A Comparison of Two Tree Detection Methods for Estimation of Forest Stand and Ecological Variables from Airborne LiDAR Data in Central European Forests

Abstract

Estimation of biophysical variables based on airborne laser scanning (ALS) data using tree detection methods concentrates mainly on delineation of single trees and extraction of their attributes. This study provides new insight regarding the potential and limits of two detection methods and underlines some key aspects regarding the choice of the more appropriate alternative. First, we applied the multisource-based method implemented in reFLex software (National Forest Centre, Slovakia), which uses the information contained in the point cloud and a priori information. Second, we applied the raster-based method implemented in OPALS software (Vienna University of Technology, Austria), which extracts information from several ALS-derived height models. A comparative study was conducted for a part of the university forest in Zvolen (Slovakia, Central Europe). ALS-estimated variables of both methods were compared (1) to the ground reference data within four heterogonous stands with an area size of 7.5 ha as well as (2) to each other within a comprehensive forest unit with an area size of 62 ha. We concluded that both methods can be used to evaluate forest stand and ecological variables. The overall performance of both methods achieved a matching rate within the interval of 52%–64%. The raster-based method provided faster and slightly more accurate estimate of most variables, while the total volume was more precisely estimated using the multisource-based method. Specifically, the relative root mean square errors did not exceed 7.2% for mean height, 8.6% for mean diameter, 21.4% for total volume, 29.0% for stand density index, and 7.2% for Shannon’s diversity index. Both methods provided estimations with differences that were statistically significant, relative to the ground data as well as to each other (p < 0.05).

Combined Impact of Sample Size and Modeling Approaches for Predicting Stem Volume in Eucalyptus spp. Forest Plantations Using Field and LiDAR Data

“…The analysis of LiDAR data combined with field data has been used by several authors to produce highly accurate retrievals of tree density, stem total, and assortment volumes, basal area, aboveground carbon, leaf area index, and thereby, can be an effective way to predict and map forest attributes at unsampled locations [14][15][16][17][18][19]. Current predictive modeling methods include parametric (i.e., multiple linear regression) and non-parametric (i.e., machine learning algorithms) approaches [20].…”

Individual tree detection using template matching of multiple rasters derived from multispectral airborne laser scanning data

“…ITD performance was evaluated on the basis of six metrics (height, point density, vegetation ratio, and intensity from three channels) and various combinations of these metrics. Height is the metric most commonly used for ITD based on ALS data (Eysn et al 2015;Sačkov, Kulla, and Bucha 2019;Aubry-Kientz et al 2019) even though its limitations with respect to detecting understorey trees have been widely noted. The results presented here show that this deficiency can be overcome by using additional metrics.…”

A Transferable and Effective Method for Monitoring Continuous Cover Forestry at the Individual Tree Level Using UAVs

“…The BOVW framework goes through the following steps for ITD (Figure 2): (a) the CHM resolution is set. If the CHM is too-course then a higher number of undetected trees is expected and if too fine then the potential for false positives increases [51]; (b) a Gaussian smoothing window is passed over the CHM to reduce spurious points. Previous ITD studies have shown that the strength of smoothing to achieve optimum segmentation results depends on species composition, but generally, overly heavy smoothing results in an increase in undetected trees [52]; (c) the local maximum is detected in a circular variable search window (VW) which scaling is determined by a h-cr relationship; and (d) the segmentation accuracy (F-score) is determined using reference tree data.…”