bluuebunny/crossref_metadata_2025 huggingface dataset:
Chemical Weathering of Granite in Ice and Its Implication for Weathering in Polar Regions
Authors: Chung Hyun Young , Jung Jaewoo , Lee Du Hyeong , Kim Sunghan , Lee Min Kyung , Lee Jae Il , Yoo Kyu-Cheul , Lee Yong Il , Kim Kitae
Published: February 2020

Recently, it has been reported that some chemical reactions are enhanced in below-freezing conditions. Despite the high denudation typical of polar regions, chemical weathering that occurs under ice has not been investigated. In this study, we investigated the dissolution of granite in ice. The mixture of granite and deionized water (DW) or solution adjusted to pH 2 or 3 was split into two groups: the test group was frozen at −20 °C, while the control was maintained at room temperature. After 29 days of batch experiments, the filtrate was analyzed to measure the concentrations of cations and silica. The filtered powder was analyzed to investigate the mineral compositions and crystallinities of the granite before and after the experiments. Despite the low temperature, a significant quantity of cations (Na+, K+, Mg2+, Ca2+) were dissolved out, even from the ice samples. During X-ray diffraction (XRD) analysis, the decreased crystallinities of granite in ice samples were identified regardless of the pH condition. To verify the observed freeze concentration effect, the concentration of granite in the ice grain boundaries was observed using optical microscopy with a cold chamber. The low concentration of silica in the ice samples could explain the silica anomaly in polar regions. This study also provides a new perspective for the dissolution mechanism in polar regions.
Pickeringite from the Stone Town Nature Reserve in Ciężkowice (the Outer Carpathians, Poland)
Authors: Marszałek Mariola , Gaweł Adam , Włodek Adam
Published: February 2020

Pickeringite, ideally MgAl2(SO4)4·22H2O, is a member of the halotrichite group minerals XAl2(SO4)4·22H2O that form extensive solid solutions along the joints of the X = Fe-Mg-Mn-Zn. The few comprehensive reports on natural halotrichites indicate their genesis to be mainly the low-pH oxidation of pyrite or other sulfides in the Al-rich environments of weathering rock-forming aluminosilicates. Pickeringite discussed here occurs within the efflorescences on sandstones from the Stone Town Nature Reserve in Ciężkowice (the Polish Outer Carpathians), being most probably the first find on such rocks in Poland. This paper presents mineralogical and geochemical characteristics of the pickeringite (based on SEM-EDS, XRPD, EPMA and RS methods) and suggests its possible origin. It belongs to the pickeringite–apjohnite (Mg-Mn joints) series and has the calculated formula Mg0.75Mn0.21Zn0.02Cu0.01Al2.02(S0.99 to 1.00O4)4·22H2O (based on 16O and 22H2O). The unit cell parameters refined for the monoclinic system space group P21/c are: a = 6.1981(28) Å, b= 24.2963(117) Å, c = 21.2517(184) Å and β = 100.304(65)°. The Raman spectra (SO4) bands are the intensive 994 cm−1 and a low-intensive 975 cm−1 (ν1), low-intensive 1081, 1123 and 1145 cm−1 (ν3), 524, 467 and 425cm−1 (ν2), 615 cm−1 (ν4), while those at 344 and 310 cm−1 are attributed to νg H2O and at 223 cm−1 to the lattice modes. Crystallization of pickeringite within the particular tor resulted from a certain set of conditions: climatic (e.g., season, temperature, humidity), physicochemical (e.g., pH, concentration), mineral (the presence of pyrite), and site-related (location and efflorescence protection). The sulfate ions could have been derived from oxidation of pyrite in the Ciężkowice sandstones and possibly are related to local mineral waters.
The Formation of Barite and Celestite through the Replacement of Gypsum
Authors: Forjanes Pablo , Astilleros José , Fernández-Díaz Lurdes
Published: February 2020

Barite (BaSO4) and celestite (SrSO4) are the end-members of a nearly ideal solid solution. Most of the exploitable deposits of celestite occur associated with evaporitic sediments which consist of gypsum (CaSO4·2H2O) or anhydrite (CaSO4). Barite, despite having a broader geological distribution is rarely present in these deposits. In this work, we present an experimental study of the interaction between gypsum crystals and aqueous solutions that bear Sr or Ba. This interaction leads to the development of dissolution-crystallization reactions that result in the pseudomorphic replacement of the gypsum crystals by aggregates of celestite or barite, respectively. The monitoring of both replacement reactions shows that they take place at very different rates. Millimeter-sized gypsum crystals in contact with a 0.5 M SrCl2 solution are completely replaced by celestite aggregates in less than 1 day. In contrast, only a thin barite rim replaces gypsum after seven days of interaction of the latter with a 0.5 M BaCl2 solution. We interpret that this marked difference in the kinetics of the two replacement reactions relates the different orientational relationship that exists between the crystals of the two replacing phases and the gypsum substrate. This influence is further modulated by the specific crystal habit of each secondary phase. Thus, the formation of a thin oriented layer of platy barite crystals effectively armors the gypsum surface and prevents its interaction with the Ba-bearing solution, thereby strongly hindering the progress of the replacement reaction. In contrast, the random orientation of celestite crystals with respect to gypsum guarantees that a significant volume of porosity contained in the celestite layer is interconnected, facilitating the continuous communication between the gypsum surface and the fluid phase and guaranteeing the progress of the gypsum-by-celestite replacement.
Alleviating Climate Change and Pollution with Nanomaterials
Authors: Paramsothy Muralidharan
Published: February 2020

