###### 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 (R\({}^{2}\) = 0.77) than that between population density and total imperviousness (R\({}^{2}\) = 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. urban development; population growth; linear spectral mixture analysis (LSMA); landsat; land use +
Footnote †: journal: Applied Sciences  
such as counties or suburbs [10, 11]. On the other hand, urban built-up areas, known as total imperviousness, have been derived at small (e.g., buildings) to regional areas from satellite images [12, 13, 14]. Imperviousness derived from remote sensing techniques represents total imperviousness (residential and non-residential). To extract total imperviousness over large areas, previous studies used medium spatial resolution satellite images (e.g., Landsat) [10, 11, 14, 15]. For example, [PERSON] et al. [16] used Landsat images to derive imperviousness at county scale in Haiti. Per-pixel and sub-pixel classification techniques have been used [17, 18, 19] to extract total imperviousness from Landsat images. Per-pixel methods (e.g., maximum likelihood and support vector machine) based on medium spatial resolution images often overestimate or underestimate total imperviousness, due to presence of mixed pixels especially in urban areas [20]. However, sub-pixel classification approaches such as Artificial Neural Network (ANN) and Spectral Mixture Analysis (SMA) have shown a better performance to quantify the fraction of each land use in each pixel [21, 22]. A few simple methods have been used to predict total imperviousness from population density data in large areas [23, 24]. [PERSON] et al. [25] used a coefficient, showing total imperviousness per person, to predict the total imperviousness from population projections. Other studies predicted total imperviousness from population density using established regression models [26, 27, 28]. [PERSON] [29] developed a linear regression model between population data and urban area, derived from nighttime satellite imagery, to estimate imperviousness for a large number of urban centers in the United States. [PERSON] [30] used a nonlinear regression model to predict total imperviousness from population density for two catchments in the US. The above-mentioned methods have overestimated total imperviousness in dense residential suburbs, where the most of built-up areas are residential buildings. This was mainly because non-residential impervious area is not related to the local population density. Thus, in this study, we aimed to develop a new model to predict the total impervious area in dense residential PDAs in SEQ, Australia.