Multi-scale framework for landslide evolution

Kun Fang

doi:10.1016/j.jrmge.2026.09.001

Multi-scale framework for landslide evolution

2026-09-07

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DOI：10.1016/j.jrmge.2026.09.001

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Landslide classification systems have evolved considerably over the past decades, encompassing not only material and movement types but also activity states, velocity, triggers, and temporal descriptors. However, a standardized, machine-readable encoding structure that systematically integrates spatial scales, temporal evolutionary stages, and quantitative process attributes within a unified matrix representation has not yet been established. This study proposes a complementary encoding framework that builds upon established classification systems by organizing these recognized dimensions as multi-stage, multi-scale processes that progress from preparatory deformation through catastrophic failure to post-failure adjustment and possible reactivation. We first clarify the physical meaning of “landslide evolution” and systematically dissect spatial (sample-model-slope-region) and temporal (event-seasonal-geological) scales that control observed behavior. We then critically review classical movement-material taxonomies and complementary schemes based on magnitude, velocity, triggers, and geomorphic context, highlighting their inability to represent cross-scale coupling and life-cycle transitions. Building on advances in laboratory testing, physical modeling, field investigation, and multi-source monitoring (including InSAR and LiDAR), we propose a multi-scale landslide evolution (MLE) framework that encodes landslide histories in a matrix-vector form along a unified time-stage plane. This framework couples internal conditioning factors, external triggers, kinematics, runout, and human impacts, and is designed to remain compatible with existing classifications while extending them into an explicitly evolutionary domain. We demonstrate the framework’s operability through diverse case studies, including the high-mobility Baige landslide and the recurrent Outang paleo-landslide, demonstrating its capacity to encode both short-term catastrophic failures and polycyclic, geological-scale reactivations. This unified approach not only provides a structured means to bridge the paradox of decoupled spatiotemporal scales but also provides a machine-readable foundation for future data-driven modeling and artificial intelligence applications in geohazard risk governance.

Keywords:

landslide evolution

multi-scale classification

landslide life cycle

monitoring and InSAR

landslide hazard and risk

Multi-scale framework for landslide evolution

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