Nanoparticles can be utilized to extract carbon from air, dyes from water and sludge from waste, and are gradually emerging as useful for tackling threats to our planet’s health [...]
Bisphenol A and Phthalates in Diet: An Emerging Link with Pregnancy Complications
Authors: Filardi Tiziana , Panimolle Francesca , Lenzi Andrea , Morano Susanna
Published: February 2020

Endocrine-disrupting chemicals (EDCs) are exogenous substances that are able to interfere with hormone action, likely contributing to the development of several endocrine and metabolic diseases. Among them, Bisphenol A (BPA) and phthalates contaminate food and water and have been largely studied as obesogenic agents. They might contribute to weight gain, insulin resistance and pancreatic β-cell dysfunction in pregnancy, potentially playing a role in the development of pregnancy complications, such as gestational diabetes mellitus (GDM), and adverse outcomes. Pregnancy and childhood are sensitive windows of susceptibility, and, although with not univocal results, preclinical and clinical studies have suggested that exposure to BPA and phthalates at these stages of life might have an impact on the development of metabolic diseases even many years later. The molecular mechanisms underlying this association are largely unknown, but adipocyte and pancreatic β-cell dysfunction are suspected to be involved. Remarkably, transgenerational damage has been observed, which might be explained by epigenetic changes. Further research is needed to address knowledge gaps and to provide preventive measure to limit health risks connected with exposure to EDCs.
Iron Status and Cancer Risk in UK Biobank: A Two-Sample Mendelian Randomization Study
Authors: Yuan Shuai , Carter Paul , Vithayathil Mathew , Kar Siddhartha , Giovannucci Edward , Mason Amy M. , Burgess Stephen , Larsson Susanna C.
Published: February 2020

We conducted a two-sample Mendelian randomization study to explore the associations of iron status with overall cancer and 22 site-specific cancers. Single-nucleotide polymorphisms for iron status were obtained from a genome-wide association study of 48,972 European-descent individuals. Summary-level data for breast and other cancers were obtained from the Breast Cancer Association Consortium and UK Biobank. Genetically predicted iron status was positively associated with liver cancer and inversely associated with brain cancer but not associated with overall cancer or the other 20 studied cancer sites at p < 0.05. The odds ratios of liver cancer were 2.45 (95% CI, 0.81, 7.45; p = 0.11), 2.11 (1.16, 3.83; p = 0.02), 10.89 (2.44, 48.59; p = 0.002) and 0.30 (0.17, 0.53; p = 2 × 10−5) for one standard deviation increment of serum iron, transferrin saturation, ferritin and transferrin levels, respectively. For brain cancer, the corresponding odds ratios were 0.69 (0.48, 1.00; p = 0.05), 0.75 (0.59, 0.97; p = 0.03), 0.41 (0.20, 0.88; p = 0.02) and 1.49 (1.04, 2.14; p = 0.03). Genetically high iron status was positively associated with liver cancer and inversely associated with brain cancer.
Programming Skeletal Muscle Metabolic Flexibility in Offspring of Male Rats in Response to Maternal Consumption of Slow Digesting Carbohydrates during Pregnancy
Authors: Salto Rafael , Girón María D. , Manzano Manuel , Martín María J. , Vílchez Jose D. , Bueno-Vargas Pilar , Cabrera Elena , Pérez-Alegre Mónica , Andujar Eloisa , Rueda Ricardo , Lopez-Pedrosa Jose M.
Published: February 2020

Skeletal muscle plays a relevant role in metabolic flexibility and fuel usage and the associated muscle metabolic inflexibility due to high-fat diets contributing to obesity and type 2 diabetes. Previous research from our group indicates that a high-fat and rapid-digesting carbohydrate diet during pregnancy promotes an excessive adipogenesis and also increases the risk of non-alcoholic fatty liver disease in the offspring. This effect can be counteracted by diets containing carbohydrates with similar glycemic load but lower digestion rates. To address the role of the skeletal muscle in these experimental settings, pregnant rats were fed high-fat diets containing carbohydrates with similar glycemic load but different digestion rates, a high fat containing rapid-digesting carbohydrates diet (HF/RD diet) or a high fat containing slow-digesting carbohydrates diet (HF/SD diet). After weaning, male offspring were fed a standard diet for 3 weeks (weaning) or 10 weeks (adolescence) and the impact of the maternal HF/RD and HF/SD diets on the metabolism, signaling pathways and muscle transcriptome was analyzed. The HF/SD offspring displayed better muscle features compared with the HF/RD group, showing a higher muscle mass, myosin content and differentiation markers that translated into a greater grip strength. In the HF/SD group, metabolic changes such as a higher expression of fatty acids (FAT/CD36) and glucose (GLUT4) transporters, an enhanced glycogen content, as well as changes in regulatory enzymes such as muscle pyruvate kinase and pyruvate dehydrogenase kinase 4 were found, supporting an increased muscle metabolic flexibility and improved muscle performance. The analysis of signaling pathways was consistent with a better insulin sensitivity in the muscle of the HF/SD group. Furthermore, increased expression of genes involved in pathways leading to muscle differentiation, muscle mass regulation, extracellular matrix content and insulin sensitivity were detected in the HF/SD group when compared with HF/RD animals. In the HF/SD group, the upregulation of the ElaV1/HuR gene could be one of the main regulators in the positive effects of the diet in early programming on the offspring. The long-lasting programming effects of the HF/SD diet during pregnancy may depend on a coordinated gene regulation, modulation of signaling pathways and metabolic flexibility that lead to an improved muscle functionality. The dietary early programming associated to HF/SD diet has synergic and positive crosstalk effects in several tissues, mainly muscle, liver and adipose tissue, contributing to maintain the whole body homeostasis in the offspring.
Nutritional Intake Influences Zinc Levels in Preterm Newborns: An Observational Study
Authors: Terrin Gianluca , Boscarino Giovanni , Di Chiara Maria , Iacobelli Silvia , Faccioli Francesca , Greco Carla , Onestà Elisa , Sabatini Giulia , Pietravalle Andrea , Oliva Salvatore , Conti Maria Giulia , Natale Fabio , De Curtis Mario
Published: February 2020

