Multi-temporal glacier inventories provide key information about the glaciers, their characteristics and changes and are inevitable for glacier modelling and investigating geodetic mass changes. However, to date, no consistent multi-tempo glacier inventory for the whole of the Karakoram exists, negatively affecting the monitoring of spatiotemporal variations of glaciers’ geometric parameters and their related applications. We used a semi-automatic method by combining automatic segmentation and manual correction and produced multi-temporal Karakoram glacier inventories (KGI) for 1990s, 2000s, 2010s, and 2020s, compiled from Landsat TM/ETM+/OLI images. Our assessments using independent multiple digitization of 37 glaciers show that the KGI is sufficiently accurate, with an overall uncertainty of ±3.68%. We attached more than 20 attributes for each analyzed glacier, including glacier ID, area, topographical information and surface types. The data are provided as ESRI Shapefile files with detailed attributes. The KGI data in this study can provide basic glacier outlines for GLIMS and the forthcoming planned release of the next versions of the RGI, as well as for other glacier-related research applications.
|collect time||1990/07/01 - 2020/10/01|
|collect place||Multi-temporal glacier inventory from Remote sensing data in Karakoram mountians, western Tibetan|
|altitude||1300.0m - 8600.0m|
|data size||173.8 MiB|
We selected Level 1 terrain-corrected products of the Landsat TM/ETM+/OLI scenes (L1TP) representing the years 1990~2020, including 65 TM images, 39 ETM+ images and 82 OLI images; several Sentinel-2 10 meter resolution images and 2 m resolution Planet images are used in estimation of the uncertainty of glacier inventory accuracy.
The data are processed by the combination of band threshold method and manual correction, including preliminary extraction of glacier boundary, manual correction, ice divides, attribute assignment and uncertainty evaluation. See the data description document for details.
The glacier inventory is sufficiently accurate, with an overall mapping error of ±3.68%.
|1||2021YFE0116800||cooperation research and demonstration application of monitoring technologies for the snow,glaciers and geohazards||National key R & D plan|
|2||2021KF01||Development of the Karakoram Interdecadal Glacier Inventory Dataset||other|
|3||42171129||Characterizing the long-term effect of large glaciers on water resource in Asian higu mountains and plateaus||National Natural Science Foundation of China|
|1||The Second Glacier Inventory Dataset of the Kunlun Mountains in China（V1.0）|
|2||Surface velocity of Rimo Glacier in the Karakoram (1990-2019)|
|3||Data Set on Surface Elevation Changes of Rimo Glacier in the Karakoram Mountains (2000-2019)|
|4||Dataset of Glacier Surface Elevation Changes in the Hongzha River Basin of the Karakoram Mountains (2000-2020)|
|5||Glacier Surface Motion Velocity Dataset in the Hongzha River Basin of the Karakoram Mountains (1990-2019)|
|6||Glacier inventory dataset of Bhutan|
|7||An updated ALASKA Glacier Inventory by using Landsat 8 OLI in 2018|
|8||Glacial catalogue data set of Nepal|
|9||Glacier inventory dataset in Sikkim region, India|
|10||The second glacial catalogue data set of China (v1.0)|
©Copyright 2005-. Northwest Institute of Eco-Environment and Resources, CAS.
Donggang West Road 320, Lanzhou, Gansu, China (730000)