Na . Haza ds Ea h Sys . Sci., 18, 2455–2469, 2018
h ps://doi.o g/10.5194/nhess-18-2455-2018
© Au ho (s) 2018. This wo k is dis ibu ed unde
he C ea i e Commons A ibu ion 4.0 License.
E ec i e su eyed a ea and i s ole in s a is ical landslide
suscep ibili y assessmen s
Txomin Bo nae xea1, Mau o Rossi2, I an Ma chesini2, and Massimiliano Al ioli2
1Depa men o Geog aphy, P ehis o y and A chaeology, Facul y o A s o he Uni e si y o he Basque Coun y UPV/EHU,
c/ Tomás y Valien e, s/n, 01006, Vi o ia-Gas eiz, Spain
2Consiglio Nazionale delle Rice che, Is i u o di Rice ca pe la P o ezione Id ogeologica, ia Madonna Al a 126,
06128 Pe ugia, I aly
Co espondence: Txomin Bo nae xea ( xomin.bo nae x[email p o ec ed])
Recei ed: 28 Ma ch 2018 – Discussion s a ed: 9 Ap il 2018
Re ised: 13 July 2018 – Accep ed: 21 Augus 2018 – Published: 14 Sep embe 2018
Abs ac . Geomo phological ield mapping is a con en ional
me hod used o p epa e landslide in en o ies. The app oach
is ypically hampe ed by he accessibili y and isibili y, du -
ing ield campaigns o landslide mapping, o he di e en
po ions o he s udy a ea. S a is ical signi icance o land-
slide suscep ibili y maps can be signi ican ly educed i he
classi ica ion algo i hm is ained in unsu eyed egions o
he s udy a ea, o which landslide absence is ypically as-
sumed, while igno ance abou landslide p esence should ac-
ually be acknowledged. We compa e di e en landslide sus-
cep ibili y zona ions ob ained by aining he classi ica ion
model ei he in he en i e s udy a ea o in he only po ion o
he a ea ha was ac ually su eyed, which we name e ec i e
su eyed a ea. The la e was delinea ed by an au oma ic p o-
cedu e speci ically de ised o he pu pose, which uses in o -
ma ion ga he ed du ing su eys, along wi h landslide loca-
ions. The me hod was es ed in Gipuzkoa P o ince (Basque
Coun y), no h o he Ibe ian Peninsula, whe e digi al he-
ma ic maps we e a ailable and a landslide su ey was pe -
o med. We p epa ed he landslide suscep ibili y maps and
he associa ed unce ain y wi hin a logis ic eg ession model,
using bo h slope uni s and egula g id cells as he e e ence
mapping uni . Resul s indica e ha he use o e ec i e su -
eyed a ea o landslide suscep ibili y zona ion is a alid ap-
p oach ha minimises he limi a ions s emming om unsu -
eyed egions a landslide mapping ime. Use o slope uni s
as mapping uni s, ins ead o g id cells, mi iga es he unce -
ain ies in oduced by aining he au oma ic classi ie wi hin
he en i e s udy a ea. Ou me hod pe ains o da a p epa a ion
and, as such, he ele ance o ou conclusions is no limi ed
o he logis ic eg ession bu a e alid o i ually all he ex-
is ing mul i a ia e landslide suscep ibili y models.
1 In oduc ion
Landslide suscep ibili y is de ined as he likelihood o a
landslide occu ing in an a ea on he basis o he local e -
ain and en i onmen al condi ions (B abb, 1984; Guzze i
e al., 2005). Landslide suscep ibili y zona ion (LSZ) is im-
po an o landslide mi iga ion plans, since i supplies plan-
ne s and decision make s wi h essen ial in o ma ion (Van
Den Eeckhau e al., 2012). A la ge numbe o LSZ s udies
based on s a is ical me hodologies (Reichenbach e al., 2018)
and compa a i e s udies (Cascini, 2008; Das e al., 2010;
Schicke , 2010; Amo im, 2012; Blais-S e ens e al., 2012;
T igila e al., 2015; Wang e al., 2015) we e published in he
las decades. Many s a is ical me hods, aimed a es ima ing
he p opensi y o a e i o y o expe ience slope ailu es, ely
on landslide in en o y maps and spa ial hema ic laye s as
p edisposing ac o s (E mini e al., 2005; Van Den Eeckhau
e al., 2006; Camilo e al., 2017).
In s a is ical landslide suscep ibili y models, such as he
logis ic eg ession (LR) model adop ed in his wo k, he
p epa a ion o he aining da a se is a undamen al and c i -
ical s ep. Commonly, his equi es he selec ion o a sample
o s able (wi hou landslides) and uns able (wi h landslides)
mapping uni s. While ensu ing he p esence o a landslide
is s aigh o wa d and can be suppo ed by he geomo pho-
logical signa u es on he slope o by di ec obse a ion o
Published by Cope nicus Publica ions on behal o he Eu opean Geosciences Union.
2456 T. Bo nae xea e al.: E ec i e su eyed a ea and i s ole in s a is ical landslide suscep ibili y assessmen s
he e en s, he selec ion o landslide- ee a eas is mo e c i i-
cal. Assuming as landslide- ee he loca ions o a s udy a ea
whe e no landslides we e epo ed in a ield su ey is co ec
only in he unlikely ci cums ance ha he landslide in en o y
has been p epa ed by su eying e e y single si e o he s udy
a ea and ollowing homogeneous c i e ia. In o he wo ds, any
landslide- ee loca ion in an in en o y map should ha e been
explici ly checked o be ee om landslides.
