Abstract
The characterization of canopy structure is crucial for modeling eco-physiological processes. Two commonly used metrics for characterizing canopy structure are the gap fraction and the effective Plant Area Index (PAIe). Both have been successfully retrieved with terrestrial laser scanning. However, a systematic assessment of the influence of the laser scan properties on the retrieval of these metrics is still lacking. This study investigated the effects of resolution, measurement speed, and noise compression on the retrieval of gap fraction and PAIe from phase-shift FARO Photon 120 laser scans. We demonstrate that FARO’s noise compression yields gap fractions and PAIe that deviate significantly from those based on scans without noise compression and strongly overestimate Leaf Area Index (LAI) estimates based on litter trap measurements. Scan resolution and measurement speed were also shown to impact gap fraction and PAIe, but this depended on leaf development phase, stand structure, and LAI calculation method. Nevertheless, PAIe estimates based on various scan parameter combinations without noise compression proved to be quite stable.
Highlights
Information about forest canopy structure is crucial for understanding the significant role forest canopies play in global processes such as water and carbon cycling
This paper investigates the effects of scanner and scan properties on the retrieval of gap fraction and Plant Area Index (PAIe) derived from phase-shift scanner data
This study investigated the effects of scan resolution, measurement speed, and noise compression on the retrieval of gap fraction and effective Plant Area Index from phase-shift FARO Photon
Summary
Information about forest canopy structure is crucial for understanding the significant role forest canopies play in global processes such as water and carbon cycling. In addition to simple forest stand-based descriptors, such as stem density or mean tree height, descriptors related to the amount, distribution, and orientation of foliage within the canopy are vitally important for understanding plant physiology and growth [1]. These foliage metrics include the Leaf Area Index (LAI), commonly defined for flat leaves as half the total leaf area per unit ground surface area [2], and the foliage area volume density (FAVD), defined as the volume density function of foliage area [3]. As the direct methods are costly, labor intensive and time-consuming [4,6], indirect LAI methods are more commonly applied
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