Landslide susceptibility assessment using the bivariate ... - Springer Link

Environ Earth Sci (2011) 63:397–406 DOI 10.1007/s12665-010-0724-y

ORIGINAL ARTICLE

Landslide susceptibility assessment using the bivariate statistical analysis and the index of entropy in the Sibiciu Basin (Romania) Mihaela Constantin • Martin Bednarik Marta C. Jurchescu • Marius Vlaicu

•

Received: 19 June 2009 / Accepted: 13 August 2010 / Published online: 5 September 2010 Ó Springer-Verlag 2010

Abstract The Sibiciu Basin is located in Romania between the Buza˘u Mountains and the Buzau Subcarpathians (Curvature Carpathians and Subcarpathians). The geology of the basin consists of Paleogene flysch deposits represented by an alternation of sandstones, marls, clays and schists and Neogene deposits represented by marls, clays and sands. The area is affected by different types of landslides (shallow, medium-deep and deep-seated failures). In Romania, in the last decades, direct and indirect methods have been applied for landslide susceptibility assessment. The most utilized before 2000 were based on qualitative approaches. This study evaluates the landslide susceptibility in the Sibiciu Basin using a bivariate statistical analysis and an index of entropy. A landslide inventory map was prepared, and a susceptibility estimate was assessed based on the following parameters which influence the landslide occurrence: slope angle, slope aspect, curvature, lithology and land use. The landslide susceptibility map was divided into five classes showing very low to very high landslide susceptibility areas. Keywords Landslide susceptibility Bivariate statistical analysis Index of entropy Sibiciu Basin Buza˘u Mountains Romania

M. Constantin (&) M. C. Jurchescu M. Vlaicu Institute of Geography, Romanian Academy, Str. D. Racovita 12, 023993 Bucharest 20, Romania e-mail: [email protected] M. Bednarik Department of Engineering Geology, Comenius University, Mlynska dolina G-127, 84215 Bratislava, Slovak Republic

Introduction Varnes (1984) defined landslides hazard as ‘‘the probability of occurrence of a potentially damaging phenomenon (landslide) within a given area and in a given period of time’’. Landslide susceptibility is defined as the spatial probability of landslide occurrence (Glade 2001; Sorriso-Valvo 2002; Paudits and Bednarik 2002; Moreiras 2005; van Westen 2004; Guzzetti et al. 1999, 2006). Different methods to prepare landslide susceptibility and hazard maps exploiting statistical methods and GIS tools were developed in the last years (Carrara et al. 1995; Glade 2001; Sorriso-Valvo 2002; Paudits and Bednarik 2002; Moreiras 2005; van Westen 2004; Guzzetti et al. 2006). Direct and indirect methods have been applied to prepare landslide susceptibility maps (Soeters and Van Westen 1996; Carrara et al. 1995; van Westen 2004). In Romania, before year 2000, direct and indirect methods (represented by the qualitative analysis of landslide occurrence conditions) have been used by geomorphologists (Ba˘lteanu 1983; Ba˘lteanu et al. 1994; Cioaca˘ et al. 1993; Constantin et al. 2005; Constantin 2006a, b, 2008; Ielenicz et al. 1999) and were based especially on the expert knowledge (Constantin 2008). This qualitative method delineates landslide susceptibility classes using different types of conventional signs and was used by many Romanian researchers dealing with geomorphologic risk evaluation over the period 1986–2000. After 2000, the development of GIS applications prompted also the usage of GIS tools in landslide susceptibility assessment. The approaches were based mainly on the analysis of landslide occurrence conditions. The thematic maps presenting lithology, slope aspect, slope angle, etc., were classified, each class receiving a score according

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to the landslide density. The landslide susceptibility map was prepared using the summation of the scores (Constantin 2006a, b, 2008). This paper presents a landslide susceptibility assessment in the Sibiciu Basin using a bivariate statistical analysis. Bivariate statistical analysis is based on the comparison between a landslide inventory map as a dependent variable and the single input parametric maps (lithology/landslides, slope angle/landslides, etc.). The approach used in the study area allows calculation of weights for each input variable and is based on the methodology proposed by Vlcko et al. (1980) where the weight value for each parameter is expressed as an entropy index.

