研究目的
To develop and apply an interval-parameter stochastic programming (ISP) approach for optimizing ecological restoration of degraded mud flat ecosystems under cost uncertainties, aiming to maximize return on investment by balancing ecosystem service values and restoration costs.
研究成果
The ISP approach effectively handles cost uncertainties in mud flat ecological restoration, generating solution blocks that provide decision alternatives. The method allows managers to optimize restoration investments by maximizing ecosystem services and minimizing costs, with results showing equivalence between ISP and linear programming under certain confidence levels. Future work should expand to other ecosystems and sensor types.
研究不足
The study is limited to a specific geographic area (Dafeng City) and time period (1997 data), which may not be generalizable. The use of uniform cost distributions and specific satellite sensors (Landsat 5 TM) could restrict applicability to other contexts. The approach relies on assumptions about cost uncertainties and may not capture all real-world variabilities.
1：Experimental Design and Method Selection:
The study employs an interval-parameter stochastic programming (ISP) model to handle uncertainties in ecological restoration costs. The model integrates linear programming for optimization and uses statistical methods for hypothesis testing.
2：Sample Selection and Data Sources:
The study area is Dafeng City, Jiangsu Province, China, with data from a Landsat 5 TM satellite image acquired in
3：The image was processed for reflectance, atmospheric correction, and geometric transformation. List of Experimental Equipment and Materials:
19 Landsat 5 TM satellite sensors, ERDAS software for image processing, and computational tools for ISP modeling.
4：Experimental Procedures and Operational Workflow:
The image was interpreted digitally using ERDAS software to classify mud flat use types. The ISP model was applied to optimize restoration areas and costs, with parameters derived from the image data and cost distributions. Hypothesis tests were conducted to compare ISP and linear programming solutions.
5：Data Analysis Methods:
Statistical analysis included confidence interval estimation and hypothesis testing using t-distributions to assess solution blocks under different uncertainty scenarios.
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