Iwahashi et al. Progress in Earth and Planetary Science (2018) 5:1
DOI 10.1186/s40645-017-0157-2

Progress in Earth andPlanetary Science

RESEARCH ARTICLE

Open Access

Global terrain classification using 280 m
DEMs: segmentation, clustering, andreclassification
Junko Iwahashi1*, Izumi Kamiya2, Masashi Matsuoka3 and Dai Yamazaki4

Abstract
Polygon-based terrain classification data were created globally using 280 m digital elevation models (DEMs)
interpolated from the multi-error-removed improved-terrain DEM (MERIT DEM). First, area segmentation was
performed globally with the logarithmic value of slope gradient and the local convexity calculated from the DEM.
Next, by adding surface texture, k-means clustering was performed globally and the polygons were grouped into
40 clusters. Then, we tried to reclassify these 40 clusters into geomorphologic terrain groups. In this study, we
attempted reclassification and grouping using local information from Japan as a test case. The 40 clusters werecompared with Japanese geological and geomorphological data and were then reclassified into 12 groups that had
different geomorphological and geological characteristics. In addition, large shape landforms, mountains, and hills
were subdivided by using the combined texture. Finally, 15 groups were created as terrain groups. Cross
tabulations were performed with geological or lithological maps of California and Australia in order to investigate if
the Japanese grouping of the clusters was also meaningful for other regions. The classification is improved fromprevious studies that used 1-km DEMs, especially for the representation of terrace shapes and landform elements
smaller than 1 km. The results were generally suitable for distinguishing bedrock mountains, hills, large highland
slopes, intermediate landforms (plateaus, terraces, large lowland slopes), and plains. On the other hand, the crosstabulations indicate that in the case of gentler landforms under different geologic provinces/climates, similar
topographies may not always indicate similar formative mechanisms and lithology. This may be solved by locallyreplacing the legend; however, care is necessary for mixed areas where both depositional and erosional gentle
plains exist. Moreover, the limit of the description of geometric signatures still appears in failure to detect narrowvalley bottom plains, metropolitan areas, and slight rises in gentle plains. Therefore, both global and local
perspectives regarding geologic province and climate are necessary for better grouping of the clusters, andadditional parameters or higher resolution DEMs are necessary. Successful classification of terrain types ofgeomorphology may lead to a better understanding of terrain susceptibility to natural hazards and landdevelopment.
Keywords: Geomorphological map, Terrain classification, MERIT DEM, Japan, Geomorphometry, Landform* Correspondence: iwahashi-j96pz@mlit.go.jp
1
Geospatial Information Authority of Japan, Geography and Crustal Dynamics
Research Center, Kitasato 1, Tsukuba, Ibaraki 305-0811, Japan
Full list of author information is available at the end of the article© The Author(s). 2018, corrected publication February 2018 Open Access This article is distributed under the terms of theCreative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits
unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s)and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Iwahashi et al. Progress in Earth and Planetary Science (2018) 5:1

Introduction
This study was performed to develop global terrain
classification data that depict landform patterns as
polygon data and that will help to estimate theground characteristics of land. We use only digital
elevation models (DEMs) as a data source and create
a global polygon data set that can be flexibly classified. In common geomorphological maps, geomorphic
provinces are shown as homogeneous regions of thesame landform characteristics. Various landform characteristics are used: tectonic or volcanic landforms,
exogenic landforms, and lithological features that are
rather close to those used in engineering geologicalmaps. Geomorphological maps are usually produced
by interpretation of aerial photographs and various
other documents that are concerned with slope materials
and chronology. In the 1980s, some geomorphologists
highlighted the numerical characteristics of landformelements (e.g., Evans 1980; Speight 1984; Pike 1988;
Zevenbergen and Thorne 1987). Automated classificationof topography by terrain measurements using DEMs
(hereafter, terrain classification) has been developed byuse of this technology. During the early period of terrain
classification using DEMs, Dikau et al. (1991)
presented unmanned landform classification maps.
Research in the twenty-first century using only DEMs
for terrain classification appears to be converging on
two approaches; one is the classification of landformelements with reference to hydrological geomorphology and transportation of soils using high-resolution
DEMs such as laser scanning data (e.g., van Asselen
and Seijmonsbergen 2006; Drăguţ and Blaschke 2006;
MacMillan et al. 2003; del Val et al. 2015), and theother is classification of physiographic regions using
medium or small-scale DEMs (e.g., Jasiewicz et al.
2014). In recent years, many studies have combined
other parameters with DEMs; for example, Shafiqueet al. (2012) produced a seismic site characterization
map by combining terrain classification with Vs30
(average shear wave velocity for the top 30 m); Guida
et al. (2016) produced hydro-geomorphologicalscenario maps by combining API (air-photo interpretation) maps with flow accumulation data calculated
from DEMs and hydro-chemographs from field surveys, and Martha et al. (2010) extracted landslides
using terrain classification with satellite images. Hengl
et al. (2017) produced global gridded soil information
at 250-m resolution by machine learning using derivatives calculated from DEMs, land cover data fromsatellite data, lithologic units based on the Global
Lithological Map (Hartmann and Moosdorf 2012),
landform classes based on the USGS Map of Global
Ecological Land Units (Sayre et al. 2014), and soil
data from field surveys. Recent terrain classification

Page 2 of 31

studies, especially with high-resolution DEMs, show thatpolygon-based outputs are increasingly using object-based
area segmentation (Baatz and Schäpe 2000).
As Hough et al. (2010), Shafique et al. (2012), andYong et al. (2012) described, one recent practical use of
geomorphological information on the medium to small
scale is the prediction of earthquake ground motion
amplification. In Japan, the Cabinet Office recommends
the local government to use geomorphological maps forearthquake disaster prevention measures. In addition, anationwide 7.5 arc-second geomorphological map (Japan
Engineering Geomorphologic Classification Map; JEGM,
Wakamatsu and Matsuoka 2013) is used nationally forseismic hazard assessments. In California, Wills et al.
(2000, 2015) proposed a Vs30 map with the same uses.
The map was created by reclassification of the Geologic
map of California (Jennings 1977) with the addition ofsoft-ground regions. As in the above studies, terrain
classification is often cited as being related to the lithological condition of the ground.
The applications of terrain classification are diverse.
Therefore, in this study, we prepare global terrain classification data as polygon data to which other thematic information could be added easily. In addition, we present
the geomorphological grouping results of terrain in
Japan as a test case and set out thematic informationand conditions that need to be added in the future.
Iwahashi and Pike (2007) arranged a procedure forraster-GIS that became popular around 2000 and produced
a 1-km grid worldwide raster terrain classification data set
created from the Shuttle Radar Topography Mission 30
arc-second DEM (SRTM30) of the U.S. Geological Survey(USGS) using slope gradient, local convexity, and surface
texture. At present, there are also some other global terrain
classification data sets besides Iwahashi and Pike (2007)(Meybeck et al. 2001; Drăguţ and Eisank 2012; Sayre et al.
2014). However, all data have coarse (1 km) resolution or
do not represent terraces and fans that are important tohuman habitats. Iwahashi et al. (2015) tried to create
polygon data of terrain classification by object-based
area segmentation (Baatz and Schäpe 2000) using thegeometric signatures of Iwahashi and Pike (2007). In
the current study, we develop this approach andintroduce a trial of a new procedure of polygon-basedclassification using 280 m DEMs interpolated fromthe multi-error-removed improved-terrain DEM(MERIT DEM; Yamazaki et al. 2017).
The important point of terrain classification using a
DEM is that the result will depict geomorphologicallyappropriate classification if the DEM used is accurate.
Iwahashi and Pike (2007), however, gave only
numbers for map legends as a result of numericalclassification. In this study, we try to attach geomorphological meanings to the classification results

Iwahashi et al. Progress in Earth and Planetary Science (2018) 5:1

according to cross tabulation with geological or geomorphological maps.

Methods
DEM

A serious problem in calculating geometric signaturesfrom a fine-resolution DEM is caused by the precision
unevenness in different DEM sources and also by theheight errors in space-borne DEMs. The differences inspatial resolution or observation instrument (i.e., radaror optical) in source DEMs will cause different patternsof geometric signatures (De Reu et al. 2013; Maynard
and Johnson 2014; Ariza-Villaverde et al. 2015). Such
unevenness may not be apparent in an image of slope
gradient, which is the destination of a primary difference(Evans 1980); however, the noise is more evident in animage uses a concept of secondary difference (e.g.,
Laplacian filter or curvature) in the calculation process(Guth 2006). Moreover, an image of surface texture(Iwahashi and Pike 2007), which uses a noise detector(median filter) in the calculation process, shows veryclear unevenness (Fig. 1).
This study used the MERIT DEM that Yamazaki et al.
(2017) produced by removing multiple error components from existing space-borne DEMs. The original
MERIT DEM was developed with 3 arc second resolution by merging several baseline DEMs (Fig. 2). The
data south of 60° N are based on the Shuttle Radar Topography Mission 3 arc-second DEM (SRTM3) ver. 2.1(National Aeronautics and Space Administration;NASA), and the data north of 60° N are based on the

Page 3 of 31

AW3D-30 m DEM ver. 1 (Japan Aerospace Exploration
Agency; JAXA). Gaps in these DEMs are filled by theViewfinder Panoramas DEM (available at http://
www.viewfinderpanoramas.org/dem3.html). Yamazaki etal. (2017) used a 2D-Fourier-transform filter to remove
striping and also used ICESat (Ice, Cloud, and land Elevation Satellite) laser altimetry data (NASA) to remove
absolute bias and vegetation canopy bias. Speckle random noise was removed by applying an adaptive-scalesmoothing filter (Gallant 2011). We resampled the
MERIT DEM at 9 arc-second resolution by minimum
elevation values for the terrain classification. The minimum values were chosen because the MERIT DEM includes buildings that need to be removed as far as
possible to bring the values closer to ground data.
In Fig. 3a, the surface texture in another global
DEM, GMTED2010 (Danielson and Gesch 2011)shows marked stripe noise (Nelson et al. 2009) originating from the SRTM DEM, whereas the MERITDEM (Fig. 3b) is almost free from such unevenness.
Small areas of the MERIT DEM, which still show
unevenness (Fig. 1), were omitted from the terrain
classification (Fig. 2). These errors coincided with theareas where high-quality DEMs (i.e., SRTM or
AW3D) were not available as baseline data for the
MERIT DEM. For example, the areas omitted northof 60° N correspond to the observation gap of the
AW3D DEM, where the elevations originated fromthe Viewfinder Panoramas DEM, which has lower
effective resolution than AW3D (Fig. 1a, darker
areas). The areas omitted in desert regions also

Fig. 1 Grayscale images of surface texture from the 280 m MERIT DEM. Unevenness due to DEM source differences is visible: a around the
Scandinavian Peninsula and b in the central part of the Sahara Desert

Iwahashi et al. Progress in Earth and Planetary Science (2018) 5:1

Page 4 of 31

Fig. 2 Individual tiles and data sources of the MERIT DEM. Each tile covers 20° latitude by 30° longitude. The red areas are omitted in this study
due to unevenness of the DEM (see Fig. 1)

correspond to the observation gap of the AW3D
DEM, where removal of the stripe noise was not satisfactory (Fig. 1b, brighter areas).
The MERIT DEM includes elevations of water bodies
on land. Masking of water bodies was mostly achieved
using the water bodies in the GlobCover2009 land covermap ver. 2.3 (Bontemps et al. 2013), whose resolution(10 arc-seconds) is close to 9 arc-seconds. The areas atelevations under 0 m in the MERIT DEM were used as
masks for two tiles (50S030 E and 30S150 W in Fig. 2)that show an obvious loss of islands in GlobCover2009.
The DEM resolution is set to 280 m as this corresponds to the spatial resolution of a 9 arc-second grid in
the mid-latitudes. As Speight (1984) noted, at thisresolution, a DEM draws landform patterns rather thanlandform elements.

