Read Books Online and Download eBooks, EPub, PDF, Mobi, Kindle, Text Full Free.
Artificial Intelligence Analysis Of Hyperspectral Remote Sensing Data For Management Of Water Weed And Nitrogen Stresses In Corn Fields
Download Artificial Intelligence Analysis Of Hyperspectral Remote Sensing Data For Management Of Water Weed And Nitrogen Stresses In Corn Fields full books in PDF, epub, and Kindle. Read online Artificial Intelligence Analysis Of Hyperspectral Remote Sensing Data For Management Of Water Weed And Nitrogen Stresses In Corn Fields ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!
Book Synopsis Artificial Intelligence Analysis of Hyperspectral Remote Sensing Data for Management of Water, Weed, and Nitrogen Stresses in Corn Fields by : Tahir Waheed
Download or read book Artificial Intelligence Analysis of Hyperspectral Remote Sensing Data for Management of Water, Weed, and Nitrogen Stresses in Corn Fields written by Tahir Waheed and published by . This book was released on 2005 with total page 516 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This study investigated the possibility of using ground-based remotely sensed hyperspectral observations with a special emphasis on detection of water, weed and nitrogen stresses contributing towards in-season decision support for precision crop management (PCM)." --
Book Synopsis Advanced Applications in Remote Sensing of Agricultural Crops and Natural Vegetation by : Prasad S. Thenkabail
Download or read book Advanced Applications in Remote Sensing of Agricultural Crops and Natural Vegetation written by Prasad S. Thenkabail and published by CRC Press. This book was released on 2018-12-07 with total page 385 pages. Available in PDF, EPUB and Kindle. Book excerpt: Written by leading global experts, including pioneers in the field, the four-volume set on Hyperspectral Remote Sensing of Vegetation, Second Edition, reviews existing state-of-the-art knowledge, highlights advances made in different areas, and provides guidance for the appropriate use of hyperspectral data in the study and management of agricultural crops and natural vegetation. Volume IV, Advanced Applications in Remote Sensing of Agricultural Crops and Natural Vegetation discusses the use of hyperspectral or imaging spectroscopy data in numerous specific and advanced applications, such as forest management, precision farming, managing invasive species, and local to global land cover change detection. It emphasizes the importance of hyperspectral remote sensing tools for studying vegetation processes and functions as well as the appropriate use of hyperspectral data for vegetation management practices. The concluding chapter provides readers with useful guidance on the highlights and essence of Volume IV through the editors’ perspective. Key Features of Volume IV: Guides readers to harness the capabilities of the most recent advances in applying hyperspectral remote sensing technology to the study of terrestrial vegetation. Includes specific applications on agriculture, crop management practices, study of crop stress and diseases, crop characteristics based on inputs (e.g., nitrogen, irrigation), study of vegetation impacted by heavy metals, gross and net primary productivity studies, light use efficiency studies, crop water use and actual evapotranspiration studies, phenology monitoring, land use and land cover studies, global change studies, plant species detection, wetland and forest characterization and mapping, crop productivity and crop water productivity mapping, and modeling. Encompasses hyperspectral or imaging spectroscopy data in narrow wavebands used across visible, red-edge, near-infrared, far-infrared, shortwave infrared, and thermal portions of the spectrum. Explains the implementation of hyperspectral remote sensing data processing mechanisms in a standard, fast, and efficient manner for their applications. Discusses cloud computing to overcome hyperspectral remote sensing massive big data challenges. Provides hyperspectral analysis of rocky surfaces on the earth and other planetary systems.
