A manuscript entitled “Terrestrial lidar remote sensing of forests: Maximum likelihood estimates of canopy profile, LAI, and leaf angle distribution” was accepted by Agricultural and Forest Meteorology (IF=3.894).
This study aims to improve the utility of terrestrial lidar for vegetation survey by forging new TLS analyses to estimate foliage profile, leaf area index, and leaf angle distribution. We applied physical-based gap theories to build a maximum likelihood estimator (MLE) of vegetation structural parameters. Unlike classical gap-based algorithms, MLE explicitly accommodates laser scanning geometries, fully leverages raw information of laser ranging data, and simultaneously derives foliage profile and leaf angle distribution, with many additional theoretical advantages.
A forest canopy in three contrasting yet linked spaces: Physical, measurement, and parameter.