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Titlebook: Digital Mapping of Soil Landscape Parameters; Geospatial Analyses Pradeep Kumar Garg,Rahul Dev Garg,Hari Shanker Sri Book 2020 The Editor(

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發(fā)表于 2025-3-21 18:17:30 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱Digital Mapping of Soil Landscape Parameters
副標(biāo)題Geospatial Analyses
編輯Pradeep Kumar Garg,Rahul Dev Garg,Hari Shanker Sri
視頻videohttp://file.papertrans.cn/280/279545/279545.mp4
概述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
叢書(shū)名稱Studies in Big Data
圖書(shū)封面Titlebook: Digital Mapping of Soil Landscape Parameters; Geospatial Analyses  Pradeep Kumar Garg,Rahul Dev Garg,Hari Shanker Sri Book 2020 The Editor(
描述.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 sm
出版日期Book 2020
關(guān)鍵詞Digital Mapping; Soil-landscape Parameters; Prediction Model; Environmental Covariates; Remote Sensing a
版次1
doihttps://doi.org/10.1007/978-981-15-3238-2
isbn_softcover978-981-15-3240-5
isbn_ebook978-981-15-3238-2Series ISSN 2197-6503 Series E-ISSN 2197-6511
issn_series 2197-6503
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor
The information of publication is updating

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發(fā)表于 2025-3-21 23:49:37 | 只看該作者
4Real: Performance and Authenticityprediction algorithms for crop cover mapping. The various methods that have been, or could be used for crop cover mapping are discussed. The potential indicators that have been, or could be, used in crop prediction modelling are also discussed.
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發(fā)表于 2025-3-22 15:35:17 | 只看該作者
Prediction Models for Crop Mapping,prediction algorithms for crop cover mapping. The various methods that have been, or could be used for crop cover mapping are discussed. The potential indicators that have been, or could be, used in crop prediction modelling are also discussed.
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發(fā)表于 2025-3-23 00:39:51 | 只看該作者
Performance, Gender and Rock MusicModel performance depends on the variables used to represent soil–landscape relationships.
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發(fā)表于 2025-3-23 04:20:05 | 只看該作者
Different Approaches on Digital Mapping of Soil-Landscape Parameters,In soil-landscape parameters mapping, the implementation of geomatics-GIS, GPS, remote sensing, and DEM, suggests new alternatives. Different approaches have been applied for retrieval of soil-landscape parameters. In recent years, machine learning algorithms have received increasing attention for digital mapping.
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發(fā)表于 2025-3-23 08:12:07 | 只看該作者
Selection of Suitable Variables and Their Development,Model performance depends on the variables used to represent soil–landscape relationships.
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