Investigation Of Earth Research Articles

An investigation of earth and sea-return impedances of power electrical cables

This paper investigates the numerical performance of the Wedepohl series and of the Gauss-Kronrod quadrature to calculate ground- and sea-return impedances of power cables. Many computational routines to calculate ground-return impedances of cables used in EMTP-type programs have been proposed without technical justification of their application range. In this paper, we explore the convergence and accuracy of the Wedepohl series based on the number of terms in the series and as a ratio test. As a result, an accurate and stable numerical algorithm to compute ground- and sea-return impedances is proposed. The proposed method is capable of accounting for very low resistivity values and large depths.

Land cover classification using Land Parcel Identification System (LPIS) data and open source Eo-Learn library

Depending on the development of remote sensing technologies, data at various spatial, temporal and spectral resolutions are obtained and play a key role in Earth investigation. Land cover/use maps are also created using these data. These data play an important role as a base for determining land cover and land use classes, detecting changes, monitoring forest, agriculture and wetlands, as well as updating the LPIS data used by countries. As well as being reference data in the LPIS update, current land cover and land use classification results with satellite images also provide statistical information about which class has changed over the years and how much the class or classes need to be updated, and will also provide the planning of the update to be made on a national scale. In this study, land cover classification for 2020 covering Çivril-Baklan Plain, which is between the borders of Denizli Province (Turkey), Baklan, Çal and Çivril Districts, was performed. The LPIS classes of the study area drawn in 2015 and the land cover classes as a result of the 2020 classification were compared as a result of the classification process, and particularly the areas with change were referenced in the LPIS update. In the classification process, the open source Eo-Learn library, which uses machine learning and deep learning algorithms in remote sensing studies and Sentinel-2 images that can be accessed directly in this library were used. The open source Eo-Learn library has been preferred because it provides great convenience to users in the classification process with ready-made machine learning models and easy access to satellite images, image processing steps (cloud masking, calculate index, feature extraction, temporal interpolation) are carried out in a certain workflow. Instead of land cover classification from a single image, 17 different time (multi-temporal) images between 01.02.2020 and 31.11.2020 were used. In the classification process, 12 different features were used for the image taken on each date, including NDVI, NDWI, NDBI indices and Tasseled Cap transformations, as well as (Blue) B02, (Green) B03, (Red) B04, (NIR) B08, (SWIR-1) B11, (SWIR-2) B12 bands. In the classification process, the Light Gradient Boosting Machines (LightGBM) algorithm in the Eo-Learn library and the physical blocks produced within the scope of the Land Parcel Identification System (LPIS) project were used as ground truth data; arable land, bare land, forest, artificial surface, shrubland, tree crops and water classes were created. The classification results were evaluated using the K fold (k = 5) cross validation method, with F1 score, recall, and precision values calculated for each class, and the overall accuracy %92.2. When the classification result is compared with the LPIS data, it is seen that the arable land, shrub land, and water classes have changed. This study determined that these classes, in particular, need to be updated.

Climate education in secondary science: comparison of model-based and non-model-based investigations of Earth’s climate

ABSTRACT In this mixed method study, we analyse the effectiveness of two pedagogical approaches – one model-based and another non-model-based – for developing secondary students’ understanding of the phenomenon of increase in Earth’s average surface temperatures, a core dimension of global climate change (GCC). Building on past research on teaching and learning about Earth’s climate, we use an Evidence-Based Reasoning framework to assess student tasks and interviews from a 3-week, project-developed, model-based curriculum. We observed that the use of a climate model allowed students to reason more effectively about the Earth’s increasing temperatures. They were able to establish the premise and interpret evidence for the phenomenon more effectively with the climate model. Using temperature and carbon dioxide data sets, students observed and quantified the behaviour of climate variables, establishing correlation and causation through data-integrated claims. In doing so, students were able to develop scientific knowledge about climate science as well as understand the processes undertaken by climate scientists in analysing climate data. Given the importance of fostering climate literacy in K-12 students, study findings have implications for both teaching and learning about GCC using climate models, as well as in shifting students’ focus from acquiring knowledge to constructing their own knowledge.

New Sensing Strategies for Estimation of Global, Exospheric Density

AbstractReliable quantification of the global and time‐dependent structure of the Earth's outermost atmosphere, a vast region known as the exosphere, has long been elusive, owing mainly to the sparse spatial and temporal sampling afforded by exospheric sensing platforms deployed to date. In this paper, we introduce new observing schemes that overcome these historical limitations and enable high‐fidelity, high‐cadence reconstruction of the exospheric density distribution on a global scale. Our approach leverages several state‐of‐the‐art remote sensing capabilities, including (1) wide‐field photometric imaging of exospheric emission, which is created through resonant photon scattering by the exosphere's constituent hydrogen atoms; (2) constellation sensor deployment near lunar orbit, which provides common‐volume sensing; and (3) tomographic inversion of a discretized exospheric emission model that explicitly accounts for the Poisson‐distributed shot noise inherent in the photometric data constraints. Using ensembles of synthetic emission data acquired at various realistic temporal cadences, we evaluate the fidelity of both static density reconstructions, in which the solution is formulated in the context of maximum a posteriori (MAP) estimation, as well as dynamic density reconstructions, which incorporate temporal evolution via Kalman filtering. The numerical results demonstrate that the new sensing strategies are capable of retrieving reliable estimates of exospheric density distribution at unprecedented spatial scales and temporal cadence, in support of numerous high‐priority investigations of Earth's near‐space environment.

Measuring the Inaccessible Earth: Geomagnetism, In situ Measurements, Remote Sensing, and Proxy Data

The usual problems of measurement and its meaning are complicated and magnified when the object of study is in principle and in fact inaccessible. When a phenomenon occurs in a place where our instruments cannot reach, what can the relation between the instrument, its reading, and the phenomenon be? This essay asks how researchers have addressed questions about inaccessible processes of Earth's magnetic field on the surface, at the edge of space and under its surface. This case takes us beyond simple contrasts of inductive and deductive methods, or field sciences versus basic sciences, to a messier, more nuanced view of inherently complex sciences. The article focuses on investigations of Earth's magnetism and electricity, but its perspectives are applicable to many areas of recent science.

Observations of triboelectric charging effects on Langmuir‐type probes in dusty plasma

Investigation of Earth's mesosphere using sounding rockets equipped with a myriad of instruments has been a highly active field in the last 2 decades. This paper presents data from three separate instruments: an RF impedance probe, a DC fixed bias Langmuir probe, and an electric field probe, that were flown on a mesospheric sounding rocket flight investigating the presence of charged dust within and/or around a sporadic metal layer. The combined data set indicates a case of payload surface charging, the causes of which are investigated within this paper. A generic circuit model is developed to analyze payload charging and behavior of Langmuir‐type instruments. The application of this model to the rocket payload indicates that the anomalous charging event was an outcome of triboelectrification of the payload surface from neutral dust particles present in the Earth's mesosphere. These results suggest caution in interpreting observations from the Langmuir class of instrumentation within dusty environments.

Design and investigation of earth and rockfill dams

Design and investigation of earth and rockfill dams