LEADER 03912nam 22006015 450 001 9910484233703321 005 20200705041509.0 010 $a981-15-3238-9 024 7 $a10.1007/978-981-15-3238-2 035 $a(CKB)4100000010348454 035 $a(MiAaPQ)EBC6118518 035 $a(DE-He213)978-981-15-3238-2 035 $a(PPN)243767714 035 $a(EXLCZ)994100000010348454 100 $a20200220d2020 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aDigital Mapping of Soil Landscape Parameters $eGeospatial Analyses using Machine Learning and Geomatics /$fby Pradeep Kumar Garg, Rahul Dev Garg, Gaurav Shukla, Hari Shanker Srivastava 205 $a1st ed. 2020. 210 1$aSingapore :$cSpringer Singapore :$cImprint: Springer,$d2020. 215 $a1 online resource (159 pages) 225 1 $aStudies in Big Data,$x2197-6503 ;$v72 311 $a981-15-3237-0 327 $aChapter 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. 330 $aThis book addresses the mapping of soil-landscape parameters in the geospatial domain. It begins by discussing the fundamental concepts, and then explains how machine learning and geomatics can be applied for more efficient mapping and to improve our understanding and management of ?soil?. The judicious utilization of a piece of land is one of the biggest and most important current challenges, especially in light of the rapid global urbanization, which requires continuous monitoring of resource consumption. The book provides a clear overview of how machine learning can be used to analyze remote sensing data to monitor the key parameters, below, at, and above the surface. It not only offers insights into the approaches, but also allows readers to learn about the challenges and issues associated with the digital mapping of these parameters and to gain a better understanding of the selection of data to represent soil-landscape relationships as well as the complex and interconnected links between soil-landscape parameters under a range of soil and climatic conditions. Lastly, the book sheds light on using the network of satellite-based Earth observations to provide solutions toward smart farming and smart land management. . 410 0$aStudies in Big Data,$x2197-6503 ;$v72 606 $aComputational intelligence 606 $aBig data 606 $aRemote sensing 606 $aComputational Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/T11014 606 $aBig Data$3https://scigraph.springernature.com/ontologies/product-market-codes/I29120 606 $aRemote Sensing/Photogrammetry$3https://scigraph.springernature.com/ontologies/product-market-codes/J13010 615 0$aComputational intelligence. 615 0$aBig data. 615 0$aRemote sensing. 615 14$aComputational Intelligence. 615 24$aBig Data. 615 24$aRemote Sensing/Photogrammetry. 676 $a631.470223 700 $aGarg$b Pradeep Kumar$4aut$4http://id.loc.gov/vocabulary/relators/aut$0860953 702 $aGarg$b Rahul Dev$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aShukla$b Gaurav$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aSrivastava$b Hari Shanker$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910484233703321 996 $aDigital Mapping of Soil Landscape Parameters$91921333 997 $aUNINA