Assessment of Multi-spectral Vegetation Indices using Remote Sensing and Grid Computing

A primary goal of many remote sensing projects is to characterize the type, size and condition of vegetation present within a region. By combining data from two or more spectral bands we obtain what is commonly known as a vegetation index (VI), which enhances the vegetation signal, while minimizing solar irradiance and soil background effects. This study addresses the computation of Normalized Difference Vegetation Index (NDVI), Ratio Vegetation Index (RVI), Enhanced Vegetation Index (EVI), Atmospherically Resistant Vegetation Index (ARVI), Normalized Difference Snow Index (NDSI) and Normalized Burn Ratio (NBR) based on satellite imagery. As the analysis is performed on large data sets, we used Grid Computing to implement a service for using on Computational Grids with a Web-based client interface, which will be greatly useful and convenient for those who are studying the growth and vigor of green vegetation by using environmental remote sensing, and have typical workstations, with no special computing and storing resources for computationally intensive satellite image processing and no license for a commercial image processing tool. Keywords—Grid Computing, Remote Sensing, Vegetation Indices