Estimates of forest aboveground biomass are fundamental for carbon monitoring and accounting; delivering information at very high spatial resolution is especially valuable for local management, conservationand selective logging purposes. In tropical areas, hosting large biomass and biodiversity resources whichare often threatened by unsustainable anthropogenic pressures, frequent forest resources monitoring isneeded. Lidar is a powerful tool to estimate aboveground biomass at fine resolution; however its applica-tion in tropical forests has been limited, with high variability in the accuracy of results. Lidar pulses scanthe forest vertical profile, and can provide structure information which is also linked to biodiversity. Inthe last decade the remote sensing of biodiversity has received great attention, but few studies focusedon the use of lidar for assessing tree species richness in tropical forests.This research aims at estimating aboveground biomass and tree species richness using discrete returnairborne lidar in Ghana forests. We tested an advanced statistical technique, Multivariate AdaptiveRegression Splines (MARS), which does not require assumptions on data distribution or on the rela-tionships between variables, being suitable for studying ecological variables.We compared the MARS regression results with those obtained by multilinear regression and foundthat both algorithms were effective, but MARS provided higher accuracy either for biomass (R2= 0.72)and species richness (R2= 0.64). We also noted strong correlation between biodiversity and biomass fieldvalues. Even if the forest areas under analysis are limited in extent and represent peculiar ecosystems, thepreliminary indications produced by our study suggest that instrument such as lidar, specifically usefulfor pinpointing forest structure, can also be exploited as a support for tree species richness assessment.