Book Synopsis Dissertation Abstracts International by :
Download or read book Dissertation Abstracts International written by and published by . This book was released on 2007 with total page 924 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Computational Intelligence for Remote Sensing by : Manuel Grana
Download or read book Computational Intelligence for Remote Sensing written by Manuel Grana and published by Springer Science & Business Media. This book was released on 2008-06-05 with total page 397 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a composition of different points of view regarding the application of Computational Intelligence techniques and methods to Remote Sensing data and applications. It is the general consensus that classification, its related data processing, and global optimization methods are core topics of Computational Intelligence. Much of the content of the book is devoted to image segmentation and recognition, using diverse tools from different areas of the Computational Intelligence field, ranging from Artificial Neural Networks to Markov Random Field modeling. The book covers a broad range of topics, starting from the hardware design of hyperspectral sensors, and data handling problems, namely data compression and watermarking issues, as well as autonomous web services. The main contents of the book are devoted to image analysis and efficient (parallel) implementations of these analysis techniques. The classes of images dealt with throughout the book are mostly multispectral-hyperspectral images, though there are some instances of processing Synthetic Aperture Radar images.
Book Synopsis Remote Sensing in Precision Agriculture by : Salim Lamine
Download or read book Remote Sensing in Precision Agriculture written by Salim Lamine and published by Elsevier. This book was released on 2023-02-15 with total page 554 pages. Available in PDF, EPUB and Kindle. Book excerpt: Remote Sensing in Precision Agriculture compiles the latest applications of remote sensing in agriculture using spaceborne, airborne and drones' geospatial data. The book presents case studies, new algorithms and the latest methods surrounding crop sown area estimation, determining crop health status, assessment of vegetation dynamics, crop diseases identification, crop yield estimation, soil properties, drone image analysis for crop damage assessment, and other issues in precision agriculture. This book is ideal for those seeking to explore and implement remote sensing in an effective and efficient manner with its compendium of scientifically and technologically sound information. Presents a well-integrated collection of chapters, with quality, consistency and continuity Provides the latest RS techniques in Precision Agriculture that are addressed by leading experts Includes detailed, yet geographically global case studies that can be easily understood, reproduced or implemented Covers geospatial data, with codes available through shared links
Book Synopsis HYDROLOGY AND WATERSHED MANAGEMENT by : K. Ramamohan Reddy
Download or read book HYDROLOGY AND WATERSHED MANAGEMENT written by K. Ramamohan Reddy and published by Allied Publishers. This book was released on 2014-10-20 with total page 1310 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Proceeding contains the following sections: i) Groundwater Exploration and Exploitation; (ii) RS&GIS Applications in Water Resources; (iii) Watershed Management: Hydrological, Socio-Economic and Cultural Models; (iv) Water and Wastewater Treatment Technologies; (v) Rainwater Harvesting and Rural and Urban Water Supplies; (vi) Floods, Reservoir Sedimentation and Seawater Intrusion; (vii) Water Quality, Pollution and Environment; (viii) Irrigation Management; (ix) Water Logging and Water Productivity in Agriculture; (x) Groundwater Quality; (xi) Hydrologic Parameter Estimation and Modelling; (xii) Climate Change, Water, Food and Environmental Security; (xiii) Groundwater Recharge and Modelling; (xiv) Computational Methods in Hydrology; (xv) Soil and Water Conservation Technologies.
Book Synopsis Hyperspectral Remote Sensing of Agriculture and Vegetation by : Simone Pascucci
Download or read book Hyperspectral Remote Sensing of Agriculture and Vegetation written by Simone Pascucci and published by MDPI. This book was released on 2021-01-20 with total page 266 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book shows recent and innovative applications of the use of hyperspectral technology for optimal quantification of crop, vegetation, and soil biophysical variables at various spatial scales, which can be an important aspect in agricultural management practices and monitoring. The articles collected inside the book are intended to help researchers and farmers involved in precision agriculture techniques and practices, as well as in plant nutrient prediction, to a higher comprehension of strengths and limitations of the application of hyperspectral imaging to agriculture and vegetation. Hyperspectral remote sensing for studying agriculture and natural vegetation is a challenging research topic that will remain of great interest for different sciences communities in decades.
