Special Issue "2nd Edition of Data in Astrophysics & Geophysics: Research and Applications"

A special issue of Data (ISSN 2306-5729). This special issue belongs to the section "Spatial Data Science and Digital Earth".

Deadline for manuscript submissions: 31 October 2022 | Viewed by 6660

Special Issue Editors

Dr. Vladimir Sreckovic
E-Mail Website
Guest Editor
The Astrophysics and Ionospheric Laboratory, Institute of Physics Belgrade, University of Belgrade, Belgrade, Serbia
Interests: solar and stellar astrophysics; space weather studies of upper atmosphere; astrogeoinformatics; astroinformatics; databases; data-mining; natural hazards
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Milan S. Dimitrijević
E-Mail Website
Guest Editor
Astronomical Observatory, Volgina 7, 11060 Belgrade, Serbia
Interests: astrophysics; spectroscopy; line profiles; atomic and molecular processes; history of astronomy; archaeoastronomy
Special Issues, Collections and Topics in MDPI journals
Dr. Zoran Mijic
E-Mail Website
Guest Editor
Institute of Physics Belgrade, University of Belgrade, 11080 Belgrade, Serbia
Interests: statistical modeling in atmospheric physics; multivariate receptor modeling; ground-based remote sensing for retrieval of the atmospheric composition; aerosol optical properties; aerosol physical and chemical characterization and climatic role; air quality
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The space and Earth’s layers are mediums permanently exposed to influences of numerous perturbations characterized with time- and space-dependent intensity. For this reason, detection of the astrophysical and terrestrial events and their influences, as well as the development and application of various models, must be based on observation data.

The challenges related to data volume, variety, and data flow are similar in astro- and geo-observations. This Special Issue aims to encourage communication among the disciplines by identifying and grouping relevant research solutions. Its goals are to engage a broad community of researchers, both users and contributors, to make new discoveries enabled by the growth of data and technology and to continue interdisciplinary exchanges of ideas and methodologies with other fields.

We would like to invite you to submit articles addressing data collection in astrophysics and geophysics, its acquisition, processing, and management, so that these results will be used by other scientists and that the compilation of such data sets will be useful to data producers as well. Potential topics include but are not limited to:

  • Big data in astrophysics and geophysics
  • Data processing, visualization, and acquisition
  • Line profile data
  • Interstellar spectra data
  • Atomic and molecular data in astrophysics
  • Earth observation data
  • Climate data records
  • Natural hazards and disasters
  • Remote sensing

Dr. Vladimir Sreckovic
Prof. Dr. Milan S. Dimitrijević
Dr. Zoran Mijic
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Data is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (10 papers)

