Generating confidence intervals for composition-based landscape indexes

Abstract

Many landscape indexes with ecological relevance have been proposed, including diversity indexes, dominance, fractal dimension, and patch size distribution. 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 hypothesized 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 researchers have used error matrices to adjust extent estimates derived from classified land cover data. Because landscape diversity indexes depend only on landscape composition – the extent of each cover in a landscape – adjusted extent estimates may be used to calculate diversity indexes. We used a bootstrap procedure to extend this approach and generate confidence intervals for diversity indexes. Bootstrapping is a technique that allows one to estimate sample variability by resampling from the empirical probability distribution defined by a single sample. Using the empirical distribution defined by an error matrix, we generated a bootstrap sample of error matrixes. The sample of error matrixes was used to generate a sample of adjusted diversity indexes from which estimated confidence intervals for the diversity indexes were calculated. We also note that present methods for accuracy assessment are not sufficient for quantifying the uncertainty in landscape indexes that are sensitive to the size, shape, and spatial arrangement of patches. More information about the spatial structure of error is needed to calculate uncertainty for these indexes. Alternative approaches should be considered, including combining traditional accuracy assessments with other probability data generated during the classification procedure.