Book Synopsis Hyperspectral Remote Sensing of Vegetation by : Prasad S. Thenkabail
Download or read book Hyperspectral Remote Sensing of Vegetation written by Prasad S. Thenkabail and published by CRC Press. This book was released on 2016-04-19 with total page 766 pages. Available in PDF, EPUB and Kindle. Book excerpt: Hyperspectral narrow-band (or imaging spectroscopy) spectral data are fast emerging as practical solutions in modeling and mapping vegetation. Recent research has demonstrated the advances in and merit of hyperspectral data in a range of applications including quantifying agricultural crops, modeling forest canopy biochemical properties, detecting crop stress and disease, mapping leaf chlorophyll content as it influences crop production, identifying plants affected by contaminants such as arsenic, demonstrating sensitivity to plant nitrogen content, classifying vegetation species and type, characterizing wetlands, and mapping invasive species. The need for significant improvements in quantifying, modeling, and mapping plant chemical, physical, and water properties is more critical than ever before to reduce uncertainties in our understanding of the Earth and to better sustain it. There is also a need for a synthesis of the vast knowledge spread throughout the literature from more than 40 years of research. Hyperspectral Remote Sensing of Vegetation integrates this knowledge, guiding readers to harness the capabilities of the most recent advances in applying hyperspectral remote sensing technology to the study of terrestrial vegetation. Taking a practical approach to a complex subject, the book demonstrates the experience, utility, methods and models used in studying vegetation using hyperspectral data. Written by leading experts, including pioneers in the field, each chapter presents specific applications, reviews existing state-of-the-art knowledge, highlights the advances made, and provides guidance for the appropriate use of hyperspectral data in the study of vegetation as well as its numerous applications, such as crop yield modeling, crop and vegetation biophysical and biochemical property characterization, and crop moisture assessment. This comprehensive book brings together the best global expertise on hyperspectral remote sensing of agriculture, crop water use, plant species detection, vegetation classification, biophysical and biochemical modeling, crop productivity and water productivity mapping, and modeling. It provides the pertinent facts, synthesizing findings so that readers can get the correct picture on issues such as the best wavebands for their practical applications, methods of analysis using whole spectra, hyperspectral vegetation indices targeted to study specific biophysical and biochemical quantities, and methods for detecting parameters such as crop moisture variability, chlorophyll content, and stress levels. A collective "knowledge bank," it guides professionals to adopt the best practices for their own work.
Book Synopsis Hyperspectral Indices and Image Classifications for Agriculture and Vegetation by : Prasad S. Thenkabail
Download or read book Hyperspectral Indices and Image Classifications for Agriculture and Vegetation written by Prasad S. Thenkabail and published by CRC Press. This book was released on 2018-12-07 with total page 333 pages. Available in PDF, EPUB and Kindle. Book excerpt: Written by leading global experts, including pioneers in the field, the four-volume set on Hyperspectral Remote Sensing of Vegetation, Second Edition, reviews existing state-of- the-art knowledge, highlights advances made in different areas, and provides guidance for the appropriate use of hyperspectral data in the study and management of agricultural crops and natural vegetation. Volume II, Hyperspectral Indices and Image Classifications for Agriculture and Vegetation evaluates the performance of hyperspectral narrowband or imaging spectroscopy data with specific emphasis on the uses and applications of hyperspectral narrowband vegetation indices in characterizing, modeling, mapping, and monitoring agricultural crops and vegetation. This volume presents and discusses topics such as the non-invasive quantification of foliar pigments, leaf nitrogen concentration of cereal crop, the estimation of nitrogen content in crops and pastures, and forest leaf chlorophyll content, among others. The concluding chapter provides readers with useful guidance on the highlights and essence of Volume II through the editors’ perspective. Key Features of Volume II: Provides the fundamentals of hyperspectral narrowband vegetation indices and hyperspectral derivative vegetation indices and their applications in agriculture and vegetation studies. Discusses the latest advances in hyperspectral image classification methods and their applications. Explains the massively big hyperspectral sensing data processing on cloud computing architectures. Highlights the state-of-the-art methods in the field of hyperspectral narrowband vegetation indices for monitoring agriculture, vegetation, and their properties such as plant water content, nitrogen, chlorophyll, and others at leaf, canopy, field, and landscape scales. Includes best global expertise on hyperspectral remote sensing of agriculture, crop water use, plant species detection, crop productivity and water productivity mapping, and modeling.