Fig. 1 The location of the Sibiciu Basin

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Environ Earth Sci (2011) 63:397–406

The study area The Sibiciu Basin (Fig. 1) is located between the Buza˘u Mountains and the Buza˘u Subcarpathians (the Curvature Carpathians and Subcarpathians). The basin has an area of 47.11 km2. The elevation is higher in the northern part of the basin (Zboiu Peak, 1,114 m a. s. l.; Oii Peak, 1,037 m a. s. l) and decrease towards the Buzau Valley in the south. The drainage density has different values: in the Buza˘u Mountains values are typical for medium size fragmented mountains (5–6 km/km2) while in the Buza˘u Subcarpathians, values are of 3–4 km/km2, typical for the hilly area. Slopes with declivity of 12°–24° covers the higher area in the basin (43.2%) followed by slopes with declivity of 24°–36° (27%).


399

The lithology is represented by Paleogene, Neogene and Quaternary deposits. The Paleogene deposits out crop in the Buza˘u Mountains in two different facies: the Colt¸ i facies (Pg1?2 in age) and the Kliwa Sandstone Formation (lf-ch in age). The Colti deposits are represented by sandstones, clays and marls. The Kliwa Sandstone Formation is represented by sandstones interbedded by bituminous shales and clays. These deposits cover a large area of the basin (78%) and are responsible for the landslide occurrence in the basin. The Neogene formations cover an area of 21.4%, and consist of Helvetian, Badenian and Sarmatian deposits (marls, clays and sands) occurring in southeastern part of the basin. (the Buza˘u Subcarpathians). The Helvetian deposits were mapped using the geological map 1:200,000 (year 1968); nowadays, these deposits are considered to be Burdigalian (bd).The Quaternary (qh1– qh2) deposits (0.6%) are eluvial, slope and alluvial sediments represented by sands, clays and loess deposits. Landslides are frequent on Paleogene and Neogene deposits (especially on Sarmatian deposits represented by clays and sands). The land use is represented by forests (67.9%), pastures (23.8%), orchards (6.3%) and a small percentage (2%) of rivers, roads and settlements. The main landslide triggering factors are rainfall and snow-melting. The mean annual temperature is 9.6°C at Pa˘taˆrlagele (10 km South West from the Colt¸ i village). The largest amount of rainfall measured during one month was 282.2 mm as recorded on July 1975, whereas the largest amount of rainfall measured during 24 h was 177.8 mm as recorded on 02 July 1975. An examination of the available rainfall data recorded during 1961–1980 in the Curvature Carpathians and Subcarpathians revealed that precipitation in excess of 200–300 mm can occur during the summer months when the amount of rainfall can be 2–5 times higher than the monthly average quantities 300

Materials The bivariate statistical analysis required the preparation of landslide conditioning maps (lithology, slope angle, slope aspect, curvature and land use) and the landslide distribution map (Fig. 4). Based on field surveys a landslide inventory was prepared. Aerial photos at 1:5,000 scale were also analyzed. Maps of morphometric conditioning factors (slope angle, slope aspect, curvature) were derived from the digital elevation model. The geological information was obtained from maps at 1:200,000 scale, whereas the land use map was elaborated using aerial photos 1:5,000 scale (year 2005).

