A 30 m resolution dataset of China's urban impervious surface area and green space, 2000–2018
Authors: Kuang Wenhui , Zhang Shu , Li Xiaoyong , Lu Dengsheng 
Published: January 2021

Abstract. Accurate and timely maps of urban underlying land
properties at the national scale are of significance in improving habitat
environment and achieving sustainable development goals. Urban impervious
surface (UIS) and urban green space (UGS) are two core components for
characterizing urban underlying environments. However, the UIS and UGS are
often mosaicked in the urban landscape with complex structures and
composites. The “hard classification” or binary single type cannot be used
effectively to delineate spatially explicit urban land surface property.
Although six mainstream datasets on global or national urban land use and land cover
products with a 30 m spatial resolution have been developed, they only provide
the binary pattern or dynamic of a single urban land type, which cannot
effectively delineate the quantitative components or structure of
intra-urban land cover. Here we propose a new mapping strategy to acquire
the multitemporal and fractional information of the essential urban land
cover types at a national scale through synergizing the advantage of both big
data processing and human interpretation with the aid of geoknowledge. Firstly,
the vector polygons of urban boundaries in 2000, 2005, 2010, 2015 and 2018
were extracted from China's Land Use/cover Dataset (CLUD) derived from
Landsat images. Secondly, the national settlement and vegetation percentages
were retrieved using a sub-pixel decomposition method through a random forest
algorithm using the Google Earth Engine (GEE) platform. Finally, the products of
China's UIS and UGS fractions (CLUD-Urban) at a 30 m resolution were
developed in 2000, 2005, 2010, 2015 and 2018. We also compared our products
with six existing mainstream datasets in terms of quality and accuracy. The
assessment results showed that the CLUD-Urban product has higher accuracies
in urban-boundary and urban-expansion detection than other products and in
addition that the accurate UIS and UGS fractions were developed in each
period. The overall accuracy of urban boundaries in 2000–2018 are over
92.65 %; and the correlation coefficient (R) and root mean square errors
(RMSEs) of UIS and UGS fractions are 0.91 and 0.10 (UIS) and 0.89 and 0.11 (UGS),
respectively. Our result indicates that 71 % of pixels of urban land were
mosaicked by the UIS and UGS within cities in 2018; a single UIS
classification may highly increase the mapping uncertainty. The high spatial
heterogeneity of urban underlying covers was exhibited with average
fractions of 68.21 % for UIS and 22.30 % for UGS in 2018 at a national
scale. The UIS and UGS increased unprecedentedly with annual rates of
1605.56 and 627.78 km2 yr−1 in 2000–2018, driven by fast
urbanization. The CLUD-Urban mapping can fill the knowledge gap in
understanding impacts of the UIS and UGS patterns on ecosystem services and
habitat environments and is valuable for detecting the hotspots of waterlogging
and improving urban greening for planning and management practices. The
datasets can be downloaded from https://doi.org/10.5281/zenodo.4034161
(Kuang et al., 2020a).