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Landslides
DOI 10.1007/s10346-017-0847-2
Received: 9 February 2017
Accepted: 22 May 2017
© Springer-Verlag Berlin Heidelberg 2017
Dragana ĐurićIAna MladenovićIMilica Pešić-Georgiadis IMilošMarjanovićIBiljana
Abolmasov
Using multiresolution and multitemporal satellite data
for post-disaster landslide inventory in the Republic
of Serbia
Abstract This paper focuses on a specific event-based land-
slide inventory compiled after the May 2014 heavy rainfall
episode in Serbia as a part of the post-disaster recovery
actions. The inventory was completed for a total of 23 affected
municipalities, and the municipality of Krupanj was selected
as the location for a more detailed study. Three sources of
data collection and analysis were used: a visual analysis of the
post-event very high and high (VHR-HR) resolution images
(Pléiades, WorldView-2 and SPOT 6), semi-automatic landslide
recognition in pre- and post-event coarse resolution images
(Landsat 8) and a landslide mapping field campaign. The
results suggest that the visual and semi-automated analyses
significantly contributed to the quality of the final inventory,
including the associated planning strategies for conducting
future field campaigns (as a final stage of the inventorying
process), all the more so because the field-based and image-
based inventories were focused on different types of land-
slides. In the most affected municipalities that had very high
resolution satellite image coverage (19.52% of the whole study
area), the density of the recognized landslides was approxi-
mately three times higher than that in those municipalities
without satellite image coverage (where only field data were
available). The total number of field-mapped landslides for
the 23 municipalities was 1785, while image-based inventories,
which were available only for the municipalities with satellite
image coverage (77.43% of the study area), showed 1298 land-
slide records. The semi-automated landslide inventory in the
test area (Krupanj municipality), which was based on coarse
resolution multitemporal images (Landsat 8), counted 490
landslide instances and was in agreement with the visual
analysis of the higher resolution images, with an overlap of
approximately 40%. These results justify the use of prelimi-
nary inventorying via satellite image analysis and suggest a
considerable potential use for preliminary visual and semi-
automated landslide inventorying as an important supplement
to field mapping.
Keywords Post-disaster .Landslide inventory .Remote
sensing .VHR-HR satellite image .Rainfall .Serbia
Introduction
A massive low-pressure cyclone, BTamara^, hit the Western
Balkan countries in May 2014, resulting in extensive flooding
and landslide damages in Serbia and Bosnia and Herzegovina.
The floods, flash floods and landslides resulted in 51 casualties,
approximately 32,000 people being evacuated and more than 1.6
million people being directly or indirectly affected in Serbia
alone. The Serbian government declared a state of emergency
covering the whole territory of Serbia on May 15, 2014. The
United Nations Disaster Assessment and Coordination Team
(UNDAC) assisted immediately and estimated that roughly
more than 2000 landslides were activated in the western and
central parts of Serbia (UNDAC 2014). Some of the locations in
western Serbia were affected by many flow-type landslides,
which had never previously been reported in these areas. These
landslides caused severe damage to the local municipalities
(residential areas, roads, infrastructure facilities, cultivated
lands, pastures and forests), as well as to the highly urbanized
areas in the city of Belgrade, the capital of Serbia. In the
framework of the post-disaster recovery led by the United Na-
tions Development Programme (UNDP) Office in Serbia, several
actions were taken with the aim of supporting landslide risk
assessment and management in the affected municipalities. Fol-
lowing these activities, attention was focused on developing a
fast and efficient methodology for a post-event landslide inven-
tory, which had to be completed within 5 months for more than
14,500 km
2
, with limited financial resources and personnel. It
entailed integrating and homogenizing all of the landslide data
in the National Landslide Database, which is yet to be
completed.
Manyrecentstudieshavehighlightedtheimportanceof
using remote sensing technologies for fast landslide mapping
in similar situations (Tralli et al. 2005;Linetal.2011;Joyce
et al. 2014; Kwan and Ransberger 2010;Guzzettietal.2012;Lira
et al. 2013;Bhambrietal.2016;Marthaetal.2015; Ray et al.
2016). In particular, during the response and recovery phases
of the disaster and crisis cycle, only very fast delivery of up-to-
date, accurate and comprehensive image analysis products can
significantly assist in the assessment of large-scale disaster
damage (Voigt et al. 2007; Denis et al. 2016). Event-based
inventories are important for documenting the full extent of
a landslide disaster caused by rainfall (Bucknam et al. 2001;
Guzzetti et al. 2004; Cardinali et al. 2006;Linetal.2011;
Marjanovićand Abolmasov 2015) or earthquakes (Sato et al.
