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Although the land-cover-based expert matrices have been widely used for ecosystem service assessment, criticisms about the accuracy and reliability of this method have never ceased. In this study, we introduced a weight distribution approach called Full Permutation Polygon Synthetic Indicator (FPPSI) into the land-cover-based expert matrices, trying to reduce the systemic flaws caused by the default weight assignment and accumulative calculation form of general matrices method. Taking Hangzhou, a representative city in the rapid urbanization process of China as an example, we used the weight optimization expert matrices to quantify the dynamics of ecosystem service supply, demand, and budget during the period of dramatic land cover change from 1990 to 2020. An indicator named supply–demand balanced index (SDBI) was formulated to describe the supply–demand situation among ecosystem services. The results indicated that with the continuous encroachment of the artificial surface on suburban cultivated land, the supply and demand of the 12 selected ecosystem services had experienced a decreasing trend in varying degrees. The evaluation results derived from the general method indicated that the ecosystem service budget of the study area had dropped by an average of 11.1% in the past 30 years, while the percentage was only 5.7% through the weight optimization method. Through literature review and discussion, we note that ecosystem services assessment is a process fraught with uncertainty. The weight optimization method has the advantage of being convenient, intuitive, and adjustable. It can also export more conservative evaluation results compared with the general method, which can better reflect the dynamic and non-linear process between land use types and ecosystem services to a certain extent. The above findings provide new insights for reducing uncertainty in land-cover-based expert matrices assessments, which can be applied to the practical assessment of ecosystem supply and demand with dramatic land use/land cover changes. |
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