Ecosystem health monitoring in the Shanghai-Hangzhou Bay Metropolitan Area: A hidden Markov modeling approach.

Ecosystem health assessment is an important method for obtaining information on ecosystem conditions, and it plays a vital role in preserving and enhancing ecosystem health status. In addition, it provides useful information and knowledge for urban agglomeration development decision makers. However, ecological phenomena often vary considerably from one observation to the next, which makes it difficult to distinguish different status of the ecosystem health. In this study, hidden Markov model (HMM) was employed to simulate the internal-external correlations of ecosystem status through establishing the relationships between internal ecological health level and combination state of external observation. Based on the statistics and land use data in 2001, 2007 and 2013, the Vigor-Organization-Resilience (VOR) framework was employed to identify the ecosystem health in Shanghai-Hangzhou Bay Metropolitan (SHBM), in which the ecosystem health state was considered as a hidden state that could be estimated according to the conditions of vigor, organization and resilience. In addition, two parameter learning cases including mathematical statistics and extensible sequence method were employed to solve the iterative convergence problem of parameters in short-time series of ecosystem health simulation. Results show that HMM not only provides a comparable descriptive ability to that of the VOR model, but also can monitor ecosystem health at the optimal grid scale in SHBM. The combination of HMM and VOR greatly expands the spatiotemporal characteristics and provides a new research approach for the study of ecosystem health assessment of urban agglomerations.