Sîrbu et al. Progress in Earth and Planetary Science
https://doi.org/10.1186/s40645-019-0290-1(2019) 6:44

Progress in Earth andPlanetary Science

RESEARCH ARTICLE

Open AccessScaling land-surface variables for landslide
detection
Flavius Sîrbu1, Lucian Drăguț1* , Takashi Oguchi2, Yuichi Hayakawa2,4 and Mihai Micu3

Abstract
As geomorphological processes operate at various spatial scales, their morphological expressions, i.e., land-surface
variables (LSVs) should be scaled accordingly. Most approaches on landslide susceptibility modeling and landslide
detection have been performed based on arbitrarily scaled LSVs. We propose a methodology to improve automatedlandslide detection by fitting each LSV to its optimal scale. We test our approach on two landslide inventories, withdifferent landslide morphology. First, we derive seven LSVs from a DEM in a standard 3 × 3 moving window. Then, we
rescale each LSV using focal mean statistics in increasingly larger moving windows until the optimal scale is found, i.e.,
scale at which logistic regression shows the best fit between the existence of landslide scarps and individual LSVs. The
LSVs at the optimal scale are used as input data in a random forest (RF) model. In order to calculate the effect ofscaling predictors on the accuracy of the model, we compare the results, using the area under the curve (AUC), against
the results from an RF model with unscaled LSVs as input data. The results show (i) that different LSVs have different
optimal scales, and (ii) the multi-scale approach improved the models significantly, from AUC = 0.73 to 0.80 for the firststudy area and from AUC = 0.59 to 0.73 for the second study area. Based on these results, we conclude that a multiscale approach should be considered when automated models are used in order to detect landslides, in complex
terrain settings.
Keywords: Landslide scarp, Random forest, Landslide modeling, Landslide inventory, Romania, Japan

Introduction
The concept of scale stands at the basis of geomorphology
and geomorphometry (Pike et al. 2008) and yet it is notstraightforwardly defined in both theory and practicalapplications (Bishop et al. 2012; Zhilin 2008). Most often,
the concept of scale has different meanings in different
fields of study (Goodchild 2001). In cartography it represents the ratio between the real dimensions and the represented dimensions, while in geomorphology scale usuallyrefers to the size of the study area (e.g., local, regional,
national, or continental scale) (Broeckx et al. 2016; Gariano
et al. 2017; Segoni et al. 2018). In order to have a more
coherent approach, the concept of scale can be further
divided into sub-concepts (Dekavalla and Argialas 2017).
Relevant to our study are the observational scale, the oper-* Correspondence: lucian.dragut@fulbrightmail.org
1
Department of Geography, West University of Timisoara, Bd. V. Parvan 4,300223 Timisoara, Romania
Full list of author information is available at the end of the article

ational scale, and the computational scale. These concepts
can have different meanings depending on the type of data
and the field of study. In geomorphometry, where the data
are usually in raster format, the observational scale refers
to the spatial resolution or the cell size of a digital elevation
model (DEM), the operational scale describes the scale atwhich different process occurs and the computational scaleis the scale at which the analysis is conducted, i.e., the sizeof the moving window used in preprocessing the predictors(Goodchild 2001).
Most often, the land surface is a product of a numberof processes and control factors (Bishop et al. 2012) thathave different operational scales while sharing the same
physical space. When performing (space) prediction
studies, the challenge is to find the most appropriatecomputational and observational scales of the predictor
data at which they best match the process of interest, orat least the process features employed in modeling.
Because the computational scale is strongly related tothe observational scale, they are often discussed together
or in an interchangeable manner. Furthermore, both the© The Author(s). 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, andreproduction 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.

Sîrbu et al. Progress in Earth and Planetary Science(2019) 6:44

DEM resolution and the size of the moving window caninfluence the modeling process and the way in which
the data is scaled to better fit the operational scale.
The effect of the spatial resolution of a DEM on geomorphological modeling results has been tested in different disciplines and with somewhat different results. Somestudies conclude that the best option is to use the DEM
with the highest available spatial resolution for modeling
erosion, deposition, and rill network (Lu et al. 2017), or
catchment hypsometry (Liffner et al. 2018). Other studies,
however, showed that a higher DEM resolution does notnecessarily produce better results (Garosi et al. 2018), andsometimes induces more uncertainty or produce worse results in applications such as connectivity index modeling(Cantreul et al. 2018), hydrological modeling (Zhang andMontgomery 1994; Li and Wong 2010;), and catchment
area modeling (Becker et al. 2017).
Landslides research is an important topic in geomorphology and a significant number of studies dealt with landslide susceptibility and landslide detection. Landslide
inventory maps are created traditionally by interpretationof aerial photographs and/or field mapping. In recent years,
however, (semi)automated models have been utilized for
producing landslide inventories (Guzzetti et al. 2012;
Scaioni et al. 2014; Peppa et al. 2019), landslide susceptibility maps (Reichenbach et al. 2018), and for numerical simulation of a landslide (Iverson et al. 2015).
The methods of landslide modeling are getting increasingly sophisticated, with the use of additive statistics (Goetz
et al. 2011), machine learning or artificial intelligence algorithms (Catani et al. 2013; Lagomarsino et al. 2017) and numerical modeling (Lombardo et al. 2018), and the numberof studies comparing different algorithms has been increasing (Reichenbach et al. 2018). Landslide modeling, however,still depends on the input data, with various aspects, suchas sampling strategy, size or scale (Arnone et al. 2016;
Reichenbach et al. 2018; Schlögel et al. 2018).
Spatial resolution of a DEM is important in modelinglandslide susceptibility (Sulaiman et al. 2017; Wang et al.
2017; Schlögel et al. 2018), and it should be adapted to thesize of the study area (Reichenbach et al. 2018) or the
average size of the landslides (Claessens et al. 2005). Other
studies showed that scaling the predictors can improve
the overall accuracy of the model. A straightforward
upscaling method is resampling a DEM to lower resolutions, and then deriving the predictors and testing thescale (spatial resolution) that produces the best model(Catani et al. 2013). This methodology has shown that not
all predictors perform best at the same scale, thus, Paudel
et al. (2016) introduced an approach to find the best scalefor each predictor and used it in the model for landslide
susceptibility prediction. These findings were further supported by Pawluszek et al. (2018) who used scaled predictors for automated landslide detection.

