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19/12/2014 Quantitative hazard and risk assessment for slow-moving landslides from Persistent Scatterer Interferometry - Springer

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Landslides

Journal of the International Consortium on Landslides

© Springer-Verlag Berlin Heidelberg 201310.1007/s10346-013-0432-2

Original Paper

Quantitative hazard and risk assessmentfor slow-moving landslides from PersistentScatterer Interferometry

Ping Lu 1, 2 , Filippo Catani 3, Veronica Tofani 3 and Nicola Casagli 3

College of Surveying and Geo-Informatics, Tongji University, Siping Road 1239, Shanghai, China

Center for Spatial Information Science and Sustainable Development Applications, Tongji University,Siping Road 1239, Shanghai, China

Department of Earth Sciences, University of Firenze, Via La Pira 4, Florence, Italy

Ping LuEmail: [email protected]

Received: 24 October 2012

Accepted: 28 August 2013

Published online: 8 September 2013

Abstract

Preparation of reliable landslide hazard and risk maps is crucial for hazard mitigation and risk

management. In recent years, various approaches have been developed for quantitative

assessment of landslide hazard and risk. However, possibly due to the lack of new data, very

few of these hazard and risk maps were updated after their first generation. In this study,

aiming at an ongoing assessment, a novel approach for updating landslide hazard and risk

maps based on Persistent Scatterer Interferometry (PSI) is introduced. The study was

performed in the Arno River basin (central Italy) where most mass movements are slow-

moving landslides which are properly within the detection precision of PSI point targets. In

the Arno River basin, the preliminary hazard and risk assessment was performed by Catani et

al. (Landslides 2:329–342, 2005) using datasets prior to 2002. In this study, the previous

hazard and risk maps were updated using PSI point targets processed from 4 years (2003–

2006) of RADARSAT images. Landslide hazard and risk maps for five temporal predictions

of 2, 5, 10, 20 and 30 years were updated with the exposure of losses estimated in Euro (€).



In particular, the result shows that in 30 years a potential loss of approximate €3.22 billion is

expected due to these slow-moving landslides detected by PSI point targets.

Keywords Slow-moving landslides – Landslide hazard and risk assessment – PersistentScatterer Interferometry – Remote sensing

Introduction

Landslide hazard and risk assessment is the common concern of researchers, communities

and administrators (Aleotti and Chowdhury 1999). A reliable hazard and risk mapping helps

to address further risk management activities such as mitigation works, early warning

systems and sustainable development planning. In recent years, various methodologies have

been developed using different statistical models for quantitative landslide hazard and risk

assessment. Overviews and summaries regarding these approaches can be found in previous

literature such as Aleotti and Chowdhury (1999), Guzzetti et al. (1999), Dai et al. (2002),

Glade et al. (2005), and van Westen et al. (2006, 2008). In addition, a guideline for landslide

susceptibility, hazard and risk mapping with a definition of general framework and uniform

terminologies was proposed by Fell et al. (2008).

However, with increasing case studies of landslide hazard and risk mapping, very few of

them were further updated. This is fairly contradictory to what landslide hazard and risk

assessment originally targets on, since a reliable hazard and risk mapping should be

considered as an ongoing work which is fundamentally needed to be updated as frequently as

possible (Aleotti and Chowdhury 1999). In particular, for those mapping results derived from

dynamic data, the assessment needs to be updated regularly and continuously (van Westen et

al. 2008). The current challenge to keeping landslide hazard and risk maps updated is often

due to the availability of new data, thereby making an update of previous assessment

difficult.

To some extent, remote sensing has the potential to solve this problem of lacking new data

owing to the scheduled revisiting time and orbit which ensure a frequent update of acquired

data. In practical terms, remote sensing has already shown its usefulness in landslide hazard

and risk assessment, particularly in landslide hazard identification, spatial extent prediction

and triggering factors detection (Metternicht et al. 2005). For rapid-moving shallow

landslides and debris flows, the hazard identification and inventory mapping can be carried

out using optical imageries by recognizing the removal of vegetation from spectral behaviors

(e.g., Sato et al. 2007; Martha et al. 2010; Lu et al. 2011). For slow-moving landslides, the

mass movement can be detected through image correlations of sequential high resolution

optical data (e.g., Casson et al. 2003; Delacourt et al. 2004, 2007). However, this method is

affected by weather conditions and illumination changes, and it has difficulty in operation



during night (Travelletti et al. 2012).

