A critical review of forest biomass estimation equations in India

Abstract

Plant biomass is an integral part of the global carbon cycle and a renewable energy source that can deaccelerate the rising global temperature. India has 71 million ha (M ha) of land under forests represented by tropical to alpine ecosystems. Numerous direct and indirect species-specific and mixed-species equations have been used for biomass estimation in India. Biomass estimation equations that facilitate the prediction of aboveground biomass (AGB) stocks non-destructively across India are still lacking. Therefore, the objective of this review is to (i) assess the existing species-specific biomass estimation equations for trees, bamboos, palms, and bananas in India, (ii) assess and identify the most appropriate multi-species biomass estimation equations for AGB estimation across India, and (iii) define the critical research gaps in biomass estimation in India. The literature search found 85 species-specific and six multi-species AGB estimation equations reported from India. It was also found that a 50% of these equations were based on the power-law function using diameter at breast height (D) as the predictor variable. We carried out a multi-fold validation to compare the multi-species equation's compatibility by comparing the root mean square error (RMSE). The estimated RMSE values of the six reported multi-species equations showed that the following two equations could be effectively used for estimation of AGB: (i) lnAGB= 0.349+1.316 lnGBH and (ii) AGB= (0.18D2.16) × 1.32. These are adequate for predicting biomass of any woody species across a range of conditions in India.

Keywords

• Biomass equation
• Power-law function
• Root mean square error
• Diameter at breast height
• Species-specific equation
• Mixed species equation