Page 2 of 13

Choosing the appropriate input data in terms of DEM
resolution (observational scale) and the appropriate computational scale to derive different predictors (operational
scales) are still challenges in landslide detection. We
propose a methodology that solves the problem of adapting
the best operational scale in landslide detection by iteratively rescaling each predictor and finding its best fit. We
illustrate this by modeling two different landslide scarp
inventories without preprocessing to evaluate the successof the proposed approach. The new approach has beendeveloped as an algorithm for the open source R platform(R_Core_Team 2017) and can be freely downloaded to use
or improve.
All the models that we build and present in this paper
are used for automated landslide detection rather thanlandslide susceptibility modeling.

Methods/Experimental
Study areas and landslide inventory characteristics

In order to test if our hypothesis works on different settings, we have chosen two distinct study areas that are different in geology and land cover and thus they produce
landslides with different morphogenetic typology. By using
the two areas, we decrease the possibility that our results
are site specific and thus increase the confidence ingeneralization of our conclusion. The modeling results, forthe two areas, are not directly compared against eachother, but are utilized to compare for the scaled models
against the unscaled models. For both areas, compilationof the landslide inventory precedes the acquisition of theDEMs, so that the landslide scarps should be recognizable
in the DEMs.
The first study area is located in the Shizuoka Prefecture,in the southeast of Honshu Island, Japan (Fig. 1) and covers
an area of about 125 km2. Hillslopes in this area are mostly
covered by forest. Lithology is dominated by a melange
matrix of the Late Eocene to Early Miocene accretionary
complex with chaotic facies with intrusions of limestone
and marble blocks of the same accretionary complex.
Along the river valley, in the eastern study area, Late Pleistocene to Holocene fan deposits can be found. The available data consist of a DEM based on airborne LiDAR (lightdetection and ranging) at a 5 m spatial resolution, and an
inventory of 371 landslide scarps (Uchiyama et al. 2012).
The inventory was provided by the National Research Institute for Earth Science and Disaster Resilience, Japan(NIED) (Shimizu et al. 2002; Oyagi et al. 2015). The inventory was derived by visual interpretation of topographic
discontinuities using stereo-paired aerial photographs at a
1:40,000 scale, acquired in the 1970s. It includes historicallandslides that have occurred in the past decades to centuries, wider than 150 m, but does not include smaller shallow
slope failures. The majority of landslides are shallow slides
occurring on moderate to steep slopes but deep-seatedSîrbu et al. Progress in Earth and Planetary Science(2019) 6:44

Page 3 of 13

Fig. 1 Landslide scarps inventories in Shizuoka, Japan (1) and Buzău, Romania (2), and their locations (right)

landslides are also contained in the inventory. The use ofhistorical landslide inventories is advantageous for thelandslide modeling in that it summarizes past multiple
landslide events reflecting distinct topographic features in a
DEM and various environmental conditions (Fig. 2).
The second study area is situated in the Buzău County(Fig. 1), Romania, and covers about 800 km2 at the contact between the Romanian Curvature Carpathian
Mountains and the Subcarpathian Hills, which are partof the Vrancea seismic region. Cretaceous and Palaeogene flysch (i.e., alternations of thick cohesive sandstone
with schistose intercalations of marls, clays or bitumen)is specific to the mountainous section, while hills anddepressions are built on less cohesive Neogene molasses
deposits, i.e., a heterogeneous mixture of clays, marls,
salt breccias, loose conglomerates, sands, and loess-like
deposits. The morphology of landslides reflects thelitho-structural conditioning. Large, dormant (partiallyrelict), landslides prevail in the mountainous flysch sector featuring numerous reactivations in the form of shallow translational slides across the broad terminal
accumulation of landslide deposits, mainly due to the active river undercut (Fig. 2). The molasses sector features
very frequent but low-magnitude landslides and is characterized by the widespread presence of shallow translational earth slides and earth flows, reflecting at least
three propitious triggering frameworks: spring showers
and snowmelt, torrential convective summer showers

and less heavy but long-lasting autumn rains. This leads
to numerous sequences of first-time failures and subsequent reactivations which are responsible for the development of large landslide complexes, typically 5 to 10
ha, with a polycyclic evolution (Fig. 2).
Less than 10% of the inventory could be consideredmulti-temporal, since it covers the period of the last 40
years. The majority of the inventory is being represented byshallow and medium-seated earth and debris slides in thesouth-eastern half, which corresponds to the molasses formations of the Subcarpathian Hills; while for the Carpathians, it gathers both depletion and accumulation
sectors of shallow to deep-seated landslides. It was compiled from different sources. Some of them have been reconstructed from archive data (Institute of Geography,
Romanian Academy), while others resulted from detailed
geomorphological field mapping and databases of the local
authority: Buzau County Inspectorate for Emergency Situations (Zumpano 2014; Zumpano et al. 2014). The largest
proportion (> 90%) of data is represented by a single-period
inventory obtained in 2014 through digital stereographic
photo interpretation using color aerial ortho-photographs(cell size 2 m, resampled from an original 0.5 m) taken in2005 by ANCPI (Agentia Nationala de Cadastru si Publicitate Imobiliara) Bucharest. The anaglyph 3D visualization
was created using the “stereopair from DTM” (DTM—
digital terrain model) module of ILWIS Version 3.4 software. The 10 m resolution DEM used for the landslideSîrbu et al. Progress in Earth and Planetary Science(2019) 6:44

Page 4 of 13

Fig. 2 High magnitude deep-seated landslides in the Buzău Carpathians, upper left; low magnitude, clearly individualized shallow earth slide in
the Buzău Subcarpathians, upper right; mountain slopes with historical landslides, that are mostly covered by vegetation in Shizuoka Prefecture,
lower image

interpretation was obtained from linear interpolation of
topographic contour lines (20 and 5 m interval) derived
from 1:25,000 topographic maps published by the military
topographic direction (Damen et al. 2014). The DEM used
for model construction is an optically derived DTM with aspatial resolution of 4 m.
The landslide scarps from the two study areas show
significant differences in size (Fig. 3). The average size ofthe landslide scarp in the Buzau County study area is8.64 ha and the median size is 4.28 ha, while the mean
size of the landslide scarps in the Shizuoka Prefecture
study area is 2.35 ha and the median value is 1.44 ha.
The landslides analyzed in the present study are not differentiated based on their origin or morphologic traits.
Thus, the term landslide in this paper includes shallow or
deep-seated slides, flows, and avalanches.