2007; Sato and Harp 2009;Tangetal.2015;YangandChen
2010;Xuetal.,2014;Marthaetal.2016;Shafiqueetal.2016)
and for damage assessment related to post-disaster manage-
ment activities (Martha and Kumar 2013; Ciampalini et al.
2015). Landslide event-based inventory maps in the post-
disaster phase are usually created through the interpretation
of stereoscopic aerial photographs taken after the event
(Bucknam et al. 2001; Guzzetti et al. 2004,Tangetal.2015)
using visual (Marjanovićet al. 2016,Linetal.2011)orsemi-
automatic analysis (Martha et al. 2010,2012;Mondinietal.
2011) of the multispectral and multitemporal satellite images,
using high resolution digital elevation model analysis (Chen
et al. 2006; Booth et al. 2009; Kwan and Ransberger 2010;
Iwahashi et al. 2012), using extensive field surveys (Cardinali
Landslides
Recent Landslides
et al. 2006;Marjanovićand Abolmasov 2015)orusingacom-
bination of these techniques (Mondini et al. 2011;Xuetal.
2013, Murillo-García et al. 2015; Alkevli and Ercanoglu 2011).
In relation to all the abovementioned case studies, this case
study differs in some respects. Firstly, the regional extent of
similar studies is much smaller than the one considered here
(Lin et al. 2011,MarthaandKumar2013), except for the
earthquake-induced landslide cases (Tang et al. 2015;Martha
et al. 2016). This finding implies that very high resolution
(VHR) image coverage of sufficient quality was difficult to
obtain for the entire area of interest, as highlighted in Xu
et al. (2014). Secondly, previous case studies combined the
same resolution of pre-event and post-event (VHR or high
resolution (HR)) images for visual (Martha et al. 2015;Lira
et al. 2013) or VHR satellite data for semi-automatic recogni-
tion (Martha and Kumar 2013). Although highly recommended,
this is a demanding and expensive approach for large areas,
such as the one in this case study. Instead, it is herein pro-
posed that only some parts of the whole area should have VHR
coverage, and these should be used for control of the semi-
automated landslide mapping, performed by using lower reso-
lution images. Thereby, semi-automated mapping can be ex-
trapolated outside the VHR coverage. The pilot area in the
Krupanj municipality was herein used to explore this option.
The main objective of this research was an analysis of the
available multiresolution satellite image data for a fast post-
event landslide inventory of the 23 municipalities affected by
the extreme rainfall event during May 2014. VHR and HR
Pléiades, SPOT 6 and WorldView-2 (WV2) multispectral satel-
lite images were used for visual landslide recognition and to
build an inventory to support an extensive landslide mapping
field campaign. The VHR and HR satellite images covered a
9167.11-km
2
territory, and a total of 1298 landslides of various
types were visually recognized in these images. Additionally,
the free-to-access HR Landsat 8 multitemporal satellite images
and open source QGIS software were used for the analysis of
the Krupanj municipality (341 km
2
)asatestareaforthesemi-
automatic recognition of landslide occurrences. The objective
of this additional analysis was to show that the semi-automatic
landslide recognition in HR satellite images could be a useful
tool for other affected territories where VHR satellite images
are unavailable. In addition, another objective was to show
how a preliminary landslide inventory obtained by remote
sensing analysis can be an essential support for a field cam-
paign, as well as a reliable source for landslide inventorying in
inaccessible terrains. An event-based landslide inventory for a
particular rainfall trigger event was undertaken for the first
time on a previously unheard of scale in Serbia. Additionally,
forthefirsttime,theentiredatabasehasbeenmadepublicly
available via an interactive web portal (http://
geoliss.mre.gov.rs/beware/).
Study area
The study area covered 11.840 km
2
,i.e.23ofthe27municipal-
ities included in the UNDP post-disaster BEWARE Project ac-
tivities in the western and central parts of the Republic of Serbia
(Fig. 1). Four municipalities were excluded from the analysis
because there were no landslides that occurred during the
May 2014 rainfall event, only floods and flash floods. In the 23
municipalities that were included, the total population was
approximately 1,000,000 people. It is also important to mention
that the majority of these municipalities are characterized as
underdeveloped and undeveloped (11 underdeveloped and 4
undeveloped of the 23 chosen municipalities), with fragile econ-
omies, poor industrial potentials and high unemployment rates
(http://www.regionalnirazvoj.gov.rs/ data from 2014, accessed
January 2017). All these factors make these areas even more
vulnerable to disastrous landslide events, such as the ones that
occurred in May 2014.
Geological settings
The geological and geomorphological settings and other envi-
ronmental conditions are very complex. The central and west-
ern parts of Serbia are situated in the region of the so-called
Dinaric fold-and-thrust belt, encompassing several continental
and oceanic tectonic units that were left behind after the exten-
sive processes of the Jurassic closure of the Neotethys Ocean
(Schmid et al. 2008) and the Cretaceous–Oligocene nappe stack-
ing (Schefer 2010;Ustaszewskietal.2010;Mladenovićet al.
