Forest classification on the basis of satellite images is a promising technique both for primary map production and for map updating and forest monitoring. For accurate forest classification into three classes, using mapping by canopy cover density "high spatial resolution satellite images have to be used in order to obtain the required spatial detail" [Schneider, 1999]. At the same time, the spectral information necessary for identifying certain class types can most economically be derived

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... multi-spectral images of medium spatial resolution. Fusion techniques have to be used to combine information from both sources. In this paper, a method was developed for object-level fusion of IRS-1C/1D pan images (5.8 m pixel size) and LANDSAT TM multispectral images (30 m pixel size) and subsequent classification to produce a canopy cover classification of the northern forests of Iran. The study area is located in Sari and its forest regions in 60,000Hec. (Figure 1) The individual processing steps included segmentation of a multi-band image consisting of both the high-spatial-resolution pan image band and medium-spatial-resolution mul-tispectral bands, with proper weighting of the individual bands in the segmentation procedure in order to obtain both fine detail from the pan image and coarser boundary de-lineations which show up only in multispectral images. For classification, fuzzy logic membership functions were used. Verification of the classification was carried out and checked with error matrix and kappa calculation on a selected transect from a newly classified map. The results showed that employing object-based fusion procedure using medium and high-resolution data was an appropriate method that improved classification. Comparing the hard work of creating a new topographic map, a pixel-based fusion procedure was demonstrated to be an acceptable method to create a satellite image map (Sat-map) for visual monitoring activities and programs. The overall accuracy of the map produced was calculated as a topo-map of the region.