Land-surface variables

We have chosen only predictors that can be extracted froma DEM and ignored predictors like land cover and lithology, which are known to hold an important influence onlandslides but fall outside the scope of the study. Furthermore, these land-surface variables (LSVs) had to satisfy two
additional conditions: they can be extracted using moving
windows of variable size, and their influence on landslide
scarps has a clear geomorphological explanation. Thus,

Fig. 3 Area of landslide scarps in the two study areas, in hectares.
Boxplots represent 25–75% of values, whiskers represent 10–90% ofvalues, and the line within the box represents the median. Extreme
values are omittedSîrbu et al. Progress in Earth and Planetary Science(2019) 6:44

LSVs like total catchment area or hillshade were notconsidered.
The LSVs were derived, in a standard 3 × 3 moving
window, from the available DEMs using the RSAGA
package (Brenning 2008) that implements the algorithmsof SAGA (System for Automated Geoscientific Analyses;
Conrad et al. 2015) into the R software as follows: Elevation, as expressed by DEM values, is regularly














used as a predictor in modeling of landslides (Catani
et al. 2013; Reichenbach et al. 2018)
Mean curvature is a second derivative of theelevation and was computed according to themethodology proposed by Moore et al. (1991)
Plan curvature is calculated perpendicular to thedirection of flow and is generally used to describe
the divergence or convergence of flow (Catani et al.
2013)
Profile curvature is calculated on the direction offlow and is generally used to characterize erosion/
deposition potential of a slope (Catani et al. 2013)
Slope gradient is the most widely used predictor inlandslide modeling (Reichenbach et al. 2018)
The topographic positioning index (TPI) gives anestimate of the slope position of landslides and wasfound to have a scale dependency (Wang et al. 2018)
The topographic roughness index (TRI) measures theunevenness of a terrain by calculating the difference inelevation between a cell and the average of thesurrounding cells, in a given window size (Olaya 2004)
Terrain surface texture (texture), emphasizing finedifferences in elevation of different pixels, wasderived with the method proposed by Iwahashi andPike (2007).

The values of these LSVs were associated with one
randomly sampled point per scarp, as well as an equivalent number of randomly sampled non-scarp points(Santacana et al. 2003). It is possible for the non-scarp
points to be located on landslide bodies.
Scaling of LSVs

For scaling of LSVs, we propose a methodology that can(i) analyze the degree of fitting between each predictor
and the presence/absence of landslide scarps, (ii) adapt todifferent study areas or scenarios, and (iii) easily integrate
into a (semi)automated modeling approach (Fig. 4).
At first, each LSV was re-scaled to successively
broader representations of topography with focal mean
statistics in increasing squared windows, starting from3 × 3 cells (Dragut et al. 2009), using the RSAGA package (Brenning 2008).
For each LSV, the best scale was evaluated in an iterative process, by fitting a simple binary logistic regression

Page 5 of 13(Brenning 2005; Deluigi et al. 2017; Cauzzi et al. 2018;),
where each scale of the LSV is, in turn, the predictor
variable and the scarp presence/absence is the predictedvariable. The process continues as long as the result of
the logistic regression improves, and it stops when thescale that best describes the presence/absence of scarps
is found. All the available points for presence/absence oflandslide scarps are used for the logistic regression.
The performance of each model was evaluated using
the AUC (area under the curve) metric. The AUC is obtained by plotting all possible sensitivity (true positive)
rates against 1-sensitivity (false positive) rates, andreturns values between 0.5 (no discrimination between
presence/absence) and 1 (perfect discrimination) (Hosmer and Lemeshow 2000). The AUC values are computed using the ROCR package (Sing et al. 2005).
Because the aim of the test is to evaluate the goodnessof fit, the data used for model evaluation was the sameas the training data.
Prediction of landslide scarps

In order to assess the impact of scaling the predictors inmodeling, two models were produced and compared:
one that used the scaled predictors as input data, while
the other used the default LSVs (derived in a 3 × 3 moving window, without further smoothing). The latter wasconsidered the baseline in evaluating the scaling performance. Because the aim is to test the prediction
power of the model, the input data has been split into
70% and 30%, for training and validation respectively.
The employed modeling technique is random forest(RF; Breiman 2001). RF is a robust, easy to use, andcomputationally efficient classification algorithm (Chenet al. 2018) with a high level of accuracy (Youssef et al.
2016; Chen et al. 2017; Behnia and Blais-Stevens 2018).
It produces a number of classifications, based on a binary decision algorithm, called decision trees and uses allof them, with a majority voting, to form the best prediction model. Thus, each classification is assigned to a certain class (presence or absence) if the majority ofdecision trees do so. Furthermore, it is possible to obtain
the results as probabilities of class assignment for a certain sample by evaluating the strength of the majority ofdecision trees. RF has several characteristics linked with
the input data that are useful for our purposes: (i) there
is no need for the input data to have a specific frequency
distribution, (ii) it is not sensitive to outliers in the input
data, (iii) it can use a great number of predictor variables, and (iv) it is not sensitive to collinearity in thepredictor variables (Catani et al. 2013).
The modeling was performed using the package “randomForest” (Liaw and Wiener 2002) in the R software,with the settings ntree (number of classification trees) =501 and mtry (number of candidate variables) = 3. The

Sîrbu et al. Progress in Earth and Planetary Science(2019) 6:44

Page 6 of 13

Fig. 4 Experimental work flow

ntree value was set in a “trial and error” method inwhich the model was run with a high number of trees andthe results were plotted against the OOB (out of bag
error). The ntree value at which the curve flattened wasselected as the appropriate one for modeling because afurther increase in ntree would not improve the model(Catani et al. 2013). The mtry value was obtained by thepﬃﬃﬃ
standard formula, p, where p is the number of variables.
Another output of an RF model is the ranking of predictor importance in creating the model, the so-called
variable importance (VI). There are many ways for computing this but we used the simplest method, based on thedecrease in mean accuracy. The OOB is calculated using
all the predictors and then, in turn, omitting one predictor. The differences between the two OOB values areaveraged for all the decision trees and are normalizedusing the standard deviation of the differences. The predictors that are found to reduce the OOB when used, areregarded as the most important (Liaw and Wiener 2002).
In order to account for the random part in RF, themodels were run for 25 times and the results were averaged and evaluated using the validation data set and AUC.

