A comprehensive framework for assessing the spatiotemporal impact of land use and cover change on lake eutrophication

Excessive nitrogen and phosphorus (N/P) input is the important reason for lake eutrophication, a widespread environmental problem that harms freshwater ecosystems worldwide. Land use land cover changes (LULCCs) strongly affect the migration and transformation process on land of N/P. Aiming to improve the accuracy of N/P estimation and deeply understand the spatiotemporal impact of LULCCs on N/P distribution, this study proposed an optimized framework, InVEST-UM, by integrating an unmixing algorithm with the conventional InVEST model. A comprehensive and intuitive index LPI (LULCCs purification capacity index) is developed for the impact assessment and spatialization. Validation shows a strong correlation (0.94) between the LPI and eutrophication degree of Dianshan Lake. This suggests the LPI effectively captures the spatiotemporal influence of LULCCs on lake eutrophication. Based on the sub-pixel results of N/P estimation, dramatic changes of LPI with an average rate of 46.182% were explored. There is a strong positive correlation (0.91) between vegetation area and LULCCs purification capacity for N/P. Conversely, negative relationships are found between LULCCs purification capacity with bare lands (−0.86), and artificial surfaces (−0.75). Changes in the water bodies have weak influence (−0.33) on LULCCs purification capacity. The increase in vegetation area can not only enhance the lake purification capacity, but also help maintain low ecological sensitivity. By employing this comprehensive framework, this study provides valuable insights into the long-term dynamics of LULCCs changes and their implications for lake ecosystems, and could support the protection and land use planning for the sustainability and ecological balance of the lake.