Hydro-Topographic Controls on the Influence Distance of Forest Road Networks on Shallow Landslides under Heavy Rainfall

Youngeun Kang1*, Ji-Hyeok Park2, Suhyun Lee1, Seonghun Jeong3,4

(1Department of Forest Resource, Gyeongsang National University,

2Kasuya Research Forest, Kyushu University,

3Department of Forest Environmental Resources, Gyeongsang National University,

4Institute of Agriculture and Life Science, Gyeongsang National University)

Abstract:  Forest road networks are critical infrastructure for forest management and disaster mitigation. However, poorly maintained road sections can destabilize slopes during heavy rainfall by impairing drainage and disturbing hillslope materials. Conventional fixed-distance assessments may not capture such condition-dependent road effects. This study examined four counties of Gyeongsangnam-do province, the Republic of Korea (Geochang, Sacheon, Hamyang, and Hapcheon) where shallow landslides were concentrated following localized heavy rainfall in July 2025. A Bayesian logistic regression model was applied to assess how shallow landslide occurrence varies with distance to roads while accounting for environmental covariates (i.e., rainfall, hydro-topographic, and vegetation factors). Distance to forest road was treated as continuous variable and scaled per 106.6 m increase. Results demonstrated that proximity to forest road was significantly related to shallow landslide occurrence (OR = 0.362 per 106.6 m increase), indicating higher odds of shallow landslides closer to roads. In addition, effect of road distance varied across topo-climatic conditions: the near-road effect strengthened most with increasing slope angle, followed by rainfall, and tangential curvature. This can make the near-road increase in landslide odds more pronounced in steeper and more rainfall-intensive environments. Our findings can inform the prioritization of shallow landslide-vulnerable zones and targeting forest road maintenance based on site-specific hydro-topographic conditions.

Keywords: Forest road network, Landslide susceptibility, Bayesian logistic regression, Environmental factors, Gyeongsangnam-do

Acknowledgement: This study was carried out with the support of ´R&D Program for Forest Science Technology (Project No. "RS-2025-02213493") provided by Korea Forest Service(Korea Forestry Promotion Institute).