Mean

250

Thematic maps

Maximum Minimum

200

mm

(Bogdan and Niculescu 1999). Figure 2 shows the maximum, minimum and mean monthly rainfall values measured at Pa˘taˆrlagele during the period of 1961–1995. This information shows that rainfall values higher than 200 mm were registered in May and July and were three times higher than the monthly quantities. The freeze–thaw phenomenon was observed to occur within a depth of about 55 cm from the ground surface during the months of December through March according to the temperature readings taken by several thermometers installed at different depths into the soil (Bogdan and Mihai 1977). Among the mass movements (mainly rock falls and landslides) affecting the basin, landslides have an important role in the landscape modeling. The main types of landslides occurring in the Sibiciu Basin comprise shallow, medium-deep and deep-seated landslides (Fig. 3). Based on field observation (Balteanu 1983), the landslides were classified according to the depth and their volume. The shallow landslides have a depth of 1.5–2 m and a volume of 10–104 m3, the medium deep-seated have a depth of 2–10 m and a volume of 104–105 m3 while the deep-seated have a depth of more than 10 m and a volume higher than 105 m3 (Balteanu 1983; Ielenicz 1984).

Lithology 150

100

50

0 I

II

III

IV

V

VI

VII

VIII

IX

X

XI

XII

month

Fig. 2 The mean, maximum and minimum monthly precipitation at Pa˘taˆrlagele (1961–1995)

The geological information was obtained from the available map at 1:200,000 scale (maps at a larger scale are not available for the basin). Six geological classes were identified: Paleocene–Eocene (Pg1?2)-sandstones, clays and marls; Lattorfian–Chattian (lf-ch) sandstones, bituminous shales and clays; Helvetian-marls; Badenian-sandstones and marls; Sarmatian-clays and sands) and Quaternary (qh1–qh2) loess, sands and clays. The distribution of the lithological units is shown in Fig. 4a.

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was produced in ArcGIS and was checked for elevation errors. Slope angle The slope angle map derived from the DEM was reclassified in seven angle classes for the analysis. The slope map was classified according to regional field observations of the frequency of the slope mass movements (Balteanu 1983) (Fig. 4b). Slope aspect The slope aspect map was also obtained from the digital elevation model and eight classes of 45° were considered in our study (Fig. 4c). Curvature The curvature map was calculated as a second derivation of the DEM. Three classes—concave, flat, convex—were considered (Fig. 4d) for the analysis. Land use The map of land use was prepared using aerial photos interpretation considering the following classes: pastures, forests, orchards, rivers, roads and settlements (Fig. 4e). Landslide inventory map Spatial information about registered (mapped) landslides represents the most important input factor in the process of landslide susceptibility assessment. The landslide inventory parametric map presents a binary dependent (dichotomic) variable which is compared with all input parametric maps in the process of bivariate statistical analysis. The binary grid (raster) includes the values 0 and 1 only (True/False), where a value of 1 indicates the presence of a landslide in a cell, and the value 0 denotes its absence (Bednarik 2007). For bivariate analysis, the areas corresponding to the main scarps were distinguished as a polygon shape. In the study area we mapped 169 landslides (Fig. 4f).

Fig. 3 The main types of landslides in the Sibiciu Basin: a shallow, b medium-deep and c deep-seated landslides

Methods Bivariate statistical analysis

The following morphometric parameters were assessed: slope angle, slope aspect, curvature. The digital elevation model was provided by the National Geodetic Fund in form of a 10-m-density grid of elevation points. A digital elevation model (DEM) with a resolution of 10 m 9 10 m

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To evaluate landslide susceptibility, we used the bivariate analysis and the index of entropy (Brabb 1985; Carrara et al. 1991; van Westen 2004; Bednarik 2007). A 10 m 9 10 m grid was utilized.


401

Fig. 4 Thematic maps: a Geology, b slope angle, c slope aspect, d curvature, e land use, f landslide inventory

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402


The bivariate statistical analysis is based on the comparison, within a GIS environment, between the landslide distribution and the selected parametric maps (lithology/ landslides, slope angle/landslides, etc.). This approach allows calculation of the weight for each input variable. Herein, the weighting process is based on the methodology proposed by Vlcko et al. (1980). The weight value for each parameter taken separately is expressed as an entropy index.

