Generating confidence intervals for composition-based landscape indexes

Many landscape indexes with ecological relevance have been proposed, includ ing diversity indexes, dominance, fractal dimension, and patch size distrib ution. Classified land cover data in a geographic information system (GIS) are frequently used to calculate these indexes. However, a lack of methods for quantifying uncertainty in these measures makes it difficult to test hy pothesized relations among landscape indexes and ecological processes. One source of uncertainty in landscape indexes is classification error in land cover data, which can be reported in the form of an error matrix. Some rese archers have used error matrices to adjust extent estimates derived from cl assified land cover data. Because landscape diversity indexes depend only o n landscape composition - the extent of each cover in a landscape - adjuste d extent estimates may be used to calculate diversity indexes. We used a bo otstrap procedure to extend this approach and generate confidence intervals for diversity indexes. Bootstrapping is a technique that allows one to est imate sample variability by resampling from the empirical probability distr ibution defined by a single sample. Using the empirical distribution define d by an error matrix, we generated a bootstrap sample of error matrixes. Th e sample of error matrixes was used to generate a sample of adjusted divers ity indexes from which estimated confidence intervals for the diversity ind exes were calculated. We also note that present methods for accuracy assess ment are not sufficient for quantifying the uncertainty in landscape indexe s that are sensitive to the size, shape, and spatial arrangement of patches . More information about the spatial structure of error is needed to calcul ate uncertainty for these indexes. Alternative approaches should be conside red, including combining traditional accuracy assessments with other probab ility data generated during the classification procedure.