2015).Theoceanictectonicunitsshowatypicalsequenceof
peridotite, gabbro and basalt covered by an ophiolitic mélange
(Dimitrijević1997). The continental units are mostly composed
of slightly metamorphosed sandstones and shales covered by
shallow limestone sequences (Dimitrijević1997). All the tectonic
units are covered by Cretaceous flysch deposits. During the
Miocene, this area was affected by an extension, which provided
the conditions leading to smaller basins in the so-called Dinaric
Lake system (Krstićet al. 2003). These basins are mostly filled
with limestones, sandstones, marls and shales (Dimitrijević
1997).
The geomorphological settings are mostly characterized by
the fluvial and fluvial-denudation features that are dominant in
western and central Serbia. Erosional features prevail on the
slopes of the hills and mountains, while depositional features
occupy basins, valleys and other depressions. These deposits are
mainly within the clay fraction and are hence considered to be
responsible for the development of the most common types of
landslides in this region (Menkovićet al. 2003;MihalićArbanas
et al. 2013; Abolmasov et al. 2015). However, the landslides in
Serbia, with all their diverse mechanisms and types of mobilized
material (Cruden and Varnes 1996; Cruden and VanDine 2013;
Hungr et al. 2014), involve many other factors, such as the local
geomorphological characteristics, engineering geological mate-
rial properties, degree and depth of the weathering substratum,
land cover and land use as well as the characteristics of the
factors that trigger them (e.g. the unusually high precipitation
rate in May 2014).
The municipality of Krupanj, one of the most affected areas
in May 2014, was selected as a test site for more detailed
research. This area is located in western Serbia and covers
341 km
2
, with a population of 17,295 inhabitants (Fig. 1). The
majority of the territory consists of Devonian-Carboniferous
weathered low-crystalline metamorphic rocks, including mud
Recent Landslides
Landslides
and clay shales, phyllites and argillaceous schist (approximately
80%). Permian meta-sediments (clay shales, sandstone and
flysch), Triassic limestone and sandstone and Cretaceous lime-
stonemakeuptheremaining20%oftheterritory.The
weathering zone thickness in these rocks is between 1 to 5 m,
depending on the dominant lithological units and structures.
Quaternary deposits are generally represented by alluvial and
diluvial sediments. The topography is a typical low
Fig. 1 Geographical position of the Republic of Serbia and the position of the included municipalities in the research
Landslides
mountainous to hilly relief landscape with predominantly gentle
to moderate slopes (7–21°) and many micro-relief forms
enclosed in relatively small catchments.
Rainfall event
In the third week of May 2014, the massive low-pressure
cyclone Tamara swept through the Western Balkans, resulting
in extreme precipitation over a short period, which caused
floods, flash floods and massive landslides in the western and
central parts of Serbia. The highest 48-h rainfall was registered
at the Loznica Main Meteorological Station (MMS) in the
western part of Serbia, exceeding 160 mm/48 h, which corre-
sponds to an extreme rainfall that statistically occurs once
every 1000 years over that area (1000-year return period).
MMS in Valjevo and Belgrade recorded precipitation of a
400-year return period for the same duration (Prohaska et al.
2014). The highest values of 72-h rainfall were also recorded in
Loznica (213 mm), Valjevo (190 mm) and Belgrade (174 mm).
The following flood events and landslides were triggered in-
stantly,butitispossiblethatthemassivelandslideswerealso
initiated by the antecedent rainfall, which was unusually high
from April 15 to May 13, 2014 (Fig. 2), when the monthly
precipitation exceeded half of the previous annual average.
Namely, the annual precipitation averages for the Loznica,
Valjevo and Belgrade MMSs were 868, 788 and 691 mm/year,
respectively (reference period, 1981–2010). Even though these
values are seemingly low in comparison to other latitudes (e.g.
tropical or Mediterranean scenarios), 48–72-h rainfall of ap-
proximately 100–200 mm (10–20% of annual total) at these
three MMS represents an extreme level of soil saturation,
especially when coupled with the antecedent cumulative rain-
fall effects (causing constant, gradual deterioration of the soil
strength by decreasing the effective stresses).
Materials and methods
Data sources
The main input sets for the visual analysis were all available in
the cloud-free (less than 5% cloud) and snow-cover-free series
of the VHR and HR images from May 23, 2014 to November
04, 2014, provided by the UNDP Office in Serbia through the
post-disaster BEWARE Project. The general characteristics of
the available satellite images, including their date of acquisi-
tion, resolution, number of scenes and name of the mission,
are given in Table 1.TheVHRandHRsatelliteimagescovered
9167.11 km
2
or 77.43% of the study area (Table 2and Fig. 3).
