Label-Free Detection of Hidden Forest Degradation Using Sentinel-2, Landsat Thermal Stress and AlphaEarth Embeddings

It may be argued that forest monitoring based only on land-cover conversion can miss functional decline inside areas that remain spectrally forest-like. This study presents a label-free remote-sensing framework for mapping candidate hidden forest degradation from annual satellite data. The method combines physically interpretable Sentinel-2 vegetation, moisture and burn-sensitive indices with Landsat land surface temperature and year-to-year AlphaEarth embedding drift. These signals are normalised and fused into an annual hidden functional degradation score, then separated into two pathways: candidate spectral cover-loss/conversion pixels and candidate hidden degradation within pixels that still satisfy relaxed forest-like spectral conditions. Dynamic World probabilities are excluded from model construction and used only afterwards as a public land-cover transition reference, which avoids direct label leakage while retaining the limitation that both Dynamic World and part of the model input use Sentinel-2 information. It should be noted that, across six annual transitions from 2019 to 2025, AlphaEarth drift and the combined score separated Dynamic World transition pixels from stable-forest reference pixels with AUC values of 0.948–0.999 and 0.939–0.999, respectively. At the top 2% threshold for 2024–2025, F1 reached 0.937 for AlphaEarth drift and 0.916 for the combined score. These validation results apply to the full-domain score layers rather than to direct confirmation of the hidden stable-forest pathway itself. The combined score does not always outperform AlphaEarth drift on F1, but it provides a more physically interpretable candidate early-warning layer for forest-condition assessment and follow-up validation.