. We show that 63% of vegetated areas experienced significant trends during the 2000–2018 period, of which only 8% were negative. We identify (i) a climatic control on spring and autumn phenology with contrasting effects depending on plant type and elevation, and (ii) land-use change dynamics, such as shrub encroachment on abandoned pastures and colonization of new surfaces at high elevation. Below 1500 m, warming temperatures promote incremental greening in the transition from spring to summer, but not in fall, suggesting either photoperiod or water limitation. In the alpine and sub-alpine belts (>1800 m asl), snow prevents vegetation development until late spring, despite favorable temperatures. Instead, at high elevation greening acts both in summer and autumn. However, photoperiod limitation likely prevents forested ecosystems from fully exploiting warmer autumn conditions. We furthermore illustrate two emblematic cases of prominent greening: recent colonization of previously glaciated/non vegetated areas, as well as shrub/tree encroachment due to the abandonment of agricultural practices. Our results demonstrate the interplay of climate and land-use change in controlling greening dynamics in the Western Alps.
Automated Extraction of Antarctic Glacier and Ice Shelf Fronts from Sentinel-1 Imagery Using Deep Learning
Authors: Baumhoer Celia A. , Dietz Andreas J. , Kneisel C. , Kuenzer C.
Published: October 2019

Sea level rise contribution from the Antarctic ice sheet is influenced by changes in glacier and ice shelf front position. Still, little is known about seasonal glacier and ice shelf front fluctuations as the manual delineation of calving fronts from remote sensing imagery is very time-consuming. The major challenge of automatic calving front extraction is the low contrast between floating glacier and ice shelf fronts and the surrounding sea ice. Additionally, in previous decades, remote sensing imagery over the often cloud-covered Antarctic coastline was limited. Nowadays, an abundance of Sentinel-1 imagery over the Antarctic coastline exists and could be used for tracking glacier and ice shelf front movement. To exploit the available Sentinel-1 data, we developed a processing chain allowing automatic extraction of the Antarctic coastline from Seninel-1 imagery and the creation of dense time series to assess calving front change. The core of the proposed workflow is a modified version of the deep learning architecture U-Net. This convolutional neural network (CNN) performs a semantic segmentation on dual-pol Sentinel-1 data and the Antarctic TanDEM-X digital elevation model (DEM). The proposed method is tested for four training and test areas along the Antarctic coastline. The automatically extracted fronts deviate on average 78 m in training and 108 m test areas. Spatial and temporal transferability is demonstrated on an automatically extracted 15-month time series along the Getz Ice Shelf. Between May 2017 and July 2018, the fronts along the Getz Ice Shelf show mostly an advancing tendency with the fastest moving front of DeVicq Glacier with 726 ± 20 m/yr.
Integrating Water Observation from Space Product and Time-Series Flow Data for Modeling Spatio-Temporal Flood Inundation Dynamics
Authors: Huang Chang , Chen Yun , Zhang Shiqiang , Li Linyi , Shui Junfeng , Liu Qihang
Published: October 2019

Periodic inundation of floodplains and wetlands is critical for the well being of ecosystems. This study proposes a simple but efficient model that integrates time series daily flow data and the Landsat-derived Water Observation from Space (WOfS) product to model the spatio-temporal flood inundation dynamics of the Murray-Darling Basin. A zone-gauge framework is adopted in order to reduce the hydrologic complexity of the large river basin. Under this framework, flood frequency analysis was conducted at each gauge station to identify historical peak flows and their annual exceedance probabilities. The results were then linked with the WOfS dataset through date to model the inundation probability in each zone. Inundation frequency was derived by simply overlaying the yearly inundation extent from 1988 to 2015 and counting the inundation times. Both the resultant inundation frequency map and inundation probability map are of ecological significance for the survival and prosperity of riparian ecosystems. The assumptions of the model were validated carefully to enhance its theoretical basis. The WOfS dataset was also compared with another independent water observation dataset to cross-validate its reliability. It is hoped that with the development of more and more global high-resolution surface water datasets, this study could inspire more studies that integrate surface water datasets with hydrological observations for flood inundation modeling.
Automatic Proba-V Processor: TREX—Tool for Raster Data Exploration
Authors: Suliga Joanna , Bhattacharjee Joy , Chormański Jarosław , van Griensven Ann , Verbeiren Boud
Published: October 2019