Nowadays, he e a e me hods based on he isual in e p e-
a ion o ae ial pho og aphs o digi al p ocessing o emo ely
acqui ed op ical and ada image y (Ca ani e al., 2005; He -
e a e al., 2009; Fio ucci e al., 2011, 2018; Casagli e al.,
2017; Mondini, 2017; Al ioli e al., 2018b), which allow his-
o ical and e en landslide in en o ies o be p epa ed. How-
e e , he adop ion o such me hods can be hampe ed by he
lack o image y o image in e p e a ion expe ise, low pe o -
mance o au oma ic classi ica ion and o he ac o s. Al e na-
i ely, bibliog aphic sou ces like newspape s and news eeds,
adminis a i e epo s o scien i ic li e a u e can be used o
ob aining landslide in o ma ion. Ne e heless, he downside
o hese ype o da a is ha hey a e a ely as accu a e as
equi ed by LSZ s udies. As a consequence, some imes he
bes op ion o ob aining a eliable landslide in en o y is a
s aigh o wa d geomo phological ield mapping. A de ailed
discussion abou he cha ac e is ics, ad an ages and limi a-
ions o di e en app oaches o landslide mapping can be
ound in Guzze i e al. (2012) San angelo e al. (2015) and
Fio ucci e al. (2018).
An ope a ional disad an age o ield-based landslide map-
ping is he di icul y in su eying he whole a ea whe e he
LSZ mus be ca ied ou , since some places can be inacces-
sible o no isible om he accessible places. Di icul ies
in su eying he landscape a ec he comple eness and he
spa ial ep esen a i eness o he landslide in en o y and, as
a esul , inclusion o non- isible a eas wi hin a landslide in-
en o y in oduces a bias, since he p esence o absence o
landslides canno be asce ained in po ions o he landscape.
This unce ain y has ha dly been conside ed in exis ing s ud-
ies ha use ield-based landslide in en o ies (Yesilnaca and
Topal, 2005; Mu illo-Ga cía e al., 2015; Wang e al., 2017).
On he o he hand, selec ion o an app op ia e e ain sub-
di ision is also a c i ical s ep in LSZ analysis. The land
su ace can be di ided in o po ions ollowing geomo pho-
logic ea u es using e ain uni s, opog aphic uni s, geo-
hyd ological uni s o slope uni s bu also conside ing he-
ma ic laye s esul ing in unique condi ion uni s o admin-
is a i e uni s, as well as egula g id cells pa i ions (Van
Den Eeckhau e al., 2006; Reichenbach e al., 2018). Se-
lec ion o di e en mapping uni s can esul in conside -
able di e ences in he suscep ibili y assessmen (Ca a a
e al., 2008). In his wo k, we conside ed g id cells and
slope uni s (Ca a a e al., 1991, 1995; Guzze i e al., 2006;
Al ioli e al., 2016; Zêze e e al., 2017; Rosi e al., 2018;
Ba e al., 2018) and in es iga ed he e ec o he di e en
ways o aining LSZ models wi hin bo h ypes o mapping
uni s.
We p opose an au oma ic and ep oducible p ocedu e o
delinea e he ac ual a ea which was explici ly su eyed in
p epa ing a landslide in en o y by geomo phological ield
mapping, i.e. he e ec i e su eyed a ea (ESA), and o use
such ele an in o ma ion in s a is ical analyses. The p o-
cedu e allows us o ca y ou he calib a ion o a s a is ical
model wi hin he ESA and hen o apply he esul ing sus-
cep ibili y model o he whole a ea (WA) unde in es iga-
ion. Mo eo e , we implemen ed an au oma ic app oach o
he delinea ion o he ESA in a newly de eloped GRASS GIS
module named .su ey.py. The so wa e delinea es he he-
o e ical isible a eas om he poin s o iew eco ded du -
ing a ield campaign by he GPS acks. Mos impo an ly,
he ESA delinea ed by .su ey.py is an objec i e and ep o-
ducible po ion o he s udy a ea di ec ly obse ed by he ge-
omo phologis s, hus allowing us o a oid a bi a y assump-
ions abou which si es we e ac ually su eyed and which
ones we e no .
This wo k aims a demons a ing ha he calib a ion o a
landslide suscep ibili y model wi hin he ESA, ins ead o he
WA ( he whole s udy a ea, encompassing he ESA), enhances
he pe o mance o model i sel . In a es s udy a ea, we cali-
b a ed he mul i a ia e logis ic eg ession model o landslide
suscep ibili y in ou di e en ways, combining wo di e en
calib a ion a eas (ESA and WA) wi h wo di e en mapping
uni ypes: (i) a egula g id cell pa i ion wi h a g ound eso-
lu ion o 5 m ×5 m and (ii) a slope uni (SU) pa i ion (con-
sis ing o i egula e ain subdi isions bounded by d ainage
and di ided lines).
The pape is o ganised as ollows. Sec ion 2 p o ides an
o e iew o he s udy a ea. Sec ion 3 shows he de ails abou
da a acquisi ion, in pa icula he .su ey is desc ibed in
Sec . 3.3 and SU delinea ion in Sec . 3.4. Sec ion 4 con ains
a gene al desc ip ion abou he mul i a ia e me hod applied
o model landslide suscep ibili y and he app oach ollowed
o alida e model esul s, as well as a de ailed desc ip ion
abou he se -up o he di e en model assessmen s. Resul s
a e desc ibed in Sec . 5 and a e u he discussed in Sec . 6.
E en ually, ou conclusions a e d awn in Sec . 7.
2 S udy a ea
The Gipuzkoa P o ince was selec ed as es s udy a ea. I is
loca ed in he no h o he Ibe ian Peninsula along he wes -
e n end o he Py enees and co e s an a ea o 1980 km2, wi h
al i ude anging om he sea le el o 1528ma.s.l. Six wa-
e sheds o di e en size d ain he s udy a ea and each he
Can ab ian Sea (Fig. 1). The p o ince is cha ac e ised by a
s eep mo phology wi h 55% o i s su ace ha ing a slope
la ge han 15◦.