Automatic procedure

Catani et al. (2013) and Paudel et al. (2016) show that scaling of all or individual predictors can improve the modelof landslide susceptibility, and Reichenbach et al. (2018)
and Schlögel et al. (2018) show that choosing the appropriate DEM resolution is important and can influence theoverall accuracy of the model. However, preprocessing of
input data (predictors) in regard to scale is still not a common practice in landslide modeling. Moreover, our literature review on landslide modeling indicates that there are
more studies for choosing the right method than studies
for calibration of input data to the model.
For easier preprocessing of the predictors in order to
fit them to the most appropriate scale of analysis, we
provided a script that can run with the free software R,
available in Additional file 1. The necessary input data
for the script is a DEM from which the predictors will
be extracted, and a shapefile containing landslide presence/absence data for the variable that is to be modeled.
Note that for this study, we have chosen to work with
only eight predictors, but the script can extract morepredictors from an input DEM.

Sîrbu et al. Progress in Earth and Planetary Science(2019) 6:44

Page 7 of 13

Results and discussion

Prediction of landslide scarps

Scaling of predictors

The results show a significant improvement in the accuracy of the models for both study areas when the predictors have been scaled. The increase is particularly
significant in Buzau, where AUC changed from 0.59 to
0.73, corresponding to a poor to a good model. For
Shizuoka, the increase in accuracy is more limited, from
AUC = 0.73 to 0.80, but is still important, especiallyconsidering that the model with default predictors had
already shown a good accuracy (Fig. 7).
Building a model for detecting landslide scarps getsincreasingly more complex as the terrain is more variable
and the landslides are of different type. The relation
between landslide scarps and LSVs is not linear and generally not intuitive. For example, there is no clear relation
between the slope of a scarp and the slope of the terrain
before the landslide occurred. A deep-seated slide canoccur on a gentle slope but the slope of its scarp can besteep. And also an older, deep-seated slide can occur on amuch steeper slope but the scarp can be eroded and thus
less steep.

LSVs calibrated to scale could better predict the scarp
presence/absence in five out of eight cases for the Shizuoka study area, and in six out of eight cases for the Buzau
study area (Fig. 5). Elevation and profile curvature display
the best prediction of the landslide scarps at their default
scale in both areas, and plan curvature also shows the best
prediction at the default scale for Shizuoka, but at a 17 ×
17 pixels for Buzau. Three predictors (elevation, profile
curvature, and mean curvature) were found to perform
best at the same scale in both study areas (Fig. 5).
The performance of the predictors for both study areas
shows that two predictors (plan curvature and texture)display a higher scale for Buzau, while three others(slope, TRI, and TPI) show a higher scale for Shizuoka.
Pawluszek et al. (2018) also found that TPI performs
better when it is scaled using a relatively larger moving
window. These results suggest that the relationship between the size of landslides and the scale of predictors
is not straightforward. If such a relationship exists, thenit is most probably defined for each individual predictor, and is more complex than a simple linear one.
This confirms the findings of Paudel et al. (2016) andPawluszek et al. (2018) in that there is no universal
scale that works for all LSVs, but each LSV performs
best at its own scale.
The curvatures, with one exception, perform better atfine scales, while slope, texture, TPI, and TRI performbetter at broad scales. When scaled, the prediction
power of individual LSVs increases in both study areas.
The most significant increase in the Shizuoka study area
is for slope (ΔAUC = 0.10) and TRI (ΔAUC = 0.11). For
the Buzau study area, the biggest increase is for mean
curvature (ΔAUC = 0.07). The increase in AUC for ascaled LSV compared to the default is greater for the
Shizuoka study area, with the exception of elevation,plan, and profile curvatures (Fig. 6).

Variable importanceOur study found out that not all the LSVs have theirbest fit at the same scale. This controls the final accuracyof the model, because using each LSV at its appropriate
scale not only improves the modeling accuracy (Paudel
et al. 2016) but also strongly affects the ranking of predictor importance (Pawluszek et al. 2018). Indeed, for
the Buzau study area, the ranking changes significantly
when the model is constructed with scaled predictors(Fig. 8). For the default model, elevation stands as themost important predictor, followed by a group of otherthree predictors with similar importance (TRI, TPI, andslope). Curvatures have lower importance while texture
has a negative importance coefficient, which indicates anegligible contribution to the model. For the scaled
model, two predictors stand out as important, slope, andFig. 5 Size of the moving window at which each LSV predicts best the landslide scarps

Sîrbu et al. Progress in Earth and Planetary Science(2019) 6:44

Page 8 of 13

Fig. 6 AUC results for landslide scarps modeling with individual predictors

TPI, and the variable importance graph shows all thepredictors influence the model, even texture. For the
Shizuoka study area, the default model’s predictor ranking is quite similar to that of the Buzau default model,
elevation stands as the most important predictor,
followed by TRI, slope, and TPI, while curvatures andtexture are the list important. For the scaled model,
there is an even bigger gap between elevation and theother predictors. Notable is the rise in importance of the
texture when scaled, which becomes the second mostimportant predictor (Fig. 8).
These findings are supported by similar results presented by Paudel et al. (2016) and Pawluszek et al. (2018).
Also, by testing the effect of DEM resolution on the accuracy of landslide models, Catani et al. (2013) found thatusing a DEM with a different spatial resolution changes

Fig. 7 Model accuracy, measured using AUC

the ranking of variable importance. This is in strong contrast with the vast majority of studies on landslide modeling that use all the LSVs at the same scale. The problem
of model accuracy can be, partially, solved by using an algorithm that can have a high number of predictor variables from which those that improve the model arechosen (e.g., RF). However, the possibility that some LSVs
can be ranked of little or no importance to the model occurs, because they are not used at the proper scale, affecting the geomorphological interpretation of the model.
It is important to note that there are great differencesin the ranking of variable importance between resultsfrom RF (Fig. 8) and those from the logistic regression(Fig. 6). The fact that different modeling approaches
produce different variable importance rankings, for thesame input data, has also been reported in previousSîrbu et al. Progress in Earth and Planetary Science(2019) 6:44

