The problem of image mining combines the areas of content- based image retrieval (CBIR), image understanding, data mining and databases. Image mining in remote sensing is more challenging due to its multi-spectral and spatio-temporal characteristics. To deal with the phenomena that have imprecise interpretation in remote sensing applications, images should be identified by the similarity of their attributes rather than exact matching. Fuzzy spatio-temporal objects are modeled by spatial feature values combined with geographic temporal metadata and climatic data. This paper focuses on the implementation of a remotely sensed image databased with fuzzy characteristics, and its application to data mining. A comprehensive series of calibrated, geo- registered, daily observations, and biweekly maximum NDVI composite AVHRR images are processed and used to build the database. The particularity of the NDVI composite images that our experiments are conducted on is that they cover large geographic areas, and are suitable to observe seasonal changes in biomass (greenness). Based on the characterization of land cover and statistical analysis of climatic data related to NDVI, spatial and temporal data mining such as abnormality detection and similar time sequences detection were carried out by fuzzy object queries.