Rapid climate change demands frequent habitat monitoring in alpine ecosystems. We compare post-classification vs. direct change detection using geospatial AI (U-Net, Prithvi, Clay, ChangeViT) on high-res multimodal data from Gesäuse National Park, Austria. Post-classification with foundation models achieves superior multi-class accuracy. LiDAR integration boosts segmentation from 30% to 50% accuracy. Results show promise for operational monitoring despite class imbalance challenges.