An Ecological Study of Two Adjacent Microtine Populations

Daily movements of two adjacent microtine populations were correlated with the utilization of various types of microenvironments. Animals were trapped, marked and released on a gridded area which transected three ecological zones, a riparian woodland, an edge, and an old field. Average distance traveled between recaptures and standard diameters were used to estimate activity patterns and home ranges of the meadow vole, ..Microtus oennsylvanicus, and the red-backed vole, Clethrionomys gapperi. Microtus restricted their activity to grassy habitats while Clethrionomys remained in the woodland. There was no ecological overlap between the two species. In spite of relatively high population densities, meadow and red-backed voles restricted activities to their preferred grassy and wooded habitats. Despite intraspecific population pressure, interspecific competition was an important factor restricting these species to their respective habitats. Periods of precipitation or drought temporarily affected the activity patterns of meadow voles. The number of captures increased immediately following precipitation; dry periods had the opposite effect. These conditions had no apparent effect on the activity of Clethrionomys. However, there was no positive correlation between periods of precipitation and the average distances traveled by either species on successive days. In general, Clethrionomys had larger home ranges than Microtus. Adults of both species had larger home ranges than juveniles; therefore, they appeared to disperse further than juveniles

Deriving Spectral and Spatial Features to Establish a Hierarchical Classification System

Automatic processing of remotely sensed data has to date been constrained to using training sets to classify a small number of categories within the context of a limited geographical area. In order to promote a more flexible user-oriented data processing system, a hierarchical taxonomic structure is proposed. This structure incorporates data inputs from several different sensors together with a priori information on the characteristics of different materials of interest to facilitate efficient design of feature sets to classify those materials. A Boolean approach may be used to assign these feature sets including both spectral and spatial criteria to different hierarchical levels