As a remote sensing product from active microwave sensor, Persistent Scatterer

Interferometry (PSI) is an Interferometric Synthetic Aperture Radar (InSAR) technique that

employs a multi-interferogram analysis of temporal Synthetic Aperture Radar (SAR) images,

for extracting long-term high phase stability benchmarks of coherent PSI point targets,

namely Persistent Scatterers (PS). PSI is not affected by weather conditions and illumination

changes. In the past years, several approaches have been developed to obtain these PSI point

targets. For example, the PSInSAR™ technique, which can estimate the phase stability of the

scattering barycenter with low differential atmospheric contribution and can extract PS

regardless of normal and temporal baseline, was firstly proposed by Ferretti et al. (2000,

2001) and further improved by Colesanti et al. (2003). Also, the Stanford Method for

Persistent Scatterers (StaMPS), which utilizes the spatial correlation of interferogram phase

to find persistent benchmarks, was introduced by Hooper et al. (2004) and further modified

by Hooper et al. (2007). Similarly, the Interferometric Point Target Analysis (IPTA), which

has advantage in finding stable benchmarks in areas of low coherence and can use large

baselines for phase interpretation, was presented by Werner et al. (2003) and Strozzi et al.

(2006). Besides, the Coherence Pixel Technique (CPT), which enables an estimation of linear

and nonlinear components of ground deformation, was reported by Mora et al. (2003) and

Blanco-Sanchez et al. (2008). Moreover, the Small Baseline Subset (SBAS), an approach

performed on small-baseline interferograms and combining unwrapped differential InSAR

(DInSAR) interferograms through the Singular Value Decomposition (SVD) method, was

described by Berardino et al. (2002), Lanari et al. (2004) and Casu et al. (2006). Furthermore,

the Spatio-Temporal Unwrapping Network (STUN) algorithm was proposed by Kampes

(2006) which combines displacement model with spatial network for phase unwrapping

based on single-master interferograms. Additionally, the Stable Point Network (SPN) was

suggested by Crosetto et al. (2008) and Herrera et al. (2011), focusing on pixels with stable

behaviors in SAR amplitude stability, interferometric coherence and spectral coherence.

More recently, Zhang et al. (2012) demonstrated a method of temporarily coherent point

(TCP) InSAR (TCPInSAR) which can estimate deformation signals without the need for

phase unwrapping.

Owing to the millimeter precision, PSI is suitable for studying slow-moving landslides in

several aspects. First, PSI can be used for detection of slow-moving landslides. Bovenga et

al. (2006) combined PS information with ground data and monitoring controls to detect

landslides in the Daunia Apennine Mountains in Southern Italy. Besides, landslides can be

potentially detected by the hotspot mapping of PS clustering (Bianchini et al. 2012; Lu et al.

2012). Second, PSI shows its usefulness in landslide mapping and inventory updating. In

particular, by integrating with optical images, ancillary maps and ground measurements, the

PSI information can be used to modify the existing inventory with an assessment of the state



of landslide activity (Farina et al. 2006; Righini et al. 2012; Calò et al. 2012). Also, Cascini et

al. (2009) used PSI to check and update the existing landslide inventory at 1:25,000 scale and

to test the reliability of inventory based on the geomorphologic criteria. Third, PSI provides

an effective tool for landslide monitoring. This can be fulfilled by integrating PS with

leveling data and GPS for ground deformation monitoring (Colesanti et al. 2003). Greif and

Vlcko (2012) monitored the post-failure behavior of landslides in Central Slovakia using

transformation of line-of-sight (LOS) displacement rate to slope vector direction. Besides,

Herrera et al. (2011) detected and monitored the Portalet landslide in Spain using a

combination of X-band TerraSAR-X data and C-band ERS and ENVISAT data. The

monitoring capacity was further improved by Herrera et al. (2013) to figure out different

moving directions, measure different velocity patterns within the same moving mass and

identify triggering factors. Additionally, Bovenga et al. (2012) indicated that X-band sensors,

which have higher spatial resolution and shorter revisiting time, can estimate the surface

displacement using fewer images and the monitoring can be done in shorter time for high risk

cases. Fourth, PSI is valuable for landslide investigation. PS can be used for landslide

investigation at regional scale by combining visual interpretation of optical images (Farina et

al. 2006). PS can also refine the boundaries and the state of activity of landslides for

understanding the deformation pattern and relation with triggering factors (Tofani et al.

2013). Cascini et al. (2010) proposed a method for landslide feature investigation which can

be used for both full and low resolution analysis. Cigna et al. (2012) employed a PSI-based

matrix approach to evaluate the state of activity and intensity of slow-moving landslides.

