Global polygons for terrain classification divided into uniform slopes and basins

Abstract Global terrain classification data have been used for various issues related to topography such as the estimation of soil types and ground vulnerability earthquakes creation seismic hazard maps. However, due resolution digital elevation models (DEMs), from previous studies could not discriminate small landforms plains at bottom narrow valleys rises in plains. Owing greater regional variation landforms, there is trade-off between DEMs higher global geomorphological legends. To address this problem, we first merged regions with similar topographic characteristics using slope gradients HAND (height above nearest drainage) calculated by 90-m-spatial-resolution interpolated multi-error-removed improved-terrain DEM (MERIT DEM), united polygons unit catchments MERIT-Basins dataset, so that contain measurements (slope gradient, HAND, surface texture, local convexity, sinks) noise attributes, well ID number catchment. In addition, performed k -means clustering on dataset which can be combined a simple classification. The results were prepared 15 40 uniform clusters each basin understand appearance provide zoning problem-solving. observe outline without any processing, whereas group reclassify create region. This showed improvements terms capturing compared authors' data. proxy expected contribute modeling applications are known topography. datasets article available [ https://gisstar.gsi.go.jp/terrain2021/ ].