Quantifying urban expansion using Landsat images and landscape metrics: a case study of the Halton Region, Ontario

Abstract

The Halton Region, as part of the Greater Toronto Area (GTA), is regarded as one of the fastest growing regions in Canada, generating 20% of national gross domestic product. It is also one of the most desirable places for living and for thriving businesses. This research attempts to assess the urban expansion in the Halton Region, Ontario, Canada from 1989 to 2019 using satellite images, analysis approaches, and landscape metrics. Multitemporal Landsat images and the supervised learning algorithms in GIS software were used to explore the dynamic changes and to classify the urban and nonurban areas. The temporal urban expansion in the Halton Region experienced a dramatic rise, and it mainly occurred from the centre of the area. The analysis of landscape metrics based on different methods including the Land Use in Central Indiana (LUCI) model, the vegetation-impervious surface-soil (V-I-S) model, and the census data of Canada was carried out to understand the transition mode of the urbanization in the Halton Region. Also, the population growth in the centre of the Halton Region was considered as one of the driving forces affecting urban expansion. The results showed that most of the landscape metrics rose between 1989 and 2019, indicating that leapfrog pattern of urbanization occurred over the entire period. The purpose of this research is to evaluate urbanization in the Halton Region and give the city managers data to make appropriate decisions in further urban planning.