## 5 Discussion  
The accurate change detection of the ecosystem and vegetation remains a challenge, particularly in sub-tropical and tropical regions. A few of researches attempted to address this problem (Table 3). The BFAST algorithm was utilized to detect the forest loss in the Aberdare National Park, Kenya, using the 500-m resolution MODIS EVI data [60]. However, it only detected 72% of forest loss accurately, due to a large amount of mixing and missing pixels in the MODIS pixels. Furthermore, it cannot perform the temporal detection. Another study focused on the accurate detection of forest change in Sierra Madre Occidental mountain and achieved a high detection accuracy (97.6%) using the VCT method [61]. The discontinuous Landsat data used in [61] result in the inability of obtaining high accuracy in time scale. Thus, the change region was only analyzed annually in the research. The BFAST algorithm was also used to detect the forest and grassland change over the homogeneous area in Queensland, Australia, taking the STARFM fused time series as input [9]. Despite the 96.2% changepoint estimation accuracy of the clearing event and 83.6% of the re-clearing event were achieved, the timing accuracy was seriously delayed (40 days). For improving image fusion and increasing the detection precision of change types, the ESTARFM model and random forest classification algorithm were adopted [42]. But the uncertainty in the detection procedure was not estimated and the slow disturbance event (such as diseases and pests) could not be detected. In general, the methods in [9; 42] are not suitable for monitoring the vegetation dynamics in the Dongting Lake area as the high heterogeneity in this region where the STARFM and ESTARFM algorithms could not provide accurate fused images. Figure 14: (**a**) Annual proportion (%) of pixels with the maximum NDVI value and the most significant seasonal and trend changes. (**b**) the annual average temperature (\({}^{\circ}\)C) and precipitation (mm) in the study area. We used the cross-fusion strategy and FSDAF model to generate the dense Landsat time series stack, which made the detection more sensitive and robust. For example, the _Pinus massoniana_ forest suffered pine moth was accurately identified. Compared with the BFAST algorithm, the BEAST can estimate more extra parameters, including probability distribution of timing, number of changepoints and the rate of change in trend signal, so it can achieve satisfying performance in both spatial and temporal domains. The spatial and temporal validation shows high OA of 87.37% and Kappa coefficient of 0.85%, and a satisfactory timing accuracy with an interval intersection of 86.57% and absolute difference of mean interval length of 6.8 days. However, there are commission and omission errors in the detection process over the time series. The commission errors are possibly due to the noise from the Savitzky-Golay filter in MODIS processing and the prediction errors in the FSDAF fusion. The Savitzky-Golay filter was used to smooth the NDVI time series by reducing noise and cloud-contaminated pixels, but it influenced the abrupt changes in time series and induced commission errors [9]. In this study, the cross-fusion and FSDAF algorithm were used to blend MODIS and Landsat observations to generate a continuous monthly NDVI time series. However, the fusion results have uncertainties, despite the high correlation between the fused and actual images. On one hand, there are some prediction errors. Indistinguishable changes cannot be reflected in the base coarse-resolution MODIS images [8]. On the other hand, the residual cloud pixels in a monthly MODIS NDVI are ubiquitous, particularly in subtropical and tropical areas, which may also result in fusion errors that are mistaken as changes. Additionally, the main reason for omission errors are the slight and continuous transformation of vegetation to other land cover types or the inter-conversion between vegetation, which induce indistinguishable spectral variation that cannot be detected by BEAST.