An inventory of retrogressive thaw slumps along the vulnerable Qinghai-Tibet engineering corridor (2019)- A Big Earth Data Platform for Three Poles

Retrogressive thaw slumps (RTSs) are slope failures caused by the thawing of ice-rich permafrost. Once developed, they usually retreat at high speeds (meters to tens of meters) towards the upslope direction, and the mudflow may destroy infrastructure and release carbon stored in frozen ground. RTSs are frequently distributed in permafrost areas and increase dramatically but lack investigation. Qinghai Tibet Engineering Corridor crosses the permafrost, links the inland and the Tibet. However, in this critical area, we lack knowledge of the distribution and impact of RTSs. To compile the first comprehensive inventory of RTSs, this study uses an iterative semi-automatic method based on deep learning and manual inspection to delineate RTSs in 2019 images. The images from PlanetScope CubeSat have a resolution of 3 meters, have four bands, cover a corridor area of approximately 54,000 square kilometers. The method combines the high efficiency and automation of deep learning and the reliability of the manual inspection to map the entire region ninth, which minimize the missings and misidentification.

The manual inspection is based on geomorphic features and temporal changes (2016 to 2020) of RTSs. The inventory which includes 875 RTSs with their attributes, including identification, Longitude and Latitude, possibilities and time, provides a benchmark dataset for quantifying permafrost degradation and its impact.

File naming and required software

The vector file in geopackage / gpkg format contains the boundary of each hot melt collapse. Relevant attribute tables include number, confidence probability, time of satellite image, source of the satellite image, near roads labels, initial year, longitude and latitude, area (unit: m2), and depth learning model used. The corresponding names of the table fields are "Id", "probability", "year-month", "source image", "near road", "initial year", "longitude", "latitude", "area", "deep learning model". "Probability" represents our confidence in the described hot melt collapse, which is divided into "high", "medium" and "low". The file in GIF format shows the time change of each hot melt collapse (from 2016 to 2020), in which the file name format is "rts_id", "Id" corresponds to the vector file.

Data Citations Data citation guideline What's data citation?

Cite as:

Xia, Z., Huang, L., Liu, L. (2021). An inventory of retrogressive thaw slumps along the vulnerable Qinghai-Tibet engineering corridor (2019). A Big Earth Data Platform for Three Poles, DOI: 10.11888/Cryos.tpdc.272672. CSTR: 18406.11.Cryos.tpdc.272672. (Download the reference： RIS | Bibtex )

Related Literatures:

1. Xia, Z., Huang, L., Fan, C., Jia, S., Lin, Z., Liu, L., Luo, J., Niu, F.,& Zhang, T. (2022). Retrogressive thaw slumps along the Qinghai--Tibet Engineering Corridor: a comprehensive inventory and their distribution characteristics. Earth System Science Data, 14(9), 3875--3887.(https://doi.org/10.5194/essd-14-3875-2022)( View Details | Bibtex)

Using this data, the data citation is required to be referenced and the related literatures are suggested to be cited.

Copyright & License

To respect the intellectual property rights, protect the rights of data authors, expand services of the data center, and evaluate the application potential of data, data users should clearly indicate the source of the data and the author of the data in the research results generated by using the data (including published papers, articles, data products, and unpublished research reports, data products and other results). For re-posting (second or multiple releases) data, the author must also indicate the source of the original data.

License： This work is licensed under an Attribution 4.0 International (CC BY 4.0)