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Research

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Article
An Evaluation of the OpenWeatherMap API versus INMET Using Weather Data from Two Brazilian Cities: Recife and Campina Grande
Data 2022, 7(8), 106; https://doi.org/10.3390/data7080106 - 30 Jul 2022
Viewed by 341
Abstract
Certain weather conditions are inadvertently related to increased population of various mosquitoes. In order to predict the burden of mosquito populations in the Global South, it is imperative to integrate weather-related risk factors into such predictive models. There are a lot of online [...] Read more.
Certain weather conditions are inadvertently related to increased population of various mosquitoes. In order to predict the burden of mosquito populations in the Global South, it is imperative to integrate weather-related risk factors into such predictive models. There are a lot of online open-source weather platforms that provide historical, current and future weather forecasts which can be utilised for general predictions, and these electronic sources serve as an alternate option for weather data when physical weather stations are inaccessible (or inactive). Before using data from such online source, it is important to assess the accuracy against some baseline measure. In this paper, we therefore evaluated the accuracy and suitability of weather forecasts of two parameters namely temperature and humidity from the OpenWeatherMap API (an online weather platform) and compared them with actual measurements collected from the Brazilian weather stations (INMET). The evaluation was focused on two Brazilian cites, namely, Recife and Campina Grande. The intention is to prepare an early warning model which will harness data from OpenWeatherMap API for mosquito prediction. Full article
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Article
Handling Dataset with Geophysical and Geological Variables on the Bolivian Andes by the GMT Scripts
Data 2022, 7(6), 74; https://doi.org/10.3390/data7060074 - 01 Jun 2022
Cited by 1 | Viewed by 659
Abstract
In this paper, an integrated mapping of the georeferenced data is presented using the QGIS and GMT scripting tool set. The study area encompasses the Bolivian Andes, South America, notable for complex geophysical and geological parameters and high seismicity. A data integration was [...] Read more.
In this paper, an integrated mapping of the georeferenced data is presented using the QGIS and GMT scripting tool set. The study area encompasses the Bolivian Andes, South America, notable for complex geophysical and geological parameters and high seismicity. A data integration was performed for a detailed analysis of the geophysical and geological setting. The data included the raster and vector datasets captured from the open sources: the IRIS seismic data (2015 to 2021), geophysical data from satellite-derived gravity grids based on CryoSat, topographic GEBCO data, geoid undulation data from EGM-2008, and geological georeferences’ vector data from the USGS. The techniques of data processing included quantitative and qualitative evaluation of the seismicity and geophysical setting in Bolivia. The result includes a series of thematic maps on the Bolivian Andes. Based on the data analysis, the western region was identified as the most seismically endangered area in Bolivia with a high risk of earthquake hazards in Cordillera Occidental, followed by Altiplano and Cordillera Real. The earthquake magnitude here ranges from 1.8 to 7.6. The data analysis shows a tight correlation between the gravity, geophysics, and topography in the Bolivian Andes. The cartographic scripts used for processing data in GMT are available in the author’s public GitHub repository in open-access with the provided link. The utility of scripting cartographic techniques for geophysical and topographic data processing combined with GIS spatial evaluation of the geological data supported automated mapping, which has applicability for risk assessment and geological hazard mapping of the Bolivian Andes, South America. Full article
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Data Descriptor
An Inventory of Large-Scale Landslides in Baoji City, Shaanxi Province, China
Data 2022, 7(8), 114; https://doi.org/10.3390/data7080114 - 15 Aug 2022
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Abstract
Landslides are a typical geological hazard that endangers people’s lives and property in the Loess Plateau. The destructiveness of large-scale landslides, in particular, is incalculable. For example, traffic disruptions, river blockages, and house collapses may all result from landslides. Thus, it is urgent [...] Read more.
Landslides are a typical geological hazard that endangers people’s lives and property in the Loess Plateau. The destructiveness of large-scale landslides, in particular, is incalculable. For example, traffic disruptions, river blockages, and house collapses may all result from landslides. Thus, it is urgent to compile a complete inventory of landslides in a specific region. The investigation object of this study is Baoji City, Shaanxi Province, China. Using the multi-temporal high-resolution remote sensing images from Google Earth, we preliminarily completed the cataloging of large-scale (area > 5000 m2) landslides in the study area through visual interpretation. The inventory was subsequently compared with the existing literature and hazard records for improvement and supplement. We decoded 3422 landslides with a total area of 360.7 km2 and an average area of 105,400 m2 for each individual landslide. The largest landslide had an area of 1.71 km2, while the smallest one was 6042 m2. In previous studies, we analyzed these data without describing the data sources in detail. We now provide a shared dataset of each landslide in shp format, containing geographic location, boundary information, etc. The dataset is significantly useful for understanding the distribution characteristics of large-scale landslides in this region. Moreover, it can serve as basic data for the study of paleolandslide resurrection. Full article
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Data Descriptor
Three-Dimensional, Km-Scale Hyperspectral Data of Well-Exposed Zn–Pb Mineralization at Black Angel Mountain, Greenland
