LiDAR Change Detection: Coupling remote sensing with permanent plot data

Forest landscape change depends on fine-scale patterns of tree growth, mortality, and recruitment.  While long-term forest inventory plots provide some of the most robust assessments of individual- to stand-level changes, their spatial extent (< 4 ha) limits their capacity to represent dynamics across the entire landscape. In this study, we are using high-resolution remote sensing of vegetation structure (i.e., light detection and ranging, or LiDAR) from multiple years coupled with our long-term measurements and stem maps to assess patterns of tree mortality and growth over complete landscapes. This research aims to both (1) understand the landscape drivers of forest growth and mortality, and (2) map areas of forest vulnerable to changing environmental stressors.LiDAR change detection

     Examples of canopy height losses from 2008 to2014 associated with individual tree mortality and canopy gap formation.