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[1.] Analyse:Hja/Fragment 005 04 - Diskussion
Bearbeitet: 13. January 2015, 22:47 141.76.115.208
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Chacon et al 2006, Fragment, Hja, SMWFragment, Schutzlevel, Verschleierung, ZuSichten

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Untersuchte Arbeit:
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1.2 Literature review

Late 1960s, a number of maps were prepared in the United States showing slope stability conditions (Blanc and Cleveland, 1968), incidence of landslides expressed by relative amount of landslide deposits (Radbruch-Hall, 1970; Radbruch-Hall and Crowther, 1973), landslide deposits (Brabb and Pampeyan, 1972) and qualitative landslide susceptibility (Dobrovoiny, 1971; Scott, 1972; Davis, 1974a, j; Pomeroy, 1974, etc.). The qualitative susceptibility assessment was firstly based on field reconnaissance of geology based recognition of instability factors around the observed landslides in order to make susceptibility zonations (see section 3.1 for the definitions of susceptibility and hazard zonation) in the area with the landslide inventory being a basic step. The method drew on the subjective expertise of each author.

Similar qualitative landslide incidence maps have been made in different countries using the terms zones exposed to landslide risks or slope instability (ZERMOS program by French Laboratoire de Ponts et Chausse´s, Paris: Antoine, 1977; Humbert, 1977; Landry, 1979; Meneroud and Calvino, 1976; Meneroud 1978, etc.; Mahr and Malgot, 1978 in Slovakia; Kienholtz, 1978 in Switzerland, Rodrıguez Ortiz et al., 1978; Hinojosa and Leon, 1978 in Spain, etc.). An example of the main qualitative susceptibility maps published by the USGS (Radbruch, 1970; Scott, 1972; Davies, 1974; Pomeroy, 1974, etc.) is a map showing landslide areas susceptible to landsliding in the Morrison Quadrangle, Jefferson County, Colorado, Scott (1972) which distinguished four zones.

Semi-quantitative susceptibility hazard or slope instability maps based on analysis of slope angles, lithology and relative amounts of landslip material have been published (Blanc and Cleveland, 1968; Bowman, 1972; Radbruch and Crowther, 1973; Dobrovolny and Schmoll, 1974; Nilsen and Brabb, 1977; Nilsen and Wrigth, 1979). The landslide map of California was made by Radbruch and Crowther (1973) into 1: 1,000,000 scales. Here, the rating were related to slope angle below 50 and rainfall of less than 10 inch (25.4 cm) with very little evidence of landsliding as unit 1 and at the opposite extreme, to areas heavily covered by large amount of landslides as unit 6. Nilsen and Wrigth (1979) in a 1:125,000 scale landslide map of the San Francisco Bay region distinguished slope angle units of < 50, 5–150 and > 150, and lithological groups of no landslide deposits, susceptible bedrock, susceptible superficial deposits and landslide deposits. Combining these two criteria of slope angle and lithological groups they classified the region into six zones: (1) stable, (2) generally stable, (3) moderately stable, (4) moderately unstable and (5) unstable. The areas subject to liquefaction was defined as Zone 1A. These maps, at different scales, were oriented toward the classification of land units based on the evidence of landsliding. However, as there was no attempt at temporal forecasting, from the point of view of Varnes (1978) they would be considered closer to landslide inventories than to landslide hazard maps.

Stevenson (1977), proposed hazard and risk maps which was based on numerically rated or weighted slope and geological factors with geotechnical data. Other significant contributions were linear risk maps of roads (Meneroud, 1978) and geotechnical stability maps which rate soil and rock mechanics parameters such as cohesion, friction angle or rock massif discontinuities (Vecchia, 1978).


Blank, R. P., Clevelend, G. B. (1968): Natural slope stability as related to geology, San Clemente Area, Orange and San Diego Countries [sic], California. California Division of Mines and Geology, Special report 98, pp 19.

