Accuracy of Remotely Sensed Estimates of Landscape Change using Patch- and Boundary-based Pattern Statistics

In this paper, we compare the relative amount of error in estimates of change in each of two different classes of spatial pattern statistics calculated from remotely sensed imagery. The more commonly used patch-based statistics require image classification and identification of individual patches. The advantage is that this process isolates particular information classes (e.g., forest) and focuses the analysis on a ecologically meaningful unit of analysis (i.e., the patch). However, classification and patch-delineation are processes that are sensitive to image variability and uncertainty (Brown et al., 2000). Boundary-based statistics, on the other hand, operate directly on continuous-variable surfaces (e.g., NDVI) to identify areas of rapid spatial change (Barbujani et al., 1989), referred to here as boundaries. Our analysis, based on multitemporal remotely sensed data, shows that a selected set of boundary-based statistics was substantially less sensitive to image variability than are several patch-based statistics. We attribute this decreased sensitivity to differences in approach, i.e., identifying classes and patches versus boundaries. Further, because edge effects are important causes of the ecological impact from forest fragmentation, boundary-based statistics may be as ecologically meaningful as patch-based statistics.