The in es iga ed a ea is li hologically he e ogeneous
(Fig. 1), wi h ma e ials anging om Paleozoic ocks o Qua-
Na . Haza ds Ea h Sys . Sci., 18, 2455–2469, 2018 www.na -haza ds-ea h-sys -sci.ne /18/2455/2018/
T. Bo nae xea e al.: E ec i e su eyed a ea and i s ole in s a is ical landslide suscep ibili y assessmen s 2457
Figu e 1. Loca ion o he Gipuzkoa P o ince s udy a ea and simpli ied li hological map de eloped acco ding o he o iginal map o he
spa ial da a se ice o he Basque Coun y. Coo dina es in deg ees, Uni e sal T ans e sal Me ca o (UTM) Zone 30N, Eu opean Da um
ETRS 1989.
e na y deposi s (EVE, 2010), and i co esponds o a hilly
and moun ainous A lan ic landscape (Müche e al., 2010).
The a e age annual p ecipi a ion is 1597mm (González-
Hidalgo e al., 2011) wi h wo maximum pe iods: 34% in
No embe –Janua y and 10% in Ap il. E en hough ain all
is he p ima y igge ing ac o o shallow landslides (Pe -
ley e al., 2005; Al ioli e al., 2018a), an h opogenic slope
modi ica ions such as slope clea ings and o es ex ac ion
ac i i ies also s ongly a ec landslide occu ence (Co omi-
nas e al., 2017) in he a ea.
3 Da a p epa a ion
3.1 Landslide in en o y
We p epa ed a landslide in en o y by a di ec geomo pholog-
ical ield su ey, du ing he pe iod om June o Augus 2016.
We collec ed in o ma ion abou he loca ion o each obse ed
landslide, ou GPS poin s (c own, oe and wo lanks), pho-
og aphs, ea u es o he su ounding a ea and in o ma ion
abou he landslide ype, acco ding o he C uden and Va nes
(1960) classi ica ion. Each documen ed landslide was d awn
and digi ised using i s ou eco ded GPS waypoin s and
pho og aphs as a e e ence. The QGIS so wa e and Google
Ea h sa elli e image y we e used o he pu pose. Mo eo e ,
and mos impo an ly o de ine he ESA, we digi ised he
ou e ollowed du ing he ield su ey. This in o ma ion was
hen elabo a ed using a GRASS GIS module de eloped o
he pu pose and included in his wo k as he Supplemen .
As a esul o se e al ield ips, 793 indi idual land-
slides we e collec ed; 746 o hem we e classi ied as shal-
low mo emen s (Fig. 2a). Ou obse a ions oge he wi h
he exis ing li e a u e (INGEMISA, 1995; IDE de Euskadi,
2014; Gipuzkoako Fo u Aldundia, unpublished da a) con-
i med ha shallow slides a e he mos equen ype o land-
slide in he s udy a ea. Consequen ly, in o de o conside
only landslides igge ed by he same mechanisms, only shal-
low mo emen s we e used o de e mine landslide p esence
when de ining he dependen a iable in he suscep ibili y as-
sessmen . Figu e 2b and c show he dis ibu ion o landslide
sizes, highligh ing ha a di e ence o 5 o de s o magni ude
exis s be ween he smalles and he la ges in en o ied shal-
low slide.
3.2 Explana o y a iables
The selec ion o he app op ia e explana o y a iables o
build a landslide suscep ibili y model is an impo an s ep
(Ayalew and Yamagishi, 2005; Schlögel e al., 2018), and no
uni e sal c i e ia no guidelines exis o he pu pose.
We ob ained ele an en i onmen al digi al laye s om
he Spa ial Da a Se ice o he Basque Coun y1and c e-
a ed 13 maps desc ibing he di e en explana o y a iables
(see Table 2). To p oduce de i ed mo phome ic con inuous
a iables, such as slope, sinusoidal slope, su ace a ea a-
io (SAR), e ain we ness index (TWI), cu a u e, plan cu -
a u e and p o ile cu a u e, we used a DEM as e laye
wi h 5 m ×5 m spa ial esolu ion. sinusoidal slope is a de-
i ed mo phome ic a iable p oposed by San acana Quin-
as (2001) and Amo im (2012) o emphasise he ac ha
shallow slides ypically occu in medium slope a eas, while
hey seldom occu on slopes s eepe han 45◦. Fo ca ego ical
a iables, such as li hology, pe meabili y, egoli h hickness,
1h p://www.geo.euskadi.eus (las access: 23 Janua y 2017)
www.na -haza ds-ea h-sys -sci.ne /18/2455/2018/ Na . Haza ds Ea h Sys . Sci., 18, 2455–2469, 2018
2458 T. Bo nae xea e al.: E ec i e su eyed a ea and i s ole in s a is ical landslide suscep ibili y assessmen s
Figu e 2. (a) Dis ibu ion o he shallow slides in en o y along he s udy a ea and ex ension o he e ec i e su eyed a ea (ESA). (b) P ob-
abili y densi y plo o he shallow landslide size (a ea in m2) dis ibu ion. (c) Box plo o he same dis ibu ion.
land use, ege a ion and aspec , we compu ed equency a io
(FR) alues o each class and used hem as ela i e alues
o hei ans o ma ion in o con inuous a iables (Lee and
Min, 2001; Yilmaz, 2009; T igila e al., 2015). We acknowl-
edge ha he FR alues can a y depending on he po ion o
he e i o y conside ed o be he o al a ea (ESA o WA). In
o de o pe o m a di ec compa ison, we decided o main-
ain he same FR alues (calcula ed conside ing he WA) in
all egula g id-cell-based suscep ibili y analyses.
In his wo k, we i s adop ed g id cells as mapping uni s,
and we applied a simpli ied and s a is ically o ien ed wo k-
low ha ensu ed ha only signi ican a iables we e aken
in o accoun as well as he non- edundancy o he con-
ibu ed in o ma ion by each co a ia e (Ayalew and Yamag-
ishi, 2005). To do his, he whole se o 13 a iables was con-
side ed wi hin he LR analysis, and co ela ion coe icien s
we e compu ed. We conside ed wo a iables o be collinea
when hei co ela ion coe icien is g ea e han 0.5 wi h a
signi icance le el o 0.01. In such a case, as an objec i e c i-
e ion o a iable selec ion, he a iable wi h he highes p
alue be ween he wo (see Sec . 4.1) was no aken in o con-
side a ion in he inal un o he suscep ibili y LR model. Ad-
di ionally, a iables wi h a p alue highe han he h eshold
o 0.05 we e ejec ed.
