Search results for " mining"
showing 10 items of 1548 documents
Melting temperature prediction by thermoelastic instability: An ab initio modelling, for periclase (MgO)
2021
Abstract Melting temperature (TM) is a crucial physical property of solids and plays an important role for the characterization of materials, allowing us to understand their behavior at non-ambient conditions. The present investigation aims i) to provide a physically sound basis to the estimation of TM through a “critical temperature” (TC), which signals the onset of thermodynamic instability due to a change of the isothermal bulk modulus from positive to negative at a given PC-VC-TC point, such that (∂P/∂V)VC,TC = -(∂2F/∂V2) VC,TC = 0; ii) to discuss the case of periclase (MgO), for which accurate melting temperature observations as a function of pressure are available. Using first princip…
A Regional Geography Approach to Understanding the Environmental Changes as a Consequence of the COVID-19 Lockdown in Highly Populated Spanish Cities
2021
Spain has been highly impacted by the COVID-19 pandemic, which is reflected at different scales. From an economic point of view, lockdowns and the reduction of activities have damaged the country (e.g., complete lockdown from March 13 to June 21, 2020). However, it is not clear if the associated environmental impacts could be observed in 2020. Currently, studies on the effects of the lockdown (e.g., decrease in economic activities, transport and social communication) on specific parameters related to climate change, such as air temperature or air pollution, due to a drastic decrease in human activities are rare. They are focused on specific cities and short periods of time. Therefore, the m…
Transferring deep learning models for cloud detection between Landsat-8 and Proba-V
2020
Abstract Accurate cloud detection algorithms are mandatory to analyze the large streams of data coming from the different optical Earth observation satellites. Deep learning (DL) based cloud detection schemes provide very accurate cloud detection models. However, training these models for a given sensor requires large datasets of manually labeled samples, which are very costly or even impossible to create when the satellite has not been launched yet. In this work, we present an approach that exploits manually labeled datasets from one satellite to train deep learning models for cloud detection that can be applied (or transferred) to other satellites. We take into account the physical proper…
Channel forms recovery in an ephemeral river after gravel mining (Palancia River, Eastern Spain)
2017
[EN] During the 1970s, the Palancia River was intensively affected by gravel mining instream. This activity completely destroyed the fluvial forms, devastating the original wandering pattern. At the end of the 1980s, gravel mining ceased and the river started a process of recovery, only altered by several clearing operations. The aim of this work is to describe these processes of change, analyzing the river's morphosedimentary conditions through a GIS analysis of aerial photographs previous to, simultaneous with, and subsequent to the intense gravel mining activity. Results explain the current difficulties of some ephemeral rivers to recover their original forms, because of the sediment and…
Hyperspectral dimensionality reduction for biophysical variable statistical retrieval
2017
Abstract Current and upcoming airborne and spaceborne imaging spectrometers lead to vast hyperspectral data streams. This scenario calls for automated and optimized spectral dimensionality reduction techniques to enable fast and efficient hyperspectral data processing, such as inferring vegetation properties. In preparation of next generation biophysical variable retrieval methods applicable to hyperspectral data, we present the evaluation of 11 dimensionality reduction (DR) methods in combination with advanced machine learning regression algorithms (MLRAs) for statistical variable retrieval. Two unique hyperspectral datasets were analyzed on the predictive power of DR + MLRA methods to ret…
Optimizing Gaussian Process Regression for Image Time Series Gap-Filling and Crop Monitoring
2020
Image processing entered the era of artificial intelligence, and machine learning algorithms emerged as attractive alternatives for time series data processing. Satellite image time series processing enables crop phenology monitoring, such as the calculation of start and end of season. Among the promising algorithms, Gaussian process regression (GPR) proved to be a competitive time series gap-filling algorithm with the advantage of, as developed within a Bayesian framework, providing associated uncertainty estimates. Nevertheless, the processing of time series images becomes computationally inefficient in its standard per-pixel usage, mainly for GPR training rather than the fitting step. To…
Geomeasure: GIS and Scripting for Measuring Morphometric Variability
2019
This paper presents Geomeasure, a methodological tool developed to recover typometric information with a twofold objective. First, to speed up the process of gathering data by automatizing the way in which it is recovered. Second, it adds higher accuracy and the possibility of re-measuring archeological items without further directly interacting with the piece. Based on a combination of R scripting with GIS features, Geomeasure is at the time able to automatically gather 125–130 typometric variables per archaeological item, with the only input of vectorized photographs. It can be used as a reliable methodological aid to extract detailed information on patterns and trends of shape variabilit…
Comment on the letter of the Society of Vertebrate Paleontology (SVP) dated April 21, 2020 regarding “Fossils from conflict zones and reproducibility…
2020
Motivation for this comment Recently, the Society of Vertebrate Paleontology (SVP) has sent around a letter, dated 21st April, 2020 to more than 300 palaeontological journals, signed by the President, Vice President and a former President of the society (Rayfield et al. 2020). The signatories of this letter request significant changes to the common practices in palaeontology. With our present, multi-authored comment, we aim to argue why these suggestions will not lead to improvement of both practice and ethics of palaeontological research but, conversely, hamper its further development. Although we disagree with most contents of the SVP letter, we appreciate this initiative to discuss scien…
Summarizing the state of the terrestrial biosphere in few dimensions
2020
Abstract. In times of global change, we must closely monitor the state of the planet in order to understand the full complexity of these changes. In fact, each of the Earth's subsystems – i.e., the biosphere, atmosphere, hydrosphere, and cryosphere – can be analyzed from a multitude of data streams. However, since it is very hard to jointly interpret multiple monitoring data streams in parallel, one often aims for some summarizing indicator. Climate indices, for example, summarize the state of atmospheric circulation in a region. Although such approaches are also used in other fields of science, they are rarely used to describe land surface dynamics. Here, we propose a robust method to crea…