This study aims to estimate forest above-ground biomass and biomass components in a stand of Picea crassifolia
(a coniferous tree) located on Qilian Mountain, western China via low density small-footprint airborne LiDAR data. LiDAR points were first classified into ground points and vegetation points. After, vegetation statistics, including height quantiles, mean height, and fractional cover were calculated. Stepwise multiple regression models were used to develop equations that relate the vegetation statistics from field inventory data with field-based estimates of biomass for each sample plot. The results showed that stem, branch, and above-ground biomass may be estimated with relatively higher accuracies; estimates have adjusted R2
values of 0.748, 0.749, and 0.727, respectively, root mean squared error (RMSE) values of 9.876, 1.520, and 15.237 Mg·ha−1
, respectively, and relative RMSE values of 12.783%, 12.423%, and 14.163%, respectively. Moreover, fruit and crown biomass may be estimated with relatively high accuracies; estimates have adjusted R2
values of 0.578 and 0.648, respectively, RMSE values of 1.022 and 5.963 Mg·ha−1
, respectively, and relative RMSE values of 23.273% and 19.665%, respectively. In contrast, foliage biomass estimates have relatively low accuracies; they had an adjusted R2
value of 0.356, an RMSE of 3.691 Mg·ha−1
, and a relative RMSE of 26.953%. Finally, above-ground biomass and biomass component spatial maps were established using stepwise multiple regression equations. These maps are very useful for updating and modifying forest base maps and registries.