Then, conside ing he a iables ac ually used o he ap-
plica ion o he s a is ical models wi h g id cells, we ha e
u he es ic ed he se o a iables o be used wi h slope
uni s (see Sec . 5.2).
3.3 De ini ion o he e ec i e su eyed a ea
In his wo k we sugges he concep o ESA and aining o
s a is ical models he ein, as an app oach o be used o ain
a landslide suscep ibili y model, a oiding assump ions abou
he p esence o absence o landslides in a eas no explici ly
obse ed. We delinea ed he ESA by means o he newly de-
eloped GRASS GIS py hon module .su ey.py (see Sup-
plemen ). Inpu da a ha de ine he isible a ea (i.e. ESA
in ou case) a e (i) a sample o poin s o be conside ed he
poin s o iew, (ii) a DEM o he a ea and (iii) he maximum
isible dis ance. The sample o poin s o iew, in ou case,
was de ined esampling a gi en numbe o poin s along he
eco ded pa h du ing he ield campaigns. This numbe o
poin s depends on he maximum dis ance se be ween hem,
and oge he wi h he selec ed DEM esolu ion he esul s can
be di ec ly a ec ed. In a 10km2subse o he s udy a ea, we
es ed he so wa e ou pu using (i) he maximum dis ance
be ween sampled poin s o 50, 100, 200 and 500 m; (ii) he
o iginal DEM a 5m esolu ion and esampled e sions o
he DEM a 20, 50 and 100 m esolu ion; and (iii) maxi-
mum isible dis ance o 500 m ( he la e was dic a ed by he
la ges dis ance be ween he digi ised ield pa h and he a -
hes landslide pixel in he subse o he s udy a ea). Resul s
o he es a e summa ised in Table 1.
We conside ed ha he bes se ing was he one which al-
lows he o ali y o he landslides o be co e ed using he
smalles numbe o poin s (la ge Dmax alue) and he lowe
DEM esolu ion in o de o op imise he calcula ion ime. In
ou case, conside ing he whole s udy a ea, he maximum
isible dis ance was se o 1100m, in iew ha he la ges
dis ance be ween he digi ised ield pa h and he a hes
landslide pixel was 1092m. Then, and acco ding o he e-
sul s o Table 1, we se he maximum sampling dis ance o
200 m and adop ed a DEM esolu ion o 100m.
We can make sense o he nume ical alues o he pa ame-
e s used in he .su ey.py module conside ing ha he min-
imum size Ao an objec isible om a dis ance 1is gi en
Na . Haza ds Ea h Sys . Sci., 18, 2455–2469, 2018 www.na -haza ds-ea h-sys -sci.ne /18/2455/2018/
T. Bo nae xea e al.: E ec i e su eyed a ea and i s ole in s a is ical landslide suscep ibili y assessmen s 2459
Table 1. Resul s o he se ing es o .su ey in a 10km2subse
o he s udy a ea. The bes combina ion o se ings is highligh ed in
bold.
Name Resolu ion Dmax Pe cen age o
(m) landslides wi hin (%)
Su ey 5 5 50 35
Su ey 6 20 50 70
Su ey 7 50 50 95
Su ey 8 100 50 100
Su ey 9 5 100 30
Su ey 10 20 100 60
Su ey 11 50 100 95
Su ey 12 100 100 100
Su ey 13 5 200 30
Su ey 14 20 200 55
Su ey 15 50 200 85
Su ey 16 100 200 100
Su ey 17 5 500 0
Su ey 18 20 500 35
Su ey 19 50 500 60
Su ey 20 100 500 95
by Rod igues e al. (2010) and Minelli e al. (2014):
A=2512
c,(1)
whe e cis a s e adian o squa e minu es con e sion ac o ,
c≃1.18 ×107. Using 1=1100 m in Eq. (1), we ge A=
2.6 m2, meaning ha he smalles landslide in ou in en o y,
wi h size 7.3 m2, would ac ually be iden i iable om a leas
one poin along he ou e i he landslide si s wi hin he ESA.
The esul ing ESA co e s 44.24 % o he en i e s udy a ea
and i is shown in Fig. 2a.
3.4 Slope uni delinea ion
Fo SU delinea ion we ha e adop ed he .slopeuni s so wa e
desc ibed in Al ioli e al. (2016). The so wa e is a GRASS
GIS module, as is he .su ey.py code p esen ed in his wo k,
and i was designed o he au oma ic and adap i e delin-
ea ion o SUs gi en a DEM and a se o use -de ined inpu
pa ame e s. The code can be used o p oduce se e al SU pa -
i ions, using di e en combina ions o he inpu pa ame e s,
which can hus be uned acco ding o use -de ined c i e ia.
We pa ially ollowed Al ioli e al. (2016), in ha we se-
lec ed he bes SU pa i ion conside ing he quali y o e ain
aspec segmen a ion. In addi ion, we pe o med p elimina y
es s using he LR suscep ibili y model, showing ha he use
o e y small SUs p o ides un ealis ic esul s, which can be
unde s ood conside ing he limi ed a iabili y o a iables
wi hin such small SU polygons. We concluded ha , in he
case o he Gipuzkoa P o ince he mos sui able SU pa i ion
o landslide suscep ibili y zona ion should be ob ained wi h
he ollowing .slopeuni s inpu pa ame e s: low accumula-
ion a ea h eshold =1 km2, minimum SU planime ic a ea
a=0.15 km2, minimum ci cula a iance o e ain aspec
wi hin each SU c=0.2, educ ion ac o =5 and h eshold
alue o he cleaning p ocedu e cleansize=0.025 km2. As
a esul , we ob ained a se o SUs which ange in size om
0.026 o 3.6 km2wi h an a e age o 0.28km2. A discussion
o SU delinea ion and op imisa ion o inpu pa ame e s can
be ound in Al ioli e al. (2016) and Schlögel e al. (2018),
and i is beyond he scope o his wo k.