(1) Background: Zinc is a key element for protein synthesis in preterm newborns. Early aggressive nutrition, promoting protein synthesis, may increase zinc consumption; (2) Methods: We performed a prospective observational study, to assess the relationship between early macronutrients intake and serum zinc levels, in preterm newborns with Gestational Age (GA) of 24–35 weeks, consecutively observed in Neonatal Intensive Care Unit (NICU). (3) Results: We enrolled 130 newborns (GA 31.5 ± 2.8). A significant negative correlation between serum zinc level at 28 days of life and energy (r −0.587, p < 0.001) and protein intake (r −0.556, p < 0.001) in the first week of life was observed. Linear regression analysis showed that zinc levels depended on energy (β −0.650; p < 0.001) and protein (β −0.669; p < 0.001) intake given through parenteral nutrition (PN) in the first week of life; (4) Conclusions: zinc status of preterm neonates was influenced by early protein and energy intake. An additional zinc supplementation should be considered when high protein and energy intake are received by preterm newborns in the first week of life.
High Consumption of Soft Drinks Is Associated with an Increased Risk of Fracture: A 7-Year Follow-Up Study
Authors: Chen Li , Liu Ruiyi , Zhao Yong , Shi Zumin
Published: February 2020

(1) Background: Fracture causes a substantial burden to society globally. Some studies have found that soft drinks consumption was associated with the risk of fractures. We aimed to assess the association in the Chinese population; (2) Methods: Data from 17,383 adults aged 20 to 75 years old attending the China Health and Nutrition Survey (CHNS) between 2004 and 2011 were analyzed. Soft drinks consumption and fracture occurrence were self-reported. The cross-sectional and longitudinal associations between soft drink and fracture was assessed using multivariable mixed-effect logistic regression and Cox regression; (3) Results: After adjusting for sociodemographic and lifestyle factors and dietary patterns, compared with those who did not consume soft drinks, participants with daily consumption of soft drinks had an odds ratio (95%CI) of 2.72 (95%CI: 1.45–5.09) for fracture. During a mean 5-year follow-up, there were 569 incident fracture cases. Compared with non-consumers, those with daily soft drinks consumption had a hazard ratio (95%CI) of 4.69 (95%CI: 2.80–7.88) for incident fracture; (4) Conclusions: Soft drinks consumption is directly associated with the risk of fracture. Reducing soft drinks consumption should be considered as an important strategy for individual and population levels to maintain bone health.
Breast Milk Lipids and Fatty Acids in Regulating Neonatal Intestinal Development and Protecting against Intestinal Injury
Authors: Ramiro-Cortijo David , Singh Pratibha , Liu Yan , Medina-Morales Esli , Yakah William , Freedman Steven D. , Martin Camilia R.
Published: February 2020

Human breast milk is the optimal source of nutrition for infant growth and development. Breast milk fats and their downstream derivatives of fatty acids and fatty acid-derived terminal mediators not only provide an energy source but also are important regulators of development, immune function, and metabolism. The composition of the lipids and fatty acids determines the nutritional and physicochemical properties of human milk fat. Essential fatty acids, including long-chain polyunsaturated fatty acids (LCPUFAs) and specialized pro-resolving mediators, are critical for growth, organogenesis, and regulation of inflammation. Combined data including in vitro, in vivo, and human cohort studies support the beneficial effects of human breast milk in intestinal development and in reducing the risk of intestinal injury. Human milk has been shown to reduce the occurrence of necrotizing enterocolitis (NEC), a common gastrointestinal disease in preterm infants. Preterm infants fed human breast milk are less likely to develop NEC compared to preterm infants receiving infant formula. Intestinal development and its physiological functions are highly adaptive to changes in nutritional status influencing the susceptibility towards intestinal injury in response to pathological challenges. In this review, we focus on lipids and fatty acids present in breast milk and their impact on neonatal gut development and the risk of disease.
Influence of a Functional Nutrients-Enriched Infant Formula on Language Development in Healthy Children at Four Years Old
Authors: Nieto-Ruiz Ana , Diéguez Estefanía , Sepúlveda-Valbuena Natalia , Catena Elvira , Jiménez Jesús , Rodríguez-Palmero María , Catena Andrés , Miranda M. Teresa , García-Santos José Antonio , G. Bermúdez Mercedes , Campoy Cristina
Published: February 2020

