Vast parts of Arctic Siberia are underlain by ice-rich permafrost, which is exposed to different processes of degradation due to global warming. Thermal erosion as a key process for landscape degradation causes the recent reactivation and formation of new landforms like thermoerosional valleys and gullies. However, a statistical assessment about the decisive factors and the locations most susceptible for this phenomenon is still missing. This study investigates the influence of different geomorphological parameters on the occurrence of recently observed thermal erosion using a GIS-based approach and statistical modeling by logistic regression. The study site is located on an island within the Arctic Lena River Delta and is mainly composed of ice- and organic-rich deposits. Field surveys and mapping of high-resolution remotely sensed data revealed that thermal erosion occurs predominantly i) on very steep slopes along the margins of the island, ii) in the upper reaches of deeply incised thermo-erosional valleys and iii) in thermo-erosional gullies. Several potentially influencing environmental parameters were derived by a combination of high-resolution satellite imagery and 2 m-DEM. The full set of parameters was reduced stepwise within the logistic regression model. This approach allows the selection of a parsimonious model, i.e. a best-fit model using as few variables as possible. The parameters Contribution of warm open surface water, Relief ratio, Direct solar radiation and Snow accumulation turned out be the decisive factors for thermal erosion. Uncertainties in the model due to sampling and model selection were evaluated statistically and spatially through the generation of 100 models. Receiver Operating Characteristics (ROCs) were used to validate the spatial predictive capability of each model run. The consensus map as the median of all susceptibility models represents the final susceptibility map. The agreement between mapped and predicted erosion is generally very high within the study site, confirmed by an Area under the ROC curve (AUC) of 0.957 for the consensus map. The variability of predicted erosion probabilities between the single models is about four percentage points per cell within the study site and thus, very low. Mismatches between observed and predicted erosion could be attributed to the generation of the explanatory environmental parameters and the modeling approach. Model results seem promising for the spatial prediction of susceptible sites for thermal erosion, but require external validation on other sites with comparable environmental conditions.