4 Modelling amewo k
We p epa ed ou landslide suscep ibili y maps (LS maps),
by means o a mul i a ia e LR model. Classi ica ion pe o -
mances we e measu ed by means o a se o alida ion es s
explained in he ollowing sec ions. We p epa ed he i s wo
maps using 5 m ×5 m egula g id cells as mapping uni s.
The wo maps di e because in one case he LR model was
calib a ed wi hin he WA, and in he o he case wi hin he
ESA (desc ibed in Sec . 3.3). The hi d and ou h LS maps,
ins ead, we e p epa ed wi h di e en mapping uni s, namely
wi h SUs (desc ibed in Sec . 3.4) ins ead o g id cells, whe e
calib a ion da a we e also changed conside ing da a wi hin
WA in one case and wi hin ESA in he o he . We end up wi h
ou maps, which we name as ollows: WA-PM (whole-a ea
pixel map), ESA-PM (e ec i e su eyed a ea pixel map),
WA-SUM (whole a ea o he slope uni s map) and ESA-
SUM (e ec i e su eyed a ea o slope uni s map).
4.1 Logis ic eg ession
We used logis ic eg ession (Hosme J . e al., 2013), one
o he mul i a ia e s a is ical app oaches a ailable in he
LAND-SE so wa e (Rossi and Reichenbach, 2016), o build
he landslide suscep ibili y model in he es s udy a ea. The
me hod is he mos used in he scien i ic li e a u e (Reichen-
bach e al., 2018) and p o ed o be use ul and eliable in
se e al s udies (Ne eslioglu e al., 2008; Van Den Eeckhau
e al., 2012; T igila e al., 2015). The LR model wo ks wi h
ei he con inuous o ca ego ical independen a iables, o a
combina ion o he wo ypes, ega dless o whe he hey a e
no mally dis ibu ed o no (Cos anzo e al., 2014).
The ma hema ical ela ionship be ween he dependen di-
cho omous a iable (p esence o absence o a landslide in
he mapping uni ; Yin he ollowing) and he nindependen
a iables (e.g. slope, li hology; X1,...,Xn), wi hin he LR
model, eads as ollows:
Y=β0+β1X1+... +βnXn,(2)
whe e β0is he in e cep o he model and β1,...,βn he
linea eg ession es ima e coe icien s. The independen (ex-
plana o y) a iables, X1,...,Xn, included in ou case bo h
con inuous and ca ego ical laye s ( he la e we e p e i-
ously ans o med in o con inuous a iables, as desc ibed in
www.na -haza ds-ea h-sys -sci.ne /18/2455/2018/ Na . Haza ds Ea h Sys . Sci., 18, 2455–2469, 2018
2460 T. Bo nae xea e al.: E ec i e su eyed a ea and i s ole in s a is ical landslide suscep ibili y assessmen s
Table 2. Se o en i onmen al a iables in oduced o he whole-a ea pixel-based (WA-PM) and e ec i e su eyed-a ea pixel-based (ESA-
PM) model calcula ion, oge he wi h he signi ican p- alue es ima e co esponding o each explana o y a iable (c . Sec . 4.1). The bes
p edic o s we e labelled wi h an as e isk.
Name Desc ip ion Signi ican p alue
Con inuous WA-PM ESA-PM
Slope The slope g adien in deg ees. 1.17 ×10−189 1.06 ×10−111
Sinusoidal slope The sinusoidal ma hema ical ans o ma ion applied o he slope
a iable (Amo im, 2012)
1.00 ×10−155 7.57 ×10−134*
Su ace a ea a io The ela ion be ween he heo e ical olume and he su ace o
each pixel.
3.743 ×10−203* 1.89 ×10−99
Te ain we ness index The spa ial dis ibu ion o soil mois u e o sa u a ion (Yilmaz,
2009)
9.864 ×10−10* 0.126807342
Cu a u e The spa ial a ia ion o he slope g adien . 0.909592654 0.525989188
Plan cu a u e The cu a u e o he su ace pe pendicula o he di ec ion o
he maximum slope.
0.9094261 0.525836679
P o ile cu a u e The cu a u e o he su ace in he di ec ion o he maximum
slope.
0.909605174 0.526032985
Ca ego ical
Li hology The o iginal ca ego ies ha e been eclassi ied by expe c i e ia
(Geoeuskadi).
0* 0*
Pe meabili y The o iginal ca ego ies ha e been eclassi ied by expe c i e ia
(Geoeuskadi).
1.496 ×10−33* 7.632 ×10−72*
Regoli h hickness The laye o he s udy a ea has been ob ained om he li ho-
logical map (Geoeuskadi).
0* 0*
Land use The o iginal ca ego ies ha e been eclassi ied by expe c i e ia
(Geoeuskadi).
5.14 ×10−291 1.42 ×10−87
Vege a ion The o iginal ca ego ies ha e been eclassi ied by expe c i e ia
(Geoeuskadi).
0* 1.596 ×10−173*
Aspec I ep esen s he downslope di ec ion measu ed in deg ees clas-
si ied in nine classes.
0* 0*
Sec . 3.2); see Table 2 o he ull lis o a iables used in
his wo k. Calib a ing an LR model amoun s o selec ing
nume ical alues o he {βi}i=n
i=1coe icien s in Eq. (2) ha
maximise he ag eemen be ween model ou pu , i.e. landslide
p obabili y:
P=1
1+e−Y,(3)
and empi ical landslide da a, in a aining a ea. The same al-
ues o he coe icien s can hen be used o alida e he model
p edic ion skills in a di e en a ea, whe e landslide condi-
ions a e unknown o he model bu he same explana o y
a iables laye s exis .
