Samuel Kusangaya

Vegetation discrimination and analysis

From multi-spectral to hyper-spectral image analysis

• Broschiertes Buch

Produktbeschreibung

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 derivativetransformation, 4-point interpolation, Lagrangian interpolation, continuum removal, absorption depth analysis and linear pixel un-mixing methods will also be shown through different application cases.

Produktdetails

• Verlag: LAP Lambert Academic Publishing
• Aufl.
• Seitenzahl: 96
• Englisch
• ISBN-13: 9783848484348
• ISBN-10: 384848434X
• Artikelnr.: 35472350

Autorenporträt

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.