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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 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.
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 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.
A volume in the three-volume Remote Sensing Handbook series, Remote
Sensing of Water Resources, Disasters, and Urban Studies documents
the scientific and methodological advances that have taken place
during the last 50 years. The other two volumes in the series are
Remotely Sensed Data Characterization, Classification, and
Accuracies, and Land Resources Monitoring, Modeling, and Mapping
with Remote Sensing. In true handbook style, this volume
demonstrates in-depth, extensive and comprehensive coverage of
Remote Sensing of Water Resources, Disasters, and Urban Studies.
The book provides fundamental as well as practical knowledge of
remote sensing of myriad topics pertaining to water resources,
disasters, and urban areas such as hydrology, water resources,
water use, water productivity, floods, wetlands, snow and ice,
nightlights, geomorphology, droughts and drylands, disasters,
volcanoes, fire, and smart cities. Highlights include: Hydrological
studies, groundwater studies, flood studies, and crop water use and
water productivity studies Wetland modeling, mapping, and
characterization Snow and ice studies Drought and dryland
monitoring and mapping methods Volcanoes, coal fires, and
greenhouse gas emissions Urban remote sensing for disaster risk
management Remote sensing for the design of smart cities Considered
magnum opus on the subject the three-volume Remote Sensing Handbook
is edited by Dr. Prasad S. Thenkabail, an internationally acclaimed
scientist in remote sensing, GIScience, and spatial sciences. The
volume has contributions from pioneering remote sensing global
experts on specific topics. The volume gives you a knowledge base
on each of the above mentioned topics, a deep understanding the
evolution remote sensing science, and familiarity with state-of-th
A volume in the three-volume Remote Sensing Handbook series, Land
Resources Monitoring, Modeling, and Mapping with Remote Sensing
documents the scientific and methodological advances that have
taken place during the last 50 years. The other two volumes in the
series are Remotely Sensed Data Characterization, Classification,
and Accuracies, and Remote Sensing of Water Resources, Disasters,
and Urban Studies. In true handbook style, the chapters in the
volume have been carefully selected, organized, and designed to be
self-contained so that you can focus on a chapter and read it
through without having to be overly dependent on other chapters.
This volume provides comprehensive theoretical and practical
coverage of remote sensing of land resources that include
vegetation and biomass, agricultural croplands, rangelands,
phenology and food security, forests, biodiversity, ecology, land
use\land cover, carbon, and soils. Highlights include: Global
terrestrial carbon and carbon budgets Precision farming
Agricultural systems studies and soil studies Global croplands,
agricultural croplands, and rangelands Food security analysis
Biodiversity Land use and land cover mapping Measuring
photosynthesis from space Vegetation characterization and above
ground biomass measurements and modeling Hyperspectral remote
sensing Ecological studies Tropical forest characterization Habitat
mapping and monitoring In a very practical way, the book
demonstrates the experience, utility, methods, and models used in
studying a wide array of land applications. Considered magnum opus
on the subject, the three-volume Remote Sensing Handbook is edited
by Dr. Prasad S. Thenkabail, a renowned interna
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.
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 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 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.
Methods and approaches of mapping irrigated areas at different
resolutions using remote sensing data. Irrigated areas were mapped
using: (a) AVHRR pathfinder 10-km data, (b) MODIS 500-m (c) MODIS
250-m and (d) Landsat 30-m data. The study was conducted in the
Krishna River, India using satellite sensor data for nominal year
2000. The methods analyzing multiple sensors data, time-series and
consisted of image segmentation approaches using SRTM data,
creation of mega-file data cube, spectral matching techniques,
ideal spectra data bank creation, class spectral generation, 2
dimensional brightness-greenness wetness (BGW) plot, space
time-spiral curves, comprehensive protocols for class
identification and labeling, resolving the mixed classes,
calculation of sub-pixel areas (SPAs), and fuzzy classification
accuracy assessments. The class identification protocols involved
matching class spectra with ideal spectra to group classes, use of
extensive ground truth data, innovative use of very high resolution
Google Earth Data, and secondary data. The results clearly showed
that finer the resolution greater was the irrigated areas.
A volume in the three-volume Remote Sensing Handbook series, Land
Resources Monitoring, Modeling, and Mapping with Remote Sensing
documents the scientific and methodological advances that have
taken place during the last 50 years. The other two volumes in the
series are Remotely Sensed Data Characterization, Classification,
and Accuracies, and Remote Sensing of Water Resources, Disasters,
and Urban Studies. In true handbook style, the chapters in the
volume have been carefully selected, organized, and designed to be
self-contained so that you can focus on a chapter and read it
through without having to be overly dependent on other chapters.
This volume provides comprehensive theoretical and practical
coverage of remote sensing of land resources that include
vegetation and biomass, agricultural croplands, rangelands,
phenology and food security, forests, biodiversity, ecology, land
use\land cover, carbon, and soils. Highlights include: Global
terrestrial carbon and carbon budgets Precision farming
Agricultural systems studies and soil studies Global croplands,
agricultural croplands, and rangelands Food security analysis
Biodiversity Land use and land cover mapping Measuring
photosynthesis from space Vegetation characterization and above
ground biomass measurements and modeling Hyperspectral remote
sensing Ecological studies Tropical forest characterization Habitat
mapping and monitoring In a very practical way, the book
demonstrates the experience, utility, methods, and models used in
studying a wide array of land applications. Considered magnum opus
on the subject, the three-volume Remote Sensing Handbook is edited
by Dr. Prasad S. Thenkabail, a renowned international expert in
remote sensing science, GIScience, and spatial sciences, with
contributions from some of the very best leading global experts,
the handbook gives you a knowledge base on the evolution remote
sensing science, state-of-the-art technology, and a future vision
for the field.
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