Fig. 3 Grayscale images of surface textures in northern Australia from the a 250 m GMTED2010 DEM and b 280 m MERIT DEM

Iwahashi et al. Progress in Earth and Planetary Science (2018) 5:1

Calculation of geometric signatures

Figure 4 shows the method for generating geometric signatures as classifying parameters and area segmentation
using the MERIT DEM. First, the MERIT DEM was aggregated into 9 arc-seconds as described in the previous

Page 5 of 31

section, a projection transform applied and 280 m DEMs
were produced by interpolation with the nearest neighbor (DEM1) and the bilinear (DEM2) methods. These
two types of DEMs were prepared in order to avoid thenoise generated from features of raster image processing

Fig. 4 Flowchart for creating the base polygons for terrain classification. The generation of the 280 m DEM, calculation of the geometricsignatures, and the area segmentation method are describedIwahashi et al. Progress in Earth and Planetary Science (2018) 5:1

in projection transformation becoming obvious in subsequent processing. Next, following Iwahashi and Pike
(2007), three geometric signatures were calculated: slope
gradient (in degrees calculated over 3 × 3 cells), local
convexity (spatial density of convex points extracted by
the Laplacian filter over 3 × 3 cells), and surface texture(spatial density of pits and peaks extracted by the median filter over 3 × 3 cells). The three geometric signatures can be calculated by common image processing
software, e.g., ArcGIS Spatial Analyst (ESRI).
Iwahashi (1994) and Iwahashi and Kamiya (1995)
introduced the slope gradient to extract mountainslopes from other slopes. In this study, slope gradient
was converted logarithmically because, in Iwahashi
and Pike (2007), thresholds of classification were
defined to be graduated values by a nested-means
method, that is, using successively averages of thetotal slope, the gentler half, the gentlest quarter, andthe gentlest quarter-half. This follows a common
geomorphological map style in which mountainous
steep slopes tend to be lumped together while plains
are divided into details. Thresholds of slope gradient for
the nested-means method do not decrease linearly likethose of surface texture and local convexity, but decrease
logarithmically (Iwahashi and Pike 2007). As aconsequence, logarithmic converted slope gradient (hereafter LN Slope) was used as a classifying parameter inorder to replicate the decrease of thresholds in Iwahashi
and Pike (2007).
The local convexity tends to be high in terrace surfaces (Iwahashi and Kamiya 1995). However, the local
convexity calculated from different DEMs or using different thresholds that separate convex points and others
showed very different patterns (Fig. 5, the Kanto Districtin Japan). The threshold value for extracting convex
points used by Iwahashi and Pike (2007) was zero. In
this study, we set the threshold value as 2. This value
was chosen because we could obtain local convexity images to separate out terrace features and could reducenoise from artificial land as far as possible. Understanding of terrace features is important for information onhuman habitat, ground condition, and flooding in alluvial topography.
In Japan, surface texture shows relatively low values
on debris slopes such as Holocene volcanic slopes
and alluvial fans, or high permeability slopes such as
weathered granites (Iwahashi and Kamiya 1995).
Iwahashi and Pike (2007) showed that the surface texture calculated from the 1-km DEM using 3 × 3 cell
windows depicted well the density of mountain ridges
and valleys and divided large volcanic cones of
Holocene mafic volcanos from older high-texture
mountains. The surface texture values calculated froma 50-m DEM showed some correlation with Vs30 data

Page 6 of 31

in Japan (Iwahashi et al. 2010). However, patterns of surface texture will change in response to the window sizefor extracting pits and peaks (Iwahashi and Pike 2007).
However, small-scale geomorphological elements, such as
cones of small volcanos could be distinguished in 3 × 3
surface texture of the 280 m DEM. A new signature, combined texture, was introduced to handle these scale issues.
The combined texture was calculated as the minimum
value of the normalized 3 × 3 and 13 × 13 surface texture
values for each segmented polygon (Fig. 6). Finally, allgeometric signatures were normalized to 8 bit to reduce
data volume for dealing with processing ability.

Region segmentation

Homogeneous geomorphological elements (Fig. 4) wereclassified by area segmentation. We used a multiresolution segmentation method (Baatz and Schäpe
2000) that was implemented in the software eCognition
Developer ver.9 (Trimble Inc.). The multi-resolution
segmentation method is a technique derived from moving image compression as used in the field of computer
vision. The method aggregates neighboring cells thathave values closer than a given threshold and thenoutputs polygons. The advantage is that the method canaggregate cells which include a little unevenness. Drăguţ
and Blaschke (2006) used this method for landform classification with 5-, 46-, and 57-m DEMs. Martha et al.
(2010) used the method for landslide detection.
The shapes of polygons output by multi-resolution
segmentation change with the choice of geometric signatures and their weights. Figure 7 shows some test casesin the Kanto District of Japan. When only LN Slope wasused (Fig. 7b) for area segmentation, outlines of terraces
and fans were described accurately. However, low terraces whose cliffs are not clearly defined such as the
Omiya Upland (Fig. 7a) could not be outlined clearly.
With high-accuracy domestic DEMs, a weight ratio of
1:1:1 for LN Slope:surface texture:local convexity gave
good area segmentation (Iwahashi et al. 2015). However,with the 280 m DEM, the weight ratio 1:1:1 gave inaccurate terrace shapes (Fig. 7c). After trial and error, wechose a weight ratio of 2:0:1 for area segmentation using
the 280 m DEM. The size of each destination polygon ismainly controlled by the scale parameter (Baatz and
Schäpe 2000). We found that a scale parameter of 50 or
100 outputs segments that are too large and omits
slightly elevated areas in lowlands (Fig. 7d, e). We finallychose a value of 20, which subdivided homogeneous
landforms adequately but not excessively (Fig. 7f ). The
shape parameter, which provides a balance between
shape and color for segmentation (Kavzoglu and Yildiz
2014), was set to zero following Drăguţ and Eisank(2012). The compactness was also set to zero.

Iwahashi et al. Progress in Earth and Planetary Science (2018) 5:1

Page 7 of 31

Fig. 5 Local convexity in the 280 m DEM from the Kanto District in Japan, calculated with different threshold values (TH) for separating out
convex points. a JEGM (Wakamatsu and Matsuoka 2013) which shows terraces in orange. (b-1 to b-5) 280 m MERIT DEM. c 250 m GMTED2010
DEM. d 1 km SRTM30 DEM

Mean values for each geometric signature (LN slope,surface texture (3 × 3 and 13 × 13), combined texture,and local convexity) within the destination polygons
were calculated and stored as representative values. We
found much artificial unevenness in residential land.
Therefore, we think that the standard deviation should
not be used for representative values for the destination
polygons, and we used only mean values. After all signatures were normalized and the combined textures were
calculated from the normalized values of 3 × 3 and 13 ×
13 surface texture, the base polygon datasets were prepared (Fig. 4).
Base classification

There are two kinds of classification method using
geometric signatures: image thresholding and data
mining. An image thresholding method using LN
Slope is conceptually similar to the Iwahashi and Pike
(2007) method, if thresholds are configured at equalintervals. The expected advantages are that large data
sets can be classified using subdivided data, andclassification from mixed DEMs of different data
sources may be possible if corrected thresholds areused (Iwahashi et al. 2015). One weakness is that aninappropriate configuration of thresholds will givenonsensical results. To attach geomorphologicalmeanings to classification results, geometric signatures
should be compared with reference materials, andthresholds should be configured after comparison(Iwahashi et al. 2015). However, reference materials
are unlikely to be supported globally. Therefore, theimage thresholding method is not suitable for globalclassification.
We instead adopted a machine learning method, kmeans clustering (MacQueen 1967), for terrain classification (Fig. 8). The advantages of the clustering are thatit can deal with large data and create groups without requiring training data. Unsupervised classification excluding subjective judgment will be suitable for comparing
physical properties of ground with topography. We classified the base polygons created from the 280 m DEM
using normalized LN slope, local convexity and surface
texture by k-means clustering. The initial number ofclusters was set to 40, assuming that the clusters would
be reclassified into fewer groups. IBM SPSS Statistics
ver.22 was used for k-means clustering and the calculation of all study areas (Fig. 2) converged to 40 clusters at
the 682th iteration.

Iwahashi et al. Progress in Earth and Planetary Science (2018) 5:1

Page 8 of 31

Fig. 6 Flowchart of calculation of the combined textureGrouping of the clusters and reclassification

The 40 clusters indicate globally unified categories classified by machine learning. In order to identify the typeof terrain they represent, we must compare them withexisting thematic maps. Moreover, because 40 legends
are too numerous for practical use, similar clusters must
be merged. We thought that hierarchical clustering,using composition ratios and specialization coefficientsof the comparison with thematic maps or data, was adequate to understand the similarity of the clusters. It is
not necessary for thematic data to be global because it ispossible to apply results in representative areas to otherareas. Thematic data can be of any form as long as there
is a correlation with terrain classification, and groupingof various themes is possible. On the other hand, comparing the clusters with thematic data that have no

relation to the topography may give meaningless groups.
Therefore, we need thematic data that have a wide
range, are related to topography, and a map scale thatfits the 280 m resolution. Furthermore, as we explainedfor combined texture, due to the scale of the terrain, theoptimal window size for calculating geometric signatures
is likely to depend on the region, and additional reclassification may be necessary. In this study, we tested
grouping and reclassification using Japanese GIS data in
the form of geomorphological maps, geological maps,
and landslide distribution maps. Flowcharts of thegrouping and reclassification are shown in Fig. 8 and described in detail below.
In Japan, the distributions of the 40 clusters were numerically compared with existing nationwide geological,
geomorphological, and landslide distribution maps (nos.

