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. , 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/m2). During thes..., , # UAV-LiDAR point clouds from the Forest and Biodiversity Experiment 2 (2022)

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). The names of the files refers to the date of data collection.

Code/Software

Point clouds can be opened using QGIS, [CloudCompare,](https://www.danielgm.net...

Introduction

This dataset contains extensive, long-term monitoring data on the Belvedere Glacier, a debris-covered glacier located on the east face of Monte Rosa in the Anzasca Valley of the Italian Alps. The data is derived from photogrammetric 3D reconstruction of the full Belvedere Glacier and includes:

dense point clouds obtained with UAV-based MVS covering the entire glacier body

high-resolution orthophotos

high-resolution DEMs

Since 2015, in-situ survey of the glacier have been conducted annually using fixed-wing UAVs until 2020 and quadcopters from 2021 to 2022 to remotely sense the glacier and build high-resolution photogrammetric models. A set of ground control points (GCPs) were materialized all over the glacier area, both inside the glacier and along the moraines, and surveyed (nearly-) yearly with topographic-grade GNSS receivers (Ioli et al., 2022).

For the period from 1977 to 2001, historical analog images, digitalized with photogrammetric scanners and acquired from aerial platforms, were used in combination with GCPs obtained from recent photogrammetric models (De Gaetani et al., 2021).

Before downloading them, you can explore the photogrammetric point clouds of the Belvedere Glacier within web app based on Potree from https://thebelvedereglacier.it/ (use a web browser from a desktop/laptop for the best experience). Additionally, from here you can also visualize and download the coordinates of the GCPs measured by GNSS every year since 2015.

Belvedere Glacier

The Belvedere Glacier is an important temperate alpine glacier located on the east face of Monte Rosa in the Anzasca Valley of Italy. The Belvedere Glacier is of particular importance among alpine glaciers because it is a debris-covered glacier and it reaches its lowest elevation at about 1800 m a.s.l. Over the last century, the Belvedere Glacier has experienced extraordinary dynamics, such as a surge-like movement or the formation of a supraglacial lake, which seriously threatened the nearby community of Macugnaga.

Data organization

The data are organized by year in compressed zip folders named belvedere_YYYY.zip, which can be downloaded independently. Each folder contains all data available for that year (i.e. photogrammetric point clouds, orthophotos, and DEMs) and the corresponding metadata. Metadata is provided as a .json file which contains all the main information for data usage. Point clouds are saved in compressed las format (.laz) and they can be inspected e.g., with CloudCompare. Orthophotos and DEMs are georeferenced images (.tif) that can be inspected with any GIS software (e.g., QGIS).

Large point clouds are subdivided into regular tiles, which are numbered in a progressive row-wise order from the bottom-left corner of the point cloud bounding box.

All the files are named according to the following naming schema:

"belv_YYYY_surveyplatform_datatype[_resolution][vertical_datum][-tile_number].extension"

where:

YYYY: is the year of the survey

surveyplatform: can be either "uav" for the UAV-based photogrammetry survey or "histo" for the historical aerial datasets.

datatype: can be either "pcd" for point clouds, "orthophoto" for orthophotos and "dsm" for DSMs.

resolution: on-ground resolution of each pixel in meters. This applies only to raster data (orthophoto and DSMs)

vertical_datum: if the DSM is given in orthometric coordinates, the label "ortho" is present in the filename, otherwise the height of the dataset is supposed to be ellipsoidal.

tile: tile number, if the data is tiled to avoid large files.

Data Usage

This dataset can be used to estimate glacier velocities, volume variations, study geomorphological processes such as the process of moraine collapse, or derive other information on glacier dynamics. If you have any requests on the data provided, data acquisition, or the raw data themselves, you are encouraged to contact us.

Contributions

The monitoring activity carried out on the Belvedere Glacier was designed and conducted jointly by the Department of Civil and Environmental Engineering (DICA) of Politecnico di Milano and the Department of Environment, Land and Infrastructure Engineering (DIATI) of Politecnico di Torino. The DREAM projects (DRone tEchnnology for wAter resources and hydrologic hazard Monitoring), involving teachers and students from Alta Scuola Politecnica (ASP) of Politecnico di Torino and Milano, contributed to the campaign from 2015 to 2017.

Acknowledgements

The authors thank CGR SpA for digitizing the historical images (1977, 1991, 2001, 2009) and making them available to the authors for the photogrammetric processing.

The authors thank all students and collaborators contributing to the Alta Scuola Politecnica projects DREAM 1, DREAM 2, and DREAM 3 (DRone tEchnnology for wAter resources and hydrologic hazard Monitoring).

If you use the data, please, cite these our pubblications:

Ioli, F., Dematteis, N., Giordan, D., Nex, F., Pinto, L., Deep Learning Low-cost Photogrammetry for 4D Short-term Glacier Dynamics Monitoring. PFG (2024). https://doi.org/10.1007/s41064-023-00272-w

Ioli, F.; Bianchi, A.; Cina, A.; De Michele, C.; Maschio, P.; Passoni, D.; Pinto, L. Mid-Term Monitoring of Glacier’s Variations with UAVs: The Example of the Belvedere Glacier. Remote Sensing, 14, 28 (2022). https://doi.org/10.3390/rs14010028

De Gaetani, C.I.; Ioli, F.; Pinto, L. Aerial and UAV Images for Photogrammetric Analysis of Belvedere Glacier Evolution in the Period 1977–2019. Remote Sensing, 13, 3787 (2021). https://doi.org/10.3390/rs13183787