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

ISBN-13: 9789811532405 / Angielski / Miękka / 2021 / 142 str.

Pradeep Kumar Garg; Rahul Dev Garg; Gaurav Shukla

Spis treści

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.

O autorze

Professor Pradeep Kumar Garg is faculty in Civil Engineering Department of Indian Institute of Technology, Roorkee. He has also served as vice chancellor, Uttarakhand Technical University, Dehradun. He completed his B.Tech. in 1980 and his M.Tech. in 1982, both from the University of Roorkee, India (now IIT Roorkee). He did a Ph.D. from University of Bristol, UK, and postdoctoral research work at the University of Reading, UK. He joined the Department of Civil Engineering at IIT Roorkee in 1982. Dr. Garg has published about 93 research papers, guided 7 Ph.D. thesis, 47 M.Tech. thesis, authored one textbook on Remote Sensing, organized 22 training programmes, and prepared two educational films under UGC programme. He has completed 13 research projects and 25 consultancy projects. He is Fellow member of 5 Technical Societies and life member of 15 Technical Societies. His main areas of research interest are remote sensing and GIS applications. 

Professor Rahul Dev Garg is faculty in Civil Engineering Department of Indian Institute of Technology, Roorkee. He graduated with a bachelor’s in technology in Civil Engineering in 1989, master’s in technology in 1993, and a Ph.D. in 2004 from IIT Roorkee. He has also served as a scientist from 1993 to 2007 in Indian Institute of Remote Sensing (IIRS), Dehradun. Dr. Garg has published about 110 research papers, guided 8 Ph.D. thesis, 47 M.Tech. thesis, authored one textbook on Remote Sensing, organized 22 training programmes, and prepared two educational films under UGC programme. He has completed 11 research projects and 24 consultancy projects. He is Fellow member of 3 Technical Societies and life member of 10 Technical Societies. His main areas of interest are land surveying, remote sensing, GIS, GPS, digital image processing, SAR interferometry, and GPR. 

Dr. Gaurav Shukla is currently working as a faculty member in surveying and geomatics section, Civil Engineering Department, Maharishi Markandeshwar (Deemed to be University) University, Mullana, Haryana, India. He is also a nodal coordinator of MHRD initiative, virtual lab programme. He is postgraduated in geomatics from the Indian Institute of Technology (ISM), Dhanbad, in 2011 and completed his Ph.D. from Indian Institute of Technology, Roorkee, India, in 2018. Dr. Shukla has published 7 journal papers and organized 2 training programmes. His main areas of research interest include nonparametric approaches to retrieval of Earth’s parameters, GNSS reflectometry, remote sensing, and GIS applications. 

Dr. Hari Shanker Srivastava (Scientist G) is currently working as Scientist/Engineer-SG  in Agriculture and Soils Department of Indian Institute of Remote Sensing (IIRS), Indian Space Research Organization (ISRO), Dehradun, India. He did a Ph.D. in Physics on synthetic aperture radar. He explored multiparametric microwave data from ground-based scatterometer, RADARSAT-2, hybrid polarimetric RISAT-1 SAR, passive AMSR-E and SMOS for various applications in agriculture, soil moisture, surface roughness, forestry, wetland, and human settlement.

Blurb

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.