China has witnessed rapid urban growth over the past 3 decades resulting in vast ecological and environmental issues. Understanding the process of construction land (Cl) expansion and its driving factors are crucial for urban growth planning and management to mitigate the adverse impacts of such growth. Based on remote sensing data for 1975, 1990, 2000, and 2008, we monitored Cl changes in Xi'an City over the past 3 decades. The land use transform matrices were calculated. The results showed that from 1975 to 2008, remarkable changes in Cl occurred in Xi'an City. Over the past 30 years, the Cl concentration spread from one centre to multiple centres. The elevation, slope, traffic condition, wetland, education industry, rebuilding of city village, and historical and cultural sites may be the most important driving forces. We therefore recommend discussing the interactions of these potential driving factors within the Cl sprawl.

Abstract. The China–Pakistan Economic Corridor (CPEC) is one of the flagship projects of the One Belt One Road Initiative, which faces threats from water shortage and mountain disasters in the high-elevation region, such as glacial lake outburst floods (GLOFs). An up-to-date high-quality glacial lake dataset with parameters such as lake area, volume, and type, which is fundamental to water resource and flood risk assessments and prediction of glacier–lake evolutions, is still largely absent for the entire CPEC. This study describes a glacial lake dataset for the CPEC using a threshold-based mapping method associated with rigorous visual inspection workflows. This dataset includes (1) multi-temporal inventories for 1990, 2000, and 2020 produced from 30 m resolution Landsat images and (2) a glacial lake inventory for the year 2020 at 10 m resolution produced from Sentinel-2 images. The results show that, in 2020, 2234 lakes were derived from the Landsat images, covering a total area of 86.31±14.98 km2 with a minimum mapping unit (MMU) of 5 pixels (4500 m2), whereas 7560 glacial lakes were derived from the Sentinel-2 images with a total area of 103.70±8.45 km2 with an MMU of 5 pixels (500 m2). The discrepancy shows that Sentinel-2 can detect a large quantity of smaller lakes compared to Landsat due to its finer spatial resolution. Glacial lake data in 2020 were validated by Google Earth-derived lake boundaries with a median (± standard deviation) difference of 7.66±4.96 % for the Landsat-derived product and 4.46±4.62 % for the Sentinel-derived product. The total number and area of glacial lakes from consistent 30 m resolution Landsat images remain relatively stable despite a slight increase from 1990 to 2020. A range of critical attributes has been generated in the dataset, including lake types and mapping uncertainty estimated by an improved equation of Hanshaw and Bookhagen (2014). This comprehensive glacial lake dataset has the potential to be widely applied in studies on water resource assessment, glacial lake-related hazards, and glacier–lake interactions and is freely available at https://doi.org/10.12380/Glaci.msdc.000001 (Lesi et al., 2022).

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