Delineation of Impervious Surface from Multispectral Imagery and Lidar Incorporating Knowledge Based Expert System Rules

An attempt to delineate impervious surfaces in the City of Scottsbluff, Nebraska, was made using multispectral high spatial resolution imagery and lidar data. An ISODATA classification was performed and results aggregated into two parent classes, impervious and pervious. The ISODATA classification yielded an overall accuracy of 91.0 percent with a Kappa of 82.0 percent. A Knowledge Based Expert System (KBES) set of rules was designed incorporating the imagery classification with lidar data to derive two models, Cover Height and Cover Slope, to provide critical information not available from multispectral imagery. The rules were applied to the initial ISODATA classification to improve the classification accuracy to an overall accuracy of 94.0 percent with a Kappa of 87.9 percent. In this study, it was shown that lidar holds promise for improving the accuracy of impervious surface measurement, as well as the potential identification and measurement of other significant planimetric features such as buildings and trees.