Predictive modeling of bedrock outcrops and associated shallow soil in upland glaciated landscapes

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Identifying the areal extent of bedrock outcrops and shallow soils has important implications for understanding spatial patterns in vegetation composition and productivity, stream chemistry gradients, and hydrologic and soil properties of landscapes. Manual methods of delineating bedrock outcrops and associated shallow soils are still commonly employed, but they are expensive to implement over broad areas and often limited by representation of polygon units. Few studies have automated the delineation of bedrock outcrops. These focused on delineation approaches in landscapes with rapidly eroding hillslopes and sparse vegetation. The objectives of this study were to assess the accuracy of visually interpreting high-resolution relief maps for locating bedrock outcrops and associated shallow soil (BOSS) <50 cm deep in a heavily forested landscape, to use visually interpreted point locations to train predictive models, and to compare predictions with manually delineated polygons in upland glaciated landscapes. Visual interpretation of Lidar-derived 1 m shaded relief maps at Hubbard Brook Experimental Forest (HBEF), USA resulted in a 79% accuracy of interpreting deep soil locations and 84% accuracy in distinguishing BOSS. We explored four probabilistic classifications of BOSS using multiple Lidar-derived topographic metrics as predictive variables. All four methods identified similar predictors for BOSS, including slope and topographic position indices with a 15, 100 and 200 m circular analysis window, respectively. Although all classifiers yielded similar results with little difference in interpretation, a generalized additive model had slightly higher accuracy predicting BOSS presence, yielding 85% overall accuracy using independent validation data across the primary study area, and 86% overall accuracy in a second validation area.