Abstract
This study demonstrates the use of high resolution IRS1C LISS-III and PAN merged data for growing stock assessment in Timli Forest Range, west of Dehradun. The merged data set was generated using principal component-based image fusion. The merged data had advantage of colour and high resolution from LISS-III and PAN respectively. It facilitated in differentiation and mapping of a number of forest categories in terms of type and density. The homogeneous forest strata were field inventoried for individual tree height and diameter using sample plots following two-phase sampling design. The plot inventory data was analysed to arrive at image level growing stock estimates. The study revealed that pure sal forest has maximum growing stock followed by sal mixed forest and miscellaneous forest. The study also shows good scope of high resolution data for growing stock assessment.
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