Bovenga et al. (2013) integrated X-band COSMO-SkyMed, C-band ENVISAT and GNSS

measurements for landslide investigation in Assisi, Italy. Fifth, PSI has potential in landslide

mechanism understanding. For quantitative estimation, PS are suggested to be combined with

ground truth, field survey and analysis of acquisition geometry to understand landslide

mechanism (Colesanti and Wasowski 2006). The seasonality of ground acceleration revealed

by PS can be combined with the precipitation data to analyze the dynamics of slow-moving

landslide (Hilley et al. 2004). Zhao et al. (2012) indicated that by comparing PS velocity with

precipitation record, it can correlate landslide displacement with rainfall peak for further

investigation of landslide mechanism and defining rainfall threshold for early warning

purpose.

Although PSI has been extensively applied in landslide studies, none of the above-mentioned

works deals with the direct use of PS for quantitative landslide hazard and risk assessment. In

order to fill this gap, and also for an ongoing update of existing landslide hazard and risk

maps, a novel approach of quantitative landslide hazard and risk assessment using PSI point

targets is presented. The purpose is to quantify the potential hazard and risk resulting from

slow-moving landslides. The Arno River basin in central Italy was chosen as the study area

because most of the mass movements in the basin are slow-moving rotational landslides



which are properly within the detection precision of PSI point targets (Lu et al. 2012). Catani

et al. (2005) have accomplished a hazard and risk mapping at catchment scale for all types of

landslides in the Arno River basin. This paper continues the work of Catani et al. (2005), but

will focus only on slow-moving landslides within the detection range of PSI. The novelty of

this paper is to provide a new application of PSI in quantitative landslide hazard and risk

assessment and to develop a new method to integrate PSI for an ongoing update of landslide

hazard and risk maps.

Study area and PS datasets

The Arno river basin is located in central Italy (Fig. 1), mostly within the Tuscany region.

The whole area of the basin is about 9130 km2. Since the basin is across the Northern

Apennines orogenic belt, 78 % of the area (ca. 7190 km2) is situated in mountainous and hilly

areas. The basin is strongly affected by landslides. More than 27,000 landslides were

previously mapped at a scale of 1:10,000, with a total affected area of more than 800 km2

(Catani et al. 2005; Farina et al. 2006). 74 % of these landslides are slow-moving rotational

slides, which can be periodically reactivated by prolonged and intense rainfall (Catani et al.

2005; Lu et al. 2012). These landslides pose great threat to human lives and vulnerable

elements considering the dense population (2.6 million inhabitants) within the basin, which

includes the major cities of Firenze, Pisa and Arezzo (Fig. 1).



Fig. 1

The geographic location of the Arno River basin

The PS datasets were processed by Tele-Rilevamento Europa (TRE) on behalf of the Arno

River basin Authority using PSInSAR™ technique, which is only slightly affected by

temporal/geometric decorrelation and atmospheric disturbances as described by Ferretti et al.

(2000, 2001). 102 RADARSAT-1 SAR images (54 ascending and 48 descending scenes)

spanning from March 2003 to January 2007 were processed. These images were collected

with the beam mode of S3 which provides an incident angle ranging between 30° and 37°.

The orbiting tracks of the satellite are 54 for descending and 247 for ascending acquisitions.

These two tracks cover about 6,300 km2, approximately 70 % of the whole basin. With a

definition of coherence level above 0.60, more than 700,000 PS were identified. The

precision of displacement rates along the LOS varies from 0.1 to 2 mm/year and the

geocoding accuracy of PS position is within 5 m in the north–south and 10 m in the east–west

direction. The PS density is 54 points/km2 for ascending data and 60 points/km2 for

descending orbit. PS located in the flat area were masked out so that only PS situated in the

mountainous and hilly areas were used for further landslide hazard and risk mapping. The

point density decreased to 31 PS/km2 for ascending data and 32 PS/km2 for descending data

after the masking of flat areas.



Methodology and results

PSI Hotspot and Clustering Analysis

The first step for landslide hazard and risk assessment from PSI is to employ the spatial

statistics approach of PSI Hotspot and Clustering Analysis (PSI-HCA) as proposed by Lu et

al. (2012). The purpose is to obtain a continuous estimation of PS distribution from their

spatially correlated velocities. PSI-HCA is composed of two spatial statistic approaches: (1)

Getis-Ord G i * statistics (Getis and Ord 1992) calculated for each single PSI point target and

(2) kernel density estimation (Silverman 1986) derived based on calculated G i * values. The

output of PSI-HCA is a PS hotspot map represented by the kernel density values. In this

study, PSI-HCA was performed on both ascending and descending PS datasets. Figure 2

shows part of the PS hotspot map in the Arno River basin. Pixels with positive kernel density

values are displayed with blue colors whereas pixels with negative kernel density values are

rendered on red colors. Both blue and red hotspots indicate where potential mass movements

exist, with deeper color indicating higher clustering level of high velocity PS. Full details and

explanations of PSI-HCA can be found in the work of Lu et al. (2012).



