Modelling growing stock volume of forest stands with various ALS area-based approaches

Abstract

Airborne laser scanning (ALS) is one of the most innovative remote sensing tools with a recognized important utility for characterizing forest stands. Currently, the most common ALS-based method applied in the estimation of forest stand characteristics is the area-based approach (ABA). The aim of this study was to analyse how three ABA methods affect growing stock volume (GSV) estimates at the sample plot and forest stand levels. We examined (1) an ABA with point cloud metrics, (2) an ABA with canopy height model (CHM) metrics and (3) an ABA with aggregated individual tree CHM-based metrics. What is more, three different modelling techniques: multiple linear regression, boosted regression trees and random forest, were applied to all ABA methods, which yielded a total of nine combinations to report. An important element of this work is also the empirical verification of the methods for estimating the GSV error for individual forest stand. All nine combinations of the ABA methods and different modelling techniques yielded very similar predictions of GSV for both sample plots and forest stands. The root mean squared error (RMSE) of estimated GSV ranged from 75 to 85 m3 ha−1 (RMSE% = 20.5–23.4 per cent) and from 57 to 64 m3 ha−1 (RMSE% = 16.4–18.3 per cent) for plots and stands, respectively. As a result of the research, it can be concluded that GSV modelling with the use of different ALS processing approaches and statistical methods leads to very similar results. Therefore, the choice of a GSV prediction method may be more determined by the availability of data and competences than by the requirement to use a particular method.

Adding single tree features and correcting edge tree effects enhance the characterization of seedling stands with single-photon airborne laser scanning

“…Adding ITD-derived features to ABA: This approach is based on averaging the ITD-derived features of segments located inside plots and using them in accordance with the ABA Ordinary -derived features to improve estimation of forest attributes compared to ABA Ordinary . This approach was introduced by Hyyppä et al (2012), then applied and proved in other studies as well (Kelley et al, 2022;Parkitna et al, 2021;Shinzato et al, 2016;Breidenbach et al, 2012;Vastaranta et al, 2012). 2.…”

Validation of the DART Model for Airborne Laser Scanner Simulations on Complex Forest Environments

“…Such results could be of high interest for the development of improved area-based models [53], that are used to predict forest stand attributes with variables derived from the analysis of ALS point clouds at the level of traditional field inventory, i.e. for forest plots with diameters ranging from about 20 to 30 m. Indeed, simulations could be used to better analyze, understand and model impacts of acquisition conditions, e.g.…”