Analysis of Sudan Vegetation Dynamics Using NOAA-AVHRR NDVIData from 1982-1993

摘要

Long term observation of space-borne remote sensing data provides a means toexplore temporal variation on the Earth's surface. This improved understanding of variabilityis required by numerous global change studies to explain annual and inter-annual trends andto separate those from individual events. This knowledge also can be included into budgetingand modeling for global change studies. The study employs daily 8 km NOAA-AVHRR dataof the Pathfinder program to study changes in the annual variability of vegetation in Sudan,during the time period from 1982 to 1993. The daily data were processed to improve 15 daycomposites using an iterative approach including metadata and robust statistical techniques.This study employs GIS to examine the relationship between rainfall and the NormalizedDifference Vegetation Index (NDVI) in the context of the Sudan and the value of NDVI istaken as a tool for drought monitoring. The relationship between rainfall and NDVI during1982 and 1993 in Sudan is examined using spatial analysis methods and a strong positivecorrelation is found. The correlation is strongest during years of heaviest rainfall, indicatingthat the relationship between rainfall and NDVI is not a simple linear one. However it isargued that the input data accuracy has affected the quality of the GIS output and theshortcomings of the data are highlighted. The study stresses the need for the use of remotesensing to provide real time data for forecasting. Whilst most countries in the Sahel includingSudan lack the resources (financial, technical and human) to establish the informationsystems necessary for drought monitoring, this study concludes that remote sensing is theonly feasible data source to fill such a gap. NDVI is a valuable first cut indicator for suchsystems, although analyzing and interpreting its relationship to rainfall is complex and mustbe based on detailed analysis of its relationship to ecological zone, vegetation type andseason.