For two of the municipalities (Paraćin and Smederevska
Palanka), there were no available satellite images (i.e. 23.57%
of study area) (Table 2). Pléiades VHR images covered only
19.52% of the study area, while the rest was covered by the HR
SPOT 6 and WV2 missions (38.57 and 19.33% of the study area,
respectively) during the required time spans (Table 2).
Multitemporal, pre-event (August 9, 2013) and post-event (Au-
gust 19, 2014) free access Landsat 8 satellite images, number 187/29
Fig. 2 Antecedent rainfall, from April 15 to May 13, 2014 for Belgrade, Loznica and Valjevo meteorological stations
Recent Landslides
Landslides
(row/path), were analysed for the Krupanj municipality as a test of
semi-automatic landslide recognition. These images were also
cloud-free, and they had similar vegetation and agricultural con-
ditions. The territory of Krupanj, as indicated earlier, was chosen
not only because it provided a diversity and multiplicity of land-
slide examples but also because it had large coverage in the VHR
Pléiades images (87.33% of the territory) (Table 2).
Data processing
The Pléiades, SPOT 6 and WorldView-2 images used in the
visual analysis were orthorectified and georeferenced by the
vendor. The images have been pan-sharpened and mosaicked.
The visual recognition was aided by a 25-m shaded relief de-
rived from the DEM (supplied by the Republic Geodetic Au-
thority of Republic of Serbia) using different transparency
settings, as well as a pre-event background image from the
Google platform. In all multispectral images, the usual true
RGB colour composite and false colour composite were used
(Fig. 4).
Data pre-processing and Landsat 8 image processing (Fig. 5)
were performed in the open source QGIS software. To maximize
the usefulness of the data, radiometric calibration and
Table 1 Post-disaster VHR and HR satellite data used for visual analysis
Mission Sensor
type
Spectral bands
(μm)
Original
resolution (m)
Resolution after
processing (m)
Acquisition
date
Number of
scenes
Pléiades MS B (0.43–0.556) 2 0.5 2014-05-21 1
G (0.49–0.61) 2014-06-08 1
R (0.60–0.72) 2014-06-29 1
2014-10-08 2
PAN Pan.(0.48–0.83) 0.5 2014-10-09 1
2014-11-04 1
SPOT 6 MS B (0.455–0.525) 6 6 2014-05-21 1
G (0.53–0.59)
R (0.625–0.695)
IR (0.760–0.890)
PAN Pan.(0.45–0.745) 1.5 1.5 2014-09-19 1
WorldView-2 MS B (0.45–0.51) 1.5 1.5 2014-05-24 4
G (0.51–0.58) 2014-07-04
R (0.63–0.96)
PAN Pan. (0.45–0.9) 1.5 1.5 2014-05-23 5
2014-06-30
Landsat 8 MS UB (0.43–0.45) 30 15 2013-08-09 1
B (0.45–0.51)
G (0.53–0.59)
R (0.64–0.67)
NIR (0.85–0.88)
SWIR1
(0.85–0.88)
SWIR2
(2.11–2.29)
CIR (1.36–1.38)
TIRS1
(10.60–11.19)
TIRS2
(11.50–12.51)
PAN Pan. (0.50–0.68) 15 2014-08-19 1
Landslides
correction procedures were applied (Chavez 1996). Pre-
processing included radiometric calibration and atmospheric
correction; the derived data were extracted for the surface
temperature calculation (Moran et al. 1992; Valor and Caselles
1996; Jiménez-Muñoz et al. 2006; Osiñska-Skotak 2007). The
surface temperature is generated using a Split Window algo-
rithm, which includes the emissivity, vapour, temperature pro-
files, transmittance and reflection (Fig. 5). Pre-processing
further included brightness optimization, pan-sharpening and
masking procedures (defining the areas covered by clouds and
their removal). Processing included Principal Component Anal-
ysis (PCA) and semi-automatic classification. PCA was conduct-
ed on six pre-processed multispectral bands (from two to
seven), and PCA2 was the one that best showed the landslides
because PCA2 gives the relationship between bands 5 and 3 (red
and infrared), which represents the Vegetation Index. A raster
calculator (Change detection) was used to compute the differ-
ence between the pre- and post-event PCA2s to compare the
differences in their vegetation cover and, thereby, the potential
presence of slides and flows. A supervised classification of land
cover was completed using a semi-automatic classification plug-
in with a Minimum Distance algorithm using four classes (Minu
and Bindhu 2016), which will be explained in BData analysis^
section.
