Abstract
The Great Lakes watershed is a major geographic feature of North America, reflecting a diverse landscape ranging from the heavily forested north to the highly industrial and agrarian south. Quantitative aspects of the region's landscape have been extracted from a combination of land cover derived from satellite (Landsat multispectral scanner (MSS)) data and topographic map layers. Synoptic evaluations of a number of landscape metrics (forest contagion, U-index, riparian corridor extent, road density) indicate strong correlations between human activity (HA) and natural vegetation (NV) characterizations. Conventional or commonly used landscape metrics are scalar quantities; however, human activities are characterized by spatial regularity. For example, the small remnant forest patches in the southern portion of the watershed exhibit a spatial distribution that is associated with historic settlement patterns that led to widespread deforestation of the land and conversion to agriculture. It is shown through cellular automata simulations of deforestation that the observed values of scalar landscape metrics and their interrelationships can only be understood by taking into account the spatial pattern aspects associated with causal human drivers of the deforestation process.
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