###### Abstract  
The dynamic monitoring and analysis of wetland vegetation play important roles in revealing the change, restoration and reconstruction of the ecosystem environment. The increasing availability of high spatial-temporal resolution remote sensing data provides an unprecedented opportunity for wetland dynamic monitoring and change detection. Using the reconstructed dense monthly Landsat time series, this study focuses on the continuous monitoring of vegetation dynamics in Dongting Lake wetland, south China, in the last two decades (2000-2019) by using the Bayesian estimator of abrupt change, seasonal change, and trend (BEAST) method. Firstly, the flexible spatiotemporal data fusion (FSDAF) model is applied to blend Landsat and moderate-resolution imaging spectroradiometer (MODIS) images on the basis of the input image pair selection strategy named \"cross-fusion\" to generate the monthly time-series normalized difference vegetation index (NDVI) with the spatial resolution of 30 m. Then, the abrupt changes, trend, and seasonality of the vegetation in the study area as well as the uncertainties of change detection are estimated by the BEAST method. Results show that there is a close relationship between the ground true data and the estimated changepoints. A high overall accuracy (OA) of 87.37% and Kappa coefficient of 0.85 were achieved by the proposed framework. Additionally, the temporal validation got the interval intersection of 86.57% and the absolute difference of mean interval length of 6.8 days. All of the results demonstrate that the vegetation changes in the Dongting Lake wetland varied spatially and temporally in the last two decades, because of extreme weathers and anthropogenic factors. The presented approach can accurately identify the vegetation changes and time of disturbance in both the spatial and temporal domains, and also can retrieve the evolution process of wetland vegetation under the influence of climate changes and human activities. Therefore, it can be used to reveal potential causes of the degradation and recovery of wetland vegetation in subtropical areas. wetland vegetation; Landsat time series; FSDAF; time-series decomposition algorithm; dynamic monitoring; change detection +
Footnote †: journal: Applied  
is extremely sensitive to climate and environmental changes. In the last several decades, due to the global climate change and human activities, the wetland ecosystem has faced many degradation risks, such as area shrinkage [4], ecological degradation [5], invasion of alien species [6] and pollution [7]. Therefore, a long-term dynamic monitoring of wetland vegetation is of practical significance and is important and essential to sustainable development. With the increasing availability of high spatial-temporal resolution data, remote sensing technology provides spatially comprehensive information for monitoring and detecting vegetation disturbance and land cover changes [8; 9; 10]. Coarse spatial resolution data, such as MODIS and advanced very high-resolution radiometer (AVHRR) images, have been used to monitor the surface change in regional or large area, because of the high temporal resolution and wide spatial coverage. For example, the multi-temporal MODIS NDVI 16-day composite data have been used to detect the land cover change in the Albemarle-Pamlico Estuary System (APES) region in the US [11]; the MODIS imagery was employed to monitor the land cover change in Borneo from 2002 to 2005 [12]; the AVHRR data were used in monitoring the land cover dynamics in Bangladesh [13]. However, these moderate-resolution images caused mixed pixels. Furthermore, these images cannot be used to extract the detailed land cover changes or vegetation disturbance, due to their low spatial resolution. Remote sensing images of a 30-m resolution can identify most vegetation disturbances and land cover changes [14]. Thus, the free Landsat archive with a spatial resolution of 30 m provides an opportunity for the continuous dynamic monitoring of land cover changes.