Data analysis
Visual analysis and interpretation
Visual analysis and interpretation were performed on the
available Pléiades, SPOT 6 and WorldView-2 satellite images
provided by UNDP. Originally, the high- to low-resolution
(1.5–6 m) multispectral and panchromatic SPOT 6 and
WorldView-2 satellite images and their acquisition dates had
limited usefulness (Table 1) because their visual interpretation
was time consuming and constrained only to large slides,
according to Metternicht and Hurni (2005). On the other
hand,theVHRPléiadesimages(0.5m)wereveryhelpful
for fast detection and interpretation of all landslides types.
The landslides in these images were detected on the basis of a
set of standard criteria: their tone changes and the morpho-
logical footprints in the relief. Tone changes (for black-and-
white images) or changes in colour (colour images) indicated
Table 2 List of municipalities and spatial coverage of municipality areas by VHR and HR satellite data
No. Municipality Municipality area (km
2
) Coverage of municipality area (km
2
) Coverage of municipality area (%)
Pléiades SPOT 6 WV 2 Pléiades SPOT 6 WV 2
1 Kragujevac 833.94 293.21 –388.92 35.16 0.00 46.64
2 Kraljevo 1528.7 268.73 ––17.58 0.00 0.00
3 Krupanj 341.65 298.36 314.65 –87.33 92.10 0.00
4 Lazarevac 383.3 322.31 187.65 –84.09 48.96 0.00
5 Ljubovija 355.67 46.43 355.67 163.51 13.05 100.00 45.97
6 Loznica 609.63 81.66 609.63 –13.40 100.00 0.00
7 M. Zvornik 183.43 17.42 183.43 –9.50 100.00 0.00
8 Obrenovac 409.89 188.63 ––46.02 0.00 0.00
9 Osečina 318.4 13.03 318.4 –4.09 100.00 0.00
10 Trstenik 447.83 96.81 –313.2 21.62 0.00 69.94
11 Ub 456.36 235.97 27.67 –51.71 6.06 0.00
12 Valjevo 903.64 33.99 899.6 –3.76 99.55 0.00
13 B. Bašta 668.6 350.89 363.76 295.73 52.48 54.41 44.23
14 Čačak 635.86 63.89 306.08 –10.05 48.14 0.00
15 Šabac 796.89 –391.89 –0.00 49.18 0.00
16 Kosjerić358.42 –358.41 –0.00 100.00 0.00
17 Koceljeva 257.29 –250.12 –0.00 97.21 0.00
18 Jagodina 468.7 ––254.71 0.00 0.00 54.34
19 Svilajnac 325.9 ––316.4 0.00 0.00 97.08
20 V. Plana 344.89 ––335.02 0.00 0.00 97.14
21 Varvarin 249.2 ––221.33 0.00 0.00 88.82
22 Paraćin 540.38 –––0.00 0.00 0.00
23 S. Palanka 421.02 –––0.00 0.00 0.00
Total area (km
2
) 11,839.59 2311.33 4566.96 2288.82
Recent Landslides
Landslides
achangeinsoilmoisture.Visualinterpretationalloweda
relatively confident discerning of flows, as elongated irregular
forms developed down the slope and shallow slides in irreg-
ular elliptical forms (Figs. 6and 7). Some of the flows follow-
ed the existing local micro-relief depressions on the slopes,
but others were formed in the gullies of the upstream zones.
Flash flood zones were also routinely digitized (Fig. 8).
All landslide occurrences were interpreted with a specified
degree of certainty. The level of certainty was determined based
on the number of satisfied interpretation criteria, such as the
morphological footprints in the relief, tone/colour changes, soil
moisture changes, gullies, vegetation, textures (including
cracks), surface disturbances and artificial object disturbances
(Haugerud et al. 2003;Schulz2007). Independent of the
landslide size, if the phenomenon satisfied all six of these
criteria, the highest level of certainty was assigned to it. The
highest level of certainty was labelled B1^(890 landslides),
medium was labelled B2^(214 landslides, which satisfied a
minimum 3/6 criteria), while the lowest level of certainty was
marked B3^(179 landslides); see the example in Fig. 9. Approx-
imately 516 of the 890 landslides with the highest interpretation
certainty were flow-type landslides, suggesting that this type of
mechanism provides the easiest and most reliable interpreta-
tion. In contrast, approximately 100 of the 179 occurrences with
the lowest interpretation certainties belonged to slide-type land-
slides, suggesting that this type has the least reliable interpreta-
tion variants. Since 70% of all of the interpreted instabilities fell
in the B1^category, where all criteria for interpretation were
Fig. 3 Spatial coverage of municipality areas by satellite data used in the analysis (list of municipalities is given in Table 2)
Fig. 4 Flow chart of visual landslide recognition
Landslides
Fig. 5 Flow chart of applied pre-processing and processing procedures for semi-automatic landslide recognition
Fig. 6 Left: Pléiades satellite image (resolution 0.5 m, RGB 3, 2, 1). Right: Recognition of landslides, Krupanj municipality
Recent Landslides
Landslides
satisfied, it is already clear that VHR-HR images provide an
efficient and reliable source of information.
