# Mean Shift Segmentation Assessment for Individual Forest Tree Delineation from Airborne Lidar Data
## Materials and Methods
### Test Data  
Three types of plots were used in this study to test the tree segmentation methods: a) A synthetically generated mixed-deciduous woodland, b) a monoculture coniferous stand, and c) two forest plots with a mixture of coniferous and deciduous species.  
The synthetic dataset (Figure 2) was simulated by the open-source software HELIOS [45]. Main advantages of using synthetic data include: a) Accurate knowledge on the tree locations and crown parameters, and b) control over the number and species of trees. Four species, black tupelo (Nyssa  
Figure 1: The workflow of individual forest tree delineation from airborne light detection and ranging (lidar) data.  
sylvatica Marshall), sassafras (Sassafras albidum), tamarack (Larix laricina ([PERSON]) [PERSON]), and weeping willow (Salix babylonica L.), were fed into the RIEGL LMS-Q780 simulator to simulate fifty trees randomly located on a 100 m by 100 m square (one hectare). Their heights are 27.891 m, 24.351 m, 22.116 m, and 13.599 m, respectively.  
Apart from the synthetic data, an airborne laser scanning (ALS) dataset (8.4 points per m\({}^{2}\)) of a monoculture plantation stand located in the Queen Elizabeth Forest Park (Aberfoyle, UK) is used for the experiment (Figure 3a). The data were collected by the UK Natural Environment Research Council Airborne Research Facility using the Leica ALS50 Scanner in August 2014. The plot was planted in year 1965, and was composed of lodgepole pine (Pinus contorta Dougl.). Tree parameters, including locations and heights of 45 trees, were surveyed during the field campaign. Tree locations were measured by total station at the bottoms of trees, whilst heights were measured using a vertex hypsometer [46]. The average recorded tree height was 16.18 m, with a standard deviation of 2.12 m. Note that the whole plot contains much more trees, but only this subset covering different tree sizes and densities was measured for validation. Ideally, given a valid tree delineation approach, only a small plot needs to be ground measured to validate the approach and to choose parameters or configurations, which will then be applied to the whole area without further parameter tuning.  
Figure 3: Experiment data (scale in meters). (**a**) A plot of the Aberfoyle forest in Scotland, UK. (**b**) Plot B1 from an international benchmark. (**c**) Plot B2 from an international benchmark. Red points represent ground measured tree tops.  
Figure 2: Synthetic data simulated by HELIOS (scale in meters). (**a**) Tree models and simulated data in bird’s eye view, (**b**) Perspective view. There are 50 trees in total, of four species at different heights. Red points represent tree tops.  
The final data of two plots (Plots B1 Figure 3b and B2 Figure 3c, approx. 8 points per m\({}^{2}\) ) were taken from an international benchmark [22]. They both have a mixture of species, including Norway spruce (Picea abies L.), Scots pine (Pinus sylvestris L.), Downy birch (Betula sp. L.), and Aspen (Populus tremula L.). Plot B1 is predominantly composed of Norway spruce (80%), and the average tree height is 16.8 m with a standard deviation of 6.4 m. Plot B2 has around 55% Norway spruce, and the average tree height is 16.1 m with a standard deviation of 7.31 m. Both plots have multiple crown layers, i.e., dominant, co-dominant, intermediate, and suppressed. The ALS data were collected in June 2004 using an Optech 2033 airborne scanner. Field measurements were collected with a terrestrial laser scanner (TLS), Faro LS880 HE. The locations and heights of trees were manually measured from the TLS data.