Non susceptibility slideshare

NH 3.8, Vienna, 02-05-2014, Ivan Marchesini

Non-Susceptible Landslide Areas in Italy and in the Mediterranean Region

Massimiliano Alvioli1, Francesca Ardizzone1, Fausto Guzzetti1, Ivan Marchesini1, and Mauro

Rossi1,2

1) Consiglio Nazionale delle Ricerche, Istituto di Ricerca per la Protezione Idrogeologica, via Madonna Alta 126, I-06128 Perugia, Italy 2) Universita` degli Studi di Perugia, Dipartimento di Scienze della Terra, Piazza Universita`, 1, I-06123, Perugia, Italy

NH 3.8, Vienna, 02-05-2014


Why this work?

● A large number of methods and techniques were proposed and tested to ascertain landslide susceptibility

● A few attempts were made to define landslide susceptibility at the continental and even at the global scale (e.g. Van Den Eeckhaut et al., 2012; Gunther et al., 2013)

● Little effort was made to define where landslides are not expected, i.e. where landslide susceptibility is null, or negligible (Godt et al. 2012)


What we did

● In this work, we discuss some methods for the definition of non-susceptible landslide areas, at the synoptic scale.

● We apply the best method in Italy and to the landmasses surrounding the Mediterranean Sea

10° W 40° E

10° W 40° E

50° N

30° N

50° N

30° N


Data

● Digital terrain elevation: – 40 SRTM data tiles

covering the Mediterranean area

● Landslide information:– 13 inventories of

polygons including geomorphological, event, and multi-temporal inventory maps in Italy


SRTM

● We exploited two morphometric parameters computed from the SRTM DEM:– relative relief R (in meters)

– terrain slope S (in degrees)

● We computed – R using a circular moving window with a diameter of 15 cells

– S in a 3 × 3 - cell square moving window.


Landslide inventories

● The maps cover 8.9% of the Italian territory

● 93,538 landslides● Mapped area: 2726 km2 ● Landslide area is 10.1% of

the mapped areas– Rotational and translational

slides,

– Earth flows

– Complex and compound movements


Methods

● We defined the areas that are expected to be non susceptible to landslides in Italy, using two different methods:

1.The first method is derived from the work of Godt et al. (2012) (method I)

2.The second method was developed specifically for this work (method II)


Method I

● We computed the frequency distribution of the relative relief R and of the terrain slope S for all the grid cells in each single landslide in each inventory.

0 10 20 30 40 50 60Local Terrain Slope, S [°]

Em

piric

al C

umul

ativ

e P

roba

bilit

y

0.2

0.4

0.6

0.8

1.0

0.0

0 20 40 60Local Terrain Slope, S [°]

0.2

0.4

0.6

0.8

1.0

0.0

● For each inventory, we prepared the Empirical Cumulative Distribution Functions (ECDFs) for the 50th percentile of the two terrain variables, R and S, in all the mapped landslides.

● Next we arbitrary chose the 5% cumulative frequency of both slope and relief of the ECDFs of the different inventories

0.0

50.

05


Method I

● Plot of the 5th percentile pairs (R50, S50).

● Data fitting with a Linear Regression model (LR):

Non susceptibleSusceptib

le

S50 = 3.448 + 0.040 R50

A-M: landslide inventories


Method I

● We used the Linear Regression model (LR) to prepare the binary zonation of the Italian territory.

● The orange color shows areas where landslide susceptibility is expected to be null or negligible.


Method II

● 354,406 (R,S) pairs of slope and relief values

● Corresponding to all the cells inside the landslide polygons

● We searched for a lower threshold to the cloud of points.


Method II

● Quantile regression ● We tested

– A Quantile Linear Regression model (QLR)

– A Quantile Non-Linear regression (exponential) model (QNL)

● We instructed the quantile regression to model the 5th percentiles i.e., to search for a regression line that would leave, below the line, 5% of the empirical data points.

95% of data points

5% of data points

?


