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Caminos, Canales y Puertos. Centro Politécnico de Fuentenueva, Universidad de Granada, 18071,. Granada (E-mails: [email protected], [email protected], ...
Natural Hazards 30: 297–308, 2003. © 2003 Kluwer Academic Publishers. Printed in the Netherlands.

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Methodology for Landslide Susceptibility Mapping by Means of a GIS. Application to the Contraviesa Area (Granada, Spain) T. FERNÁNDEZ1, C. IRIGARAY2 , R. EL HAMDOUNI2 and J. CHACÓN2 1 Departamento de Ingeniería Cartográfica, Geodésica y Fotogrametría. Escuela Politécnica

Superior de Jaén. Universidad de Jaén. Virgen de la Cabeza, 2, 23071 Jain, Spain (E-mail: [email protected]); 2 Departamento de Ingeniería Civil. Escuela Técnica Superior de Ingenieros de Caminos, Canales y Puertos. Centro Politécnico de Fuentenueva, Universidad de Granada, 18071, Granada (E-mails: [email protected], [email protected], [email protected]) (Received 23 April 2001; accepted 12 November 2001) Abstract. This article presents a method to map landslide susceptibility in rock massifs using Geographical Information Systems (GIS). The method is based on making an inventory of rupture zones of different types of slope movements and then analysing the bivariate correlation of these with the factors that determine instability. After determining the factors that present the highest correlation with each type of movement, a matrix is created to combine these factors and to determine the percentage of the rupture zone in each combination, which provides an expression of the susceptibility of the terrain. The map thus obtained is divided into susceptibility classes. The susceptibility maps (made in 1995) for each type of movement are first calibrated with the inventory of the movements from which they are derived (previous to 1995), and subsequently validated by another inventory elaborated after the susceptibility maps (in 1997). In both cases, significant correlation coefficients were obtained (the Goodman–Kruskal coefficients were over 0.8 and sometimes exceeded 0.9). The relative error (degree of accumulated fit for very low to low susceptibility classes) was always less than 5%, while the relative success rate was always above 50%. These results illustrate the adequacy of the method and of the maps obtained. Key words: slope movements, susceptibility maps, GIS, validation, determinant factors, rock massifs.

1. Introduction Of all geological risks, landslides are among those that cause most damage, producing thousands of deaths every year and material losses of billions of dollars (Brabb, 1991). Among the measures that have been taken to reduce these losses are the creation of maps of susceptibility, danger and risk (Brabb, 1984; Ayala, 1987; Corominas, 1987; Chacón et al., 1992, 1994, 1996). These maps are elaborated by means of deterministic and non-deterministic (probabilistic) models. The probabilistic ones are more frequently used, and so a large number of methodologies have been developed (Rengers et al., 1998), based on the inventory of landslides, geo-

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Figure 1. Location of the study area.

morphological analysis, qualitative and statistical bivariate analysis (Brabb et al., 1972; Degraff and Romersburg, 1980; Jade and Sarkar, 1993; Chung and Fabbri, 1993; Irigaray, 1995) and multivariate analysis (Carrara, 1983; Carrara et al., 1991; Baeza, 1994; Chung et al., 1995). This article presents a bivariate analysis methodology to obtain maps of landslide susceptibility (spatial prediction) developed on the SPANS Geographic Information System (GIS) (Intera-Tydac, 1993). The methodology is easy to apply and implement completely on a GIS; any type of variable (qualitative or quantitative) can be used for the analysis; moreover, it produced the best results in the calibration and validation of the different methods applied in the study zone (Contraviesa area) and at the work scale (1 : 25,000) (Fernández et al., 2000; Fernández, 2001). The methodology has been also calibrated for other sectors of the Betic Cordillera and for different materials, including both rock massifs of metamorphic origin (Fernández et al., 1994, 1996, 1997, 2000; El Hamdouni et al., 1996), and soils (Irigaray, 1995; Irigaray et al., 1997, 1999a).

