Classification trees to discriminate forest structural ...

2014 ForestSAT conference, 4-7 November, Riva del Garda (Italy)

Classification trees to discriminate forest structural clusters through metrics from discrete return ALS data Chiara Torresan1, Gianfranco Scrinzi2, Piermaria Corona3, Joan Valls Marsal4 1

Department for Innovation in Biological, Agro-food and Forest Systems, University of Tuscia, Viterbo, Italy 2 Consiglio per la Ricerca e la Sperimentazione in Agricoltura, Forest Monitoring and Management Research Unit (CRA-MPF), Trento, Italy 3 Consiglio per la Ricerca e la Sperimentazione in Agricoltura, Forestry Research Centre (CRA-SEL), Arezzo, Italy 4 Biostatistics Unit, Biomedical Research Institute of Lleida, Spain

Introduction

Forest managers use basal area distribution into tree diameter classes (e.g. understory, mid-story, and overstory trees) as criterion to classify the structure of forest for management purposes. Remote sensing is a valuable source of information in mapping and monitoring forest features (Corona et al., 2012), and machine learning techniques have been used exploiting satellite imagery (e.g. Franklin et al., 2002; Moghaddam et al., 2002) and satellite imagery integrated with airborne laser scanner data (e.g. Lefsky et al., 1999; Hudak et al., 2002). In this research we studied the problem of forest structure prediction from lidar point cloud-derived metrics using classification trees and we investigated the performance of the developed tree model.

Materials and methods

Field data collection

Results Table 2 - Pearson correlation coefficient (rP ) with the associated Bonferroni corrected p-values (p-valueBC) between lidar metrics and proportion of basal area of understory (%BAu), mid-story (%BAm), and overstory (%BAo) for only the metrics with p-valueBC