Semi-automatic detection of landslides for the test area
The spectral plot, original bands and soil temperature interme-
diate products described in BData processing^section were
analysed with a semi-automatic supervised classification in or-
der to determine the target classes. The following classes were
selected: areas that included clouds and/or shadows—class 0;
areas of cultivated land—class 1; areas under water and
vegetation—class 2; and areas with bare land (eroded areas)—3
(Fig. 10). The process of automatic classification misclassified
alluvial plains and river terraces as eroded areas (that poten-
tially contain landslides); therefore, they were manually re-
moved from class 3. It was found that instabilities that
represent a single body were disaggregated into multiple poly-
gons. All such cases were identified and merged into logical
entities in class 3. A semi-automatic classification of the PCA2
difference (PCA/before and PCA/after) (Krupanj) singled out
more than 4500 occurrences of instabilities in the test area.
The obtained polygons were plotted and correlated with a slope
map derived from the digital elevation model (25 × 25 m). Ac-
cording to the morphological parameters in the test area (the
Krupanj municipality), it was assumed that slopes ranging be-
tween 5° and 25° were possible hosts of landslides according to
field data and statistical analysis of slope angle range vs.
landslides (the average slope angle for all landslides is 23°). All
zones outside of this range were excluded from further semi-
automatic analysis for simplification, and the number of total
polygons was reduced. Many of these were split polygons that
belonged to the same landslide and had to be aggregated into
single logical entities. The remaining split polygons were
visually/manually grouped into logical entities.
Results and discussion
Results of the analysis of the whole study area
The resolutions of the satellite images that covered the whole
research area and included all 23 municipalities varied from
0.5 m for the Pléiades images to 1.5–6mfortheSPOT6and
WV2 images. This variance was the source of the uneven inter-
pretation qualities and different numbers of recognized land-
slides throughout the area. The field mapping campaigns, on the
other hand, had lesser but more consistent areal coverage
(Table 3).
Based on the visual interpretation and analysis of the VHR
and HR satellite images of the study area, a total of 1298
landslides (592 slides and 691 debris/earth flows) were recog-
nized, while 15 instances of movements were not assigned to be
slide- or flow-type movements since they represent complex
types of movement. The total number of visually recognized
landslides per municipality included in the research is given in
Fig. 7 Left: Pléiades satellite image (resolution 0.5 m, RGB 3, 2, 1). Right: Recognition of earth flow (yellow) and debris flows (pink), Krupanj municipality
Fig. 8 Left: Pléiades satellite image (resolution 0.5 m, RGB 3, 2, 1). Right: Recognition of flash flood zone Krupanj municipality urban area
Landslides
Table 3. Several municipalities were covered by the relatively
low-resolution SPOT 6 and WorldView-2 (MS) satellite images,
or they were not covered by any satellite images at all. In these
municipalities, subsequent field work has only established
slides. Because it is relatively difficult to identify slides with
relatively small displacements (below 12 m) and small magni-
tudes (below 500 m
2
) without stereo images and considering the
resolution of the available satellite images in the territory of
these municipalities, it has not been possible to visually inter-
pret slides.
The largest landslide/debris flow was recognized in the
municipality of Ljubovija, near the village of Selanac, with an
area of 85,222.23 m
2
(Table 4). This landslide/debris flow was
1.5 km long and, at its wider part, was 220 m wide (Figs. 11
and 12).
Results of the analysis of the test area (Krupanj municipality)
Results of the visual interpretation of the satellite images in
the Krupanj test area are shown in Fig. 13. A total of 507
landslides (Table 5) were assigned certainty labels of B1^and
B2^.
A semi-automatic analysis was applied in order to highlight
the differences in the relief for the period after a heavy rainfall
by comparing the pre-event and post-event images (Fig. 14). The
red/orange colour represents a deficit of mass, while the blue
colour represents a surplus of mass (accumulation of
transported material). Since the resolution of the Landsat 8
images was 30 m, only instabilities longer/wider than 30 m were
recognized. White zones in the image represent a mask for
clouds and shadows. These results were burdened with unde-
sired interferences, such as alluvial fans and agricultural zones,
which were shown as surplus/deficit masses. Given the spectral
signature of the target classes (Fig. 10), the minimum distance
algorithm had difficulties coping with the classifications in
spectral overlap domains, especially for cultivated land, vegeta-
tion and bare land.
