Estimating tree attributes using airborne laser scanning

There are a number of tree attributes which are routinely assessed in forest inventory, research trials, and tree breeding programmes. Those attributes can be placed into four groups: tree size, tree form (determining tree and log quality), wood quality, and disease. There is potential for cost savings and new approaches to tree assessment if methods could be developed to evaluate attributes representing each of these groups using remotely sensed ALS data.

1.4.1 Tree size

Researchers have used crown metrics derived from ALS data to estimate individual tree height, DBH or volume in boreal and savannah forest types (Chen et al. 2007; Lindberg et al. 2012;

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Vauhkonen et al. 2010; Yu et al. 2011), and for planted forests (Chen and Zhu 2012; Lo and Lin 2013). Estimates of tree height typically had higher accuracy than estimates of DBH and volume. Estimates of height, DBH and volume for trees in boreal forest in southern Sweden had RMSEs of 4%, 15% and 35% respectively (Lindberg et al. 2012). Very similar results were obtained in a study of boreal forest in southern Finland, where RMSEs on estimates of height, DBH and volume were 3%, 13%, and 31% respectively (Vauhkonen et al. 2010). While there is only limited international research, results indicate good potential for the use of crown metrics from ALS data to estimate tree size attributes in New Zealand radiata pine stands.

1.4.2 Tree and log quality

Stem form variables of interest in forest management are straightness, including absence of butt sweep, and absence of malformations, in order to maximise the merchantable volume and quality of the stem. Tree breeds with multi-nodal branching are also favoured as they tend to have smaller branches, as well as better growth rate and straighter, less malformed, stems (Burdon 2001). Trees with straight, defect-free stems and small branches achieve higher log grades and are therefore of higher value. A strong relationship was found between tree maximum branch diameter and crown radius measured from the ground in one study, and the potential to use ALS data for estimating branch size was proposed by the authors (Groot and Schneider 2011). However review of the literature found no examples of using ALS data to directly estimate stem form or branching attributes for individual trees.

1.4.3 Wood quality

Wood stiffness and density are key attributes for structural timber and are therefore important to forest managers and tree breeders. In a review paper the potential use of ALS data to estimate various wood quality variables for forest management purposes was discussed (Van Leeuwen et al. 2011). Moderate success has been achieved in estimating wood properties from ground measured crown variables and the authors suggested the possibility of using remote sensing data, such as ALS, in the future (Groot et al. 2015; Lenz et al. 2012).

Introduction

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There are some examples of estimating wood quality from ALS, although these studies are confined to estimates at the plot, rather than tree, level. In a pilot study (Pont et al. 2012b), crown metrics were used to estimate plot mean standing tree acoustic velocity, a measure which is highly correlated with timber stiffness, with an R2 of 0.69 (Watt et al. 2013b). Wood fibre attributes, including wood density and microfibril angle, were estimated at the plot level with low to moderate precision (R2 from 0.18 to 0.53 and RMSE from 2% to 14%) using metrics derived from ALS data (Luther et al. 2014). In a study using area-based metrics describing canopy height, canopy depth and canopy light zones, about half of the observed variance at the plot level was explained for fibre attributes (Hilker et al. 2012). Crown structural metrics derived from a canopy height model (CHM) from terrestrial laser scanning data were used to estimate plot mean wood fibre properties with R2 from 0.63 to 0.72 for black spruce stands (Blanchette et al. 2015). In summary, a review of the literature has shown potential, but has not identified any research that has estimated wood quality metrics at the individual tree level from ALS data.

1.4.4 Disease

The negative impacts of defoliation on tree growth due to disease or pests is an important issue in New Zealand and internationally. Review of the literature found few studies using ALS data to characterise levels of infection or needle loss on individual trees. Metrics from area-based analysis of ALS data were found to be correlated with plot level assessments of needle loss due to pine beetle infestation (Coops et al. 2014). In a study of loblolly pine it was noted that crown metrics are known to be correlated with individual tree leaf area, but the ability to predict leaf area index (LAI) was limited by the ability to accurately detect tree crown diameters and lengths (Roberts et al. 2005). Nearest neighbour methods were used to establish relationships allowing determination of two defoliation classes for individual trees, to an accuracy of over 80%, based on ground measured training data and point cloud metrics (Kantola et al. 2013; Kantola et al. 2010). Several studies have investigated the use of laser scanning in the estimation of leaf area index or the related measure leaf area density, which might be used to quantify needle loss due to disease (Beets et al. 2011; Korhonen and Mosdorf 2014; Solberg et

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al. 2006b; Tang et al. 2014). A number of those studies have relied on full waveform or terrestrial laser scanner data (Adams et al. 2012; Kato et al. 2013). Measurement of LAI for individual trees is very difficult (Breda 2003), and the few studies which looked at the individual tree level were for isolated trees, not groups of trees which had been delineated (Oshio et al. 2015). While the potential use of ALS data to quantify needle loss on individual trees has been recognised, to date there has been little research into methods to carry this out.