## 5 Discussion
We focused on the detection of abrupt changes in the vegetation area caused by disturbance, vegetation recovery and regrowth, and also the phenology and species succession as well as the diseases and insect pests. The phenological shift caused by species succession, plant diseases and insect pests would only show as slow changes that cannot be immediately detected by the proposed framework. The slow changes caused by other issues (e.g., urban expansion and forest cutting) would also delay the change detection. Besides, the quality of the reconstructed NDVI time series is a key factor to influence the detection accuracy at temporal scale. A 250-m pixel with small changes that might not be observed in the base MODIS image may be taken as an unchanged pixel to extend the detection interval and lead to an inconsistent intersection. The maximum separation distance in trend was set as 12 months (referred to one year) to reduce pseudo changes and this setting is suitable for most pixels. That means the setting only achieved local optimum but not global optimum, which would lead to changepoints that were too close. Thus, the delay of detection timing is also attributed to this situation, because the real cycle of some disturbances is shorter than 12 months. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method and Source**} & \multicolumn{2}{c}{**Accuracy**} & \multirow{2}{*}{**Region**} & \multirow{2}{*}{**Research Period**} \\ \cline{2-2} \cline{4-4}  & **Spatial Domain** & & & \\ \hline MODIS EVI + BFAST [60] & 72.0\% & — & The Aberdare National Park, Kenya & 2000–2015 \\ \hline Landsat + VCT [61] & 97.6\% & — & Sierra Madre Occidental mountain, Durango, Mexico & 1986–2012 \\ \hline STARFM + BFAST [9] & \textgreater{}83.6\% & within 40 d & Homogeneous forestland and grassland & 2000–2011 \\ \hline ESTARFM + BFAST + RF [42] & 87.8\% &
\begin{tabular}{c} over 1/5 pixels within or over 2 months \\ \end{tabular} & Mainly in forested wetland & 2000–2018 \\ \hline The proposed method & 86.5\% & 6.8 d & Heterogeneous wetland & 2000–2019 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of methods for monitoring vegetation disturbance. In summary, the proposed method preforms well in extracting the vegetation changes from monthly time series, as it benefits from the synthetic use of FSDAF and the BEAST algorithm. However, there are several limitations in practical applications, which might be solved by the following strategies. Data: This study only used NDVI to characterize the vegetation growth for detecting the disturbance. Other vegetation indices (e.g., red-edge band vegetation index) may also be useful and flexible for vegetation monitoring. Besides, SAR data are immune to the cloudy and rainy weather and sensitive to the structure, moisture and dielectric properties of ground targets, so can be employed for continuous monitoring of vegetation. The red-edge and shortwave infrared reflectance is critical for the early detection of vegetation diseases and insect pests [62]; the temporal rate of change of the red-edge chlorophyll index and the structure-related NDVI can be used to detect forest loss [63]; multivariate SAR images have been employed to detect vegetation changes and have got promising results [64]. Thus, exploring other time-series resources that are more sensitive and robust to vegetation variation is one of the key points in future work. Algorithm: The BEAST algorithm has solved the following three issues of the conventional decomposition method. Ecologically, the definition of the change components (abrupt changes, seasonality and trend) in BEAST is more inclusive, which helps to detect the indiscernible abnormality in time series and investigate the potential driver of phenological shift. Mathematically, the BEAST algorithm uses BMA and MCMC strategies to fit the seasonal and trend signals and quantify the uncertainty of fitting and detection, but not to seek a single beast model that may result in poor fitting.