Fig. 2

The landslide hotspot map covering the area between Volterra and Poggibonsi in the Arno River basin:a hotspot map derived from a kernel density estimation using ascending RADARSAT PS: red hotspotscorrespond to PS moving downwards and/or eastwards, whereas blue hotspots correspond to PSmoving upwards and/or westwards; b hotspot map derived from a kernel density estimation usingdescending RADARSAT PS: red hotspots correspond to PS moving downwards and/or westwards,



whereas blue hotspots correspond to PS moving upwards and/or eastwards

Susceptibility mapping

The landslide susceptibility in the Arno River basin was previously mapped by Catani et al.

(2005). Since susceptibility assessment only concentrates on spatial probability of landslide

occurrences, regardless of temporal prediction, the susceptibility map created by Catani et al.

(2005) was also used in this study with the same area focused. Catani et al. (2005) described

the methodology of landslide susceptibility mapping in the Arno River basin in detail. Five

preparatory factors related to slope instability (slope angle, profile curvature, upslope

contributing area, land cover and lithology) were selected for the susceptibility analysis. The

slope angle, profile curvature and upslope contributing area factors were derived from a 10-m

DTM (created from topographic maps in 2002), classified into five, three and three classes,

respectively. The land cover factor, obtained from a 1:50,000 land cover map, was classified

into nine classes according to the legend of CORINE (Coordination of Information on the

Environment) land cover project (Heyman et al. 1994). The lithology factor, acquired from

the lithology map published by Canuti et al. (1994), was reclassified into eight classes. A

summary of these five susceptibility parameters and their classification algorithms is listed in

Table 1. The artificial neural network (ANN) was then deployed for statistical estimation of

landslide susceptibility because of its loose hypothesis on the variable distribution and the

possibility to involve mixed parameters (Ermini et al. 2005). The statistical prediction was

performed on the basis of unique condition unit (UCU), which is the subdivided

homogeneous terrain unit referring to the landslide preparatory factors. Those UCUs were

used to build a number of model vectors for training and testing of ANNs. For an accurate

ANN training, the training data were chosen with reference to the landslide inventory and

were validated in the field for accuracy and completeness. By checking the landslide

inventory, the percentage of UCU area subject to landslides was determined as predicted

variable. As ANN output, each UCU was assigned a degree of membership to a susceptibility

value from 0 to 100. The derived susceptibility map was then reclassified into four classes (S

0, S 1, S 2 and S 3, assorted with increasing susceptibility levels) by comparing the cumulative

density function of ANN outputs within mapped landslides with the cumulative distribution

of total ANN outputs, as proposed by Ermini et al. (2005) and modified by Catani et al.

(2005). The final output of the susceptibility mapping in the Arno River basin is illustrated in

Fig. 3.



Table 1

The susceptible parameters and their classifications used for landslide susceptibility assessment in theArno River basin (Catani et al. 2005)

Susceptible

parameterClassification

Slope angle 0–5°, 5–10°, 10–20°, 20–33°, 33–90°

Profile

curvatureConcave; planar; convex

Upslope

contributing

area

0–1,000, 1,000–1,500, >1,500 m2

Land coverArtificially modified land; crops and permanent cultivation; forest; grassland; heterogeneous

cultivated land; rangeland; scrubland; wetland

LithologyCohesive soils; complex mainly pelitic units; granular soils; indurated rocks; marls and compact

clays; rocks with pelitic layers; weak rocks; weakly cemented conglomerates and carbonate rocks



Fig. 3

The landslide susceptibility map of the Arno River basin (modified after Catani et al. 2005)

Hazard mapping

The landslide hazard map was accomplished based on the previously derived landslide

hotspot maps (Fig. 2). Five hazard levels (H 0, H 1, H 2, H 3 and H 4, assorted by increasing

hazard levels) were initially determined from the kernel density values of the hotspot maps.

Ascending and descending hotspot maps were analyzed separately for hazard assessment.