Iwahashi et al. Progress in Earth and Planetary Science (2018) 5:1

Page 9 of 31

Fig. 7 The area segmentations using different weights and scale parameters on the Kanto District in Japan. The a JEGM geomorphological map
is displayed as a reference (Wakamatsu and Matsuoka 2013). b–f Area segmentation by the 280 m DEM using different scale parameters or
different weights (LN slope: surface texture: local convexity)

1 to 3 in Table 1). Cross tabulation results and graphs
are shown in the Additional file 1 with the numbers ofclusters.
For the cross tabulations, over 200 types of geologic
units from the Seamless Digital Geological Map of Japan(SDGM) (Table 1) were reclassified for statistical convenience into 14 types according to lithology codes androck age information in the data attributes. For example,“Sedimentary rocks” in the geological map was subdivided at the age 22 Ma (Middle Miocene), which is
known as a boundary between linear and random plots
in log–log plots of slope gradient and sedimentary rockage in Japan (Iwahashi et al. 2015). The Miocene is an
important era for Japan, as it is the era when the Japan
Sea began to open (Otofuji et al. 1985). Subdivision at
the Miocene is also useful for imaging boundaries
between consolidated and half- or unconsolidated sedimentary strata, because sedimentary rocks younger thanthe Neogene are often treated as soft rocks in the fieldof engineering geology in Japan (Japan Society of Civil
Engineers 2016). We used full map units (22) of the
JEGM (Table 1) for cross tabulation. The JEGM divides
populated lowlands and terraces into several groups, but
mountains are in one group, except for Holocene

volcanos. Older volcanos are depicted as mountains in
the JEGM.
We performed hierarchical clustering using composition ratios and specialization coefficients from the comparison with geological and geomorphological maps andused percentages of landslide masses to assist grouping.
The result shows three meaningful major branches(Fig. 9) compared with the characteristics of each clustershown in the Additional file 1, i.e., (1) alluvial or coastal
plains and non-volcanic terraces, (2) volcanos, hills, fans,
and terraces, and (3) mountains. However, in the lower
hierarchy, some groups seemed less useful. The result of
hierarchical clustering (Fig. 9) was the first priority guidein grouping; however, visual observation of the thematic
maps was used to recombine cases where the same
mountain masses or terraces were found in different
branches. In addition, we used point diagrams of convergence values of the clustering (Fig. 10) to check that theclusters within the same group did not differ markedly
from each other.
The clusters were grouped into 12 groups. However,
the grouping in mountains and hills tended to mix
volcanic and non-volcanic mountains that could easily
be distinguished by visual investigation with geologicalIwahashi et al. Progress in Earth and Planetary Science (2018) 5:1

Page 10 of 31

Fig. 8 Flowchart of terrain classification used to generate a geomorphological map

maps. Some, but not all, could be divided by surface texture
calculated from a 1-km resolution DEM (Iwahashi and Pike
2007); therefore, the mixing was caused by the use of smaller
sized windows for surface texture. We used the combined
texture as an additional threshold parameter. Thresholds
were decided by visual observation of the image of combined
texture compared with the landform elements of JGEM andthe 1:25,000 Land Condition Map of the Geospatial
Information Authority of Japan (no. 4 in Table 1). The latterare geomorphological maps of urban plains and their surroundings, which have been published from the 1960s. The
extra three groups found by this reclassification of mountains
and hills gave a total of 15 definitive terrain groups. Table 2
gives quantitative descriptions of the 15 terrain groups.

Results
Cross tabulations with geological and geomorphologicaldata

The terrain classification using DEM objectively distinguishes homogeneous slopes. In this study, we groupedslopes of similar properties by hierarchical clustering using
Japanese thematic maps and reclassified mountains andhills by the combined texture. The final 15 groups werecross tabulated and visually compared with Japanese thematic maps again. Moreover, we also conducted the same
analysis for overseas areas where public data can be obtained. We chose three scopes: an area that lies in a young
orogenic belt like Japan (California), an area totally different from Japan in geological and climate settings

Australia

7

Regolith Map of Australia

Geological Regions of Australia

Vs30 map

Land Condition Map

Landslide Distribution Maps

Japan Engineering Geomorphologic
Classification Map (JEGM) ver.3

Seamless Digital Geological Map
of Japan (SDGM)

Dataset (map)Geological Survey of Japan, AIST [2015]b
National Research Institute for Earth Science and Disaster Resiliencec
Geospatial Information Authority of Japand
Geoscience AustraliaaCalifornia

Japan (urban area)

4

Australia

Japan (nationwide)

3

5

Japan (nationwide)

2

6

Japan (nationwide)

1

Range

Major landform

Geology

Chan et al. 1986, GA (ed)Geological ground condition

Landform

GSIc
Blake and Kilgour (1998), GAd (ed.)

Landslides

NIEDb

Wills et al. (2015)

Landform

Wakamatsu and Matsuoka (2013)

Subject
Geology

Creator/publisher
GSJa (ed.) (2015)

Table 1 Thematic map data used for comparison with the terrain classification of the current study
Map scale/resolution

1:5,000,000

1:5,000,000

1:24,000

1:25,000

1:50,000

250 m grid

1:200,000

Usage in this study

Comparison

Comparison

Comparison

Subdivision of the groups

Grouping decision

Grouping decision, subdivisionof the groups

Grouping decision, subdivisionof the groups

Iwahashi et al. Progress in Earth and Planetary Science (2018) 5:1
Page 11 of 31

Iwahashi et al. Progress in Earth and Planetary Science (2018) 5:1

Fig. 9 (See legend on next page.)

Page 12 of 31

Iwahashi et al. Progress in Earth and Planetary Science (2018) 5:1

Page 13 of 31(See figure on previous page.)
Fig. 9 Dendrogram of the 40 clusters obtained by hierarchical clustering in Japan. This figure shows a dendrogram calculated by the Wardmethod, using composition ratios and specialization coefficients of the comparison with JEGM and SDGM, and percentages of landslide masses(Table 1). Numbers in the left column of the graph are the reference numbers of the 40 clusters in the Additional file 1. The text in this graphshows typical landforms for the three branches

Fig. 10 Point diagrams of the convergence values (Zscore) of the 40 clusters. The colors and captions show the 12 groups

Iwahashi et al. Progress in Earth and Planetary Science (2018) 5:1

Page 14 of 31(Australia), and glacier regions that are rare in Japan butvery important for landform formation in high-latitude
areas. These analyses were important in order to construct
the final legend.
Comparisons with the geological and geomorphologicalmaps of Japan

We compared the statistics of the 15 groups and geomorphological and lithological data (Table 3). Table 3
lists not only composition ratios (CR) but also particularly
large values (> two times) of specialization coefficients(SC) were described in Table 3, because map scales differ
substantially (Table 1). SC describes the level of concentration of a certain map unit in the clusters. SC exceeding
one indicates concentration of the distribution. This trend
is more pronounced with larger SC values.
The result of grouping using Japanese geomorphologicaland geological data (SDGM and JEGM) generally implied
the following. Groups 1 and 2 were bedrock mountains.
In group 3, there were more sediments that were probably
talus or dissected terraces, and the group indicatedmiddle-relief mountains. Therefore, group 3 was hills.
Among groups 1 to 3 (i.e., bedrock mountains and hills),
there was a difference in major rock types in the subcategories “a” and “b” as intended. Sub-category a seemed
to be hard rocks, and b included debris and highpermeability rocks. On the other hand, Table 2 shows thedifference between b of bedrock mountains and hills. Subcategory b in bedrock mountains was smooth on bothlarge and small scales; however, b in hills was rough on
the large scale. Groups 4 and 5 were highland slopes thatwere steep but very smooth (Table 2), and they wereclosely related to Holocene mafic volcanos and their surroundings. Groups 6 to 12 were terraces, fans, and plains
formed by unconsolidated strata. Their slope gradientswere small and their average surface texture gradually decreased (Table 2). Groups 6 and 8 coincided closely with
the Japanese major terrace type, i.e., terraces covered withvolcanic ash soils (Wakamatsu and Matsuoka 2013).
Group 7 was also terraces in a broad sense; however, specifically, it was rather fans or valley bottoms that weremainly distributed in shallow valleys in terraces. Group 9
mainly represented Japanese major alluvial fans that wereformed by sands and gravels. Groups 10 to 12 covered alluvial plains, coastal plains, and their surroundings. Group
10 was plains at the foot of mountains. Groups 11 and 12,
especially group 12, were very gentle slopes and widelydistributed in muddy and silty plains.
The categories with low CR (< 5%) that were not described in Table 3 were mainly as follows. Plutonic rocks(Middle Miocene to Holocene), ultramafic rocks, andfelsic intrusive rocks had high SC values in group 1a.
High SC values also appeared in groups 3b, 6, and 9 for
Holocene tephra; groups 5, 6, and 7 for rocky strath terraces; groups 8, 10, and 11 for sand bars and dunes incoastal plains; and groups 6, 8, and 10 for filled land.
Additional visual observation revealed that distinctive
terrain with long slopes, such as caldera escarpments(e.g., Aso caldera, Kumamoto Prefecture, Kyushu), faultblock mountains (e.g., Yourou Mts., Gifu Prefecture,
Honshu), and inselberg (e.g., Mt. Tsukuba, Ibaraki
Prefecture, Honshu) were classified as group 1b.

Table 2 Quantitative values of the terrain groups, calculated globally
Terraingroup

Cluster reference numbers(see Additional file 1 forJapanese conditions)Average of slope gradient Average of surface texture Average of local convexity Threshold of Average ofcombined
combined%
Z score%Z scoreDegrees
Z scorea
texturetexture1a6, 15, 22, 40

14.8
14.9

2.25

63

0.42

42

0.75

≦ 0.410.15

5, 17, 19, 29

7.8

1.54

73

0.94

41

0.62

> 0.15

0.69

1b
2a

2.25

70

0.77

43

0.85

> 0.41

0.72

2b

6.5

1.34

68

0.65

38

0.43

≦ 0.15− 0.203a4, 7, 10, 14, 20, 24, 25, 31, 39 2.8

0.50

71

0.79

40

0.55

> 0.1

0.51

≦ 0.1

3b

− 0.43

1.7

0.10

71

0.83

39

0.46

4

18

7.5

1.49

55

− 0.0345

0.94− 0.085

16, 34

2.7

0.49

57

0.06

44

0.85

− 0.03

6

2, 21, 28, 37, 38

0.8− 0.38

67

0.63

40

0.55

− 0.067

9

1.3− 0.10

61

0.28

37

0.29

0.10

8

3, 26, 300.4− 0.7255− 0.0534

0.08− 0.46

9

1, 11, 12, 13, 360.9− 0.3547− 0.4635

0.18− 0.58

10

35

0.3

− 0.8242− 0.7225− 0.62− 0.91

11

27, 32, 33

0.2− 0.9330− 1.3716− 1.38− 1.45

12

8, 23

0.1

− 1.0414− 2.203− 2.45− 2.20a1 Normalized value using global average and standard deviation

