Digital Mapping of Soil Landscape Parameters Geospatial Analyses using Machine Learning and Geomatics

Language: English

Published by Springer Nature Singapore, 2020

9811532370 / 9789811532375

Series: Book 66 of 95 - Studies in Big Data

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This book addresses the mapping of soil-landscape parameters in the geospatial domain. It begins by discussing the fundamental concepts, and then explains how machine learning and geomatics can be applied for more efficient mapping and to improve our understanding and management of 'soil'. The judicious utilization of a piece of land is one of the biggest and most important current challenges, especially in light of the rapid global urbanization, which requires continuous monitoring of resource consumption. The book provides a clear overview of how machine learning can be used to analyze remote sensing data to monitor the key parameters, below, at, and above the surface. It not only offers insights into the approaches, but also allows readers to learn about the challenges and issues associated with the digital mapping of these parameters and to gain a better understanding of the selection of data to represent soil-landscape relationships as well as the complex and interconnected links between soil-landscape parameters under a range of soil and climatic conditions. Lastly, the book sheds light on using the network of satellite-based Earth observations to provide solutions toward smart farming and smart land management. Sprache: Englisch Gewicht in Gramm: 366.…

Title
Digital Mapping of Soil Landscape Parameters Geospatial Analyses using Machine Learning and Geomatics
Author
Gaurav Shukla, Pradeep Kumar Garg, Rahul Dev Garg, Hari Shanker Srivastava
Publisher
Springer Nature Singapore
Publication year
2020
Condition
Neu
Binding
Gebundene Ausgabe
Language
English
ISBN 10
9811532370
ISBN 13
9789811532375
Item weight
366 grams
Series
Book 66 of 95: Studies in Big Data

Home-set

Mannheim, Germany

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Home-set

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