Estimation of forest biophysical parameters using small-footprint lidar with low density in a coniferous forest

Abstract

This study aimed to estimate forest stand variables, such as mean height, mean crown diameter, mean diameter breast height (DBH), basal area, tree density, and aboveground biomass in coniferous tree species of Picea crassifolia stand in the Qilian Mountain, western China using low density small-footprint airborne LiDAR data. Firstly, LiDAR points were classified into ground points and vegetation points. Then the statistics of vegetation points, including height quantiles, mean height, and fractional cover was calculated. The stepwise multiple regression models were used to develop the equations relating the statistics of vegetation points to field inventory data and field-based estimates of biomass for each sample plot. The result shows that the mean height, biomass and basal area have a higher accuracy with R2 of 0.830, 0.736 and 0.657, respectively, while the mean diameter breast height DBH, crown diameter and tree density have a lower accuracy with R2 of 0.491, 0.356 and 0.403, respectively. Finally, the spatial forest stand variable maps were established using the stepwise multiple regression equations. These maps were very useful for updating and modifying forest base maps and forest register.