Brabb, E. E., Pampeyan, E. H. (1972): Preliminary map of landslide deposits in San Mateo Country [sic], California. US Geological Survey Miscellaneous Field Studies, Map MF-360, scale 1:62,500.

Davies, W. E. (1974a): Landslide susceptibility map of part of the Bridgeville 7-1/2 minutes [sic] quadrangle. Allegheny Country [sic] and vicinity, Pennsylvania. US Geological Survey Open-File Report 74-274, scale 1:24,000, pp 8.

Davies, W. E. (1974b): Landslide susceptibility map of part of the Canonsburg 7-1/2 minutes [sic] quadrangle. Allegheny Country [sic] and vicinity, Pennsylvania. US Geological Survey Open-File Report 74-276, scale 1:24,000, pp 8.

Davies, W. E. (1974c): Landslide susceptibility map of part of the Donora 7-1/2 minutes [sic] quadrangle. Allegheny Country [sic] and vicinity, Pennsylvania. US Geological Survey Open-File Report 74-277, scale 1:24,000, pp 8.

Davies, W. E. (1974d): Landslide susceptibility map of part of the Freeport 7-1/2 minutes [sic] quadrangle. Allegheny Country [sic] and vicinity, Pennsylvania. US Geological Survey Open-File Report 74-278, scale 1:24,000, pp 8.

Davies, W. E. (1974e): Landslide susceptibility map of part of the McKees-port 7-1/2 minutes [sic] quadrangle. Allegheny Country [sic] and vicinity, Pennsylvania. US Geological Survey Open-File Report 74-280, scale 1:24,000, pp 8.

Davies, W. E. (1974f): landslide susceptibility map of part of the Monongahela 7-1/2 minutes [sic] quadrangle. Allegheny Country [sic] and vicinity, Pennsylvania. US Geological Survey Open-File Report 74-281, scale 1:24,000, pp 8.

Davies, W. E. (1974g): Landslide susceptibility map of part of the New Kensington East 7-1/2 minutes [sic] quadrangle. Allegheny Country [sic] and vicinity, Pennsylvania. US Geological Survey Open-File Report 74-283, scale 1:24,000, pp 8.

Davies, W. E. (1974h): landslide susceptibility map of part of the New Kensington West 7-1/2 minutes [sic] quadrangle. Allegheny Country [sic] and vicinity, Pennsylvania. US Geological Survey Open-File Report 74-284, scale 1:24,000, pp 8.

Davies, W. E. (1974i): Landslide susceptibility map of part of the Braddock 7-1/2 minutes [sic] quadrangle. Allegheny Country [sic] and vicinity, Pennsylvania. US Geological Survey Open-File Report 74-273, scale 1:24,000, pp 8.

Davies, W. E. (1974j): landslide susceptibility map of part of the Curtisville 7-1/2 minutes [sic] quadrangle. Allegheny Country [sic] and vicinity, Pennsylvania. US Geological Survey Open-File Report 74-276, scale 1:24,000, pp 8.

Dobrovolny, E. (1971): Landslide susceptibility in and near anchorage as interpreted from topographic and geologic maps in the great Alaska earthquake of 1964-Geology volume. Publication 1603. U.S. Geological Survey Open_Field Report 86-329, National Research Council, committee on the Alaska earthquake, National Academy of Sciences, USA, pp 735-745.

Meneroud, J.P. (1978): Cartographie des risques dans les Alps-Maritimes (France). In: Proceedings of the IIIrd I.A.E.G. Congress, II, Chap. 46, pp 98–107.

Nilsen, T. H., Wright, R. H. (1979): Relative slope stability and landuse planning in the San Francisco Bay region, California. US Geological Survey Professional Paper 944, US Department of Interior, Washington, pp 103.

Radbruch, D. H. (1970): Map of relative amount of landslides in California. US Geological Survey open file report 70-1485, pp 36, map scale 1:500,000. US Geological Survey Open-File Report 85-585.

