Mapping 10 m global impervious surface area (GISA-10m) using multi-source geospatial data

[摘要] Artificial impervious surface area (ISA) documents thehuman footprint. Accurate, timely, and detailed ISA datasets are thereforeessential for global climate change studies and urban planning. However, dueto the lack of sufficient training samples and operational mapping methods,global ISA datasets at a 10 m resolution are still lacking. To this end, weproposed a global ISA mapping method leveraging multi-source geospatialdata. Based on the existing satellite-derived ISA maps and crowdsourcedOpenStreetMap (OSM) data, 58 million training samples were extracted via aseries of temporal, spatial, spectral, and geometric rules. We then produceda 10 m resolution global ISA dataset (GISA-10m) from over 2.7 millionSentinel optical and radar images on the Google Earth Engine platform. Basedon test samples that are independent of the training set, GISA-10m achievesan overall accuracy of greater than 86 %. In addition, the GISA-10mdataset was comprehensively compared with the existing global ISA datasets,and the superiority of GISA-10m was confirmed. The global road area wasfurther investigated, courtesy of this 10 m dataset. It was found that Chinaand the US have the largest areas of ISA and road. The global rural ISA wasfound to be 2.2 times that of urban while the rural road area was found tobe 1.5 times larger than that of the urban regions. The global road areaaccounts for 14.2 % of the global ISA, 57.9 % of which is located in thetop 10 countries. Generally speaking, the produced GISA-10m dataset and theproposed sampling and mapping method are able to achieve rapid and efficientglobal mapping, and have the potential for detecting other land covers. Itis also shown that global ISA mapping can be improved by incorporating OSMdata. The GISA-10m dataset could be used as a fundamental parameter forEarth system science, and will provide valuable support for urban planningand water cycle study. The GISA-10m can be freely downloaded from https://doi.org/10.5281/zenodo.5791855 (Huang et al., 2021a).

[学科分类] 眼科学