A comparative assessment of the vertical distribution of forest components using full-waveform airborne, discrete airborne and discrete terrestrial laser scanning data

Abstract

[EN] Laser scanning has the potential to accurately detect the vertical distribution of forest vegetative components. However, limitations are present and vary according to the system's platform (i.e., terrestrial or airborne) and recording method (i.e., discrete return or full-waveform). Terrestrial configurations detect close objects (i.e., lower vegetation strata) in more detail while airborne configurations detect a more detailed upper strata, with weak backscattered signals from lower strata. Moreover, discrete lidar systems record single or multiple hits from a given pulse at intercepted features in contrast to full-waveform systems, which register the pulse's complete backscattered signal providing complete vertical profiles. In this study, we examine for a boreal and a Mediterranean forest with contrasted conifer canopy densities: (i) the characterization of the vertical distribution and signal occlusion from three laser scanning configurations: full-waveform airborne (ALS(FW)), discrete airborne (ALS(D)), and discrete terrestrial (TLS); (ii) the comparison in the detection of understory vegetation by ALS(FW) and ALS(D) using TLS as reference; and (iii) the use of a methodological procedure based on the Gini index concept to group understory vegetation in density classes from both ALS(FW) and ALS(D) configurations. Our results demonstrate, firstly, that signal occlusion can be quantified by the rate of pulse reduction independently for data from all three laser scanning configurations. The ALS(D) configuration was the most affected by signal occlusion, leading to weak signal returns at the lower strata (z < 4 m) where the rate of pulse reduction was highest as a result of dense canopy covers. Secondly, we demonstrated the capabilities for both airborne laser scanning configurations to detect understory vegetation, albeit significantly more accurately with ALS(FW). Lastly, we demonstrated the use of the Gini index as an indicator to determine understory vegetation density classes, particularly for ALS(FW) data in dense canopy cover. We proceed to explain the limitations in detecting the vertical distribution from different configurations, and indicate that understory vegetation density classes may be successfully assigned with ALS(FW) in contrasted conifer canopy densities.