Page 9 of 13

Fig. 8 Variable importance ranking, based on the random forest modelstudies (Goetz et al. 2015; Chen et al. 2017), and the reason for this is beyond the scope of this paper. The main
point in our approach is to compare the scaled variableagainst the unscaled variable for a better fit based on asimple regression, since more complex modeling techniques, like RF, work best when they use multiple predictors (Catani et al. 2013).
Scaling factor and landslide size

Our results show that, with the exception of profile
curvature, all others LSVs have a better fit to the landslide scarps when they were scaled, and that there is nouniversal scale at which the LSVs should be used, eachindividual LSV has a particular scale for a particularstudy area. When we transform the scaling factor, frompixels to hectares, in order to compare the results withthe average size of the landslides, we find that there are
significant differences between predictors and between
the two study areas (Fig. 9). For the Shizuoka study area,
five LSVs (plan curvature, profile curvature, mean curvature, texture, elevation) that are not scaled or scaled torepresent an area less than 0.25 ha (50 × 50 m) and only
three variables are scaled broadly. In the Buzau study
area, by contrast, three LSVs (profile curvature, mean
curvature, elevation) are scaled to finer sizes and five arescaled broadly.

It has long been acknowledged that the multitudes ofprocesses, at different spatial and temporal scales, occupy the same physical space (Schmidt and Andrew
2005) and that DEMs with different pixel sizes portray
different processes and landforms for the same area
(Claessens et al. 2005). This is important in all geomorphological modeling and is particularly important inmodels to detect past landslides, because the vast majority of landslides are relatively big, and on both theirscarp and body other processes, at smaller scales, cantake place and develop a local microtopography (e.g., rill
networks and gullies). All the LSVs that are scaled tofiner areas seem to improve the modeling accuracy due
to a better representation of topography through elimination of local noise in the form of microtopography(Fig. 10). Furthermore, the area that they are scaled toseems to fit the range of spatial resolutions at which
other studies have found to be most appropriate tomodel landslides. Thus, when studying the effects of
DEM resolution on landslide modeling in the past,
DEMs with spatial resolutions ranging from 2 to 500 m
were investigated and the conclusion was that the best
results were obtained using DEMs with a spatial resolution of 10 m (Arnone et al. 2016; Wang et al. 2017;
Schlögel et al. 2018;), 20 m (Sulaiman et al. 2017; Pawluszek et al. 2018) and even 50 m (Catani et al. 2013).

Sîrbu et al. Progress in Earth and Planetary Science(2019) 6:44

Page 10 of 13

Fig. 9 A comparisons between the area that scaled variables represent (bars) and the average area of landslides (horizontal lines)

These results suggest that greater consideration should
be given to the choice of DEMs, and their spatial resolution, when modeling landslides. Also, the fact that thelowest pixel size (i.e., highest spatial resolution) is not always the best option is highly important today with theincreasing availability and use of DEMs with high to very
high resolution.
The LSVs that are scaled broadly, to an area of morethan 2500 m2 (50 × 50 m), seem to be less related with themicrotopography developed on the landslides and morewith the size of the landslides themselves. They are related
to the findings of Claessens et al. (2005) who concluded

that there is no universal DEM resolution for landslide
modeling, and recommended that the DEM to be usedshould be adapted to the average size of the landslides.
For the Shizuoka study area, the three LSVs (slope,
TRI, and TPI) that are scaled broadly represent an area
greater than that of the average area of landslides
whereas, the broadly scaled LSVs for the Buzau study
area represent an area of approximately three times
smaller than that of the average landslide. This can be
related to the type and accuracy of the DEMs. The Lidar
DEM used for the Shizuoka study area has a higher accuracy than that of the optically derived DEM for the

Fig. 10 Example of a scaled predictor in relation to a landslide scarp in the Shizuoka area. Left image: plan curvature at the default scale. Right
image: plan curvature scaled in a 9 × 9 window. Note a north-south trench inside the landslide scarp on the left image is filtered out on theright imageSîrbu et al. Progress in Earth and Planetary Science(2019) 6:44

Buzau study area, and the latter has more artifacts resulted from the transformation from a DSM (digital surface model) to a DTM (digital terrain model). However,an even greater uncertainty in interpreting the relation
between the area at which the LSVs were scaled and thearea of the landslides is the variability in landslide sizefor the two study areas and that in size within eachstudy area. This is valid especially in the Buzau area
where the size differences between the more frequent
smaller landslides and the bigger landslides are significant, and using only the average to express the size isstatistically unrepresentative. The complex relation between the scaling factors of different predictors and thesizes of landslides under different conditions should be
addressed in future studies.
Scaling LSVs for landslide detection models vs landslide
susceptibility models

In recent years, more and more landslide inventories are
created using automated or semi-automated models thatcan detect changes in the topography and/or land coveror that can be trained to identify the characteristics oflandslides. The input for these models can be DEMs, remote sensing data or a combination of both.
The methodology that we propose here was developed
and tested for detecting landslides in order to build a
new inventory. We have chosen this approach, insteadof a landslide susceptibility study because (i) it allows us
to use landslides that are different in size and scarp
characteristics, making the modeling more challenging
and also reducing the risk of overfitting (creating an algorithm with good results on our data but with poortransferability), and (ii) it is easy to validate, since wealready have the inventory of landslides that the algorithm predicts.
Different methods of scaling input data have been used
for landslide susceptibility studies (Catani et al. 2013;
Paudel et al. 2016). We estimate that our model for scaling can also be used in landslide susceptibility models.
From the R script that is provided in Additional file 1,
the scaling subroutine can be run independently of therest of the script and can be used as a preprocessing stepin a workflow of landslide susceptibility modeling. However, this should be tested in further studies.