Iwahashi et al. Progress in Earth and Planetary Science (2018) 5:1

Page 15 of 31

Table 3 CRa and SCb of geomorphological and geological units for each terrain group in Japan
Terrain Landform element of JEGM
group

Geological unit of SDGM

1a

Mountain (84%), volcanoc (8%), others (8%)

Accretionary complex (30%), mafic volcanic rocks (Jurassic toPleistocene) (20%), plutonic rocks (Silurian to Middle Miocene)(13%), felsic volcanic rocks (9%), metamorphic rocks (9%) (× 2.3),
others (19%)

1bMountain (53%), volcano (32%)(× 7.0), volcanic footslope(9%), others (6%)

Mafic volcanic rocks (Jurassic to Pleistocene) (39%) (× 3.1),
accretionary complex (13%), Plutonic rocks (Silurian to
Middle Miocene) (10%), sediments (8%), felsic volcanic rocks (7%),
pyroclastic flow deposits (6%)

2aMountain (79%), others (21%)

Accretionary complex (25%), mafic volcanic rocks (Jurassic toPleistocene) (18%), plutonic rocks (Silurian to Middle Miocene)(13%), sediments (13%), felsic volcanic rocks (12%), sedimentaryrocks (Silurian to Middle Miocene) (7%), metamorphic rocks (6%),
others (6%)

2bMountain (70%), hill (9%), volcano (7%), others (14%)

Sediments (23%), accretionary complex (22%), mafic volcanic rocks(Jurassic to Pleistocene) (17%), sedimentary rocks (Silurian to
Middle Miocene) (13%) (× 2.3), felsic volcanic rocks (9%), plutonic
rocks (6%), others (10%)

3aMountain (36%), hill (23%), gravelly terrace (11%), valley bottom
lowland (10%) (× 2.0), volcanic footslope (5%), others (15%)

Sediments (38%), accretionary complex (13%), Plutonic rocks(Silurian to Middle Miocene) (12%), mafic volcanic rocks (Jurassic
to Pleistocene) (9%), pyroclastic flow deposits (8%), sedimentaryrocks (Silurian to Middle Miocene) (7%), felsic volcanic rocks (7%),
others (6%)

3bHill (32%) (× 3.1), mountain (20%), gravelly terrace (10%), volcanic
footslope (9%), valley bottom lowland (8%), terrace covered withvolcanic ash soil (6%), volcanic hill (6%) (× 2.4), others (9%)

Sediments (54%), pyroclastic flow deposits (13%) (× 2.3),
accretionary complex (7%), sedimentary rocks (Silurian to Middle
Miocene) (7%), mafic volcanic rocks (Jurassic to Pleistocene) (5%),
others (14%)

4Volcano (55%) (× 12.5), volcanic footslope (36%) (× 7.7), others (9%)

Mafic volcanic rocks (Holocene) (47%) (× 84.3), volcanic debris(Miocene to Holocene) (21%) (× 12.6), mafic volcanic rocks(Jurassic to Pleistocene) (16%), pyroclastic flow deposits (8%),
others (8%)

5Volcanic footslope (60%) (× 12.8), gravelly terrace (10%), terrace
covered with volcanic ash soil (9%) (× 2.3), volcano (6%), others (15%)

Sediments (35%), volcanic debris (Miocene to Holocene) (23%)(× 13.9), pyroclastic flow deposits (18%) (× 3.1), mafic volcanic
rocks (Holocene) (14%) (× 25.8), mafic volcanic rocks (Jurassic toPleistocene) (7%), others (3%)

6

Terrace covered with volcanic ash soil (19%) (× 4.8), gravelly terrace(16%) (× 2.4), valley bottom lowland (14%) (× 3.0), hill (12%), volcanic
footslope (7%), back marsh (7%) (× 2.5), alluvial fan (7%) (× 2.3), delta
and coastal lowland (5%) (× 3.1), others (13%)

Sediments (79%) (× 2.3), pyroclastic flow deposits (8%), others (13%)

7

Gravelly terrace (29%) (× 4.2), terrace covered with volcanic ash soil
(14%) (× 3.5), hill (13%), alluvial fan (12%) (× 4.3), valley bottom
lowland (10%) (× 2.2), volcanic footslope (8%), others (14%)

Sediments (81%) (× 2.4), pyroclastic flow deposits (5%), others (14%)

8

Terrace covered with volcanic ash soil (19%) (× 4.8), alluvial fan (16%) Sediments (95%) (× 2.8), others (5%)(× 5.7), gravelly terrace (15%) (× 2.2), back marsh (14%) (× 5.2), delta
and coastal lowland (8%) (× 4.7), others (28%)

9

Alluvial fan (30%) (× 10.5), gravelly terrace (30%) (× 4.4), terrace
covered with volcanic ash soil (16%) (× 4.1), volcanic footslope(10%) (× 2.1), others (14%)

Sediments (86%) (× 2.5), pyroclastic flow deposits (5%), others (9%)

10Back marsh (28%) (× 9.9), alluvial fan (22%) (× 7.5), delta andcoastal lowland (14%) (× 8.3), gravelly terrace (8%), natural
levee (7%) (× 11.2), others (21%)

Sediments (99%) (× 2.9), others (1%)

11

Back marsh (36%) (× 13.1), delta and coastal lowland (18%) (× 10.9),
alluvial fan (12%) (× 4.3), natural levee (11%) (× 17.1), reclaimed land(6%) (× 13.3), others (17%)

Sediments (100%) (× 2.9)

12Back marsh (56%) (× 20.0), delta and coastal lowland (21%) (× 13.0),
natural levee (14%) (× 21.0), reclaimed land (7%) (× 14.7), others (2%)

Sediments (100%) (× 2.9)

Legends with CR of 5% or more are shown. Clusters with SC over × 2.0 are shown in bold text and the factors are in parenthesesaComposition ratiobspecialization coefficientclimited to Holocene volcano in JEGM

Iwahashi et al. Progress in Earth and Planetary Science (2018) 5:1

Comparisons with geohazard data and densely inhabited
district data in Japan

In Japan, the Ministry of Land, Infrastructure and Transport(MLIT) has published some national statistics as GIS data,“National Land Numerical Information”. Wakamatsu (2011)
collected spatial data of historic (745 A.D. to 2008 A.D.)
liquefaction in Japan. We analyzed the relationship between
the subjects concerning such land conditions and each terrain group. Table 4 shows CR and SC of terrain group vs.
densely inhabited districts (DID), sediment disaster hazard
areas, and historic liquefaction. The sediment disaster hazard
areas, which describe dangerous zones susceptible to collapse, mud or debris flows, and landslides, are not specified
in regions with no or few downstream habitations. Therefore, we calculated corrected specialization coefficients(CSC) in Table 4 by using distribution percentages of the
DID areas (CSC: SC/CR in the DID areas for each group).
The GIS data of historic liquefactions (Wakamatsu2011) describe newer liquefactions after the World War
II that can be delineated on maps or aerial photographs
as polygons and describe the first liquefaction that appears in Japanese historical documents (745 A.D.) to thelast liquefaction within the data collection period (2008
A.D.) as point data of the centers of liquefied areas. The
point data include 3717 liquefaction sites, but many ofthese are near seashores and overlap with the water body

Page 16 of 31

of the GlobCover 2009. Therefore, 2914 of the point
data were used in the analysis.
The DID areas in Japan were remarkably concentrated
in groups 6 to 12. Although the highest SC value was ingroup 8 that was mainly low terraces, the distributionsof errors caused by reclaimed land around coastal areas
may have raised SC. The liquefaction sites in polygon
form were mainly distributed in groups 11 and 12, whichwere very gentle slopes. However, point data of liquefaction sites were concentrated on steeper slopes whichcorrespond to terrains, alluvial fans, and hills. According
to individual investigation of those sites, hilly cases weredivided into three types: banking on residential areas,
exceedingly strong earthquakes which occurred in hillsof sedimentary rocks (e.g., the Chuetsu Earthquake on
23 October 2004), and insufficient DEM resolution thatmissed narrow plains along valleys or coastlines.
The zones susceptible to collapse in Japan and officiallyconsidered dangerous are defined by the MLIT as steep
slopes (over 30° and 5 m high) and their surrounding habitats. These zones were concentrated not only in the groups
representative of bedrock mountains (groups 1 and 2) but
also in highland slopes that were thought to be formed bydebris or high permeability rocks (groups 4 and 5), especially in group 4. CSC values were also very high in the caseof the officially defined zones of landslides and debris flow.

Table 4 CRa, SCb, and CSCc of DIDd and geohazards for each terrain group in Japan
All
Japan

DID

Terrain group

CR (%)

CR (%)SC

CR (%)CSC

CR (%)CSC

CR (%)

1a

13.2

0.1

0.0

5.3

5.7

11.0

11.9

1b

2.6

0.1

0.0

1.0

7.3

2.1

16.0

2a

31.9

1.0

0.0

32.4

1.0

43.5

2b

4.0

0.5

0.1

3.4

1.9

7.7

3a

20.6

8.2

0.4

37.9

0.2

3b

9.6

7.7

0.8

12.9

0.2

4

0.3

0.0

0.0

0.1

5

0.6

0.5

0.8

0.5

6

6.5

23.0

3.5

7

1.2

3.9

3.3

8

4.6

34.3

9

1.1

3.1

10

1.4

11

2.2

12

0.4

a

Dangerous zones for the sediment disaster hazard area

Historic liquefaction

Collapse

Center point (745 A.D.
to 2008 A.D.)

