APPLICATION OF REMOTE SENSING TO STUDY THE ENVIRONMENT AND ECOSYSTEM. A CASE STUDY FOR PRIMARY ANALYSIS OF VEGETATION

Abstract

Four major types of forest vegetation viz, moist sal forest, moist mixed forest, dry mixed forest and. seral vegetation were identified at the Kanha National Park using remote sensing data products viz., satellite imageries (Landsat - TM-FCC 1:50,000 scale) and aerial photographs (B/W panchromatic 1:10,000 scale). Primary analysis of the vegetation has revealed that sal (Shorea robusta) was more abundant (60.13) with highest importance value index (IVI) of 180.11 followed by Terminalia tomentosa in moist sal group of forests in valleys. T.tomentosa was found to be a close associate of sal. In moist miked forests (on hill slopes), I. tomentosa were abundant with high frequency, density and highest IVI (94.88). Total absence of sal and less abundance of 1, tomentosa were the significant aspects in characterizing the dry mixed forest. In dry mixed forest, Bosewellia serrata and Lagerstroemia parviflora are the most abundant species constituting the ridge vegetation. The seral vegetation was dominated by Butea monosperma and L. parviflora which were invading the grasslands in pure patches, respectively, into meadows and dadar grasslands.