Quantifying forest residual biomass in Pinus halepensis Miller stands using Airborne Laser Scanning data

Abstract

The estimation of forest residual biomass is of interest to assess the availability of green energy resources. This study relates the Pinus halepensis Miller forest residual biomass (FRB), estimated in 192 field plots, to several independent variables extracted from Airborne Laser Scanner (ALS) data in Aragón region (Spain). Five selection approaches and four non-parametric regression methods were compared to estimate FRB. The sample was divided into training and validation sets, composed of 144 and 48 plots, respectively. The best-fitted model was obtained using the Support Vector Machine method with the radial kernel. The model included three ALS metrics: the 70th percentile, the variance of the return heights, and the percentage of first returns above mean height. The root-mean-square error (RMSE) after validation was 8.85 tons ha−1. The influence of point density, scan angle, canopy pulse penetration, terrain slope, and shrub presence in model performance was assessed using graphical and statistical approaches. Point densities higher than 1 point m−2, scan angles lower than 15°, canopy pulse penetration over 25%, and terrain slopes under 30% generated a smaller variability in mean predictive error (MPE) values, thus increasing model accuracy in 0.56, 1.94, 1.44, and 5.47 tons ha−1, respectively. Shrub vegetation caused greater variability in MPE values but slightly decreased model accuracy (0.10 tons ha−1). No statistically significant differences were found between the categories in the influencing variables, except for canopy pulse penetration. The mapping of Pinus halepensis Miller FRB using the best-fitted model summed up a total of 3,627,021.25 tons, which equals to 1,584.91 thousand tonnes of oil (ktoe).

Keywords

• forest residual biomass
• bioenergy
• mediterranean forest
• als-pnoa
• model performance