Estimation of biomass and volume of shrub vegetation using LiDAR and spectral data in a Mediterranean environment

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.