Dynamics of Forest Disturbance in the Canopy of Permanent Production Forests: A Multitemporal Analysis (2004–2025) Using Spectral Unmixing in the Southeastern Peruvian Amazon

Monitoring forest degradation using medium-resolution optical sensors often results in an underestimation of the actual ecological impacts, limiting conservation strategies in threatened regions. We evaluated the forest disturbance dynamics (2004–2025) in the Permanent Production Forests of Tahuamanu, Madre de Dios, Peru. We processed multitemporal Landsat images in Google Earth Engine to map change trajectories by applying Spectral Mixture Analysis to derive the Normalized Difference Fraction Index (NDFI) integrated with a stratified area estimator. This approach yielded overall accuracies of ≥93%. Our findings show that structural degradation is replacing deforestation as the main driver of forest alteration. By 2025, the footprint of this disturbance, which silently affects the understory, had quadrupled the extent of deforestation, a trend evidenced by the stratified adjustment that revealed over 10,000 hectares of structural damage previously hidden under the label of intact forest, demonstrating the typical omission bias of passive sensors. Relying on raw maps means underestimating biomass lost to understory degradation. To address this systematic bias, it is necessary to operationalize the NDFI model along with a rigorous stratified estimation. With this combined approach, a scalable, cost-effective, and statistically robust framework is offered to monitor the subtle degradation that traditional mapping systems overlook. To our knowledge, this is the first long-term, area-corrected assessment of forest disturbance within Peru’s oldest formal timber concessions, and it shows that degradation persists and accelerates even under a regulated management model.