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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.

The rate and pattern of snow melt control both hydrological and ecological factors. Snow cover maps derived by different satellite sensors can differ considerably from surface observations due to different spatial resolutions and snow cover classification algorithms. This article addresses issues related to the validation of three daily snow cover products over Canada: MODIS and GOES+SSM/I snow maps derived at 500m and 4km resolution, respectively for 2001, and VEGETATION snow maps derived at 1km resolution for 2000. The validation is based on surface snow depth observations from almost two thousand meteorological stations across Canada. The analysis is performed on a daily basis for the period of six months (January-June). A land cover map of Canada at 1km resolution is used to relate the differences within the validation to land cover types. The SPOT product shows an average agreement of 83% and considerably high percentage of omission error. The MODIS and GOES+SSM/I products have similar percentage average agreements, 93% and 92%, respectively. Generally, less agreement is seen within the evergreen forest cover types, earlier in the snow season and during snow melt. The MODIS product exhibits a high percentage commission error for evergreen forests. The GOES+SSM/I product shows relatively similar ratios of omission and commission errors for all land cover types except deciduous forest.