Detecting Terrain Surface Changes in High-Alpine Environments in the Austrian Alps from 2017 to 2025 Through a Multiple Pairwise Image Correlation Approach and a Novel Terrain Activity Index.

Alpine environments in the Austrian Alps are undergoing significant geomorphological transformations driven by glacier retreat, permafrost degradation, and increased terrain instability linked to climate change. This study introduces a multiple pairwise image correlation (MPIC) approach in detecting temporal surface changes from PlanetScope (3m resolution) satellite imagery. Yearly time series data is collected between 2017 and 2025, in which image pairs (e.g. 2017-2022, 2018-2019) are compared using a normalized cross-correlation (NCC) algorithm to quantify pixel reflectance shifts between years. Summary statistics from the MPIC results are then transformed into a novel Terrain Activity Index (TAI) proposed in this study. Spatial clustering algorithms are applied to the TAI for detecting hotspot and coldspot regions of spatial significance. The three study sites across the Austrian Alps contain networks of trails and mountain huts in which findings can additionally support trail damage assessments. This framework offers a scalable and efficient tool for monitoring subtle, climate-driven landscape changes, with potential applications across all environmental terrains and locations requiring temporal change monitoring.