Method II

● Quantile Linear Regression model (QLR) resulted in:

S = 0.245 + 0.032 R

● Quantile Non-Linear regression model (QNL) resulted in the exponential function:

S = 3.539*e(0.0028 × R)


QLR


QLR

Non susceptible


QLR, QNL


QLR, QNL

Non susceptible


LR, QLR, QNL


LR, QLR, QNL

Non susceptible


QLR zonation

● We prepared a zonation, showing non-susceptible areas in Italy, both using the quantile linear model QLR ...


QNL zonation

… and using the quantile non linear model QNL


Models performance

● Percentage of non-susceptible Italian territory:– LR: 62%,

– QLR: 22%,

– QNL: 42%,

● Quantile Linear model (QLR) is very conservative respect to the other two models

LR QLR QNL


Models performance

● Test dataset: IFFI, Italian Landslide Inventory (Trigila et al., 2010).

● Obtained though the IFFI WMS service and setting ground resolution at 5 m × 5 m

● The dataset contains: – falls and/or topples, – slow flows, – rapid flows, – complex movements,– rotational/translational slides, – lateral spreads, – sinkholes, – undefined slope movements.

From Trigila et al., 2010.

Progetto IFFI - ISPRA - Dipartimento Difesa del Suolo-Servizio Geologico d'Italia -

www.sinanet.isprambiente.it/progettoiffi


Models performance

LR QLR QNL

FPR [%] 43.6 6.06 6.33

● We searched the proportion of landslide cells that overlaid non-susceptible areas: the False Positive Rate: – FPR = FP / (FP+TN)

● The more the FPR get close to 5% the better is the model performance

● The QLR and QNL models performed significantly better than the LR model

● QLR model is conservative, and so we concluded that QNL is the best QNL is the best model.model.

FP TNNon

sus

cept

ible

are

a

Landslide


QNL model performance

The QNL model performed better for translational and rotational slides

Results are not far from the expected 5% for slow flows, complex and undefined movements

These landslide types represent 92% of the IFFI landslides (in terms of covered area)

The QNL model failed to detect non-susceptible areas for lateral spreads, sinkholes, rapid flows and for falls and topples.

Landslide types FPR [%]

Rotational, translational slides 5.3

Undefined 7.2

Slow flows 7.2

Complex movements 7.4

Falls and topples 8.3

Rapid flows 11.6

Sinkholes 13.8

Lateral spreads 20.9



We further investigated the performance of the QNL model in the 20 administrative regions in Italy.● Size and shape deformations

depend on the IFFI landslide density

● Orange and red colors show high values of False Positive Rate.

● High values of FPR are frequently associated with scarce density of the inventory



● We applied the non linear model QNL to the landmasses surrounding the Mediterranean Sea

● Non-susceptible cells cover 3,652,683 km2, 63% of the area.



We tested the synoptic-scale terrain zonation using independent landslide information in Spain:

● Three inventories: – Pyrenees, Murcia, and the Tramuntana range in Majorca,

– total of 521 landslides,

– total landslide area 27.24 km2.

● The resulting False Positive Rate (FPR) was: 6.11%


Conclusions

● Exploiting accurate landslide information for 13 study areas in Italy we identified areas non-susceptible to landslides.

● We tested the Italian landslide non-susceptibility map against independent landslide information (IFFI) and we obtained promising results.

● We extended the application of the non-susceptibility model to landmasses surrounding the Mediterranean Sea, and we successfully tested the synoptic subdivision using independent landslide information for three areas in Spain.


Conclusions

● Our work showed the importance of landslide information for the production of maps of non-susceptible landslide areas, and confirmed the importance of preparing accurate landslide inventory maps.

● We expect that our synoptic-scale zonation for Italy and for the landmasses surrounding the Mediterranean Sea can be used for insurance and re-insurance purposes, for large areas land planning, and in operational landslide warning systems.


Thank you for your attention

[email protected]

NHESSD Open Discussion:

I. Marchesini, F. Ardizzone, M. Alvioli, M. Rossi, and F. Guzzetti, (2014). Non-susceptible landslide areas in Italy and in the Mediterranean region. Nat. Hazards Earth Syst. Sci. Discuss., 2, 2813-2849, 2014

Technology

Non susceptibility slideshare