Polygon(after WW2)

CSC

CR (%)SC

CR (%)

SC

3.9

4.3

0.0

0.0

0.0

0.0

1.5

11.0

0.0

0.0

0.0

0.0

1.3

33.3

1.0

0.1

0.0

0.1

0.0

4.2

4.5

2.5

0.0

0.0

0.0

0.0

24.6

0.1

40.1

0.2

4.1

0.2

1.1

0.1

10.0

0.1

9.7

0.1

9.2

1.0

4.0

0.4

44.0

0.1

60.8

1.0

402.9

0.0

0.0

0.0

0.0

1.7

0.2

0.6

2.5

8.5

0.0

0.1

0.0

0.0

4.7

0.0

0.6

0.0

1.7

0.0

17.6

2.7

9.0

1.4

0.9

0.2

0.2

0.0

1.4

0.3

1.3

1.1

0.4

0.3

7.5

0.6

0.0

0.1

0.0

0.2

0.0

34.4

7.5

29.9

6.6

2.9

0.2

0.1

0.1

0.0

0.2

0.1

0.2

0.2

0.7

0.6

6.3

4.6

0.1

0.0

0.0

0.0

0.0

0.0

29.0

21.1

12.8

9.3

9.5

4.3

0.0

0.0

0.0

0.0

0.0

0.0

3.7

1.7

34.9

15.8

1.9

5.1

0.0

0.0

0.0

0.0

0.0

0.0

0.3

0.7

7.2

19.0

Landslide

Debris flow

Composition ratiobspecialization coefficientcspecialization coefficient corrected by the composition ratio of units in the DID area of terrain groupsdDensely inhabited districts

Iwahashi et al. Progress in Earth and Planetary Science (2018) 5:1

In these cases, smooth steep slopes (groups 1b, 2b, and 4)
show higher CSC values.
Comparison with the Vs30 data in JapanThe ground Vs30, which is observed or estimated byborehole data, is a predictor of earthquake groundmotion amplification. We compared the calculated
Vs30 based on published shear-wave velocity profile
data in Japan (Strong-motion Seismograph Networks,
K-NET and KIK-net by NIED; http://www.kyoshin.bosai.go.jp) with the terrain classification (Fig. 11). In
extremely steep slope areas (Additional file 1) likegroups 1a and 1b, there is a possibility that Vs30
values were measured on narrow valley floors that are
not described by the 280 m DEM.
The box plot in Fig. 11 shows relatively low medians
for smooth slopes (1b, 2b, and 3b) in mountains andFig. 11 Boxplots of Vs30 for each terrain group in Japan

Page 17 of 31

hills. However, among intermediate landforms, thought
to be formed by half- or unconsolidated strata, fanpredominant slopes (groups 7 and 9) showed higher
Vs30 values than terrace-predominant slopes (groups 6
and 8) that were usually rougher than fans. In plains,(groups 10 to 12), Vs30 values decreased along withslope gradients.
Comparisons with data from California, Australia, andglacier regions

Cross tabulations were performed with geological or
lithological maps of California and Australia in order toinvestigate if the Japanese grouping of the clusters was
also meaningful for other regions (Table 5). California
lies on a young orogenic belt like Japan, but there are no
Holocene volcanos and the climate is drier than inJapan. Australia mostly lies on very dried shields andCrystalline rocks (61%) (×2.1), Franciscan complex rocks (17%) (×2.7),
Tertiary volcanic units (8%), Tertiary sandstone units (6%), others (8%)

Crystalline rocks (59%) (×2.0), Tertiary volcanic units (19%), Tertiary
sandstone units (5%), others (17%)

Crystalline rocks (45%), Tertiary sandstone units (14%), Franciscancomplex rocks (13%) (×2.0), Tertiary volcanic units (9%), Tertiary shaleand siltstone units (7%), others (12%)

Crystalline rocks (36%), Tertiary volcanic units (15%), Tertiary sandstone
units (14%), Holocene alluvium (steep slopes) (12%), Cretaceous
sandstone (6%) (×2.8), Tertiary shale and siltstone units (6%), others(11%)

Crystalline rocks (32%), Tertiary volcanic units (18%), Tertiary sandstone
units (11%), Holocene alluvium (steep slopes) (9%), Franciscan complex
units (7%), Tertiary shale and siltstone units (6%), Pleistocene alluvium(6%), Pleistocene - Pliocene alluvial deposits (5%), others (6%)

Holocene alluvium (steep slopes) (22%) (×2.1), crystalline rocks (16%),
Sedimentary rocks (62%), Cenozoic regolith (10%),
Tertiary volcanic units (14%), Tertiary sandstone units (14%), Pleistocene
granites (10%), metamorphics (6%), others (12%)
alluvium (12%), Pleistocene–Pliocene alluvial deposits (7%) (×2.9), Tertiary
shale and siltstone units (7%), others (8%)

Tertiary volcanic units (39%) (×3.5), crystalline rocks (25%), Holocene
alluvium (steep slopes) (18%), Pleistocene alluvium (8%), others (10%)

Holocene alluvium (steep slopes) (44%) (×4.3), Pleistocene alluvium(19%), crystalline rocks (15%), Tertiary volcanic units (13%), others (9%)

Tertiary sandstone units (19%) (×2.3), Tertiary volcanic units (19%),
Pleistocene alluvium (18%), Holocene alluvium (steep slopes) (13%),
crystalline rocks (10%), Holocene alluvium (moderate slopes) (8%),
Pleistocene–Pliocene alluvial deposits (7%) (×3.0), others (6%)

Tertiary volcanic units (24%) (×2.1), Pleistocene alluvium (21%) (×2.0),
Holocene alluvium (steep slopes) (21%) (×2.0), crystalline rocks (10%),
Tertiary sandstone units (8%), Pleistocene - Pliocene alluvial deposits(5%), others (11%)

Holocene alluvium (moderate slopes) (28%) (×4.2), Pleistocene alluvium Cenozoic regolith (42%), sedimentary rocks (36%),(23%) (×2.2), tertiary volcanic units (21%), Holocene alluvium (very low
granites (8%), metamorphics (5%), others (9%)
slopes) (8%), tertiary sandstone units (8%), Holocene alluvium (steep
slopes) (6%), others (6%)

Holocene alluvium (moderate slopes) (34%) (×5.1), Holocene alluvium(steep slopes) (32%) (×3.1), Pleistocene alluvium (21%) (×2.0), Tertiary
volcanic units (5%), others (8%)

1a

1b

2a

2b

3a

3b

4

5

6

7

8

9

Australia, major landform unit of Chan et al. (1986)

Hills (35%) (×3.2), mountains (34%) (×5.8), lowhills (7%)

Hills (24%) (×2.2), mountains (23%) (×4.0),
plateau (17%), low hills (12%), rises (7%)

Sedimentary rocks (36%), Cenozoic regolith (32%),
granites (18%), metamorphics (6%), others (8%)

Sedimentary rocks (57%), Cenozoic regolith (15%),
granites (11%), metamorphics (6%), others (11%)

Sedimentary rocks (47%), Cenozoic regolith (20%),
granites (13%), metamorphics (6%), mafic volcanics(6%) (×2.1), others (8%)

Sedimentary rocks (65%), Cenozoic regolith (15%),
Cenozoic mafic volcanics (7%) (×5.0), others (13%)

Sedimentary rocks (58%), mafic volcanics (13%) (×4.8),
Cenozoic mafic volcanics (8%) (×5.2), metamorphics(6%), Cenozoic regolith (5%), others (10%)

Plateau (23%) (×2.1), erosional plain (16%),
dunefield (14%), sand plain (10%), low hills (9%),
rises (8%), hills (6%), depositional plain (6%)

Dunefield (23%) (×2.2), sand plain (13%), plateau(12%), erosional plain (11%), rises (9%), low hills(8%), hills (8%), depositional plain (6%)

Hills (23%) (×2.1), low hills (17%), plateau (14%),dunefield (12%), rises (10%), mountains (9%),
erosional plain (6%)

Hills (25%) (×2.3), low hills (18%), mountains(11%), plateau (10%), rises (9%), dunefield (8%),
sand plain (6%), erosional plain (5%)

Low hills (30%) (×2.8), hills (22%) (×2.1), coastal
dunes (7%) (×14.8), dunefield (7%), lava plain(6%) (×9.3), mountains (6%)

Hills (37%) (×3.4), low hills (22%) (×2.0),
mountains (20%) (×3.5), lava plain (7%)(×10.3), plateau (6%)

Hills (23%) (×2.1), low hills (20%), mountains(14%) (×2.4), plateau (13%), rises (9%),
erosional plain (6%)

Sedimentary rocks (55%), granites (17%), mafic volcanics Hills (30%) (×2.8), low hills (23%) (×2.1),(6%) (×2.2), felsic volcanics (6%) (×4.2), others (16%)
mountains (22%) (×3.9), plateau (13%)

Sedimentary rocks (66%), granites (12%), metamorphics(7%), others (15%)

Sedimentary rocks (58%), granites (16%), felsic volcanics Mountains (45%) (×7.7), hills (21%), low hills(6%) (×4.4), Cenozoic mafic volcanics (6%) (×4.1), others (11%)(14%)

Sedimentary rocks (49%), granites (29%) (×2.9),
metamorphics (5%), others (17%)

Sedimentary rocks (51%), granites (19%), felsic volcanics Mountains (59%) (×10.1), plateau (15%), hills(8%) (×5.8), mafic intrusives (6%) (×9.1), Cenozoic mafic (12%), low hills (7%)
volcanics (6%) (×4.1), metamorphics (6%), others (4%)

California, lithological unit of Wills et al. (2015)

Terraingroup

Australia, geological unit reclassifiedc from Blake andKilgour (1998)

Table 5 CRa and SCb of geomorphological and geological units for each terrain group in California and Australia

Iwahashi et al. Progress in Earth and Planetary Science (2018) 5:1
Page 18 of 31

Holocene alluvium (moderate slopes) (42%) (×6.3), Holocene alluvium(very low slopes) (23%), Pleistocene alluvium (21%) (×2.0), others (14%)

Holocene alluvium (very low slopes) (53%) (×6.6), Pleistocene alluvium(23%) (×2.2), Holocene alluvium (moderate slopes) (18%) (×2.7),
others (6%)

Holocene alluvium (very low slopes) (72%) (×8.9), Pleistocene alluvium(26%) (×2.5), others (2%)

10

11

12

Cenozoic regolith (72%), sedimentary rocks(17%), others (11%)

Cenozoic regolith (46%), sedimentary rocks(33%), granites (9%), others (12%)

Cenozoic regolith (38%), sedimentary rocks (38%),
granites (10%), metamorphics (6%), others (8%)

Australia, geological unit reclassifiedc from Blake andKilgour (1998)

Legends with CR of 5% or more are shown. Clusters with SC over × 2.0 are shown in bold text and the factors are in parenthesesaComposition ratiobSpecialization coefficientcGeological units were aggregated by lithology and era. The units without an era are rocks older than Mesozoic

California, lithological unit of Wills et al. (2015)

Terraingroup

Table 5 CRa and SCb of geomorphological and geological units for each terrain group in California and Australia (Continued)

Erosional plain (25%), depositional plain (15%),
alluvial plain (12%) (×2.8), sand plain (11%), flood
plain (8%) (×4.3), plateau (6%), low hills (6%)

Erosional plain (22%), sand plain (12%), depositional
plain (11%), plateau (10%), low hills (9%), dunefield(9%), rises (8%)

Erosional plain (18%), dunefield (14%), plateau(12%), sand plain (10%), rises (10%), low hills (9%),
depositional plain (8%)

Australia, major landform unit of Chan et al. (1986)

