Detailed point cloud data on stem size and shape of Scots pine trees

This data set is comprised of three packed zip files and they include text files of 3D information from terrestrial laser scanning (TLS) and aerial imagery from unmanned aerial vehicle (UAV) from individual Scots pine trees within 27 sample plots from three test sites located in southern Finland. TLS data acquisition was carried out with Trimble TX5 3D laser scanner (Trible Navigation Limited, USA) for all three study sites between September and October 2018. Eight scans were placed to each sample plot and scan resolution of point distance approximately 6.3 mm at 10-m distance was used. Artificial constant sized spheres (i.e. diameter of 198 mm) were placed around sample plots and used as reference objects for registering the eight scans onto a single, aligned coordinate system. The registration was carried out with FARO Scene software (version 2018). Aerial images were obtained by using an UAV with Gryphon Dynamics quadcopter frame. Two Sony A7R II digital cameras were mounted on the UAV in +15° and -15° angles. Images were acquired in every two seconds and image locations were recorded for each image. The flights were carried out on October 2, 2018. For each study site, eight ground control points (GCPs) were placed and measured. Flying height of 140 m and a flying speed of 5 m/s was selected for all the flights, resulting in 1.6 cm ground sampling distance. Total of 639, 614 and 663 images were captured for study site 1, 2, and 3, respectively, resulting in 93% and 75% forward and side overlaps, respectively. Photogrammetric processing of aerial images was carried out following the workflow as presented in Viljanen et al. (2018). The processing produced photogrammetric point clouds for each study site with point density of 804 points/m², 976 points/m², and 1030 points/m² for study site 1, 2, and 3, respectively. The sample plots within the three test sites have been managed with different thinning treatments in either 2005 or 2006. The experimental design of the sample plots includes two levels of thinning intensity and three thinning types resulting in six different thinning treatments, namely i) moderate thinning from below, ii) moderate thinning from above, iii) moderate systematic thinning, iv) intensive thinning from below, v) intensive thinning from above, and vi) intensive systematic thinning, as well as a control plot where no thinning has been carried out since the establishment. More information about the study sites and samples plots as well as the thinning treatments can be found in Saarinen et al. (2020a). The data set includes stem points of individual Scot pine trees extracted from the point clouds. More about the method of extraction can be found in Saarinen et al. (2020a, 2020b) and Yrttimaa et al. (2020). The title of the zip file refers to the study sites 1, 2, and 3. The title of the text files includes the information on the test site, the plot within the test site, and the tree within the plot. The text files contain stem points extracted from the TLS point clouds. The columns “x” and “y” contain x- and y-coordinates in a local coordinate system (in meters), in column “h” is the height of each point in meters above ground, and treeID is the tree identification number. The columns are separated by space. Based on the study site and plot number, files from different thinning treatments can be identified by using the information in Table 1 in Saarinen et al. (2020b). <strong>References</strong> Saarinen, N., Kankare, V., Yrttimaa, T., Viljanen, N., Honkavaara, E., Holopainen, M., Hyyppä, J., Huuskonen, S., Hynynen, J., Vastaranta, M. 