Nutrition during early life is essential for brain development and establishes the basis for cognitive and language skills development. It is well established that breastfeeding, compared to formula feeding, has been traditionally associated with increased neurodevelopmental scores up to early adulthood. We analyzed the long-term effects of a new infant formula enriched with bioactive compounds on healthy children’s language development at four years old. In a randomized double-blind COGNIS study, 122 children attended the follow-up call at four years. From them, 89 children were fed a standard infant formula (SF, n = 46) or an experimental infant formula enriched with functional nutrients (EF, n = 43) during their first 18 months of life. As a reference group, 33 exclusively breastfed (BF) were included. Language development was assessed using the Oral Language Task of Navarra-Revised (PLON-R). ANCOVA, chi-square test, and logistic regression models were performed. EF children seemed to show higher scores in use of language and oral spontaneous expression than SF children, and both SF and EF groups did not differ from the BF group. Moreover, it seems that SF children were more frequently categorized into “need to improve and delayed” in the use of language than EF children, and might more frequently present “need to improve and delayed” in the PLON-R total score than BF children. Finally, the results suggest that SF children presented a higher risk of suffering language development than BF children. Secondary analysis also showed a slight trend between low socioeconomic status and poorer language skills. The functional compound-enriched infant formula seems to be associated with beneficial long-term effects in the development of child’s language at four years old in a similar way to breastfed infants.
The Gastrointestinal Exertional Heat Stroke Paradigm: Pathophysiology, Assessment, Severity, Aetiology and Nutritional Countermeasures
Authors: Ogden Henry B. , Child Robert B. , Fallowfield Joanne L. , Delves Simon K. , Westwood Caroline S. , Layden Joseph D.
Published: February 2020

Exertional heat stroke (EHS) is a life-threatening medical condition involving thermoregulatory failure and is the most severe condition along a continuum of heat-related illnesses. Current EHS policy guidance principally advocates a thermoregulatory management approach, despite growing recognition that gastrointestinal (GI) microbial translocation contributes to disease pathophysiology. Contemporary research has focused to understand the relevance of GI barrier integrity and strategies to maintain it during periods of exertional-heat stress. GI barrier integrity can be assessed non-invasively using a variety of in vivo techniques, including active inert mixed-weight molecular probe recovery tests and passive biomarkers indicative of GI structural integrity loss or microbial translocation. Strenuous exercise is strongly characterised to disrupt GI barrier integrity, and aspects of this response correlate with the corresponding magnitude of thermal strain. The aetiology of GI barrier integrity loss following exertional-heat stress is poorly understood, though may directly relate to localised hyperthermia, splanchnic hypoperfusion-mediated ischemic injury, and neuroendocrine-immune alterations. Nutritional countermeasures to maintain GI barrier integrity following exertional-heat stress provide a promising approach to mitigate EHS. The focus of this review is to evaluate: (1) the GI paradigm of exertional heat stroke; (2) techniques to assess GI barrier integrity; (3) typical GI barrier integrity responses to exertional-heat stress; (4) the aetiology of GI barrier integrity loss following exertional-heat stress; and (5) nutritional countermeasures to maintain GI barrier integrity in response to exertional-heat stress.
Comprehensive Evaluation of Hepatitis E Serology and Molecular Testing in a Large Cohort
Authors: Anastasiou Olympia E. , Thodou Viktoria , Berger Annemarie , Wedemeyer Heiner , Ciesek Sandra
Published: February 2020

Introduction: Reliable and cost-effective diagnostics for hepatitis E virus (HEV) infection are necessary. The aim of our study was to investigate which diagnostic test is most accurate to detect HEV infection in immunocompetent and immunosuppressed patients in a real world setting. Patients and Methods: We performed a retrospective analysis of 1165 patients tested for HEV antibodies and HEV PCR at the same time point. Clinical, laboratory and virological data were taken from patient charts. HEV IgA was measured in a subgroup of 185 patients. Results: HEV RNA was detectable in 61 patients (5.2%); most of them (n = 49, 80.3%/n = 43, 70.5%) were HEV IgM+ and IgG+; however, 12 patients (19.6%) were HEV RNA positive/HEV IgM negative and 17 patients (27.8%) were HEV RNA positive/HEV IgG negative. Ten HEV RNA positive patients (16.4%) had neither HEV IgG nor IgM antibodies. Importantly, all of them were immunosuppressed. HEV IgA testing was less sensitive than HEV IgM for HEV diagnosis. Conclusions: HEV infection can be overlooked in patients without HEV specific antibodies. Performing PCR is necessary to diagnose or exclude HEV infection in immunocompromised hosts. In immunocompetent patients, a screening based on HEV antibodies (IgG/IgM) is sufficient.
Editorial for the Special Issue “Combining Different Data Sources for Environmental and Operational Satellite Monitoring of Sea Ice Conditions”
Authors: Dierking Wolfgang , Mäkynen Marko , Similä Markku
Published: February 2020

Satellite remote sensing is an important tool for continuous monitoring of sea ice covered ocean regions and spatial and temporal variations of their geophysical characteristics [...]
ScienceEarth: A Big Data Platform for Remote Sensing Data Processing
Authors: Xu Chen , Du Xiaoping , Yan Zhenzhen , Fan Xiangtao
Published: February 2020