Conclusion
We tested the hypothesis that scaling LSVs to better fit
the observational scale improves modeling the presence/
absence of landslide scarps. The results show that whenusing scaled predictors, the models outperform themodels with unscaled predictors. In terms of AUC
values, an increase of 0.07 (0.73 to 0.80) was achieved
for the Shizuoka area and an increase of 0.14 (0.59 to0.73) for the Buzau area. We further found that the

Page 11 of 13

importance of predictors in modeling changes as LSVs
become more relevant at appropriate scales. These results highlight the importance of considering the scale ofthe input data in relation to the scaled of the modeledlandslides. Therefore, we recommend that a multiscale
analysis of the LSVs should be performed as a preprocessing of the input data before the modeling.
The proposed upscaling approach was integrated intoa script available in Additional file 1, with a description
and instructions to use in Additional file 2. The script
can run automatically, as a preprocessing step in modeling, or it can run on a line by line base, in order to better analyze and understand the input data. This script
can be applied to any DEM resolution in order to scaleup the input data to the size of geomorphologicalfeatures.
In this paper, the errors that can be present in the two
DEMs, and the other factors that can influence the modeling results, such as sampling strategy or modelingtechniques are not discussed. Also, predictors that are
known to have an important role in landslide modelingbut are not derived from a DEM, like lithology and landcover, were not used. Those factors are beyond the scopeof this paper and in order to test solely the influence ofscaling the DEM-derived input variables, we had to keep
all other factors constant, at the expense of potential loss
in the overall accuracy of the model. Furthermore, we
do not discuss the differences in the ranking of variable
importance when computed with different methods. Allof this should be addressed in future research.

Additional files
Additional file 1: R scalling script. (R 7 kb)
Additional file 2: R script user guide. (DOCX 14 kb)
Abbreviations
ANCPI: Agentia Nationala de Cadastru si Publicitate Imobiliara (Romaniannational agency for mapping and real estate); AUC: Area under the curve;
DEM: Digital elevation model; DSM: Digital surface model; DTM: Digitalterrain model; LSV: Land-surface variable; mtry: Number of candidate
variables; ntree: Number of classification trees; OOB: Out of beg error;
RF: Random forest; VI: Variable importanceAcknowledgements
This work was supported by a grant of Ministry of Research and Innovation,
CNCS - UEFISCDI, project number PN-III-P4-ID-PCE-2016-0222, within PNCDI
III.
Authors’ contributions
FS performed the analyses, contributed to the research design, drafted thefirst version of the manuscript, and developed the script. LD contributed tothe development of the idea, drafted the research design, and contributed
to the final version of the manuscript. TO contributed to the development of
the idea and to the final version of the manuscript. YH provided the data for
the Japanese case study and contributed to the final version of themanuscript. MM provided the data for the Romanian case study andcontributed to the final version of the manuscript. All authors read andapproved the final manuscript.

Sîrbu et al. Progress in Earth and Planetary Science(2019) 6:44

Funding
This work was supported by a grant of Ministry of Research and Innovation,
CNCS-UEFISCDI, project number PN-III-P4-ID-PCE-2016-0222, within PNCDI III.
The grant facilitated the acquisition of the DEM for the Romanian case study,
payment of salaries for Flavius Sîrbu and Lucian Drăguț, fieldtrips and project
meetings, conferences, and publication costs.
Availability of data and materials
The data for the Japanese case study (DEM and landslide inventory) are
publicly available by the National Research Institute for Earth Science andDisaster Prevention (NIED), Japan. The DEM for the Romanian case study has
been acquired under a license that forbids public release. The landslide
inventory for the Romanian case study is the property of the RomanianAcademy of Sciences; we do not have permission to make it publicly
available. Preprocessing of these data can be performed by everyone withthe tool available on the project’s website: http://mapslide.projects.uvt.ro/wp-content/uploads/2017/12/Scalling-Tool.7z
Competing interests
The authors declare that they have no competing interests.
Author details
1
Department of Geography, West University of Timisoara, Bd. V. Parvan 4,300223 Timisoara, Romania. 2Center for Spatial Information Science, The
University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa-shi, Chiba 277-8568, Japan.
3
Institute of Geography, Romanian Academy, 023993 Bucharest, Romania.
4
Faculty of Environmental Earth Science, Hokkaido University, N10W5 Kita-ku,
Sapporo, Hokkaido 060-0810, Japan.
Received: 17 December 2018 Accepted: 27 May 2019

References
Arnone E, Francipane A, Scarbaci A, Puglisi C, Noto LV (2016) Effect of raster
resolution and polygon-conversion algorithm on landslide susceptibility
mapping. Environ Model Softw 84:467–481. https://doi.org/10.1016/j.envsoft.
2016.07.016
Becker D, de Andres-Herrero M, Willmes C, Weniger GC, Bareth G (2017)
Investigating the influence of different DEMs on GIS-based cost distancemodeling for site catchment analysis of prehistoric sites in Andalusia. ISPRSInt Geo-Inf 6:28. https://doi.org/10.3390/ijgi6020036
Behnia P, Blais-Stevens A (2018) Landslide susceptibility modelling using thequantitative random forest method along the northern portion of the Yukon
Alaska highway corridor, Canada. Nat Hazards 90:1407–1426. https://doi.org/
10.1007/s11069-017-3104-z
Bishop MP, James LA, Shroder JF, Walsh SJ (2012) Geospatial technologies anddigital geomorphological mapping: concepts, issues and research.
Geomorphology 137:5–26. https://doi.org/10.1016/j.geomorph.2011.06.027
Breiman L (2001) Random forests. Mach Learn 45:5–32. https://doi.org/10.1023/a:
1010933404324
Brenning A (2005) Spatial prediction models for landslide hazards: review,comparison and evaluation. Nat Hazards Earth Syst Sci 5:853–862. https://doi.
org/10.5194/nhess-5-853-2005
Brenning A (2008) Statistical geocomputing combining R and SAGA: the example
of landslide susceptibility analysis with generalized additive models. In:
Boehner J, Blaschke T, Montanarella L (eds) SAGA--Seconds Out, vol 19.
Hamburger Beitraege zur Physischen Geographie und Landschaftsoekologie,
Hamburg, pp 23–32
Broeckx J, Vanmaercke M, Balteanu D, Chendes V, Sima M, Enciu P, Poesen J(2016) Linking landslide susceptibility to sediment yield at regional scale:application to Romania. Geomorphology 268:222–232. https://doi.org/10.
1016/j.geomorph.2016.06.012
Cantreul V, Bielders C, Calsamiglia A, Degre A (2018) How pixel size affects a
sediment connectivity index in Central Belgium. Earth Surf Process Landf 43:
884–893. https://doi.org/10.1002/esp.4295
Catani F, Lagomarsino D, Segoni S, Tofani V (2013) Landslide susceptibility
estimation by random forests technique: sensitivity and scaling issues. Nat
Hazards Earth Syst Sci 13:2815–2831. https://doi.org/10.5194/nhess-13-28152013
Cauzzi C, Fah D, Wald DJ, Clinton J, Losey S, Wiemer S (2018) ShakeMap-based
prediction of earthquake-induced mass movements in Switzerland calibrated

