INTEGRATION OF HIGH AND LOW-RESOLUTION NDVI DATA FOR MONITORING VEGETATION IN MEDITERRANEAN ENVIRONMENTS

The integration of the useful features of high and low spatial and tem poral resolution satellite data is a major issue in remote sensing stu dies. The current work presents the development and testing of a proce dure based on classification and regression analysis techniques for ge nerating an NDVI data set with the spatial resolution of Landsat TM im ages and the temporal resolution of NOAA AVHRR maximum-value composite s. The procedure begins with a classification of the high resolution T M data which yield land use references. These are degraded to low spat ial resolution in order to procedure 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 pur e classes, which are then merged to the high spatial resolution classi fication outputs to generate an integrated data set. Experiments carri ed 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) ide ntified an optimum methodology composed of fuzzy classification, mean degradation, and multivariate regression procedures. (C) Elsevier Scie nce Inc., 1998.