Book Synopsis Hyper-spectral Remote Sensing for Weed and Nitrogen Stress Detection by : Pradeep Kumar Goel
Download or read book Hyper-spectral Remote Sensing for Weed and Nitrogen Stress Detection written by Pradeep Kumar Goel and published by . This book was released on 2003 with total page 420 pages. Available in PDF, EPUB and Kindle. Book excerpt: "The results of the study suggested that spectral data acquired from airborne platforms can provide vital information on weed presence and nitrogen levels in cornfields, which might then be used effectively in the development of PCM systems." --
Book Synopsis Fundamentals, Sensor Systems, Spectral Libraries, and Data Mining for Vegetation by : Prasad S. Thenkabail
Download or read book Fundamentals, Sensor Systems, Spectral Libraries, and Data Mining for Vegetation written by Prasad S. Thenkabail and published by CRC Press. This book was released on 2018-12-07 with total page 449 pages. Available in PDF, EPUB and Kindle. Book excerpt: Written by leading global experts, including pioneers in the field, the four-volume set on Hyperspectral Remote Sensing of Vegetation, Second Edition, reviews existing state-of-the-art knowledge, highlights advances made in different areas, and provides guidance for the appropriate use of hyperspectral data in the study and management of agricultural crops and natural vegetation. Volume I, Fundamentals, Sensor Systems, Spectral Libraries, and Data Mining for Vegetation introduces the fundamentals of hyperspectral or imaging spectroscopy data, including hyperspectral data processes, sensor systems, spectral libraries, and data mining and analysis, covering both the strengths and limitations of these topics. This book also presents and discusses hyperspectral narrowband data acquired in numerous unique spectral bands in the entire length of the spectrum from various ground-based, airborne, and spaceborne platforms. The concluding chapter provides readers with useful guidance on the highlights and essence of Volume I through the editors’ perspective. Key Features of Volume I: Provides the fundamentals of hyperspectral remote sensing used in agricultural crops and vegetation studies. Discusses the latest advances in hyperspectral remote sensing of ecosystems and croplands. Develops online hyperspectral libraries, proximal sensing and phenotyping for understanding, modeling, mapping, and monitoring crop and vegetation traits. Implements reflectance spectroscopy of soils and vegetation. Enumerates hyperspectral data mining and data processing methods, approaches, and machine learning algorithms. Explores methods and approaches for data mining and overcoming data redundancy; Highlights the advanced methods for hyperspectral data processing steps by developing or implementing appropriate algorithms and coding the same for processing on a cloud computing platform like the Google Earth Engine. Integrates hyperspectral with other data, such as the LiDAR data, in the study of vegetation. Includes best global expertise on hyperspectral remote sensing of agriculture, crop water use, plant species detection, crop productivity and water productivity mapping, and modeling.
Book Synopsis Hyperspectral Remote Sensing of Vegetation, Second Edition, Four Volume Set by : Prasad S. Thenkabail
Download or read book Hyperspectral Remote Sensing of Vegetation, Second Edition, Four Volume Set written by Prasad S. Thenkabail and published by CRC Press. This book was released on 2022-07-30 with total page 1637 pages. Available in PDF, EPUB and Kindle. Book excerpt: Written by leading global experts, including pioneers in the field, the four-volume set on Hyperspectral Remote Sensing of Vegetation, Second Edition, reviews existing state-of-the-art knowledge, highlights advances made in different areas, and provides guidance for the appropriate use of hyperspectral data in the study and management of agricultural crops and natural vegetation. Volume I, Fundamentals, Sensor Systems, Spectral Libraries, and Data Mining for Vegetation introduces the fundamentals of hyperspectral or imaging spectroscopy data, including hyperspectral data processes, sensor systems, spectral libraries, and data mining and analysis, covering both the strengths and limitations of these topics. Volume II, Hyperspectral Indices and Image Classifications for Agriculture and Vegetation evaluates the performance of hyperspectral narrowband or imaging spectroscopy data with specific emphasis on the uses and applications of hyperspectral narrowband vegetation indices in characterizing, modeling, mapping, and monitoring agricultural crops and vegetation. Volume III, Biophysical and Biochemical Characterization and Plant Species Studies demonstrates the methods that are developed and used to study terrestrial vegetation using hyperspectral data. This volume includes extensive discussions on hyperspectral data processing and how to implement data processing mechanisms for specific biophysical and biochemical applications such as crop yield modeling, crop biophysical and biochemical property characterization, and crop moisture assessments. Volume IV, Advanced Applications in Remote Sensing of Agricultural Crops and Natural Vegetation discusses the use of hyperspectral or imaging spectroscopy data in numerous specific and advanced applications, such as forest management, precision farming, managing invasive species, and local to global land cover change detection.
