Monitoring Mangrove Forests Responses to Kaolin Pollution Using LandTrendr Time-Series Analysis

Chronic coastal pollution can drive progressive mangrove degradation, yet its spatiotemporal trajectories and post-disturbance recovery remain poorly quantified from satellite observations. In this study, Landsat time-series imagery and the LandTrendr algorithm implemented on Google Earth Engine (GEE) were used to characterize mangrove responses to a kaolin pollution event in Tieshan Port, Guangxi, China. NDVI, NDMI, and NBR trajectories were first compared to identify the most sensitive indicator of contamination-induced stress, and LandTrendr was then applied to extract the timing, magnitude, duration, and spatial extent of mangrove disturbance and recovery. Results showed that kaolin contamination imposed persistent chronic stress on mangroves from 2017 to 2021, with degradation first occurring near Langen Village and then expanding northward across the port. Moderate and severe degradation were mainly distributed along tidal creeks and patch edges, indicating strong spatial control by local hydrodynamics and geomorphology. Among the tested indices, NDVI provided the earliest and clearest response to contamination, whereas NDMI and NBR showed delayed or less consistent responses. The disturbance mapping achieved an overall accuracy of 86.5% with a Kappa coefficient of 0.73. Recovery remained limited after pollution discharge ceased, suggesting persistent environmental constraints on mangrove regeneration. These findings demonstrate that Landsat–LandTrendr trajectories provide an effective framework for monitoring chronic pollution-driven mangrove degradation and recovery in coastal wetlands.