## 4 Results
### Spatial-Temporal Dynamics of Wetland Vegetation in the Dongting Lake Area  
In this study, the total number of seasonal and trend changepoints and the slope of trend were estimated by the BEAST algorithm in the period 2000-2019 over the study area. Figure 10(a),b visualize all the seasonal and trend changepoints in Dongting Lake wetland, suggesting that the change in trend is more sharply than that in seasonality. The cropland (e.g., the paddy rice field in the central part of the study area) has less changes in seasonality than in trend, indicating that it has stable intra-annual phenology but frequent crop rotations. The wetland herb (the northeastern part of the study area, surrounding the Dongting Lake) has similar change features with cropland, but the trend has a negative slope (\(\sim\)\(-\)0.1/20 a) (Figure 10(c)). This may be caused by the climate change. Most changepoints in seasonality was detected in the _Pinus massoniana_-dominated area in the southwestern part of the study area, because that area suffered from pine moth. The changepoints of the permanent water body were extracted precisely, which are the least in both seasonality and trend. This further demonstrates the feasibility of the BEAST method. In Figure 10(a),c, the stable area has positive slope of trend, and the area with changes has negative slope of trend. The area with stable vegetation has a greening trend, which is possible due to the global warming. The natural wetland has most changing areas and faces the risk of degradation, because of the frequent human activities and environment changes. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Degradation** & **Recovery** & **Stable** \\ \hline PA (\%) & 85.72 & 86.83 & 88.50 \\ UA (\%) & 85.32 & 86.44 & 86.89 \\ OA (\%) & & 87.37 & \\ Kappa coefficient & & 0.85 & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The changepoint detection accuracies of the presented method. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Types of Change**} & \multirow{2}{*}{**Interval Intersection (\%)**} & \multirow{2}{*}{**Numbers of Plots**} & \multicolumn{2}{c}{**Mean Interval Length (days)**} \\ \cline{3-4}  & & & **Validation** & **BEAST** \\ \hline Degradation & 84.71 & 76 & 98.9 & 107.5 \\ Recovery & 88.42 & 67 & 127.3 & 132.2 \\ Average & 86.57 & — & 113.1 & 119.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The assessment of detection accuracy at temporal scale. We calculated the proportion of pixels with single/multiple changepoint (s) (Figure 12a) and the proportion of stable and changed pixels in different DEM ranges (Figure 12b). In general, the seasonal changepoints are less than the trend changepoints, which means that permanent changes (e.g., deforestation) are more than ephemeral changes (e.g., flooding). Most areas have two or less seasonal changepoints but at least three trend changepoints. In addition, 80% pixels are changed (Figure 12b), indicating the dramatic vegetation changes over the whole Dongting Lake wetland during 2000-2019. Besides, the changed pixels mainly distribute in the area lower than 20 m (22% of the total pixels) and between 30-40 m (18% of the total pixels), which are the core wetland and built-up area. Forested wetland (southwestern of the study area) higher than 40 m, especially the region higher than 50 m, is the relatively stable vegetation area. The regions with poplar and wetland herb surrounding the water body have negative slopes in trend during the research period (Figure 11c), and they have the most frequent changes. To better understand the evolution mechanism of wetland vegetation, we extracted the time with the most significant disturbances in seasonality and trend (Figure 13a,b), and measured the year with the maximum NDVI (Figure 13c). As Figure 13a shows, many significant changes intensively occurred in 2008, because the most serious frozen disaster in more than a hundred years struck southern China and led to the phenological shift and vegetation death.