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An Inventory of Large-Scale Landslides in Baoji City, Shaanxi Province, China
Landslides are a typical geological hazard that endangers people’s lives and property in the Loess Plateau. The destructiveness of large-scale landslides, in particular, is incalculable. For example, traffic disruptions, river blockages, and house collapses may all result from landslides. Thus, it i...
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Published in: | Data 2022, Vol.7 (8) |
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Main Authors: | , , , , |
Format: | Report |
Language: | English |
Subjects: | |
Online Access: | Get full text |
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Summary: | Landslides are a typical geological hazard that endangers people’s lives and property in the Loess Plateau. The destructiveness of large-scale landslides, in particular, is incalculable. For example, traffic disruptions, river blockages, and house collapses may all result from landslides. Thus, it is urgent to compile a complete inventory of landslides in a specific region. The investigation object of this study is Baoji City, Shaanxi Province, China. Using the multi-temporal high-resolution remote sensing images from Google Earth, we preliminarily completed the cataloging of large-scale (area > 5000 m[sup.2] ) landslides in the study area through visual interpretation. The inventory was subsequently compared with the existing literature and hazard records for improvement and supplement. We identified 3422 landslides with a total area of 360.7 km[sup.2] and an average area of 105,400 m[sup.2] for each individual landslide. The largest landslide had an area of 1.71 km[sup.2] , while the smallest one was 6042 m[sup.2] . In previous studies, we analyzed these data without describing the data sources in detail. We now provide a shared dataset of each landslide in shp format, containing geographic location, boundary information, etc. The dataset is significantly useful for understanding the distribution characteristics of large-scale landslides in this region. Moreover, it can serve as basic data for the study of paleolandslide resurrection. Dataset: Dataset License: Creative Commons Attribution 4.0 International. |
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ISSN: | 2306-5729 2306-5729 |
DOI: | 10.3390/data7080114 |