2026-10-13T11:06:20Z https://www.scidb.cn/oai

10.57760/sciencedb.35165 2026-05-26T09:18:51Z

2026-05-26 Source Code and Data for Glacial Lake Classification in the Parlung Zangbo Basin (PLZB) version 1.0 doi:10.57760/sciencedb.35165 en Glacial lakes in the Third Pole are critically important for climate change and ecological environments, and their classification is essential for dynamic monitoring and GLOF risk assessment. However, automatic classification remains challenging. The proposed method integrates glacier centerlines, glacial meltwater flow paths, and glacier retreat zones to classify lakes into five types: supraglacial, ice-contact, glacier-proximal, glacier-distal, and non-glacially-fed lakes. This dataset contains the source code, input data, intermediate outputs, and final classification results for automatically identifying glacial lake types in the Parlung Zangbo Basin (PLZB), Tibetan Plateau. Specifically, it includes (1) multi‑source input data (glacier/lake inventories, NASADEM, flow direction raster, glacier retreat zones, basin boundaries); (2) Python scripts for extracting glacier minimum points and performing the full classification (with detailed annotations); and (3) all intermediate (cache) and output files, including classification results in shapefile and Excel formats, meltwater inflow channels, and summary statistics at sub‑basin and micro‑basin levels. The results show that out of 1,429 glacial lakes classified, PLZB contains 13 supraglacial, 41 ice‑contact, 521 glacier‑proximal, 235 glacier‑distal, and 619 non‑glacially‑fed lakes. The scheme is sensitive to glacier extent changes and can accurately identify non‑glacially‑fed lakes within 10 km of glaciers. This dataset supports reproducible research, facilitates large‑scale glacial lake mapping, and provides a foundation for enhanced GLOF susceptibility assessment. glacial lake type; automatic classification; glacier centerline; glac-ier retreat zone Dahong Zhang Shiqiang Zhang PUBLIC https://creativecommons.org/licenses/by/4.0/ dataset http://www.doi.org/10.3390/rs18101467 Science Data Bank