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In geodesy and geoinformation science, as well as in many other technical disciplines, it is often not possible to directly determine the desired target quantities. Therefore, the unknown parameters must be linked with the measured values by a mathematical model which consists of the functional and the stochastic models. The functional model describes the geometrical–physical relationship between the measurements and the unknown parameters. This relationship is sufficiently well known for most applications. With regard to the stochastic model, two problem domains of fundamental importance arise: 1. How can stochastic models be set up as realistically as possible for the various geodetic observation methods and sensor systems? 2. How can the stochastic information be adequately considered in appropriate least squares adjustment models? Further questions include the interpretation of the stochastic properties of the computed target values with regard to precision and reliability and the use of the results for the detection of outliers in the input data (measurements). In this Special Issue, current research results on these general questions are presented in ten peer-reviewed articles. The basic findings can be applied to all technical scientific fields where measurements are used for the determination of parameters to describe geometric or physical phenomena.
This second edition of Mathematical Geosciences book adds five new topics: Solution equations with uncertainty, which proposes two novel methods for solving nonlinear geodetic equations as stochastic variables when the parameters of these equations have uncertainty characterized by probability distribution. The first method, an algebraic technique, partly employs symbolic computations and is applicable to polynomial systems having different uncertainty distributions of the parameters. The second method, a numerical technique, uses stochastic differential equation in Ito form; Nature Inspired Global Optimization where Meta-heuristic algorithms are based on natural phenomenon such as Particle Swarm Optimization. This approach simulates, e.g., schools of fish or flocks of birds, and is extended through discussion of geodetic applications. Black Hole Algorithm, which is based on the black hole phenomena is added and a new variant of the algorithm code is introduced and illustrated based on examples; The application of the Gröbner Basis to integer programming based on numeric symbolic computation is introduced and illustrated by solving some standard problems; An extension of the applications of integer programming solving phase ambiguity in Global Navigation Satellite Systems (GNSSs) is considered as a global quadratic mixed integer programming task, which can be transformed into a pure integer problem with a given digit of accuracy. Three alternative algorithms are suggested, two of which are based on local and global linearization via McCormic Envelopes; and Machine learning techniques (MLT) that offer effective tools for stochastic process modelling. The Stochastic Modelling section is extended by the stochastic modelling via MLT and their effectiveness is compared with that of the modelling via stochastic differential equations (SDE). Mixing MLT with SDE also known as frequently Neural Differential Equations is also introduced and illustrated by an image classification via a regression problem.
Global Navigation Satellite Systems (GNSS), such as GPS, have become an efficient, reliable and standard tool for a wide range of applications. However, when processing GNSS data, the stochastic model characterising the precision of observations and the correlations between them is usually simplified and incomplete, leading to overly optimistic accuracy estimates. This work extends the stochastic model using signal-to-noise ratio (SNR) measurements and time series analysis of observation residuals. The proposed SNR-based observation weighting model significantly improves the results of GPS data analysis, while the temporal correlation of GPS observation noise can be efficiently described by means of autoregressive moving average (ARMA) processes. Furthermore, this work includes an up-to-date overview of the GNSS error effects and a comprehensive description of various mathematical methods.
Significant advances in the scientific use of space based data were achieved in three joint interdisciplinary projects based on data of the satellite missions CHAMP, GRACE and GOCE within the R&D program GEOTECHNOLOGIEN. It was possible to explore and monitor changes related to the Earth’s surface, the boundary layer between atmosphere and solid earth, and the oceans and ice shields. This boundary layer is our habitat and therefore is in the focus of our interests. The Earth’s surface is subject to anthropogenetic changes, to changes driven by the Sun, Moon and planets, and by changes caused by processes in the Earth system. The state parameters and their changes are best monitored from space. The theme “Observation of the System Earth from Space” offers comprehensive insights into a broad range of research topics relevant to society including geodesy, oceanography, atmospheric science (from meteorology to climatology), hydrology and glaciology.