Wj ¼ Ij pij :

ð6Þ

In Table 1, At represents the area of the category and Asd is the area of landslides within the given category. Based upon calculated probability density (pij), each input parametric map was secondarily reclassified (column recl_2) for final susceptibility map elaboration. Hj and Hjmax represent entropy values, Ij is the information coefficient and Wj represents the resultant weight value for the parameter as a whole.

Weighting process

Landslide susceptibility map

The weight parameter was obtained from the defined level of entropy representing the approximation to normal distribution of the probability. The entropy index indicates the extent of disorder in the environment. It also expresses which parameters in a natural environment are most relevant for the development of mass movements. The calculated weights of individual parameters form the Wi value in the landslide susceptibility assessment equation (Bednarik et al. 2009). The equations used to calculate the information coefficient Wj representing the weight value for the parameter as a whole are:

The final landslide susceptibility map was prepared by summation of weighted multiplications of the secondarily reclassified parametric maps. The equation used for the creation of the landslide susceptibility map has the following form: y ¼ landuse recl2 Wj of land use þ geol recl2 Wj of geology þ slope recl2 Wj of slope þ aspect recl2 Wj of aspect þ curvat recl2 Wj of curvatureÞ ð7Þ

pij ¼

Asd At

ð1Þ

pij pij ¼ Psj

j¼1

Hj and Hj Hj ¼

sj X

pij

ð2Þ

:

max

represent entropy values (Eqs. 3, 4).

ðpij Þ log2 pij ;

j ¼ 1; . . .; n

ð3Þ

sj number of classes

ð4Þ

i¼1

Hjmax ¼ log2 sj ;

Ij is the information coefficient (Eq. 5) and Wj represents the resultant weight value for the parameter as a whole (Eq. 6). Ij ¼

Hj max Hj ; Hj max

I ¼ ð0; 1Þ;

j ¼ 1; . . .; n

ð5Þ

where y is value of landslide susceptibility in the final map; /landuse_recl2/, value in a particular cell for the secondarily reclassified parametric map of actual landuse; / geol_recl2/, value in a particular cell for the secondarily reclassified parametric map of lithological units; /slope_recl2/, value in a particular cell for the secondarily reclassified parametric map of slope angles; /aspect_recl2/, value in a particular cell for the secondarily reclassified parametric map of aspect; /curvat_recla2/, value in a particular cell for the secondarily reclassified parametric map of curvature. The result of this summation is a continuous interval of values from 0.0090 to 0.0957, and these represent the various levels of the landslide susceptibility. Since these intervals should be divided into three or five classes, here, a natural breaks classification method was used to divide the interval into five classes based on the Eq. 8 below:

Table 1 The result of weight calculation applied for geology Parameter

Class

Lithology

1 (Pg1?2)

123

Asdðkm2 Þ

Pij

(Pij)

Hj

Hj max

Ij

Wj

2.1969

2.5850

0.1501

0.0068

recl_2

6.9903

0.6989

0.1000

0.3704

29.7266

1.3963

0.0470

0.1740

4

3 (he)

4.5669

0.1463

0.0320

0.1187

2

4 (qh1–qh2)

0.2858

0.0000

0.0000

0.0000

0

5 (sm)

4.3496

0.2212

0.0509

0.1884

5

6 (bn)

1.1898

0.0477

0.0401

0.1485

3

47.1090

2.5104

0.2699

1.0000

2 (lf-ch)

Summation

Atðkm2 Þ

6


SSDi...j ¼

j X

ðA½n medianÞ2

403

ð8Þ

n¼i

where SSD is the sum of the squared differences; A, data set (in ascending order). Natural breaks method follows the Gauss normal distributions and it was used for classifying the results.

