EGU24-16723, updated on 11 Mar 2024
https://doi.org/10.5194/egusphere-egu24-16723
EGU General Assembly 2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Monitoring the changes in glacial lakes in the Southern Alps, New Zealand from 2000-2023 using an Object-Based Image Analysis (OBIA) approach in Google Earth Engine (GEE)

Tomos Morgan, Robert McNabb, and Paul Dunlop
Tomos Morgan et al.
  • Ulster, University, School of Geography and Environmental Sciences, Coleraine, United Kingdom of Great Britain – England, Scotland, Wales (morgan-t15@ulster.ac.uk)

Glacial lakes are growing rapidly, driven by climatic change and glacial retreat. The growth of glacial lakes may increase the magnitude and frequency of glacial lake outburst floods (GLOFs), posing a hazard to downstream populated regions. Satellite remote sensing provides a way to improve monitoring efforts, though automatic methods are needed to accurately and rapidly monitor changes in these lakes. In this study, we develop and apply an Object-Based Image Analysis (OBIA) approach to 71 multispectral Landsat 5-9 Top-Of-Atmosphere (TOA) satellite imagery in Google Earth Engine (GEE) to monitor the changes of 14 lake-terminating glacial lakes across the Southern Alps of New Zealand outside of the winter season (June-September) between 2000-2023. The Southern Alps of New Zealand are experiencing increasing glacial mass loss and despite previous glacial lake monitoring it remains necessary to continue monitoring these glacial lakes to understand the magnitude of their contribution to past regional ice mass loss. Our results show that the collective area of these 14 glacial lakes increased by 69% between 2000-2023, from 12.84 ± 0.06 km2 to 21.71 ± 0.1 km2. We evaluate the accuracy of this method by comparing automatically generated classification to manually classified points, using a stratified random sampling approach. Preliminary results derived for the accuracy of Landsat 9 satellite imagery resulted in an overall accuracy of 89%, with a producer’s accuracy and user’s accuracy of 98% and 96% respectively, for water. These preliminary results suggest that the method has the potential to map glacial lakes accurately and rapidly and can be applied to other glaciated regions.

How to cite: Morgan, T., McNabb, R., and Dunlop, P.: Monitoring the changes in glacial lakes in the Southern Alps, New Zealand from 2000-2023 using an Object-Based Image Analysis (OBIA) approach in Google Earth Engine (GEE), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16723, https://doi.org/10.5194/egusphere-egu24-16723, 2024.