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

Abstract

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 km² 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.

Acknowledgments

The authors would like to thank the Tahoe Regional Planning Agency (TRPA) and the Watersheds and Environmental Sustainability (WES) centre at the Desert Research Institute for co-funding this study. The assistance of Tim Hagan of TRPA and Joe Pepi of the California Tahoe Conservancy is also greatly appreciated.