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Books > Earth & environment > Geography > Cartography, geodesy & geographic information systems (GIS) > Remote sensing
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Spatial Modeling Principles in Earth Sciences (Paperback, Softcover reprint of the original 2nd ed. 2016)
Loot Price: R4,387
Discovery Miles 43 870
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Spatial Modeling Principles in Earth Sciences (Paperback, Softcover reprint of the original 2nd ed. 2016)
Expected to ship within 10 - 15 working days
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This is a revised and updated second edition, including new
chapters on temporal and point uncertainty model, as well as on
sampling and deterministic modeling. It is a comprehensive
presentation of spatial modeling techniques used in the earth
sciences, outlining original techniques developed by the author.
Data collection in the earth sciences is difficult and expensive,
but simple, rational and logical approaches help the reader to
appreciate the fundamentals of advanced methodologies. It requires
special care to gather accurate geological, hydrogeological,
meteorological and hydrological information all with risk
assessments. Spatial simulation methodologies in the earth sciences
are essential, then, if we want to understand the variability in
features such as fracture frequencies, rock quality, and grain size
distribution in rock and porous media. This book outlines in a
detailed yet accessible way the main spatial modeling techniques,
in particular the Kriging methodology. It also presents many unique
physical approaches, field cases, and sample interpretations. Since
Kriging's origin in the 1960s it has been developed into a number
of new methods such as cumulative SV (CSV), point CSV (PCSV), and
spatial dependence function, which have been applied in different
aspects of the earth sciences. Each one of these techniques is
explained in this book, as well as how they are used to model earth
science phenomena such as geology, earthquakes, meteorology, and
hydrology. In addition to Kriging and its variants, several
alternatives to Kriging methodology are presented and the necessary
steps in their applications are clearly explained. Simple spatial
variation prediction methodologies are also revised with up-to-date
literature, and the ways in which they relate to more advanced
spatial modeling methodologies are explained. The book is a
valuable resource for students, researchers and professionals of a
broad range of disciplines including geology, geography, hydrology,
meteorology, environment, image processing, spatial modeling and
related topics. Keywords "Data mining - Geo-statistics - Kriging -
Regional uncertainty - Spatial dependence - Spatial modeling -
geographic data - geoscience - hydrology - image processing
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