Geospatial Insights Into Landslide Relics in the Northern Half of the Taihang Mountains: Topography, Geology and Beyond

ABSTRACT Landslides are among the most widespread and recurrent natural hazards, posing significant threats due to their sudden onset and unpredictable nature. Understanding their spatial distribution patterns is crucial for improving disaster prediction and mitigation strategies. This study investigates the spatial distribution of 8349 landslide relics in the northern half of the Taihang Mountains, China, by analysing their relationship with eight influencing factors: elevation, slope, aspect, curvature, distance from rivers, distance from faults, lithology and land cover. The analysis is based on four key metrics: Landslide Number, Landslide Area, Landslide Number Density (LND) and Landslide Area Percentage (LAP). Results reveal that 66.06% of landslides (5515 events) and 40.79% of the total landslide area (61.84 km 2 ) occur within the elevation range of 800–1300 m. The highest LND (1.04 km −2 ) and LAP (2.65%) appear in areas with curvature less than −10, indicating a preference for concave terrain. Slopes of 15°–20° host the most landslides (1713 events), whereas areas with slopes > 45° show the densest distribution (LAP = 1.44%). North‐ and northwest‐facing slopes exhibit the highest susceptibility, with LNDs of 0.51 and 0.43 km −2 , respectively. Landslides are concentrated within 0–4 km of rivers (81.35% of total number), and proximity to faults strongly influences landslide size and density. The Quaternary loess and Neoproterozoic clay strata are particularly prone to landslides, with the Neoproterozoic displaying the highest LND (5.59 km −2 ). In terms of land cover, grasslands account for 48.4% of landslide occurrences, whereas forests contain over half of the total landslide area. Barren lands, though limited in extent, show the highest LND (0.95 km −2 ) and LAP (1.6%). These findings contribute to a deeper understanding of landslide susceptibility in the region and provide a scientific basis for future landslide risk assessments. Additionally, the results offer valuable insights for local governments in formulating targeted landslide prevention and mitigation strategies.