The processing tool TREX, standing for ‘Tool for Raster data EXploration’ is presented and evaluated in the Biebrza wetlands in northeastern Poland. TREX was designed for the automatization of processing satellite data from the Proba-V satellite into maps of NDVI or LAI in any defined by the user projection, spatial resolution, or extent. The open source and access concept of TREX encourages the potential community of users to collaborate, develop, and integrate the tool with other satellite imagery and models. TREX reprojects, shifts, and resamples original data obtained from the Proba-V satellite to deliver reliable maps of NDVI and LAI. Validation of TREX in Biebrza wetlands resulted in correlations between 0.79 and 0.92 for NDVI data (measured with ASD Field Spec 4) and 0.92 for LAI data (measured with LiCOR—LAI-2000 Plant Canopy Analyzer).
Detection of Pine Shoot Beetle (PSB) Stress on Pine Forests at Individual Tree Level using UAV-Based Hyperspectral Imagery and Lidar
Authors: Lin Qinan , Huang Huaguo , Wang Jingxu , Huang Kan , Liu Yangyang
Published: October 2019

In recent years, the outbreak of the pine shoot beetle (PSB), Tomicus spp., has caused serious shoots damage and the death of millions of trees in Yunnan pine forests in southwestern China. It is urgent to develop a convincing approach to accurately assess the shoot damage ratio (SDR) for monitoring the PSB insects at an early stage. Unmanned airborne vehicles (UAV)-based sensors, including hyperspectral imaging (HI) and lidar, have very high spatial and spectral resolutions, which are very useful to detect forest health. However, very few studies have utilized HI and lidar data to estimate SDRs and compare the predictive power for mapping PSB damage at the individual tree level. Additionally, the data fusion of HI and lidar may improve the detection accuracy, but it has not been well studied. In this study, UAV-based HI and lidar data were fused to detect PSB. We systematically evaluated the potential of a hyperspectral approach (only-HI data), a lidar approach (only-lidar data), and a combined approach (HI plus lidar data) to characterize PSB damage of individual trees using the Random Forest (RF) algorithm, separately. The most innovative point is the proposed new method to extract the three dimensional (3D) shadow distribution of each tree crown based on a lidar point cloud and the 3D radiative transfer model RAPID. The results show that: (1) for the accuracy of estimating the SDR of individual trees, the lidar approach (R2 = 0.69, RMSE = 12.28%) performed better than hyperspectral approach (R2 = 0.67, RMSE = 15.87%), and in addition, it was useful to detect dead trees with an accuracy of 70%; (2) the combined approach has the highest accuracy (R2 = 0.83, RMSE = 9.93%) for mapping PSB damage degrees; and (3) when combining HI and lidar data to predict SDRs, two variables have the most contributions, which are the leaf chlorophyll content (Cab) derived from hyperspectral data and the return intensity of the top of shaded crown (Int_Shd_top) from lidar metrics. This study confirms the high possibility to accurately predict SDRs at individual tree level if combining HI and lidar data. The 3D radiative transfer model can determine the 3D crown shadows from lidar, which is a key information to combine HI and lidar. Therefore, our study provided a guidance to combine the advantages of hyperspectral and lidar data to accurately measure the health of individual trees, enabling us to prioritize areas for forest health promotion. This method may also be used for other 3D land surfaces, like urban areas.
Modelling the Wealth Index of Demographic and Health Surveys within Cities Using Very High-Resolution Remotely Sensed Information
Authors: Georganos Stefanos , Gadiaga Assane Niang , Linard Catherine , Grippa Tais , Vanhuysse Sabine , Mboga Nicholus , Wolff Eléonore , Dujardin Sébastien , Lennert Moritz
Published: October 2019