Data 2022, 7(8), 104; https://doi.org/10.3390/data7080104 - 28 Jul 2022
Viewed by 625
Abstract
Hyperspectral imaging is an innovative technology for non-invasive mapping, with increasing applications in many sectors. As with any novel technology, robust processing workflows are required to ensure a wide use. We present an open-source hypercloud dataset capturing the complex but spectacularly well exposed [...] Read more.
Hyperspectral imaging is an innovative technology for non-invasive mapping, with increasing applications in many sectors. As with any novel technology, robust processing workflows are required to ensure a wide use. We present an open-source hypercloud dataset capturing the complex but spectacularly well exposed geology from the Black Angel Mountain in Maarmorilik, West Greenland, alongside a detailed and interactive tutorial documenting relevant processing workflows. This contribution relies on very recent progress made on the correction, interpretation and integration of hyperspectral data in earth sciences. The possibility to fuse hyperspectral scans with 3D point cloud representations (hyperclouds) has opened up new possibilities for the mapping of complex natural targets. Spectroscopic and machine learning tools allow or the rapid and accurate characterization of geological structures in a 3D environment. Potential users can use this exemplary dataset and the associated tools to train themselves or test new algorithms. As the data and the tools have a wide range of application, we expect this contribution to benefit the scientific community at large. Full article
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Data Descriptor
A Database of Topo-Bathy Cross-Shore Profiles and Characteristics for U.S. Atlantic and Gulf of Mexico Sandy Coastlines
Data 2022, 7(7), 92; https://doi.org/10.3390/data7070092 - 06 Jul 2022
Viewed by 553
Abstract
A database of seamless topographic and bathymetric cross-shore profiles along with metrics of the associated morphological characteristics based on the latest available lidar data ranging from 2011–2020 and bathymetry from the Continuously Updated Digital Elevation Model was developed for U.S. Atlantic and Gulf [...] Read more.
A database of seamless topographic and bathymetric cross-shore profiles along with metrics of the associated morphological characteristics based on the latest available lidar data ranging from 2011–2020 and bathymetry from the Continuously Updated Digital Elevation Model was developed for U.S. Atlantic and Gulf of Mexico open-ocean sandy coastlines. Cross-shore resolution ranges from 2.5 m for topographic and nearshore portions to 10 m for offshore portions. Topographic morphological characteristics include: foredune crest elevation, foredune toe elevation, foredune width, foredune volume, foredune relative height, beach width, beach volume, beach slope, and nearshore slope. This database was developed to serve as inputs for current and future morphological modeling studies aimed at providing real-time estimates of coastal change magnitudes resulting from imminent tropical storm and hurricane landfall. Beyond this need for model inputs, the database of cross-shore profiles and characteristic metrics could serve as a tool for coastal scientists to visualize and to analyze varying local, regional, and national variations in coastal morphology for varying types of studies and projects related to Atlantic and Gulf of Mexico sandy coastline environments. Full article
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Data Descriptor
Daily Precipitation Data for the Mexico City Metropolitan Area from 1930 to 2015
Data 2022, 7(7), 88; https://doi.org/10.3390/data7070088 - 29 Jun 2022
Viewed by 402
Abstract
The Metropolitan Zone of Mexico City, as well as the associated basin, includes the territories of Mexico City, some municipalities of the State of Mexico and the state of Hidalgo. In addition, this area is the most densely populated in Mexico. The region [...] Read more.
The Metropolitan Zone of Mexico City, as well as the associated basin, includes the territories of Mexico City, some municipalities of the State of Mexico and the state of Hidalgo. In addition, this area is the most densely populated in Mexico. The region is influenced by mid-latitude and tropical weather systems and is vulnerable to extreme hydrometeorological events. In this context, we developed a dataset from the records of 136 geolocated sites that includes daily precipitation data from the CLImate COMputing (CLICOM) project and the Mexico City Water System. The data spans the period from 1930 to 2015 for the rainy months (June–October) from stations with records of 20 or more years. In each recording site, automatic and manual data quality control were performed to verify the consistency of the daily precipitation data. We believe that our highly dense precipitation dataset will be useful for climate, trend and extreme events analysis. Additionally, the data will allow validating simulations of numerical atmospheric models. The dataset is public, and it was previously used in other research to determine areas susceptible to flooding due to heavy rain events and to develop a web mapping application of daily precipitation data. Full article
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Data Descriptor
Quality Control Impacts on Total Precipitation Gauge Records for Montane Valley and Ridge Sites in SW Alberta, Canada
Data 2022, 7(6), 73; https://doi.org/10.3390/data7060073 - 30 May 2022
Viewed by 652
Abstract
This paper presents adjustment routines for Geonor totalizing precipitation gauge data collected from the headwaters of the Oldman River, within the southwestern Alberta Canadian Rockies. The gauges are situated at mountain valley and alpine ridge locations with varying degrees of canopy cover. These [...] Read more.
