SimpleTree —An Efficient Open Source Tool to Build Tree Models from TLS Clouds

By Jan Hackenberg, Heinrich Spiecker, Kim Calders, Mathias I. Disney, Pasi Raumonen. 2015

331 citations recorded by the source corpus.

An open source tool named SimpleTree, capable of modelling highly accurate cylindrical tree models from terrestrial laser scan point clouds, is presented and evaluated. All important functionalities, accessible in the software via buttons and dialogues, are described including the explanation of all necessary input parameters. The method is validated utilizing 101 point clouds of six different tree species, in the main evergreen and coniferous trees. All scanned trees have been destructively harvested to get accurate estimates of above ground biomass with which we assess the accuracy of the SimpleTree-reconstructed cylinder models. The trees were grouped into four data sets and for each one a Concordance Correlation Coefficient of at least 0.92 (0.92, 0.97, 0.92, 0.94) and an total relative error at most ~8 % (2.42%, 3.59%, –4.59%, 8.27%) was achieved in the comparison of the model results to the ground truth data. A global statistical improvement of derived cylinder radii is presented as well as an efficient optimization approach to automatically improve user given input parameters. An additional check of the SimpleTree results is presented via comparison to the results of trees reconstructed using an alternative, published method.

Subfields: Environmental Engineering, Global and Planetary Change, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/a2ad2bb90e9b665c10ea527132a3c14b69bc0e62

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In Environmental Engineering

In Global and Planetary Change

In Nature and Landscape Conservation

Citations in the corpus

International benchmarking of terrestrial laser scanning approaches for forest inventories (2018, methodology): “The remaining individual clouds were de-noised using filters described in (Hackenberg et al., 2015), e.g., the radius outlier filter, the voxel-grid filter and the stem/major branch point detection.”
Sensing and Automation in Pruning of Apple Trees: A Review (2018, methodology): “…et al. [60] used RGB and 3D cameras to detect and reconstruct the branches that could be used for determining the shaking points for the mechanical sweet cherry harvesting (Figure 5A); and Zhang et al. [61] used a Microsoft Kinect v2 (ToF camera, Microsoft, Seattle, DC, USA) and a convolutional…”