DOI: 10.23729/fd-aa4d04a7-7b4c-34ca-ac4a-c8c3ec26c13b
title: Daily permanent laser scanning point clouds time-series of 106 individual Scots pines over the 2021 growth season in central Finland

description: DATA SOURCE AND TIME SERIES DESCRIPTION:  

This permanent laser scanning (PLS) dataset comprises a four-month-long LiDAR point cloud time-series with near-daily temporal resolution. The dataset includes repeated 3D point cloud observations of 106 individual Scots pine (Pinus sylvestris L.) trees located in central Finland. Data were acquired between 1 April and 30 July 2021 using the Finnish Geospatial Institute (FGI) LiDAR Phenology Station (LiPhe). LiPhe is a PLS system located at the Hyytiälä Forestry Field Station in southern Finland. As described by Lindenbergh et al. (2025), PLS can be defined as a fixed, automated terrestrial laser scanning system designed for the continuous monitoring of surfaces and environments, providing high-precision 4D (3D + time) data for change-detection analysis. As of 2026, the LiPhe system is the only PLS actively collecting data in forest environments. LiPhe is equipped with a RIEGL VZ-2000i terrestrial laser scanner (RIEGL Laser Measurement Systems GmbH, Horn, Austria). The scanner is mounted on a 35 m tower, positioned approximately 15 m above the forest canopy, and operated with a 60° tilt angle to provide an oblique view of the forest stand.  Additional details about the LiPhe system are available at Campos et al. (2021).  

Temporal Resolution: The median temporal interval between consecutive scans is 24 hours, with a standard deviation of 8.42 hours, totaling 116 time-points. Near-daily coverage was achieved by selecting scans based on favorable meteorological conditions; only acquisitions collected under relative humidity below 80% and wind speeds below 3 m s⁻¹ were included.  

Spatial Resolution: The scanner provides a minimum spatial point spacing of approximately 0.01 m at ranges up to 100 m.  

Georeferencing: The dataset consists of georeferenced individual-tree point clouds with three-dimensional coordinates referenced to EPSG:3067 (ETRS89 / TM35FIN) 

Point Cloud Attributes: Each point in the point clouds includes the following attributes: Return number (integer, 1–15), Number of returns (integer, 1–15), Intensity (amplitude), Reflectance (dB), Return pulse deviation (a measure of pulse shape distortion).  

Nomenclature: The filename denotes the tree id. The single tree point cloud filenames include date and time (YYMMDD_HHMMSS) and treeID: 'YYMMDD_HHMMSS_TREE_TreeId.laz (210401_150501_TREE_5087.laz)'. 

Metadata: 

Tree information can be obtained using  Tree2Attributes pipeline: 
https://gitlab.com/fgi_nls/public/liphekit_tree2attributes.git 

Pine_study_tree_metadata.xlsx contains the IDs and coordinates (E, N, H) of the trees studied in this study. All study trees are Scots pine. The coordinates are in the ETRS89/TM35FIN coordinate system. 

Tree_height_area_timeseries.xlsx contains the height and crown area timeseries of the 106 study trees. Tree height has been calculated using the Tree2Attributes.height function while crown area was calculated with the Tree2Attributes.area function. Date is in yymmdd format and Time is in hhmmss format. Area contains the crown area in m2. Height_99_5, Height_99_95, Height_100, represent tree height in 99.5th, 99.95th and 100th meter percentiles, respectively. Height_99_95 was used in our study. 

Pine_study_tree_attributes.xlsx contains the calculated metrics for each study tree. The table contains the following attributes: 

               *    ID: Study tree ID 

               *    E, N coordinates: correspond to East, North. The coordinates are in the ETRS89/TM35FIN coordinate system.
 
               *    Elevation: Elevation of study trees. Calculated using the study tree positions (E, N) and a 0.2-m resolution digital terrain model (DTM) from the airborne laser scanning campaign (available at LiPheStream). One of the ouputs from the function Tree2Attributes.height. 

               *   TWI:   Topographic wetness index calculated from a 0.5-m resolution DTM. The TWI value of each tree represents the mean TWI within a 6-m radius from the study tree.  

               *   PAI: Plant area index calculated from the same 0.5-m resolution DTM. The PAI value of each tree represents the mean PAI within a 5-m radius from the study tree. 

              *   VCI: Vertical complexity index calculated from the same 0.5-m resolution DTM. The VCI value of each tree represents the mean VCI within a 5-m radius from the study tree. 

             *    Neighbors_total: The number of neighbors within a 5-m radius from the study tree, calculated from the forest inventory data, which includes neighborhood tree positions and their heights. This inventory data is produced in collaboration with another party and cannot be published for this reason. One of the ouputs from the function Tree2Attributes.neighbors. 

