Treffer: Cloud computing-based freshwater ice mapping using synthetic aperture radar imagery.

In regions where ice coverage significantly impacts local economies and daily life, the continuous mapping of ice types is indispensable. This study focuses on developing an automated methodology for monitoring freshwater ice, which is crucial for ensuring the safety of winter activities and transportation. The proposed processing workflow integrates cloud computing capabilities utilizing services from Google Earth Engine (GEE) and Google Colab, along with Synthetic Aperture Radar (SAR) imagery from the open Sentinel-1 collection. Despite GEE’s extensive remote sensing capabilities, it lacks built-in support for Grey Level Co-occurrence Matrix (GLCM) texture calculations. Our approach addresses this gap by incorporating GLCM data analysis and clustering techniques into the GEE workflow. The methodology employs Sentinel-1 backscatter information and GLCM texture analysis within the GEE Python API framework to enhance the ice condition monitoring. A key component of this approach is the separability analysis, which identifies the most effective GLCM parameters for distinguishing different types of ice. The classification of freshwater ice types using Sentinel-1 C-band VV backscattering and GLCM texture features provided valuable insights into the challenges of ice classification. The unsupervised model achieved an overall accuracy of 79 % , demonstrating good performance in distinguishing between freshwater ice types. We demonstrate the practical application of this methodology in two study regions: Lake Saint-Pierre and Yamaska River in Quebec, Canada. Furthermore, the proposed method in this study can be applied to other regions. [ABSTRACT FROM AUTHOR]