Geomatics, the handling and processing of information and data about the Earth, is one geoscience discipline that has seen major changes in the last decade, as mapping and observation systems become ever more sensitive and sophisticated. This book is a unique and in-depth survey of the field, which has a central role to play in tackling a host of environmental issues faced by society. Covering all three strands of geomatics - applications, information technology and surveying - the chapters cover the history and background of the subject, the technology employed both to collect and disseminate data, and the varied applications to which geomatics can be put, including urban planning, assessment of biodiversity, disaster management and land administration. Relevant professionals, as well as students in a variety of disciplines such as geography and surveying, will find this book required reading. This rapidly developing field uses increasingly complex and accurate systems. Today, technology enables us to capture geo-data in full 3D as well as to disseminate it via the Web at the speed of light. We are able to continuously image the world from space at resolutions of up to 50 cm. Airborne LiDAR (laser surveying) sensors can be combined with digital camera technology to produce geometrically correct images of the Earth's surface, while integrating these with large-scale topographic maps and terrestrial as well as aerial images to produce 3D cityscapes that computer users can explore from their desktops.
Spatial information technology and its integration, such as remote sensing, geographic information systems (GIS), and global navigation satellite systems (GNSS), known as 3S technology, have been extensively utilized in managing and monitoring natural disasters. This book illustrates the 3S integrated applications in the field of meteorology and promotes the role of 3S in developing precise and intelligent meteorology. It presents the principles of 3S technology and the methods for monitoring different meteorological disasters and hazards as well as their application progress. The case studies from the United States, Japan, China, and Europe were conducted to help all countries understand the 3S technology functions in handling and monitoring severe meteorological hazards. FEATURES Presents integral observations from GNSS, GIS, and remote sensing in estimating and understanding meteorological changes Explains how to monitor and retrieve atmospheric parameter changes using GNSS and remote sensing Shows three-dimensional modelling and evaluations of meteorological variation processing based on GIS Helps meteorologists develop and use space-air-ground integrated observations for meteorological applications Illustrates the practices in monitoring meteorological hazards using space information techniques and case studies This book is intended for academics, researchers, and postgraduate students who specialize in geomatics, atmospheric science, and meteorology, as well as scientists who work in remote sensing and meteorology, and professionals who deal with meteorological hazards.
These proceedings include selected papers from the International Review Workshop on Satellite Altimetry Cal/Val Activities and Applications, held in Chania, Crete, Greece, on 23-26 April 2018. Organised in the context of the European Space Agency Project “Fiducial Reference Measurements for Altimetry” the workshop was cosponsored by the International Association of Geodesy (in particular by the IAG Commission 2, Gravity Field), the European Space Agency, the European Union (the Copernicus Programme), the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT), Space Geomatica P.C., and the Municipality of Chania. The workshop presented the latest research in the field of satellite altimetry calibration and altimetry applications for monitoring ocean changes and improving Earth observation in an objective, continuous, homogeneous and reliable manner, free of errors and biases. Further, it supported long-term monitoring of climate change by providing a better understanding of environmental changes in the world's oceans, terrestrial surface waters, and Arctic and Antarctic Regions. The outcome was the creation of a scientific roadmap with procedures, protocols, guidelines, and best practices to help international groups working on satellite altimetry to establish SI (Système International d'Unités) traceability of their measurements, results and data products.
Contents Abstract xi Samenvatting xv Curriculum vitae xix Acknowledgements xxii 1. Introduction 1 2. Gravity field modeling from SST data: an overview 9 3. Gravity field modeling from CHAMP data 53 4. Gravity field modeling from GRACE hl-SST data 81 5. Gravity field modeling from GRACE ll-SST data 91 6. Analysis of results obtained from the 3RC approach 133 7. Summary, conclusions and recommendations 203 Bibliography 209 A. Autocorrelation 223 B. Gaussian Filtering 225
This comprehensive handbook covers Geospatial Artificial Intelligence (GeoAI), which is the integration of geospatial studies and AI machine (deep) learning and knowledge graph technologies. It explains key fundamental concepts, methods, models, and technologies of GeoAI, and discusses the recent advances, research tools, and applications that range from environmental observation and social sensing to natural disaster responses. As the first single volume on this fast-emerging domain, Handbook of Geospatial Artificial Intelligence is an excellent resource for educators, students, researchers, and practitioners utilizing GeoAI in fields such as information science, environment and natural resources, geosciences, and geography. Features Provides systematic introductions and discussions of GeoAI theory, methods, technologies, applications, and future perspectives Covers a wide range of GeoAI applications and case studies in practice Offers supplementary materials such as data, programming code, tools, and case studies Discusses the recent developments of GeoAI methods and tools Includes contributions written by top experts in cutting-edge GeoAI topics This book is intended for upper-level undergraduate and graduate students from different disciplines and those taking GIS courses in geography or computer sciences as well as software engineers, geospatial industry engineers, GIS professionals in non-governmental organizations, and federal/state agencies who use GIS and want to learn more about GeoAI advances and applications.