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The composition of a landscape is a fundamental indicator in land-cover pattern assessments. The objective of this paper was to evaluate a landscape composition indicator called 'landscape mosaic' as a framework for interpreting land-cover dynamics over a 9-year period in a 360,000 km2 study area in the southern United States. The indicator classified a land parcel into one of 19 possible landscape mosaic classes according to the proportions of natural, developed, and agriculture land-cover types in a surrounding 4.41-ha neighborhood. Using land-cover maps from remote sensing, the landscape mosaics were calculated for each 0.09-ha pixel in the study area in 1996 and 2005. Mosaic transition matrices estimated from the pixel change data were then used to develop two Markov chain models. A “landscape mosaic” model was a temporal model of the shifting landscape mosaic, based on the probability of landscape mosaic change for all pixels. A “forest security” model was the same, except that the Markov states were defined by both the landscape mosaic and the land-cover of each pixel, which allowed interpreting forest land-cover dynamics in the context of a shifting landscape mosaic. In the forest security model, the overall percentage of forest decreased from 33% in 2005 to 17% at steady-state, and there was little change in the relative distribution of existing forest area among landscape mosaic classes. In contrast, the landscape mosaic steady-state was reached later, and indicated that a maximum of 10% of total area was available for forest. The implication was that forest security depended ultimately on the dynamics of the landscape mosaics that contained forest, not on forest dynamics within those landscape mosaics.
