Unveiling spatial pattern in slope units-based landslides using a data-driven GAMM and InSAR techniques.

Spatial slope deformation pattern is crucial for landslide early warning and management. Quantitative modelling of the effects of multiple driving factors on the landslide spatial pattern has not yet been fully explored. To address this, this study utilizes data-driven models and Interferometric Synthetic Aperture Radar (InSAR) techniques to unveil the spatial pattern of landslides based on SUs (slope units). Specifically, we propose a methodology that establishes a correlation between InSAR deformation and topographic/meteorological factors by constructing a Generalized Additive Mixed Model (GAMM). Using this method, we analysed ALOS-2 ascending SAR images from April 2020 to September 2021 and successfully obtained cumulative minimum deformation during the period. Through the GAMM, we linked the observed cumulative minimum deformation in each SU to various factors, including topographic and meteorological factors. In terms of accuracy, there is an excellent agreement between the observed and fitted deformation, with a Pearson Correlation Coefficient (PCC) of 0.83. In terms of interpretability, our method can well explain the contribution of each factor to landslide deformation. We conclude that using the data-driven GAMM and InSAR techniques can effectively reveal the spatial pattern of landslides based on SUs. Furthermore, based on the method proposed in this study, it is possible to predict landslide deformation in the future. [ABSTRACT FROM AUTHOR]