Yale University, Yale School of Forestry & Environmental Studies, New Haven, CT 06511, USA.
Yale University, Yale Center for Research Computing, New Haven, CT 06511, USA.
Sci Data. 2018 Mar 20;5:180040. doi: 10.1038/sdata.2018.40.
Topographic variation underpins a myriad of patterns and processes in hydrology, climatology, geography and ecology and is key to understanding the variation of life on the planet. A fully standardized and global multivariate product of different terrain features has the potential to support many large-scale research applications, however to date, such datasets are unavailable. Here we used the digital elevation model products of global 250 m GMTED2010 and near-global 90 m SRTM4.1dev to derive a suite of topographic variables: elevation, slope, aspect, eastness, northness, roughness, terrain roughness index, topographic position index, vector ruggedness measure, profile/tangential curvature, first/second order partial derivative, and 10 geomorphological landform classes. We aggregated each variable to 1, 5, 10, 50 and 100 km spatial grains using several aggregation approaches. While a cross-correlation underlines the high similarity of many variables, a more detailed view in four mountain regions reveals local differences, as well as scale variations in the aggregated variables at different spatial grains. All newly-developed variables are available for download at Data Citation 1 and for download and visualization at http://www.earthenv.org/topography.
地形变化是水文、气候、地理和生态学中诸多模式和过程的基础,也是理解地球上生命变化的关键。一个完全标准化和全球化的、多种地形特征的多元产品,有可能支持许多大规模的研究应用,但迄今为止,这样的数据集还不存在。在这里,我们使用全球 250 米 GMTED2010 的数字高程模型产品和近全球 90 米 SRTM4.1dev 来生成一系列地形变量:海拔、坡度、方位、东经、北纬、粗糙度、地形粗糙度指数、地形位置指数、向量粗糙度度量、剖面/切向曲率、一阶和二阶偏导数,以及 10 种地貌地形类别。我们使用了几种聚合方法,将每个变量聚合到 1、5、10、50 和 100 公里的空间粒度上。虽然相关性分析强调了许多变量的高度相似性,但在四个山区的更详细视图中,揭示了局部差异,以及在不同空间粒度上聚合变量的尺度变化。所有新开发的变量都可在数据引用 1 中下载,也可在 http://www.earthenv.org/topography 上下载和可视化。