A systematic and precise understanding of urban socio-economic spatial inequalities in developing regions is needed to address global sustainability goals. At the intra-urban scale, access to detailed databases (i.e., a census) is often a difficult exercise. Geolocated surveys such as the Demographic and Health Surveys (DHS) are a rich alternative source of such information but can be challenging to interpolate at such a fine scale due to their spatial displacement, survey design and the lack of very high-resolution (VHR) predictor variables in these regions. In this paper, we employ satellite-derived VHR land-use/land-cover (LULC) datasets and couple them with the DHS Wealth Index (WI), a robust household wealth indicator, in order to provide city-scale wealth maps. We undertake several modelling approaches using a random forest regressor as the underlying algorithm and predict in several geographic administrative scales. We validate against an exhaustive census database available for the city of Dakar, Senegal. Our results show that the WI was modelled to a satisfactory degree when compared against census data even at very fine resolutions. These findings might assist local authorities and stakeholders in rigorous evidence-based decision making and facilitate the allocation of resources towards the most disadvantaged populations. Good practices for further developments are discussed with the aim of upscaling these findings at the global scale.
Suppressing Paired Echoes Caused by Stair-Step Antenna Steering in TOPS SAR Imaging
Authors: He Feng , Zhang Yongsheng , Yi Tianzhu , Jin Guanghu , Dong Zhen
Published: October 2019

The use of electronically steered antennas in the azimuth dimension typically leads to a staircase-like antenna beam steering law in the Terrain Observation by Progressive Scan (TOPS) wide-swath synthetic aperture radar (SAR) data acquisition mode, which will introduce paired echoes in the focused images. This paper proposes a new approach for removing such paired echoes from TOPS SAR images based on the generalization of the ideal optimum filtering concept, which can be implemented easily in the SAR data processing. Modeling the amplitude-modulated azimuth signal shows that the absolute phase of the introduced paired echoes cannot be determined due to the random rotation angle jump time for each target, which will prevent the precise use of optimum filtering. An extended optimum filtering approach, which is originally proposed for suppressing the azimuth ambiguities in SAR images, is reintroduced in this particular case, and a new approximated and generalized form of the deconvolving filtering in the approach is redefined to accommodate the undetermined phase for both the strongest paired distortion peaks and the other peripheral peaks in the distorted impulse response function (IRF). Simulated data from a TOPS SAR mode with staircase-like beam steering are used to verify the improvement in image quality by using the new method.
3D GPR Image-based UcNet for Enhancing Underground Cavity Detectability
Authors: Kang Man-Sung , Kim Namgyu , Im Seok Been , Lee Jong-Jae , An Yun-Kyu
Published: October 2019

This paper proposes a 3D ground penetrating radar (GPR) image-based underground cavity detection network (UcNet) for preventing sinkholes in complex urban roads. UcNet is developed based on convolutional neural network (CNN) incorporated with phase analysis of super-resolution (SR) GPR images. CNNs have been popularly used for automated GPR data classification, because expert-dependent data interpretation of massive GPR data obtained from urban roads is typically cumbersome and time consuming. However, the conventional CNNs often provide misclassification results due to similar GPR features automatically extracted from arbitrary underground objects such as cavities, manholes, gravels, subsoil backgrounds and so on. In particular, non-cavity features are often misclassified as real cavities, which degrades the CNNs’ performance and reliability. UcNet improves underground cavity detectability by generating SR GPR images of the cavities extracted from CNN and analyzing their phase information. The proposed UcNet is experimentally validated using in-situ GPR data collected from complex urban roads in Seoul, South Korea. The validation test results reveal that the underground cavity misclassification is remarkably decreased compared to the conventional CNN ones.
Spatial–Temporal Analysis of Land Cover Change at the Bento Rodrigues Dam Disaster Area Using Machine Learning Techniques
Authors: Luo Dong , Goodin Douglas G. , Caldas Marcellus M.
Published: October 2019

