Application of Tree Detection Methods Over Lidar Data for Forest Volume Estimation

Abstract

Abstract. Lidar (light detection and ranging) data are becoming more and more important in the analysis of the most relevant forest parameters. This study aims to compare the most recent segmentation methods for single trees using the ALS (Airborne Laser Scanning) point cloud and the CHM (Canopy Height Model). The methods used were the Li et al., method developed in 2012 and the Multi CHM method developed in 2015. The parameters analysed were the height and diameter for the individual trees and the volume and density for the entire forest. The efficiency of each method was verified by comparing the estimated parameters with those measured through 30 test areas. To better identify the useful parameters for the correct calibration of the algorithms, the population was divided into three layers according to the vertical structure and chronological class. From the comparison of the volumes obtained with the above methods and those calculated for the test areas, it emerges a tendency to over-segment for the Multi CHM method, while for the appropriately calibrated Li method there is a better correspondence to reality. The F-score values for the volumes obtained for the Li method are between 0.52 and 0.69 while for those obtained for the Multi CHM method are between 0.47 and 0.55. When compared with relascopic measures for each of the 48 parcels, a mean absolute difference ∼127 m3/ha and ∼141 m3/ha were found for Li2012 and MultiCHM respectively.

Estimated Biomass Loss Caused by the Vaia Windthrow in Northern Italy: Evaluation of Active and Passive Remote Sensing Options

“…All samples are in the Alpine region, with an average height above sea level of 1422 m and, higher and lower heights above sea level of 2012 m and 852 m, respectively. Trento plots were sampled during the 2019 campaign [25], while UMA was sampled in 2018 right before the Vaia event. To compute AGB plot values from DBH, species, and height information, the [26] formulas were used.…”

“…The data were used to detect single trees with calibrated parameters according to vertical structure and chronological class. This procedure uses an adaptive distance threshold that affects the precision of tree detection, allowing for the limitation of over-and under-estimation [25]. The tree heights and positions were extracted by segmenting the trees from the point cloud using the method defined in [27].…”