Field measured estimates of aboveground biomass (AGB) for 15  transects in Bwindi Impenetrable National Park (BINP), Uganda were  used to generate a number of prediction models for estimating above ground biomass (AGB) over the full extent of the park. AGB estimates were extrapolated from the field data using dual-polarization radar satellite data alone, optical satellite data, and a combination of both.

Field assistants Benon (r) and Damazo (l), measuring the dbh of a crooked tree.

The effectiveness of the dual-polarization radar remote sensing data alone was limited due to the difficulties of geocoding and terrain correction in this mountainous region, producing problems with layover and shadowing. The optical-only method demonstrated that perhaps thermal bands may be more sensitive to biomass in tropical forests than visible bands.

The radar and optical combined method, generated using the non-parametric algorithm called 'Random Forest' in implemented in R, provided the lowest RMSE error (~120 Mg ha^-1). The analysis also demonstrated that a number of radar backscatter variables had greater utility for generating a predictive model of biomass than many optical bands in this mountainous region.

The combined optical and radar model was used to produce a final AGB map over the full 331 km² extent of BINP.  AGB in BINP was estimated at 89.1 ± 3.9 million Mg, with a total carbon stock of 44.5 ± 1.9 million Mg C.

Melissa (r) and Peter (l) comparing notes in the field