Loránd, SzabóMinaya, Reynaldo2026-06-012026-06-012026-04-30https://hdl.handle.net/2437/407604Palm swamp peatlands in the Peruvian Amazon are recognized as ecosystems of remarkable ecological and climatic significance. Despite their importance, these ecosystems have been underrepresented in remote sensing-based monitoring frameworks, and the subtlety of their degradation process, which frequently proceeds without producing detectable canopy gaps, has posed challenges for conventional change-detection approaches. A harmonic linear regression model was fitted to 12-year Landsat 8 time series (2013–2025) of three spectral indices — the Normalized Difference Moisture Index (NDMI), the Normalized Difference Vegetation Index (NDVI), and a custom Vegetation–Moisture Divergence Index (VMDI) — at 20 randomly selected palm swamp locations, additionally, 28 validation points were evaluated, 14 fire-affected and non-forest classified points were frame as degraded points and 14 core forest of Palm Swamp points were featured as non-degraded points. All analyses were conducted within Google Earth Engine. A composite degradation score and noise score were created. Non-parametric trend assessment was performed using the Mann-Kendall test and the Theil-Sen slope estimator applied to annual index means. It was concluded that satellite-based detection of degradation in Amazonian palm swamp peatlands is feasible using freely available Landsat imagery, and that NDMI-based harmonic trend analysis provides a methodologically sound and operationally accessible foundation for ecosystem monitoring in this context. Good level of correspondence was observed between composite degradation and noise scores, and Mann-Kendall significance outcomes, supporting the scoring framework. Elevated noise scores were found to be associated with degraded validation sites, suggesting that this temporal disturbance may function as a diagnostic complement to ecosystem degradation or negative trend beginning. The composite scoring and classification framework developed in this study is proposed as a practical and exchangeable tool for degradation monitoring across Amazonian palm swamps landscapes.74enDegradation, Palm Swamp, Remote SensingEarth Sciences::Természeti földrajzEarth SciencesEarth Sciences::Environmental GeographyHozzáférhető a 2022 decemberi felsőoktatási törvénymódosítás értelmében.