The Land Use and Cover Area frame Statistical survey (LUCAS) aimed at the collecting harmonised data about the state of land use/cover over the extent of European Union (EU). Among these 2·105 land ...use/cover observations selected for validation, a topsoil survey was conducted at about 10% of these sites. Topsoil sampling locations were selected as to be representative of European landscape using a Latin hypercube stratified random sampling, taking into account CORINE land cover 2000, the Shuttle Radar Topography Mission (SRTM) DEM and its derived slope, aspect and curvature.
In this study we will discuss how the LUCAS topsoil database can be used to map soil properties at continental scale over the geographical extent of Europe. Several soil properties were predicted using hybrid approaches like regression kriging. In this paper we describe the prediction of topsoil texture and related derived physical properties. Regression models were fitted using, along other variables, remotely sensed data coming from the MODIS sensor. The high temporal resolution of MODIS allowed detecting changes in the vegetative response due to soil properties, which can then be used to map soil features distribution. We will also discuss the prediction of intrinsically collinear variables like soil texture which required the use of models capable of dealing with multivariate constrained dependent variables like Multivariate Adaptive Regression Splines (MARS).
Cross validation of the fitted models proved that the LUCAS dataset constitutes a good sample for mapping purposes leading to cross-validation R2 between 0.47 and 0.50 for soil texture and normalized errors between 4 and 10%.
•The LUCAS harmonised soil survey comprising 20,000 observations was used in this study.•Soil texture and coarse fragments were mapped over the extent of Europe using MARS.•MARS modelled soil texture with good accuracy whilst constraining their values.•AWC, soil bulk density and USDA textural classes were derived from soil texture maps.•These maps constitute a first approximation of the GlobalSoilMap products for Europe.
A maar crater is the top of a much larger subsurface diatreme structure produced by phreatomagmatic explosions and the size and shape of the crater reflects the growth history of that structure ...during an eruption. Recent experimental and geophysical research has shown that crater complexity can reflect subsurface complexity. Morphometry provides a means of characterizing a global population of maar craters in order to establish the typical size and shape of features. A global database of Quaternary maar crater planform morphometry indicates that maar craters are typically not circular and frequently have compound shapes resembling overlapping circles. Maar craters occur in volcanic fields that contain both small volume and complex volcanoes. The global perspective provided by the database shows that maars are common in many volcanic and tectonic settings producing a similar diversity of size and shape within and between volcanic fields. A few exceptional populations of maars were revealed by the database, highlighting directions of future research to improve our understanding on the geometry and spacing of subsurface explosions that produce maars. These outlying populations, such as anomalously large craters (>3000m), chains of maars, and volcanic fields composed of mostly maar craters each represent a small portion of the database, but provide opportunities to reinvestigate fundamental questions on maar formation. Maar crater morphometry can be integrated with structural, hydrological studies to investigate lateral migration of phreatomagmatic explosion location in the subsurface. A comprehensive database of intact maar morphometry is also beneficial for the hunt for maar-diatremes on other planets.
•MaarVLS is a global database of maar volcano shapes size and distribution.•Maar craters are elongate but not simple ellipses reflecting multiple growth directions.•Morphometry of maar craters suggests lateral migration of explosion locations is common.•Maar crater sizes and shapes vary globally and within individual volcanic fields.•The database highlights universal characteristics of maars and anomalous populations.
