Vegetation biomass and carbon stock assessment using geospatial approach

Natural forests of tropical region are the world's largest terrestrial carbon sinks and harbor-rich biological diversity that deliver an array of ecosystem services. Quantifying carbon stock from such ecosystems is very important in understanding the global carbon cycle. Geospatial technology plays an important role in this context, offers quick and accurate forest biomass carbon assessments at larger spatial scales. We combined remote sensing and field measured data for spatial mapping of aboveground biomass (AGB) and carbon stock in varied vegetation types of northern part of Karnataka, India. Moderate resolution imaging spectro-radiometer (250 m) satellite data was utilized in development of relationship between normalized difference vegetation index (NDVI) and field measured AGB and carbon stock. Field measured AGB ranged between 30.09 and 301.51 Mg ha−1 across different vegetation types. Regression model (Y = 52.904X − 10.362) with the highest coefficient of determination (R2 = 0.613) was obtained between AGB (Y) and NDVI (X) of the December month. The regression model developed further was used in predicting the AGB and carbon stock at regional level. Total AGB was estimated at 0.20–30.80 Mt and carbon stock of 0.09–14.10 Mt. This study demonstrates that remote sensing data combined with field inventory provide reliable estimates of both AGB and carbon stock at spatial scales.