As a result of the semi-automatic classification and reclas-
sification (explained in BSemi-automatic detection of
Fig. 9 Left: Pléiades satellite image (resolution 0.5 m, RGB 3, 2, 1). Right: Illustration of different level of certainty of landslide (red polygon certainty 1, yellow
polygon certainty 2, green polygon certainty 3)
Fig. 10 Diagram of wavelength for different classes
Recent Landslides
Landslides
Table 3 List of visually recognized landslides per municipality
No. Municipality Total number of landslides (all type)
Satellite image data Field campaign data
Slides Debris/earth flows Complex Total Slides and flows
1 Kragujevac 25 27 52 155
2 Kraljevo 28 23 51 188
3 Krupanj 220 277 10 507 160
4 Lazarevac 62 18 80 36
5 Ljubovija 96 104 3 203 66
6 Loznica 5 18 2 25 108
7 M. Zvornik 34 59 93 26
8 Obrenovac 23 3 26 15
9 Osečina 1 1 210
10 Trstenik 2 2 4 66
11 Ub 7 14 21 26
12 Valjevo
a
222
13 B. Bašta 75 143 218 144
14 Čačak
a
126
15 Šabac
a
42
16 Kosjerić
a
62
17 Koceljeva
a
55
18 Jagodina
a
9
19 Svilajnac
a
27
20 V. Plana
a
1
21 Varvarin 15 2 17 7
22 Paraćin
a
26
23 S. Palanka
a
8
Total 592 691 15 1298 1785
a
Municipalities without VHR image coverage (less than 10%)
Table 4 Details of visually recognized landslides per municipality
No. Municipality No. of landslides Landslide metric parameters
Min size (m
2
) Max size (m
2
) Average size (m
2
) Total area (m
2
)
1 Kragujevac 52 194.90 11,014.58 1607.06 83,567.02
2 Kraljevo 51 146.00 19,874.6 1952.43 103,479.24
3 Krupanj 507 68.60 17,137.5 1862.36 938,630.30
4 Lazarevac 80 168.64 8010.57 2056.06 164,484.43
5 Ljubovija 203 93.19 85,222.23 1886.50 377,300.00
6 Loznica 25 164.19 13,650.16 2946.36 91,337.16
7 M. Zvornik 93 113.22 10,388.12 1540.58 124,787.20
8 Obrenovac 26 152.46 12,437.35 3143.44 81,729.52
9 Osečina 1 2665.12 2665.12 2665.12 2665.12
10 Trstenik 4 816.57 8935.02 3418.02 13,672.10
11 Ub 21 263.66 10,180.63 2445.30 51,351.33
13 B. Bašta 218 62.40 8218.70 1026.10 230,868.80
21 Varvarin 17 596.61 8409.48 2866.90 48,738.88
Landslides
landslides for the test area^section), 490 instances of mass
differences were identified. Those polygons were interpreted
as landslides without a more precise determination of the
mechanism of transport (slides/flows). After their interpreta-
tion,itwasconcludedthatmostoftheidentifiedinstances
were positioned on slopes with values of approximately 20°
and were close to or within gullies.
The total number of recognized landslides in the Krupanj
municipality based on the different source data and applied meth-
odology of inventorying is shown in Table 5.
The visual satellite image analysis, supervised classifications
and field observations were cross-compared (Fig. 15). Since
the lowest common resolution for these three sources was
30 m (Landsat 8 image resolution), only landslides larger than
900 m
2
in the area were considered. The field data were
loosely considered to be the ground truth but were mostly
used as a reference. The reason for this lies in the fact that
the flows were recognized with much higher certainty in the
satellite images than the slides (i.e. flows were preferred via
image analysis), while one of the focuses of the field work
was mapping of slides or other types that could not be clearly
identified from satellite images (i.e. slides were preferred via
field work). The cross-comparison for Krupanj shows some
matching between the visual interpretation and the ground
truth, i.e. an approximately 26% match, especially for the
larger slides. A similar match of approximately 20% was
noted between the semi-automatic analysis and ground truth.
A cross-comparison between the visual and semi-automatic
analyses gives a better match, with an overlap of 41% for
larger slides.
Considering the number of identified instabilities and the
resolution of the available data, the visual interpretation pro-
vided reliable results, especially for the mapping of inaccessi-
ble flows. Field data (160 landslides and debris/earth flows)
cover only accessible terrain, which is the reason behind the
relatively small number of detected instabilities in this dataset
compared to the number found via the remote sensing meth-
od (507 landslides and flows). The second reason was that the
Fig. 11 Example of a representative visually recognized Selanac landslide/debris flow
Fig. 12 Photo of the he Selanac landslide/debris flow (from Fig. 11)
Recent Landslides
Landslides
field campaign data were concentrated at predefined locations.