What is a Lexicographical Database? Bergenholtz, Henning; Nielsen, Jesper Skovgård
Lexikos,
01/2013, Letnik:
23, Številka:
23
Journal Article
Recenzirano
Odprti dostop
Fifty years ago, no lexicographer used a database in the work process. Today, almost all dictionary projects incorporate databases. In our opinion, the optimal lexicographical database should be ...planned in cooperation between a lexicographer and a database specialist in each specific lexicographical project. Such cooperation will reach the highest level of success if the lexicographer has at least a basic knowledge of the topic presented in this paper: What is a database? This type of knowledge is also needed when the lexicographer describes an ongoing or a finished project. In this article, we provide the description of this type of cooperation, using the most important theoretical terms relevant in the planning of a database. It will be made clear that a lexicographical database is like any other database. The only difference is that an optimal lexicographical database is constructed to fulfil the requirements for a specific lexicographical project. PUBLICATION ABSTRACT
•A novel 1-D CNN learning framework for soil spectral analysis is proposed.•Multiple spectral sources are combined to maximize the spectral information.•All soil properties are simultaneously ...predicted to utilize the cross-correlations.•A local adaptive error-correction mechanism enhances the prediction performance.•The framework produced interpretable and accurate results in the LUCAS SSL.
The use of visible near-infrared and shortwave-infrared (VNIR-SWIR) diffuse reflectance spectroscopy for the estimation of soil properties is increasingly maturing with large-scale soil spectral libraries (SSLs) of laboratory spectra developed across the globe. Such an SSL is the publicly available LUCAS topsoil database with approximately 20,000 soil samples encompassing 23 countries of the European Union. A wide variety of machine learning tools have been applied to the LUCAS SSL to predict some of the soil samples’ physicochemical properties with different degrees of accuracy. In this paper, we developed and examined the use of a novel one-dimensional convolutional neural network (CNN) to simultaneously predict ten physicochemical properties of the LUCAS SSL. Leveraging on the use of multiple-input channels it uses as model inputs the absorbance spectra along with some pre-processed spectra developed using standard techniques. Moreover, it exploits the use of local spectral neighborhoods to perform an adaptive error-correction mechanism. This novel localized multi-channel 1-D CNN was applied to all the available physicochemical properties of the LUCAS SSL and was statistically compared with the current state-of-the-art where it was shown to statistically outperform its counterparts, as well as with other CNNs where it exhibited the best performance. In particular, for the mineral soil samples, the RMSE for the Clay content was 4.80% (R2 0.86), for soil organic carbon the RMSE was 10.96 g kg−1 (R2 0.86), while for total nitrogen the RMSE was 0.66 g kg−1 (R2 0.83).
There is a growing need for raster-based soil data to support modelling at regional and continental scales. The GlobalSoilMap consortium aims to satisfy this need with the production of a suite of ...digital soil maps of various soil properties at six standard depths for most of the land surface of the Earth. Initially, the maps will be produced using legacy soil data (soil data already available). In the United States, the State Soil Geographic (STATSGO2) database is a rich source of legacy soil information, but the STATSGO2 map is vector-based and soil components' soil property data is organised by horizon. We therefore applied the equal-area spline function to the soil components of STATSGO2 map units in order to obtain estimates of soil organic carbon (SOC) content at the GlobalSoilMap standard depth increments. Using these estimates, we calculated the weighted mean of SOC for each STATSGO2 map unit at each GlobalSoilMap.net depth increment and gridded these weighted means at 100m resolution for the contiguous United States. The result is a set of maps of SOC content for each GlobalSoilMap depth increment accompanied by an indication of the within-map unit variability. In addition, we show how the use of other “metadata maps” is essential for avoiding misunderstandings about reported values and discuss some of the limitations of the approach.
► We apply equal-area splines to soils of the U.S. STATSGO2 database. ► We use splines to estimate soil organic carbon at GlobalSoilMap depth increments. ► We map soil organic carbon content for the contiguous U.S. from spline estimates. ► We demonstrate how use of metadata maps augments comprehension of reported values.
