Drought Pattern Invostingation through processing Normalized Vegetation Index-Based Satellite images

Abstract

he emergence of satellite remote sensing technology has provided people with various appropriate, more accurate and easy to use tools for monitoring environmental conditions like health of vegetation. Using the red and infrared band reflectances, for instance, enables the derivation of a vegetation index called Normalized Difference Vegetation Index (NDVI) in spatial and temporal domains. This index is vital to assess the evolution of drought as well as predict crop yield. The aim of this study is to analyze the series of deviation of NDVI images, extract virtual drought objects from the series, and investigate for drought patterns from historical images for growing season after appropriate preprocessing and segmentation of the images. In this study, the virtual drought objects extracted from images over the growing season (June -September) were found to exhibit a given (similar) pattern for the historical drought years, taken in Ethiopia. The graphical pattern exhibited by historical occurrences of drought for specific area on the ground demonstrated nearly a similar time series except the fact that the intensities vary. This variance is an indicative of the difference in the severity level of the droughts at each specific area. Hence, given the implementation of appropriate prediction tool, this similarity in the time series analysis of the historical data over a drought will give new views for ways in drought prediction for early warning and crop condition monitoring at near real-time.