Disasters are an unpredictable way to change land use and land cover. Improving the accuracy of mapping a disaster area at different time is an essential step to analyze the relationship between human activity and environment. The goals of this study were to test the performance of different processing procedures and examine the effect of adding normalized difference vegetation index (NDVI) as an additional classification feature for mapping land cover changes due to a disaster. Using Landsat ETM+ and OLI images of the Bento Rodrigues mine tailing disaster area, we created two datasets, one with six bands, and the other one with six bands plus the NDVI. We used support vector machine (SVM) and decision tree (DT) algorithms to build classifier models and validated models performance using 10-fold cross-validation, resulting in accuracies higher than 90%. The processed results indicated that the accuracy could reach or exceed 80%, and the support vector machine had a better performance than the decision tree. We also calculated each land cover type’s sensitivity (true positive rate) and found that Agriculture, Forest and Mine sites had higher values but Bareland and Water had lower values. Then, we visualized land cover maps in 2000 and 2017 and found out the Mine sites areas have been expanded about twice of the size, but Forest decreased 12.43%. Our findings showed that it is feasible to create a training data pool and use machine learning algorithms to classify a different year’s Landsat products and NDVI can improve the vegetation covered land classification. Furthermore, this approach can provide a venue to analyze land pattern change in a disaster area over time.
A Rapidly Assessed Wetland Stress Index (RAWSI) Using Landsat 8 and Sentinel-1 Radar Data
Authors: Walter Matthew , Mondal Pinki
Published: October 2019

Wetland ecosystems are important resources, providing great economic benefits for surrounding communities. In this study, we developed a new stress indicator called “Rapidly Assessed Wetlands Stress Index” (RAWSI) by combining several natural and anthropogenic stressors of wetlands in Delaware, in the United States. We compared two machine-learning algorithms, support vector machine (SVM) and random forest (RF), to quantify wetland stress by classifying satellite images from Landsat 8 and Sentinel-1 Synthetic Aperture Radar (SAR). An accuracy assessment showed that the combination of Landsat 8 and Sentinel SAR data had the highest overall accuracy (93.7%) when used with an RF classifier. In addition to the land-cover classification, a trend analysis of the normalized difference vegetation index (NDVI) calculated from Landsat images during 2004–2018 was used to assess changes in healthy vegetation. We also calculated the stream sinuosity to assess human alterations to hydrology. We then used these three metrics to develop RAWSI, and to quantify and map wetland stress due to human alteration of the landscape. Hot-spot analysis using Global Moran’s I and Getis-Ord Gi* identified several statistically significant hot spots (high stress) in forested wetlands and cold spots (low values) in non-forested wetlands. This information can be utilized to identify wetland areas in need of further regulation, with implications in environmental planning and policy decisions.
Landsat Time Series Assessment of Invasive Annual Grasses Following Energy Development
Authors: Villarreal Miguel , Soulard Christopher , Waller Eric
Published: October 2019

Invasive annual grasses are of concern in much of the western United States because they tolerate resource variability and have high reproductive capacity, with propagules that are readily dispersed in disturbed areas like those created and maintained for energy development. Early season invasive grasses “green up” earlier than most native plants, producing a distinct pulse of greenness in the early spring that can be exploited to identify their location using multi-date imagery. To determine if invasive annual grasses increased around energy developments after the construction phase, we calculated an invasives index using Landsat TM and ETM+ imagery for a 34-year time period (1985–2018) and assessed trends for 1755 wind turbines installed between 1988 and 2013 in the southern California desert. The index uses the maximum Normalized Difference Vegetation Index (NDVI) for early season greenness (January-June), and mean NDVI (July–October) for the later dry season. We estimated the relative cover of invasive annuals each year at turbine locations and control sites and tested for changes before and after each turbine was installed. The time series was also mapped across the region and temporal trends were assessed relative to seasonal precipitation. The results showed an increase in early season invasives at turbine sites after installation, but also an increase in many of the surrounding control areas. Maps of the invasive index show a region-wide increase starting around 1998, and much of the increase occurred in areas surrounding wind development sites. These results suggest that invasions around the energy developments occurred within the context of a larger regional invasion, and while the development did not necessarily initiate the invasion, annual grasses were more prevalent around the developments.
Correcting Position Error in Precipitation Data Using Image Morphing
Authors: Le Coz Camille , Heemink Arnold , Verlaan Martin , ten Veldhuis Marie-claire , van de Giesen Nick
Published: October 2019

