Remote sensing technologies can provide accurate, cost-effective and real time information for sustainable forest management. Present study demonstrates the use of high resolution IRS1C data and Principal Component Analysis (PCA) with supervised classification to define different types of forest cover in protected and unprotected areas for growing forest stock assessment of Shorea robusta Gaertn. F. (Sal) dominated forest. Further, a map of the same was generated which was subsequently validated using phyto-sociological field data in Dehradun, India. Three forest canopy density classes, viz., 10-30%, 30-70% and <70% could be differentiated. Aim of this study was to test usefulness of LISS-III and test its efficacy for assessment of total growing stock and further in generalising the method for the whole area. The homogeneous forest strata were field inventoried for individual tree (≥10 cm dbh) diameter using sample quadrats at each of the study sites. The plot inventory data was analysed to arrive at image level growing stock estimates. An inspection review was carried out to be familiar with the study area using hard copy of LISS-III data to correlate the image. A scheme of forest classification was developed and the forests were stratified into moist Siwalik sal forest (3C/C2a), dry Siwalik sal forest (5B/C1a) and non-forest. High level of accuracy was achieved by ground truthing. The objective of the stratification was to categorize the forest into homogeneous strata. The biomass measurement from same sample plots was also integrated into the remote sensing technique for large spatial information on AGB distribution. The study revealed that protected sal forest has maximum volume and growing stock per hectare followed by unprotected sal forest. Same was also true for sal associates. The study also shows good scope of high resolution data for growing stock and forest carbon assessment, opening scope in estimating carbon sequestration potential of forest which may help researchers and policy makers to understand global and regional CO<sub>2</sub> cycle.