Fusing diameter distributions predicted by an area-based approach and individual-tree detection in coniferous-dominated forests

Abstract

An area-based approach (ABA) is the most common method used to predict forest attributes with airborne laser scanning (ALS) data. Individual-tree detection (ITD) offers an alternative to ABA; however, few studies have examined the selection of these two alternatives for the prediction of diameter distributions. We predicted diameter distributions by applying ABA and ITD in coniferous-dominated boreal forests using ALS data and examined their predictive performance based on the shapes of the diameter distributions (Gaussian, bimodal, and reverse-J). We proposed an ABA–ITD fusion for diameter distribution prediction. Firstly, the fusion was optimized and its potential was evaluated using an error index. Secondly, we offer two alternatives to incorporate the fusion into ALS-based forest inventories. Our results indicate that ITD is more prone to errors than ABA and that the predictive performance of ITD is more sensitive than ABA to the shape of the diameter distribution. The results show that ITD outperforms ABA with Gaussian diameter distributions. In contrast, ABA was seen as preferable to ITD with bimodal- or reverse-J-shaped diameter distributions. The findings indicate that ABA–ITD fusion has potential for predicting diameter distributions, although the predictive capability of ITD is limited compared with that of ABA.

Improvements in forest structural type assessment using airborne laser scanning

“…Several studies have used ABA to describe the relationship between forest variables and ALS metrics. These include the prediction of ℎ, basal area, volume, biomass or height using linear regression (Means et al 2000;Naesset 2002), non-linear regression (Packalén et al 2011) or non-parametric approaches (Packalén and Maltamo 2006;Yu et al 2010;Andersen et al 2011;Räty et al 2020). Some studies have also identified the factors that affect the performance of ABA, such as plot size (Gobakken and Naesset 2008), sample size (Junttila et al 2013), errors in plot positions (Gobakken and Naesset 2009;Rana et al 2014), and the resolution of the cell (Packalen et al 2019).…”

“…Some studies have also identified the factors that affect the performance of ABA, such as plot size (Gobakken and Naesset 2008), sample size (Junttila et al 2013), errors in plot positions (Gobakken and Naesset 2009;Rana et al 2014), and the resolution of the cell (Packalen et al 2019). However, this method is most often applied in operational forest inventories that employ ALS data (Maltamo et al 2014), and is more flexible and robust for diameter predictions, for example, in boreal managed forests dominated by coniferous species (Räty et al 2020).…”

Estimating Species-Specific Stem Size Distributions of Uneven-Aged Mixed Deciduous Forests Using ALS Data and Neural Networks

“…Following previous research [26,32,33,35,43,44], 46 ALS features were calculated for each segment (Figure 3VIII). Height metrics were calculated considering points above 2 m. Intensity metrics were calculated considering points above the 85th height percentile inside segments for both channels (C1 and C2).…”

“…Machine learning techniques were also used to predict individual tree DBH [34]. The ABA and ITD approaches were also combined to predict SSD by fusing the two predicted SSDs [35], employing distribution matching techniques [36], using replacement or histogram matching methods [37], or even using stand density and crown radius distribution through a distribution matching step [38]. Secondly, the interest in SSD estimation was focused on more complex forests, especially deciduous and tropical stands.…”

“…Tree-size frequency distribution has also been estimated from ITD and tree allometries [42]. Concerning species discrimination, most studies have usually not considered species at all [35,38,41], used a preliminary plot stratification by main species during sampling design [40], or predicted only broadleaf proportion [39], while species-specific predictions have mainly been made for coniferous species [21]. ALS data were sometimes combined with spectral data [16,17,21], however nowadays multi-spectral ALS offers new possibilities for differentiating tree species [43] and estimating forest stand variables [44].…”