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Written by leading global experts, including pioneers in the field, the four-volume set on Hyperspectral Remote Sensing of Vegetation, Second Edition, reviews existing stateof- 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. Hyperspectral remote sensing or imaging spectroscopy data has been increasingly used in studying and assessing the biophysical and biochemical properties of agricultural crops and natural 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. The concluding chapter provides readers with useful guidance on the highlights and essence of Volume III through the editors’ perspective. Key Features of Volume III: Covers recent abilities to better quantify, model, and map plant biophysical, biochemical water, and structural properties. Demonstrates characteristic hyperspectral properties through plant diagnostics or throughput phenotyping of plant biophysical, biochemical, water, and structural properties. Establishes plant traits through hyperspectral imaging spectroscopy data as well as its integration with other data, such as LiDAR, using data from various platforms (ground-based, UAVs, and earth-observing satellites). Studies photosynthetic efficiency and plant health and stress through hyperspectral narrowband vegetation indices. Uses hyperspectral data to discriminate plant species and\or their types as well as their characteristics, such as growth stages. Compares studies of plant species of agriculture, forests, and other land use\land cover as established by hyperspectral narrowband data versus multispectral broadband data. Discusses complete solutions from methods to applications, inventory, and modeling considering various platform (e.g., earth-observing satellites, UAVs, handheld spectroradiometers) from where the data is gathered. Dwells on specific applications to detect and map invasive species by using hyperspectral data.
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.
Global climate change, reductions in arable land, and food security demands that plant breeding will continue to play an imperative role in feeding 9 billion people sustainably by 2050. In order to face this challenge, modern plant breeding will necessitate the adoption of new technologies and practices to boost production of cultivated plants by capturing or generating more favorable genetic diversity. In crop plants, the majority of agronomically important traits are quantitatively inherited, controlled by multiple genes each with a small effect (quantitative trait loci, QTLs). The most common approach to pre-breeding is to use genetic mapping to identify QTLs for key phenotypic variation followed by introgressing those QTLs into the elite gene pool with marker-assisted selection (MAS), which can enhance the selection criteria of phenotypes comparing to conventional breeding with the selection of genes. As the cost of genotyping continues to decline, the use of genotyping-by-sequencing (GBS) technologies or whole genome re-sequencing, coupled with the release of the genome sequences of plant species have permitted the development of dense arrays of single nucleotide polymorphisms (SNPs) covering the entire genome, which have in turn paved the way to genome-wide association studies (GWAS). Meanwhile, fine mapping guided by genome sequences of many plant species have facilitated the exploration of functional genes; in addition, pan-genomes constructed from various available resources such as the reference sequence and its variants, raw reads and haplotype reference panels provide a new perspective on QTL locations and potential molecular targets for plant breeding. Similarly, new approaches to marker-trait association analyses such as quantitative trait locus sequencing (QTL-seq) and quantitative trait gene sequencing (QTG-seq) that are based on bulked-segregant analysis (BSA) and whole-genome resequencing will help accelerate QTL fine-mapping and identification of the causal genes. In conclusion, the tools and strategies for MAS in modern plant breeding have been expanding in recent years. By embracing a broad array of conventional and new molecular techniques, modern plant breeding has a bright future in delivering new crop cultivars to keep our food, fiber and biobased economy diverse and safe.
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.
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.
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.
Authored by world-class scientists and scholars, The Handbook of Natural Resources, Second Edition, is an excellent reference for understanding the consequences of changing natural resources to the degradation of ecological integrity and the sustainability of life. Based on the content of the bestselling and CHOICE-awarded Encyclopedia of Natural Resources, this new edition demonstrates the major challenges that the society is facing for the sustainability of all well-being on the planet Earth. The experience, evidence, methods, and models used in studying natural resources are presented in six stand-alone volumes, arranged along the main systems of land, water, and air. It reviews state-of-the-art knowledge, highlights advances made in different areas, and provides guidance for the appropriate use of remote sensing and geospatial data with field-based measurements in the study of natural resources. Volume 3, Wetlands and Habitats, provides fundamental information on wetlands and their integral functions as a productive ecosystem. The topics it covers include wetlands biodiversity, wetlands classification and monitoring, floods, river ecosystems, pollution, and more. New to this edition are discussions on wetland vegetation, assessment of current wetland health status, restoration, sea-level rises and coastal storm, vulnerability to human impacts, and lakes and wetlands remote sensing. This volume demonstrates the key processes, methods, and models used through many case studies from around the world. Written in an easy-to-reference manner, The Handbook of Natural Resources, Second Edition, as individual volumes or as a complete set, is an essential reading for anyone looking for a deeper understanding of the science and management of natural resources. Public and private libraries, educational and research institutions, scientists, scholars, and resource managers will benefit enormously from this set. Individual volumes and chapters can also be used in a wide variety of both graduate and undergraduate courses in environmental science and natural science at different levels and disciplines, such as biology, geography, earth system science, and ecology.
Authored by world-class scientists and scholars, the Handbook of Natural Resources, Second Edition, is an excellent reference for understanding the consequences of changing natural resources to the degradation of ecological integrity and the sustainability of life. Based on the content of the bestselling and CHOICE awarded Encyclopedia of Natural Resources, this new edition demonstrates the major challenges that the society is facing for the sustainability of all wellbeing on planet Earth. The experience, evidence, methods, and models used in studying natural resources are presented in six stand-alone volumes, arranged along the main systems: land, water, and air. It reviews state-of-the-art knowledge, highlights advances made in different areas, and provides guidance for the appropriate use of remote sensing data in the study of natural resources on a global scale. The six volumes in this set cover: Terrestrial Ecosystems and Biodiversity; Landscape and Land Capacity; Wetlands and Habitats; Fresh Water and Watersheds; Coastal and Marine Environments; and finally Atmosphere and Climate. Written in an easy-to-reference manner, the Handbook of Natural Resources, Second Edition, as a complete set, is essential for anyone looking for a deeper understanding of the science and management of natural resources. Public and private libraries, educational and research institutions, scientists, scholars, and resource managers will benefit enormously from this set. Individual volumes and chapters can also be used in a wide variety of both graduate and undergraduate courses in environmental science and natural science courses at different levels and disciplines, such as biology, geography, Earth system science, ecology, etc.
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.