Integration of High and Low Resolution NDVI Data for Monitoring Vegetation in Mediterranean Environments

Abstract

The integration of the useful features of high and low spatial and temporal resolution satellite data is a major issue in remote sensing studies. The current work presents the development and testing of a procedure based on classification and regression analysis techniques for generating an NDVI data set with the spatial resolution of Landsat TM images and the temporal resolution of NOAA AVHRR maximum-value composites. The procedure begins with a classification of the high resolution TM data which yields land use references. These are degraded to low spatial resolution in order to produce abundance images comparable with the AVHRR data. Linear regressions are then applied between the AVHRR NDVI data and the abundance images to estimate the profiles of the pure classes, which are then merged to the high spatial resolution classification outputs to generate an integrated data set. Experiments carried out in an area of Tuscany (Central Italy) intercomparing different strategies for each methodological step (hard and fuzzy classification, mean and Gaussian degradation, uni- and multivariate regression) identified an optimum methodology composed of fuzzy classification, mean degradation, and multivariate regression procedures.