Last Updated: 2022/06/04 JST
The contents of this website are subject to change without notice.

About

This site was created as a download site to publish data of global polygons for terrain classification.
This work was supported by JSPS KAKENHI Grant Number JP18H00769.

Aim and Scope

The datasets on this site were created by calculating the terrain measurements from a 90-m DEM and then dividing the unit regions to create polygon data. The datasets may serve as material for creating terrain classification maps anywhere on the globe. We hope that this data will be used for scientific research and education.
If you have any questions or concerns about the use of our product, please contact the first author.

Citation

Iwahashi, J., Yamazaki, D. (2022) Global polygons for terrain classification divided into uniform slopes and basins. Prog Earth Planet Sci 9, 33. https://doi.org/10.1186/s40645-022-00487-2

Materials

DEM (Digital Elevation Model) MERIT DEM v1.0.3 (based on Yamazaki et al., 2017 MERIT DEM) was used as the primary material for these datasets.
Masking data of NoData For lakes, lake polygons from HydroLAKES (Messager et al., 2016) were used. For major rivers and brackish lakes, the Code2 major rivers and water sections extracted from the OSM Water Layer was used (OSM Water Layer is available for download on the IIS U-Tokyo webpage). Please note that these mask data were used as masks to minimize errors in areas where the slopes change rapidly and does not necessarily reflect the actual extent of the land and water areas.
Basin data The unit catchment polygons of MERIT-Basins (Lin et al., 2019 webpage) were used as thematic data for segmentation.
Noise area Polygon data of noise regions (self-made by manual interpretation, mainly for stripe noise and ice sheets) were used as thematic data for segmentation.

Terrain measurements

  Table 1
Attribute Calculation method Description
lnSLOPE ln(SLOPE + 1)
SLOPE: slope gradient (degrees) calculated from the 90-m DEM interpolated by the bilinear option
SLOPE was calculated within 3 by 3 cells windows, using QGIS 3.14, by the Horns method
lnHAND ln(HAND + 1)
HAND: Height Above the Nearest Drainage (m)
HAND was calculated by the method of Yamazaki et al. (2012) and Yamazaki et al. (2019).
TEXTURE Density within a 10-cell radius of pits and peaks obtained by the difference between the Original DEM from the 3x3 median filtered DEM.
Original DEM: the 90-m DEM interpolated by the nearest-neighbour option
The surface texture of Iwahashi and Pike (2007). TEXTURE was calculated by the "terrain surface texture" tool of SAGA (Conrad, 2012a) in QGIS 3.4 (Threshold: 5-m, radius: 10-cells, no distance weighting)
CONVEXITY Density within a 10-cell radius of convex points obtained by processing the DEM with a 3x3 Laplacian filter.
Original DEM: the 90-m DEM interpolated by the nearest-neighbour option
The local convexity of Iwahashi and Pike (2007). CONVEXITY was calculated by the "terrain surface convexity" tool of SAGA (Conrad, 2012b) in QGIS 3.4 (Threshold: 1-m, radius: 10-cells, no distance weighting)
Sinks Sinks is the following region:
((Filled DEM) - (Original DEM)) > 0
Original DEM: the 90-m DEM interpolated by the bilinear option
Filled DEM was calculated by the "Fill Sinks (Wang & Liu)" (Wang and Liu, 2006) tool of SAGA in QGIS 3.4
* Please note that while basically following the method of Iwahashi et al. (2021), the calculation methods of HAND and CONVEXITY are different.

Data partitioning

The polygon data sets are provided for each of MERIT-Basin's major hydrological boundaries (Lin et al., 2019) and boundaries drawn in the sea (below).

Download

Please see the figure above for the index numbers.The numbers 11 to 91 coinside with the basin numbers of MERIT-Basin (Lin et al., 2019).
11 (391MB, unzipped 2.13GB)12 (495MB, unzipped 2.69GB)13 (603MB, unzipped 3.32GB)14 (454MB, unzipped 2.54GB)15 (749MB, unzipped 4.20GB)16 (256MB, unzipped 1.43GB)17 (340MB, unzipped 1.90GB)18 (79.6MB, unzipped 436MB)21 (176MB, unzipped 969MB)22 (311MB, unzipped 1.68GB)23 (210MB, unzipped 1.13GB)24 (218MB, unzipped 1.18GB)25 (91.6MB, unzipped 494MB)26 (131MB, unzipped 731MB)27 (15.2MB, unzipped 82.9MB)28 (387MB, unzipped 2.12GB)29 (680MB, unzipped 3.76GB)31 (388MB, unzipped 2.15GB)32 (358MB, unzipped 1.93GB)33 (152MB, unzipped 833MB)34 (346MB, unzipped 1.86GB)35 (392MB, unzipped 2.08GB)36 (39.4MB, unzipped 217MB)41 (64.3MB, unzipped 354MB)42 (394MB, unzipped 2.12GB)43 (580MB, unzipped 3.14GB)44 (289MB, unzipped 1.56GB)45 (553MB, unzipped 3.02GB)46 (255MB, unzipped 1.41GB)47 (175MB, unzipped 974MB)48 (198MB, unzipped 1.08GB)49 (79.2MB, unzipped 424MB)51 (87.3MB, unzipped 490MB)52 (160MB, unzipped 890MB)53 (123MB, unzipped 687MB)54 (730KB, unzipped 3.80MB)55 (7.3MB, unzipped 40.6MB)56_1 (350MB, unzipped 1.99GB)56_2 (481MB, unzipped 2.72GB)57 (38.6MB, unzipped 210MB)61 (248MB, unzipped 1.35GB)62 (811MB, unzipped 4.48GB)63 (274MB, unzipped 1.48GB)64 (407MB, unzipped 2.25GB)65 (176MB, unzipped 978MB)66 (150MB, unzipped 812MB)67 (69.9MB, unzipped 384MB)71 (236MB, unzipped 1.28GB)72 (366MB, unzipped 1.92GB)73 (130MB, unzipped 735MB)74 (408MB, unzipped 2.23GB)75 (274MB, unzipped 1.49GB)76 (27.6MB, unzipped 154MB)77 (346MB, unzipped 1.88GB)78 (201MB, unzipped 1.05GB)81 (246MB, unzipped 1.30GB)82 (249MB, unzipped 1.33GB)83 (140MB, unzipped 769MB)84 (44.2MB, unzipped 238MB)85 (77.2MB, unzipped 419MB)86 (61.3MB, unzipped 333MB)91 (244MB, unzipped 1.37GB)101 (2.13MB, unzipped 11.8MB)102 (946KB, unzipped 5MB)103 (743KB, unzipped 4.1MB)104 (68.8KB, unzipped 347KB)105 (70.6KB, unzipped 350KB)106 (1.25MB, unzipped 6.83MB)

