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.
Section I: Hyperspectral Vegetation Indices
- Hyperspectral vegetation indices
- Derivative hyperspectral vegetation indices in characterizing forest biophysical and biochemical quantities
- Hyperpsectral image classification methods in vegetation and agricultural
- Big Data Processing on Cloud Computing Architectures for Hyperspectral Remote Sensing
- Non-invasive Quantification of Foliar Pigments: Principles and Implementation
- Hyperspectral Remote Sensing of Leaf Nitrogen Concentration in Cereal Crops
- Optical remote sensing of vegetation water content
- Estimation of nitrogen content in herbaceous plants using hyperspectral vegetation indices
- Hyperspectral remote sensing of leaf chlorophyll content: from leaf, canopy, to landscape scales
- Fifty-years of Advances in Hyperspectral Remote Sensing of Agriculture and Vegetation: Summary, Insights, and Highlights of Volume II: Hyperspectral Vegetation Indices and Image Classifications for Agriculture and Vegetation
[Dar A. Roberts, Keely L. Roth, Erin B. Wetherley, Susan K. Meerdink, and Ryan L. Perroy]
[Quan Wang, Jia Jin, Rei Sonobe, and Jing Ming Chen]
Section II: Hyperspectral Image Classification Methods and Approaches
[Edoardo Pasolli, Saurabh Prasad, Melba M. Crawford, and James C. Tilton]
[Zebin Wu, Jin Sun, and Yi Zhang]
Section III: Hyperspectral Vegetation Indices Applications to Agriculture and Vegetation
[Anatoly Gitelson and Alexei Solovchenko]
[Tao Cheng, Yan Zhu, Dong Li, Xia Yao, and Kai Zhou]
[Colombo Roberto, Busetto Lorenzo, Meroni Michele, Rossini Micol, and Panigada Cinzia]
[D. Stroppiana, F. Fava, M. Boschetti, and P.A. Brivio]
Section IV: Conclusions
[Prasad S. Thenkabil, John G. Lyon, and Alfredo Huete]