Abstract
The prediction of tree stem volumes has conventionally been based on simple field measurements and applicable allometric functions, but terrestrial laser scanning (TLS) has enabled new opportunities for extracting stem volumes of single trees. TLS-based tree dimensions are commonly estimated by automatized cylinder- or circle-based fitting approaches which, given that the stem cross-sections are relatively round and the whole stem is sufficiently covered by TLS points, enable an accurate prediction of the stem volume. The results are, however, often deteriorated by co-registration errors and occlusions, i.e., incompletely visible parts of the stem, which easily lead to poorly fitted features and problems in locating the actual treetop. As these defects are difficult to be controlled or totally avoided when collecting data at a plot level, taking advantage of additional field measurements is proposed to improve the fitting process and mitigate gross errors in the prediction of stem volumes. In this paper, this is demonstrated by modelling the stems first as cylinders by only using TLS data, after which the results are refined with the assistance of field data. The applied data consists of various field-measured stem dimensions which are used to define the acceptable diameter estimation limits and set the correct vertical extents for the analyzed tree. This approach is tested using two data sets, differing in the scanning setup, location, and the measured field variables. Adding field data improves the results and, at best, enables almost unbiased volumetric predictions with an RMSE of less than 5%. According to these results, combining TLS point clouds and simple field measurements has the potential to produce stem volume information at a considerably higher accuracy than TLS data alone.
Highlights
Reliable and unbiased measurements on tree stem volumes have important implications for the sustainable planning of forest resources, providing data for harvesting decisions and for helping to estimate the impacts of forestry activities on carbon stock, and further effects on climate regulation (Bonan, 2008; Patenaude, et al, 2005; Vaunkonen and Packalen, 2018; Zianis, et al, 2005)
The errors and completeness of tree extraction regarding dataset I are presented in tables 3 and 4, and errors related to dataset II in table 5, respectively
Applications relying on terrestrial laser scanning (TLS) data have gradually matured to a stage where the replication of simple field measurements has turned into providing relevant information on more complex dimensions, including stem volumes
Summary
Reliable and unbiased measurements on tree stem volumes have important implications for the sustainable planning of forest resources, providing data for harvesting decisions and for helping to estimate the impacts of forestry activities on carbon stock, and further effects on climate regulation (Bonan, 2008; Patenaude, et al, 2005; Vaunkonen and Packalen, 2018; Zianis, et al, 2005). The challenge with stem volumes, lies in their measurement: as determining ac curate stem dimensions is tedious and prohibitively expensive, a con ventional and practical solution is to predict the volumes using allometric equations which require only a few simple parameters, such as diameter at breast height and tree height (Laasasenaho, 1982; West, 2015; Zianis, et al, 2005). Voxel-based strategies are used as well to simplify complicated
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