Digital mapping of soil landscape parameters : geospatial analyses using machine learning and geomatics
Author(s)
Bibliographic Information
Digital mapping of soil landscape parameters : geospatial analyses using machine learning and geomatics
(Studies in big data, v. 72)
Springer, c2020
- : [hardback]
Available at 2 libraries
  Aomori
  Iwate
  Miyagi
  Akita
  Yamagata
  Fukushima
  Ibaraki
  Tochigi
  Gunma
  Saitama
  Chiba
  Tokyo
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  Niigata
  Toyama
  Ishikawa
  Fukui
  Yamanashi
  Nagano
  Gifu
  Shizuoka
  Aichi
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  Shiga
  Kyoto
  Osaka
  Hyogo
  Nara
  Wakayama
  Tottori
  Shimane
  Okayama
  Hiroshima
  Yamaguchi
  Tokushima
  Kagawa
  Ehime
  Kochi
  Fukuoka
  Saga
  Nagasaki
  Kumamoto
  Oita
  Miyazaki
  Kagoshima
  Okinawa
  Korea
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  United Kingdom
  Germany
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Note
Includes bibliographical references
Description and Table of Contents
Description
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.
Table of Contents
Chapter 1. Concept of Digital Mapping.- Chapter 2. Different Approaches on Digital Mapping of Soil.- Chapter 3. Selection of Suitable Variables and Their Development.- Chapter 4. Digital Soil Mapping: Implementation and Assessment.- Chapter 5. Prediction Modelsfor Crop Mapping.- Chapter 6. Spatial Soil Moisture Prediction Model over an Agricultural Land.
by "Nielsen BookData"