**4. Discussion**

#### *4.1. The Accuracy of Landslide Susceptibility Maps in the Protected and Non-Protected Forests*

The results of the model assessment indicated the high accuracy of the obtained landslide susceptibility maps from the RF model with the contribution of influential conditioning and triggering variables for both the protected and non-protected forests. However, the landslide susceptibility map of the protected forest showed a higher AUROC value than the landslide susceptibility map of the non-protected forest (Table 3). The high performance of RF for landslide susceptibility mapping has also been verified in previous studies [41,42,57,58,60]. This study adds that the application of an object-based random forest resulted in a high accuracy of landslide susceptibility mapping, whereas the pixel-based random forest was the model of interest by the aforementioned researchers.

#### *4.2. The Importance of Conditioning Factors for Mapping Landslide Susceptibility in Protected and Non-Protected Forests*

Our analysis of comparing the influential variables revealed that the topographic factors obtained the highest scores for mapping landslide susceptibility in the protected forest; however, there was a relative balance between the scores of topographic, hydrologic, and triggering factors in the non-protected forest. The topographic features obtained about 60% of the total importance values in the protected forest; 36% of the values were assigned to the TRI (19.5%) and slope (16.5%) (Figure 4). The majority of landslide events fell in the old type in the protected forest, which are scattered in the steeped slopes and coarse rugged surfaces [72]. Furthermore, our analysis showed that the spatial probability of landslide significantly increased from 0.75 to 1 when the TRI increased from 14 to 27 (Figure 6a) and the slope increased from 25◦ to 51◦ (Figure 6b) in the protected forest. The high importance of the TRI [42,52] and slope [41,48,50,132] for mapping the landslide susceptibility has also been reported in several studies. Nevertheless, some research has addressed the low importance of slope for mapping landslide susceptibility [42,47,58].

Although topographic features gained about 36% of importance in the non-protected forest, their score was lower than the score of the topographic features in the protected forest. Both studied forests showed almost similar topographic characteristics; however, the aspect (Figure 7b) and elevation (Figure 7c) recorded higher scores among the topographic features in the non-protected forest (Figure 4). Likewise, several studies have confirmed the high importance of aspect and elevation for landslide susceptibility mapping [49–53].

The hydrological features obtained about 18% of scores with the top variables of TWI (7%) and distance to rivers (4.7%) in the protected forest. While in the non-protected forest, the importance of hydrological features increased to 28.5% with the top variables of river density (Figure 7a) and sediment transportation index (STI) (Figure 7e). We can infer from these results that it is likely that increasing human activities such as deforestation may cause changes in the hydrological system and increase the sediment [133,134] through the rivers with the consequences of increasing the susceptibility of landslide [11]. For example, Swanson and Dyrness [16] concluded that clear-cutting-induced landslides has substantially increased transported sediment materials in forest areas. The importance of the TWI [48,53,132] and distance to river [4,50,53] has also been reflected in earlier studies mapping landslide susceptibility.

The importance values of natural triggering factors were relatively equal between the two forests. The top variables of this category were earthquake (9.3%) and rainfall (4.6%) in the protected forest (Figure 4a), while all three variables roughly gained equal values in the non-protected forest (Figure 4b). Although the importance of natural triggering factors such as earthquake [52] and rainfall [49,52,53,78] has been reported for mapping landslide susceptibility, earthquakes trigger landslides by generating primary slips and intensifying liquefaction in the saturated soils [52]. The intensification of natural hazards due to human intervention can increase the landslide susceptibility, as the importance of flood in the mapping of landslide susceptibility increased from 2.3% in the protected forest to 5.6% in the non-protected forest.