              *    NSR: Neighborhood species richness is the number of different species within a 5-m radius. Only Scots pine, Norway spruce, and silver birch are included in the calculation. The values range between 1 (only one species as neighbor) and 3 (all three species as neighbors). NSR is calculated using the forest inventory data, which includes neighborhood tree positions and their heights. This inventory data is produced in collaboration with another party and cannot be published for this reason. One of the ouputs from the function Tree2Attributes.neighbors. 

              *    CI6: Competition index 6 is calculated using the Tree2Attributes.competition function, which uses the forest inventory data as input. However, this inventory data is produced in collaboration with another party and cannot be published for this reason. 

             *     CI8: Competition index 8 is calculated using the Tree2Attributes.competition function, which uses the forest inventory data as input. However, this inventory data is produced in collaboration with another party and cannot be published for this reason. 

             *     CV: Competition index calculated from point clouds of the individual trees and their neighborhood in a 5-m radius. CV is calculated using the Tree2Attributes.competition3D function. The input study tree and neighborhood point clouds were calculated from the full point cloud 210402_180501_R_georef_resample.laz (additional data). 

             *      Dvert: Competition index calculated from point clouds of the individual trees and their neighborhood in a 5-m radius. Dvert is calculated using the Tree2Attributes.competition3D function. The input study tree and neighborhood point clouds were calculated from the full point cloud 210402_180501_R_georef_resample.laz (additional data). 

             *     Growth_onset: The date of the onset of height growth detected using the Tree2Attributes.growth_detection function. A tree height time series has to first be calculated using the Tree2Attributes.height function for all time points. A ready height time series is available in Tree_height_area_timeseries.xlsx. The onset day is given in dd-mm-yy format. 

             *    Growth_onset_DOY: Same as above but date is given in Day Of Year format. 

             *    Absolute_height_change: Absolute difference in height between the mean value in April (before growth) and the mean height in July. Values are in meters. Calculated using the Tree2Attributes.growth_detection function. 

             *    Relative_height_change: Relative difference in height between the mean value in April (before growth) and the mean height in July. The mean height in April is used as base height for the tree to calculate its relative growth in height. Calculated using the Tree2Attributes.growth_detection function. 

Additional data: A full view point cloud from the LiPhe data collection conducted in April 2021 is also available here (resampled with 5 cm resolution), enabling evaluation of tree spatial locations and neighborhood conditions at the beginning of the time series.  For this application, as well as height estimation, the 5 cm resample do not affect the results accuracy. Full unsampled Liphe point clouds are available upon request.  Additional complementary data related to this dataset are available through LiPheStream (https://doi.org/10.1038/s41597-024-04143-w), which includes a Digital Terrain Model (DTM) of the study area as well as example full point clouds (PLS and ALS).

Campos, M. B., Litkey, P., Wang, Y., Chen, Y., Hyyti, H., Hyyppä, J., & Puttonen, E. (2021). A long-term terrestrial laser scanning measurement station to continuously monitor structural and phenological dynamics of boreal forest canopy. Frontiers in Plant Science, 11, 606752. https://doi.org/10.3389/fpls.2020.606752 

Lindenbergh, R., Anders, K., Campos, M., Czerwonka-Schröder, D., Höfle, B., Kuschnerus, M., ... & Vos, S. (2025). Permanent terrestrial laser scanning for near-continuous environmental observations: Systems, methods, challenges and applications. ISPRS Open Journal of Photogrammetry and Remote Sensing, 100094. https://doi.org/10.1016/j.ophoto.2025.100094 

DATA PROCESSING 

This dataset is a subset of LiPhe data collection. Plot-level LiPhe point clouds were processed to generate near-daily individual-tree point clouds using the LiPheKit processing pipeline. The workflow includes point cloud registration, georeferencing, and individual tree segmentation. Further information on the LiPheKit pipeline is available at Wittke et al. (2024).  Attention: the goal of this dataset was to estimate tree height over time and subsequently derive tree height curves to identify the onset of vertical growth in Scots pine during the 2021 growing season in Finland. During data processing, the treetops were preserved and prioritized while the data were cleaned; as a result, some structural elements, such as branches, may have been removed due to aggressive automatic tree segmentation applied to eliminate neighboring trees. Most of those trees can be also found on LiPheStream, which is a LiPhe point cloud time series dataset with different temporal resolutions (weekly level).  

LiPheKit is available at: https://gitlab.com/fgi_nls/public/liphekit 

Wittke, S., Campos, M., Ruoppa, L., Echriti, R., Wang, Y., Gołoś, A., ... & Puttonen, E. (2024). LiPheStream-A 18-month high spatiotemporal resolution point cloud time series of Boreal trees from Finland. Scientific Data, 11(1), 1281. https://doi.org/10.1038/s41597-024-04143-w