This is due to the independent PSI processing approaches of long-term SAR images for

ascending and descending orbits, reflected by their differences in acquisition dates, master

images, reference points and coherence maps. For each orbit, blue and red hotspots,

indicating different moving directions of mass movements along LOS, were individually

analyzed for initial hazard zonation. The boundary and threshold for hazard zonation were

derived from heuristic determination by classifying hotspot maps into different levels, using

aerial photo interpretation and field surveys as supplementary references. A preliminary

hazard zonation was determined based on the Z-score values of hotspot map pixels. The Z-

score of 2.5 was chosen as the basic zonation unit since it corresponds to an approximate

99 % confidence interval of kernel density pixels. The kernel density values were divided

into five classes, with thresholds defined as 0, 2.5, 5 and 10 standard deviations for red

hotspots, and 0, 5, 10, 20 standard deviations for blue hotspots. The corresponding result for

assigning five hazard levels is summarized in Table 2. The selection of different zonation

conditions for red and blue hotspots is due to their different moving directions and

acquisition geometries. Red hotspots (negative kernel density) indicate the clustering of PS

moving away from the sensor, whereas blue hotspots (positive kernel density) suggest the

clustering of PS moving towards the sensor. Considering the incidence angle ranging

between 30o and 37o for RADARSAT, this can be interpreted as follows:

Table 2

The conditions of classifying hazard levels from kernel density values of hotspot maps

Ascending orbit

Red hotspot

H 4 Kernel density ≤ −280

H 3 −280 < kernel density ≤ −140


H 1 −35 < kernel density < 0




H 0 Kernel density = 0

Blue hotspot

H 4 Kernel density ≥ 560

H 3 560 > kernel density ≥ 280


H 1 70 > kernel density > 0


Descending orbit

Red hotspot

H 4 Kernel density ≤ −200




H 0 kernel density = 0

Blue hotspot

H 4 Kernel density > = 400



H 1 50 > kernel density > 0


– For the ascending orbit: red hotspots correspond to PS moving downwards and/or

eastwards, whereas blue hotspots correspond to PS moving upwards and/or westwards.



(1)

– For the descending orbit: red hotspots correspond to PS moving downwards and/or

westwards, whereas blue hotspots correspond to PS moving upwards and/or eastwards.

For both ascending and descending orbits, blue hotspots correspond to PS moving upwards.

Although upward movements are typical features in lower portions of rotational landslides,

they are also possibly related to fluid injection (Doubre and Peltzer 2007), sedimentation of

rivers (Smith 2002) and tectonic uplift (Vilardo et al. 2009; Massironi et al. 2009; Morelli et

al. 2011). Therefore, the hazard levels for blue hotspots were more carefully defined with

larger standard deviations.

After, the hazard levels estimated from the hotspot maps were compared with the

susceptibility classes. For each pixel, if the initial hazard level is higher than the

corresponding susceptibility class, the new hazard level is determined by the former. Instead,

if the hazard level from the hotspot map is lower than the corresponding susceptibility class,

the final hazard level was assigned by the values of the latter. This is due to the fact that

underestimation of mass movements from PSI techniques possibly exists, resulting from a

lack of stable benchmarks with high coherence values.

For each of these five new hazard levels, a conventional recurrence time T was assigned (H 0:

10,000 years, H 1: 1,000 years, H 2: 100 years, H 3: 10 years, H 4: 1 year) as described by

Catani et al. (2005). The temporal probability was then calculated for each hazard level, using

the following algorithm (Canuti and Casagli 1996):

where T is the recurrence time, N is the time period considered for temporal probability

assessment which was calculated here for 2, 5, 10, 20, and 30 years, respectively. P{H(N)} is

the temporal probability of landslide occurrences in a given time span N. The result of

occurrence probability for each hazard level is listed in Table 3. It was assessed by five

classes (from H 0 to H 4), with each corresponding probability of occurrences (from 0 to 1)

over five periods. An example of a derived hazard (temporal probability) map over the period

of 30 years is displayed in Fig. 4.

P{H(N)} = 1 − ,(1 − )1

T

N



Table 3

The probability of landslide occurrence for different hazard levels and time spans

Recurrence

time T

(years)

P{H(2 years)} P{H(5 years)} P{H(10 years)} P{H(20 years)}

H

41 1 1 1 1

H

310 0.1900 0.4095 0.6513 0.8784

H

2100 0.0200 0.0490 0.0956 0.1821

H

11,000 0.0020 0.0049 0.0099 0.0198

H

010,000 0.0000 0.0005 0.0010 0.0019



Fig. 4

The landslide hazard (temporal probability) map of the Arno River basin for 30 years, updated from PSIpoint targets