Rainfall estimates based on satellite data are subject to errors in the position of the rainfall events in addition to errors in their intensity. This is especially true for localized rainfall events such as the convective rainstorms that occur during the monsoon season in sub-Saharan Africa. Many satellite-based estimates use gauge information for bias correction. However, bias adjustment methods do not correct the position errors explicitly. We propose to gauge-adjust satellite-based estimates with respect to the position using a morphing method. Image morphing transforms an image, in our case a rainfall field, into another one, by applying a spatial transformation. A benefit of this approach is that it can take both the position and the intensity of a rain event into account. Its potential is investigated with two case studies. In the first case, the rain events are synthetic, represented by elliptic shapes, while the second case uses real data from a rainfall event occurring during the monsoon season in southern Ghana. In the second case, the satellite-based estimate IMERG-Late (Integrated Multi-Satellite Retrievals for GPM ) is adjusted to gauge data from the Trans-African Hydro-Meteorological Observatory (TAHMO) network. The results show that the position errors can be corrected, while preserving the higher spatial variability of the satellite-based estimate.
Nitrogen and Phosphorus Effect on Sun-Induced Fluorescence and Gross Primary Productivity in Mediterranean Grassland
Authors: Martini David , Pacheco-Labrador Javier , Perez-Priego Oscar , van der Tol Christiaan , El-Madany Tarek S. , Julitta Tommaso , Rossini Micol , Reichstein Markus , Christiansen Rune , Rascher Uwe , Moreno Gerardo , Martín M. Pilar , Yang Peiqi , Carrara Arnaud , Guan Jinhong , González-Cascón Rosario , Migliavacca Mirco
Published: October 2019

Sun-Induced fluorescence at 760 nm (F760) is increasingly being used to predict gross primary production (GPP) through light use efficiency (LUE) modeling, even though the mechanistic processes that link the two are not well understood. We analyzed the effect of nitrogen (N) and phosphorous (P) availability on the processes that link GPP and F760 in a Mediterranean grassland manipulated with nutrient addition. To do so, we used a combination of process-based modeling with Soil-Canopy Observation of Photosynthesis and Energy (SCOPE), and statistical analyses such as path modeling. With this study, we uncover the mechanisms that link the fertilization-driven changes in canopy nitrogen concentration (N%) to the observed changes in F760 and GPP. N addition changed plant community structure and increased canopy chlorophyll content, which jointly led to changes in photosynthetic active radiation (APAR), ultimately affecting both GPP and F760. Changes in the abundance of graminoids, (%graminoids) driven by N addition led to changes in structural properties of the canopy such as leaf angle distribution, that ultimately influenced observed F760 by controlling the escape probability of F760 (Fesc). In particular, we found a change in GPP–F760 relationship between the first and the second year of the experiment that was largely driven by the effect of plant type composition on Fesc, whose best predictor is %graminoids. The P addition led to a statistically significant increase on light use efficiency of fluorescence emission (LUEf), in particular in plots also with N addition, consistent with leaf level studies. The N addition induced changes in the biophysical properties of the canopy that led to a trade-off between surface temperature (Ts), which decreased, and F760 at leaf scale (F760leaf,fw), which increased. We found that Ts is an important predictor of the light use efficiency of photosynthesis, indicating the importance of Ts in LUE modeling approaches to predict GPP.
Mapping Photosynthesis Solely from Solar-Induced Chlorophyll Fluorescence: A Global, Fine-Resolution Dataset of Gross Primary Production Derived from OCO-2
Authors: Li Xing , Xiao Jingfeng
Published: October 2019