Radbruch, D. H., Crowther, K. C. (1973): Map showing areas of estimated relative susceptibility to landsliding in California. US Geological Survey Miscellaneous Geologic Investigations Map I-747, scale 1:1,000,000.

Scott, G. R. (1972): Map showing landslides and areas susceptible to landslides in the Morrison Quadrangle, Jefferson Country, Colorado. US Geological Survey. Map I-790-B. USA.

Stevenson, P. C. (1977): An empirical method for the evaluation of relative landslide risk. Int. Ass. Eng. Geol. Bull., 16:69–72.

Varnes, D. J. (1978): Slope movement types and processes. In Landslide Analysis and Control, edited by M. Clark, pp. 11-33, Special Report 176, Transportation Research Board, National Academy of Sciences, National Research Council Washington, DC.

Vecchia, O. (1978): A simple terrain index for the stability of hillsides or scarps. In: Geddes J. D. (ed) Large ground movements and structures. Wiley, New York Toronto, pp 449-461.

[Page 346]

From the late 1960s, a number of maps were made in the United States showing slope stability conditions (Blanc and Cleveland 1968), the incidence of landslides expressed by relative amount of landslide deposits (Radbruch-Hall 1970; Radbruch-Hall and Crowther 1973), landslide deposits (Brabb and Pampeyan 1972) and qualitative landslide susceptibility (Dobrovolny 1971; Scott 1972; Davies 1974a, j; Pomeroy 1974, etc.).

[Page 347]

The susceptibility assessment was firstly based on field reconnaissance of instability factors producing a given susceptibility level for a particular area or field zone, with the landslides inventory being a basic step towards analysis and mapping. The method drew on the subjective expertise of each author. The basic analysis was a field geology-based recognition of instability factors around the observed landslides in order to make a zonation of the presence of these factors within the susceptibility zones. Indications about levels of landslide activity were also included (Scott 1972). Similar qualitative landslide incidence maps have been made in different countries using the terms zones exposed to landslide risks or slope instability, etc. (ZERMOS program by French Laboratoire de Ponts et Chausse´s, Paris: Antoine 1977; Humbert 1977; Landry 1979; Méneroud and Calvino 1976; Méneroud 1978, etc.; Mahr and Malgot 1978 in Slovakia; Kienholtz 1978 in Switzerland, Rodríguez Ortiz et al. 1978; Hinojosa and Leon 1978 in Spain, etc.).

An example of the main qualitative susceptibility maps published by the USGS (Radbruch 1970; Scott 1972; Davies 1974; Pomeroy 1974, etc.) is a map showing landslide areas susceptible to landsliding in the Morrison Quadrangle, Jefferson County, Colorado, Scott (1972) which distinguished four zones:

[...]

Also semi-quantitative susceptibility, hazard or slope instability maps based on analysis of slope angles, lithology and relative amounts of landslip material have been published (Blanc and Cleveland 1968; Bowman 1972; Radbruch and Crowther 1973; Dobrovolny and Schmoll 1974; Nilsen and Brabb 1977; Nilsen and Wrigth 1979).

As an example, the landslide map of California (at a scale 1:1,000,000) was made by Radbruch and Crowther (1973) with units indicating ‘‘...only the estimated relative amounts of area covered by landslides for each map unit as well as can be determined within the limitations of the present study’’. The authors indicate that ‘‘no quantitative measurements were made and many areas that were neither observed in the field nor covered by published or unpublished data were assigned numbers on the basis of the general aspect of similar geologic units.’’ The general criteria for the rating were related first to slope angle below 5° and rainfall of less than 10 in. (25.4 cm) with very little evidence of landsliding (unit number 1) and, at the opposite extreme, to areas heavily covered by large amount of landslides, as in most of the Franciscan terrain in the northern Coast Ranges (unit 6). [...]

