Martin H. Trauth – författare

MATLAB® is used in a wide range of geoscientific applications, e.g. for image processing in remote sensing, for creating and processing digital elevation models, and for analyzing time series. This book introduces readers to MATLAB-based data analysis methods used in the geosciences, including basic statistics for univariate, bivariate and multivariate datasets, time-series analysis, signal processing, the analysis of spatial and directional data, and image analysis. The revised and updated Fifth Edition includes seven new sections, and the majority of the chapters have been rewritten and significantly expanded. New sections include error analysis, the problem of classical linear regression of log-transformed data, aligning stratigraphic sequences, the Normalized Difference Vegetation Index, Aitchison’s log-ratio transformation, graphical representation of spherical data, and statistics of spherical data. The book also includes numerous examples demonstrating how MATLAB can be used on datasets from the earth sciences. The supplementary electronic material (available online through SpringerLink) contains recipes that include all the MATLAB commands featured in the book and the sample data.

This textbook introduces methods of geoscientific data acquisition using MATLAB in combination with inexpensive data acquisition hardware such as sensors in smartphones, sensors that come with the LEGO MINDSTORMS set, webcams with stereo microphones, and affordable spectral and thermal cameras. The text includes 35 exercises in data acquisition, such as using a smartphone to acquire stereo images of rock specimens from which to calculate point clouds, using visible and near-infrared spectral cameras to classify the minerals in rocks, using thermal cameras to differentiate between different types of surface such as between soil and vegetation, localizing a sound source using travel time differences between pairs of microphones to localize a sound source, quantifying the total harmonic distortion and signal-to-noise ratio of acoustic and elastic signals, acquiring and streaming meteorological data using application programming interfaces, wireless networks, and internet of things platforms, determining the spatial resolution of ultrasonic and optical sensors, and detecting magnetic anomalies using a smartphone magnetometer mounted on a LEGO MINDSTORMS scanner. The book’s electronic supplementary material (available online through Springer Link) contains recipes that include all the MATLAB commands featured in the book, the example data, the LEGO construction plans, photos and videos of the measurement procedures.

Python is used in a wide range of geoscientific applications, such as in processing images for remote sensing, in generating and processing digital elevation models, and in analyzing time series. This book introduces methods of data analysis in the geosciences using Python that include basic statistics for univariate, bivariate, and multivariate data sets, time series analysis, and signal processing; the analysis of spatial and directional data; and image analysis. The text includes numerous examples that demonstrate how Python can be used on data sets from the earth sciences. Codes are available online through GitHub.

MATLAB® is used in a wide range of geoscientific applications, such as for image processing in remote sensing, for generating and processing digital elevation models, and for analyzing time series. This book introduces methods of data analysis in the earth sciences using MATLAB, such as basic statistics for univariate, bivariate, and multivariate data sets, time series analysis, signal processing, spatial and directional data analysis, and image analysis. The text includes numerous examples demonstrating how MATLAB can be used on data sets from the earth sciences. The supplementary electronic material (available online through Springer Link) contains recipes that include all the MATLAB commands featured in the book and example data.

MATLAB® is used for a wide range of applications in geosciences, such as image processing in remote sensing, the generation and processing of digital elevation models and the analysis of time series. This book introduces methods of data analysis in geosciences using MATLAB, such as basic statistics for univariate, bivariate and multivariate datasets, time-series analysis, signal processing, the analysis of spatial and directional data and image analysis. The revised and updated Fourth Edition includes sixteen new sections and most chapters have greatly been expanded so that they now include a step by step discussion of all methods before demonstrating the methods with MATLAB functions. New sections include: Array Manipulation; Control Flow; Creating Graphical User Interfaces; Hypothesis Testing; Kolmogorov-Smirnov Test; Mann-Whitney Test; Ansari-Bradley Test; Detecting Abrupt Transitions in Time Series; Exporting 3D Graphics to Create Interactive Documents; Importing, Processing andExporting LANDSAT Images; Importing and Georeferencing TERRA ASTER Images; Processing and Exporting EO-1 Hyperion Images; Image Enhancement; Correction and Rectification; Shape-Based Object Detection in Images; Discriminant Analysis; and Multiple Linear Regression. The text includes numerous examples demonstrating how MATLAB can be used on data sets from earth sciences. The book’s supplementary electronic material (available online through Springer Link) includes recipes that include all the MATLAB commands featured in the book and the example data.

This second edition is an intensively revised and updated version of the book MATLAB® and Design Recipes for Earth Sciences. It aims to introduce students to the typical course followed by a data analysis project in earth sciences. A project usually involves searching relevant literature, reviewing and ranking published books and journal articles, extracting relevant information from the literature in the form of text, data, or graphs, searching and processing the relevant original data using MATLAB, and compiling and presenting the results as posters, abstracts, and oral presentations using graphics design software. The text of this book includes numerous examples on the use of internet resources, on the visualization of data with MATLAB, and on preparing scientific presentations. As with the book MATLAB Recipes for Earth Sciences–4rd Edition (2015), which demonstrates the use of statistical and numerical methods on earth science data, this book uses state-of-the art software packages, including MATLAB and the Adobe Creative Suite, to process and present geoscientific information collected during the course of an earth science project. The book''s supplementary electronic material (available online through the publisher''s website) includes color versions of all figures, recipes with all the MATLAB commands featured in the book, the example data, exported MATLAB graphics, and screenshots of the most important steps involved in processing the graphics.

Trauth, Martin H.

MATLAB® - Rezepte für Geowissenschaften, 1. deutschsprachige Auflage, basierend auf der korrigierten  5. englischsprachigen Auflage

MATLAB® wird in einer Vielzahl von geowissenschaftlichen Anwendungen eingesetzt, z.B. zur Bildverarbeitung in der Fernerkundung, zur Erzeugung und Verarbeitung digitaler Höhenmodelle und zur Analyse von Zeitreihen. Dieses Buch führt in Methoden der Datenanalyse in den Geowissenschaften mit MATLAB ein, wie z.B. grundlegende Statistik für univariate, bivariate und multivariate Datensätze, Zeitreihenanalyse, Signalverarbeitung, die Analyse räumlicher und gerichteter Daten und Bildanalyse. Der Text enthält zahlreiche Beispiele, die zeigen, wie MATLAB auf Datensätze aus den Geowissenschaften angewendet werden kann. Das ergänzende elektronische Material des Buches (online verfügbar über Springer Link) enthält Rezepte, die alle im Buch vorgestellten MATLAB-Befehle und die Beispieldaten enthalten. Das Buch soll Student:innen, Doktorand:innen, Postdoktorand:innen und Fachleuten helfen, schnelle Lösungen für gängige Datenanalyseprobleme in den Geowissenschaften zu finden.

Systemanforderungen

Benutzer:innen dieses Buches benötigen die MATLAB®-Software, die für Windows, macOS und Linux verfügbar ist. Die M-Files und Beispieldaten, die online über Springer Link verfügbar sind, sollten auf allen Plattformen laufen, ohne dass eine Modifikation erforderlich ist. Für diese Ausgabe haben wir MATLAB Version 9.11 (Release 2021b), die Bioinformatics Toolbox Version 4.15.2, die Image Processing Toolbox Version 11.4, die Mapping Toolbox Version 5.2, die Signal Processing Toolbox Version 8.7, Simulink 3D Animation Version 9.3, die Statistics and Machine Learning Toolbox Version 12.2 und die Wavelet Toolbox Version 6.0 verwendet.