Highly homogenous α zein protein was isolated from maize kernels in an environment‐friendly process using 95% ethanol as solvent. Due to the polyploidy and genetic polymorphism of the plant source, ...the application of high resolution separation methods in conjunction with precise analytical methods, such as MALDI‐TOF‐MS, is required to accurately estimate homogeneity of products that contain natural zein protein. The α zein protein product revealed two main bands in SDS‐PAGE analysis, one at 25 kDa and other at 20 kDa apparent molecular mass. Yet, high resolution 2DE revealed approximately five protein spot groups in each row, the first at ca. 25 kDa and the second at ca. 20 kDa. Peptide mass fingerprinting data of the proteins in the two dominant SDS‐PAGE bands matched to 30 amino acid sequence entries out of 102 non‐redundant data base entries. MALDI‐TOF‐MS peptide mapping of the proteins from all spots indicated the presence of only α zein proteins. The most prominent ion signals in the MALDI mass spectra of the protein mixture of the 25 kDa SDS gel band after in‐gel digestion were found at m/z 1272.6 and m/z 2009.1, and the most prominent ion signals of the protein mixture of the 20 kDa band after in‐gel digestion were recorded at m/z 1083.5 and m/z 1691.8. These ion signals have been found typical for α zein proteins and may serve as marker ion signals which upon chymotryptic digestion reliably indicate the presence of α zein protein in two hybrid corn products.
This article is a contribution to the Informatik Spektrum special issue „Cross-Domain Fusion“ – Heft 2. Terminologies are paramount to establish robust communication within interdisciplinary working ...groups inside and outside academia. To find the “common language” is hence essential and sometimes a long way to go. Within the idea of Cross Domain Fusion, we want to tackle this issue from the very beginning. Therefore, we set up a database based on the open source MediaWiki content management system. In this dictionary, a dedicated consortium from different disciplines evaluates terminologies used in Cross Domain Fusion and provides them within the Dialogue:Wiki. The aim is to provide accessible insight into commonalities and differences between different domain-specific terminologies to foster cross domain exchange.
Lithology describes the geochemical, mineralogical, and physical properties of rocks. It plays a key role in many processes at the Earth surface, especially the fluxes of matter to soils, ecosystems, ...rivers, and oceans. Understanding these processes at the global scale requires a high resolution description of lithology. A new high resolution global lithological map (GLiM) was assembled from existing regional geological maps translated into lithological information with the help of regional literature. The GLiM represents the rock types of the Earth surface with 1,235,400 polygons. The lithological classification consists of three levels. The first level contains 16 lithological classes comparable to previously applied definitions in global lithological maps. The additional two levels contain 12 and 14 subclasses, respectively, which describe more specific rock attributes. According to the GLiM, the Earth is covered by 64% sediments (a third of which are carbonates), 13% metamorphics, 7% plutonics, and 6% volcanics, and 10% are covered by water or ice. The high resolution of the GLiM allows observation of regional lithological distributions which often vary from the global average. The GLiM enables regional analysis of Earth surface processes at global scales. A gridded version of the GLiM is available at the PANGEA Database (http://dx.doi.org/10.1594/PANGAEA.788537).
Key Points
Global lithological map of high resolution
Three levels of lithological information are provided
A gridded version of the map is available
To facilitate the assessment of hazards and risk from volcanoes, we have created a comprehensive global database of Quaternary Large Magnitude Explosive Volcanic Eruptions (LaMEVE). This forms part ...of the larger Volcanic Global Risk Identification and Analysis Project (VOGRIPA), and also forms part of the Global Volcano Model (GVM) initiative (
http://www.globalvolcanomodel.org
). A flexible search tool allows users to select data on a global, regional or local scale; the selected data can be downloaded into a spreadsheet. The database is publically available online at
http://www.bgs.ac.uk/vogripa
and currently contains information on nearly 3,000 volcanoes and over 1,800 Quaternary eruption records. Not all volcanoes currently have eruptions associated with them but have been included to allow for easy expansion of the database as more data are found. Data fields include: magnitude, Volcanic Explosivity Index (VEI), deposit volumes, eruption dates, and rock type. The scientific community is invited to contribute new data and also alert the database manager to potentially incorrect data. Whilst the database currently focuses only on large magnitude eruptions, it will be expanded to include data specifically relating to the principal volcanic hazards (e.g. pyroclastic flows, tephra fall, lahars, debris avalanches, ballistics), as well as vulnerability (e.g. population figures, building type) to facilitate risk assessments of future eruptions.
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