Abstract
Tree stem detection is a key step toward retrieving detailed stem attributes from terrestrial laser scanning (TLS) data. Various point-based methods have been proposed for the stem point extraction at both individual tree and plot levels. The main limitation of the point-based methods is their high computing demand when dealing with plot-level TLS data. Although segment-based methods can reduce the computational burden and uncertainties of point cloud classification, its application is largely limited to urban scenes due to the complexity of the algorithm, as well as the conditions of natural forests. Here we propose a novel and simple segment-based method for efficient stem detection at the plot level, which is based on the curvature feature of the points and connected component segmentation. We tested our method using a public TLS dataset with six forest plots that were collected for the international TLS benchmarking project in Evo, Finland. Results showed that the mean accuracies of the stem point extraction were comparable to the state-of-art methods (>95%). The accuracies of the stem mappings were also comparable to the methods tested in the international TLS benchmarking project. Additionally, our method was applicable to a wide range of stem forms. In short, the proposed method is accurate and simple; it is a sensible solution for the stem detection of standing trees using TLS data.
Highlights
Forests are important ecological and economic resources because they contain 80% of the earth’s plant biomass [1], which is important to understand the global carbon balance [2]
This study proposed a novel and simple approach for the stem detection from terrestrial laser scanning (TLS) data, which achieved comparable accuracies of the stem point extraction compared with the reported accuracies in the literature, which ranged from 8% to 98% [25,31,48]
We proposed a novel and simple segment-based approach to detect tree stems from plot-level TLS data
Summary
Forests are important ecological and economic resources because they contain 80% of the earth’s plant biomass [1], which is important to understand the global carbon balance [2]. The stem generally accounts for more than 65% of the tree biomass [3] and is thereby considered crucial for the accurate determination of tree-level aboveground biomass. In forestry, retrieving detailed stem attributes, such as stem curve, is essential for determining the inflection points or cut points along the stem, calculating the total and merchantable stem volume, and evaluating the quality of stems [4]. The detailed stem attributes can improve the accuracy of biomass estimation of individual trees [5,6]. Many different stem curve models have been developed for various tree species [7]. These models cannot be used to describe irregular stems
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