A multilevel dataset of landform mapping and geomorphologic descriptors for the Loess Plateau of China.

IF 5.8 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES Scientific Data Pub Date : 2024-11-25 DOI:10.1038/s41597-024-04027-z
Sijin Li, Liyang Xiong, Yue Li, Xin Yang, Fayuan Li, Guoan Tang
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Abstract

The Loess Plateau is a region of importance in geomorphologic research because of its typical loess layers and intense surface erosion. Analysing the landforms on the Loess Plateau is helpful for understanding changes in the surface environment. However, geomorphologic data with high resolution are lacking for the Loess Plateau, which limits the progress of geomorphologic studies at finer scales. This study provides the first 30 m resolution landform classification and characteristics dataset for the Loess Plateau (LPL30). Considering morphological characteristics, dominant dynamics, and material conditions, the landforms on the Loess Plateau were categorized into three levels with 28 landform types. Moreover, we calculated 6 metrics to quantify the spatial distribution and category composition of the landforms in 16 analytical units with different scales and shapes, resulting in 96 geomorphologic descriptors. This dataset provides fundamental data for the study of landform formation processes and evolution mechanisms at various scales and can be employed as a geomorphological gradient to support ecology and environmental research.

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中国黄土高原地貌图和地貌描述符的多层次数据集。
黄土高原因其典型的黄土层和强烈的地表侵蚀而成为地貌研究的重要区域。分析黄土高原的地貌有助于了解地表环境的变化。然而,黄土高原缺乏高分辨率的地貌数据,这限制了更精细尺度的地貌研究进展。本研究首次提供了 30 米分辨率的黄土高原地貌分类和特征数据集(LPL30)。综合考虑形态特征、主导动力和物质条件,我们将黄土高原的地貌分为三个等级,共 28 种地貌类型。此外,我们还计算了 6 个指标,以量化不同尺度和形状的 16 个分析单元中地貌的空间分布和类别组成,从而得出 96 个地貌描述符。该数据集为研究不同尺度的地貌形成过程和演化机制提供了基础数据,并可用作地貌梯度,为生态学和环境研究提供支持。
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来源期刊
Scientific Data
Scientific Data Social Sciences-Education
CiteScore
11.20
自引率
4.10%
发文量
689
审稿时长
16 weeks
期刊介绍: Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.
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