Improving 30-meter global impervious surface area (GISA) mapping: New method and dataset

Abstract

Timely and accurate monitoring of impervious surface areas (ISA) is crucial for effective urban planning and sustainable development. Recent advances in remote sensing technologies have enabled global ISA mapping at fine spatial resolution (<30 m) over long time spans (>30 years), offering the opportunity to track global ISA dynamics. However, existing 30 m global long-term ISA datasets suffer from omission and commission issues, affecting their accuracy in practical applications. To address these challenges, we proposed a novel global long-term ISA mapping method and generated a new 30 m global ISA dataset from 1985 to 2021, namely GISA-new. Specifically, to reduce ISA omissions, a multi-temporal Continuous Change Detection and Classification (CCDC) algorithm that accounts for newly added ISA regions (NA-CCDC) was proposed to enhance the diversity and representativeness of the training samples. Meanwhile, a multi-scale iterative (MIA) method was proposed to automatically remove global commissions of various sizes and types. Finally, we collected two independent test datasets with over 100,000 test samples globally for accuracy assessment. Results showed that GISA-new outperformed other existing global ISA datasets, such as GISA, WSF-evo, GAIA, and GAUD, achieving the highest overall accuracy (93.12 %), the lowest omission errors (10.50 %), and the lowest commission errors (3.52 %). Furthermore, the spatial distribution of global ISA omissions and commissions was analyzed, revealing more mapping uncertainties in the Northern Hemisphere. In general, the proposed method in this study effectively addressed global ISA omissions and removed commissions at different scales. The generated high-quality GISA-new can serve as a fundamental parameter for a more comprehensive understanding of global urbanization.