Sensitivity of landscape pattern metrics to map spatial extent

Abstract

Computation of landscape pattern metrics from spectrally classified digital images is becoming increasingly common, because the characterization of landscape spatial structure provides valuable information for many applications. However, the spatial extent (window size) from which pattern metrics are estimated has been shown to influence and produce biases in the results of these spatial analyses. In this study, the sensitivity of eight commonly used landscape configuration metrics to changes in map spatial extent is analyzed using simulated thematic landscape patterns generated by the modified random clusters method. This approach makes it possible to control and isolate the different factors that in-fluence the behavior of spatial pattern metrics, as well as taking into account a wide range of landscape configuration possi-bilities. Edge Density is found to be the most robust metric and is recommended as a fragmentation index where the effect of spatial extent is concerned. The metrics that attempt to quantify the irregularity and complexity of the shapes in the pattern (Mean Shape Index, Area Weighted Mean Shape Index, and Perimeter Area Fractal Dimension) are by far the most sensitive. In particular, it is suggested that the Mean Shape Index should be avoided in further landscape studies. For the eight analyzed pattern metrics, quantitative guidelines are provided to estimate the systematic biases that may be introduced by the use of a given extent, so that the metric values derived from data of different spatial extents can be properly compared.

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