Lidar point clouds of three oak trees

The data consists of a 3-dimensional point cloud of trees produced by a laser scanner, where each point is a sample of the tree’s surface. As for a single scanner location, a large part of the tree is occluded, multiple scans are commonly performed, and their data are co-registered (Li et al., 2020; Raumonen et al., 2015; Wan et al., 2019). It is important to not only place the points in a consistent coordinate scheme during co-registration, but also the scanner locations and to keep track of which points were produced by which scanner location for uncertainty propagation. To showcase our methodology, we applied it to terrestrial laser scanning data of 80-year-old oak trees of three different sizes (small, medium, and large). The raw point clouds were recorded at Alice Holt Forest, UK (51.1546°N, 0.8520°W) using a single-return phase shift Leica HDS- 6100 terrestrial laser scanner (Leica Geosystems, n.d.). The scans were conducted in March 2014 under dry conditions and low wind speeds (..., , # Lidar point clouds of three oak trees

Data is provided on behalf of Forest Research UK.\
Dataset DOI: [10.5061/dryad.7h44j1086](https://doi.org/10.5061/dryad.7h44j1086)

## Description of the data

### Files and variables

TLS scanner locations are given in TLS_Positions_*_Oak.txt with format

x y z #scanner

For each tree, there are six scanner locations, each of which has a point cloud stored in an .xyz file (i.e. Bigoak_M_P1 for the first scanner location for the big tree) with format

x y z

## Code/software

The following Matlab code was used to read the data (Data_Processing/EC_Data_Reader.m) and used to fit QSMs to it with TreeQSM 2.4.1 and quantify their uncertainty.

[https://github.com/InverseTampere/Uncertain_TreeQSM](https://github.com/InverseTampere/Uncertain_TreeQSM)
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