## 4 Discussion  
### Comparison with Automated Mapping Methods  
Rapid response to a major landslide event, such as hurricanes or earthquakes, is nowadays feasible due to the abundance of open remote sensing data available shortly after an event, from satellite imagery (e.g., Sentinel-1 & Sentinel-2, Landsat 8/9). The first satellite images available during or shortly after a meteorological event can provide a fast and comprehensive map of significant slides and effects, as described in the previous section. During the period that follows first response and early reconstruction, there is the need for more thorough and detailed landslide inventories. While this can be performed by manually digitizing landslides and effects using very high-resolution satellite imagery or other remote sensing data such as UAS surveys, new tools and a wealth of open satellite data can automate this workflow [42]. The ability to have access and process huge amounts of satellite imagery on the cloud through Google Earth Engine [43] and similar platforms has revolutionized remote sensing in geohazard response and analysis. We present a comparison of our manual rapid mapping using Sentinel-2 images, with a series of recent workflows and codes that use the multi-temporal analysis of satellite imagery in Google Earth Engine [44; 45; 46; 47]. While this comparison is not straightforward due to the different workflow, data and time frame used by either rapid manual mapping or automated multi-temporal analysis, this is an interesting case study to compare them. The wide extent and large number of landslides in the area of Karditsa, Thesaly is suitable for this use and comparison (Figure 15). We selected four change detection techniques, that use multi-temporal analysis of open satellite data (Sentinel-1, Sentinel-2, Landsat): (a) method from [46] that calculates relative difference in the normalized difference vegetation index (rdNDVI) calculated from cloud-free composites of Sentinel-2, (b) HazMapper code by [44] that is based on the normalized difference vegetation index (dNDVI) calculated from cloud-free composites of Sentinel-2 [48], (c) ALDI, automated landslide detection index algorithm based on normalized difference vegetation index (NDVI) differencing of Landsat time series within Google Earth Engine taking into account seasonality [47] and (d) a SAR backscatter and amplitude change approach that uses multi-temporal stacks of Copernicus Sentinel-1 images [45]. Figure 14: Sentinel-2 multi-temporal composites (Band 4) of central Cephalonia. (**Left**): Mirtos beach area; (**Right**): Site of collapsed bridge on the E.O. Argostoliou-Fiskardou (38.3195\({}^{\circ}\) N, 20.5081\({}^{\circ}\) E). Red colors mark the location of debris flows, landslides and deposition of debris along the coast. Treshold values for positive and negative landslide and debris flow identification was determined for each method results based on proposed values and also local conditions after cross comparing with mapped landslides and imagery (Table 2 and Figure S2). Examining the different change detection results we obtained from these four methods, we can observe that all methods captured the large debris flows that occurred along the mountainous watersheds such as Mouzakiotikos river (Figure 15) and Megalos river in Filakti, near Lake Plastira (Figure 16). Large landslides with significant dimensions and/or long run-out distance were also identified. As the majority of landslides triggered during Medicare Iancos were of small dimensions (less than 20-30 m), these were mostly missed or were undifferentiated from the background scatter noise. A quick estimate of the positive or negative detection was performed by extracting pixel values on the mapped landslide inventory from the various methods. Table 2 presents the final percentage and number of positively and negatively detected landslides.