Development of a Scale Independent Approach for Landscape Pattern Metrics using Fourier Vector Analysis of Satellite Images

Mapping land-cover land-use change (LCLUC) over regional and continental scales and long time scales can be accomplished using class maps derived from Earth observing satellites. Observations of landscape change can be related to processes like hydrologic or ecological function by employing image processing tools that produce landscape pattern metrics such as dominance, fractal dimension, and contagion. These landscape pattern metrics arise from local kernel operations that detect edges and gradients in class maps, and suffer from a lack of statistical independence and scalability. This paper describes an approach to generating landscape pattern metrics based on a Fourier system analysis of the entire class map or any subset of the class map such as a watershed. Three metric improvements are made. First, correlations between the metrics are removed. Second, image sourced statistical independence is abated. Third, the proposed metrics become, in a least squares sense, optimally scalable.