Assessment of biomass and net primary productivity of a dry tropical forest using geospatial technology

Abstract

This study quantifies biomass, aboveground and belowground net productivity, along with additional environmental factors over a 2–3 year period in Barnawapara Sanctuary of Chhattisgarh, India through satellite remote-sensing and GIS techniques. Ten sampling quadrates 20 × 20, 5 × 5 and 1 × 1 m were randomly laid for overstorey (OS), understorey (US) and ground vegetation (GS), respectively. Girth of trees was measured at breast height and collar diameters of shrubs and herbs at 0.1 m height. Biomass was estimated using allometric regression equations and herb biomass by harvesting. Net primary productivity (NPP) was determined by summing biomass increment and litter crop values. Aspect and slope influenced the vegetation types, biomass and NPP in different forests. Standing biomass and NPP varied from 18.6 to 101.5 Mg ha⁻¹ and 5.3 to 12.7 Mg ha⁻¹ a⁻¹, respectively, in different forest types. The highest biomass was found in dense mixed forest, while net production recoded in Teak forests. Both were lowest in degraded mixed forests of different forest types. OS, US and GS contributed 90.4, 8.7 and 0.7%, respectively, for the total mean standing biomass in different forests. This study developed spectral models for the estimation of biomass and NPP using Normalized Difference Vegetation Index and other vegetation indices. The study demonstrated the potential of geospatial tools for estimation of biomass and net productivity of dry tropical forest ecosystem.

Keywords

Allometric regression equations / Fine root biomass / Litter fall / LAI / NDVI / Spectral models

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