###### Abstract
Moreover, other monitoring approaches such as the vegetation change tracker (VCT) [33], continuous change detection and classification (CCDC) method [34], and exponentially weighted moving average change detection (EWMACD) [35] were proposed. The VCT algorithm reconstructs recent forest disturbance history based on the spectral-temporal properties of land cover and forest change processes. The EWMACD algorithm uses the exponentially weighted moving average chart and Landsat images to detect the multitemporal forest change and can achieve an accuracy of 85% [35]. By the CCDC approach, land cover change can be detected when the difference between the observed and predicted (based on a time series model) pixels exceeds a threshold three times consecutively and then the kinds of land cover change could be identified by the CCDC method (based on random forest classifier). Although these methods have been widely used, they have some defects. First, these methods should preset a range of parameters. Second, some of these algorithms cannot estimate the seasonal changes. Third, these methods ignore the real problems in ecosystem evolution, because they only adopt the single best-fitting model (i.e., linear model or piecewise linear model) in a signal estimation. Foremost, the single best-fitting model may lead to opposite results that vegetation greening is suggested by linear model but this trend only reflects in a previous segmentation of time series when use piecewise linear modeling [36; 37; 38]. To solve these problems, the BEAST method was proposed to utilize a Bayesian-ensemble-based approximator to fit arbitrarily complex trends of time series, which can quickly and accurately characterize individual components and credible uncertainty within the time series [39]. To some extent, this ensemble strategy makes the method more ecologically meaningful and is beneficial to the mechanistic interpretations of the terrestrial ecosystem and climate-biosphere interactions as well as the monitoring performance [39]. The STIF and time-series decomposition algorithm were often used separately, and have been seldomly used for long-term monitoring in wetland areas. On one hand, the cloudy and rainy weather in the subtropical regions make the acquisition of continuous remote sensing data with high spatial and temporal resolution difficult, which hampers the studies related to wetland monitoring [17; 40]. On the other hand, previous time-series algorithms focus on seeking a single best model for change detection, but ignore the interpretation of the ecosystem change mechanism, so they may result in controversy because of the opposite results [16; 41]. In this research, we integrate the FSDAF model with the BEAST algorithm to continuously monitor the 20-year vegetation changes of the Dongting Lake wetland in south China, for a better understanding of the long-term evolution process of wetland ecosystem under global warming. The rest of this paper is structured as follows: Section 2 briefly introduces the Dongting Lake wetland and the dataset used in this study. Section 3 describes how to fuse time series using the FSDAF model based on the cross-fusion strategy and how the BEAST algorithm is applied to detect the abrupt changes, trend, and seasonality of wetland vegetation. Section 4 demonstrates the fusion and change detection results. Section 5 discusses the implication of the results and some ideas for further work. In the final Section, some conclusions are presented.