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Open access

Alvin Spivey and Anthony Vodacek

Abstract

Extending the Landscape Pattern Metric (LPM) model analysis in Smith et al. (2001) into a LPM decision model, decadal scale prediction of fecal coliform compromised South Carolina watersheds is developed. The model’s parameter variability identifies the greatest contributors to a compromised watershed’s prediction. The complete set of model parameters include Land Cover Land Use (LCLU) & slope, along stream proportion, Fourier Metric of Fragmentation (FMF), Fourier Metric of Proportion (FMP), and Least Squares Fourier Transform Fractal Dimension (LsFT). The 1992 National Land Cover Data (NLCD) Land Cover Land Use (LCLU) within fecal coliform compromised watersheds is used to train the model parameters, and the 2001 NLCD LCLU is used to test the LPM model. The most significant model parameters are along stream bare rock LsFT, FMF between urban/recreational grasses and evergreen forests, and FMF between deciduous forests and high density residential areas. These metrics contribute significantly more than the best proportion descriptor: proportion of urban/recreational grasses. In training, the proposed model correctly identified 92 % of the compromised watersheds; while the Smith et al. (2001) model 94 % of the compromised watersheds were correctly identified. This study reveals the ability of Fourier metrics to interpret ecological processes, and the need for more appropriate landscape level models.

Open access

Alvin Spivey and Anthony Vodacek

Abstract

A factor analysis of 67 landscape pattern metrics was performed to quantify the ability of landscape pattern metrics to explain land cover pattern, and to report individual landscape pattern metric values that are statistically independent. This land cover pattern is measured from 7.68 x 7.68 [km] GeoTiff image tiles of the conterminous United States Geological Survey (USGS) 1992 National Land Cover Dataset (NCLD). Using factor analysis to rank independent landscape pattern information, each landscape pattern metric produces the explanatory power of that landscape pattern metric amongst the other 66 landscape pattern metrics—any landscape pattern metrics that report similar values contribute redundant information. The metrics that contribute the most information are Jackson’s Contagion statistic (P005), typically contributing to 97 % of the explained variability; the Fourier Metric of Fragmentation (FMF), typically contributing to 65 % of the explained variability; and average LCLU class lacunarity (TLAC), typically contributing to 62 % of the explained variability. Two other Fourier-based landscape pattern metrics we tested, the Least Squares Fourier Transform Fractal Dimension Estimation (LsFT) and the Fourier Metric of Proportion (FMP), contributed 50 % and 12 % to the explained variability, respectively. In addition, the values reported by each of the Fourier metrics are revealed to be relatively independent amongst commonly used landscape pattern metrics and are thus demonstrated to be appropriate for explaining general landscape pattern variability.