GISD30: global 30 m impervious-surface dynamic dataset from 1985 to 2020 using time-series Landsat imagery on the Google Earth Engine platform

• Published in: Earth system science data 2022-04, Vol.14 (4), p.1831-1856
• Main Authors: Zhang, Xiao, Liu, Liangyun, Zhao, Tingting, Gao, Yuan, Chen, Xidong, Mi, Jun
• Format: Article
• Language: English
• Subjects: Accuracy; Anthropogenic factors; Carbon emissions; Classification; Cloud computing; Consistency; Datasets; Dynamic models; Dynamics; Earth resources technology satellites; Emissions; Engines; Environment models; Imagery; Land cover; Landsat; Reflectance; Remote sensing; Satellite imagery; Spatial distribution; Spectra; Statistical methods; Surface dynamics; Sustainable development; Sustainable urban development; Time series; Urbanization
• ISSN: 1866-3516; 1866-3508; 1866-3516
• DOI: 10.5194/essd-14-1831-2022

Accurately mapping impervious-surface dynamics has great scientific significance and application value for research on urban sustainable development, the assessment of anthropogenic carbon emissions and global ecological-environment modeling. In this study, a novel and automatic method of combining the advantages of spectral-generalization and automatic-sample-extraction strategies was proposed, and then an accurate global 30 m impervious-surface dynamic dataset (GISD30) for 1985 to 2020 was produced using time-series Landsat imagery on the Google Earth Engine cloud computing platform. Firstly, the global training samples and corresponding reflectance spectra were automatically derived from prior global 30 m land-cover products after employing the multitemporal compositing method and relative radiometric normalization. Then, spatiotemporal adaptive classification models, trained with the migrated reflectance spectra of impervious surfaces from 2020 and transferred pervious-surface samples in each epoch for every 5∘×5∘ geographical tile, were applied to map the impervious surface in each period. Furthermore, a spatiotemporal-consistency correction method was presented to minimize the effects of independent classification errors and improve the spatiotemporal consistency of impervious-surface dynamics. Our global 30 m impervious-surface dynamic model achieved an overall accuracy of 90.1 % and a kappa coefficient of 0.865 using 23 322 global time-series validation samples. Cross-comparisons with five existing global 30 m impervious-surface products further indicated that our GISD30 dynamic product achieved the best performance in capturing the spatial distributions and spatiotemporal dynamics of impervious surfaces in various impervious landscapes. The statistical results indicated that the global impervious surface has doubled in the past 35 years, from 5.116×105 km2 in 1985 to 10.871×105 km2 in 2020, and Asia saw the largest increase in impervious surface area compared to other continents, with a total increase of 2.946×105 km2. Therefore, it was concluded that our global 30 m impervious-surface dynamic dataset is an accurate and promising product and could provide vital support in monitoring regional or global urbanization as well as in related applications. The global 30 m impervious-surface dynamic dataset from 1985 to 2020 generated in this paper is free to access at https://doi.org/10.5281/zenodo.5220816 (Liu et al., 2021b).