2. Location of the Study Area The study zone covers an area of 94 km2 and is located on the northern slopes of the Contraviesa and Lújar mountains in the province of Granada (Spain), bounded to the north by the Alpujarra valley and to the south by the Mediterranean coast (Figure 1). From a geological standpoint, this area is part of the Alpujárride Complex within the inner zone of the Betic Cordillera. This complex forms a series of units superimposed by thrusting and low-angled normal faults. The outcropping materials are Paleozoic dark schists and quartzites, Permotriassic phyllites and quartzites, and Triassic marbles and dolostones (Aldaya et al., 1979).

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3. Methodology The methodology employed to map landslide susceptibility was as follows (Chacón et al., 1992, 1994, 1996): – Make the inventory and data base of slope movements – Analyse the factors that determine susceptibility – Create the susceptibility maps Finally, the maps were calibrated and validated (Fernández et al., 1997, 2000; Irigaray et al., 1997, 1999a). 3.1. INVENTORY OF LANDSLIDES This was carried out by means of interpretation of aerial photography (corresponding to a flight made by the Regional Government of Andalucía in 1992) and ground surveys, during 1993–1994, before the heavy rainfall of 1996–1997. Various types of slope movements were identified, according to the mechanism of movement (falls, slides, flow), size and materials involved. These were then grouped into a series of basic types (Varnes, 1978; Corominas and García-Yague, 1997): – Rock and block falls, caused by the detachment of rock fragments from cliff faces and subsequent movement through the air, by collision, or rolling. – Debris flows: the rapid movement of detritic materials with predominantly large particles, and usually channelled along gullies or ravines. We distinguished between those comprising carbonate materials and those comprising metapelites with a larger proportion of fine materials. – Collapses. Soil or rock movements with a fundamentally vertical trajectory through the rupture surface, on steep slopes, both natural and artificial, after undermining by a river or disintegration of materials from the base. – Rock slides maintaining a certain degree of coherence over a plane or gently curved surface (translational) or spoon-shaped (rotational) surface. – Complex movements, with rock falls at the head of the movement due to the presence of more fragile materials (carbonate rocks). The field inventory derived from aerial photography was then put on the 1 : 25,000 National Topographic Map. For the purposes of our methodology, we distinguished between whole movements and rupture zones (Chacón et al., 1992, 1994, 1996; Irigaray, 1995). The two inventories were digitised and entered into a Geographical Information System (GIS). The first one was used to create the data base of parameters and characteristics of movements, while the second one was required for all the subsequent analysis, because only the rupture conditions and not the deposit conditions were statistically analysed for determining the susceptibility. A similar inventory was made of landslides that occurred or were reactivated after the winter rains of 1996–1997. During this period, some rainfall stations recorded historical maximum monthly values and considerable instability was produced in the rock slopes in the area (Irigaray et al., 1999b). In this case, too, rupture

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Figure 2. Inventory of slope movements.

Figure 3. Rockslide susceptibility maps by the matrix method.

zones were distinguished from slope movements for use in subsequent validation of the susceptibility maps. Figure 2 shows the inventory of slope movements in the study area, while Table I provides the results of the area analysis of the rupture zones, both before and after 1997.

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Table I. Area occupied (km2 ) by rupture zones of slope movements (all ruptures 1997 includes natural and human-related ruptures produced in this year) Slope movements

Previous ruptures

All ruptures 1997

Natural ruptures 1997

Rock falls Debris flows in carbonate rocks Debris flows in metapellite rocks Collapses Rockslides Complex slides