Landslide intensity

Landslide intensity can be measured from the kinetic energy of mass movement, which is

primarily considered as its volume and velocity, or a more complicated estimation of its run-

out distance (Hungr 1995). Due to the difficulty in measuring velocity of slow-moving deep-

seated landslides over large areas, the intensity is mainly measured from its estimated

volume. In the Arno River basin, Catani et al. (2005) measured the intensity of deep-seated

landslides from the estimation of landslide volume using the post-failure geometry based on

the assumption that the shape of landslide is half-ellipsoidal. In this study, the intensity was

additionally measured from the velocity of landslide, thanks to the technique of PSI which

enables a detection of slow movement of millimeters per year. Moreover, the PSI technique

provides the complete time series record of landslides velocity over the period of processed

SAR images, thus making a selection of maximum velocity of mass movement possible. This

is especially useful for landslide intensity estimation due to the fact that landslide intensity is

often determined by its maximum velocity instead of an average velocity over a period of

time (Hungr 1997).

In this study, for each single PS, in order to remove noise, the time series data of PS were

firstly smoothed using a moving average filtering with a smooth span of five consecutive

SAR acquisitions, namely five records of the time series data of each single PSI point target.

Since the PSI-derived velocities are calculated as averages of observations over a period, they

are found to be lower than the peak velocities of mass movements (Cascini et al. 2010; Cigna

et al. 2012). As a result, the maximum velocity was selected from the time series of velocity

for each single PSI point target for both ascending and descending orbits. The intensity field

was then interpolated from the maximum velocity of PS incorporating the geostatistical

approach of ordinary kriging (Stein 1999), firstly quantifying the spatial structure of PS and

subsequently performing a spatial prediction of other areas uncovered by PS data. The

kriging model employed the statistical relationships of spatial autocorrelation among the

measured maximum velocity of PS for a spatial prediction. This was done by calculating its

empirical semivariogram which estimated the squared difference between the velocity values

for all pairs of PS datasets. The kriging is an exact interpolator which preserves the PS

velocity at known locations. Considering the PS located in the Arno River basin are spatially

clustered (Lu et al. 2012), the kriging can also be used to compensate the spatial clustering

effect of those PSI point targets, assigning each single PS within a cluster less weight than an

isolated PS. The root mean square (RMS) standardized errors are 1.146 and 1.068 for

ascending and descending data, respectively. Both RMS standardized errors are close to 1,



indicating the variability was assessed correctly in prediction. Besides, the mean standardized

prediction errors are 0 and 0.012 for ascending and descending data, respectively. Both mean

standardized prediction errors are close to 0, suggesting that prediction errors are unbiased.

The interpolated velocity field was then classified into four classes: v 4 (velocity

>10 mm/24 days), v 3 (10 mm/24 days > velocity > 4 mm/24 days), v 2 (4 mm/24 days >

velocity > 2 mm/24 days) and v 1 (velocity <2 mm/24 days). Here, the time span of 24 days is

the revisiting time of the RADARSAT satellite, namely the time interval between two

consecutive time series records. The reason 10 mm/24 days was selected as the threshold to

classify v 4 and v 3 is that it approximates the typical velocity of an active slow-moving

landslide (1.6 m/year), according to the classification reported by Cruden and Varnes (1996).

Similarly, 4 mm/24 days was chosen as the boundary between v 3 and v 2 due to the typical

velocity for differentiating very slow and extremely slow landslides (Cruden and Varnes

1996). Furthermore, 2 mm/24 days was defined as the typical velocity for extremely slow-

moving landslide to separate the velocity level of v 2 and v 1. These four classes of velocity

were used to define the new intensity levels by comparing with the initial intensity levels

(five classes from I 0 to I 4, with a significance of increasing intensity levels). The

comparison was based on a heuristic approach using the classification matrix indicated in

Fig. 5. The intensity classification was performed for both ascending and descending orbits,

which were subsequently merged into a unique intensity map based on the algorithm that

applies higher intensity classes if one pixel contains both values from two orbits. The final

derived intensity map is displayed in Fig. 6.

Fig. 5

The matrix for rendering new intensity levels based on the kriging-interpolated velocity level v and theinitial intensity level I mapped from the landslide inventory



Fig. 6

The derived landslide intensity map in the Arno River basin

Vulnerability and exposure

The vulnerability is generally defined as a function of a given intensity, measured as the

expected degree of loss for an element at risk due to landslide occurrence, ranging between 0

(without damage) to 1 (full destruction) (Varnes and IAEG Commission on Landslides 1984;

Fell 1994). Exposure instead is related to the practical use of vulnerability, usually considered

as the number of lives or the value of properties exposed at risk (Schuster and Fleming 1986).

