Prediction of Total Imperviousness from Population Density and Land Use Data for Urban Areas (Case Study: South East Queensland, Australia)

Abstract

Total imperviousness (residential and non-residential) increases with population growth in many regions around the world. Population density has been used to predict the total imperviousness in large areas, although population size was only closely related to residential imperviousness. In this study, population density together with land use data for 154 suburbs in Southeast Queensland (SEQ) of Australia were used to develop a new model for total imperviousness estimation. Total imperviousness was extracted through linear spectral mixing analysis (LSMA) using Landsat 8 OLI/TIRS, and then separated into residential and non-residential areas based on land use data for each suburb. Regression models were developed between population density and total imperviousness, and population density and residential imperviousness. Results show that (1) LSMA approach could retrieve imperviousness accurately (RMSE < 10%), (2) linear regression models could be used to estimate both total imperviousness and residential imperviousness better than nonlinear regression models, and (3) correlation between population density and residential imperviousness was higher (R2 = 0.77) than that between population density and total imperviousness (R2 = 0.52); (4) the new model was used to predict the total imperiousness based on population density projections to 2057 for three potential urban development areas in SEQ. This research allows accurate prediction of the total impervious area from population density and service area per capital for other regions in the world.

Mapping impervious surface change from remote sensing and GIS data: A case study in Hochiminh city, Vietnam

“…Several recent studies have developed regression models between the percentage of impervious surfaces and the population density. Variables such as population growth and trade are also included in some predicting models to improve the accuracy of predictive results of impervious surface distribution (Azimand et al, 2020;Li et al, 2021;Ramezani et al, 2021). Machine learning techniques such as SVM, RF and so on are often used when extracting impervious surfaces from remote sensing data, while artificial neural networks (ANN) and regression techniques are often applied to predictive models of future impervious surface distributions (Mahyoub et al, 2022).…”

Relevance of tyre wear particles to the total content of microplastics transported by runoff in a high-imperviousness and intense vehicle traffic urban area.

“…MPs tend to be at high levels in areas with large population worldwide (Bronwe et al, 2011;Vaughan et al, 2017;Jiang et al, 2019, Nematollahi et al, 2022, due to the generation of high amounts of litter with non-appropriate waste disposal and high number of vehicles (Li et al, 2020;Koutnik et al, 2021). The escalation in population density, associated with urbanisation, is associated with an increase in impervious areas (Ramezani et al, 2021;Kawakubo et al, 2019;Wu et al, 2020), which subsequently affects the response of an area after a rainfall events, typically increasing runoff in the area (Miller et al, 2014;Huang et al, 2008;Jacobson, 2011). Runoff from storms washes out impervious surfaces in densely populated areas, carrying the previous build-up of plastics and its degradation products into water bodies (Triebskorn et al, 2019;Grbić et al, 2020;Lange et al, 2021;Smyth et al, 2021).…”