**5. Conclusions**

This study investigated the performance of a geographic object-based random forest for modeling the susceptibility of protected and non-protected forests to landslides. Various object features of conditioning (topographic, hydrologic, geologic, geology, soil, and vegetation) and triggering factors (rainfall, flood, earthquake, deforestation, forest fragmentation, logging, and mining) were applied

as a database for the mapping of landslide susceptibility in the two forest areas using the random forest algorithm.

Although the random forest exhibited good performance for the mapping of landslide susceptibility in both the protected and non-protected forests, its sensitivity in the protected forest was higher than that in the non-protected forest. The influential variables controlling the susceptibility of these two forests to landslides were di fferent. Approximately 88% of the susceptibility of protected forests were explained by the conditioning factors focusing on the topographic (60%) and hydrologic (18%) features. Moreover, triggering factors recorded 22% of importance, focusing on natural triggering factors (16%). The top five variables were TRI, slope, earthquake, elevation, and TCI for the mapping of landslide susceptibility in the protected forest. In contrast, the importance values were distributed among the object features of both the conditioning and triggering factors in the non-protected forests. While the importance of topographic factors has significantly decreased, the importance of triggering factors focusing on anthropogenic features has substantially increased from less than 1% in the protected forest to about 17%—focusing on forest fragmentation and logging—in the non-protected forest. Moreover, the e ffects of some features of hydrologic and natural triggering factors such as sediment transport index and flood frequency were amongs<sup>t</sup> the top variables that control landslide susceptibility in the non-protected forest. The e ffects of these features could be caused or intensified by human activities such as deforestation, forest fragmentation, logging, and mining. These results provide managers and decision-makers with information in which to assess the consequences of developing destructive schemes such as road building, logging, and mining before any intervention in forest areas. The importance of geology and soil features was lower than the importance of other variables in the non-protected forest.

This study indicates that di fferent forest areas can be a ffected by di fferent conditioning and triggering factors that control their susceptibility to landslides. Consequently, there are no uniformly predefined influential variables for mapping landslide susceptibility in forest areas.

**Author Contributions:** Z.S. contributed to writing all parts of this article including the design of the work, image processing, and object-based classification using eCognition ®, modeling of landslide susceptibility in R, and mapping in ArcGIS ®. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Acknowledgments:** The author expresses her acknowledgment to the three anonymous reviewers whose comments/suggestions helped improve and clarify this manuscript. The author acknowledges support by the German Research Foundation and the Open Access Funding by the Publication Fund of the TU Dresden.

**Conflicts of Interest:** The author declare no conflicts of interest.
