Effect of pixel size on cartographic representation of shallow and deep-seated landslide, and its collateral effects on the forecasting of landslides by SINMAP and Multiple Logistic Regression landslide models

Abstract

It is important to evaluate the influence of pixel resolution on cartographic representations of landslides to assess models for detection of unstable hillslopes. However, little work has been done on the effect of the resolution of Digital Elevation Models on landslide modeling. This paper uses artificial landslides to evaluate how pixel size affects the cartographic representation of shallow and deep-seated landslides. Published landslide sizes and manual modification of contour lines are used to produce artificial depletion landslide topography. Two landslide models are used: Stability Index MAPping (SINMAP) and Multiple Logistic Regression (MLR). The two models are embedded in a GIS system to facilitate the analysis. The evaluation is conducted at 1 m, 5 m, 10 m, and 30 m pixel resolutions. As the pixel size increases, the landslide loses cartographic representation. The result is a biased model prediction. In tests on real topography in landslide terrain, MLR predictions match existing landslides better than do SINMAP predictions, if the MLR model has enough pixels to obtain reliable statistics. SINMAP more consistently produces a similar susceptibility map over a range of pixel resolutions. In general, MLR over-predicts while SINMAP under-predicts landslides as pixels coarsen.