Abstract
Landscape integrity data-sets represent a measure of a landscape's naturalness, or its inverse, the level of human modification. Generally referred to as human footprint modeling, a hybrid approach was developed here by incorporating distance, density, and spatially explicit fuzzy logic methods for quantifying the impacts of anthropocentric infrastructure on the landscape. Integrity scores varied markedly across this large and heterogeneous landscape. A comparative analysis among peer data products revealed that this model exhibited the highest level of correlation when compared to an independently derived expert survey of expected scores. Moreover, differences in correlation were found to be statistically significant in two cases indicating robust model performance. Data products such as these may be leveraged to quantify the nature and extent of human modifications on the landscape, identify highly natural areas for conservation purposes, and may serve as an overarching umbrella for guiding and coordinating large and local-scale planning efforts.
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