Although anthropogenic triggering factors obtained less than one percent of importance in the protected forest, their importance was recorded at roughly 17% in the non-protected forest. The features of forest fragmentation (Figure 8) ranked the highest among the anthropogenic factors, which resulted from forest conversion and road-network expansion for logging, rural usages, and transporting mine materials in the non-protected forest since the 1970s [82]. For example, the length of the rural roads have increased from 113 to 752 km between 1970 and 2016, and about 245 and 155 km of logging and mine roads were built before 2016, respectively. All the fragmentation metrics showed higher values in the non-protected forest in comparison to the protected forest (Figures 4 and 8). Moreover, the importance of logging and mining was 2.6% and 1.5% in the non-protected forest, respectively. The number of parcels for timber harvesting increased from 0 to 404 between 1970 and 2016; the area of mining plans also expanded to 12,520 ha in the non-protected forest prior to 2016.

Most previous studies have frequently pointed to the anthropogenic triggering factors such as distance to roads [51,53,78,105,130], road density [76,105,135], land-use/land-cover types [42,47,49,58,78,106], and land-use changes [3,136,137] for the mapping of landslide susceptibility.

However, the current study explicitly localized and classified the significant anthropogenic triggering factors depending on the human footprint including forest fragmentation, forest conversion, timber harvesting, and mining within the forests. The influences of building forest roads [15,18,21–23], logging [4,11–15], deforestation [2–8], forest fragmentation [5], and mining [17] on the occurrence, frequency, and distribution of landslides have been demonstrated in the forest areas. For example, Guns and Vanacker [7] highlighted that anthropogenic activities such as forest conversion increased the occurrence of small landslides and sediment deposition in tropical forests. Borga et al. [18] concluded that forest roads changed the stream flows and increased the susceptibility of the forest to shallow landslides on steep slopes. Guthrie [13] reported that the frequency and density of landslides have significantly increased, following timber harvesting in the forested watersheds.

Although a number of studies have reported geological features as the main causes of increasing landslide susceptibility [49,51,76,132], our analysis revealed that the importance of these variables was lower than the topographic, hydrologic, and natural triggering factors in both the protected and non-protected forests as well as lower than the importance of anthropogenic triggering factors in the non-protected forest. Distance to faults with a value of 4.6% was the top variable of the geological features in the non-protected forest. In addition, some studies reported the low importance of lithology [41,50,58,78], but the high importance of distance to faults [58] for mapping landslide susceptibility.

Moreover, forest type did not show considerable importance for landslide susceptibility mapping in both forests. With respect to the importance of forest loss and forest fragmentation in the non-protected forest, we can argue that forest dynamics are superior to the forest type in landslide susceptibility mapping. Soil variables showed neutral influence on landslide susceptibility [50] in both forests.

This study indicated that the influential conditioning and triggering factors that control the susceptibility of the protected and non-protected forests to landslides are di fferent. Likewise, some studies have verified the variety of landslide triggering factors for di fferent regions [48,63]. The triggering factors of landslides have regional di fferences and the types of data in di fferent study areas are not exactly the same.

**Figure 6.** The layers of the top influential factors that control landslide susceptibility in the protected forest in NE Iran: terrain ruggedness index (TRI) (**a**); slope (**b**); earthquake (EQ) (**c**); elevation (Elev.) (**d**); topographic wetness index (TWI) (**e**); and profile curvature (Profile Curv.) (**f**).

**Figure 7.** The layers of the top influential factors that control landslide susceptibility in the non-protected forest in NE Iran: river density (**a**); aspect (**b**); elevation (Elev.) (**c**); rainfall (**d**); sediment transport index (STI) (**e**); and flood (**f**).

**Figure 8.** The top influential anthropogenic triggering factors for mapping landslide susceptibility in the non-protected forest, NE Iran: the edge density and mean shape index indicating the forest fragmentation induced by road-network expansion and forest conversion (**<sup>a</sup>**,**<sup>c</sup>**); and the aggregation of logging volumes (**b**) from 1970 to 2016.

The integration of random forest and an object-based approach yielded a good performance for mapping the landslide susceptibility in our forest regions. However, the comparison of the integration of other machine learning algorithms with the object-based approach needs to be considered to improve the best method to map the landslide susceptibility in the forest regions. Furthermore, this research used multiple conditioning and triggering factors to assess the susceptibility of forest areas to landslides. However, other factors may trigger landslide hazards such as ground water flow [138] in forest regions that need to be explored in the upcoming studies.
