Detecting and discriminating impervious cover with high-resolution IKONOS data using principal component analysis and morphological operators

The objective of this study was to directly detect impervious cover using new high-resolution IKONOS imagery in South Lake Tahoe, California, USA. The research presented was a pilot analysis to assess the ability of satellite imagery to derive accurate estimates of impervious cover, critical for assessing impacts to water quality, wildlife, and fish habitat. A combination of image processing methods based on principal component analysis and spatial morphological operators was developed for a 25 km(2) urban area in the Lake Tahoe Basin. The methodology produced very accurate identification of both commercial and residential impervious cover in an area dominated by dense conifer canopy. Sub-canopy and sub-shadow surfaces were not only detectable, but also discernible with respect to the underlying substrate. An overall accuracy of 92.94% was obtained, with an even higher user accuracy of 95.83% based on 170 ground truth points. Impervious cover is a difficult feature to delineate accurately and efficiently using direct methods. For this application, spatial resolution proved a better operator than spectral resolution. Results from this analysis will be used to better understand the impacts of urban development on the ecology of the Lake Tahoe Basin.