## 6 Conclusions  
The study presents a strategy integrating FSDAF and BEAST algorithms to detect and characterize the long-term vegetation changes in wetland areas by using the fused time series. The strategy considers both similarity and consistency in selecting base image pairs for FSDAF fusion. A high correlation was found between the FSDAF-fused and the actual NDVIs, with \(R^{2}\) = 0.93 and \(RMSE\) = 0.051. The fused NDVI time series is suitable for monitoring dynamic disturbance in heterogeneous areas. Besides, the BEAST algorithm robustly detected abrupt changes, seasonality and trend as well as uncertainties of the vegetation in the study area in the past two decades. In addition to the indiscernible deforestation and degradation, the plant diseases and insect pests and climate-driven gradual vegetation change events were reflected by the seasonality and trend. Both the spatial and temporal accuracy of the detection were evaluated, with 87.37% OA and 86.57% interval intersection. In the research period, the wetland vegetation in the Dongting Lake area changed dramatically in distribution and durations, because of the combined impacts of climate changes and human activities. Results indicate that the frozen disaster in early 2008 over southern China is the main factor leading to phenological shift and vegetation death in Dongting Lake wetland, and the ecological conservation policies instituted by the governments (i.e., poplar cleaning and replacement) are the key points to explain the abrupt vegetation changes in trend. Generally, the extreme weather, (frozen disaster) and anthropogenic factors (government policies, variation of economic profits, land reclamation for developing agriculture and industry as well as rapid urbanization) are the primary reasons for vegetation changes. Additionally, the stable area (e.g., cropland and relatively high-altitude forest) had a greening trend during the last 20 years, which is an evident of the global warming bringing vegetation greening. The proposed method fills the gap of continuous vegetation monitoring in heterogeneous wetland areas using long-term data with high spatial and temporal resolutions. This method can reveal the potential drivers for the degradation and recovery of wetland vegetation in subtropical areas, which can be extended to other large-scale vegetation monitoring.  
Conceptualization, Y.C.; data curation, Y.C. and S.L.; formal analysis, Y.C. and S.L.; funding acquisition, H.L.; investigation, S.L.; methodology, Y.C.; project administration, Y.C. and H.L.; software, Y.C.; supervision, H.L.; validation, S.L.; visualization, Y.C. and S.L.; writing--original draft, Y.C.; writing--review & editing, Y.C. and H.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Forestry Remote Sensing Application System based on GF satellites (Phase 2) (21-Y30B02-9001-19/22-2), the Postgraduate Scientific Research Innovation Project of Hunan Province, and in part by the Scientific Innovation Fund for Post-graduates of Central South University of Forestry and Technology.  
We would like to thank the editors and anonymous reviewers for valuable comments, which are important for improving this manuscript.  
The authors declare no conflict of interest.