## 2 Materials and Methods
### Semi-Automated Landslide Mapping  
For semi-automatically mapping the Butangbunasi landslide area on each Landsat image, OBIA was used. The analyses were conducted using the eCognition (Trimble) software. We defined a set of knowledge-based classification rules for mapping the landslide area per image. Since images from 20 different points in time were used, efforts were made to design a transferable classification routine that could be applied to all images without or with only minor adaptations of the classification thresholds (Table 4). The classification rules were developed based on the Landsat 5 image from 1984 and then transferred to the other images. As a first step, the normalized difference vegetation index (NDVI) and the modified soil-adjusted vegetation index (MSAVI), as well as a brightness layer were calculated. Next, we applied the multiresolution segmentation [67] for creating image objects as a basis for the classification. The multiresolution segmentation in eCognition is a bottom-up region merging technique. It starts with single pixel objects and merges them stepwise to larger objects based on local homogeneity criteria that describe the similarity of adjacent image objects [67]. The scale parameter and homogeneity criteria (shape vs. color weighting, compactness vs. smoothness weighting) determine the maximum allowed heterogeneity and control the average size and shape of the resulting image objects. The segmentation parameters (Table 4) were selected based on an expert-guided trial and error approach and a visual assessment of resulting image objects. The knowledge-based classification primarily relied on the usage of the calculated spectral indices. The main indication for mapping the landslide area was the absence of vegetation, which leads to a distinctive spectral contrast between the landslide-affected area and its surroundings, especially in densely vegetated regions [49; 58; 68]. This change in land cover related to landslide occurrence can \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline \multirow{2}{*}{**Data**} & \multicolumn{2}{c}{**Parameters for**} & \multirow{2}{*}{**Bands for Segmentation**} & \multirow{2}{*}{**Classification Parameters**} \\  & **Multiresolution Segmentation** & & & Mean NDVI \(<\) 0.5 \\ \hline Landsat 5 (1984, 1989, 1990, 1992, 1994, 1996, 1998, 2004, September 2005, October 2005, August 2008, 2009, 2010) & Scale parameter: 10; Shape criterion: 0.1; Compactness criterion: 0.4 & blue, green, red, nir, brightness & Mean MSAVI \(<\) 0.7 \\ \hline Landsat 5 (March 2008) & Scale parameter: 10; Shape criterion: 0.1; Compactness criterion: 0.4 & blue, green, red, nir, brightness & Mean brightness \(>\) 20 \\ \hline Landsat 7 (2000, 2001) & Scale parameter: 10; Shape criterion: 0.1; Compactness criterion: 0.4 & blue, green, red, nir, brightness & Mean NDVI \(<\) 0.6 \\ \hline Landsat 7 (2000, 2001) & Scale parameter: 10; Shape criterion: 0.1; Compactness criterion: 0.4 & blue, green, red, nir, brightness & Mean MSAVI \(<\) 0.75 \\ \hline Landsat 8 (2013, 2015, 2016, 2018) & Scale parameter: 150; Shape criterion: 0.1; Compactness criterion: 0.4 & blue, green, red, nir, brightness & Mean NDVI \(<\) 0.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Segmentation and classification parameters used for object-based landslide mapping. be well represented with spectral indices such as the NDVI [51]. The same classification parameters were applied to all Landsat 5 images, except the March 2008 image, where the thresholds for the NDVI and MSAVI were slightly adapted. The reason for this was that this image is the only one acquired in spring, i.e., before the rainy season, and the seasonal variation leads to a slightly different spectral reflection. Minor adaptations were also needed for the Landsat 7 and Landsat 8 images, while the same classification rules and thresholds were used per sensor.