1. Introduction
Landslide, the most serious and common geological disaster, causes severe casualties and economic loss yearly [
1,
2]. To mitigate the risk of landslide hazards and guide disaster response, knowing where landslides may happen is indispensable. Toward this end, efforts have been made to develop landslide prediction models. Following landslide mechanics, these models often used natural factors, such as geology, topography, hydrology and rainfall, as predictors [
3,
4,
5]. Nonetheless, landslide is a complicated phenomenon, and adopting measures to predict landslide is difficult due to the heterogeneous environments between regions. In forest-covered mountainous areas, alternations of forest type are expected to insert a substantial impact on landslide occurrence, which, however, has not been well investigated.
One of the primary changes in forest type during recent years is the extensive expansion of plantations in particular economic forests. China has experienced a rapid transition from natural forests to forest plantations, with the area of plantations expanding from 20.57 million hectares in 2000 to 79.54 million hectares in 2019 [
6]. The positive roles of natural forests in preventing landslides are well known. For example, tree roots reinforce soil layers and form buttresses against soil movement. However, the extent of protection varies with forest type. In plantation forests, intensive management is routinely applied, leading to changes in conditions, especially understory conditions, thereby reducing their roles in mitigating landslides. While the importance of land-use changes on landslide occurrence has been recognized [
5,
7,
8,
9], previous studies [
10,
11,
12] often grouped all forest types together as forestland without accounting for the differences between them. Overall, our understanding of how landslide occurrence varies with forest type remains far from complete.
Lin’an, the target area of this study, is rich in natural forests composed of conifer, hardwood, moso bamboo, and shrub. Driven by an increasing desire for higher economic returns, large-scale forest-type changes, primarily the conversion of natural forests to economic plantations (for example, bamboo (
Phyllostachys praecox) and Chinese hickory (
Carya cathayensis Sarg) plantations), have become normal phenomena [
13,
14,
15]. In the past three decades, hickory plantations have continuously increased and reached an area of 28,700 hectares in Lin’an [
16]. Both bamboo and Chinese hickory plantations are pure stands (i.e., single tree species) with a relatively simple canopy structure; these plantations are intensively managed by frequently applying herbicides to control understory vegetation, as well as fertilizers and insecticides to improve productivity [
17,
18,
19]. This intensive management likely exacerbates soil erosion from rainfall-induced runoff and causes slope instability and other landslide triggers, particularly in hilly regions [
17,
20]. There are differences between hickory and bamboo plantations, with the former having a lower density and greater distribution on steeper slopes [
16]. Evidently, conversions will continue due to their high economic benefits. People have expressed great concerns that intensively managed hickory and bamboo plantations may increase landslides, but information on this phenomenon is limited.
Landslide susceptibility (LS) mapping plays an important role as it presents the spatial distribution and occurrence probability of landslides using key landslide conditioning factors (LCFs) [
21]. The effectiveness of LS mapping always depends on model accuracy. Over the years, various models, e.g., physical models [
22], heuristic models [
23], and statistical models [
24], have been developed. Among the statistical methods, logistical regression is especially attractive due to its simple structure and strong interpretability [
3,
25]. More recently, machine learning techniques have become popular in this field of study [
5,
26] due to their relative objectiveness [
27]. Decision trees [
11] and random forests [
11,
28] are also widely used in modeling landslides. Decision trees are used to classify landslides by constructing a tree-structured classifier feasible for visualizing the classification of landslides [
29], while random forests combine multiple decision trees to improve model performance, accompanied by a loss in model interpretability [
30]. However, no consensus currently exists on which methods are most suitable for modeling LS, as the choice of method is often dependent on data. At present, logistical regression, decision trees, and random forests paired with GIS technologies have been widely used in modeling landslides [
3,
11,
28,
31,
32,
33].
In this case study, we modeled landslide occurrences in the forest-covered areas of Lin’an. Specifically, the objectives of this study were: (1) to compare the efficiency in modeling landslides among the models including logistical regression, decision tree, and random forest; (2) to identify key LCFs, particularly factors related to forest cover; and (3) to construct LS maps. This research represents one of the few studies comparing landslide occurrences between different forest types and provides important information for decision-making in forest planning and management in landslide-prone areas. These results could be used to develop warning systems and thereby help governments and farmers take measures for landslide prevention and mitigation.