Book Synopsis Remote Sensing in Precision Agriculture by : Salim Lamine
Download or read book Remote Sensing in Precision Agriculture written by Salim Lamine and published by Elsevier. This book was released on 2023-10-20 with total page 555 pages. Available in PDF, EPUB and Kindle. Book excerpt: Remote Sensing in Precision Agriculture: Transforming Scientific Advancement into Innovation compiles the latest applications of remote sensing in agriculture using spaceborne, airborne and drones’ geospatial data. The book presents case studies, new algorithms and the latest methods surrounding crop sown area estimation, determining crop health status, assessment of vegetation dynamics, crop diseases identification, crop yield estimation, soil properties, drone image analysis for crop damage assessment, and other issues in precision agriculture. This book is ideal for those seeking to explore and implement remote sensing in an effective and efficient manner with its compendium of scientifically and technologically sound information. Presents a well-integrated collection of chapters, with quality, consistency and continuity Provides the latest RS techniques in Precision Agriculture that are addressed by leading experts Includes detailed, yet geographically global case studies that can be easily understood, reproduced or implemented Covers geospatial data, with codes available through shared links
Book Synopsis Methodologies Used in Remote Sensing Data Analysis and Remote Sensors for Precision Agriculture by : Jiyul Chang
Download or read book Methodologies Used in Remote Sensing Data Analysis and Remote Sensors for Precision Agriculture written by Jiyul Chang and published by Mdpi AG. This book was released on 2023-02-08 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: When adopting remote sensing techniques in precision agriculture, there are two main areas to consider: data acquisition and data analysis methodologies. Imagery and remote sensor data collected using different platforms provide a variety of information volumes and formats. For example, recent research in precision agriculture has used multispectral images from different platforms, such as satellites, airborne, and, most recently, drones. These images have been used for various analyses, from the detection of pests and diseases, growth, and water status of crops to yield estimations. However, accurately detecting specific biotic or abiotic stresses requires a narrow range of spectral information to be analyzed for each application. In data analysis, the volume and complexity of data formats obtained using the latest technologies in remote sensing (e.g., a cube of data for hyperspectral imagery) demands complex data processing systems and data analysis using multiple inputs to estimate specific categorical or numerical targets. New and emerging methodologies within artificial intelligence, such as machine learning and deep learning, have enabled us to deal with these increasing data volumes and the analysis complexity.
