## 4 Results
### Abrupt Changes, Trend, and Seasonality of Wetland Vegetation Derived by the BEAST Method  
The BEAST algorithm was used to decompose the NDVI time series into abrupt changes, trend and seasonality components to monitor vegetation dynamics. Seasonality is the periodic variation with abrupt changes caused by intra-annual climatic change or phenological factors. Trend signal is considered as the gradual change resulted from long-term environmental trends, successional dynamics and chronic disturbances, reflecting the stable, browning and greening trend of vegetation. Abrupt changes are related to severe disturbances in both seasonality and trend. Specifically, most disturbance results in the turning of time series, such as flooding, insects, fire, logging, weeding, replantation post-deforestation, urbanization, extreme weather, crop rotation and climate change, can be detected as abrupt changes by BEAST. Additionally, the possibility and uncertainty of the distribution of these three components were quantified in the study area. Figure 8: Scatter density plot between the observed and the fused NDVI in December 2017. Figure 7: The monthly NDVI data covering the whole study area in December, 2017: (a) MODIS; (b) Landsat; (c) FSDAF. Figure 9 shows an example of an NDVI time-series decomposition in a forest stand. In this example, the BEAST algorithm has detected two seasonal changepoints (Figure 9a) and four trend changepoints (Figure 9b) with the 95% credible interval of occurrence time, which possibly demonstrates the unstable characteristics of forest growth. The probabilities of both the seasonal and trend change points were estimated using the BMA strategy. The threshold of the probability of abrupt changes can be controlled manually to determine the location of change points according to the prior knowledge. BEAST is beneficial from repeating sampling (10,000 times in this experiment) and the BMA method. The standard deviations of fitting in BEAST can be controlled by the BMA strategy (Figure 9e,f). In other words, BEAST improves the accuracy and stability of fitting in seasonality and trend. Figure 9g,h show the estimated harmonic order for fitting seasonal and trend signals, respectively. In Figure 9i,j, the probabilities distribution of seasonal and trend change points are present. In order to understand the evolution of wetland vegetation during the last two decades, the slope of trend with the 95% confident interval and its standard deviations (Std) were measured (Figure 9k,l). Figure 9m depicts the probability of having a positive rate of change in trend that assists in identifying the disturbance types (e.g., deforestation and recovery). The Landsat images corresponding to the detected changes are shown in Figure 10 to help understand the changing process. A total of six abrupt changes were detected (Figure 9a,b) and one abrupt change (approximately occur in 2012) was observed by both the seasonal and trend signals. Firstly, a sudden change occurred in the seasonality of September 2006, which might be caused by phenological shifts (Figure 9a). In Figure 10, the area has no vegetation on 5 May 2006, but has forest on 27 May 2007. In this duration, the stand recovered and this change has been accurately detected by BEAST. The vegetation was a stable from May 2007 to January 2009, but a sudden drop of NDVI  
Figure 9: The decomposition of NDVI time series in a forest stand: (**a**,**b**) represent the best fitted seasonal and trend signals (red line), respectively. The 95% credible interval (gray envelope) of corresponding signals, the most possible changepoints (dotted line) as well as the possible locations of the break time (lemon envelope) were also calculated by BEAST.