Forecasting water-associated vegetation dynamics of a Ramsar wetland using multivariate and remote sensing approaches

Water-associated vegetation in a unique Villu ecosystem within a Ramsar site was projected for 2029 using remote sensing technology, and the relationships between Villu-associated vegetation and water quality parameters were examined through multivariate analysis. Temperature (r = -0.581, p < 0.001), potential of Hydrogen (pH) (r = -0.826, p < 0.001), salinity (r = -0.627, p < 0.001), total dissolved solids (r = -0.736, p < 0.001), and biochemical oxygen demand (r = -0.370, p < 0.001) showed a negative relationship with the species richness of Villu-associated vegetation, indicating that higher values of these parameters may create stressful conditions that reduce plant diversity. In contrast, chlorophyll a concentration (r = 0.800, p < 0.001) showed a positive relationship, suggesting that nutrient-rich waters may promote the growth and diversity of Villu-associated vegetation. The multiple regression model developed using chlorophyll a concentration and pH explained 79.26% of the variation in species richness of Villu-associated vegetation (p < 0.001). These relationships imply that water quality parameters of Villus directly shape the ecological balance of the entire Villu ecosystem. Remote sensing analysis showed a continuous reduction in grassland vegetation cover of the area of Villus from 2015 (2.95%) to 2029 (1.34%), and sparse vegetation cover from 2015 (16.11%) to 2029 (8.29%), indicating a potential for future hydrological change in Villus in WNP. The findings highlight the requirement for continuous monitoring and conservation of this sensitive ecosystem using cost-effective remote sensing and a field-based approach. This method can be improved by recalibration to other climatic regions to approach this for the conservation of other global wetlands. • Water associate vegetation dynamics in a selected unique Villu ecosystem within Wilpattu National Park (WNP) were predicted using multivariate and Remote Sensing approaches. • Water quality strongly affects the Villu associate vegetation. • A regression model developed using log-transformed chlorophyll a concentration and pH explained 79.26% of the variation in species richness of Villu associate vegetation. • Remote sensing analysis showed a continuous reduction in grassland vegetation cover of the Villu associate area from 2015 (2.95%) to 2029 (1.34%) indicating risk for water of Villu in future. • These new findings can be used for early water quality monitoring, vegetation prediction for habitat restoration, and the management of wetlands as long-term, cost-effective conservation methods.