Glacier Monitoring in the Alps: Leveraging GEDI Altimetry for Surface Elevation Change Detection

Glaciers are vital components of Alpine ecosystems and are increasingly threatened by climate change. Therefore monitoring glacier elevation change over time is an essential task and aids in modeling future freshwater availability. By leveraging remote sensing technologies with high revisit frequencies, we can gain a comprehensive understanding of glacier dynamics, including retreat rates, the influence of landslides, and overall glacier health.Unmanned Aerial Vehicles (UAVs) provide the most precise means of tracking glacier surface changes, however, their use is often constrained by high costs and the difficulty of conducting in-situ measurements in extreme weather or remote locations. In these cases, remote sensing and satellite altimetry offer a practical and viable alternative.In this study, we present a novel methodology utilizing Global Ecosystem Dynamics Investigation (GEDI) altimetry data. GEDI is a LiDAR (Light Detection and Ranging) sensor collecting altimetric data with a 25 m footprint size and 60 m along-track spacing from the International Space Station [1,2]. GEDI was active from early 2019 till 2023 when it was temporarily hibernated and has recently been reactivated.The proposed method relies exclusively on available GEDI bands and is fully implemented within Google Earth Engine (GEE). We have applied the methodology to three Alpine glaciers using nine GEDI acquisitions and evaluated its performance through comparisons with reference Digital Surface Models (DSMs) generated from aerial and drone photogrammetry and LiDAR data.After applying outlier detection techniques solely based on GEDI bands, GEDI-derived glacier profiles along the tracks provided valuable surface elevation information. The results showed a strong correlation (r = 0.99) with reference DSMs along with low dispersion and R2 of 0.99, based on an average of 135 GEDI footprints per glacier. Additionally, the analysis indicated that GEDI could capture seasonal variations in glacier surfaces, detecting the melt and gain in the snowpack.While GEDI lacks the capability to map an entire glacier extent as photogrammetric block imagery does, its higher acquisition rate, including coverage of smaller glaciers, offers a significant advantage. Integrating GEDI with traditional approaches thus enables more continuous and comprehensive glacier monitoring.References:[1] Hamoudzadeh, A., Ravanelli, R., and Crespi, M.: Glacier Monitoring Using GEDI Data in Google Earth Engine: Outlier Removal and Accuracy Assessment, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10176, https://doi.org/10.5194/egusphere-egu24-10176, 2024.[2]  Hamoudzadeh, A., Ravanelli, R., and Crespi, M.: GEDI DATA WITHIN GOOGLE EARTH ENGINE: PRELIMINARY ANALYSIS OF A RESOURCE FOR INLAND SURFACE WATER MONITORING, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-M-1-2023, 131–136, https://doi.org/10.5194/isprs-archives-XLVIII-M-1-2023-131-2023, 2023.