The selection of the elements at risk for vulnerability assessment in this study was extracted

based on the regional digital topographic maps at the scale of 1:10,000, and an updated

CORINE land cover map of 2002 from European Space Agency at the scale of 1:50,000

(Heyman et al. 1994). A geodatabase of the elements at risks was then built, including the

exposure values and vulnerability as a function of intensity which was previously

determined. The elements at risk were classified into five categories: building, complex, road,

railway and land cover. Each category was further subdivided according to their practical

uses which render the exposure and vulnerability value for each element. For example,

complexes used for hospitals and schools are considered more vulnerable than sport facilities,



thus receiving higher values for exposure and vulnerability. A detailed description of this

geodatabase regarding vulnerability and exposure can be found in the work of Catani et al.

(2005).

Quantitative landslide risk assessment

The quantitative risk assessment was performed with the direct application of the following

algorithm: Risk = Hazard × Vulnerability × Exposure, as suggested by Varnes and IAEG

Commission on Landslides (1984), Fell (1994), van Westen et al. (2006) and Remondo et al.

(2008). The calculation was performed on each pixel with a spatial resolution of 10 m,

completed for five different time spans of 2, 5, 10, 20 and 30 years, respectively. The final

output is a 10-m resolution landslide risk map with each pixel indicating the amount of

expected loss in Euro. An overview of landslide risk maps for 2, 5, 10, 20 and 30 years is

rendered in Fig. 7.



Fig. 7

The landslide risk map estimated from PSI in the Arno river basin: a the shaded relief map, b–f riskmaps for 2, 5, 10, 20, 30 years, respectively. See the corresponding amount of losses in Table 4

The total estimated economic loss is summarized in Table 4, indicating the potential losses

(in Euro) of 2, 5, 10, 20 and 30 years. In particular, approximately €3.22 billion loss was

expected in the upcoming 30 years throughout the Arno River basin, due to the slow-moving

landslides within the detection range of PSI technique. The approximate losses for 20, 10, 5

and 2 years are €2.72 billion, €1.86 billion, €1.14 billion and €0.54 billion, respectively.



Table 4

Landslide risks (losses estimated in Euro) in the Arno River basin calculated from PSI for five timespans

Time span (years) Expected economic losses (in Euro)

2 543,980,444

5 1,143,746,730

10 1,864,851,052

20 2,721,273,302

30 3,224,446,172

Discussion

To validate the result of risk assessment, some financial data concerning the expenses spent

on landslide prevention and risk mitigation in the Arno River basin during 5 years (2001–

2005) were collected. These expenses come from Italian national and regional funds,

allocated by two national laws, Department of Italian Civil Protection, regional decrees and

the landslide mitigation program in the Arno River basin. The detailed amount of expenses

from 2001 to 2005 is listed in Table 5. The financial data indicates that a total amount of

€0.52 billion was spent on landslide risk mitigation in the Arno River basin during these

5 years. The estimated economic loss for 5 years in this study is about €1.14 billion, which is

higher than the collected financial data. This difference between the actual funds for

mitigation measures and the expected loss is possibly due to two facts. First, the financial

data were not completely collected. Not all the public funding information was collected and

additionally none of the private expense was obtained. This renders potential underestimation

of actual expenses spent on landslide risk mitigation in the Arno River basin. Second,

mitigation measures were not built for all landslides in the basin during these 5 years. The

priority was given to landslides with higher intensity and more vulnerable elements at risk.

Therefore, the actual expenses spent may be lower than the estimated economic loss.



Table 5

The amount of expenses used for landslide risk mitigation in the Arno River basin during 5 years (2001–2005)

Financial source Amount of expenses (€)

Italian National Law L. 183/89 4,068,176

Italian National Law D.L. 180/98 16,186,881

Department of Italian Civil Protection 185,283,204

Regional Allocation 201,470,084

Landslide Mitigation Program 109,072,488

Total 516,080,833

The result of risk assessment in this study was also compared to the previous study of Catani

et al. (2005). Similar to the observation of Catani et al. (2005), in this study the increase of

risk with time is nonlinear. However, compared to the previous risk mapping result, the risk

value in this study is significantly lower. Catani et al. (2005) expected ca. €6 billion loss for

30 years, whereas the estimation in this study is ca. €3.22 billion. The decrease of the

predicted risk is possibly due to the fact that the risk assessment performed by Catani et al.