* We used the multiresolution segmentation tool of eCognition v10 (Trimble) for segmentation. The parameters are as follows.
Weight: lnSLOPE:lnHAND = 2:1, Scale parameter = 5, shape parameter and compactness = 0.
As a new attempt compared to Iwahashi et al. (2021), the unit catchment polygons from MERIT-Basins (Lin et al., 2019) were superimposed as a thematic layer and designed so that slopes of similar shape are divided by the ridge line of the catchment area. Noise areas (if available) were also superimposed.
* The k-means clustering tool of SPSS v26 was used for clustering. Used parameters (lnSLOPE, lnHAND and TEXTURE), data pre-processing (weight by area, standardization of the parameters) are in the same as in Iwahashi et al. (2021).
* 56_1 and 56_2 are the same basin, but due to the amount of data, the segmentation was done in two parts divided by boundaries of unit catchments. The clustering was done in a lumped manner.

Usage of the dataset and notes

References

Conrad O (2012a) Module Terrain Surface Texture / SAGA-GIS Module Library Documentation (v2.2.5). http://www.saga-gis.org/saga_tool_doc/2.2.5/ta_morphometry_20.html
Conrad O (2012b) Module Terrain Surface Convexity / SAGA-GIS Module Library Documentation (v2.2.5). http://www.saga-gis.org/saga_tool_doc/2.2.5/ta_morphometry_20.html
Iwahashi J, Pike RJ (2007) Automated classifications of topography from DEMs by an unsupervised nested-means algorithm and a three-part geometric signature, Geomorphology, 86, 409-440. https://doi.org/10.1016/j.geomorph.2006.09.012 Data download site
Iwahashi J, Kamiya I, Matsuoka M and Yamazaki D (2018) Global terrain classification using 280 m DEMs: segmentation, clustering, and reclassification. Progress in Earth and Planetary Science, 5:1. https://doi.org/10.1186/s40645-017-0157-2 Data download site
Iwahashi J, Yamazaki D, Nakano T, Endo R (2021) Classification of topography for ground vulnerability assessment of alluvial plains and mountains of Japan using 30 m DEM. Progress in Earth and Planetary Science, 8:3. https://doi.org/10.1186/s40645-020-00398-0 Data download site
Lin P, Pan M, Beck HE, Yang Y, Yamazaki D, Frasson R, David CH, Durand M, Pavelsky TM, Allen GH, Gleason CJ, Wood EF (2019) Global reconstruction of naturalized river flows at 2.94 million reaches. Water Resources Research https://doi.org/10.1029/2019WR025287
Messager ML, Lehner B, Grill G, Nedeva I, Schmitt O (2016) Estimating the volume and age of water stored in global lakes using a geo-statistical approach. Nature Communications: 13603. https://doi.org/10.1038/ncomms13603
Wang L, Liu H (2006) An efficient method for identifying and filling surface depressions in digital elevation models for hydrologic analysis and modelling. International Journal of Geographical Information Science, Vol. 20, No. 2, 193-213.
Yamazaki D, Baugh CA, Bates PD, Kanae S, Alsdorf DE, Oki T (2012) Adjustment of a spaceborne DEM for use in floodplain hydrodynamic modeling. Journal of Hydrology, Vol. 436-437, 81-91.
Yamazaki D, Ikeshima D, Tawatari R, Yamaguchi T, O'Loughlin F, Neal JC, Sampson CC, Kanae S, Bates PD (2017) A high accuracy map of global terrain elevations, Geophysical Research Letters, vol.44, pp.5844-5853, https://doi.org/10.1002/2017GL072874
Yamazaki D, Ikeshima D, Sosa J, Bates PD, Allen GH, Pavelsky TM (2019) MERIT Hydro: a high‐resolution global hydrography map based on latest topography dataset. Water Resources Research, 55,5053–5073. https://doi.org/10.1029/2019WR024873