**1. Introduction**

Despite the importance of physical-resisting forces of forests to the propensity for landslide occurrence, human and non-human variables can accelerate the spatial probability of landslide occurrence through slope stability in a given area [1]. However, the holistic understanding of the importance of conditioning and triggering factors that control the susceptibility of forest areas to

landslides has not been appropriately customized yet. Anthropogenic triggering factors may reduce the resisting forces of forests to landslides by deforestation [2–10], logging [4,11–16], and mining [17], or may increase the susceptibility of forest areas to landslides by the fragmentation induced by infrastructure development such as road-network expansion [4,5,16,18–23] with the consequences of mass movements and slope failures. Likewise, natural triggering factors such as earthquake [24–27], rainfall [28–32], and flooding [33,34] may increase the propensity for occurring landslides by reducing the resisting forces in forest areas. Meanwhile, a comparison between the importance of conditioning and triggering factors in protected and non-protected forests may reveal the effects of anthropogenic activities on the susceptibility of disturbed forests to landslides.

Various methods have been developed for assessing landslide susceptibility with respect to knowledge-driven approaches, physical and statistical models, and machine learning algorithms [35,36]. Knowledge-driven approaches are subjective, and determines the influence of a variable through an expert's opinion [37] that may affect the real expectation of landslide susceptibility [38]. Moreover, the physical models are appropriate for assessing the susceptibility of small areas to landslide in the presence of detailed geological, pedological, hydrological, and geomorphological information [39,40]. The statistical models are dependent on the input data characteristics, where any uncertainty in data may lead to a huge error in mapping the landslide susceptibility [40,41]. In contrast, machine learning applies algorithms for modeling through learning data, where their high ability in the estimation of a model has made them more popular for analyzing the landslide susceptibility at a regional scale [42] such as artificial neural networks (ANN) [43], decision trees (DT) [44], Bayesian network (BN) and naïve Bayes [45], support vector machines (SVM) [46], and random forest (RF) [42,47–53]. RF, as an ensemble machine learning algorithm, is known for its ability in handling both parametric and non-parametric variables, working with big data without any selection, reduction, or preprocessing, handling missing values automatically, avoiding the risk of over-fitting, self-testing using "out of bag" data, and yielding high satisfactory accuracy in modeling [54]. Furthermore, RF has achieved robust performance for the mapping of landslide susceptibility in comparison with the conventional statistical models such as weights-of-evidence [55], logistic regression [41,55,56], and generalized additive models (GAM) [55]; or even other machine learning techniques such as boosted regression trees [42,57], regression tree [58], ANN [56,59], and SVM [55,60]. For example, Vorpahl et al. [57] concluded that RF indicated a higher performance than statistical and other machine learning methods such as GAM, generalized linear models (GLM), the maximum entropy method (MEM), classification tree analysis, multivariate adaptive regression splines, and ANN for analyzing influential variables that control natural landslides in a montane tropical forest, South Ecuador. Likewise, Dou et al. [61] reported that RF performed higher overall efficiency than DT for mapping rainfall-induced landslide susceptibility at a regional scale in Japan.

Recent studies have criticized the current derived landslide susceptibility mapping in terms of applying similar geo-environmental factors over different regions and times [36,62,63], considering fixed effects of a variable [2,63] such as distance to roads [63] and land-use/land-cover derived from the current available images without assessing their dynamic changes [2,62]. However, the current land-cover may not reflect its actual status during the time a landslide occurs in a specific area [2,64], and human-induced triggering factors such as logging and road construction may reduce slope stability over time. For example, Wolter et al. [12] showed that landslide events were observed in forests that had been opened by logging activities or fragmented by road construction in the Chilliwack River Valley, British Columbia, and reported that other geo-environmental variables did not show significant effects on the slope instability.

The Hyrcanian ecoregion has been degraded by different human and natural triggering factors such as forest and rangeland conversion [65,66], forest fires [67,68], flooding [69], landslides [70–72], soil erosion [73], and climate hazards [74,75] in northeast (NE) Iran. Several studies have accomplished mapping landslide susceptibility in Hyrcanian forests, but have mostly focused on applying models

and using common conditioning factors [51,76–79] with less attention given to the temporal dynamics of natural and anthropogenic triggering factors.

Less is known about the influence of di fferent conditioning factors such as natural and particularly anthropogenic triggering factors for mapping landslide susceptibility in forest areas. For this purpose, a holistic approach needs to be developed to model the actual importance of conditioning and triggering factors, which control the susceptibility of protected and disturbed forests to landslides. Therefore, this research was designed to evaluate the performance of applying object-based random forest for mapping landslide susceptibility in a protected forest and a non-protected forest in NE Iran. Furthermore, we compared the importance of influential variables that control the susceptibility of these forests to landslides. Specifically, we aimed to find appropriate answers to the following questions: (i) Does object-based random forest show a satisfactory performance for modeling landslide susceptibility in protected and non-protected forests? (ii) Which conditioning and triggering factors are at the top in modeling the susceptibility of these two forest areas to landslides? and (iii) How do natural and anthropogenic triggering factors a ffect the susceptibility of protected and non-protected forests to landslides?

#### **2. Materials and Methods**

#### *2.1. Description of Study Area*

We selected a protected and a non-protected forest for analyzing landslide susceptibility in the eastern part of the Hyrcanian forests, southeast Caspian Sea, Iran. The largest Iranian National Park, Golestan, is assigned as a protected forest (approx. 500 out of 920 km2) (Figure 1a). The protection of this park has taken place since 1957 and was registered by UNESCO as a biosphere reserve in 1976 as it contains fifty percent of the total of Iran's mammal species and above 1400 plant species registered by UNESCO [80,81]. A non-protected forest was selected in the neighborhood of this protected area (approx. 1500 km2). This area has been a ffected by a variety of human activities such as deforestation [65], logging, mining, and road construction [82]. The annual rate of deforestation was reported at about 0.85% [65], the number of forest logging parcels increased to 400 (34,000 ha), the number of mines reached 12 plans (12,520 ha), and the length of roads increased from 120 to 1257 km between 1966 and 2016 (Figure 1b). The average elevation, slope, and rainfall of the two studied forests are about 1280 m, 30◦ and 600 mm, with the predominant forest type of *Quercus castaneafolia-Carpinus betulus*.

However, dominated lithology types of the protected and non-protected forests are Jl (limestone, oolitic-porous dolomitic limestone; Lar formation; Mesozoic era; Jurassic period) and Js (upper: shale, marl, sandstone, nodular Ls, Ammonite, Belemnite, and lower: shale, sandstone with thin-bedded limestone; Shemshak formation; Mesozoic era; Jurassic period), respectively.
