Social and economic considerations are among the most important drivers of landscape change, yet few studies have addressed economic and environmental influences on landscape structure, and how land ownership may affect landscape dynamics. Watersheds in the Olympic Peninsula, Washington, and the southern Appalachian highlands of western North Carolina were studied to address two questions: (1) Does landscape pattern vary among federal, state, and private lands? (2) Do land-cover changes differ among owners, and if so, what variables explain the propensity of land to undergo change on federal, state, and private lands? Landscape changes were studied between 1975 and 1991 by using spatial databases and a time series of remotely sensed imagery. Differences in landscape pattern were observed between the two study regions and between different categories of land ownership. The proportion of the landscape in forest cover was greatest in the southern Appalachians for both U.S. National Forest and private lands, compared to any landownership category on the Olympic Peninsula. Greater variability in landscape structure through time and between ownership categories was observed on the Olympic Peninsula. On the Olympic Peninsula, landscape patterns did not differ substantially between commercial forest and state Department of Natural Resources lands, both of which are managed for timber, but differed between U.S. National Forest and noncommercial private land ownerships. In both regions, private lands contained less forest cover but a greater number of small forest patches than did public lands. Analyses of land-cover change based on multinomial legit models revealed differences in land-cover transitions through time, between ownerships and between the two study regions. Differences in land-cover transitions between time intervals suggested that additional factors (e.g., changes in wood products or agricultural prices, or changes in laws or policies) cause individuals or institutions to change land management. The importance of independent variables (slope, elevation, distance to roads and markets, and population density) in explaining land-cover change varied between ownerships. This methodology for analyzing land-cover dynamics across land units that encompass multiple owner types should be widely applicable to other landscapes.
