Annual maps of global artificial impervious area (GAIA) between 1985 and 2018

不透水面 遥感 合成孔径雷达 环境科学 土地覆盖 城市化 气象学 土地利用 地理 生态学 经济增长 生物 工程类 土木工程 经济
作者
Peng Gong,Xuecao Li,Jie Wang,Yuqi Bai,Бин Чэн,Tengyun Hu,Xiaoping Liu,Bing Xu,Jun Yang,Wei Zhang,Yuyu Zhou
出处
期刊:Remote Sensing of Environment [Elsevier BV]
卷期号:236: 111510-111510 被引量:1271
标识
DOI:10.1016/j.rse.2019.111510
摘要

Artificial impervious areas are predominant indicators of human settlements. Timely, accurate, and frequent information on artificial impervious areas is critical to understanding the process of urbanization and land use/cover change, as well as of their impacts on the environment and biodiversity. Despite their importance, there still lack annual maps of high-resolution Global Artificial Impervious Areas (GAIA) with longer than 30-year records, due to the high demand of high performance computation and the lack of effective mapping algorithms. In this paper, we mapped annual GAIA from 1985 to 2018 using the full archive of 30-m resolution Landsat images on the Google Earth Engine platform. With ancillary datasets, including the nighttime light data and the Sentinel-1 Synthetic Aperture Radar data, we improved the performance of our previously developed algorithm in arid areas. We evaluated the GAIA data for 1985, 1990, 1995, 2000, 2005, 2010, and 2015, and the mean overall accuracy is higher than 90%. A cross-product comparison indicates the GAIA data are the only dataset spanning over 30 years. The temporal trend in GAIA agrees well with other datasets at the local, regional, and global scales. Our results indicate that the GAIA reached 797,076 km2 in 2018, which is 1.5 times more than that in 1990. China and the United States (US) rank among the top two in artificial impervious area, accounting for approximately 50% of the world's total in 2018. The artificial impervious area of China surpassed that of the US in 2015. By 2018, the remaining eight among the top ten countries are India, Russia, Brazil, France, Italy, Germany, Japan, and Canada. The GAIA dataset can be freely downloaded from http://data.ess.tsinghua.edu.cn.
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