DOI: 10.5061/dryad.jdfn2z3hk
title: UAV-LiDAR point clouds from the Forest and Biodiversity Experiment 2 (2022)

description: Point clouds collected at the of the Forest and Biodiversity Experiment 2
 located at the Cedar Creek Ecosystem Science Reserve (CCESR), Minnesota,
 USA. These point clouds were collected using a LiDAR onboard an Uncrewed
 Aerial Vehicle (UAV) thoughout the growing season of 2022. 
description: We collected LiDAR data across the FAB2 experiment eight times
 during 2022 on the following days of the year: 100, 138, 163, 188, 215,
 250, 261, and 297. Flights were conducted throughout the growing season
 before leaf out until after leaf senescence of the deciduous species in
 accordance with sufficient accumulated growing degree days for 2022. Data
 were collected using a Zenmuse L1 sensor onboard an Uncrewed Aerial
 Vehicle (UAV) DJI Matrice 300. This sensor integrates a Livox LiDAR
 module, an inertial measurement unit, and an RGB camera on a 3-axis
 stabilized gimbal. The LiDAR module has a conic footprint on ground with a
 field of view of 77.2° vertical and 70.4° horizontal, enabling it to
 capture multiple returns. All the surveys were conducted using autonomous
 flights programmed to take place at a speed of 8 m/s at 50 m above ground.
 These surveys were done in an area of ~5.8 ha with 85% overlap between
 sidetracks allowing capture of dense point clouds (~ 2100
 point/m<sup>2</sup>). During these flights, we also collected
 static GNSS data using an Emlid Reach RS2+ receiver to enable kinematic
 corrections or post-processing corrections in case of signal loss. The
 data collected from the LiDAR sensor and GNSS receiver were processed in
 DJI Terra, delivering true color point clouds using the optimization
 option for manufacture strip alignment. The resulting point clouds were
 then processed through BayesStripAlign 2.24 to ensure a proper alignment
 among flight lines. The resulting point clouds were treated in a series of
 steps to ensure data quality. First, points were classified as ground and
 non-ground and then normalized by height. These procedures were done using
 ‘<em>lasthin’,</em> ‘<em>lasground’</em>, and
 ‘<em>lasheight’</em> of LAStools. We provide point clouds
 using the compressed format (.LAZ) clipped to the area where the
 experiment is located. These point clouds were not filtered by noise nor
 decimated by the redundancy of points. The height provided (i.e., Z
 coordinate) is in reference to the height from the ground. The X and Y
 columns are coordinates projected to NAD83 UMT15 (EPSG:26915). The names
 of the files refers to the date of data collection.
description: # UAV-LiDAR point clouds from the Forest and Biodiversity Experiment 2
 (2022)
 [https://doi.org/10.5061/dryad.jdfn2z3hk](https://doi.org/10.5061/dryad.jdfn2z3hk) Eight point clouds were collected on the Forest and Biodiversity Experiment 2 throughout the 2022 growing season ## Description of the data and file structure We provide point clouds using the compressed format (.LAZ) clipped to the area where the experiment is located. These point clouds were not filtered by noise nor decimated by the redundancy of points. However, these were processed by stripe alignment, ground classification, and normalization by height. The height provided (i.e., Z coordinate) is in reference to the height from the ground. The X and Y columns are coordinates projected to NAD83 UMT15 ([EPSG:26915](https://epsg.io/26915)). The names of the files refers to the date of data collection. ## Code/Software Point clouds can be opened using [QGIS](https://www.qgis.org/), [CloudCompare,](https://www.danielgm.net/cc/) or [LAStools](https://github.com/LAStools/LAStools) for visualization. They can also be processed using the lidR package of R. The code used for processing and deriving forest structural metrics is available at [GitHub.](https://github.com/Cavender-Bares-Lab/UAV-LiDAR-Processing)