Understanding species-specific vegetation responses to climate variability is crucial for assessing mangrove resilience under climate change. This study presents a 12-year (2013–2024) multi-sensor satellite analysis of three dominant mangrove species ( Rhizophora mucronata , R. apiculata , and Avicennia marina ) in southern Thailand using a combined Google Earth Engine (GEE) and Python-based framework. Monthly Enhanced Vegetation Index (EVI) time series were derived from Landsat and Sentinel-2 imagery and analysed using Fourier-based time-series decomposition to characterize long-term trends, seasonal variability, and phenological timing in a tropical monsoon mangrove system. Environmental drivers – including rainfall, temperature, solar radiation, and oceanographic variables – were extracted from open-access climate datasets. Species-level analyses revealed contrasting vegetation trajectories and phenological strategies, with R. mucronata exhibiting the strongest and most consistent seasonal amplitude, R. apiculata showing pronounced monsoon-aligned seasonality, and A. marina displaying weak and irregular seasonal expression alongside sustained vegetation decline. Lagged correlation analysis (0–6 months) identified delayed and species-specific responses to climatic and oceanographic drivers, with R. apiculata demonstrating the highest climate sensitivity and shortest response lags, while A. marina showed limited climate coupling despite marked decline in the greenness of A. marina stands, suggesting a stronger influence of local non-climatic stressors on A. marina . A species-level vulnerability assessment based on long-term vegetation trends and climate sensitivity revealed distinct risk pathways, with R. apiculata and A. marina exhibiting elevated vulnerability through climate sensitivity and local degradation, respectively, and R. mucronata showing comparatively higher resilience. This study highlights the value of species-resolved phenological analysis and lag-aware remote sensing frameworks for tropical evergreen ecosystems. The integrated GEE – Python workflow provides a scalable and transferable approach for long-term mangrove monitoring and early warning of climate-induced ecosystem change in tropical monsoon coastal systems.
Koedsin et al. (Sun,) studied this question.