0.2390 1.3896 2.0724 0.9706 7.0257 2.7431

0.0072 0.1503 0.7596 0.3040 1.0047 0.2119

0.0047 0.1503 0.7596 0.2500 0.6674 0.0826

3.2. ANALYSIS OF DETERMINANT FACTORS This analysis consists of creating crossed tables (contingency tables) between the different types of movements and the determinant factors involved. On the basis of these tables, several correlation coefficients were calculated. Factors were considered determinant when the Goodman–Kruskal coefficient (Goodman and Kruskal, 1954) values were higher than 0.5. The following factors were analysed (Fernández et al., 1996, 2000; Fernández, 2001): – Those derived from the digital elevation model (DEM): altitudes, slopes, aspect, hillshading, slope curvature, slope roughness, slope area (km2 ) and qualitative classification of landforms. The DEM was obtained by digitisation of the contour lines and altitude points taken from the corresponding sheets of the National Topographic Map at scale 1 : 25,000, and by subsequent linear interpolation. From the DEM, different models were obtained, by means of more or less complex GIS procedures. – Thematic maps: geology, soils, vegetation, spatial distribution of mean and 24hour maximum precipitations, drainage basins, proximity to river channels of different level, drainage density. These were entered into the system by digitising polygonal, linear and point data. These were then rasterised by means of polygon-raster transformation, buffers, corridors or Voronoi interpolation. The pixel size was about 6 m. – Factors related to discontinuities (faults, metamorphic foliation and jointing); geometrical potential instability of plane, wedge and toppling failures, proximity to regional fractures and fracture density. These factors were entered into the system as linear or point data and subsequently modified within the GIS (buffers, corridors and Voronoi interpolation) (Burrough, 1986). The factors defined as determinant for each type of movement are listed in Table II.

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Table II. Factors selected for each type of movement Slope movements

Determinant factors

Rock falls

Slope roughness, slope wideness, lithological units, fracture density, basins Slope-angle, slope curvature and slope roughness, lithological units, soils, fracture density, basins Landforms, lithological units, soils, 2nd and 3rd order river channels Slope-angle, slope curvature and slope roughness, lithological units, soils, 4th order river channels, fracture density Slope curvature, lithological units, geological boundaries, soils, 4th and 5th order river channels, drainage density Slope curvature and slope area, geological boundaries, soils, basins, 7th order river channels

Debris flows in carbonate rocks Debris flows in metapellite rocks Collapses Rockslides Complex slides

3.3. SUSCEPTIBILITY MAPS Susceptibility maps were obtained by means of the matrix method, which consists of constructing two matrixes in which the following data are recorded (Degraff and Romersburg, 1980; Irigaray, 1995): – The area of each of the combinations of classes of factors that determine each type of movement. – The area of each of the combinations occupied by rupture zones for the different types of movements. A third matrix was established from these data, the matrix of susceptibility, in which we recorded the percentage of each of the combinations occupied by rupture zones for each type of movement. The higher the percentage, the more susceptible is the corresponding combination of factors to landslide phenomena. The maps were drawn to include a classification divided into five categories (Irigaray, 1995): – 0–1 % Very low susceptibility – 1–5 % Low susceptibility – 5–10 % Moderate susceptibility – 10–30 % High susceptibility – >30 % Very high susceptibility Figure 3 shows susceptibility map only for rockslides, and Figure 4 gives the results of the analysis of the susceptibility maps of all types of movements. 4. Calibration and Validation of Results First, an internal calibration of the susceptibility maps was carried out through the cross correlation between these maps and the previous inventory of rupture

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Figure 4. Area analysis of the susceptibility maps.

zones. In addition to this control, after the susceptibility maps were elaborated the maps were validated by means of an inventory corresponding to the period of heavy rainfall that took place in 1997 (Irigaray et al., 1997, 1999a; Fernández et al., 1997, 2000; Fernández, 2001). In each case, two quality control tests were applied to the mapping method: the determination of correlation coefficients and the determination of the degree of fit (Baeza, 1994; Irigaray, 1995). The coefficients show whether there is a significant correlation between the rupture zones of movements and the susceptibility maps. As above, if the G-K coefficient was higher than 0.5 the correlation was considered significant. The degree of fit was calculated according to the following expression: zi /si D.F. =  zi /si where zi is the area occupied by the rupture zones in the i class of susceptibility and Si is the area of the i class of susceptibility. The smaller the degree of fit in the low and very low susceptibility classes (relative error), and the higher it is in the high to very high susceptibility classes (relative success rate), the higher the quality of the susceptibility map. Finally, the percentages of the rupture zones that lay within each susceptibility class, are determined. This enabled us to estimate the absolute errors and success rates (percentage of ruptures in the low to very low and high to very high susceptibility classes, respectively). Table III presents the correlation coefficients obtained by crossing the susceptibility maps and the maps of previous and recent rupture zones of the different types of movements. Figure 5 illustrates the degrees of fit of the susceptibility classes and Figure 6 the distribution of rupture zones within the susceptibility maps. The following observations may be drawn from the above table and figure (Fernández, 2001): – The Goodman–Kruskal coefficients are always above 0.8, both for previous and recent ruptures (after 1997). Thus, the relations found are significant, al-