This paper presents adjustment routines for Geonor totalizing precipitation gauge data collected from the headwaters of the Oldman River, within the southwestern Alberta Canadian Rockies. The gauges are situated at mountain valley and alpine ridge locations with varying degrees of canopy cover. These data are prone to sensor noise and environment-induced measurement errors requiring an ordered set of quality control (QC) corrections using nearby weather station data. Sensor noise at valley sites with single-vibrating wire gauges accounted for the removal of 5% to 8% (49–76 mm) of annual precipitation. This was compensated for by an increase of 6% to 8% (50–76 mm) from under-catch. A three-wire ridge gauge did not experience significant sensor noise; however, the under-catch of snow resulted in 42% to 52% (784–1342 mm) increased precipitation. When all QC corrections were applied, the annual cumulative precipitation at the ridge demonstrated increases of 39% to 49% (731–1269 mm), while the valley gauge adjustments were −4% to 1% (−39 mm to 13 mm). Public sector totalizing precipitation gauge records often undergo minimal QC. Care must be exercised to check the corrections applied to such records when used to estimate watershed water balance or precipitation orographic enhancement. Systematic errors at open high-elevation sites may exceed nearby valley or forest sites. Full article
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Data Descriptor
Geomorphological Data from Detonation Craters in the Fehmarn Belt, German Baltic Sea
Data 2022, 7(5), 63; https://doi.org/10.3390/data7050063 - 11 May 2022
Viewed by 768
Abstract
Military munitions from World War I and II dumped at the seafloor are a threat to the marine environment and its users. Decades of saltwater exposure make the explosives fragile and difficult to dispose of. If required, the munition is blast-in-place. In August [...] Read more.
Military munitions from World War I and II dumped at the seafloor are a threat to the marine environment and its users. Decades of saltwater exposure make the explosives fragile and difficult to dispose of. If required, the munition is blast-in-place. In August 2019, 42 ground mines were detonated in a controlled manner underwater during a NATO maneuver in the German Natura2000 Special Area of Conservation Fehmarn Belt, the Baltic Sea. In June 2020, four detonation craters were investigated with a multibeam echosounder for the first time. This dataset is represented here as maps of bathymetry, slope angle, and height difference to the surrounding. The circular craters were still clearly visible a year after the detonation. The diameter and depth of the structures were between 7.5–12.6 m and 0.7–2.2 m, respectively. In total, about 321 m2 of the seafloor was destroyed along the track line. Full article
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Data Descriptor
Climate Data to Support the Adaptation of Buildings to Climate Change in Canada
Data 2022, 7(4), 42; https://doi.org/10.3390/data7040042 - 06 Apr 2022
Viewed by 1060
Abstract
Climate change will continue to bring about unprecedented climate extremes in the future, and buildings and infrastructure will be exposed to such conditions. To ensure that new and existing buildings deliver satisfactory performance over their design lives, their performance under current and future [...] Read more.
Climate change will continue to bring about unprecedented climate extremes in the future, and buildings and infrastructure will be exposed to such conditions. To ensure that new and existing buildings deliver satisfactory performance over their design lives, their performance under current and future projected climates needs to be assessed by undertaking building simulations. This study prepares climate data needed for building simulations for 564 locations by bias-correcting the Canadian Regional Climate Model version 4 (CanRCM4) large ensemble (LE) simulations with reference to observations. Technical validation results show that bias-correction effectively reduces the bias associated with CanRCM4-LE simulations in terms of their marginal distributions and the inter-relationship between climate variables. To ensure that the range of projected climate change impacts are encompassed within these data sets, and to furthermore provide building moisture and energy reference years, the reference year files were prepared from bias-corrected CanRCM4-LE simulations and are comprised of a typical meteorological year for building energy applications, a typical and extreme moisture reference year, a typical downscaled year, an extreme warm year, and an extreme cold year. Full article
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Data Descriptor
Land Cover Map for Multifunctional Landscapes of Taita Taveta County, Kenya, Based on Sentinel-1 Radar, Sentinel-2 Optical, and Topoclimatic Data
Data 2022, 7(3), 36; https://doi.org/10.3390/data7030036 - 17 Mar 2022
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Abstract
Taita Taveta County (TTC) is one of the world’s biodiversity hotspots in the highlands with some of the world’s megafaunas in the lowlands. Detailed mapping of the terrestrial ecosystem of the whole county is of global significance for biodiversity conservation. Here, we present [...] Read more.
Taita Taveta County (TTC) is one of the world’s biodiversity hotspots in the highlands with some of the world’s megafaunas in the lowlands. Detailed mapping of the terrestrial ecosystem of the whole county is of global significance for biodiversity conservation. Here, we present a land cover map for 2020 based on satellite observations, a machine learning algorithm, and a reference database for accuracy assessment. For the land cover map production processing chain, temporal metrics from Sentinel-1 and Sentinel-2 (such as median, quantiles, and interquartile range), vegetation indices from Sentinel-2 (normalized difference vegetation index, tasseled cap greenness, and tasseled cap wetness), topographic metrics (elevation, slope, and aspect), and mean annual rainfall were used as predictors in the gradient tree boost classification model. Reference sample points which were collected in the field were used to guide the collection of additional reference sample points based on high spatial resolution imagery for training and validation of the model. The accuracy of the land cover map and uncertainty of area estimates at 95% confidence interval were assessed using sample-based statistical inference. The land cover map has an overall accuracy of 81 ± 2.3% and it is freely accessible for land use planners, conservation managers, and researchers. Full article
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