Page 12 of 13

on historical observations. Nat Hazards 92:1211–1235. https://doi.org/10.
1007/s11069-018-3248-5
Chen F, Yu B, Li B (2018) A practical trial of landslide detection from singletemporal Landsat8 images using contour-based proposals and random
forest: a case study of national Nepal. Landslides 15:453–464. https://doi.org/
10.1007/s10346-017-0884-x
Chen W, Xie XS, Wang JL, Pradhan B, Hong HY, Bui DT, Duan Z, Ma JQ (2017) A
comparative study of logistic model tree, random forest, and classification
and regression tree models for spatial prediction of landslide susceptibility.
Catena 151:147–160. https://doi.org/10.1016/j.catena.2016.11.032
Claessens L, Heuvelink GBM, Schoorl JM, Veldkamp A (2005) DEM resolution
effects on shallow landslide hazard and soil redistribution modelling. Earth
Surf Process Landf 30:461–477. https://doi.org/10.1002/esp.1155
Conrad O et al (2015) System for automated geoscientific analyses (SAGA). v. 2.1.
4. Geosci Model Dev 8:1991–2007. https://doi.org/10.5194/gmd-8-1991-2015
Damen M, Micu M, Zumpano V, Van Westen CJ, Sijmons K, Balteanu D (2014)
Landslide mapping and interpretation: implications for landslide
susceptibility analysis in discontinuous data environment. Conference
Analysis and Management of Changing Risks for Natural Hazards, pp 177–
186, 705
Dekavalla M, Argialas D (2017) Evaluation of a spatially adaptive approach forland surface classification from digital elevation models. Int J Geogr Inf Sci
31:1978–2000. https://doi.org/10.1080/13658816.2017.1344984
Deluigi N, Lambiel C, Kanevski M (2017) Data-driven mapping of the potential
mountain permafrost distribution. Sci Total Environ 590:370–380. https://doi.
org/10.1016/j.scitotenv.2017.02.041
Dragut L, Schauppenlehner T, Muhar A, Strobl J, Blaschke T (2009) Optimizationof scale and parametrization for terrain segmentation: an application to soillandscape modeling. Comput Geosci 35:1875–1883. https://doi.org/10.1016/j.
cageo.2008.10.008
Gariano SL, Rianna G, Petrucci O, Guzzetti F (2017) Assessing future changes in
the occurrence of rainfall-induced landslides at a regional scale. Sci Total
Environ 596:417–426. https://doi.org/10.1016/j.scitotenv.2017.03.103
Garosi Y, Sheklabadi M, Pourghasemi HR, Besalatpour AA, Conoscenti C, Van Oost
K (2018) Comparison of differences in resolution and sources of controlling
factors for gully erosion susceptibility mapping. Geoderma 330:65–78.
https://doi.org/10.1016/j.geoderma.2018.05.027
Goetz JN, Brenning A, Petschko H, Leopold P (2015) Evaluating machine learningand statistical prediction techniques for landslide susceptibility modeling.
Comput Geosci 81:1–11. https://doi.org/10.1016/j.cageo.2015.04.007
Goetz JN, Guthrie RH, Brenning A (2011) Integrating physical and empirical landslide
susceptibility models using generalized additive models. Geomorphology 129:
376–386. https://doi.org/10.1016/j.geomorph.2011.03.001
Goodchild MF (2001) Metrics of scale in remote sensing and GIS. Int J Appl Earth
Obs Geoinf 3:114–120. https://doi.org/10.1016/S0303-2434(01)85002-9
Guzzetti F, Mondini AC, Cardinali M, Fiorucci F, Santangelo M, Chang KT (2012)Landslide inventory maps: new tools for an old problem. Earth-Sci Rev
112(1–2):42–66
Hosmer DW, Lemeshow S (2000) Applied Logistic Regression. Wiley, New York.
https://doi.org/10.1002/0471722146
Iverson RM, George DL, Allstadt K, Reid ME, Collins BD, Vallance JW, Schilling SP,
Godt JW, Cannon CM, Magirl CS, Baum RL, Coe JA, Schulz WH, Bower JB(2015) Landslide mobility and hazards: implications of the 2014 Oso disaster.
Earth Planet Sci Lett 412:197–208
Iwahashi J, Pike RJ (2007) Automated classifications of topography from DEMs by
an unsupervised nested-means algorithm and a three-part geometricsignature. Geomorphology 86:409–440. https://doi.org/10.1016/j.geomorph.
2006.09.012
Lagomarsino D, Tofani V, Segoni S, Catani F, Casagli N (2017) A tool forclassification and regression using random forest methodology: applications
to landslide susceptibility mapping and soil thickness modeling. Environ
Model Assess 22:201–214. https://doi.org/10.1007/s10666-016-9538-y
Li J, Wong DWS (2010) Effects of DEM sources on hydrologic applications.
Comput Environ Urban Syst 34:251–261. https://doi.org/10.1016/j.
compenvurbsys.2009.11.002
Liaw A, Wiener M (2002) Classification and regression by random forest. R News
2:18–22 doi:citeulike-article-id:1121494
Liffner JW, Hewa GA, Peel MC (2018) The sensitivity of catchment
hypsometry and hypsometric properties to DEM resolution andpolynomial order. Geomorphology 309:112–120. https://doi.org/10.1016/j.
geomorph.2018.02.022