4. Discussion
Landslide susceptibility mapping is needed, but remains a methodological challenge, since landslides are complex events involving many correlated factors. The LR method has been widely used in modeling LS [
52,
53]. This method, however, requires assumptions such as little multicollinearity and the linearity of predictors and lacks the ability to account for complex interactions among predictors. In this study, the limitations of this method demonstrated that (1) the model was inferior, albeit not substantially, to the DT and RF models in accuracy (
Table 3), and (2) important predictors such as understory vegetation type and height were excluded from the model, likely due to their correlations with forest type and NDVIs (
Appendix C). Nonetheless, the LR model generally performed well, with all accuracy measures being ≥0.70 (
Table 3). Furthermore, the LR model allowed us to predict the risk probability for a location using model parameter estimates (
Figure 2), which is attractive to landowners.
Machine-learning modeling techniques fit with the best-possible classification based on data, rather than a predefined relationship as in the LR method. Thus, as expected, both the DT and RF models outperformed the LR model in accuracy (
Table 3). While the DT model provided an interactive visual classification tree structure feasible for decision-making, this method remains costly in terms of the necessary sample size, and thus the split under this method is likely more accurate for the root node than the bottom leaf. Additionally, the one-tree based DT model can be unstable because small variations in data may result in the generation of a completely new decision tree. Thus, RF, which is based on multiple trees, often outperforms DT in modeling LS [
11,
26]. Nonetheless, in this study, the DT model achieved high accuracy comparable to that of the RF model (
Table 3), partly due to the use of imbalanced data. Indeed, for extremely imbalanced datasets, DT can outperform RF [
28]. Recently, more complex models such as stacking have been proposed to further improve accuracy. These approaches, however, are accompanied by reduced model interpretability [
54,
55,
56].
While the examined methods varied in their accuracy measures and interpretation, the key causal factors identified by each model were similar, including FT, NDVIs, DTRD, aspect, and MDR. Information on understory vegetation was found to be important in the machine-learning models, but not in the LR model.
Knowing the differences in landslide susceptibility among forest types is of great value in planning forest management to mitigate landslides. Natural forests, in general, can mitigate landslides [
57,
58]. However, the extent of protection varies with FT. Among the four nature FTs, the moso bamboo forests under the LR and RF models and shrub forests under the LR model presented a much higher risk than the conifer or hardwood forests (
Figure 2 and
Figure 5). Both moso bamboo and shrub forests have shallow root systems (distributed over a soil depth of 0–30 cm), which are conducive to landslides. Additionally, moso bamboo forests often feature high stand density (i.e., many trees per hectare), preventing the growth of understory vegetation and deteriorating the conditions necessary for resisting landslides. A higher risk is expected in intensively managed plantations. In this study, hickory plantations were confirmed to have a much higher probability of landslide occurrence than any other FTs (
Figure 2 and
Figure 5). Using the LR model, hickory plantations were predicted to have a probability as high as 0.60, which was twice as high as that of shrub forests (0.30), the type with the second-highest risk (
Figure 2). The high landslide risk in hickory plantations was further supported by the DT model, which showed that data were divided into hickory plantations and other types at the root node (
Figure 3). Clearly, forest management activities such as removing understory vegetation in hickory plantations could greatly increase landslide occurrence, although other characteristics, such as often planting hickory in areas with high elevations (482.93 m) and steep slopes (on average, 24.3°), might also contribute to this high probability. Nevertheless, bamboo plantations that were managed intensively as hickory plantations did not show significantly increased landslide probability over the natural FTs as expected. Under the LR model, bamboo plantations had a probability about 0.10, which was higher only than the probability values of conifer and hardwood forests (
Figure 2). Similar results were also confirmed by the RF model (
Figure 5). Bamboo plantations were mostly located in the northeast and southeast areas of Lin’an (
Appendix B), where the altitude and rainfall are relatively low and the slope (on average 15.7°) is gentle, thereby reducing landslide occurrence in the plantations. Therefore, conversion from natural forests to forest plantations paired with intensive management could increase landslide occurrences substantially. This effect, however, is complicated and may be compromised by other factors such as low rainfall and gentle slope. In recent years, researchers have identified land-use changes as important aggravators of landslide occurrences [
52,
59]. Clearly, this topic deserves further investigation.
