Technology is changing the way scientists see the landscape, with new data providing accuracy never before available. Scientists in many fields are looking for concise, effective methods to predict vegetation by classifying landscapes based on digital elevation model-derived terrain metrics such as slope, aspect, curvature and topographic indices. With newly available lidar-derived elevation data, the accuracy and resolution of is greatly improved but scale is so dramatically different that new approaches should be used to classify topographic features. By identifying lidar-derived patterns of curvature, major gradients impacting vegetation in a catchment including water accumulation, soil characteristics and nutrient availability can be summarized into compact metrics. We applied this approach in the Ridge and Valley region of central Pennsylvania, in the broader basin encompassing the Shale Hills Critical Zone Observatory. The forest vegetation communities here have recolonized after disturbances from multiple stressors over the last century, including deforestation, charcoal burning, gypsy moth defoliation, atmospheric deposition, forest management, and wind-throw events. By classifying the watershed into four dominant recurring landforms using patterns of lidar-derived curvature data, dominant vegetation communities and forest structures were successfully predicted and confirmed using multivariate statistical methods. These methods are useful for comparing and classifying watersheds, and considering resilience of vegetation to disturbance.
Kristen M. Brubaker and Elizabeth W. Boyer (2011): LiDAR imagery improves classification of forest function in the Ridge and Valley physiographic province of Pennsylvania (poster). Gordon Research Conference for Catchment Science, Bates College, ME., July 2011.