Sîrbu et al. Progress in Earth and Planetary Science(2019) 6:44

Lombardo L, Opitz T, Huser R (2018) Point process-based modeling of multiple
debris flow landslides using INLA: an application to the 2009 Messina
disaster. Stoch Environ Res Risk Assess 32:2179–2198. https://doi.org/10.1007/
s00477-018-1518-0
Lu X, Li Y, Washington-Allen RA, Li Y, Li H, Hu Q (2017) The effect of grid size on
the quantification of erosion, deposition, and rill network. Int Soil Water
Conserv Res 5:241–251. https://doi.org/10.1016/j.iswcr.2017.06.002
Moore ID, Grayson RB, Ladson AR (1991) Digital terrain modeling—a review ofhydrological, geomorphological and biological applications. Hydrol Process 5:
3–30. https://doi.org/10.1002/hyp.3360050103
Olaya V (2004) A gentle introduction to SAGA GIS. Available from: http://
downloads.sourceforge.net/saga-gis/SagaManual.pdf. Accessed 4 June 2019
Oyagi N, Uchiyama S, Ogura M (2015) Explanations of landslide distribution
maps. Technical note of the National Research Institute for Earth Science andDisaster Resilience, vol 394, pp 391–314
Paudel U, Oguchi T, Hayakawa Y (2016) Multi-resolution landslide susceptibility
analysis using a DEM and random Forest. Int J Geosci 07(05):18. https://doi.
org/10.4236/ijg.2016.75056
Pawluszek K, Borkowski A, Tarolli P (2018) Sensitivity analysis of automatic
landslide mapping: numerical experiments towards the best solution.
Landslides 15(9):1851–1865
Peppa MV, Mills JP, Moore P, Miller PE, Chambers JE (2019) Automated coregistration and calibration in SfM photogrammetry for landslide change
detection. Earth Surf Process Landf 44(1):287–303
Pike RJ, Evans IS, Hengl T (2008) Geomorphometry: a brief guide. In: Hengl T,
Reuter HI (eds) Geomorphometry: concepts, software, applications, vol 33.
Elsevier, Amsterdam, p 772
R_Core_Team (2017) R: a language and environment for statistical computing. R
Foundation for statistical computing, Vienna
Reichenbach P, Rossi M, Malamud BD, Mihir M, Guzzetti F (2018) A review ofstatistically-based landslide susceptibility models. Earth-Sci Rev 180:60–91.
https://doi.org/10.1016/j.earscirev.2018.03.001
Santacana N, Baeza B, Corominas J, De Paz A, Marturia J (2003) A GIS-based
multivariate statistical analysis for shallow landslide susceptibility mapping in
La Pobla de Lillet area (Eastern Pyrenees, Spain). Nat Hazards 30:281–295.
https://doi.org/10.1023/b:nhaz.0000007169.28860.80
Scaioni M, Longoni L, Melillo V, Papini M (2014) Remote sensing for landslide
investigations: an overview of recent achievements and perspectives.
Remote Sens 6(10):9600–9652
Schlögel R, Marchesini I, Alvioli M, Reichenbach P, Rossi M, Malet JP (2018)
Optimizing landslide susceptibility zonation: effects of DEM spatial resolution
and slope unit delineation on logistic regression models. Geomorphology
301:10–20. https://doi.org/10.1016/j.geomorph.2017.10.018
Schmidt J, Andrew R (2005) Multi-scale landform characterization. Area 37:341–
350. https://doi.org/10.1111/j.1475-4762.2005.00638.x
Segoni S, Rosi A, Lagomarsino D, Fanti R, Casagli N (2018) Brief communication:using averaged soil moisture estimates to improve the performances of aregional-scale landslide early warning system. Nat Hazards Earth Syst Sci 18:
807–812. https://doi.org/10.5194/nhess-18-807-2018
Shimizu F, Yagi K, Inokuchi T, Oyagi N (2002) Landslide maps series 14 “Shizuoka”.
Technical Note of the National Research Institute for Earth Science andDisaster Resilience, p 221
Sing T, Sander O, Beerenwinkel N, Lengauer T (2005) ROCR: visualizing theperformance of scoring classifiers. Bioinformatics 21:7881
Sulaiman WNA, Rosli MH, Abu Samah MA, Kamarudin MKA (2017) Landslide
susceptibility mapping: effect of spatial resolution towards the prediction oflandslide prone area in a tropical catchment. Chiang Mai J Sci 44:494–507
Uchiyama S, Doshida S, Oyagi N, Shimizu F (2012) The NIED landslide mapping
team
Wang J, Jin W, Y-f C, W-f Z, Wu C-h, Alessandro P (2018) Earthquake-triggeredlandslides affecting a UNESCO natural site: the 2017 Jiuzhaigou earthquake
in the World National Park, China. J Mt Sci 15:1412–1428. https://doi.org/10.
1007/s11629-018-4823-7
Wang T, Liu JM, Shi JS, Wu SR (2017) The influence of DEM resolution on seismic
landslide hazard assessment based upon the Newmark displacement
method: a case study in the loess area of Tianshui. China Environ Earth Sci
76:10. https://doi.org/10.1007/s12665-017-6944-7
Youssef AM, Pourghasemi HR, Pourtaghi ZS, Al-Katheeri MM (2016) Landslide
susceptibility mapping using random forest, boosted regression tree,classification and regression tree, and general linear models andcomparison of their performance at Wadi Tayyah Basin, Asir region,

Page 13 of 13

Saudi Arabia. Landslides 13:839–856. https://doi.org/10.1007/s10346-0150614-1
Zhang WH, Montgomery DR (1994) Digital elevation model grid size, landscape
representation and hydrologic simulations. Water Resour Res 30:1019–1028.
https://doi.org/10.1029/93wr03553
Zhilin L (2008) Multi-scale digital terrain modelling and analysis. In: Zhou Q, Lees
B, Tang G (eds) Advances in digital terrain analysis. Springer, Berlin, pp 59–83.
https://doi.org/10.1007/978-3-540-77800-4_4
Zumpano V (2014) Development of a method for constructing risk scenario andrisk maps with associated uncertainties. PhD thesis, Manuscript. Institute ofGeography, Bucharest
Zumpano V, Hussin H, Reichenbach P, Bãlteanu D, Micu M, Sterlacchini S (2014) A
landslide susceptibility analysis for Buzãu County, Romania. Revue Roumainede Geographie/Rom J Geogr 58(1):9–16

Publisher’s NoteSpringer Nature remains neutral with regard to jurisdictional claims inpublished maps and institutional affiliations.