Nilsen and Wrigth (1979) in a 1:125,000 landslide map of the San Francisco Bay region distinguished slope angle units of < 5°, 5–15° and > 15°, and lithological groups of no landslide deposits, susceptible bedrock, susceptible superficial deposits and landslide deposits. Combining these two criteria of slope angle and lithological groups they classified the region into six zones: (1) stable, (2) generally stable, (3) moderately stable, (4) moderately unstable, (5) unstable. Zone 1A was defined as an area ‘‘subject to liquefaction’’.

These maps, at different scales, were oriented toward the classification of land units based on the evidence of landsliding. As there was no attempt at temporal forecasting, from the point of view of Varnes (1978) they would be considered closer to landslide inventories than to landslide hazard maps. [...]

Other hazard and risk maps have been based on numerically rated or weighted slope and geological factors with geotechnical data (Stevenson 1977). Other significant contributions were linear risk maps of roads (Méneroud 1978) and geotechnical stability maps which rate soil and rock mechanics parameters such as cohesion, friction angle or rock massif discontinuities (Vecchia 1978).


Antoine P (1977) Refléxions sur la cartographie ZERMOS et bilan de expe´riences en cours.Bull Bur Rech geol min Sec. III (1-2), 9–20

Blanc RP, Cleveland GB (1968) Natural slope stability as related to geology, San Clemente Area, Orange and San Diego Counties, California. California Division of Mines and Geology Special Report 98, 19 pp

Brabb EE, Pampeyan EH (1972) Preliminary map of landslide deposits in San Mateo County, California. US Geological Survey Miscellaneous Field Studies, Map MF-344, scale 1:62,500

Davies WE (1974a) Landslide susceptibility map of part of the Bridgeville 7-1/2 minute quadrangle, Allegheny County and vicinity, Pennsylvania. US Geological Survey Open-File Report 74-274, scale 1:24,000, 8 pp

Davies WE (1974b) Landslide susceptibility map of part of the Canonsburg 7-1/2 minute quadrangle, Allegheny County and vicinity, Pennsylvania. US Geological Survey Open-File Report 74-276, scale 1:24,000, 8 pp

Davies WE (1974c) Landslide susceptibility map of part of the Donora 7-1/2 minute quadrangle, Allegheny County and vicinity, Pennsylvania. US Geological Survey Open-File Report 74-277, scale 1:24,000, 8 pp

Davies WE (1974d) Landslide susceptibility map of part of the Freeport 7-1/2 minute quadrangle, Allegheny County and vicinity, Pennsylvania. US Geological Survey Open-File Report 74-278, scale 1:24,000, 8 pp

Davies WE (1974e) Landslide susceptibility map of the McKeesport 7-1/2 minute quadrangle, Allegheny County and vicinity, Pennsylvania. US Geological Survey Open-File Report 74-280, scale 1:24,000, 8 pp

Davies WE (1974f) Landslide susceptibility map of the Monongahela 7-1/2 minute quadrangle, Allegheny County and vicinity, Pennsylvania. US Geological Survey Open-File Report 74-281, scale 1:24,000, 8 pp

Davies WE (1974g) Landslide susceptibility map of part of the New Kensington East 7-1/2 minute quadrangle, Allegheny County and vicinity, Pennsylvania. US Geological Survey Open-File Report 74-283, scale 1:24,000, 8 pp

Davies WE (1974h) Landslide susceptibility map of part of the New Kensington West 7-1/2 minute quadrangle, Allegheny County and vicinity. US Geological Survey Open-File Report 74-284, scale 1:24,0000, 8 pp

Davies WE (1974i) Landslide susceptibility map of the Braddock 7-1/2 minute quadrangle, Allegheny County and vicinity, Pennsylvania. US Geological Survey Open-File Report 74-273, scale 1:24,000, 8 pp

Davies WE (1974j) Landslide susceptibility map of part of the Curtisville 7-1/2 minute quadrangle, Allegheny County and vicinity, Pennsylvania. US Geological Survey Open-File Report 74-276, scale 1:24,000, 8 pp

