Sensitivity of selected landscape pattern metrics to land-cover misclassification and differences in land-cover composition

investigation of the sensitivity of these measurements to clasCalcuiation of landscape metrics from land-cover data is be- sification error (Hess, 1994). he objectives of this paper are coming increasingly common. Some studies have shown that two-fold: (1) to determine the sensitivity of landscape these measurements are sensitive to differences in land-cover metrics to land-cover misclassification, and (2) to determine composition, but none are known to have tested also their the sensitivity of landscape metrics to differences in landto land-cover misc~ass~ficat~o n~ error sirnula- scape condition. These objectives are necessarily connected. tion model was written to test the sensitivity of selected land- Ideally, landscape pattern metrics would be insensitive to scape pattern metrics to misclassification, and regression misclassification but sensitive to differences in land cover. analysis was used to determine if these metrics were signifi- Land-cover data, mapped from Landsat TM for the Chescantly related to differences in land-cover composition. Corn- apeake Bay Regional Watershed, were used for this study. parison of sensitivity and regression results suggests that The data were divided into 57 eight-digit U.S. Geological differences in land-cover composition need to be about 5 Survey (us~s) hydrologic units or watersheds. Sensitivity to percent greater than the misclassification rate to be confident misclassification is tested using a simulation model based on that differences in landscape metrjcs are not due to mjsclas- a published land-cover error matrix (Green et a]., 1993). Sensification. sitivity to differences in landscape condition is tested by comparison to the amount of human land use in the watershed. Landscape condition is measured as the ratio of an