Correlation Analysis And Optimization Between Land Use Landscape Patterns And Ecosystem Service Values

Ecosystem services are the conditions and utility that human beings rely on. Human beings sustain and fulfill themselves by obtaining service values from ecosystems. However, the explosion of population, and acceleration of industrialization and urbanization have brought huge impacts to natural ecosystems. A series of ecological problems emerge and have become the world-wide problems that human beings have to face. China has made great achievements in economy and society since the reform and opening up. Meanwhile it comes with the problems of resource limits, environmental pollution, and ecological degradation. Research on land use landscape patterns change and assessment of ecosystem service values (ESVs) could help better understanding in their correlation mechanism. The results could provide theoretical supports for sustainable development in society, economy, and ecology. In addition, it provides effective and reasonable methods for decision-making.Landscape indices are applied for analyzing land use and landscape patterns. The approaches of direct market and surrogate market are adopted for ESVs assessments. The correlation analysis, gray correlation analysis, and redundancy analysis (RDA) are applied for studying the correlation. An integrated analytical framework is developed for investigating the influencing mechanism and feedback mechanism. Finally, we proposed the optimizing strategies for promoting ESVs. The structures and conclusions are presented as follows.(1) Remotely sensed data is interpreted for land use structures and landscape pattern analysis. Land use change matrix and dynamic degree are adopted for analyzing land use structures chang; Landscape indices in class level and landscape levels are selected for analyzing landscape patterns. The results show there has been a rapid urbanization in study area. Built up area is mostly transferred from agricultural area. Meanwhile, there has been several land reclamation from built up area and forest. At the landscape level, indices like NP, PD, SHDI, SHEI and IJI are becoming bigger, while AWMSI and CONTAG are getting smaller. At the class level, NP and PD in agricultural and built up class are growing; AWMSI in agricultural class is decreasing, while it is increasing in built up class. MNND in water, forest and built up class are becoming bigger, while it keeps steady in agricultural class. IJI in water, forest, and built up area is growing, while it declines in agricultural class.(2) Ecosystem services are classified into three categories, and values of the services are assessed between 1993 and 2013. First, ecosystem products and service utility are calculated. Second, price of products and services are determined by direct market and surrogate market approaches. Finally, the sum of ESVs are summarized. The results show values of provisioning services and regulating services contribute most of the whole ESVs. The total ESVs, ESVs in unit area, and provisioning services values are increasing.The regulating service has not changed significantly. Each values in ecosystem services are lower in central urban area, but getting higher in outer rural area.(3) The correlation analysis, gray correlation analysis, and redundancy analysis (RDA) are applied for studying landscape metrics and ESVs. The results show that some indices in landscape metrics are correlated. Different values differ from different services, and there are trad-offs and co-benefits between each others. The bigger values of SHDI, NP, and IJI indicate better ESVs. IJI in forest class, NP in agricultural and water class, AWMSI in forest, built up and water class have close connections with most of the ESVs. Especially, the indeices in forest class play key role in each ESVs. The driven mechanism of each landscape metrics differs. The service of provisioning air regulation, climate regulation, waste treatment, protection and culture have trad-offs with water and soil conservation service.(4) The integrated analytical framework between land use landscape patterns and ecosystem service values (ESVs) helps to understand the driven and feddback mechanism. The results show there is complicated, nonlinear and dynamic relationships between land use landscape patterns and ESVs. The spatial heterogeneity, diversity, and stability of landscape drive the ESVs. While ecosystems feed back to land use landpscae patterns by environmental, economic, social influences and its heterogeneity of service supply/demand.(5) Optimization strategies of land use and landscape patterns are proposed for improving ESVs. Engineering optimizations in land use could help change the structures and quality of land use. Landscape optimizations could help adjust the spatial configurations of landscape patterns, which improve the connection of each elements in landscapes.