Comparing small-footprint lidar and forest inventory data for single strata biomass estimation: a case study over a multi-layered mediterranean forest

Abstract

Current methods for accurately estimating vegetation biomass with remote sensing data require extensive, representative and time consuming field measurements to calibrate the sensor signal. In addition, such techniques focus on the topmost vegetation canopy and thus they are of little use over multi-layered forest ecosystems where the underneath strata hold considerable amounts of biomass. This work is the first attempt to estimate biomass by remote sensing without the need for massive in situ measurements. Indeed, we use small-footprint airborne laser scanning (ALS) data to derive key forest metrics, which are used in allometric equations that were originally established to assess biomass using field measurements. Field- and ALS-derived biomass estimates are compared over 40 plots of a multi-layered Mediterranean forest. Linear regression models explain up to 99% of the variability associated with surface vegetation, understory, and overstory biomass.