Twinning of the e-plane is the dominant crystal -plastic deformation mechanism in calcite deformed below about 400 8C. Calcite in a twin domain has a different crystallographic orientation from the host calcite grain. So-called thin twins appear as thin black lines when viewed parallel to the twin plane at 200-320 £ magnification under a petrographic microscope. Thick twins viewed in the same way have a microscopically visible width of twinned material between black lines. Calcite e-twin width and morphology has been correlated with temperature of deformation in naturally deformed coarse-grained calcite. In this paper, we present a compilation and analysis of data from limestones of the frontal Alps (France and Switzerland) and the Appalachian Valley and Ridge and Plateau provinces (eastern United States) to document this temperature dependence. Mean calcite twin width correlates directly with temperature of deformation such that thin twins dominate below 170 8C and thick twins dominate above 200 8C. Above 250 8C dynamic recrystallization is an important deformation mechanism in calcite. Mean twin intensity (twin planes/mm) correlates negatively with temperature, and a cross plot of twin intensity with twin width can yield information about both strain and temperature of deformation. These relationships provide a deformation geothermometer for rocks that might otherwise yield little or no paleotemperature data.

The purpose of this study was to detect shallow landslides using hillshade maps derived from light detection and ranging (LiDAR)-based digital elevation model (DEM) derivatives. The landslide susceptibility mapping used an artificial neural network (ANN) approach and backpropagation method that was tested in the northern portion of the Cuyahoga Valley National Park (CVNP) located in northeast Ohio. The relationship between landslides and predictor attributes, which describe landform classes using slope, profile and plan curvatures, upslope drainage area, annual solar radiation, and wetness index, was extracted from LiDAR-based DEM using geographic information system (GIS). The approach presented in this paper required a training study area for the development of the susceptibility model and a validation study area to test the model. The results from the validation showed that within the very high susceptibility class, a total of 42.6 % of known landslides that were associated with 1.56 % of total area were correctly predicted. In contrast, the very low susceptibility class that represented 82.68 % of the total area was associated with 1.20 % of known landslides. The results suggest that the majority of the known landslides occur within a small portion of the study area, consistent with field investigation and other studies. Sample probabilistic maps of landslide susceptibility potential and other products from this approach are summarized and presented for visualization to help park officials in effective management and planning.

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