These locations were chosen based on complaints obtained by
the local administration, wherein damage of objects or infra-
structure was cited. Processing and inventorying these com-
plaints were necessary for the subsequent financial restitution
from the government. The semi-automatic analysis provided
results (490 cases of instability) very similar to those of the
visual image analysis but lacked the ability to discern between
slides and flows.
Conclusion
This research considered the aftermath of a massive landslide
event that occurred in May 2014 in Serbia. This study included
the 23 municipalities heavily affected by the landslides, as well
as other rainfall-induced calamities. The research objectives
focused on two aspects. Firstly, a practical aspect was to provide
a reliable landslide inventory that will become a useful support
for subsequent field investigations, as well as to observe land-
slides in inaccessible terrains. Second, a semi-automated analy-
sis was applied over coarser (non-HR/VHR) resolution images
that are globally available (Landsat 8 images) to see whether this
approach provides any useful information. The idea behind this
approach was also a practical one since it entails the use of
widely available images to aid field campaigns or directly gen-
erates preliminary landslide inventories in a relatively short
timeframe, at practically no expense. Given that VHR/HR im-
ages are costly and do not have continuous archives (requiring
on-demand collection), generating a semi-automated landslide
Fig. 13 An example of visual recognition of landslides (red colour) in Krupanj area. Pléiades image, June 8, 2014 (transparency 50%)
Table 5 Number of recognized landslides in Krupanj municipality
Test area Number of landslides
Visual analysis (Pléiades) Field observation Supervised classification (Landsat 8)
Krupanj municipality 507 160 490
Landslides
inventory from coarse resolution but globally available images
could attract much attention for critical situations or recovery
actions, such as this one.
The results highlighted that the remote sensing-aided approach
identified a much higher density of landslides. For instance, the
field-based landslide inventory of Krupanj has three times fewer
identified landslide occurrences than the visually compiled inven-
tory from the VHR satellite images or the semi-automatically
compiled inventory. There is a similar ratio in all other heavily
affected municipalities (especially those affected by flows in
central and western Serbia) that had sufficient VHR Pléiades
satellite image coverage. It is further shown that semi-automated
and visual analyses can have a relatively good overlap (approxi-
mately 40%) even with different sources, which means that the
landslide recognition criteria were well tuned in both analyses. The
poorer results of the field data are, however, a phenomenological
and practical matter. Namely, the field work was first conducted to
recheck the preliminary inventories provided by the visual and
semi-automated analyses, but, following this, the focus of the field
campaign was switched to detect slides (confidently recognized
Fig. 14 Difference of PCA on pre-event and post-event Landsat 8 images for Krupanj municipality (red/orange colour represents deficit of mass, blue colour
represents surplus of mass)
Fig. 15 Interpretation of instabilities based on three types of data (visual analysis of VHR satellite images, yellow; field observation data, blue; semi-automatic
classification of Landsat 8 data, red)
Recent Landslides
Landslides
only in situ) that had been reported as damaging or threatening by
the local authorities of each municipality.
A successful field campaign that resulted in 1785 mapped land-
slides within 5 months of limited operational activity proves the
efficiency of the presented approach and its role in planning
detailed research. Reliable inventories, both preliminary and final,
are essential for the quality of all subsequent analyses. It is partic-
ularly important for event-based inventories because they provide
temporal support that can be put to use in a landslide hazard
context and in spatiotemporal landslide frequency analysis if
enough event-based time splits are collected in the future.
Acknowledgements
This research was part of Project BEyond landslide aWAREness
(BEWARE) funded by the People of Japan and the UNDP Office in
Serbia (grant No. 00094641). The project was implemented by the
Geological Survey of Serbia and the University of Belgrade, Faculty
of Mining and Geology. All activities are supported by the Ministry
for Energy and Mining, the Public Agency for Reconstruction and
Ministry for Education, Science and Technological Development
of the Republic of Serbia Project No. TR36009. The authors would
like to thank reviewers for constructive comments and
suggestions.
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http://www.regionalnirazvoj.gov.rs/, accessed Jan 2017
D. Đurić:A. Mladenović:M. Pešić-Georgiadis :M. Marjanović:
B. Abolmasov ())
Faculty of Mining and Geology,
University of Belgrade,
Đušina 7, Belgrade, 11000, Serbia
e-mail: biljana.abolmasov@rgf.bg.ac.rs
D. Đurić
e-mail: dragana.djuric@rgf.bg.ac.rs
A. Mladenović
e-mail: ana.mladenovic@rgf.bg.ac.rs
M. Pešić-Georgiadis
e-mail: milica.pesic@rgf.bg.ac.rs
M. Marjanović
e-mail: milos.marjanovic@rgf.bg.ac.rs
Recent Landslides
Landslides
