Vegetation discrimination and analysis: From multi-spectral to hyper-spectral image analysis - Couverture souple

Kusangaya, Samuel

Couverture souple

ISBN 10 : 384848434X ISBN 13 : 9783848484348

Editeur : LAP LAMBERT Academic Publishing, 2012

Synopsis

Vegetation discrimination and analysis using multi-spectral and hyper-spectral remote sensing imagery has gained a lot of widespread application in agricultural and vegetation applications. Such imagery has been successfully used in estimating vegetation characteristics like plant health, stress mapping, identifying disease and estimating crop yields resulting in millions of documented studies. Several characteristics of vegetation makes the use of remote sensing a desired method for vegetation description and analysis. In particular, vegetation reflectance in the red edge region has enhanced the use of hyper spectral remote sensing. Several methods for vegetation discrimination and analysis will be illustrated here among them utility of some common multispectral image classification algorithms. For hyperspectral image analysis, use of vegetation indices such as NDVI, MSAVI & MSR will be showcased. Additionally, advanced hyperspectral derivative analysis like the first derivative transformation, 4-point interpolation, Lagrangian interpolation, continuum removal, absorption depth analysis and linear pixel un-mixing methods will also be shown through different application cases.

A propos du livre

À propos de l'auteur

I am a GIS and Remote Sensing specialist with focus on applications in climate change modelling and vegetation monitoring. I have advanced statistical analysis skills in multivariate and spatio-temporal analysis of geographic, ecological and socioeconomic data. I can set up GIS academic and professional training programmes.

Détails de l'édition

• Éditeur: LAP LAMBERT Academic Publishing
• Date d'édition: 2012
• Langue: anglais
• ISBN 10: 384848434X
• ISBN 13: 9783848484348
• Reliure: Broché
• Nombre de pages: 96
• Coordonnées du fabricant: non disponible
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