CRC Press Hyperspectral Indices and Image Classifications for Agriculture and Vegetation: Hyperspectral Remote Sensing of Vegetation (Hyperspectral Remote Sensing of Vegetation, Second Edition)

Product Description 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. Review "Very comprehensive and an excellent reference, both for practitioners in the field as well as students hoping to learn more about the uses of Hyperspectral Data for characterizing a diverse set of vegetation...There are books by other authors on Hyperspectral approaches and vegetation characterization(non-hyperspectral), but I believe this book stands alone as the final word on Hyperspectral characterization of vegetation. In fact, all the premier works in literature on Hyperspectral characterization of vegetation have been authored by Thenkabail et al.!" --Dr. Thomas George, CEO, SaraniaSat Inc. "The publication of the four-volume set, Hyperspectral Remote Sensing of Vegetation, Second Edition, is a landmark effort in providing an important, valuable, and timely contribution that summarizes the state of spectroscopy-based understanding of the Earth’s terrestrial and near shore environments." --Susan L. Ustin, John Muir Institute "The second edition of the book is major revision effort and covers all the aspects most descriptively and explicitly for the students, academia and professionals across the discipline. The book provides breadth of innovative applications of mathematical techniques to extract information from the hyperspectral image data. The chapters are contributed by internationally renowned authors in their respective fields...The hand book Hyperspectral Remote Sensing of Vegetation by Prasad S. Thenkabail, John G. Lyon and Alfredo Huete is most comprehensive, designed for learning and the best book in the discipline today." --Dr. P.S. Roy, ICRISAT-CGIAR "This book is an absolute gem. The history, the contemporary and the future of hyperspectral remote sensing of vegetation is contained within these pages. New topics on data mining and machine learning are hugely helpful to understand how scientists can go about processing these massive data sets. With great societal challenges such as food se