Landsat- and Sentinel-derived glacial lake dataset in the China–Pakistan Economic Corridor from 1990 to 2020

Abstract

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).

Glacial Lake Changes and Risk Assessment in Rongxer Watershed of China–Nepal Economic Corridor

“…A semi-automated approach [22] is employed for mapping glacial lakes based on the Normalized Difference Water Index (NDWI) [34] and expert knowledge. The NDWI value of glacial lakes is higher than that of other non-water land surfaces, making it an ideal indicator for extracting the boundary of glacial lakes from remote sensing images.…”

“…The NDWI value of glacial lakes is higher than that of other non-water land surfaces, making it an ideal indicator for extracting the boundary of glacial lakes from remote sensing images. A 10 km buffer zone along RGI 6.0 glacier boundaries [35] was created to assist lake mapping, as recommended by previous studies [22,36]. The study's minimum mapping unit (MMU) is 5 pixels, equivalent to a 4500 m 2 area.…”

“…Band Green − Band NIR Band Green + Band NIR (1) where Band Green is the green band and Band NIR is the near-infrared band. Uncertainty of glacial lake mapping is calculated using an improved Hanshaw's algorithm [22]. The calculation formula is as follows:…”

“…In this study, we employ two glacial lake classification systems (GLCS) as proposed by Lesi et al [22]. In the first glacial lake classification system (GLCS1), glacial lakes are categorized as supraglacial, ice-contact, unconnected-glacier-fed lakes, and non-glacier-fed lakes based on the spatial relationship between glaciers and glacial lakes (Table 2).…”

“…Landsat images and manual mapping methods were used to investigate glacial lakes throughout the Third Pole region [21]. In the China-Pakistan Economic Corridor (CPEC), a dataset of glacial lakes was created using the NDWI mapping method on Landsat remote sensing imagery from 1990 to 2020 [22].…”