Iwahashi et al. Progress in Earth and Planetary Science (2018) 5:1
Page 19 of 31

Iwahashi et al. Progress in Earth and Planetary Science (2018) 5:1

platforms. In many regions, Japan and Australia are totally different regarding climate and geological settings.
The geological map of Australia (Blake and Kilgour
1998) has over 200 categories, so geologic units were aggregated to 15 categories, considering rock age and lithology. We used all 26 categories of the regolith terrain
map of Australia (Chan et al. 1986) as geomorphological
data. The regolith terrain map used in this study is at ascale of 1:5,000,000 and valley plains were not represented in sufficient detail. For California, we used theVs30 map of Wills et al. (2015), which aggregated andsupplemented the geological map of California with estimated seismic amplification.
In a general division between hard rocks and unconsolidated strata, Tables 3 and 5 show that Japan andCalifornia are alike in that mountains are formed bybedrocks and plains are formed by sediments. Although
there are no Holocene volcanos in California, there aresome characteristics in common with Japan; in group 4,
volcanic rocks were predominant, and in groups 3 and5, steep Holocene alluvium that may coincide with talus
was predominant. Similar to Japan, groups 6 to 8 weremainly depositional terrains in California. Group 9 was
predominant in sandy and gravelly alluvial fans in Japan,
but in dry areas of California such as the Basin andRange provinces, it was often distributed in pediplains
and bajada that were formed under a dry climate.
Although groups 11 and 12 were mainly formed by verylow Holocene alluvium like in Japan, Pleistocene alluvium was also distributed and gave some terrain age thatwas older than that in Japan.
In the case of Australia, groups 1 and 2 were composedof bedrocks, and gentler slopes included more unconsolidated strata like in Japan. However, lithological characteristics for each group were evidently different from Japanand California. All terrain groups in Australia included asimilar pair of sedimentary rocks and granites, even inplains. It is well known that bedrocks form erosional
plains in Australia after very long-term erosion on thestable continent. This was consistent with our data thaterosional plains were predominant in groups 10 to 12, andSC values of pediplains were slightly high in groups 10
and 11. Australian mountains showed different predominant lithology in the subcategories a and b, but Cenozoic
mafic volcanics appeared in a, though similar lithology
often appeared in b in Japan. On the other hand, there
were some common points with Japan, that is, alluvial
plains, flood plains, and flood outs were predominant ingroup 12, and lava plains were predominant in groups 4
and 5. It was quite strange that “hills” appeared in allgroups in Australia. We thought that there was a possibility that many valley plains were omitted because of themap scale (1:5,000,000; Table 1). Plateaus also appeared inmany groups from mountains to plains. In Japan, plateaus

Page 20 of 31

formed by bedrock table mountains as in Australia arerare except for volcanics, and most of the “plateaus” areactually diluvial uplands. In Australia, the difference between plateaus distributed in different groups probably
depended on the proportions between tablelands and plateau edges observed in the 280 m grid. Plateaus were especially predominant in group 9, where fan-shape slopes
were distributed in Japan and California. The categories
with low CR (< 5%) that were not described in Table 5
were mainly as follows. Coastal dunes showed high SC
values in group 5, and glacial features showed the highest
SC value in group 1b and high SC values in groups 3a, 1a,and 2a.
We noted glaciated mountains that were rare in Japan,
California, and Australia. Those in the Canadian Rockies
and Alps were visually observed using glacier inventory
maps (RGI Consortium 2017). The results showed thatglacier areas were mainly distributed in groups 1a and1b, and group 1b covered the areas surrounding glaciers.

Classification map and estimated legends

The final legends for the terrain groups are given in Fig. 12.
The descriptions in Fig. 12 were obtained by the methods
described in the previous sections. Although this legend
mainly shows the groups constructed from Japanese geological and geomorphological information, the main categories that divided the terrain into (1) bedrock mountains;(2) hills; (3) large highland slopes; (4) plateaus, terraces, andlarge lowland slopes; and (5) plains are probably applicableglobally. We noted the landform patterns not only in Japanbut also in California and Australia for arid landforms.
Figure 13 shows a thumbnail image of global polygon dataclassified according to the terrain groups in Fig. 12. The
classification is obviously improved from Iwahashi and Pike
(2007) in representations of terrace shapes (Fig. 14) andlandform elements smaller than 1 km (Fig. 15).
Although the global terrain was classified into groups
that were categorized using Japanese thematic data, distinct topographies of various locations were also well
depicted. For example, terrain classification data from
Krasnoyarsk (Fig. 16) clearly depicted lava of Siberian
Traps (Svensen et al. 2010). Terrain classification datafor central New Zealand (Fig. 17) and the Cameroon line
(Fig. 18) also depict well of volcanoes and other mountains in addition to alluvial or coastal lowlands.
In contrast, metropolitan areas, especially in coastal
areas, are misclassified as terrace-like groups, i.e., groups
8, 10, and 6 (e.g. Tokyo Gulf Coast in Fig. 14). This isprobably caused by DSM (digital surface model) characteristics of the 280 m DEM rather than the influence ofartificial slopes. Moreover, limits in the specification of thegeometric signatures may still prevent the detection of slightrises in gentle plains. For example, the terrain of the Central

Iwahashi et al. Progress in Earth and Planetary Science (2018) 5:1

Page 21 of 31

Fig. 12 Legend of the terrain classification

Fig. 13 A thumbnail of global terrain classification. Horizontal and vertical lines coincide with those in Fig. 2. The legend is shown in Fig. 12

Iwahashi et al. Progress in Earth and Planetary Science (2018) 5:1

Page 22 of 31

Fig. 14 Comparison of terrain classification images of the Kanto District and its surroundings. a JEGM (Wakamatsu and Matsuoka 2013). b Resultsof this study, using the 280 m DEM. c Iwahashi and Pike (2007), using a 1 km DEM

Plain of Thailand (Fig. 19) is classified poorly compared witha manual classification map (Ohkura et al. 1989).

Discussion
Validity of the geometric signatures

It is well known that the values and spatial patterns ofgeometric signatures differ according to the accuracy
and resolution of DEMs (Zhang and Montgomery 1994;
Armstrong and Martz 2003; Deng et al. 2007; Wu et al.
2007; Maynard and Johnson 2014). Unevenness of
DEMs will lead to less accurate classification of topography. For this reason, we used the MERIT DEM for terrain classification.
The three-part geometric signatures adopted in thisstudy generally gave satisfactory results for terrain classification. The new parameter, the combined texture, may
help depict both small-scale and large-scale smoothslopes. In the case of plains, the limit of the descriptionof geometric signatures was mainly caused by insufficient DEM resolution. This problem should be solvablein the future using higher resolution DEMs. For
example, the MERIT DEM is originally 3 arc-seconds
resolution. However, high-resolution DEMs may create
considerable problems in terrain classification owing to
the appearance of artificial smoothing or cutting of land.
An additional method from using the original elevations,instead of their derivatives, will be needed for more detailed classification of plains because the derivatives mayemphasize artificial errors (Erskine et al. 2007).
Tuning of geometric signatures can give very different
descriptions, for example, patterns of local convexity
changed dramatically with threshold values in calculating convex points (Fig. 7). In this study, we set thresholds of local convexity so as to extract terraces.

Iwahashi et al. Progress in Earth and Planetary Science (2018) 5:1

Page 23 of 31

Fig. 15 Comparison of the terrain classification images of the Rub’ al khali. a Results from of this study, using the 280 m DEM. b Iwahashi andPike (2007), using a 1 km DEM

However, different threshold values might possibly output better classification results for other purposes orother DEMs.
Considering the calculation cost and expected degreeof improvement, and the ability to add another parameter later because the threshold processing had been
performed using combined texture, we did not thinkthat the addition of another geometric signature wasnecessary for clustering. However, the mixture of depositional landforms and erosional landforms (e.g., fans andpediplains in group 9) seems to be a major problem incontinental regions where both alluvial landforms andarid landforms exist.
Validity of the region segmentation and clustering

In terms of region segmentation, we did not thinkthat many parameters were necessary for drawing
boundary lines of landforms, and the results of object
segmentation using LN slope and local convexity
effectively separated the range of similar landformIwahashi et al. Progress in Earth and Planetary Science (2018) 5:1

Fig. 16 Shaded terrain classification image in the Krasnoyarsk region (Russia)

Page 24 of 31

Iwahashi et al. Progress in Earth and Planetary Science (2018) 5:1

Fig. 17 Shaded terrain classification image from central New Zealand

Page 25 of 31

Iwahashi et al. Progress in Earth and Planetary Science (2018) 5:1

Page 26 of 31

Fig. 18 Shaded terrain classification image of the Cameroon line and Niger deltapatterns as considered in the section “Region segmentation”. A scale parameter of 20 seems to be rather
small on continents, but is mostly adequate (or rather
too large) for Japan where various micro geomorphological elements are distributed. Smaller values for the
scale parameter will create a large volume of polygon
data and will make it difficult to use the data. We
thought 20 was adequate globally for the scale parameter. The polygon-based format helps to edit ormerge terrain classification data easily.
The 40 clusters obtained by k-means clustering
were globally common divisions, because the clustering was done using all polygon attributes. Although
the final number of clusters is arbitrary in k-meansclustering, the calculation cost tends to increase as
the number of clusters increases, and the calculation
does not converge with too many clusters. Moreover,
too many clusters will make data usage rather
difficult. Considering this, we decided on the final
number of clusters as 40.
Validity of the grouping and reclassification

In the hierarchical clustering in Japan, bedrock mountains constituted clearly separate branches from otherterrain types. The difference of geological and geomorphological characteristics between bedrock mountains
and others was relatively clear. Among the bedrock
mountains and hills, we distinguished two types ofslopes, sub-categories a and b. Suzuki (2002) gives aschematic diagram showing the relationship between therelief and drainage density, and the strength and permeability of bedrock. The diagram shows that hard rocks
form high-relief mountains, soft rocks form low-relief
mountains (or hills), low permeability rocks form
straight slopes, and high permeability rocks form convex
slopes. Surface texture (i.e., the density of pits and peaks
that coincide with valley lines and ridges in the 280 m
grid scale) is related to drainage density, and local convexity is related to the convexity of slopes. In subcategory b, which had a lower drainage density than a,highly permeable types of rocks such as old mafic
volcanic rocks and sedimentary rocks were more predominant than very hard rocks such as accretionary
complex and metamorphic rocks. This was consistent
with the findings of Suzuki (2002) and indicated that it
may be permeability that separates a and b of mountains
and hills in Japan. Indeed, groups 1b and 2b were predominant in crushed or high permeability rocks such as

Iwahashi et al. Progress in Earth and Planetary Science (2018) 5:1

Fig. 19 Shaded terrain classification image of the Central Plain of Thailand and its surroundings

