TUM Library

Includes bibliographical references and index.

Section I: Detecting Crop Management Practices, Plant Stress, and Disease -- 1. Using Hyperspectral Data in Precision Farming Applications -- [Haibo Yao, Yanbo Huang, Lie Tang, Lei Tian, Deepak Bhatnagar, and Thomas E. Cleveland] -- 2. Hyperspectral Narrowbands and Their Indices in Study of Nitrogen Content of Cotton Crops -- [Jianlong Li, Jingjing Zhang, Chunliu Tao, Dehua Zhao, and Chengcheng Gang] -- 3. Analysis of the Effects of Heavy Metals on Vegetation Hyperspectral Reflectance Properties -- [E. Terrence Slonecker] -- Section II: Vegetation Processes and Function (ET, Water Use, GPP, LUE, Phenology) -- 4. Mapping the Distribution and Abundance of Flowering Plants Using Hyperspectral Sensing -- [Tobias Landmann, Hannes Feilhauer, Miaogen Shen, Jin Chen, and Suresh Raina] -- 5. Crop Water Productivity Estimation with Hyperspectral Remote Sensing -- [Michael Marshall, Itiya Aneece, Daniel Foley, Cai Xueliang, and Trent Biggs] -- 6. Hyperspectral Remote Sensing Tools for Quantifying Plant Litter and Invasive Species in Arid Ecosystems -- [Pamela Lynn Nagler, B. B. Maruthi Sridhar, Aaryn Dyami Olsson, Willem J. D. van Leeuwen, and Edward P. Glenn] -- 7. Hyperspectral Applications to Landscape Phenology -- [Alfredo Huete, Werapong Koedsin, and Jin Wu] -- Section III: Land Cover, Forests, and Wetland and Urban Applications Using Hyperspectral Data -- 8. The Use of Hyperspectral Earth Observation Data for Land Use/Cover Classification: Present Status, Challenges, and Future Outlook -- [Prem Chandra Pandey, Kiril Manevski, Prashant K. Srivastava, and George P. Petropoulos] -- 9. Hyperspectral Remote Sensing for Forest Management -- [Valerie Thomas] -- 10. Characterization of Pastures Using Field and Imaging Spectrometers -- [Izaya Numata] -- 11. Hyperspectral Remote Sensing of Wetland Vegetation -- [Elijah Ramsey III and Amina Rangoonwala] -- Section IV: Thermal, SWIR, and Visible Remote Sensing -- 12. Hyperspectral Remote Sensing of Fire: A Review -- [Sander Veraverbeke, Philip Dennison, Ioannis Gitas, Glynn Hulley, Olga Kalashnikova, Thomas Katagis, Le Kuai, Ran Meng, Dar Roberts, and Natasha Stavros] -- Section V: Hyperspectral Data in Global Change Studies -- 13. Hyperspectral Data in Long-Term, Cross-Sensor Continuity Studies -- [Tomoaki Miura and Hiroki Yoshioka] -- Section VI: Hyperspectral Remote Sensing of Other Planets -- 14. Hyperspectral Analysis of Rocky Surfaces on Earth and Other Planetary Bodies -- [R. Greg Vaughan, Timothy N. Titus, Jeffrey R. Johnson, Justin J. Hagerty, Laurence A. Soderblom, Paul E. Geissler, David P. Mayer, and Will M. Grundy] -- Section VII: Conclusions -- 15. Fifty Years of Advances in Hyperspectral Remote Sensing of Agriculture and VegetationSummary, Insights, and Highlights of Volume IV: Advanced Applications in Remote Sensing of Agricultural Crops and Natural Vegetation -- [Prasad S. Thenkabail, John G. Lyon, and Alfredo Huete].

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.

Also available in print format.