In addi ion o he βcoe icien s, he LR me hod o e s a
signi ican p alue o each explana o y a iable. The imple-
men a ion o he glm unc ion o he R p og amming lan-
guage lib a y2, used in he LAND-SE so wa e, is such ha
i is possible o in es iga e he es ima ed s anda d e o o a
s a is ic o he null hypo hesis o each o he coe icien s o
he linea model. The p alue ep esen s he p obabili y o
2h ps://www. -p ojec .o g/ (las access: 6 Ma ch 2017)
he pa ame e o be ze o: o p alues smalle han 0.05 he
null hypo hesis ( anishing coe icien ) is ejec ed; hus he
associa ed a iable is signi ican o he inal esul . So, he p
alue can be conside ed an objec i e indica o o he selec-
ion o he mos ele an a iables o be used in he s a is ical
model (Schlögel e al., 2018).
4.2 E alua ion o model pe o mance
The pe o mance o s a is ical suscep ibili y models, i.e. o
mul i a ia e bina y classi ie s, can be e alua ed by compa -
ing hei p edic ions wi h he landslide da a used in he model
calib a ion/ aining s ep (i.e. model i ing pe o mance) o
wi h independen landslide da a (i.e. model p edic ion pe o -
mance). The de ini ion o aining and alida ion inpu sam-
ples is c ucial o de ec how well each model i s inpu da a
bu also how good he model is a p edic ing new da a.
The s a is ical me ics commonly used in he li e a u e
(Co ominas and Ma ouli, 2011; Van Den Eeckhau e al.,
2006; Lomba do e al., 2015; Reichenbach e al., 2018) o
ha pu pose a e (i) con usion ma ices (con ingency ables)
and hei g aphical ep esen a ion ( ou - old o con ingency
Na . Haza ds Ea h Sys . Sci., 18, 2455–2469, 2018 www.na -haza ds-ea h-sys -sci.ne /18/2455/2018/
T. Bo nae xea e al.: E ec i e su eyed a ea and i s ole in s a is ical landslide suscep ibili y assessmen s 2461
plo s), (ii) ecei e ope a ing cha ac e is ic (ROC) cu es and
hei associa ed a ea unde cu e (AUC) alue, (iii) classi i-
ca ion e o plo s and (i ) Cohen’s kappa index.
Fou - old (o con ingency) plo s a e isual ep esen a ions
o he con usion ma ices epo ing he pe cen ages o he
ue posi i es (TP), ue nega i es (TN), alse posi i es (FP)
and alse nega i es (FN). ROC cu es a e a mo e complex
ep esen a ion o he classi ica ion pe o mance based on di -
e en p obabilis ic h eshold alues. The a ea unde he ROC
cu e (AUC) is an indica o o he model pe o mance in p e-
dic ing landslide suscep ibili y. AUC alues a y be ween 0
and 1, wi h highe alues indica ing be e p edic ion skills
(Fawce , 2006).
To es ima e he unce ain y associa ed wi h he landslide
suscep ibili y alue assigned o each mapping uni , i is pos-
sible o un mul iple ins ances o he LR model a ying,
andomly, he inpu da a. In each un, he inpu is p epa ed
by sampling he o iginal aining da a se wi h a boo s ap
echnique, consis ing o a andom sampling wi h eplace-
men (E on, 1992; Da ison and Hinkley, 1997; Rossi e al.,
2010; Rossi and Reichenbach, 2016). Classi ica ion e o
plo s summa ise he dis ibu ion o mul iple esul s and show
he mean p obabili y es ima e o landslide spa ial occu ence
o each mapping uni (xaxis), anked om low (le ) o
high ( igh ) alues, ela ed o he a ia ion o he model es i-
ma e (yaxis), measu ed by 2 s anda d de ia ions (2σ) o he
p obabili y es ima es ob ained by he di e en model uns
(Guzze i e al., 2006). The pa abolic model i ing equa ion
esul ing om he poin cloud (i.e. using a non-linea leas
squa e me hod), analy ically desc ibes he o e all model p e-
dic ion pe o mance a iabili y. Cohen’s kappa index (κ) is
an addi ional measu e o he eliabili y o a classi ica ion
model (Cohen, 1960; Rossi e al., 2010), wi h highe alues
ha also indica e a mo e accu a e p edic ion skill.
In his s udy he p obabili y o landslide occu ence esul -
ing om each model es ima e ( ained ei he wi hin he ESA
o wi hin he WA) and o each conside ed mapping uni (ei-
he g id cells o slope uni s) was eclassi ied in i e land-
slide suscep ibili y classes, which we e labelled as e y low
( o suscep ibili y alues in he ange 0–0.2), low (0.2–0.45),
medium (0.45–0.55), high (0.55–0.8) and e y high (0.8–1).
Mo eo e , in o de o spa ially iden i y he pai wise
ma ching deg ee be ween di e en model es ima es, we ad-
di ionally adop ed a simpli ied classi ica ion o he landslide
suscep ibili y. Each mapping uni was eclassi ied as s able
o uns able conside ing a h eshold alue o 0.5. The di e -
en maps, all o which we e p epa ed wi h he same mapping
uni pa i ion, we e o e lapped. Then, he misma ch deg ee
be ween g id cell and SU suscep ibili y maps was quan i ied
in e ms o numbe o misma ched mapping uni s and o e all
misma ched a ea.
4.3 Da a selec ion o landslide suscep ibili y
The DEM a ailable o he s udy a ea consis s o 7.91 ×107
cells wi h 5m esolu ion. Fo landslide suscep ibili y assess-
men , bo h using g id cells (i.e. pixels) and SUs, we p e-
pa ed as e laye s co esponding o each a ailable explana-
o y a iable, aligned o he DEM g id cells.
We de ised a igo ous sampling p ocedu e o minimise
possible s a is ical biases du ing aining/ alida ion pa i ion.
The p ocedu e is sligh ly di e en o he g id cell and SU
mapping uni s.
In he i s case, a g id cell is conside ed uns able i i is
loca ed wi hin any landslide polygon and s able i i is ou -
side he landslide bounda ies. In he second case, an SU was
conside ed uns able depending on he pe cen age o landslide
a ea p esen wi hin i . In any case, he 75% o he uns able
mapping uni s oge he wi h a simila numbe o s able map-
ping uni s we e used o ain he LR model, and he emaining
25 %, also oge he wi h a simila numbe o s able mapping
uni s o alida ion. The choice o an equal numbe o s a-
ble and uns able mapping uni s was done on pu pose, and
i is he s anda d p ocedu e equi ed by he LAND-SE so -
wa e o landslide suscep ibili y assessmen , because he LR
model equi es a balanced da a se , in which he numbe o
s able and uns able cases a e simila (Felicísimo e al., 2013;
Cos anzo e al., 2014).
