Sub-pixel land cover classification for improved urban area estimates using Landsat

Urban areas are Earth’s fastest growing land use that impact hydrological and ecological systems and the surface energy balance. The identification and extraction of accurate spatial information relating to urban areas is essential for future sustainable city planning owing to its importance within global environmental change and human-environment interactions. However, monitoring urban expansion using medium resolution (30-250m) imagery remains challenging due to the variety of surface materials that contribute to measured reflectance resulting in spectrally mixed pixels. This research integrates high spatial resolution orthophotos and Landsat imagery to identify differences across a range of diverse urban subsets within the rapidly expanding Perth Metropolitan Region, Western Australia. Results indicate that calibrating Landsat derived sub-pixel land cover estimates with correction values (calculated from spatially explicit comparisons of sub-pixel Landsat values to classified high resolution data which accounts for over (under) estimations of Landsat) reduces moderate resolution urban area over (under) estimates by on average 55.08% for the Perth Metropolitan Region. This approach can be applied to other urban areas globally through use of frequently available and/or low cost high spatial resolution imagery (e.g. using Google Earth). This will improve urban growth estimations to help monitor and measure change whilst providing metrics to facilitate sustainable urban development targets within cities around the world