Mass remote sensing data management and processing is currently one of the most important topics. In this study, we introduce ScienceEarth, a cluster-based data processing framework. The aim of ScienceEarth is to store, manage, and process large-scale remote sensing data in a cloud-based cluster-computing environment. The platform consists of the following three main parts: ScienceGeoData, ScienceGeoIndex, and ScienceGeoSpark. ScienceGeoData stores and manages remote sensing data. ScienceGeoIndex is an index and query system, a spatial index based on quad-tree and Hilbert curve which is combined for heterogeneous tiled remote sensing data that makes efficient data retrieval in ScienceGeoData. ScienceGeoSpark is an easy-to-use computing framework in which we use Apache Spark as the analytics engine for big remote sensing data processing. The result of tests proves that ScienceEarth can efficiently store, retrieve, and process remote sensing data. The results reveal ScienceEarth has the potential and capabilities of efficient big remote sensing data processing.
Mapping Forest Composition with Landsat Time Series: An Evaluation of Seasonal Composites and Harmonic Regression
Authors: Adams Bryce , Iverson Louis , Matthews Stephen , Peters Matthew , Prasad Anantha , Hix David M.
Published: February 2020

The Landsat program has long supported pioneering research on the recovery of forest information by remote sensing technologies for several decades, and efforts to improve the thematic resolution and accuracy of forest compositional products remains an area of continued innovation. Recent development and application of Landsat time series analysis offers unique opportunities for quantifying seasonality and trend components among different forest types for developing alternative feature sets for forest vegetation mapping. Within a large forested landscape in Southeastern Ohio, USA, we examined the use of harmonic metrics developed from time series of all available Landsat-8 observations (2013–2019) relative to seasonal image composites, including accompanying spectral components and vegetation indices. A reference dataset among three sources was integrated and used to categorize forest inventory data into seven forest type classes and gradient compositional response. Results showed that the combination of harmonic metrics and topographic variables achieved an accuracy agreement with the reference data of 74.9% relative to seasonal composites (71.6%) and spectral indices (70.3%). Differences in agreement were attributed to improved discrimination of three heterogeneous upland hardwood classes and an early-successional, young forest class, all forest types of primary interest among managers across the region. Variable importance metrics often identified the cosine and sine terms that quantify the seasonality in spectral values in the harmonic feature space, suggesting these aspects best support the characterization of forest types at greater thematic detail than seasonal compositing procedures. This study demonstrates how advanced time series metrics can improve forest type modeling and forest gradient quantifications, thus showcasing a need for continued exploration of such approaches across different forest types.
Evaluation of Gridded Precipitation Datasets in Malaysia
Authors: Ayoub Afiqah Bahirah , Tangang Fredolin , Juneng Liew , Tan Mou Leong , Chung Jing Xiang
Published: February 2020

This study compares five readily available gridded precipitation satellite products namely: Climate Hazards Group Infrared Precipitation with Station Data (CHIRPS) at 0.05° and 0.25° resolution, Tropical Rainfall Measuring Mission Multi-Satellite Precipitation Analysis (TMPA 3B42v7) and Princeton Global Forcings (PGFv3), both at 0.25°, and Global Satellite Mapping of Precipitation Reanalysis (GSMaP_RNL) at 0.1°, and evaluates their quality and reliability against 41 rain gauge stations in Malaysia. The evaluation was based on three numerical statistical scores (r, Root Mean Squared Error (RMSE) and Bias) and three categorical scores (Probability of Detection (POD), False Alarm Ratio (FAR) and Critical Success Index (CSI)) at temporal resolutions of daily, monthly and seasonal. The results showed that TMPA 3B42v7, PGFv3, CHIRPS25 and CHIRPS05 slightly overestimated the rain gauge data, while the GSMaP_RNL underestimated the value with the largest bias for monthly data. The CHIRPS25 showed the best POD score, while TMPA 3B42v7 scored highest for FAR and CSI. Overall, TMPA 3B42v7 was found to be the best-performing dataset, while PGFv3 registered the worst performance for both for numerical (monthly) and categorical (daily) scores. All products captured the intensity of heavy rainfall (20–50 mm/day) rather well, but tended to underestimate the intensity for categories of no or little rain (rain <1 mm/day) and extremely heavy rain (rain >50 mm/day). In addition, overestimation occurred for low moderate (2–5 mm/day) to low heavy rain and (10–20 mm/day). In the case study of the extreme flooding event of 2006/2007 in the southern area of Peninsular Malaysia, TMPA 3B42v7 and GSMaP_RNL performed well in capturing most heavy rainfall events but tended to overestimate light rainfalls, consistent with their performance for the occurrence intensity of rainfall at different intensity level.
GNSS-Reflectometry and Remote Sensing of Soil Moisture: A Review of Measurement Techniques, Methods, and Applications
Authors: Edokossi Komi , Calabia Andres , Jin Shuanggen , Molina Iñigo
Published: February 2020

The understanding of land surface-atmosphere energy exchange is extremely important for predicting climate change and weather impacts, particularly the influence of soil moisture content (SMC) on hydrometeorological and ecological processes, which are also linked to human activities. Unfortunately, traditional measurement methods are expensive and cumbersome over large areas, whereas measurements from satellite active and passive microwave sensors have shown advantages for SMC monitoring. Since the launch of the first passive microwave satellite in 1978, more and more progresses have been made in monitoring SMC from satellites, e.g., the Soil Moisture Active and Passive (SMAP) and Soil Moisture and Ocean Salinity (SMOS) missions in the last decade. Recently, new methods using signals of opportunity have been emerging, highlighting the Global Navigation Satellite Systems-Reflectometry (GNSS-R), which has wide applications in Earth’s surface remote sensing due to its numerous advantages (e.g., revisiting time, global coverage, low cost, all-weather measurements, and near real-time) when compared to the conventional observations. In this paper, a detailed review on the current SMC measurement techniques, retrieval approaches, products, and applications is presented, particularly the new and promising GNSS-R technique. Recent advances, future prospects and challenges are given and discussed.
Crop Growth Monitoring with Drone-Borne DInSAR
Authors: Oré Gian , Alcântara Marlon S. , Góes Juliana A. , Oliveira Luciano P. , Yepes Jhonnatan , Teruel Bárbara , Castro Valquíria , Bins Leonardo S. , Castro Felicio , Luebeck Dieter , Moreira Laila F. , Gabrielli Lucas H. , Hernandez-Figueroa Hugo E.
Published: February 2020

