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Despite  recent progress  in  landslide  susceptibility mapping,  a holistic method  is  still needed to integrate and customize influential factors with the focus on forest regions. This study was accomplished to test the performance of geographic object‐based random forest in modeling the susceptibility of protected and non‐protected forests to landslides in northeast Iran. Moreover, it investigated the influential conditioning and triggering factors that control the susceptibility of these  two  forest areas  to  landslides. After  surveying  the  landslide events,  segment objects were generated from the Landsat 8 multispectral images and digital elevation model (DEM) data. The features of conditioning factors were derived from the DEM and available thematic layers. Natural triggering factors were derived from the historical events of rainfall, floods, and earthquake. The object‐based  image  analysis  was  used  for  deriving  anthropogenic‐induced  forest  loss  and fragmentation. The  layers  of  logging  and mining were  obtained  from  available  historical data. Landslide samples were extracted from field observations, satellite images, and available database. A  single database was  generated  including  all  conditioning  and  triggering object  features,  and landslide samples for modeling the susceptibility of two forest areas to landslides using the random forest algorithm. The optimal performance of random forest was obtained after building 500 trees with the area under the receiver operating characteristics (AUROC) values of 86.3 and 81.8% for the protected and non‐protected forests, respectively. The top influential factors were the topographic and hydrologic features for mapping landslide susceptibility in the protected forest. However, the scores were loaded evenly among the topographic, hydrologic, natural, and anthropogenic triggers in the non‐protected forest. The topographic features obtained about 60% of the importance values with  the  domination  of  the  topographic  ruggedness  index  and  slope  in  the  protected  forest. Although the importance of topographic features was reduced to 36% in the non‐protected forest, anthropogenic and natural triggering factors remarkably gained 33.4% of the importance values in this area. This study confirms that some anthropogenic activities such as forest fragmentation and logging significantly intensified the susceptibility of the non‐protected forest to landslides.

remote sensing

A Holistic Analysis for Landslide Susceptibility Mapping Applying Geographic Object-Based Random Forest: A Comparison between Protected and Non-Protected Forests