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Please use this identifier to cite or link to this item: http://hdl.handle.net/2067/2962

Title: Using classification trees to predict forest structure types from LiDAR data
Authors: Torresan, Chiara
Corona, Piermaria
Scrinzi, Gianfranco
Marsal, Joan Valls
Keywords: Airborne laser scanning
Discrete return laser scanner data
Stem diameter classes
Basal area
Bivariate analysis
Unsupervised clustering
Classification tree model
Forest inventory
Forest management
Issue Date: 2016
Publisher: Forest Research and Management Institute ICAS
Citation: Torresan, C. et al. 2016. Using classification trees to predict forest structure types from LiDAR data. "Annals of Forest Research" 59 (2): 281-298
Abstract: This study assesses whether metrics extracted from airborne Li-DAR (Light Detection and Ranging) raw point cloud can be exploited to predict different forest structure types by means of classification trees. Preliminarily, a bivariate analysis by means of Pearson statistical test was developed to find associations between LiDAR metrics and the proportion of basal area into three stem diameter classes (understory, mid-story, and over-story trees) of 243 random distributed plots surveyed from 200
DOI: 10.15287/afr.2016.423
URI: http://hdl.handle.net/2067/2962
ISSN: 1844-8135
Appears in Collections:DIBAF - Archivio della produzione scientifica

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