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Table III. Correlation coefficients (Goodman–Kruskal) between the susceptibility maps and rupture zones Slope movements

Previous ruptures

All ruptures 1997

Natural ruptures 1997

Rock falls Debris flows in carbonate rocks Debris flows in metapellite rocks Collapses Rockslides Complex slides

0.981 1.983 0.814 0.968 0.884 0.976

0.940 0.972 0.825 0.900 0.821 0.941

0.995 0.972 0.825 0.955 0.824 0.979

Figure 5. Degree of fit of susceptibility classes.

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Figure 6. Distribution of movements in relation to the susceptibility classes.

though the coefficients are slightly lower for total recent rupture zones, which include artificial movements. – The relative error rates were never above 5%, neither for previous nor for recent ruptures. In some cases, the relative errors were lower for recent ruptures than for previous ones, especially if we just considered those of natural origin. – In the case of the absolute errors, in general the error rates for total recent ruptures were higher than those for natural recent ruptures and for previous ruptures. It should be taken into account that the total recent ruptures include a human factor (normally related to cuttings made in road construction) and that the susceptibility maps are designed to model conditions of natural instability. Recent ruptures of natural origin present absolute error rates that are very similar to those of previous ruptures, and even lower in some cases. – Nevertheless, in the case of rock slides, which are the movements of greatest individual dimensions, we found the error rates to be very similar in every case, including total recent ruptures and so the human related ruptures. We deduce, therefore, that these large-scale movements, require prior conditions

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of instability, irrespective of human action. In smaller ruptures a small change in stability conditions may produce the movement, even in zones of low susceptibility. – In any case, rarely was an absolute error rate of more than 10% recorded (only in recent total rock falls and collapses). – Similar comments apply to absolute success rates, as these are highest when the error rate is lowest, and vice versa. In general, the above results are indicative of the high quality of the maps obtained, for all types of movements analysed in the study area. 5. Conclusion The controls performed on the susceptibility maps obtained by the matrix method show that both the method and the maps themselves are of generally high quality for all types of movements, and therefore can be said to provide a correct model of the natural susceptibility of the terrain to landslides. The Goodman–Kruskal correlation coefficients were always lower than 0.8. The degree of fit showed that the relative error rate was always below 5%, while the absolute error rate only exceeded 10% in a few cases of small-scale movements that were artificial in origin. Large-scale movements (landslides) are predicted even in the case of human involvement. The entire methodology applied is perfectly suitable for development within a Geographic Information System. The method is a simple one, and it can incorporate various types of variables or factors, even qualitative ones. The quality of the final maps obtained, apart from the qualities of the method applied, will depend on the inventory of slope movements and on the determinant factors incorporated (derived from DEM and thematic maps). Acknowledgements This study was financed by the CICYT project “Landslide susceptibility mapping by Geographic Information Systems”. References Aldaya, F. Martínez-García, E., Avidad, J., García-Dueñas, V., Navarro-Vila, F., Gallegos, J., Díaz de Federico, A., and Puga, E.: 1979, Mapa Geológico de España 1 : 50.000 (2a Serie), hoja 1042 (Lanjarón), IGME, Madrid, Spain, 65 pp. Ayala, F. J.: 1987, Introducción a los riesgos geológicos, in F. J. Ayala (ed.), Riesgos Geológicos, IGME, Serie Geología Ambiental, Madrid, Spain, pp. 3–19. Baeza, C.: 1994, Evaluación de las condiciones de rotura y la movilidad de los deslizamientos superficiales mediante el uso de técnicas de análisis multivariante, Tesis Univ. Pol. Catalunya. Brabb, E. E.: 1984, Innovative approaches to landslide hazard and risk mapping, 4th Int. Symposium on Landslides, Vol. 1, Toronto, pp. 307–324. Brabb, E. E.: 1991, The World Landslide Problem, Episodes 14(1), 52–61.

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