Dobrovolny E (1971) Landslide susceptibility in and near anchorage as interpreted from topographic and geologic maps, in The great Alaska earthquake of 1964–Geology volume. Publication 1603. U.S. Geological Survey Open-File Report 86-329, National Research Council, Committee, on the Alaska Earthquake, National Academy of Sciences, USA, pp 735–745

Hinojosa JA, Leon CO (1978) Unstable soil mapping in Spain. In: Proceedings of 3rd International Cong. IAEG,Madrid, section I, (I), pp. 217–227

Humbert M (1977) La cartographie ZERMOS. Modalités ´détablissement [sic] des cartes des zones esposes à des risques lié s aux mouvements du sol et du sous-sol. Bull Bur Rech Geol Min III (1–2):5–8

Kienholz H (1978) Maps of geomorphology and natural hazard of Grindewald, Switzerland, scale 1:10,000. Arctic Alpine Res 10(2):169–184

Landry J (1979) Cartes ZERMOS. Zones exposés à des risques liés aix mouvements du solet du sous-sol. Région de Longle-Saunier à Oiligny (Jura). Orlèans, Bureau de RechercheGéologique [ic] et Minière, 14 pp, 1 map

Mahr T, Malgot J (1978) Zoning maps for regional and urban development based on slope stability. In: Proceedings of the IIIrd I.A.E.G. Congress I, Spain 1:14:124–137

Méneroud JP (1978) Cartographie des risques dans les Alps-Maritimes (France). In: Proceedings of the IIIrd I.A.E.G. Congress, II, Chap. 46, pp 98–107

Méneroud JP, Calvino A (1976) Carte ZERMOS. Zones exposés à des risques liés aixmouvements du sol et du sous-sol à 1:25,000 region de la Moyenne Vesubie (Alpes-Maritimes. Orléans, Bureau de Recherche Géologique et Minière, 11 pp, 1 map

Nilsen TH, Brabb EE (1977) Landslides. In: Borcherdt RD (ed) Studies for seismic zonation of the San Francisco Bay Region. US Geological Survey Professional Paper 941-A, 96 pp

Nilsen TH, Wright RH (1979) Relative slope stability and landuse planning in the San Francisco Bay region, California. US Geological Survey Professional Paper 944, US Department of Interior, Washington, 103 pp

Pomeroy JS (1974a) Landslide susceptibility and processes in the Maryland Coastal Plain. Schultz AP, Southworth CS (eds) Landslides of eastern North America. US Geological Survey Circular 1008, Chap. 2, pp 5–9

Pomeroy JS (1974b) Landslide susceptibility map of the Emsworth 7-1/2 minute quadrangle, Allegheny County, PA. US Geological Survey Open-File Report 74-75, 15 pp, 1 pl., 7 figs., scale 1:24,000

Pomeroy JS (1974c) Landslide susceptibility map of part of the Mars 7-1/2 minute quadrangle, Allegheny County and vicinity, Pennsylvania. US Geological Survey Open-File Report 74-114, 18 pp, 1 pl., 7 figs., scale 1:24,000

Pomeroy JS (1974d) Landslide susceptibility map of part of the Valencia 7-1/2 minute quadrangle, Allegheny County and vicinity, Pennsylvania. US Geological Survey Open-File Report 74-116, 18 pp, 1 pl., scale 1:24,000

Pomeroy JS (1974e) Landslide susceptibility map of the Glenshaw 7-1/2 minute quadrangle, Allegheny County, Pennsylvania. US Geological Survey Open-File Report 74-118, 18 pp, 1 pl., 7 figs., scale 1:24,000

Radbruch DH (1970) Map of relative amounts of landslides in California. US Geological Survey Open-File Report 70-1485, 36 pp, map scale 1:500,000. US Geological Survey Open-File Report 85-585

