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In this investigation, optical (SPOT-7 NAOMI), airborne LiDAR, and PolInSAR L-band data, along with forest inventories, were employed to develop models for estimating total dry aboveground biomass (AGB) over the tropical forests in the Congo Basin (Gabon) of Central Africa. Remote sensing-based variables like texture (from SPOT), median canopy height (from LiDAR), and backscattering coefficient along with canopy surface heights (from PolInSAR) were used to estimate the AGB. These variables were used individually (or combined) to develop the AGB models based on the multivariate adaptive regression splines (MARS) approach. Validation indicated that in case of the single variable models, the LiDAR-based model yielded the lowest estimation root-mean-square error (RMSE = 28%). The error decreased further when the median canopy height was combined with the texture or with the radar variables (RMSE &lt;25%). The texture derived from the Fourier transform textural ordination (FOTO) approach was more effective in improving the results as compared to the gray level co-occurrence matrices (GLCM) approach. Model validation indicated that the best performance in AGB estimation was achieved by combining optical, LiDAR, and radar data (R 2  = 0.89 and RMSE = 24%).

Estimation of the total dry aboveground biomass in the tropical forests of Congo Basin using optical, LiDAR, and radar data