Name: Hyperspectral Remote Sensing of Pasture Quality: Analysis and Prediction (en Inglés)
SKU: 25765055
Price: 1754.57 MXN
Availability: InStock
Author: Susanne Thulin
ISBN: 9783659171758

portada Hyperspectral Remote Sensing of Pasture Quality: Analysis and Prediction (en Inglés)

Hyperspectral Remote Sensing of Pasture Quality: Analysis and Prediction (en Inglés)

Formato

Libro Físico

Editorial

Lap Lambert Academic Publishing

Autor

Susanne Thulin

Año

2012

Idioma

Inglés

N° páginas

540

Encuadernación

Tapa Blanda

ISBN

3659171751

ISBN13

9783659171758

Categorías

Matemáticas

Hyperspectral Remote Sensing of Pasture Quality: Analysis and Prediction (en Inglés)

Susanne Thulin (Autor) · Lap Lambert Academic Publishing · Tapa Blanda

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Reseña del libro "Hyperspectral Remote Sensing of Pasture Quality: Analysis and Prediction (en Inglés)"

Analysis of data from imaging spectrometers to determine vegetation parameters is a new and emerging field of science. Hyperspectral remote sensing using field spectra, HyMap and Hyperion images are here used to study the feed quality of pastures. Both environmental and economical benefits can be achieved from predictive relationships between hyperspectral data and pasture quality parameters. The research described in this book provides assessments of different analysis methods, including advantages and limitations of different approaches as well as recommendations for future investigations. The work was carried out at three study sites in Victoria, Australia. Results show that there is a clear potential for hyperspectral remote sensing to provide important inputs to management decision system as pasture quality is the third crucial component needed after biomass and growth rate. The book gives both a thourough background to hyperspectral analysis of vegetation to date and a comprehensive introduction to hyperspectral remote sensing of pastures. It should therefore be very of great interest to both students and professionals in the fields of remote sensing and agronomy.