Using Texture‐Based Image Segmentation and Machine Learning With High‐Resolution Satellite Imagery to Assess Permafrost Degradation Landforms in the Russian High Arctic

Abstract Amplified climate change across the Arctic causes significant permafrost thaw and an increase of permafrost degradation landforms. These landforms range from fine‐scale degrading ice wedge‐polygon‐networks to large‐scale features such as thermo‐erosional gullies and reshape entire landscapes. In particular the expansion of thermo‐erosional gullies could have far‐reaching consequences by restructuring drainage pathways. Our study aims at finding a suitable remote sensing‐based approach for quantifying landscape‐scale permafrost degradation in gully‐dominated Arctic landscapes. We use historical and recent high‐resolution panchromatic satellite imagery allowing multi‐decadal analysis of degradation trajectories. Given that degradation stages are characterized by distinct but subtle textural characteristics in satellite imagery, we tested texture‐based machine learning segmentation methods including Random Forest (RF) using gray level co‐occurrence matrix (GLCM) texture features and deep learning Convolutional Neural Networks (CNNs) using a UNet architecture. For CNN, we tested various framework adjustments. Our results showed that CNN outperforms RF particu