Urban Impermeable Surface (UIS) and Urban Green Space (UGS) are the two core components that describe the characteristics of the urban underlying environment. However, urban impervious surfaces (UIS) and urban green spaces (UGS) are often embedded together in urban landscapes, with complex structures and composites. Hard classification or binary single type cannot effectively delineate spatially clear urban surface attributes. Although six mainstream datasets of global or national urban land use and land cover products with a spatial resolution of 30 meters have been developed, they only provide binary patterns or dynamics of a single urban land type and cannot effectively divide the quantitative components or structures of urban land cover. Here, we propose a new surveying and mapping strategy that utilizes the advantages of collaborative big data processing and manual interpretation, leveraging geographic knowledge to obtain multi temporal and segmented information on basic types of urban land cover nationwide
Vector polygons of urban boundaries in the Chinese Land Use/Cover Dataset (CLUD) derived from satellite imagery for the years 2000, 2005, 2010, 2015, and 2018
Firstly, urban boundary vector polygons for the years 2000, 2005, 2010, 2015, and 2018 were extracted from the China Land Use/Cover Dataset (CLUD) derived from satellite imagery. Secondly, using the Google Earth Engine (GEE) platform, the national settlement and vegetation ratios were retrieved using the Random Forest algorithm and sub-pixel decomposition method. Finally, we developed Chinese UIS and UGS fractional products (CLUD Urban) with 30 meter resolution in 2000, 2005, 2010, 2015, and 2018
We compared our product with six mainstream datasets in terms of quality and accuracy. The evaluation results indicate that compared to other products, the CLUD Urban product has higher accuracy in detecting urban boundaries and urban expansion. In addition, accurate UIS and UGS scores have been developed for each period. The overall accuracy of urban boundaries from 2000 to 2018 exceeded 92.65%; The correlation coefficient (R) and root mean square error (RMSE) of UIS and UGS scores are 0.91 and 0.10 (UIS) and 0.89 and 0.11 (UGS), respectively
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CSTR:11738.11.NCDC.ZENODO.DB6528.2024
10.5281/zenodo.4034161