Fo egula g id-cell-based models, we selec ed a andom
558 landslides (75%) o model aining and con e ed hem
in o as e laye s (84623 uns able pixels). The emaining 188
landslides (25 %) used o alida ion we e also as e ised
(29 247 uns able pixels). This is a a iance wi h he usual
andom selec ion o uns able pixels, in which a gi en pe -
cen age o g id cells a e sampled wi hin landslide polygons.
He e we selec whole landslides and conside all he pixels
encompassed by he landslide bodies as aining/ alida ion
samples. We an he expe imen wi h h ee di e en ain-
ing/ alida ion andom se s, con aining he abo e pe cen -
ages. This exe cise allowed us o con i m ha he andom
selec ion o he landslide in en o y does no a ec he model
esul s in a ele an way, because in all he cases he model
classi ica ion pe o mances we e e y simila . In o de o
choose one single da a se o u he compa a i e analyses,
he da a se wi h he bes classi ica ion esul was selec ed.
Then, aining se s we e selec ed as ollows: 84623 uns able
pixels and an equal numbe o s able pixels we e selec ed as
aining se s. Two di e en se s we e selec ed a andom, i s
wi hin WA and hen wi hin ESA. We made su e ha uns able
pixels we e exac ly he same in he wo cases, because we
wan ed he only di e ence o be ha he s able pixels we e
sampled wi hin he WA in he i s case and wi hin he ESA in
he second case. Finally, in o de o gua an ee he compa a-
bili y o he p edic ion pe o mances, one unique alida ion
sample was c ea ed as ollows: he emaining 29 247 uns able
pixels oge he wi h an equal numbe o s able pixels selec ed
a andom wi hin he emaining s able pixels wi hin he ESA.
www.na -haza ds-ea h-sys -sci.ne /18/2455/2018/ Na . Haza ds Ea h Sys . Sci., 18, 2455–2469, 2018
2462 T. Bo nae xea e al.: E ec i e su eyed a ea and i s ole in s a is ical landslide suscep ibili y assessmen s
Conce ning he SU-based models, we i s pa i ioned he
s udy a ea in o 6907 SUs wi h he echnique ou lined in
Sec . 3.4. SU bounda ies do no ma ch hose o he depen-
den o explana o y a iables laye s, allowing he p esence
o di e en classes o alues inside each SU. Mo eo e , he
p esence o one single landslide pixel wi hin a slope uni was
no conside ed enough o label his SU as uns able. The e-
o e, ins ead o a bi a ily de ining a gi en h eshold alue in
o de o conside an SU as uns able, we decided o use he
o e all landslide densi y in he WA. Fo his eason, we con-
side ed o be uns able hose SUs con aining 0.15 % o mo e
uns able pixels and s able o he wise. We used as explana o y
a iables he mean and he s anda d de ia ion o he mo pho-
me ic a iables o each SU and he pe cen age o he a ea
co e ed by each class o he ca ego ical laye s. In 304 cases
he SU con ained 0.15 % o mo e uns able pixels, so we se-
lec ed a andom 228 o hem (75%) o aining, and he
emaining 76 (25%) we e used o alida ion. Like in g id
cell app oaches, we c ea ed wo di e en aining samples
whe e uns able SUs we e exac ly he same, and only he s a-
ble SUs a y in each case. The i s aining sample includes
228 s able SUs selec ed a andom along he WA. The sec-
ond aining sample includes an equal numbe o s able SU
uni s selec ed a andom among hose ha a leas pa ially
o e lap he ESA. Addi ionally, 76 SUs labelled as uns able
we e selec ed om he whole se o alida ion. The alida-
ion sample was comple ed by adding a andom selec ion o
he same numbe o SUs labelled as s able and which a leas
pa ially o e lap he ESA. Thus, he alida ion sample con-
ained 152 SUs (76 uns able and 76 s able).
E en ually, since he ESA is an app oxima ion o he eal
su eyed a ea, we s ess ha we always selec ed s able map-
ping uni s o alida ion only i hey a e ully o pa ially
wi hin he ESA, because no e idence exis s ha a mapping
uni alling en i ely ou side he ESA is ac ually ee om
landslides. Mo eo e , i a po ion o an SU alls wi hin he
ESA, i implies ha a leas one pa o he SU was obse ed.
The e o e, using his app oach, we can emo e a leas hose
SUs ha we e no su eyed a all.
5 Resul s
5.1 Suscep ibili y maps using g id cells
We an he LR model using he pixel-based da a se s wice:
once using he en i e aining pixel sample and once using he
e ec i e aining pixel sample as dependen a iables. We
de ined he ob ained esul s as whole-a ea pixel map (WA-
PM) and e ec i e su eyed-a ea pixel map (ESA-PM).
In bo h WA-PM and ESA-PM, we i s used he same
13 explana o y a iables lis ed in Table 2, and hen we se-
lec ed o each model assessmen he mos ele an explana-
o y a iables conside ing he collinea i y be ween each pai
o a iables and he signi icance (p alue) o he eg ession
es ima es (see Sec . 3.2). As a esul , o each case, only he
a iables ma ked wi h an as e isk in Table 2 we e in oduced
in he inal LR analysis.
