Data · dataset · 2026
Analysis of Original Data on the Topographic Characteristics of Linzhou City
Listed in ScienceDB
(1) DEM Data.
Description
The Digital Elevation Model (DEM) data utilized in this study is derived from the ASTER GDEM V3 dataset, featuring a spatial resolution of 30 meters. This data was acquired from the Geospatial Data Cloud platform in April 2026.
Jointly released by the Ministry of Economy, Trade and Industry (METI) of Japan and the National Aeronautics and Space Administration (NASA) of the United States, the dataset provides global coverage of land surfaces, with a vertical accuracy of approximately 20 meters and a horizontal accuracy of approximately 30 meters. The primary rationale for selecting the 30-meter resolution is that the study area, covering approximately 2,046 km², pertains to regional-scale geomorphological analysis.
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This resolution effectively captures topographic relief characteristics at both macro and meso scales, thereby balancing analytical precision with computational efficiency .(2) Administrative Division Data. The vector boundary data for the administrative divisions of Linzhou City were sourced from the public version of the fundamental geographic information data provided by the National Basic Geographic Information Center via Tianditu.
These data possess good currentness and were employed to define the scope of the study area and to clip the DEM data accordingly.(3) Hydrographic Data. The vector data for the water systems within the study area were also obtained from the National Basic Geographic Information Center via Tianditu, at a scale of 1:1,000,000. These data were used to assist in river network extraction and validation during the watershed analysis process.(4) Process Data.
This category encompasses the data generated during the experimental procedures of this research.
Links
Where it is published
- DOI doi.org/10.57760/sciencedb.0129t ↗
DOI / persistent id · from scidb cn
Catalogue records · 1
- OAI-PMH record scidb.cn/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=10.57760%2… ↗
metadata API · from scidb cn
Topics
- From keywords
- Earth & Environmental Science · Engineering · Humanities · Life Sciences · Social Science
- Inferred from text
- Geoinformatics 69%
Provenance · 1 source records, 11 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| ScienceDB | 10.57760/sciencedb.0129t | 11 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| access_level | source · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].anzsrc:group:3704 | enrichment · scidb cn | taxonomy-embedding@1.0.0 | title+keywords+description (69%) |
| concepts[field].local:field:earth-environmental | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:engineering | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:humanities | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:life-sciences | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:social-science | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| description | source · scidb cn | connector:scidb_cn@1.0.0 | /metadata/dc/description |
| license | source · scidb cn | connector:scidb_cn@1.0.0 | /metadata/dc/rights |
| publication_date | source · scidb cn | connector:scidb_cn@1.0.0 | |
| title | source · scidb cn | connector:scidb_cn@1.0.0 | /metadata/dc/title |