2020a. Assessing the effects of stand dynamics on stem growth allocation of individual Scots pines. bioRxiv 2020.03.02.972521. https://doi.org/10.1101/2020.03.02.972521 Saarinen, N., Kankare, V., Yrttimaa, T., Viljanen, N., Honkavaara, E., Holopainen, M., Hyyppä, J., Huuskonen, S., Hynynen, J., Vastaranta, M. 2020b. Detailed point cloud data on stem size and shape of Scots pine trees. bioRxiv 2020.03.09.983973. https://doi.org/10.1101/2020.03.09.983973 Viljanen, N., Honkavaara, E., Näsi, R., Hakala, T., Niemeläinen, O., Kaivosoja, J. 2018. A Novel Machine Learning Method for Estimating Biomass of Grass Swards Using a Photogrammetric Canopy Height Model, Images and Vegetation Indices Captured by a Drone. Agriculture 8: 70. https://doi.org/10.3390/agriculture8050070 Yrttimaa, T., Saarinen, N., Kankare, V., Hynynen, J., Huuskonen, S., Holopainen, M., Hyyppä, J., Vastaranta, M. 2020. Performance of terrestrial laser scanning to characterize managed Scots pine (<em>Pinus sylvestris</em> L.) stands is dependent on forest structural variation. EarthArXiv. March 5. https://doi.org/10.31223/osf.io/ybs7c本数据集包含三个打包压缩文件，其中涵盖了来自芬兰南部3个试验地内27个样地的单株欧洲赤松（Scots pine）的相关数据，具体包括地面激光扫描（terrestrial laser scanning, TLS）的三维信息文本文件，以及由无人机（unmanned aerial vehicle, UAV）获取的航空影像。 2018年9月至10月期间，针对全部3个研究样地，均使用Trimble TX5三维激光扫描仪（Trimble Navigation Limited，美国）开展TLS数据采集工作。每个样地布设8站扫描，并采用10米距离处点间距约6.3毫米的扫描分辨率。 在每个样地周围布设直径固定为198毫米的人工球体，作为参考物体，用于将8站扫描数据配准至统一的对齐坐标系。配准工作通过FARO Scene软件（2018版本）完成。 航空影像由搭载Gryphon Dynamics四旋翼机架的无人机获取。该无人机上搭载两台Sony A7R II数码相机，分别以+15°与-15°角度安装。影像每2秒采集1帧，并记录每张影像的位置信息。本次飞行任务于2018年10月2日完成。 每个研究样地均布设并测量了8个地面控制点（ground control points, GCPs）。所有飞行任务均采用140米飞行高度与5米/秒的飞行速度，最终获得1.6厘米的地面采样距离。研究样地1、2、3分别拍摄了639、614、663张影像，航向重叠度与旁向重叠度分别为93%与75%。 航空影像的摄影测量处理严格遵循Viljanen等人（2018）提出的工作流。经处理后，各研究样地分别生成了点密度为804点/平方米、976点/平方米、1030点/平方米的摄影测量点云。 3个试验地内的样地于2005年或2006年实施了不同的间伐处理。本实验设计包含2个间伐强度水平与3种间伐类型，共形成6种间伐处理方案：i）下层适度间伐、ii）上层适度间伐、iii）系统性适度间伐、iv）下层强度间伐、v）上层强度间伐、vi）系统性强度间伐，以及1个自建立以来未进行间伐的对照样地。有关研究样地、样地设置与间伐处理的更多信息可参见Saarinen等人（2020a）。 本数据集包含从点云中提取的单株欧洲赤松的树干点数据。有关提取方法的更多细节可参见Saarinen等人（2020a、2020b）与Yrttimaa等人（2020）。 压缩包的文件名对应研究样地1、2、3。文本文件的文件名包含试验地信息、试验地内的样地信息，以及样地内的单株树木信息。文本文件中包含从TLS点云中提取的树干点数据。其中，列“x”与“y”为局部坐标系下的x、y坐标（单位：米），列“h”为各点相对于地面的高度（单位：米），treeID为树木识别编号。各列以空格分隔。根据研究样地与样地编号，可通过Saarinen等人（2020b）中的表1信息区分不同间伐处理对应的文件。 <strong>参考文献</strong> Saarinen, N., Kankare, V., Yrttimaa, T., Viljanen, N., Honkavaara, E., Holopainen, M., Hyyppä, J., Huuskonen, S., Hynynen, J., Vastaranta, M. 2020a. Assessing the effects of stand dynamics on stem growth allocation of individual Scots pines. bioRxiv 2020.03.02.972521. https://doi.org/10.1101/2020.03.02.972521 Saarinen, N., Kankare, V., Yrttimaa, T., Viljanen, N., Honkavaara, E., Holopainen, M., Hyyppä, J., Huuskonen, S., Hynynen, J., Vastaranta, M. 2020b. Detailed point cloud data on stem size and shape of Scots pine trees. bioRxiv 2020.03.09.983973. https://doi.org/10.1101/2020.03.09.983973 Viljanen, N., Honkavaara, E., Näsi, R., Hakala, T., Niemeläinen, O., Kaivosoja, J. 2018. A Novel Machine Learning Method for Estimating Biomass of Grass Swards Using a Photogrammetric Canopy Height Model, Images and Vegetation Indices Captured by a Drone. Agriculture 8: 70. https://doi.org/10.3390/agriculture8050070 Yrttimaa, T., Saarinen, N., Kankare, V., Hynynen, J., Huuskonen, S., Holopainen, M., Hyyppä, J., Vastaranta, M. 2020. Performance of terrestrial laser scanning to characterize managed Scots pine (<em>Pinus sylvestris</em> L.) stands is dependent on forest structural variation. EarthArXiv. March 5. https://doi.org/10.31223/osf.io/ybs7c