## 4 Discussion  
Both optical and radar data with medium or high spatial resolution have been used in the past for the semi or fully automatic detection of earthquake-triggered landslides [16; 52; 53; 54]. Our methodology and results are in accordance with many previous studies, as summarized in the following paragraphs. One of the first attempts to implement an automated method for detecting and mapping earthquake-triggered landslides was presented in 2010 using Landsat TM and ASTER data [52]. This method was based on the vegetation mass change and used the NDVI. The usefulness of the slope change for slide recognition was demonstrated in another study [55]. In a similar study [56], the authors proposed a change detection methodology based on optical data to investigate the pre-event era and SAR data for the after event period. The NDVI was used as the first selection landslide site selection criterion while the slope was used as a verification criterion. The suitability of UAVs in the identification of landslides triggered by the 2016 Kumamoto earthquake in Japan was presented [15]. These authors managed to detect 54 co-seismic landslides ranging from very small (nine m\({}^{3}\)) to very extended (more than 3994.6 m\({}^{3}\)). They also mentioned the usefulness of UAVs in such studies as the specific equipment provides both orthophotos and DSMs, in addition to other advantages, such as very high resolutions, repeatability, and mobility. [PERSON] et al. [12] used PALSAR-2 images for detecting earthquake-induced landslides after a strong earthquake (Mw 6.6) on 2018 at Hokkaido. In accordance with our study, they used slope data to ensure that the detected relief changes were indeed due to landslides. Added to this, Landsat images before and after the seismic event and a 20 m DEM were processed in order to detect earthquake-triggered landslides in Taiwan [57]. In another study, diverse remote sensing data, with UAV images, GF-6, and Landsat-8 imagery, with pixel sizes ranging from 0.2 m to 15 m, were selected to detect earthquake-induced landslides and the results were verified with in situ data [54]. It was found that as the spatial resolution ameliorates, the detection accuracy increased relatively as a result. The outcomes of the current work are in accordance with the results from previous similar studies. In a study dated from 2002 [58], the coastal cliff retreat along central California was examined. It was proved that the cliff retreat was episodic, occurring in response to single large storms or seismic events. Similar to the current study, diachronic remote sensing data before and after the occurrence of storms or earthquakes were used. In addition, to measure co-seismic vertical ground displacements in Mexico caused by the 2010 El Mayor-Cucapah earthquake, other researchers [59] followed a methodology similar to ours. A pre-earthquake LIDAR DEM was compared with a post-earthquake DEM derived from Pleiades tri-stereo imagery. Moreover, to evaluate the historic coastal cliff retreat in the area of Conway Flat (New Zealand), orthoimages with diverse spatial resolution covering the period from 1950 to 2022 were utilized [60]. It was proved that earthquakes increased the background cliff retreat rate at this specific area by approximately 45%. Furthermore, this specific area experienced widespread cliff retreat from relatively moderate ground motion, which is quite similar to our results. As these authors mentioned, the outcomes of such studies  
Figure 22: Sieves showing angular and subangular gravels with a diameter > 8 mm and the coarse sand of the first semester sample. could serve as an important example for the calculation of long-term coastal cliff retreat over multiple earthquakes [60]. Generally speaking, it is estimated that cliffs comprise almost 80% of the coastlines on Earth [61]. In areas with high population density and high seismicity, such as California, realizing how seismicity affects the long-term retreat of coastal cliffs is a prerequisite for developing efficient forecast models [62].