一种地表生态参数测算的不确定度分析方法
By using Bayesian probabilistic models and Markov chain Monte Carlo methods, the problem of improper handling of multi-source errors in traditional methods is solved, and high-precision quantification of surface ecological parameters is achieved, improving the reliability and accuracy of ecological assessment and carbon sink measurement.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- RES INST OF FOREST RESOURCE INFORMATION TECHN CHINESE ACADEMY OF FORESTRY
- Filing Date
- 2026-01-20
- Publication Date
- 2026-07-17
AI Technical Summary
Traditional uncertainty assessment methods struggle to handle the nonlinear coupling of multi-source errors, resulting in overly coarse or unrealistic uncertainty estimates that fail to meet the accuracy and reliability requirements of fields such as ecological environment monitoring and carbon sequestration.
Using a Bayesian probability model, the observed values and prior probability distributions of the target indicators are obtained. The Markov chain Monte Carlo method is used for sampling to construct a likelihood function, update the posterior distribution of each factor, calculate the uncertainty of surface ecological parameters, and adapt to various ecological measurement models.
It enables high-precision quantification of uncertainties in surface ecological parameters, improves the reliability and accuracy of ecological assessment and carbon sink measurement, and adapts to collaborative modeling and joint propagation of multi-source errors.
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Figure CN121996897B_ABST