Accurate, high-resolution maps of for crop growth monitoring are strongly needed by precision agriculture. The information source for such maps has been supplied by satellite-borne radars and optical sensors, and airborne and drone-borne optical sensors. This article presents a novel methodology for obtaining growth deficit maps with an accuracy down to 5 cm and a spatial resolution of 1 m, using differential synthetic aperture radar interferometry (DInSAR). Results are presented with measurements of a drone-borne DInSAR operating in three bands—P, L and C. The decorrelation time of L-band for coffee, sugar cane and corn, and the feasibility for growth deficit maps generation are discussed. A model is presented for evaluating the growth deficit of a corn crop in L-band, starting with 50 cm height. This work shows that the drone-borne DInSAR has potential as a complementary tool for precision agriculture.
Grassland Wildfires in the Southern Great Plains: Monitoring Ecological Impacts and Recovery
Authors: Steiner Jean L. , Wetter Jeffrey , Robertson Shelby , Teet Stephen , Wang Jie , Wu Xiaocui , Zhou Yuting , Brown David , Xiao Xiangming
Published: February 2020

Devastating wildfires in Texas, Oklahoma, and Kansas in 2016 and 2017 resulted in significant social, economic, and environmental losses, with the agricultural sector among those severely affected. Several satellite-based indices were evaluated as potential monitoring tools for post-wildfire ecological recovery and management of grasslands. All indices evaluated provided useful information and indicated rapid vegetation recovery from wildfire. The Leaf Water Stress Index (LSWI) and Gross Primary Productivity (GPP) showed a distinct response to the wildfire events, and differentiated between burned and unburned areas throughout the post-wildfire growing seasons better than the Normalized Difference Vegetative Index (NDVI) and Enhanced Vegetative Index (EVI). In particular, the LSWI may provide a useful tool for mapping the footprint of wildfire, with potential utility for organizations that provide post-fire recovery resources. The GPP, which estimates the biomass productivity of vegetation, can provide information to livestock operators to guide the re-stocking of cattle in the aftermath of wildfire. In sum, satellite-based proxies can provide timely information both to characterize a wildfire’s footprint and to guide post-fire grazing management in a manner that balances short term needs for forage with long-term productivity and ecological function.
Mapping of Post-Wildfire Burned Area Using a Hybrid Algorithm and Satellite Data: The Case of the Camp Fire Wildfire in California, USA
Authors: Syifa Mutiara , Panahi Mahdi , Lee Chang-Wook
Published: February 2020

On November 8, 2018, a devastating wildfire, known as the Camp Fire wildfire, was reported in Butte County, California, USA. Approximately 88 fatalities ensued, and 18,804 structures were damaged by the wildfire. As a response to this destructive wildfire, this study generated a pre- and post-wildfire maps to provide basic data for evacuation and mitigation planning. This study used Landsat-8 and Sentinel-2 imagery to map the pre- and post-wildfire conditions. A support vector machine (SVM) optimized by the imperialist competitive algorithm (ICA) hybrid model was compared with the non-optimized SVM algorithm for classification of the pre- and post-wildfire map. The SVM–ICA produced a better accuracy (overall accuracies of 83.8% and 83.6% for pre- and post-wildfire using Landsat-8 respectively; 90.8% and 91.8% for pre- and post-wildfire using Sentinel-2 respectively), compared to SVM without optimization (overall accuracies of 80.0% and 78.9% for pre- and post-wildfire using Landsat-8 respectively; 83.3% and 84.8% for pre- and post-wildfire using Sentinel-2 respectively. In total, eight pre- and post-wildfire burned area maps were generated; these can be used to assess the area affected by the Camp Fire wildfire as well as for wildfire mitigation planning in the future.
Vision-Based Decision Support for Rover Path Planning in the Chang’e-4 Mission
Authors: Wang Yexin , Wan Wenhui , Gou Sheng , Peng Man , Liu Zhaoqin , Di Kaichang , Li Lichun , Yu Tianyi , Wang Jia , Cheng Xiao
Published: February 2020

In planetary rover missions, rover path planning is critical to ensure the safety and efficiency of the rover traverse and in situ explorations. In the Chang’e-4 (CE-4) mission, we have proposed and developed vision-based decision support methods comprising obstacle map generation and path evaluation for the rover. At each waypoint along the rover traverse, digital elevation model (DEM) is automatically generated, which is then used for obstacle map generation and path searching. For path evaluation, the searched path and the predicted wheel trajectories are projected onto the original images captured by different cameras to coincide with the real observation scenario. The proposed methods have been applied in the CE-4 mission to support teleoperation of the rover, and examples of multiple applications used in the mission are presented in this paper. Under the support of the proposed techniques, by the end of the 14th lunar day (3 February 2020), the rover has already travelled 367.25 meters on the far side of the Moon.
Mapping Glacier Forelands Based on UAV BVLOS Operation in Antarctica
Authors: Dąbski Maciej , Zmarz Anna , Rodzewicz Mirosław , Korczak-Abshire Małgorzata , Karsznia Izabela , Lach Katarzyna , Rachlewicz Grzegorz , Chwedorzewska Katarzyna
Published: February 2020

