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Digital Mapping of Soil Landscape Parameters

Geospatial Analyses using Machine Learning and Geomatics

  • Pradeep Kumar Garg
  • Rahul Dev Garg
  • Gaurav Shukla
  • Hari Shanker Srivastava
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  • 155 Downloads

Part of the Studies in Big Data book series (SBD, volume 72)

Table of contents

  1. Front Matter
    Pages i-xix
  2. Pradeep Kumar Garg, Rahul Dev Garg, Gaurav Shukla, Hari Shanker Srivastava
    Pages 1-12
  3. Pradeep Kumar Garg, Rahul Dev Garg, Gaurav Shukla, Hari Shanker Srivastava
    Pages 13-46
  4. Pradeep Kumar Garg, Rahul Dev Garg, Gaurav Shukla, Hari Shanker Srivastava
    Pages 47-63
  5. Pradeep Kumar Garg, Rahul Dev Garg, Gaurav Shukla, Hari Shanker Srivastava
    Pages 65-92
  6. Pradeep Kumar Garg, Rahul Dev Garg, Gaurav Shukla, Hari Shanker Srivastava
    Pages 93-116
  7. Pradeep Kumar Garg, Rahul Dev Garg, Gaurav Shukla, Hari Shanker Srivastava
    Pages 117-142

About this book

Introduction

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.

 


Keywords

Digital Mapping Soil-landscape Parameters Prediction Model Environmental Covariates Remote Sensing and GIS Digital Soil Mapping Big Data Artificial Intelligence

Authors and affiliations

  • Pradeep Kumar Garg
    • 1
  • Rahul Dev Garg
    • 2
  • Gaurav Shukla
    • 3
  • Hari Shanker Srivastava
    • 4
  1. 1.Geomatics Section, Civil Engineering DepartmentIndian Institute of Technology RoorkeeRoorkeeIndia
  2. 2.Geomatics Section, Civil Engineering DepartmentIndian Institute of Technology RoorkeeRoorkeeIndia
  3. 3.Surveying and Geomatics Section, Civil Engineering DepartmentMaharishi Markandeshwar UniversityAmbalaIndia
  4. 4.Indian Institute of Remote Sensing (IIRS)DehradunIndia

Bibliographic information

  • DOI https://doi.org/10.1007/978-981-15-3238-2
  • Copyright Information The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2020
  • Publisher Name Springer, Singapore
  • eBook Packages Intelligent Technologies and Robotics
  • Print ISBN 978-981-15-3237-5
  • Online ISBN 978-981-15-3238-2
  • Series Print ISSN 2197-6503
  • Series Online ISSN 2197-6511
  • Buy this book on publisher's site