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.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
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.
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
Vendeur : Brook Bookstore On Demand, Napoli, NA, Italie
Etat : new. Questo è un articolo print on demand. N° de réf. du vendeur c552f7540453f1c5f4aac8343d1ac984
Quantité disponible : Plus de 20 disponibles
Vendeur : Ria Christie Collections, Uxbridge, Royaume-Uni
Etat : New. In. N° de réf. du vendeur ria9789811532405_new
Quantité disponible : Plus de 20 disponibles
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 164 pp. Englisch. N° de réf. du vendeur 9789811532405
Quantité disponible : 2 disponible(s)
Vendeur : moluna, Greven, Allemagne
Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Provides a framework for model development for key parameters, below, at and above the surface Presents color images for better visual interpretation and learning Includes sample satellite images for practical applications. N° de réf. du vendeur 442355241
Quantité disponible : Plus de 20 disponibles
Vendeur : Books Puddle, New York, NY, Etats-Unis
Etat : New. N° de réf. du vendeur 26384592247
Quantité disponible : 4 disponible(s)
Vendeur : Majestic Books, Hounslow, Royaume-Uni
Etat : New. Print on Demand. N° de réf. du vendeur 379311784
Quantité disponible : 4 disponible(s)
Vendeur : preigu, Osnabrück, Allemagne
Taschenbuch. Etat : Neu. Digital Mapping of Soil Landscape Parameters | Geospatial Analyses using Machine Learning and Geomatics | Pradeep Kumar Garg (u. a.) | Taschenbuch | xix | Englisch | 2021 | Springer | EAN 9789811532405 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. N° de réf. du vendeur 119601791
Quantité disponible : 5 disponible(s)
Vendeur : Biblios, Frankfurt am main, HESSE, Allemagne
Etat : New. PRINT ON DEMAND. N° de réf. du vendeur 18384592253
Quantité disponible : 4 disponible(s)
Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
Taschenbuch. Etat : Neu. Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 164 pp. Englisch. N° de réf. du vendeur 9789811532405
Quantité disponible : 2 disponible(s)
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
Taschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - 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. N° de réf. du vendeur 9789811532405
Quantité disponible : 1 disponible(s)