~ 439 ~ The Pharma Innovation Journal 2020; 9(10): 439-443 ISSN (E): 2277- 7695 ISSN (P): 2349-8242 NAAS Rating: 5.03 TPI 2020; 9(10): 439-443 © 2020 TPI www.thepharmajournal.com Received: 19-07-2020 Accepted: 21-08-2020 Arun Kumar Department of Environmental Science, G. B. Pant University of Agriculture and Technology, Pantnagar, Uttarakhand, India Vir Singh Department of Environmental Science, G. B. Pant University of Agriculture and Technology, Pantnagar, Uttarakhand, India Rajeev Ranjan Department of Agrometeorology, G. B. Pant University of Agriculture and Technology, Pantnagar, Uttarakhand, India Ajit Singh Nain Department of Agrometeorology, G. B. Pant University of Agriculture and Technology, Pantnagar, Uttarakhand, India Corresponding Author: Arun Kumar Department of Environmental Science, G. B. Pant University of Agriculture and Technology, Pantnagar, Uttarakhand, India Estimation of forest biomass in Nainital district of Uttarakhand using remote sensing technique Arun Kumar, Vir Singh, Rajeev Ranjan and Ajit Singh Nain Abstract Satellite-based remote sensing approach was developed for estimating forest biomass and productivity in large area. Present study was conducted in the sub-tropical forest of Nainital district of Uttarakhand state. In the present study two LANDSAT-ETM+ images of different seasons viz., pre-monsoon and post monsoon seasons dated 27 th May and 18 th October 2012 were used. The LANDSAT-ETM+ images acquired were processed using ENVI-4.8 image processing software and for digitization of district boundary of Nainital district, Arc-View 3.2a software was used. Subset containing Nainital and the adjoining forest region of each image were extracted and NDVIs were generated. Linear statistical model was developed to calculate the BCD value of the sub-tropical forest of Nainital. The biomass Carbon density (BCD) for 27 th May and 18 th October 2012 are 93.16MgC/ha and 111.1527MgC/ha respectively. Keywords: Supervised classification, NDVI, BCD, LANDSAT-ETM+ and ENVI-4.8 Introduction A forest comprises a major part of terrestrial ecosystems, owing to their huge biomass and high productivity. In remote sensing from satellites the electromagnetic waves are sent to earth surface. Depending upon the property of objects on Earth, the electromagnetic waves of different intensity and wavelengths are absorbed, scattered transmitted and reflected. The reflected waves in the bandwidth of infrared, thermal infrared and microwaves are picked up by sensors mounted on the satellite. A forest comprises a major part of terrestrial ecosystems occupying about 30% of the world’s land area (Dixon et al., 1994) [2] . It is estimated that over 80% of global aboveground carbon (C) is stored in forest vegetation (Lieth and Whittaker, 1975; Dixon et al., 1994) [5, 2] , and the annual C flux between forests and the atmosphere through photosynthesis and respiration is up to 90% of the total annual flux of terrestrial ecosystems (Winjum et al., 1993) [8] . Owing to their huge C pool and high productivity, forest ecosystems play a leading role in the global C cycle (Watson et al., 2000) [7] . Forest acts as a source and sink of carbon and carbon is main raw material for increasing biomass and productivity of forest. Satellite observations of vegetation have provided consistent global coverage at relatively high spatial resolution since the early 1980s (Zhou et al., 2001; Dong et al., 2003, Piao et al., 2005) [9, 3] . Compared to previous direct field measurements and inventory-based estimation for large-scale forest biomass, integrated estimation using remote sensing data and inventories data can show spatial explicit pattern for large-scale forest biomass (Piao et al., 2005) [6] . Satellite-based remote sensing approach was developed for estimating forest biomass and productivity in large area. Present study was conducted in the sub-tropical forest of Nainital district of Uttarakhand state. Forests are major contributor of terrestrial ecosystem carbon (C) pools, and are thus crucial components for assessing the global C budget. On the basis of forest inventory data and synchronous NDVI (Normalized Difference Vegetation Index) data. Material and Methods Study Area The case study conducted in Nainital district. The district of Nainital lies in the Kumaun division of Uttarakhand. It is located at 29.38°N and 79.45°E. Nainital has temperate summers, maximum temperature 27 °C (81 °F); minimum temperature 7 °C (45 °F). In winter, Nainital receives snowfall between December and February with the temperatures varying between a maximum of 15 °C (59 °F) and a minimum of ฀3 °C (27 °F). The soil structure and texture also varies from high sandy soils having 70 percent to 80 percent sand to clay soils in which