Page 27 of 31

Iwahashi et al. Progress in Earth and Planetary Science (2018) 5:1

weathered lava and old sedimentary rocks in Japan andshowed lower Vs30 values (Fig. 11). Groups 4 and 5 wereunique groups; they were clearly predominant in undissected mafic volcanos and volcanic footslopes inJapan and showed very high correlation with the sediment disaster hazard area (Table 4). Not exactly thesame, but similar lithologic trends of groups 4 and 5
could be observed even in California and Australia. The
relationship between lithology and landforms in mountains has been explored in various ways, e.g., therelationship between slope materials and slope surface(Kenter 1990) and rock control (Yatsu 1966). Moreover,lithology and rock hardness have broad study fields inengineering geology and have been widely discussed forrock mass classification since Terzaghi (1946). Somerock mass classifications focus on classification of lithology (e.g., Marinos and Hoek 2000). Thus, especially
under the same climatic and geologic province, searching for similar topography in mountains and hills mayhelp to at least grasp some common points of physicalproperties of the ground.
Intermediate landforms formed by half- or unconsolidated strata were relatively difficult to classify. They converged to the “alluvial or coastal plains and non-volcanic
terraces” branch and a “volcanos, hills, fans, terraces”
branch in Japan (Fig. 9). Among them, groups 7 and 9
were included in the “volcanos, hills, fans, terraces”
branch-like large highland slopes (groups 4 and 5) inspite of their gentler inclinations. Groups 7 and 9 sometimes appeared in volcanic footslopes and indicated thatthey were depositional slopes of debris.
In the case of plains in Japan, the clusters included
in groups 8, 10, 11, and 12, which had a high ratio of
historical liquefaction, were all categorized into the“alluvial or coastal plains and non-volcanic terraces”
branch. Group 6, which partly included the clusters
categorized into the branch, showed a slightly highratio of historical liquefactions. In total, the result ofgrouping using Japanese geomorphological and geological data is generally suitable for distinguishinglowlands, terraces, debris slopes, hills, bedrock mountains, and large slopes, especially those formed by
Holocene mafic volcanos. However, as suggested by
the comparison with liquefaction data, the limit ofthe description of geometric signatures appears in
failures to detect narrow valley bottom plains. Slightly
high terrains in gentle plains may also be missed.
Moreover, DSM characteristics of the 280 m DEM
and artificial slopes gave less accurate classification in
metropolitan areas.
Regional differences and future scope

In this study, we grouped clusters and determined thelegend using Japanese geological and geomorphologicalPage 28 of 31

information. The Japanese Islands lie on monsoonal
mountainous regions in a young orogenic belt, and Japanese mountains are mainly formed by accretionary
wedges and volcanic rocks younger than the Jurassic.
Oguchi et al. (2001) characterized Japanese landforms bysteep watersheds, heavy storms, frequent slope failures
and landslides, large flood discharge and efficient sediment transport, high sediment yields, and catastrophichydro-geomorphological events associated with earthquakes and volcanic eruption plains. There are very few
or no distributions of erosional plains, glacial landforms,
and Eolian landforms in Japan. In geomorphologicalmaps, the necessary legend may vary from region to region. For example, Oya (1995) compared the legends ofEuropean and Japanese geomorphological maps andpointed out that in Europe, the classification focused on
climatic geomorphology and erosional geomorphology,
but that in Japan, on depositional geomorphology andless on glacial geomorphology and karst geomorphology.
In recognition of this difference, it is important to determine whether the grouping based on Japan can be dealt
with by simply replacing the legend in other areas, or if
it is fundamentally insufficient. In addition, it is important to know whether both the topography and surface
material are similar, even if the landform process is different (e.g., Japanese alluvial fans and pediplains in arid
regions), or if the surface material is also different.
As mentioned earlier, mountains in Japan, California,
and Australia are mainly composed of bedrocks, and inJapan and California, plains are commonly composed of
unconsolidated strata. However, observing intermediate and gentle slopes globally, we noticed a problem
that depositional fans formed by unconsolidated strata
and erosional plateaus/pediplains formed by old rocks
were grouped into the same groups, especially into
group 9. These phenomena were observed not only inAustralia but also in other shield and platform
regions such as the Northeast China Plain, theInterior Plains of North America, and the European
Plain. We thought that information from the global
geologic province was necessary to perform better
terrain classification that responds to regional needs.
Nonetheless, although small-scale data exist (e.g., USGS
https://earthquake.usgs.gov/data/crust/type.html), we did
not have global geologic province data that matchedthe 280 m grid scale. We thought that it is possible
to solve this by replacing the legend because of theadvantages of polygon data; however, attention isneeded for mixed areas where both depositional anderosional gentle plains exist. These clear differencesof lithological conditions in gentler landforms may
be an important further research task, because
markedly different lithology with similar topography
indicates a marked difference of landform processes.

Iwahashi et al. Progress in Earth and Planetary Science (2018) 5:1

Conclusions
This study used the MERIT DEM produced by Yamazaki
et al. (2017) who removed multiple error components
from existing space-borne DEMs. As a result, it waspossible to perform terrain classification that excluded theinfluence of DEM errors as much as possible. Multiresolution segmentation was performed to classify homogeneous geomorphological elements. Terraces, plateaus,
and fans, which were likely to be missed on a coarse DEM
but important for human habitats, were effectively extracted from plains by segmentation using LN slope andlocal convexity. Terrain classification was performed byadding surface texture data to LN slope and local convexity. We adopted a machine learning method, k-meansclustering, for terrain classification. The 40 clusters obtained by k-means clustering were prepared for globallycommon divisions. We grouped clusters of similar properties by hierarchical clustering using Japanese thematicmaps, and after that, we reclassified mountains and hills
using combined texture. The final 15 groups were crosstabulated and again visually compared with Japanese thematic maps to construct a legend. We tried to attach geomorphological meanings to the classification results
according to the cross tabulations with geological or geomorphological maps. We also conducted the same analysis using data for California and Australia to check thelegend. The legend mainly showed terrain groups basedon Japanese geological and geomorphological information;
however, the main categories that separated the terrain
into (1) bedrock mountains, (2) hills, (3) large highland
slopes, (4) plateaus, terraces, large lowland slopes, and (5)plains were probably applicable globally. Distinct topographies in various specific areas were also well depicted.
In general, the terrain classification was obviously
improved from Iwahashi and Pike (2007) in the representation of terrace shape and landform elements smaller
than 1 km. However, narrow plains at the foot of mountains, metropolitan areas, and slight rises in gentle plains
were not classified sufficiently because of the limit of thedescription of geometric signatures. These problems may
be caused by insufficient resolution and the DSM characteristics of the 280 m DEM. In addition, there may beroom for improvement in tuning the geometric signatures.
Observing intermediate and gentle slopes globally, we noticed that depositional fans formed by unconsolidated
strata and erosional plateaus/pediplains formed by oldrocks were grouped into the same groups. Although wethought that it was possible to deal with this by replacing
the legend because of the advantage of polygon data, attention was necessary for mixed areas where both depositional and erosional gentle plains existed.
Further studies are needed to complete global terrain
classification, perhaps using the original 3 arc-second
MERIT DEM, with better tuning of the geometric

Page 29 of 31

signatures. Moreover, the groupings of the clusters needmore variation in response to locality. Successful classification of geomorphological terrain types may lead tobetter understanding of terrain susceptibility to natural
hazards, which is especially vital in populated areas, andwill help land development in areas where geological information does not exist.

Additional file
Additional file 1: Cross tabulation results and graphs with the reference
numbers of clusters in Japan. (PDF 530 kb)
Abbreviations
CR: Composition ratio; CSC: Corrected specialization coefficient; DEM: Digital
elevation model; DID: Densely inhabited districts; GIS: Geographic
information system; JEGM: Japan Engineering Geomorphologic Classification
Map; LN slope: Logarithmic converted slope gradient; MERIT DEM: Multierror-removed improved-terrain DEM; SC: Specialization coefficient;
SDGM: Seamless Digital Geological Map of Japan; Vs30: Average shear wave
velocity for the top 30 m
Acknowledgements
We are deeply grateful to Richard J. Pike who helped the corresponding
author with expanding the numerical terrain classification work from adomestic to a global scale, Sayuri Mochiki who helped data generation ofterrain classification, and Alan Yong of the U. S. Geological Survey andPatcharavadee Thamarux of the Tokyo Institute of Technology who
discussed the terrain classification with us. We thank Jun Matsumoto ofTokyo Metropolitan University and the anonymous referees for their mosthelpful reviews.
Funding
This work is supported by JSPS KAKENHI Grant Number 15K01176.
Authors’ contributions
JI designed the study, analyzed the data, and drafted the manuscript. IK
helped in the design of digital filtering using DEM. MM prepared the Vs30
material. DY prepared the DEM. All authors read and approved the final
manuscript.
Competing interests
The authors declare that they have no competing interest.

Publisher’s NoteSpringer Nature remains neutral with regard to jurisdictional claims inpublished maps and institutional affiliations.
Author details
1
Geospatial Information Authority of Japan, Geography and Crustal Dynamics
Research Center, Kitasato 1, Tsukuba, Ibaraki 305-0811, Japan. 2Japan Digital
Road Map Association, Hirakawa 1-3-13, Chiyoda-ku, Tokyo 102-0093, Japan.
3Interdisciplinary Graduate School of Science and Technology, Tokyo Instituteof Technology, Nagatsuta 4259-G3-2, Midori-ku Yokohama, Kanagawa
226-8502, Japan. 4Institute of Industrial Science, The University of Tokyo,
Tokyo, Japan.
Received: 31 March 2017 Accepted: 7 December 2017

References
Ariza-Villaverde AB, Jiménez-Hornero FJ, Gutiérrez de Ravé E (2015) Influence of
DEM resolution on drainage network extraction: a multifractal analysis.
Geomorphology 241:243–254
Armstrong RN, Martz LW (2003) Topographic parameterization in continentalhydrology: a study in scale. Hydrol Process 17:3763–3781

