Sensitivity of landscape pattern metrics to map spatial extent

Computation of landscape pattern metrics from spectrally classified digital images is becoming increasingly common, because the characterization of la ndscape spatial structure provides valuable information for many applicatio ns. However, the spatial extent (window size) from which pattern metrics ar e estimated has been shown to influence and produce biases in the results o f these spatial analyses. In this study, the sensitivity of eight commonly used landscape configuration metrics to changes in map spatial extent is an alyzed using simulated thematic landscape patterns generated by the modifie d random clusters method. This approach makes it possible to control and is olate the different factors that in-fluence the behavior of spatial pattern metrics, as well as taking into account a wide range of landscape configur ation possibilities. 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 co mplexity of the shapes in the pattern (Mean Shape Index, Area Weighted Mean Shape Index, and Perimeter Area Fractal Dimension) are by for the most sen sitive. In particular, it is suggested that the Mean Shape Index should be avoided in further landscape studies. For the eight analyzed pattern metric s, quantitative guidelines are provided to estimate the systematic biases t hat may be introduced by the use of a given extent, so that the metric valu es derived from data of different spatial extents can be properly compared.