SUMMARY
We modified the standard, stratified random block design used typically in aerial surveys of moose (Alces alces). We laid a grid of approximately 9 km2 cells over our study area, and GIS was then used to allocate polygons into one of 2 strata within each grid cell. The 2 strata were based upon vegetation attributes that were predicted to have either high or low moose density from previous research. We assumed that polygons of early seral forest stands (<40 yr), shrubs, and meadows would have high moose density relative to other vegetation attributes. Vegetation polygons were often <1 km2, consequently, single grid cells usually included >1 high and low density polygons. Adjacent cells were amalgamated to produce sample units with >4 km2 of high density stratum area. Real-time navigation was used and the flight track was recorded over a map of sample units, strata boundaries, and topographic features to accurately identify polygon boundaries and assign each sighted moose to the appropriate strata. We concluded that our approach was efficient and effective in fine-grained environments where the relative selection by moose for vegetation patches is well understood, and those patches are mapped in digital databases.