Multi-source data-driven spatiotemporal study on integrated ecosystem service value for sustainable ecosystem management in Lake Dianchi Basin

Abstract

Ecosystem services are pivotal in assessing environmental health and societal well-being. Focusing on Lake Dianchi Basin (LDB), China, our research evaluated the IESV (Integrated Ecosystem Service Value) from 2000 to 2020, utilizing remote sensing and multiple statistical datasets. The analysis incorporates LSV (Landscape Service Value), CSV (Carbon Sequestration Value), and NPPV (Net Primary Productivity Value). The results show that LSV and CSV exhibited an expansion of low-yield zones near urban areas, contrasted by NPPV’s growth in high-yield outskirt areas. LSV’s normal distribution indicates stability, while CSV’s bimodal structure points to partial integration and systemic divergence. IESV pronounced clustering in both low- and high-yield regions, with lowyield zones congregating near urban centers and high-yield zones dispersed along the basin’s periphery. Despite an overall downward trajectory in IESV, NPPV’s augmentation suggested an underlying systemic resilience. A southeastward shift in IESV’s focus was driven by patterns of urban expansion. Finally, we produced projections with the CAMC (Cellular Automata–Markov Chain) model to analyze the ongoing distribution of IESV areas around Kunming. By 2030, IESV’s aggregate value is expected to modestly diminish, with NPPV’s ascension mitigating the declines in LSV and CSV. In essence, IESV fluctuations within the LDB are intricately linked to urban development.