Radbruch DH, Crowther KC (1973) Map showing areas of estimated relative susceptibility to landsliding in California. US Geological Survey Miscellaneous Geologic Investigations Map I-747, scale 1:1,000,000

Rodríguez Ortiz JM, Prieto C, Hinojosa JA (1978) Regional studies on mass movements in Spain. In: Proceedings of the IIIrd I.A.E.G. Congress I, 1:29:267–278

Scott GR (1972) Map showing landslides and areas susceptible to landslides in the Morrison Quadrangle, Jefferson County, Colorado. US Geological Survey. Map I-790-B. USA

Stevenson PC (1977) An empirical method for the evaluation of relative landslide risk. Int Ass Eng Geol Bull 16:69–72

Varnes DJ (1978) Slope movement types and processes: In: Schuster RL, Krizek RJ (eds) Landslides: analysis and control. Transportation Research Board Special Report 176.

Vecchia O (1978) A simple terrain index for the stability of hillsides or scarps. In: Geddes JD(ed) Large ground movements and structures. Wiley, New York Toronto, pp 449-461

Anmerkungen

No hint is given that this text comes from another source.

There are no references for Antoine (1977), Hinojosa and Leon (1978), Humbert (1977), Kienholtz (1978), Landry (1979), Mahr and Malgot (1978), Meneroud and Calvino (1976), Nilsen and Brabb (1977), Pomeroy (1974), Rodrıguez Ortiz et al. (1978) in Hja.

Sichter
(Graf Isolan)

[2.] Analyse:Hja/Fragment 006 01 - Diskussion
Bearbeitet: 6. January 2015, 15:40 Graf Isolan
Erstellt: 5. January 2015, 22:19 (Graf Isolan)
Chacon et al 2006, Fragment, Hja, SMWFragment, Schutzlevel, Verschleierung, ZuSichten

Typus
Verschleierung
Bearbeiter
Graf Isolan
Gesichtet
No.png
Untersuchte Arbeit:
Seite: 6, Zeilen: 1-30
Quelle: Chacon et al 2006
Seite(n): 346, 347, 348-349, Zeilen: 346:right col.43-45; 347:right col. 47-53; 348:left col. 6-13.18-24.40-44 - right col. 8-13.37-44 - 349:left col. 1-5.43-51; right col. 2-6
[They generally] proposed landslide risk1 zonations or a terrain index showing the stability of hillsides. The term “risk” used here could be considered similar to landslide susceptibility. According to Varnes (1984), as the term “terrain index” is also intended to show a quantitative rating of stability, it is closer to the concept of susceptibility than hazard or risk.

Landslide susceptibility was quantitatively first approached by Brabb et al. (1972). They introduced a semi-quantitative method consisting of a bivariate analysis of landslide area percentages in slope angle intervals, expressed by relative susceptibility numbers, from which a susceptibility zonation was obtained. This pioneering paper offered a formal definition of landslide susceptibility as an indication of how prone to landsliding a land unit may be. It also offered a method to classify terrain units with a relative susceptibility number based on geological units, slope angle and percentage of landslides in the unit, which was a very difficult task to apply at that time.

Another approach to mapping landslides involves landslide density or isopleth maps. Campbell (1973) presented a nominally objective method for a statistical assessment of regional landslide distribution based on Schmidt and Mac Cannel (1955). The technique was based on a landslide inventory at a 1:24,000 scale (Campbell, 1973) by estimating the surface covered by landslide deposits using a number of contiguous circles displayed on a grid, calculating the percentage of the surface area covered by each circle and contouring equal percentage intervals.

With the computer revolution, Lessing et al. (1976) in West Virginia (USA), Newman et al. (1978) in the San Francisco Bay region and Carrara et al. (1977, 1978) in the Ferro basin (Calabria, Italy), introduced computer techniques to analyze landsliding factors in order to obtain what they called slide-prone areas, landslide susceptibility or landslide hazard zonations, all of which lacked any temporal forecasting. The widespread availability of computing power allowed statistically supported landsliding zonations to be obtained, e.g. landslide susceptibility using discriminate factors (Simons et al., 1978) and landslide hazard using bivariate (Neulands, 1976) or multivariate analysis (Carrara, 1983).