Book Synopsis Integration of Remote Sensing and Crop Growth Modeling for Nitrogen Management Decision Support in Corn by : Kelly Robert Thorp
Download or read book Integration of Remote Sensing and Crop Growth Modeling for Nitrogen Management Decision Support in Corn written by Kelly Robert Thorp and published by . This book was released on 2006 with total page 212 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation describes efforts to move toward a completely integrated remote sensing and crop growth modeling tool for developing precision nitrogen management recommendations for corn. Aerial hyperspectral remote sensing imagery collected throughout the 2004 growing season was used to estimate corn plant stand density, and a machine vision system was used to map corn population on the ground. Multiple linear regression analysis was used to assess the ability of all combinations of three reflectance bands to estimate corn plant population at resolutions of 2 m, 6 m, and 10 m. Coefficients of multiple determination of up to 0.82 were achieved in this endeavor. Although some limitations apply, remote sensing can be used as a tool to provide corn plant population inputs for crop growth simulations. A cross validation technique and bivariate confidence ellipses were used to evaluate CERES Maize simulations of spatial corn yield variability across an Iowa cornfield. Results indicated that the model performed most poorly when using the wettest or driest growing seasons to validate the model, because the model parameters fitted under the conditions of moderate growing seasons were less flexible for simulating yield in growing seasons with more extreme weather. Results also indicated that topography affects the model performance spatially. CERES-Maize was also used to simulate yield and unused nitrogen remaining in the soil at harvest for a sequence of historical weather data. Simulations were run for 13 spring-applied nitrogen rates over a cornfield divided into 100 0.2 ha grid cells. A methodology based on cumulative probability distributions was then developed to use model output for assessing the link between yield and nitrogen left behind for various nitrogen rates in each grid cell. This methodology can be used to develop precision nitrogen management strategies that address both the economic and environmental concerns of nitrogen management practices. Although the three projects in this dissertation furthered the development of remote sensing, crop growth modeling, and decision support technologies, more work is required to obtain a completely integrated tool for development of precision nitrogen management strategies in Midwestern cornfields.
Book Synopsis Remote Sensing Application for Precision Agriculture by : Matthew McCabe
Download or read book Remote Sensing Application for Precision Agriculture written by Matthew McCabe and published by Frontiers Media SA. This book was released on 2023-08-11 with total page 372 pages. Available in PDF, EPUB and Kindle. Book excerpt: Precision agriculture is used to improve site-specific agricultural decision-making based on data collection and analysis, formulation of site-specific management recommendations, and implementation of management practices to correct for factors that can limit crop growth, yield, and quality. Various approaches for the remote sensing of soil fertility, water stress, diseases and infestations, and crop growth and condition have been developed and applied for precision agricultural purposes. With developments in remote sensing technologies, the spatial and spectral resolution and return frequencies available from both satellite and other remote collection platforms have improved to the point that the promise of precision agriculture can increasingly be realized. Unmanned aerial vehicles (UAV) in particular are providing newer and deeper insights, leveraging their high resolution, sensor-carrying flexibility and dynamic acquisition schedule. This range of remote sensing platforms has been used to estimate comprehensive information related to crop health and dynamics, providing rapid retrievals of leaf area index, canopy cover, chlorophyll, nitrogen, canopy/leaf water content, canopy/leaf temperature, biomass, and yield, amongst many other variables of interest. In combination, they allow for the expansion from local to regional scales and beyond. There has never been a greater opportunity for remote sensing data to enable precision agricultural insights that can be used to better monitor, manage and respond to in-field changes that might impact crop growth, health and yield.
Book Synopsis Multi-Sensor and Multi-Temporal Remote Sensing by : Anil Kumar
Download or read book Multi-Sensor and Multi-Temporal Remote Sensing written by Anil Kumar and published by CRC Press. This book was released on 2023-04-17 with total page 178 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book elaborates fuzzy machine and deep learning models for single class mapping from multi-sensor, multi-temporal remote sensing images while handling mixed pixels and noise. It also covers the ways of pre-processing and spectral dimensionality reduction of temporal data. Further, it discusses the ‘individual sample as mean’ training approach to handle heterogeneity within a class. The appendix section of the book includes case studies such as mapping crop type, forest species, and stubble burnt paddy fields. Key features: Focuses on use of multi-sensor, multi-temporal data while handling spectral overlap between classes Discusses range of fuzzy/deep learning models capable to extract specific single class and separates noise Describes pre-processing while using spectral, textural, CBSI indices, and back scatter coefficient/Radar Vegetation Index (RVI) Discusses the role of training data to handle the heterogeneity within a class Supports multi-sensor and multi-temporal data processing through in-house SMIC software Includes case studies and practical applications for single class mapping This book is intended for graduate/postgraduate students, research scholars, and professionals working in environmental, geography, computer sciences, remote sensing, geoinformatics, forestry, agriculture, post-disaster, urban transition studies, and other related areas.
