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A Density-Based Algorithm for the Detection of Individual Trees from LiDAR Data
2021
Remote Sensing
Nowadays, LiDAR is widely used for individual tree detection, usually providing higher accuracy in coniferous stands than in deciduous ones, where the rounded-crown, the presence of understory vegetation, and the random spatial tree distribution may affect the identification algorithms. In this work, we propose a novel algorithm that aims to overcome these difficulties and yield the coordinates and the height of the individual trees on the basis of the point density features of the input point
doi:10.3390/rs13020322
fatcat:tiji5sipvzh6ndvdrjuq5adaqq