Accurately quantifying gross primary production (GPP) globally is critical for assessing plant productivity, carbon balance, and carbon-climate feedbacks, while current GPP estimates exhibit substantial uncertainty. Solar-induced chlorophyll fluorescence (SIF) observed by the Orbiting Carbon Observatory-2 (OCO-2) has offered unprecedented opportunities for monitoring land photosynthesis, while its sparse coverage remains a bottleneck for mapping finer-resolution GPP globally. Here, we used the global, OCO-2-based SIF product (GOSIF) and linear relationships between SIF and GPP to map GPP globally at a 0.05° spatial resolution and 8-day time step for the period from 2000 to 2017. To account for the uncertainty of GPP estimates resulting from the SIF-GPP relationship, we used a total of eight SIF-GPP relationships with different forms (universal and biome-specific, with and without intercept) at both site and grid cell levels to estimate GPP. Our results showed that all of the eight SIF-GPP relationships performed well in estimating GPP globally. The ensemble mean 8-day GPP was generally highly correlated with flux tower GPP for 91 eddy covariance flux sites across the globe (R2 = 0.74, Root Mean Square Error = 1.92 g C m−2 d−1). Our fine-resolution GPP estimates showed reasonable spatial and seasonal variations across the globe and fully captured both seasonal cycles and spatial patterns present in our coarse-resolution (1°) GPP estimates based on coarse-resolution SIF data directly aggregated from discrete OCO-2 soundings. SIF-GPP relationships with different forms could lead to significant differences in annual GPP particularly in the tropics. Our ensemble global annual GPP estimate (135.5 ± 8.8 Pg C yr−1) is between the median estimate of non-process based methods and the median estimate of process-based models. Our GPP estimates showed interannual variability in many regions and exhibited increasing trends in many parts of the globe particularly in the Northern Hemisphere. With the availability of high-quality, gridded SIF observations from space (e.g., TROPOMI, FLEX), our novel approach does not rely on any other input data (e.g., climate data, soil properties) and therefore can map GPP solely based on satellite SIF observations and potentially lead to more accurate GPP estimates at regional to global scales. The use of a universal SIF-GPP relationship versus biome-specific relationships can also avoid the uncertainty associated with land cover maps. Our novel, independent GPP product (GOSIF GPP), freely available at our data repository, will be valuable for studying photosynthesis, carbon cycle, agricultural production, and ecosystem responses to climate change and disturbances, informing ecosystem management, and benchmarking terrestrial biosphere and Earth system models.
A Survey of Collaborative UAV–WSN Systems for Efficient Monitoring
Authors: Popescu Dan , Stoican Florin , Stamatescu Grigore , Chenaru Oana , Ichim Loretta
Published: October 2019

Integrated systems based on wireless sensor networks (WSNs) and unmanned aerial vehicles (UAVs) with electric propulsion are emerging as state-of-the-art solutions for large scale monitoring. Main advances stemming both from complex system architectures as well as powerful embedded computing and communication platforms, advanced sensing and networking protocols have been leveraged to prove the viability of this concept. The design of suitable algorithms for data processing, communication and control across previously disparate domains has thus currently become an intensive area of interdisciplinary research. The paper was focused on the collaborative aspects of UAV–WSN systems and the reference papers were analyzed from this point of view, on each functional module. The paper offers a timely review of recent advances in this area of critical interest with focus on a comparative perspective across multiple recent theoretical and applied contributions. A systematic approach is carried out in order to structure a unitary from conceptual design towards key implementation aspects. Focus areas are identified and discussed such as distributed data processing algorithms, hierarchical multi-protocol networking aspects and high level WSN–constrained UAV-control. Application references are highlighted in various domains such as environmental, agriculture, emergency situations and homeland security. Finally, a research agenda is outlined to advance the field towards tangible economic and social impact.