The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Download
Publications Copernicus
Download
Citation
Articles | Volume XLI-B8
https://doi.org/10.5194/isprs-archives-XLI-B8-965-2016
https://doi.org/10.5194/isprs-archives-XLI-B8-965-2016
24 Jun 2016
 | 24 Jun 2016

EXAMINING URBAN EXPANSION USING MULTI-TEMPORAL LANDSAT IMAGERY: A CASE STUDY OF THE MONTREAL CENSUS METROPOLITAN AREA FROM 1975 TO 2015, CANADA

Lingfei Ma, He Zhao, and Jonathan Li

Keywords: Urban Expansion, Remote Sensing, Landsat Imagery, Classification, Change Detection, Driven Forces Analysis, Montréal Census Metropolitan Area

Abstract. Urban expansion, particularly the movement of residential and commercial land use to sub-urban areas in metropolitan areas, has been considered as a significant signal of regional economic development. In 1970s, the economic centre of Canada moved from Montreal to Toronto. Since some previous research have been focused on the urbanization process in Greater Toronto Area (GTA), it is significant to conduct research in its counterpart. This study evaluates urban expansion process in Montréal census metropolitan area (CMA), Canada, between 1975 and 2015 using satellite images and socio-economic data. Spatial and temporal dynamic information of urbanization process was quantified using Landsat imagery, supervised classification algorithms and the post-classification change detection technique. Accuracy of the Landsat-derived land use classification map ranged from 80% to 97%. The results indicated that continuous growth of built-up areas in the CMA over the study period resulted in a decrease in the area of cultivated land and vegetation. The results showed that urban areas expanded 442 km2 both along major river systems and lakeshores, as well as expanded from urban centres to surrounded areas. The analysis revealed that urban expansion has been largely driven by population growth and economic development. Consequently, the urban expansion maps produced in this research can assist decision-makers to promote sustainable urban development, and forecast potential changes in urbanization growth patterns.