Lidar and orthophotograph-derived land cover are combined with in situ vegetation measurements to assess habitat characteristics typifying four species of butterflies with differing habitat preferences across a large spatial extent (~30,000 ha) in northern Idaho, USA. Lidar data are employed to characterize both vegetation structure and topography, whereas digital orthophotographs and in situ vegetation measurements are employed to quantify surrounding land use and larval host plant cover, respectively. Non-metricmultidimensional scaling (NMDS) ordination identified nine environmental variables that were strongly related to butterfly species composition and community structure. Logistic and standard regression models were developed based on these variables to predict the presence and density of each butterfly species, respectively. We found that lidar-derived variables described more butterfly habitat characteristics than orthophotograph-derived variables or ground measurements, demonstrating the value of lidar in describing diverse habitat characteristics. However, the strongest models observed in our study use both the local- and landscape-scale variables derived from both remote-sensing data and in situ ground measurements. We conclude that combining lidar and other remotely sensed data with in situ vegetation measurements allows for an effective in-depth, large-area evaluation of local- and landscape-scale butterfly habitat structure across a diverse ecosystem.