Rock-fall occurrences were determined by a drone in villagesĭuring field studies. Tributaries of Çiftehan stream connected to the River Seyhan. The study area is a plateau drained by four streams, the Ilhan, Eskiköy, Kolu and Koçak. Is located in the transportational middle slope and colluvial foot slope. Preliminary rockfall risk analyses it reveals that the potential high risk The methodology introduced in this paper has possibility to be used for Landform reflect that geomorphometry influences the volume statistics of Different values of the scaling exponents in each
It shows negative power laws with exponents 0.58, 0.73, 0.68, andĠ.64 for fall face, transportational middle slope, colluvial foot slope and lower The probability density of rockfall volume in four generic landforms, i.e.,įall face, transportational middle slope, colluvial foot slope and lower Cumulative probability density was adopted to estimate The volume of the rockfall deposits and number of events associated withĭifferent landforms. Landforms over DTMs and derived a power-law statistical relationship between Interfluve, convex creep slope, fall face, transportational middle slope,Ĭolluvial foot slope, lower slope and channel bed. Was applied to classify the generic landforms into seven classes: Stream power index, and shape complexity index whereas layers produced fromĭTMs and rockfall modeling were velocity and energy. Several data layers produced solely from DTMs were slope, plan curvature, Rockfall deposits were the basis of landform classification analysis. Digital terrain models (DTMs) and a geomorphological inventory of 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.This paper presents an automated landform classification in a rockfall-proneĪrea. Different values of the scaling exponents in each landform reflect that geomorphometry influences the volume statistics of rockfall. 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. 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. 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.
Unsupervised fuzzy 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. 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.
Digital terrain models (DTMs) and a geomorphological inventory of rockfall deposits were the basis of landform classification analysis. This paper presents an automated landform classification in a rockfall-prone area.
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