This paper presents an automated landform classification in a rockfall-prone area. Digital terrain models (DTMs) and a geomorphological inventory of rockfall deposits were the basis of landform classification analysis. Several data layers produced solely from DTMs were slope, plan curvature, stream power index, and shape complexity index; whereas layers produced from DTMs and rockfall modeling were velocity and energy. Unsupervised fuzzy k means was applied to classify the generic landforms into seven classes: interfluve, convex creep slope, fall face, transportational middle slope, colluvial foot slope, lower slope and channel bed. We draped the generic landforms over DTMs and derived a power-law statistical relationship between the volume of the rockfall deposits and number of events associated with different landforms. Cumulative probability density was adopted to estimate the probability density of rockfall volume in four generic landforms, i.e., fall face, transportational middle slope, colluvial foot slope and lower slope. It shows negative power laws with exponents 0.58, 0.73, 0.68, and 0.64 for fall face, transportational middle slope, colluvial foot slope and lower slope, respectively. Different values of the scaling exponents in each landform reflect that geomorphometry influences the volume statistics of rockfall. The methodology introduced in this paper has possibility to be used for preliminary rockfall risk analyses; it reveals that the potential high risk is located in the transportational middle slope and colluvial foot slope.
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