The aim of this article is to show geomorphological mapping of remote Antarctic locations using images taken by a fixed-wing unmanned aerial vehicle (UAV) during the Beyond Visual Line of Sight (BVLOS) operations. We mapped landform assemblages developed in forelands of Ecology Glacier (EGF), Sphinx Glacier (SGF) and Baranowski Glacier (BGF) in Antarctic Specially Protected Area No. 128 (ASPA 128) on King George Island (South Shetland Islands) and inferred about glacial dynamics. The orthophoto and digital elevation model allowed for geomorphological mapping of glacial forelands, including (i) glacial depositional landforms, (ii) fluvial and fluvioglacial landforms, (iii) littoral and lacustrine landforms, (iv) bodies of water, and (v) other. The largest area is occupied by ground moraine and glacial lagoons on EGF and BGF. The most profound features of EGF are the large latero-frontal moraine ridges from Little Ice Age and the first half of the 20th century. Large areas of ground moraine, frequently fluted and marked with large recessional moraine ridges, dominate on SGF. A significant percentage of bedrock outcrops and end moraine complexes characterize BGF. The landform assemblages are typical for discontinuous fast ice flow of tidewater glaciers over a deformable bed. It is inferred that ice flow velocity decreased as a result of recession from the sea coast, resulting in a significant decrease in the length of ice cliffs and decrease in calving rate. Image acquisition during the fixed-wing UAV BVLOS operation proved to be a very robust technique in harsh polar conditions of King George Island.
Semantic Segmentation Using Deep Learning with Vegetation Indices for Rice Lodging Identification in Multi-date UAV Visible Images
Authors: Yang Ming-Der , Tseng Hsin-Hung , Hsu Yu-Chun , Tsai Hui Ping
Published: February 2020

A rapid and precise large-scale agricultural disaster survey is a basis for agricultural disaster relief and insurance but is labor-intensive and time-consuming. This study applies Unmanned Aerial Vehicles (UAVs) images through deep-learning image processing to estimate the rice lodging in paddies over a large area. This study establishes an image semantic segmentation model employing two neural network architectures, FCN-AlexNet, and SegNet, whose effects are explored in the interpretation of various object sizes and computation efficiency. Commercial UAVs imaging rice paddies in high-resolution visible images are used to calculate three vegetation indicators to improve the applicability of visible images. The proposed model was trained and tested on a set of UAV images in 2017 and was validated on a set of UAV images in 2019. For the identification of rice lodging on the 2017 UAV images, the F1-score reaches 0.80 and 0.79 for FCN-AlexNet and SegNet, respectively. The F1-score of FCN-AlexNet using RGB + ExGR combination also reaches 0.78 in the 2019 images for validation. The proposed model adopting semantic segmentation networks is proven to have better efficiency, approximately 10 to 15 times faster, and a lower misinterpretation rate than that of the maximum likelihood method.
Structure-Aware Convolution for 3D Point Cloud Classification and Segmentation
Authors: Wang Lei , Liu Yuxuan , Zhang Shenman , Yan Jixing , Tao Pengjie
Published: February 2020

Semantic feature learning on 3D point clouds is quite challenging because of their irregular and unordered data structure. In this paper, we propose a novel structure-aware convolution (SAC) to generalize deep learning on regular grids to irregular 3D point clouds. Similar to the template-matching process of convolution on 2D images, the key of our SAC is to match the point clouds’ neighborhoods with a series of 3D kernels, where each kernel can be regarded as a “geometric template” formed by a set of learnable 3D points. Thus, the interested geometric structures of the input point clouds can be activated by the corresponding kernels. To verify the effectiveness of the proposed SAC, we embedded it into three recently developed point cloud deep learning networks (PointNet, PointNet++, and KCNet) as a lightweight module, and evaluated its performance on both classification and segmentation tasks. Experimental results show that, benefiting from the geometric structure learning capability of our SAC, all these back-end networks achieved better classification and segmentation performance (e.g., +2.77% mean accuracy for classification and +4.99% mean intersection over union (IoU) for segmentation) with few additional parameters. Furthermore, results also demonstrate that the proposed SAC is helpful in improving the robustness of networks with the constraints of geometric structures.
High-Resolution Spatial Distribution of Bird Movements Estimated from a Weather Radar Network
Authors: Kranstauber Bart , Bouten Willem , Leijnse Hidde , Wijers Berend-Christiaan , Verlinden Liesbeth , Shamoun-Baranes Judy , Dokter Adriaan M.
Published: February 2020

Weather radars provide detailed information on aerial movements of organisms. However, interpreting fine-scale radar imagery remains challenging because of changes in aerial sampling altitude with distance from the radar. Fine-scale radar imagery has primarily been used to assess mass exodus at sunset to study stopover habitat associations. Here, we present a method that enables a more intuitive integration of information across elevation scans projected in a two-dimensional spatial image of fine-scale radar reflectivity. We applied this method on nights of intense bird migration to demonstrate how the spatial distribution of migrants can be explored at finer spatial scales and across multiple radars during the higher flying en-route phase of migration. The resulting reflectivity maps enable explorative analysis of factors influencing their regional and fine-scale distribution. We illustrate the method’s application by generating time-series of composites of up to 20 radars, achieving a nearly complete spatial coverage of a large part of Northwest Europe. These visualizations are highly useful in interpreting regional-scale migration patterns and provide detailed information on bird movements in the landscape and aerial environment.
Extending Landsat 8: Retrieval of an Orange contra-Band for Inland Water Quality Applications
Authors: Castagna Alexandre , Simis Stefan , Dierssen Heidi , Vanhellemont Quinten , Sabbe Koen , Vyverman Wim
Published: February 2020

