Land-population relationship under flood risks in transboundary river basins is crucial for sustainable development and climate change-related natural disaster prevention, tightly linked to land use and land cover (LULC), yet the optimal land use policy remains unclear. This study takes the Lancang-Mekong River Basin (LMRB) as a typical case, employing optimization and Ecosystem Service Valuation (ESV) methods to analyze land-population relationship from 2001 to 2018 and explore potential policy implications. The results show that: (1) Post-flood LULC in LMRB faced an average ESV reduction of approximately 0.03 USD/person, with forest loss being the primary driver, contributing a total of 0.04 USD/person in ecosystem services. Impervious surfaces and tree/shrub cover cropland each resulted in an additional 0.04 USD/person ESV decline. (2) In the baseline scenario, total ESV in the LMRB increased by about 6 % compared to the original post-flood LULC, mainly driven by a 11.23 % expansion of swamp areas. Under different policy scenarios, the double-strict policy (70 % basic needs and 110 % highest needs) achieved a 9.28 % ESV increase, slightly higher than the baseline. Double-relaxed policy (90 % basic needs and 130 % highest needs) only led to a 3.24 % increase. Mixed policies, such as strict basic needs (70 %) combined with relaxed highest needs (130 %), yielded a 9.73 % ESV increase, while the relaxed basic needs (90 %) and strict highest needs (110 %) scenario also resulted in a 3.24 % increase. (3) Under high urbanization requirements, ESV increased by 4 % but cropland area decreased by 20 %. In contrast, high agricultural requirements caused a 1.81 % ESV decrease despite a 10 % increase in cropland, as forest and swamp experienced significant reductions. The forest expansion scenario showed a slight ESV increase but a 20 % decrease in grassland. This study provides valuable insights and guidance for post-flood LULC management, addressing the challenging land-population relationship in transboundary river basins.
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