The imperviousness of land parcels was mapped and evaluated using high spatial resolution digitized color orthophotography and surface-cover height extracted from multiple-return lidar data. Maximum-likelihood classification, spectral clustering, and expert system approaches were used to extract the impervious information from the datasets. Classified pixels (or segments) were aggregated to parcels. The classification model based on the use of both the orthophotography and lidar-derived surface-cover height yielded impervious surface results for all parcels that were within 15 percent of reference data. The standard error for the rule-based per-pixel model was 7.15 percent with a maximum observed error of 18.94 percent. The maximum-likelihood per-pixel classification yielded a lower standard error of 6.62 percent with a maximum of 14.16 percent. The regression slope (i.e., 0.955) for the maximum-likelihood per-pixel model indicated a near perfect relationship between observed and predicted imperviousness. The additional effort of using a per-segment approach with a rule-based classification resulted in slightly better standard error (5.85 percent) and a near-perfect regression slope (1.016).
Hodgson et al. (Mon,) studied this question.