The invention belongs to the technical field of
forestry information, and particularly relates to a moso bamboo forest individual tree high-precision segmentation method based on unmanned aerial vehicle and foundation
LiDAR fusion. The method aims at solving the problems that in the prior art, in a complex bamboo forest scene,
canopy information is incomplete, under-forest
terrain and bamboo pole structure obtaining is inaccurate due to the fact that a
data source is single, and over-segmentation and under-segmentation generally exist due to the fact that a moso bamboo growth mechanism is not considered in a traditional
algorithm. Through multi-platform
LiDAR data collaborative acquisition,
point cloud accurate fusion is realized by adopting a registration
algorithm with a bamboo pole vertical structure as a constraint, and a
canopy-bamboo pole-
terrain integrated three-dimensional model is formed. A comprehensive clustering
algorithm fusing tree crown geometric attributes and a
canopy growth space competition model is innovatively provided, the moso bamboo growth competition relation is quantified into dynamic constraint, the individual tree boundary is intelligently defined, the over-segmentation and under-segmentation problems in the segmentation process are effectively solved, and the moso bamboo forest individual tree parameter extraction precision and ecological rationality are remarkably improved.