Impact of plot size and model selection on forest biomass estimation using airborne LiDAR: A case study of pine plantations in southern Spain

Abstract

We explored the usefulness of LiDAR for modelling and mapping the stand biomass of two conifer species in southern Spain. We used three different plot sizes and two statistical approaches (i.e. stepwise selection and genetic algorithm selection) in combination with multiple linear regression models to estimate biomass. 43 predictor variables derived from discrete-return LiDAR data (4 pulses per m2) were used for estimating the forest biomass of Pinus sylvestris Linnaeus and Pinus nigra Arnold forests. Twelve circular plots - six for each species - and three different fixed-radius designs (i.e. 7, 15, and 30 m) were established within the range of the airborne LiDAR. The Bayesian information criterion and R2 were used to select the best models. As expected, the models that included the largest plots (30 m) yielded the highest R2 value (0.91) for Pinus sp. using genetic algorithm models. Considering P. sylvestris and P. nigra models separately, the genetic algorithm approach also yielded the highest R2 values for the 30-m plots (P. nigra: R2 = 0.99, P. sylvestris: R2 = 0.97). The results we obtained with two species and different plot sizes revealed that increasing the size of plots from 15 to 30 m had a low effect on modelling attempts.

Keywords

• airborne laser scanning
• forest inventory
• regression
• survey design
• genetic selection methods
• pinus sp