Spatio-temporal Evolution and Future Multi-scenario Simulation of Land Use and Ecosystem Service Value in Fujian Delta Urban Agglomeration
Based on the land use data in the Fujian Delta urban agglomeration from 2000 to 2020,the changing trends of land use and ecosystem service value(ESV)were analyzed in the past 20 years.Taking 2035 as the target year,the CA-Markov model was adopted to simulate the changes in land use and ESV under four scenarios of natural development,economic develop-ment,ecological protection and cultivated land protection,so as to discuss the impact of land use change on ESV under different scenarios,and to analyze the synergy/trade-off relationship between different ecosystem service types.It is found as follows.①From 2000 to 2020,the changes of land use in the study area were obvious.The area of cultivated land decreased sharply and the area of forest land and grassland decreased slightly,while the construction land expanded in a large scale.Correspondingly,the total ESV in the study area was 228.134 billion yuan,225.854 billion yuan,219.682 billion yuan,217.581 billion yuan,and 220.069 billion yuan in 2000,2005,2010,2015 and 2020,respectively,showing a downward trend.② Under the four scenarios of natural development,cultivated land protection,ecological protection and economic development,the total ESV of the study area will be 213.842 billion yuan,213.304 billion yuan,216.635 billion yuan and 211.263 billion yuan in 2035,respectively,showing a downward trend.Among them,the total ESV under the economic development scenario is the lowest,the total ESV under the natural development scenario and the cultivated land protection scenario is also greatly reduced,and the ecological pro-tection scenario will effectively delay the downward trend of ESV,with strongest synergy between ecosystem service types,which will be the optimal scenario for land use optimization in Fujian Delta urban agglomeration in the future.
land use changeecosystem service valueCA-Markov modelmultiple scenarioFujian Delta urban agglomeration
万方数据
维普
