ASI: An artificial surface Index for Landsat 8 imagery

Abstract

Remotely sensing the spatial extent of artificial surfaces is essential for understanding human footprints and evaluating anthropogenic impacts on the global environment and climate. However, since the spectral signatures of artificial surfaces are similar to other bright natural surfaces, it is still a difficult task to separate artificial surfaces from other natural surfaces. Therefore, this study proposed a new Artificial Surface Index (ASI) to extract artificial surfaces based on the multispectral Landsat 8 imagery. We examined the performance of ASI in eight study areas with a variety of landscapes, including desert, coastal, inland urban and rural areas dominated by different climates and land cover categories. We also compared ASI with seven state-of-the-art built-up area or impervious surface indices that are developed for similar purposes. The qualitative and quantitative evaluations demonstrate that ASI has advantages in suppressing non-artificial surfaces with similar spectral signatures to artificial surfaces, such as deserts, bare soil, and harvested croplands. Furthermore, ASI improves the separability between artificial and non-artificial surfaces by 2% to 75% (measured by Jeffries–Matusita Distance), 13% to 164% (measured by Transformed Divergence), and 3% to 131% (measured by Spectral Discrimination Index) compared to the other seven indices across the eight study areas. The newly proposed ASI index has the potential to support a variety of human-activity-related studies, such as change detection of artificial surfaces, urban planning, land resource management at local, continental, and even global scales.