Margaret Kalacska, G. Arturo Sanchez-Azofeifa, "Hyperspectral Remote Sensing of Tropical and Sub-Tropical Forests"
2008 | pages: 350 | ISBN: 1420053418 | PDF | 12,3 mb
2008 | pages: 350 | ISBN: 1420053418 | PDF | 12,3 mb
While frequently used in temperate environments, hyperspectral sensors and data are still a novelty in the tropics. Exploring the potential of hyperspectral remote sensing for assessing ecosystem characteristics, Hyperspectral Remote Sensing of Tropical and Sub-Tropical Forests focuses on the complex and unique set of challenges involved in using this technology and the data it provides.
Special Features
- A CD-ROM including hyperspectral color images
- Coverage of in situ spectroscopy, airborne and satellite-based remote sensing, and fusion with other forms of data such as LiDAR
- Peer-reviewed chapters that highlight the most innovative achievements
- Discussion of the potential of hyperspectral remote sensing to provide tools for assessing ecosystem characteristics at various spatial and temporal scales
Experts from Diverse Backgrounds Share Their Successes
The book explores a range of analysis techniques, including hyperspectral reflectance indices, spectral mixture analysis, pattern classification, band selection, partial least-squares, linear discriminant analysis, and radiative transfer models. The chapter authors present a comprehensive review of the current status and innovative achievements in the field, citing approximately six hundred studies. As illustrated by the diverse backgrounds of the contributors, the most successful use of hyperspectral data requires a multidisciplinary approach spanning a wide range of fields.
Go Beyond the Basics to Actual Application
Although it begins by touching on the basics, this book is not a tutorial in remote sensing, but a reference that illustrates the potential applications and analysis techniques that can be used when facing the unique challenge of working in the tropics. It presents real-world examples and a suite of analysis techniques for using hyperspectral remote sensing in complex and diverse regions.
My Link