Cross-sensitivity analysis of land-use transformation and ecosystem service functions in Turpan City,China
Exploring the interrelationship between ecosystem service values and land class transformation in Turpan City,China during 2000-2018,it would provide a reference basis for how to reasonably plan regional land resources and realize the improvement and promotion of ecosystem service functions. In this study,a dynamic change model of land use and the revised ecosystem service value coefficient,combined with the improved cross-sensitivity coefficient model,were used to analyze the degree of sensitivity of ecosystem service values based on five periods of land-use remote-sensing imaging data. The results were as follows:1) The area comprises mainly grassland,unused land,and cultivated land. Construction and cultivated lands continued to expand,whereas water bodies and grasslands are decreasing. Among these land types,the transformations among grassland,water bodies,cultivated land,and unused land are the main focus;2) During the research period,the ecosystem service values showed a dynamic change trend of decreasing first,then increasing,and then decreasing. High-value areas are mainly distributed in the northern mountains and the central oasis plain;3) Ecosystem service functions are highly sensitive and responsive to the transformations of woodland,water bodies,grassland,and unused land into other land types. The highly sensitive areas of transformation among grassland,construction land,and unused land are widely distributed,as are the low-sensitivity areas of transformation among unused land,water bodies,and construction land. Regions should strengthen the protection of water bodies,woodlands,grasslands,and other ecosystems and pay attention to the rational planning of land resources and the improvement of ecological environment quality while developing economic construction.
land conversionecosystem service functionecosystem service valueequivalent factorcross-sensitivityspatiotemporal evolutionTurpan City
万方数据
维普
