Abstract
Several studies have addressed the biomass and volume of trees using Airborne Light
Detection and Ranging (LiDAR) data. However, little research has been conducted into
shrub vegetation, which covers a high percentage of Mediterranean forest. We used LiDAR
data and an airborne image to estimate biomass and volume of shrub vegetation. Field data
were collected in 29 square plots of 100 m2. In each plot, the percentage of the surface
covered was measured in the field. Shrub vegetation inside 3 stands for each plot was clear
cut to calculate the biomass and volume of the 29 plots. To find the best fit between LiDARspectral
data and field measurements, stepwise regressions were performed using previously
selected variables. The highest accuracy was found when variables derived from
LiDAR data and the airborne image were combined (R2 values of 0.78 and 0.84 for biomass
and volume, respectively). Biomass and volume were predicted using variables from height
metrics of LiDAR data (median and standard deviation); density metrics (percentage of
points whose height was between 0.50 m and 0.75 m, 0.75 me1 m, and higher than 1 m);
and spectral data (standard deviation of green band, mean of the vegetation index NDVI).
These results revealed the potential of LiDAR and spectral data to characterize shrub
structure and make it possible to estimate and map the biomass and volume of this
vegetation.
