A New Technique for Impervious Surface Mapping and Its Spatio-Temporal Changes from Landsat and Sentinel-2 Images

Abstract

Accurately mapping and monitoring the urban impervious surface area (ISA) is crucial for understanding the impact of urbanization on heat islands and sustainable development. However, less is known about ISA spectra heterogeneity and their similarity to bare land, wetland, and high-rise-building shadows. This study proposes a feature-based approach using decision tree classification (FDTC) to map ISAs and their spatio-temporal changes in a coastal city in southeast China using Landsat 5 TM, Landsat 8 OLI/TIRS, and Sentinel-2 images from 2009 to 2021. Atmospheric correction using simplified dark object subtraction (DOS) was applied to Landsat imagery, which enabled faster computation. FDTC’s performance was evaluated with three sensors with different spectral and spatial resolutions, with parameter thresholds held constant across remote-sensing images. FDTC produces a high average overall accuracy (OA) of 94.53%, a kappa coefficient (KC) of 0.855, and a map-level image classification efficacy (MICE) of 0.851 for ISA mapping over the studied period. In comparison with other indices such as BCI (biophysical composition index), PISI (automated built-up extraction index), and ABEI (perpendicular impervious surface index), the FDTC demonstrated higher accuracy and separability for extracting ISA and bare land as well as wetland and high-rise buildings. The results of FDTC were also consistent with those of two open-source ISA products and other remote sensing indices. The study found that the ISA in Xiamen City increased from 16.33% to 26.17% over the past 13 years due to vegetation occupation, encroachment onto bare land, and reclamation of coastal areas. While the expansion significantly reduced urban vegetation in rapidly urbanizing areas of Xiamen, ambitious park greening programs and massive redevelopment of urban villages resulted in a modest but continuous increase in urban green space.