## 4 Discussion  
Semi-automated techniques can limit the subjectivity in landslide mapping and can contribute to improving the reproducibility of landslide maps [78]. OBIA is such a technique and provides a set of suitable tools to semi-automatically map the evolution of landslides with time series of satellite images. The developed OBIA workflow was designed to be transferable across images, whereby only minor modifications for each Landsat sensor were necessary. This reduces the analysis time and increases the transferability of the approach. We used spectral indices for the landslide classification supported by ancillary DEM data to avoid the classification of specific false positives. While a DEM was available for only one point in time for our analysis, using DEMs acquired after each triggering event would increase the classification accuracy and would also allow a volume change estimation. In practice, however, multi-temporal DEM data are rarely available. The semi-automated mapping led to reasonable results. However, the determined accuracy values need to be considered with care, since any reference data created by manual mapping includes a certain degree of uncertainty and subjectivity [45]. The Landsat archive offers multispectral imagery since the 1980s suitable to identify recurrent changes in the area of large landslides such as the Butangbunasi landslide. Even if the spatial resolution of 30 m does not allow to identify very small changes, trends over time can be well depicted. However, the exact timing of landslide extension/reactivation following typhoons or tropical storms remains difficult. We employed the first cloud-free Landsat image acquired after such an event, but in some cases, the time span between a rainfall event and image acquisition date was up to several months or even longer. The accuracy of the OBIA mapping probably also depends on the time elapsed between a landslide triggering event and the acquisition of the next satellite image [37]. A short delay would allow deriving more detailed information about the landslide reactivating, the revegetation time and the formation of landslide-dammed lakes. The time span of over two years between the last image used and the last identified typhoon is probably the reason for the slightly decreasing trend in landslide area since initial revegetation happens quite fast. Several studies investigated the vegetation recovery after the occurrence of landslides. For example, [PERSON] et al. [79] estimated a vegetation recovery rate of approximately 60% two years after landslides and [PERSON] et al. [80] found a vegetation recovery rate of approximately 90% six years after landslides in central Taiwan. The new generations of satellites, for example, freely available data such as Sentinel-2 or EO data from commercial data providers, already provide a higher temporal and spatial resolution for recent years. This offers great opportunities for improving studies similar to the presented one in the future when longer time series of very high resolution (VHR) images will be available. Rainfall-triggered landslides are particularly frequent in areas heavily affected by typhoon events, such as Taiwan. Freely available rainfall data are essential to improve early-warning models, as new technologies and research opportunities emerge. In this study, we focused on two free and open rainfall data sources. The CHIRPS data are globally available since 1981, however, its coarse spatial and temporal resolution limits its usage for local studies. In addition, the CHIRPS data under/overestimate daily precipitation. In our case, rain gauge data available from the CWB typhoon database were a more suitable alternative. Our results indicate that the duration of the heavy rainfall event is the main parameter linked to the landslide area change, while cumulative rainfall and mean intensity did not show significant correlations with the extension of the Butangbunasi landslide.