Using he alida ion pixel sample, we e alua ed he p e-
dic ion skills o he pixel suscep ibili y maps. Inspec ion o
he ou - old o con ingency plo s (Fig. 3a, d) e eals ha
WA-PM co ec ly p edic ed he 63.58 % (TP+TN) o he ob-
se ed uns able and s able mapping uni s, whe eas ESA-PM
was capable o co ec ly p edic ing a highe numbe o map-
ping uni s (65.45%). The ROC cu es (Fig. 3b, e) also indi-
ca e be e p edic ion skills in ESA-PM (AUC=0.7) han in
WA-PM (AUC =0.68) and he same happens o he Cohen’s
kappa index (Fig. 3; k=0.309 e sus k=0.272). Mo eo e ,
he classi ica ion e o plo s (Fig. 3c, ) p o ide an es ima e
o he e o associa ed wi h he p edic ed suscep ibili y al-
ues, which do no exceed 0.1 s anda d de ia ions in any case,
highligh ing he eliabili y o he esul s. And inally, he mu-
ual misma ch map (Fig. 5e) shows ha 14.8% (co espond-
ing o an ex ension o 293km2) o he mapping uni s lipped
hei landslide suscep ibili y class in WA-PM and ESA-PM.
5.2 Suscep ibili y maps using slope uni s
Due o he subdi ision o ca ego ical a iables in classes and
o he use o mean and s anda d de ia ion o mo phome -
ic a iables, he in oduc ion o he o iginal 13 explana o y
a iables would esul in 56 new a iables in which many
o hem (all hose classes belonging o he same ca ego i-
cal a iable) would be highly co ela ed. Fo his eason, he
a iable selec ion app oach used in he pixel-based case is
no iable when wo king wi h SUs and a speci ic a iable
selec ion app oach o SU models would equi e u he in-
es iga ion. Thus, o his wo k, he mos app op ia e se o
explana o y a iables, among hose conside ed o be he mos
ele an in pixel-based model assessmen , was selec ed by
expe c i e ia. Conside ing his se o a iables as a s a ing
poin , we selec ed new se s o explana o y a iables o e al-
ua e landslide suscep ibili y using SUs, i.e. o calcula e he
whole-a ea slope uni map (WA-SUM) and he e ec i e a ea
slope uni map (ESA-SUM). Taking in o accoun ha he au-
oma ic p ocedu e o he SU de ini ion al eady included he
low accumula ion calcula ion, used o TWI es ima ion, and
he aspec componen , we ejec ed aspec and TWI o a oid
spu ious co ela ions. We selec ed he ollowing se o a i-
ables used o p oduce bo h pixel-based maps such as li hol-
ogy, pe meabili y, egoli h hickness and ege a ion, and we
added slope. The eason o choosing slope o e sinusoidal
slope o SAR is due o he ac ha hese wo a e de i a i e
a iables o he o me . Mo eo e , we conside slope mo e
sui able ea u e o desc ibe he a e age mo phology wi hin
SU han sinusoidal slope o SAR, so we decided o selec i
in o de o simpli y ou in e p e a ion o he esul s.
Using he alida ion SU sample, we assessed he p edic-
ion skills o he SU maps. Fo he WA-SUM he 65.13%
o he 152 alida ion mapping uni s we e co ec ly classi ied
Na . Haza ds Ea h Sys . Sci., 18, 2455–2469, 2018 www.na -haza ds-ea h-sys -sci.ne /18/2455/2018/
T. Bo nae xea e al.: E ec i e su eyed a ea and i s ole in s a is ical landslide suscep ibili y assessmen s 2463
Figu e 3. Pixel-based LR models p edic ion pe o mance esul s: summa y ables o he Cohen’s kappa index, a ea unde he ROC cu e
(AUC), o e all accu acy ((TP+TN)/(TP+TN+FP+FN)) and o e all e o a e ((FP+FN)/(TP+TN+FP+FN)). (a, d) Fou - old o con in-
gency plo s; (b, e) ROC cu es; (c, ) classi ica ion e o plo s and he quad a ic eg ession i cu es ( ed line).
(TP+TN) (Fig. 4a). The ROC cu e p o ides AUC =0.69,
and he co esponding Cohen’s kappa is k=0.302 (Fig. 4b).
Conce ning he classi ica ion e o plo (Fig. 4c), i can be
obse ed ha in he SUs wi h high and low landslide sus-
cep ibili y p obabili y (p obabili y >0.8 and <0.2) he 2σ
alue s ays below 0.2, bu a iabili y in he es ima es be-
comes la ge o in e media e suscep ibili ies. This e eals a
conside able a ia ion in he s able/uns able classi ica ion o
he e i o y, which implies low eliabili y, a leas o he in-
e media e p obabili ies (Guzze i e al., 2006). Fo he ESA-
SUM, 63.82 % o he 152 alida ion mapping uni s we e
co ec ly classi ied (TP+TN) (Fig. 4d) wi h AUC =0.71,
sligh ly la ge wi h espec o he o he SU model assessmen ,
whe eas he Cohen’s kappa index pe o med sligh ly wo se,
being k=0.276 (Fig. 4). The classi ica ion e o plo shows a
conside able a ia ion in in e media e p obabili ies (Fig. 4 ),
while he unce ain y is lowe o high and low p obabili ies.
Ne e heless, he quad a ic i cu es indica e a lowe o e all
a iabili y o ESA-SUM han o WA-SUM.
Visual inspec ion o he SU suscep ibili y maps (Fig. 5b,
d) shows simila i ies be ween WA-SUM and ESA-SUM. The
di e ence is g aphically p esen ed h ough he misma ch
map (Fig. 5 ), whe e 12.6 % o he mapping uni s (co e-
sponding o an ex ension o 247km2) change hei landslide
suscep ibili y class, be ween WA-SUM and ESA-SUM.
6 Discussion
The numbe o scien i ic publica ions ocusing on landslide
suscep ibili y zona ion has no ably inc eased o e he las
decades (Gu ié ez e al., 2010; Rossi and Reichenbach,
2016; Libe a oscioli e al., 2017; Valagussa e al., 2017; Zhou
e al., 2018; Reichenbach e al., 2018) and, nowadays, he e
is a huge a ie y o applica ions and compa isons which p o-
ide an eno mous ange o app oaches wi h which o p epa e
www.na -haza ds-ea h-sys -sci.ne /18/2455/2018/ Na . Haza ds Ea h Sys . Sci., 18, 2455–2469, 2018
