Sensitivity of landscape metrics to classification scheme

Abstract

Landscape metrics are a standard tool in the study and monitoring of landscape pattern and change, but their statistical properties and behaviour across a range of classification schemes and landscapes, as well as their sensitivity to changing landscape patterns, are still not fully understood. We therefore investigated the sensitivity of 24 metrics to a number of land cover classes for three Arizona landscapes with different spatial patterns. To do so, we applied unsupervised classification of remotely sensed data with two different nominal spatial resolutions to generate maps containing 2–35 classes. We calculated metric values for these thematic maps and classified the metrics into six groups using principal components analysis. For each group, the nature and sensitivity of responses to differences in resolution, landscape pattern, and classification detail were assessed. Our results indicated that many metrics behaved predictably with increasing classification detail, increasing or decreasing at rates that were often relatively similar and independent to sensor and landscape pattern. At lower class numbers, metrics were most sensitive to increasing classification detail, and the effects of classification scheme were most erratic and sensitive to resolution and underlying landscape pattern. Overall, this study provides a descriptive overview of the sensitivity of common metrics to changes in classification scheme, as well as a first attempt to draw some generalizations about the importance of classification scheme in conjunction with resolution effects.

Acknowledgements

We appreciate the assistance of Susan Taunton and Toby Finke on an earlier version of this manuscript. We would like to acknowledge the help of: Kurtis Thome, the Remote Sensing Group, Optical Science Centre (UA), who provided the irradiance data for image atmospheric correction; Stuart Marsh, the Arizona Remote Sensing Centre, who provided the computing facility for image processing; and the ARIA, which provided the Landsat ETM+ images used in the analyses. This research was funded in part by a grant to JAK from the National Commission on Science for Sustainable Forestry.