the images to the atmospheric radiance values; thus, merging the radiometrically-calibrated images should not produce any anomalous results in classification. However, errors were observed when a merged image was classified for entire Nepal. Therefore, we classified all images separately according to a guided classification scheme using Erdas Imagine 9.3, using hybrid methods combining unsupervised and supervised classification techniques. Initially, an unsupervised Iterative Self-Organizing Data Analysis Technique (ISODATA) routine was run on Landsat bands 1, 2, 3 and 4 of MSS and 15 and 7 of TM and ETM+ to cluster individual images into 25 classes. These classes were visually analyzed using flicker to assign specific class names. Spectral profile curves were used to examine the objects reflection by electromagnetic radiations. Pixels that correspond to clouds, land surfaces under cloud shadows, and shadows caused by the terrain (about 5-7% of the image area) were then removed from the images. The remaining portions of the image areas were then clustered into 20 classes by a second ISODATA routine. Clusters were labeled to represent specific land-cover classes, and signatures of the labeled clusters of each image were used as the basis for a supervised maximum likelihood classification of the individual images. Class names were assigned to various land use and cover classes.

Identification of roads on the classified images was reconfirmed by overlaying the vector layers available from the national Department of Survey. After the proper identification of roads and rivers, they were extracted from the images as separate raster files.<sup>11</sup> We separated these roads and rivers into layers, and merged these classes with the bare-land as bare-land and roads showed overlapping values (1550 - 1680, 1700) in the Transformed Divergence Index (TDI). A TDI calculates standard divergences between pairs of classes, and it is used to measure the separability between land use classes, such as mature forest, secondary growth, and farmlands. In satellite image classification, TDI is needed to assess the quality of the statistics prior to image classification. After careful analysis of the TDI, we ended up using eight land use classes: mature forests, secondary growth, degraded forest land, farmland, barren land, water, cloud, and no data. We classified cloud in the images if clouds were seen in all the images. However, if the 2000 and 2001 panchromatic images showed forest in a certain area and that area was under cloud cover in rest of the images, we used a mask to categorize such areas under forest for all the images.

Ideally, we would have liked to validate satellite data by using information collected on location. However, the scope at which such a ground-truthing exercise would have to be conducted would not only be prohibitively expensive but also time consuming. To a large extent, we have verified our land use and cover classes using a priori knowledge of a number of forest officers who have worked in various regions of Nepal for the past 25-30 years. Some areas that were not confirmed in the images prior to 2000 and 2001 from this a priori knowledge were cross-checked using the aerial photos and topographic maps. We also cross validated the ETM+ 2000 and 2001 classified images using the IKONOS (1m x 1m) images taken in 2003. These cross-validations were only conducted on selected 11 Road lengths were calculated using this procedure on the classified images.

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