The Operational Land Imager (OLI) onboard Landsat 8 has found successful application in inland and coastal water remote sensing. Its radiometric specification and high spatial resolution allows quantification of water-leaving radiance while resolving small water bodies. However, its limited multispectral band set restricts the range of water quality parameters that can be retrieved. Identification of cyanobacteria biomass has been demonstrated for sensors with a band centered near 620 nm, the absorption peak of the diagnostic pigment phycocyanin. While OLI lacks such a band in the orange region, superposition of the available multispectral and panchromatic bands suggests that it can be calculated by a scaled difference. A set of 428 in situ spectra acquired in diverse lakes in Belgium and The Netherlands was used to develop and test an orange contra-band retrieval algorithm, achieving a mean absolute percentage error of 5.39% and a bias of −0.88% in the presence of sensor noise. Atmospheric compensation error propagated to the orange contra-band was observed to maintain about the same magnitude (13% higher) observed for the red band and thus results in minimal additional effects for possible base line subtraction or band ratio algorithms for phycocyanin estimation. Generality of the algorithm for different reflectance shapes was tested against a set of published average coastal and inland Optical Water Types, showing robust retrieval for all but relatively clear water types (Secchi disk depth > 6 m and chlorophyll a < 1.6 mg m − 3 ). The algorithm was further validated with 79 matchups against the Ocean and Land Colour Imager (OLCI) orange band for 10 globally distributed lakes. The retrieved band is shown to convey information independent from the adjacent bands under variable phycocyanin concentrations. An example application using Landsat 8 imagery is provided for a known cyanobacterial bloom in Lake Erie, US. The method is distributed in the ACOLITE atmospheric correction code. The contra-band approach is generic and can be applied to other sensors with overlapping bands. Recommendations are also provided for development of future sensors with broad spectral bands with the objective to maximize the accuracy of possible spectral enhancements.
Phenological Dynamics Characterization of Alignment Trees with Sentinel-2 Imagery: A Vegetation Indices Time Series Reconstruction Methodology Adapted to Urban Areas
Authors: Granero-Belinchon Carlos , Adeline Karine , Lemonsu Aude , Briottet Xavier
Published: February 2020

This article presents a novel methodology for the characterization of tree vegetation phenology, based on vegetation indices time series reconstruction and adapted to urban areas. The methodology is based on a pixel by pixel curve fitting classification, together with a subsequent Savitzky–Golay filtering of raw phenological curves from pixels classified as vegetation. Moreover, the new method is conceived to face specificities of urban environments such as: the high heterogeneity of impervious/natural elements, the 3D structure of the city inducing shadows, the restricted spatial extent of individual tree crowns and the strong biodiversity of urban vegetation. Three vegetation indices have been studied: Normalized Difference Vegetation Index (NDVI) and Normalized Difference Red Edge Index 1 (NDRE1), which are mainly linked to chlorophyll content and leaf density and Normalized Burn Ratio (NBR) mostly correlated to water content and leaf density. The methodology has been designed to allow the analysis of annual and intra-annual vegetation phenological dynamics. Then, different annual and intra-annual criteria for phenology characterization are proposed and criticized. To show the applicability of the methodology, this article focuses on Sentinel-2 (S-2) imagery covering 2018 and the study of groups of London planes in an alignment structure in the French city of Toulouse. Results showed that the new method allows the ability to 1) describe the heterogeneity of phenologies from London planes exposed to different environmental conditions (urban canyons, proximity with a source of water) and 2) to detect intra-annual phenological dynamics linked to changes in meteorological conditions.
Characterizing and Mitigating Sensor Generated Spatial Correlations in Airborne Hyperspectral Imaging Data
Authors: Inamdar Deep , Kalacska Margaret , Leblanc George , Arroyo-Mora J. Pablo
Published: February 2020

In hyperspectral imaging (HSI), the spatial contribution to each pixel is non-uniform and extends past the traditionally square spatial boundaries designated by the pixel resolution, resulting in sensor-generated blurring effects. The spatial contribution to each pixel can be characterized by the net point spread function, which is overlooked in many airborne HSI applications. The objective of this study was to characterize and mitigate sensor blurring effects in airborne HSI data with simple tools, emphasizing the importance of point spread functions. Two algorithms were developed to (1) quantify spatial correlations and (2) use a theoretically derived point spread function to perform deconvolution. Both algorithms were used to characterize and mitigate sensor blurring effects on a simulated scene with known spectral and spatial variability. The first algorithm showed that sensor blurring modified the spatial correlation structure in the simulated scene, removing 54.0%–75.4% of the known spatial variability. Sensor blurring effects were also shown to remove 31.1%–38.9% of the known spectral variability. The second algorithm mitigated sensor-generated spatial correlations. After deconvolution, the spatial variability of the image was within 23.3% of the known value. Similarly, the deconvolved image was within 6.8% of the known spectral variability. When tested on real-world HSI data, the algorithms sharpened the imagery while characterizing the spatial correlation structure of the dataset, showing the implications of sensor blurring. This study substantiates the importance of point spread functions in the assessment and application of airborne HSI data, providing simple tools that are approachable for all end-users.