Analisis Spasio-Temporal terhadap Invasi Vegetasi Oportunistik di Hutan Mangrove Terdegradasi - Syaiful Eddy (Lembaga Pendidikan, Penelitian, dan Pengabdian Kamandanu, 2026)
Anthropogenic activities such as land conversion and illegal logging have triggered massive degradation of mangrove canopies, reduced blue carbon stocks, and triggered secondary succession in the form of opportunistic-invasive vegetation invasions such as Nypa fruticans. Conventional field survey-based monitoring faces significant geographical and cost constraints. This study aims to synthesize the development of spatio-temporal satellite data methodologies, identify research gaps in remote sensing sensors, and formulate a monitoring framework for degraded mangroves through several databases. This narrative literature review for the period 2016–2026 was conducted through inclusion and exclusion criteria-based screening of Google Scholar, ScienceDirect, and SpringerLink databases, with 45 selected scientific articles analyzed using qualitative-thematic synthesis techniques. The synthesis results indicate that the integration of long-term temporal data (Landsat and Sentinel-2) on the Google Earth Engine (GEE) platform with machine learning algorithms (such as random forest) is very reliable in tracking canopy damage trajectories. Spatial reconstruction successfully mapped the chronology of Nypa fruticans expansion through three main phases: initiation after upstream disturbance, aggressive colonization of open canopy gaps, and dominance of homogeneous monoculture formations. In conclusion, the use of cloud computing-based time-series satellite imagery is effective in monitoring the dynamics of degradation and opportunistic vegetation expansion chronologically. Future research should focus on multi-sensor data integration architecture (Sentinel-1 optical and radar synergy) and the validation of high-resolution drones to develop a verified early warning system for coastal restoration and carbon balance to support the achievement of SDGs 13 and 15.