Results The result of the bivariate analysis based on the total number of mapped landslides shows that each class of the conditioning factors is characterized by a certain landslide occurence density. According to the pij values (affected area/total area of each class), all thematic maps were reclassified. Fig. 5 shows the (pij) values for each class of conditioning factors. An analysis of the landslide distribution in relation to the geological formation types based on the reclassification process associated with the bivariate analysis indicates that the Paleocene–Eocene facies of Colti exhibit the highest score, followed by the Sarmatian alternation of clays and sands (Sm), and the lf-ch deposits represented by Kliwa facies (sandstones, schists and clays) (Table 1; Fig. 5a). In terms of slope angle and slope aspect, most of the existing landslides are distributed between the 12°–24° and 6°–12° slope classes (Fig. 5b), and between the east and

south facing slopes (Fig. 5c), respectively. The effect of slope curvature on landslide occurrence appears to be insignificant, as indicated by the almost equally distributed scores in Fig. 5d. With regard to land use categories, pastures and orchards seem to have the highest impact on the landslide distribution in the area (Fig. 5e). Overall, the most important factors controlling the landslide distribution derived from the weighting process described by Eq. 7 are land use and geology. The final landslide susceptibility map has been developed using the following equation: y ¼ landuse recl2 0:0073 þ geol recl2 0:0068 þ slope recl2 0:0010 þ aspect recl2 0:0005 þ curvat recl2 0:0001:

ð9Þ

This map is shown in Fig. 6 and comprises the following five landslide susceptibility classes: 1. 2. 3. 4. 5.

very low (0.0090–0.0375), low (0.0375–0.0569), medium (0.0569–0.0709), high (0.07089–0.0838), very high (0.0838–0.0957).

The landslides falling under the very high susceptibility class cover 5.4% of the study area, and are located in the N–

Fig. 5 Distribution of landslides within each conditioning factor according to the density of probability (pij): a Lithology versus landslides, b slope angle versus landslides, c slope aspect versus landslides, d curvature versus landslides, e land use versus landslides

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NW and S–SE corners of the Sibiciu Basin. The high landslide susceptibility class covers 30.45% of the area throughout the northwestern, central and southeastern parts of the basin. The low and very low landslide susceptiblity classes cover only small areas in the basin, whereas the moderate landslide susceptibility class covers 58.89% of the study area (Fig. 6). The model performance (Clerici et al. 2009) was evaluated by comparing the landslide susceptibility map with the landslide inventory map. The rate of success has been estimated by comparing the landslide area corresponding to the high and very high susceptibility classes with the total

Fig. 6 The landslide susceptibility map of the Sibiciu Basin

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area of the mapped landslides. A final success ratio of 56% was obtained, which is not a very good outcome. A potential reason for this outcome may be that no distinction was made between shallow and deep-seated landslides in the present analysis (i.e., they were considered together in our study). Additionally, the utilized lithological map had a scale of 1:200,000, thus, offering a rather scarce density of information which was difficult to be compared with the other data available at a high resolution. In a further step, the validation of the model was performed. The landslide database was split randomly into two


datasets based on a 7:3 ratio. The first data set (i.e., 70%, training set) was employed in the model construction, whereas the other data set (i.e., 30%, validation set) was used for the model testing (Clerici et al. 2006). New reclassifications of each thematic layer were made based on the not always new relationships established between the conditioning factors and the 70% landslide distribution dataset. The new scores were different only in terms of aspect and curvature. The greatest score is still associated with the east and the southeast facing slopes. Additionally, the importance of concave and convex configured slopes is switched. Considering the weight of each conditioning factor resulting from the 70% model construction, it can be seen from Eq. 10 that the order of the parameters was not changed. y ¼ landuse recl2 0:0062 þ geol recl2 0:0051 þ slope recl2 0:0009 þ aspect recl2 0:0004 þ curvat recl2 0:00001: ð10Þ Only small differences between the two models can be noticed in the percentage distribution of the susceptibility classes within the Sibiciu Basin (a maximum difference of 0.6–0.7% in the case of moderate and high classes). Discussions The bivariate and multivariate statistical analyses are mostly used for this kind of works with a high rate of reliability. A bivariate statistical analysis has been adopted for the landslide susceptibility assessment processing. From among of the landslide susceptibility zonation methods, the statistical analysis supported by GIS environment seems to be suitable for medium and large-scale (1:2,000–1:25,000) approaches (Clerici et al. 2009). The bivariate statistical analysis and the index of entropy utilized herein offer the possibility to compare the landslide distribution map with each conditioning factor and to assign weights. The methodology was applied recently for several case studies in Slovakia (Bednarik 2007; Bednarik et al. 2009) and the rate of success was high. A number of 47 rock slides and 268 earth slides were mapped out and were compared with geology, DEM, aspect, land use and slope. The findings have revealed that the most important conditioning factor was geology, the rate of success being 70%. In our case study presented herein, 169 landslides were taken into account and the result was that land use and geology are the most important factors which explain better the landslide occurrence. The rate of success was 58% which was not a very good result comparing with the case study from Slovakia where all input data were at similar

