Vegetation discrimination and analysis

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.

Autorentext

Mr Samuel Kusangaya is a Geographic Information Systems (GIS) and Remote Sensing expert by profession and training with interest in geo-information science applications in climate change modelling, vegetation monitoring, hydrology and water resources management and database management.