Subpixel land-cover classification for improved urban area estimates using Landsat

Andrew MacLachlan, Gareth Roberts, Eloise Biggs, Bryan Boruff

Research output: Contribution to journalArticlepeer-review

28 Citations (Scopus)


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 (30-250 m) 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 (PMR), Western Australia. Results indicate that calibrating Landsat-derived subpixel land-cover estimates with correction values (calculated from spatially explicit comparisons of subpixel 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 an average 55.08% for thePMR. 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.

Original languageEnglish
Pages (from-to)5763-5792
Number of pages30
JournalInternational Journal of Remote Sensing
Issue number20
Publication statusPublished - 18 Oct 2017


Dive into the research topics of 'Subpixel land-cover classification for improved urban area estimates using Landsat'. Together they form a unique fingerprint.

Cite this