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

MacLachlan, A; Roberts, G; Biggs, E; Boruff, B; (2017) Subpixel land-cover classification for improved urban area estimates using Landsat. International Journal of Remote Sensing , 38 (20) pp. 5763-5792. 10.1080/01431161.2017.1346403. Green open access

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Abstract

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 the PMR. 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.

Type: Article
Title: Subpixel land-cover classification for improved urban area estimates using Landsat
Open access status: An open access version is available from UCL Discovery
DOI: 10.1080/01431161.2017.1346403
Publisher version: https://doi.org/10.1080/01431161.2017.1346403
Language: English
Additional information: © 2017 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/ licenses/by/4.0/).
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment > Centre for Advanced Spatial Analysis
URI: https://discovery.ucl.ac.uk/id/eprint/10075656
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