When Time Prevails: The Perils of Overlooking Temporal Landscape Evolution in Landslide Susceptibility Predictions - Jinping Liu; Panxing He; Jianhua Xiao; Qingfeng Hu; Yanqun Ren; Aiding Kornejady; Huiran Gao (Remote Sensing)
This study highlights the importance of incorporating temporal landscape dynamics in landslide susceptibility assessments (LSAs). Two models are compared: one integrates multi-temporal data, while the other relies solely on present conditions. Artificial Digital Elevation Models (ADEMs) for 1960, 1980, 2000, and 2020 were generated using a landscape manipulation tool to simulate phases of land degradation and rehabilitation, thereby enabling the assessment of susceptibility over time. Landslide occurrences were simulated to increase over time—primarily as a result of anthropogenic changes, such as deforestation and land use alterations—with partial stabilization following conservation efforts. Both models achieved identical AUC values of 0.97, but the blind model misclassified stable areas and missed historically unstable regions. While conventional performance metrics such as ROC curves provide insights into model accuracy, they fail to detect misclassifications arising from temporal landscape changes, leading to overestimation in some areas and underestimation in others, especially in evolving environments. This study demonstrates that neglecting temporal landscape evolution leads to flawed susceptibility maps, potentially misguiding hazard mitigation efforts. To improve LSA accuracy, the study advocates for integrating multi-temporal thematic maps and adopting performance metrics that assess temporal robustness. It emphasizes the need for a shift from a static-to-hazard paradigm to a temporally evolved susceptibility-to-hazard framework for more accurate hazard and risk predictions.