Mapping urban land dynamics by automatic generation of ground samples from Globeland30 and random forest classification on the Google Earth Engine†

Long time series urban mapping using Landsat images remains challenging due to the difficulty of collecting representative ground samples. We proposed a mapping approach integrating a sample-transferring scheme, multiple-feature fusion classification, and temporal filtering to generate annual urban land maps in Zhengzhou during 1986 to 2021 with the aid of the Google Earth Engine (GEE). The training and validation samples for each historical year were obtained by a proposed sample transfer scheme based on stable areas derived from the GlobeLand30 datasets. Thirteen variables, such as spectral indices, spectral bands, and terrain factors, were combined to form the feature vector. Annual classification was conducted yearly using the random forest algorithm on GEE to obtain annual impervious surface (ISs) maps. Finally, the IS classification maps were postprocessed to obtain the final urban land maps. Zhengzhou was selected as the study area due to its dramatic urbanization in recent decades. The results showed that the overall accuracy and Kappa coefficient of the IS classification maps ranged from 0.8888 to 0.9578 and 0.6548 to 0.8331, respectively. Compared with other 30-m land-cover products, our urban land maps showed higher accuracy and more reliable spatial details. The proposed approach can generate massive ground samples for long time series urban mapping and is helpful for updating regional and global land-cover products. Zhengzhou's urban area increased by 1423.24 km(2) with an average expansion area of 40.66 km(2) / year from 1986 to 2021. The urban expansion in Zhengzhou showed significant stage characteristics and spatial variations. The expansion intensity indices were 0.204, 0.404, 0.736, and 2.018, respectively, at four time periods and varied across the 12 regions in Zhengzhou. (C) 2022 Society of Photo-Optical Instrumentation Engineers (SPIE)