Tree allometric equations for estimation of above, below and total biomass in a tropical moist forest: Case study with application to remote sensing

- Site-specific allometric equations to estimate biomass in a tropical moist forest.
- Tree assortments for biomass include above, trunk, crown, below and total.
- Site-specific allometric equations to estimate height and crown diameter.
- 237 destructive data with diameter varying between 1 and 121 cm were used.
- Application to remote sensing data.

The unavailability of site-specific allometric equations to estimate forest biomass has promoted the use of general equations in tropical moist forests which may result to errors in the estimates. The aim of this study was to develop site-specific allometric equations to estimate biomass of trees in tropical moist forests of Cameroon. For this study, 237 trees (1 ≤ D ≤ 121 cm) obtained by destructive method were used to develop allometric equations for the estimation of aboveground biomass. Allometric equations to estimate belowground and total biomasses were developed with 25 trees and 13 trees respectively. Trunk and crown biomass estimators were also developed in this study using 96 sample trees. Predictor variables considered were diameter, tree height, wood density and crown diameter. 237 and 235 trees were also used to develop regressions equations to estimate tree height and crown diameter respectively. For remote sensing applications, this study developed allometric equations to estimate aboveground biomass using crown diameter as predictor variable. Comparison of our biomass data to existing models showed that the equation of Djomo et al. (2016) provided the best estimator of total and mean biomass. Our study contributes to site-specific allometric equations and to the knowledge of belowground, above, trunk, crown and total biomass, which lack in most of the biomass data in tropical moist forests. Also, adding allometric equations with application to remote sensing, this study is a significant input for the implementation of REDD+ in Central Africa.