Investigation of Vegetation Dynamics using Long-Term Normalized Difference Vegetation Index Time-Series

Abstract

Problem statement: The Normalized Difference Vegetation Index (NDVI) is the most extensively used satellite-derived index of vegetation health and density. Since climate is one of the most important factors affecting vegetation condition, satellite-derived vegetation indexes have been often used to evaluate climatic and environmental changes at regional and global scale. The proposed study attempted to investigate the temporal vegetation dynamics in the whole Africa using historical NDVI time-series. Approach: For this aim, 15 day maximum value NDVI composites at 8 km spatial resolution produced from the NASA Global Inventory Mapping and Monitoring System (GIMMS) had been used. They were derived from data collected daily by NOAA AVHRR satellites. The AVHRR NDVI GIMMS dataset was freely available and gives global coverage over an extensive time period. First of all, the selected NDVI base data had been geometrically pre-processed and organized into a historical database implemented in order to grant their spatial integration. Starting from this archive, monthly and yearly NDVI historical time-series, extended from 1982-2006, had been then developed and analysed on a pixel basis. Several routines hade been developed in IDL (Interactive Data Language programming tool) with the purpose of applying suitable statistical analysis techniques to the historical information in the database in order to identify the long-term trend components of generated NDVI time-series and extract vegetation dynamics. Specific tests had been then considered in order to define the validity of results. Results: The existence of clear regional trends of NDVI, both decreasing and increasing had been showed, which helped to highlight areas subject, respectively to reduction or increase in vegetation greenness. Conclusion: As the relationship between the NDVI and vegetation productivity was well established, these estimated long-term trend components may be also, with much more caution, related to historical and ongoing land degradation or improvement processes.