405

resolution (1:10,000). The main disadvantage for our case study in Romania is the absence of data with similar resolution in digital form, which account for the unsatisfactory rate of success mentioned above.

Conclusions The Sibiciu Basin, located at the contact between the Buzau Mountains (built on Paleogene flysch deposits represented mainly by alternations of sandstones and clays) and the Buzau Subcarpathians (built by Neogene deposits consisting of clays, marls and sands), has a large incidence of landslides. Based on the consideration that in general future landslide processes in the area will occur within old landslide deposits or will be reactivations of the actual landslides, a series of direct and indirect methods involving the landslide inventory map, landslide conditioning factors and statistical tools available in a GIS environment were utilized to develop a methodology for assessing the spatial probability of landslides occurrence throughout the Sibiciu Basin. In order to assess the landslide susceptibility, we used bivariate statistical analysis and the index of entropy with definition of the weight of the parameters. The landslide inventory map was compared with all thematic maps, including lithology/landslides, slope angle/landslides, slope aspect/landslides, curvature/landslides, land use/landslides). According to the results of the bivariate statistical analysis, the highest probability of landslide occurrence is associated with Paleogene deposits (Pg1?2) comprising the Colti facies (sandstones, clays and marls) and slope angles of 12°–24°. Additionally, slope aspects falling in the east, south and southwest quadrants show the highest probability of landslide occurrence. Slope curvature is not a major factor being characterized by an equal probability of occurrence for each analyzed landslide susceptibility class. A comparison of the landslide inventory map with the land use map, revealed that the highest probability of landslide occurrence is in areas covered by pastures and orchards. The final map shows an area of very high landslide susceptibility of 2.5 km2 (i.e., 5.4% of the total study area), an area of high landslide susceptibility of 14.3 km2 (i.e., 30.45% of the total study area), and an area of moderate landslide susceptibility of 27.7 km2 (i.e., 58.9% of the total study area). The low susceptibility class covers an area of 2.3 km2 (i.e., 4.86% of the total study area), whereas the very low susceptibility class has an area of 0.19 km2 (i.e., 0.4% of the total study area). The model validation was conducted by developing a new map using 70% of randomly selected landslides from the first data set, and retaining the rest of 30% as the

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validation set. By comparing the two models, only very small differences between the areas of landslide susceptibility classes were observed. The analysis based on the entropy index characterizing the level of chaos in the environment revealed that the most unfavorable factors controlling the slope instability phenomena throughout the study area are land use (pastures) and geology (Paleocene–Eocene deposits). The methodology applied herein can also be used in landslide susceptibility assessment throughout other basins with similar landslide occurence conditions. The assessment based on the proposed methodology can be signficantly improved if different types of landslides (e.g., shallow, deep-seated) are considered and input data with similar resolution are used. The landslide susceptibility maps obtained using this method may represent a useful tool to the authorities involved with territorial and land use planning. Acknowledgments The present study was supported by the Ministry of Education and Research through the grant in aid PNIIIDEI_367 (2007–2010) funded through The National University Research Council of Romania (CNCSIS). The kind cooperation with Department of Engineering Geology, Comenius University, Bratislava, is fully appreciated.

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