The NDVI, which reflects the overall density of vegetation on the ground and the crown position, is often used as a land-cover variable in modeling landslides. As expected, the NDVIs was correlated with other variables of forest cover (r = 0.36, 0.28, and 0.28 with FT, UVT, and UVH, respectively;
Appendix C). For hickory plantations, an NDVIs of 0.60 or less reflected high landslide susceptibility, but further increasing the NDVIs quickly reduced this probability (
Figure 2 and
Figure 5). Our DT model (
Figure 3) suggested that NDVIs is especially important in predicting landslides in FTs other than hickory plantations; any forest types with NDVIs > 0.77 are unlikely to experience landslides. In parallel with our results, DTRD as a human disturbance indicator has been widely used as a causal factor to predict landslides [
38,
60]. The closer a location is to a road, the higher that area’s susceptibility to landslides. However, this trend disappeared when the location was 500 m or further from a road (
Figure 2 and
Figure 5). Although the threshold value varied, the importance of DTRD in classifying landslides was significant under all forest types (
Figure 3). In the LR model, aspect, a topographic factor, was selected as a predictor. Slopes facing east (southeast, east, and northeast) were also predicted to have higher probabilities than others in previous studies [
61]. This result might be linked to the strong summer monsoon and typhoon in the region, resulting in windward-slope rainfall contributing to landslide occurrence. Overall, aspect was not ranked as a top predictor in the LR and RF models and was not selected in the DT model.
The incidence of landslides is often inversely related to understory vegetation conditions. Denudation, such as the removal of vegetation from the hickory plantations in this study, influenced the rate of erosion from rainfall-induced runoff and generated landslides, particularly in hilly regions [
62]. Increasing UVH was accompanied by a reduction in landslide occurrence when UVH was ≤1.2 m. The trend disappeared as UVH increased further (
Figure 5), and this impact was stronger than other types in hickory plantations (
Figure 3). Unsurprisingly, grass presented a much higher probability, but our results suggest that using a different shrub type or a mix of shrub and grass could greatly reduce landslide occurrence (
Figure 5). Few studies have investigated the effects of understory vegetation on landslide occurrence, mainly due to a lack of data. In areas where understory vegetation data are limited, the potential use of radar and lidar data to reflect understory vegetation should be further explored [
63,
64]. Overall, understory vegetation conditions are important factors and should be included when modeling landslide occurrence in forest-covered areas.
According to all model results, MDR was the main triggering factor in the study areas. Based on probability, if the MDR were about 130 mm or higher, landslides would become a large concern in these areas (
Figure 2,
Figure 3 and
Figure 5). The negative impact of rainfall would also become more obvious in the hickory plantations (
Figure 3). Not all hickory plantations presented a very high risk of landslides. Plantations located in northern and southern areas had a relatively lower risk (
Figure 6), which was accompanied by lighter rainfall.
The development processes of landslides are controlled by basic predisposing factors and are induced by heavy rainfall and excessive human activities. Other studies have found predictors such as slope, curvature, RA, and soil to be important contributing factors both theoretically and empirically [
65,
66,
67]. These factors were not selected as predictors in this study under either model. Nonetheless, the roles that these factors play in influencing landslide occurrence were not negligible. Likely, these roles are overshadowed by other key predictors, such as forest cover, in model development. Effects of these basic predisposing factors could be improved by increasing the resolution of the DEM data [
68].
The developed models were applied to map landslide susceptibility in the study areas. The percentages of areas predicted to have very high or high landslide susceptibility were comparable among the maps, ranging from 10.91% (RF) to 14.71% (LR). Particular attention should be paid to the western portion of the study area, particularly the northwestern area, where landslides occur most frequently and hickory plantations are common. In LS mapping, accurately predicting very high landslide susceptibility zones is especially important. In this regard, the map produced via RF is preferred since it offers higher map-based class-specific accuracy (pi) than the other two model-based maps for the very high category.
The results of this research have important implications for mitigating future landslides in the study areas. The high landslide occurrence in the hickory plantations represents a major concern. Maintaining the understory vegetation of hickory plantations is expected to reduce landslide risks, but increase difficulty in harvesting hickory nuts. Suggestions such as planting other understory economic species in hickory plantations [
34] have been proposed; these suggestions seek not only to increase economic benefits but also improve the understory vegetation conditions. Nonetheless, the actual benefits of such interventions remain uncertain and deserve further investigation. While these bamboo plantations are not a concern in terms of landslide occurrence, the intensive management applied to plantations prevents the growth of understory vegetation, a potential driver of landslide occurrence. Thus, selecting a density that balances shoot production and understory conditions to reduce landslides is important. Natural moso bamboo forests are also a concern in the region. Moso bamboo continues to spread into adjacent natural forests and may eventually dominate the forests by outcompeting conifer and hardwood trees. From 2000 to 2020, moso bamboo forests expanded from 203 to 248 km
2, representing an increase of 22% [
16]. To reduce the risks of landslides in moso bamboo areas, forest management activities must be applied to limit bamboo expansion.