DOI: 10.5281/zenodo.10781695
title: Belvedere Glacier long-term monitoring Open Data

description: 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 Structure-from-Motion (SfM) 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).

 

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 with 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, a label "ortho" is present in the filename, otherwise the height of the dataset is supposed to be ellipsoidical.

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 request on the data provided, data acquisition or on 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.

 

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
description: Note that this is an outdated version of the dataset. Please, use the latest version.



Changes in version 1.2:



Added the data from 2023

Added orthophotos and DSMs for 2021, 2022, 2023

Added DSM with orthometric heights for 2021, 2022, 2023

Updated metadata for 2021, 2022, 2023


We are currentely updating also the previous years data and we will add orthophotos and DSM for all the years in the next release of the dataset.