Spatial interpolation of carbon stock: a case study from the Western Ghats biodiversity hotspot, India

Carbon stock distribution in different tropical forest types in India is rarely studied although India is a country with mega-diversity. The present study estimates the biomass and carbon stock of major tropical forest types in India, and attempts to identify suitable interpolation techniques for mapping carbon stock. Empirically derived allometric equations and carbon conversion coefficients were used to estimate the aboveground biomass and carbon stock, respectively. The point estimates were interpolated to spatial surface using different interpolation techniques. Two main modelling approaches were implemented: deterministic modelling and stochastic modelling. Deterministic modelling was to interpolate point information using similarities between measured points (inverse distance weighted (IDW) interpolation), and fitting a smoothing curve along the measured points (polynomial interpolation). In stochastic modelling, ordinary kriging (OK) was employed using parameters derived from semivariograms. The results showed that the average carbon stock in the study area was 84t/ha. The highest carbon stock was in evergreen forest and the lowest in thorny scrub forest. Validation of the model using the mean and RMS errors indicated that ordinary kriging performs better than IDW and polynomial interpolations.