Update infromation:
2022/10/26 JST Terrain22 (Global GeoTIFF) data was added.
2022/06/04 JST This website opened. The polygon data was published.
The contents of this website are subject to change without notice.

General Information

This site is a data publication site for data related to the following paper:

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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 website). Please note that these mask data were used as masks to minimize errors in areas where the slopes change rapidly and do 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 website) 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

Data partitioning

Segmentation and classification

Polygon data attached to Iwahashi and Yamazaki (2022): released 2022/06/04 JST

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• Properties of the polygon data (shapefile dataset, Poly_XX) XX: the index number
polyID: ID number for each polygon
lnSLOPE（calculated as in Table 1）: average value
lnHAND（calculated as in Table 1）: average value
TEXTURE（calculated as in Table 1）: average value
CONVEXITY（calculated as in Table 1）: average value
Sinks（calculated as in Table 1）: (1) The majority of the area within a polygon is sinks. (0) The majority of the area within a polygon is not sinks.
Noise（manual interpretation; datasets with apparent noise only）: (0) There is no apparent noise or ice sheet. (1) There is apparent noise on CONVEXITY. (2) There is apparent noise on TEXTURE and CONVEXITY. (3) Apparent ice sheet. (4) There is apparent noise on TEXTURE. (5) There is apparent noise on lnSLOPE. (6) There is apparent noise on lnSLOPE, TEXTURE, and CONVEXITY. * (0) to (2) are found in a wide range of areas; (3) is found in the polar regions; (4) to (6) are found only in desert areas.
COMID: ID number for each unit catchment of MERIT-Basin (Lin et al., 2019). Polygons with '-99999' are the regions which MERIT-Basin does not cover.


• Properties of the cluster file (regional or global cluster; dBASE IV, Cluster_XX or GlobalCluster_XX)
polyID: ID number for each polygon
ZlnSLOPE: standardized lnSLOPE. Noise areas (2) to (6) are not included.
ZlnHAND: standardized lnHAND. Noise areas (2) to (6) are not included.
Ztexture: standardized TEXTURE. Noise areas (2) to (6) are not included.
Cluster15, Cluster40: Cluster number by k-means clustering of 15 and 40 categories using standardized lnSLOPE, lnHAND and TEXTURE of the basin with each polygon area as the weight. The cluster number (1-15 or 1-40) is not common to other basins. A map using this data cannot be merged with different basins. Noise areas (2) to (6) are not included. On some small islands, Cluster40 was omitted.
Gcluster15, Gcluster40: Cluster number by k-means clustering of 15 and 40 categories using standardized lnSLOPE, lnHAND and TEXTURE of the global data with each polygon area as the weight. The cluster number (1-15 or 1-40) is common to other basins. A map using this data can be merged with different basins. Noise areas (2) to (6) are not included.


• Properties of the cluster convergence values (CSV, XX_convergence_values_cluster15, XX_convergence_values_cluster40, Global_convergence_values_cluster15, Global_convergence_values_cluster40)
Convergence values for each clustering of ZlnSLOPE, ZlnHAND, Ztexture.

Usage of the dataset

• Joining with the cluster file: The cluster file (dBASE IV; Cluster_XX, GlobalCluster_XX) can be joined with the shapefile dataset (Poly_XX) by polyID using GIS software.


• Grouping the cluster: If you feel that the number of clusters (e.g. 40) is too large, you can consider grouping them by your own customized method. Please find ideas for groupings in Iwahashi and Yamazaki (2022).
• Use of topographic measurements: you can create an original classification map using attributes within the polygons.
• Partial reclassification: Sinks can be used to re-classify plains.In areas where CONVEXITY does not contain noise, its use may enable you to re-classify intermediate slopes.
• Use of COMID: COMID (Lin et al., 2019) can be used as a key to join data on the upstream and downstream locations of unit catchments (additional river data of Lin et al. (2019) is needed). The relationship between upstream and downstream in the same unit catchment can probably be determined by the value of lnHAND.

Notes

• The DEM reflects the topography at the time of measurement. Due to the nature of MERIT DEM, unevenness may remain in densely built-up areas. Therefore, the classification results are not likely to be the expected ground proxies for man-made altered areas such as reclaimed land or cut-and-fill areas, and areas of dense high-rise buildings.
• Since the Noise areas were designated visually, there is a possibility that there may be some oversight or overestimation.
• In some areas of brackish lakes and large rivers, the center-line may not be NoDATA, but thin polygons. This is due to the OpenStreetMap used to extract these areas. Since the areas are very small, they do not have obvious influence on clustering, but sometimes they may need to be removed before use.
• There are often small polygons at the confluences of valleys.
• Terrestrial lands formed after the measurement period of the source DEM of MERIT have an elevation of 0. Therefore, they are classified into the same cluster as low plains. We changed the cluster number to 0 where we noticed it, but some may still exist in the dataset.
• Small lakes that are not included in Hydro LAKES (Messager et al. 2016) and OSM Water Layer (Yamazaki et al. 2017) are classified in the same cluster as low plains.
• If you find errors in the data, please contact the first author.

Terrain22 (Global GeoTIFF): released 2022/10/26 JST

Based on Gcluster15 (in addition, the clusters with more than 10% polygons of Sinks (1) were subdivided), terrain classification maps were constructed and published on GeoTIFF as Terrain22. Terrain22 maps can be joinable and allow global terrain observation in a 3-second-arc grid, WGS84, GeoTIFF format.

Terrain22 is a processed product of the polygon data of Iwahashi and Yamazaki (2022). When citing Terrain22, please cite Iwahashi and Yamazaki (2022) and also include this URL and specify Terrain22.

• There is a gap of about 1 cell at the seam with the adjacent GeoTIFF.
• Other precautions for data use are the same as those for "Notes" above.
• Please note that Terrain22 is just one of many examples that can be constructed from the polygon data. If you do not need to join different Basins, the method constructing terrain groups using regional clusters such as Cluster40 will probably give better results. (please check Iwahashi and Yamazaki 2022, Chapter "The regional clusters")

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References