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

Abstract

Impervious surface is artificial surfaces that prevent water from entering the soil. The increase in impervious surface area has led to negative impacts on the urban environment, including an increase in the risk of flooding, a decrease in vegetation cover, and the formation of urban heat islands. This paper presents the results of building a predictive model of impervious surfaces in Hochiminh city from remote sensing and GIS data. Landsat and Sentinel 2 satellite images for the period 2002–2022 are used to classify impervious surfaces and extract input layers about vegetation cover, land surface temperature, combined with GIS data (elevation, slope, aspect, distance to road, distance to hydrology, population density) for modeling and predicting impervious surface changes in future. 03 machine learning algorithms, including Support Vector Machine (SVM), Random Forest (RF), Classification and Regression Trees (CART) and maximum likelihood method are used to classify impervious surfaces from Landsat satellite images, then select the method with the highest accuracy. To predict the future distribution of impervious surface, this study uses Cellular Automata (CA) model and 02 artificial intelligence algorithms (Artificial Neural Network - ANN, Logistic Regression - LR). The results obtained in the study can be effectively used for urban planning, minimizing the impact of the process of increasing the impervious surface on the urban environment