Iwahashi et al. Progress in Earth and Planetary Science (2018) 5:1

Baatz M, Schäpe A (2000) Multiresolution segmentation: an optimization
approach for high quality multi-scale image segmentation. In: Strobl J,
Blaschke T, Greisebner G (eds) Angewandte Geographische InformationsVerarbeitung XII. Wichmann Verlag, Karlsruhe
Blake DH, Kilgour B (1998) Geological Regions of Australia 1:5 000 000 scale,Geoscience Australia. http://www.ga.gov.au/metadata-gateway/metadata/
record/32366. Accessed 15 Oct 2017
Bontemps S, Defourny P, Radoux J, Van Bogaert E, Lamarche C, Achard F, Mayaux P,
Boettcher M, Brockmann C, Kirches G, Zülkhe M, Kalogirou V, Arino O (2013)
Consistent Global Land Cover Maps for Climate Modeling Communities: Current
Achievements of the ESA's Land Cover CCI. ESA Living Planet Symposium 9–13
September 2013, Edinburgh, United Kingdom
Chan RA, Craig MA, D’Addario GW, Gibson DL, Ollier CD, Taylor G (1986) The
regolith terrain map of Australia 1:5,000,000. Bureau of Mineral Resources,
Geology and Geophysics Record 1986/27
Danielson JJ, Gesch DB (2011) Global Multi-resolution Terrain Elevation Data 2010
(GMTED2010). U. S. Geological Survey Open-File Report 2011–1073
De Reu J, Bourgeois J, Bats M, Zwertvaegher A, Gelorini V, Smedt PD, Wei C,
Antrop M, De Maeyer P, Finke P, Van Meirvenne M, Vemiers J, Crombe P(2013) Application of the topographic position index to heterogeneouslandscape. Geomorphology 186:39–49
del Val M, Iriarte E, Arriolabengoa M, Aranburu A (2015) An automatedmethod to extract fluvial terraces from LIDAR based high resolution
digital elevation models: the Oiartzun valley, a case study in the
Cantabrian Margin. Quat Int 364:35–43
Deng Y, Wilson JP, Bauer BO (2007) DEM resolution dependencies of terrain
attributes across a landscape. Int J Geogr Inf Sci 21:187–213
Dikau R, Brabb EE, Mark RM (1991) Landform classification of New Mexico bycomputer. U. S. Geological Survey Open-File Report 91–634Drăguţ L, Blaschke T (2006) Automated classification of landform elements using
object-based image analysis. Geomorphology 81:330–344Drăguţ L, Eisank C (2012) Automated object-based classification of topography
from SRTM data. Geomorphology 141-142:21–33
Erskine RH, Green TR, Ramirez JA, MacDonald LH (2007) Digital elevation
accuracy and grid cell size: effects on estimated terrain attributes. Soil
Sci Soc Am J 71:1371–1380
Evans IS (1980) An integrated system of terrain analysis and slope mapping.
Zeitschrift für Geomorphologie, Supplement Band 36:274–295
Gallant J (2011) Adaptive smoothing for noisy DEMs. Proc Geomorphometry 33:7–9
Geological Survey of Japan, AIST (ed.) (2015) Seamless digital geological map ofJapan 1: 200,000. May 29, 2015 version. Geological Survey of Japan, National
Institute of Advanced Industrial Science and Technology
Guida D, Cuomo A, Palmieri V (2016) Using object-based geomorphometry forhydro-geomorphological analysis in a Mediterranean research catchment.
Hydrol Earth Syst Sci 20:3493–3509
Guth PL (2006) Geomorphometry from SRTM: comparison to NED. Photogramm
Eng Remote Sens 72:269–277
Hartmann J, Moosdorf N (2012) The new global lithological map database GLiM:a representation of rock properties at the Earth surface. Geochemistry,
Geophysics, Geosystems 13. https://doi.org/10.1029/2012GC004370
Hengl T, Mendes de Jesus J, Heuvelink GBM, Ruiperez Gonzalez M,
Kilibarda M, Blagotić A, Wei S, Wright MN, Xiaoyuan G, Bauer-Marschallinger B,
Guevara MA, Vargas R, MacMillan RA, Batjes NH, Leenaars JGB, Ribeiro E,
Wheeler I, Mantel S, Kempen B (2017) SoilGrids250m: global gridded
soil information based on machine learning. PLoS One 12:e0169748.
https://doi.org/10.1371/journal.pone.0169748
Hough SE, Altidor JR, Anglade D, Given D, Janvier MG, Maharrey JZ, Mark
Meremonte M, Mildor BS, Prepetit C, Yong A (2010) Localized damage
caused by topographic amplification during the 2010 M 7.0 Haiti earthquake.
Nat Geosci 3:778–782
Iwahashi J (1994) Development of landform classification using the digital
elevation model. Dis Prev Res Inst Annuals 37B:141–156 (in Japanese withEnglish abstract and figures)
Iwahashi J, Kamiya I (1995) Landform classification using digital elevation modelby the skills of image processing—mainly using the Digital National Land
Information. Geoinformatics 6:97–108 (in Japanese with English abstract)
Iwahashi J, Kamiya I, Matsuoka M (2010) Regression analysis of Vs30 using
topographic attributes from a 50-m DEM. Geomorphology 117:202–205
Iwahashi J, Matsushi Y, Fukuoka H (2015) Digital terrain analysis of sea-landcombined data on the outer zone of Southwest Japan including the Nankai
Trough. Disaster Prev Res Inst Annuals 58B:102–115

Page 30 of 31

Iwahashi J, Pike RJ (2007) Automated classification of topography from DEMs by
an unsupervised nested-means algorithm and a three-part geometricsignature. Geomorphology 86:409–440
Japan Society of Civil Engineering (2016) Standard specifications for Tunnering—2016,
mountain tunnels. Maruzen Publishing Co. Ltd, Tokyo (in Japanese)
Jasiewicz J, Netzel P, Stepinski TF (2014) Landscape similarity, retrieval, andmachine mapping of physiographic units. Geomorphology 221:104–112
Jennings CW (1977) Geologic map of california. California Division of Minesand Geology
Kavzoglu T, Yildiz M (2014) Parameter-based performance analysis of objectbased image analysis using aerial and Quikbird-2 images. ISPRS Annals
Photogrammetry Remote Sens Spatial Inform Sci II-7:31–37
Kenter JAM (1990) Carbonate platform flanks: slope angle and sediment fabric.
Sedimentology 37:777–794
MacMillan RA, Martin TC, Earle TJ, McNabb DH (2003) Automated analysis andclassification of landforms using high-resolution digital elevation data:applications and issues. Can J Remote Sens 29:592–606
MacQueen JB (1967) Some methods for classification and analysis of multivariate
observations. In: Proceedings of 5-th Berkeley symposium on mathematicalstatistics and probability, vol 1. University of California Press, Berkeley, pp 281–297
Marinos P, Hoek E (2000) GSI: a geologically friendly tool for rock mass strength
estimation. In: Proceedings of GeoEng2000 conference, Melbourne.
Technomic Publishing, Pennsylvania
Martha TR, Kerle N, Jetten V, van Westen CJ, Kumar KV (2010) Characterizing spectral,
spatial and morphometric properties of landslides for semi-automatic detection
using object-oriented methods. Geomorphology 116:24–36
Maynard JJ, Johnson MJ (2014) Scale-dependency of LiDAR derived terrain
attributes in quantitative soil-landscape modeling: effects of grid resolution
vs. neighborhood extent. Geoderma 230-231:29–40
Meybeck M, Green P, Vörösmarty C (2001) A new typology for mountains andother relief cases: an application to global continental water resources andpopulation distribution. Mt Res Dev 21:34–45
Nelson A, Reuter HI, Gessler P (2009) DEM production methods and sources. In:
Hengl T, Reuter HI (eds) Geomorphometry concepts, software, applications.
Elsevier, Amsterdam
Oguchi T, Saito K, Kadomura H, Grossman M (2001) Fluvial geomorphology andpaleohydrology in Japan. Geomorphology 39:3–19
Ohkura H, Haruyama S, Oya M, Vibulsresth S, Simking R, Suwanwerakamtorn R(1989) A geomorphological land classification for the flood-inundated area in
the Central Plain of Thailand using satellite remote sensing technology. Res
Notes Nat Res Center Disaster Prev 83:25 (in Japanese with English abstract;
1 map sheet in English)
Otofuji Y, Matsuda T, Nohda S (1985) Opening mode of the Japan Sea inferredfrom the palaeomagnetism of the Japan Arc. Nature 317:603–604
Oya M (1995) Geomorphological maps of alluvial plains and their utilization for
mitigation of natural hazards: flooding and soil liquefaction. Geogr Rev Japan
68B:218–242
Pike RJ (1988) The geometric signature: quantifying landslide-terrain types fromdigital elevation models. Math Geol 20:491–511
RGI Consortium (2017) Randolph Glacier Inventory – A Dataset of Global
Glacier Outlines: Version 6.0: Technical Report, Global Land Ice
Measurements from Space, Colorado, USA. Digital Media. https://doi.org/
10.7265/N5-RGI-60
Sayre R, Dangermond J, Frye C, Vaughan R, Aniello P, Breyer S, Cribb D, Hopkins
D, Nauman R, Derrenbacher W, Wright D, Brown C, Convis C, Smith J, Benson
L, VanSistine DP, Warner H, Cress J, Danielson J, Hamann S, Cecere T, Reddy
A, Burton D, Grosse A, True D, Metzger MJ, Hartmann J, Moosdorf N, Dürr
HH, Paganini M et al (2014) A new map of global ecological land units—anecophysiographic stratification approach. Association of American
Geographers, Washington, DC https://rmgsc.cr.usgs.gov/ecosystems/docs/
AAG_Global_Ecosystems_Booklet.pdf
Shafique M, van der Meijde M, van der Werff HMA (2012) Evaluation of remotesensing-based seismic site characterization using earthquake damage data.
Terra Nova 24:123–129
Speight JG (1984) Landform. In: Australian soil and land survey field handbook,
First edn. Inkata Press, Melbourne
Suzuki T (2002) Rock control in geomorphological processes: research history inJapan and perspective. Trans Jpn Geomorphol 23:161–199
Svensen H, Planke S, Polozov AG, Schmidbauer N, Corfu F, Podladchikov YY,
Jamtveit B (2010) Siberian gas venting and the end-Permian environmental
crisis. Earth Planet Sci Lett 277:490–500

Iwahashi et al. Progress in Earth and Planetary Science (2018) 5:1

Terzaghi K (1946) Rock defects and loads on tunnel supports. Rock tunnelingwith steel supports, In
van Asselen S, Seijmonsbergen AC (2006) Expert-driven semi-automatedgeomorphological mapping for a mountainous area using a laser DTM.
Geomorphology 78:309–320
Wakamatsu K (2011) Maps for historic liquefaction sites in Japan, 745–2008.
University of Tokyo Press, Tokyo
Wakamatsu K, Matsuoka M (2013) Nationwide 7.5-arc-second Japan
engineering geomorphologic classification map and Vs30 zoning.
J Disaster Res 8:904–911
Wills CJ, Gutierrez CI, Perez FG, Branum DM (2015) A next generation Vs30 map
for California based on geology and topography. Bull Seismol Soc Am 105:3083–3091
Wills CJ, Petersen M, Bryant WA, Reichele M, Saucedo GJ, Tan S, Taylor G, Treiman
J (2000) A site-conditions map for California based on geology and shearwave velocity. Bull Seismol Soc Am 90:S187–S208
Wu S, Li J, Huang GH (2007) Modeling of the effects of elevation data resolution
on the performance of topography-based watershed runoff simulation.
Environ Model Softw 22:1250–1260
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. Geophys Res Lett 44:5844–5853
Yatsu E (1966) Rock control in geomorphology. Sozosha, Tokyo
Yong A, Hough SE, Iwahashi J, Braverman A (2012) A terrain-based site-condition
map of California with implications for the contiguous United States. Bull
Seismol Soc Am 102:114–128
Zevenbergen LW, Thorne CR (1987) Quantitative analysis of land surface
topography. Earth Surf Process Landf 12:47–56
Zhang W, Montgomery DR (1994) Digital elevation model grid size, landscape
representation, and hydrologic simulations. Water Resour Res 30:1019–1028

Page 31 of 31