(2005) focused on all types of landslides in the inventory while in this study only the slow-

moving landslides were concentrated on. Although the whole basin is predominated by slow-

moving deep-seated landslides (ca. 74 %), and rapid-moving shallow landslides and debris

flows only account for 22 %, the consequence of rapid-moving landslides is more severe than

slow-moving landslides. Another reason of the lower estimated risk is possibly due to the

limitation of PSI technique in the areas without stable reflectors or coherent targets. This

causes the potential lack of PS for some landslides, thus bringing an underestimation of

landslide intensity and hotspot quantities. However, this limitation is expected to be largely

improved with the increasing uses of higher resolution X-band SAR sensors such as

COSMO-SkyMed (Bovenga et al. 2012) and TerraSAR-X (Prati et al. 2010) and new

processing approaches such as the SqueeSAR technique (Ferretti et al. 2011) and multi-

master interferograms strategy (Prati et al. 2010).

Recently, several studies have tried to represent PS velocity not only limited to LOS. For

example, Cascini et al. (2010) proposed an approach to project LOS deformation to the

steepest slope direction with the assumption that the mechanism of mass movement is

translational landslide. This approach was also applied in monitoring post-failure landslide in



Central Slovakia (Greif and Vlcko 2012). In the Arno River basin, this projection is not used

because the dominant type of mass movement is slow-moving rotational landslides.

However, for the area susceptible to the translational landslide, it may provide an effective

approach for PSI-HCA and hazard map generation. On the other hand, if both ascending and

descending PS are available, the velocity vector can be represented on the East–

West–Zenith–Nadir plane (Lu et al. 2010). This approach has the advantage in combining

both ascending and descending orbits for PSI-HCA. However, for landslide studies, it has

significant difficulty in receiving stable radar targets from both ascending and descending

orbits due to the geometrical visibility and distortions (e.g., foreshortening, layover and

shadowing effects) in mountainous and hilly areas (Colesanti and Wasowski 2006; Cigna et

al. 2012).

Conclusion

Quantitative landslide hazard and risk assessment is essential for hazard mitigation and risk

management. For a sustainable development plan, the assessment is needed to be updated as

frequently as required. Possibly due to the lack of new data, currently very little attention has

been paid to updating previously mapped result. With scheduled revisiting time and orbits,

remote sensing products provide an important data source for a frequent update of landslide

hazard and risk assessment. In this paper, aiming at an update of previously mapped landslide

hazard and risk in the Arno River basin by Catani et al. (2005), a novel approach was

developed to evaluate the hazard and risk level of slow-moving landslides from PSI point

targets.

The quantitative risk assessment was based on the following algorithm: Risk = Hazard ×

Vulnerability × Exposure. Firstly, a susceptibility map completed by Catani et al. (2005)

using the ANN predictor was included in this study, subsequently combined with the kernel

density values of the hotspot map derived from PSI-HCA, for the generation of landslide

hazard maps for five temporal predictions of 2, 5, 10, 20 and 30 years. Moreover, a landslide

intensity map was determined by the velocity map interpolated from the maximum velocity

of PS time series data using the ordinary kriging method. With given intensity, elements at

risks were extracted from a regional digital topographic map and a CORINE land cover map.

The result of risk mapping was evaluated for 2, 5, 10, 20 and 30 years. In particular, an

expected loss of ca. €3.22 billion was estimated for the upcoming 30 years.

The estimated economic loss for 5 years in this study is higher than the collected financial

data indicating the actual expenses spent on landslide prevention and risk mitigation. This is

possibly because the collected financial data is incomplete and the mitigation works were not

built for all landslides in the Arno River basin. In addition, compared to the risk assessment



by Catani et al. (2005), the mapping result from PSI technique shows a lower estimation of

potential losses. This is possibly due to the detection range of PSI which is primarily aiming

at slow-moving landslides. Also, PS processed from C-band RADARSAT images render

lower point density, especially in landslide areas without sufficient stable reflectors, thereby

making an omission of detecting slow-moving landslides, and accordingly an

underestimation of potential landslide risk level. Further improvement should include PS

products from X-band images (e.g., TerraSAR-X and Cosmo-SkyMed) as well as new PSI

processing techniques such as the SqueeSAR (Ferretti et al. 2011) and the multi-master

interferograms strategy (Prati et al. 2010).

Acknowledgement

This work was supported by National Natural Science Foundation of China (No. 41201424), 973

National Basic Research Program (No. 2013CB733203 and No. 2013CB733204), 863 National High-

Tech R&D Program (No. 2012AA121302) and Mountain Risks FP6 project of European Commission

(MRTN-CT-2006-035798). The authors are grateful to the staff of Tele-Rilevamento Europa, a spin-off

company of Politecnico di Milano owning the patent of PSInSAR™ technique, for the data processing

and software development. The authors also thank the Arno River Basin Authority for data sharing.

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