One of the really significant contributions to landslide research comes from the pioneering work of Carrara and Merenda (1976), Carrara (1983) and Carrara et al. (1977, 1978). Varnes (1984) described the contributions of Carrara et al. (1978), on the landslides in the basin of the Calabria–Lucania border, Italy, as ‘‘one of the more advanced and accessible state-of-the-art analyses of land attributes for production of landslide hazard maps, utilizing computer processing’’. The objectives were to statistically define slope instability by multivariate analysis and, using a computer, to create a slope instability hazard map.


1 Risk is defined as the probability of meeting danger or suffering harm or loss. In relation to disaster, risk has been more specifically described as the probability that a disaster will occur, using relative terms such as high risk, average or medium risk and low risk to indicate the degree of probability.


Brabb, E.E., Pampeyan, E.H., Bonilla, M.G. (1972): Landslide susceptibility in San Mateo County, California. US Geological Survey Miscellaneous Field Studies, Map MF-360, scale 1:62,500.

Campbell, R.H. (1973): Isopleth map of landslide deposits. Point Duma Quadrangle, Los Angeles County, California: an experiment in generalizing and quantifying aerial distribution of landslides. US Geological Survey Misc. Field Investigation Map MF-535. USGS, California.

Carrara, A., Merenda, L. (1976): Landslides inventory in northern Calabria, southern Italy. Geol. Soc. Am. Bull 87:1229–1246.

Carrara, A., Pugliese, E., Merenda, L. (1977): Computer based data bank and statistical analysis of slope instability phenomena. Z Geomorph NF 21(2):187–222.

Carrara, A., Catalano, E., Sorriso-Valvo, M., Really, C., Osso, I. (1978): Digital terrain analysis for land evaluation. Geologia Applicata e Idrogeologia 13:69–127.

Carrara, A. (1983): Multivariate methods for landslide hazard evaluation. Math Geol 15:403–426.

Lessing, P., Kulander, B.R., Wilson, B.D., Dean, S.L., Woodring, S.M. (1976): West Virginia landslides and slide-prone areas. West Virginia Geol. Econ. Surv., Environ. Geol. Bull., 15:64.

Neulands, H., (1976): A prediction model of Landslip. Catena 5:215–30 engineering geology maps: landslides and GIS 405.

Newman, E. B., Paradis, A. R., Brabb, E. E. (1978): Feasibility and cost of using a computer to prepare landslide susceptibility maps of the San Francisco Bay region, California. US Geological Survey Bulletin 1443, USGS, USA, pp 23.

Schmid, R. H., MacCannel, J. (1955): Basic problems, techniques and theory of isopleth mapping. J. Am. Stat. Assoc., 50(269):220–239.

Simons, D. B., Li, R. M., Ward, T. J. (1978): Mapping of potential landslide areas in terms of slope stability. Fort Collins, Colorado. Civil Engineering Dept., Colorado State University, pp 75.

Varnes, D. J. (1984): Landslide hazard zonation: a review of principles and practice, International Association of Engineering Geology, Commission on Landslides and Other Mass Movements on Slopes, UNESCO Natural Hazards Series no. 3, pp 61.

[Page 346]

Landslide susceptibility is a measure of how prone land units are to landsliding, and was quantitatively approached by Brabb et al. (1972).

[Page 347]

They generally propose landslide risk zonations (Stevenson 1977; Méneroud 1978; Méneroud and Olivier 1978) or a terrain index showing the stability of hillsides (Vecchia 1978). The first two approaches used the term ‘‘risk’’ in a sense that could be considered similar to landslide susceptibility, although the method of Stevenson (1977) included a land use

[Page 348]

factor rated from 1 for woodland to 1.25 for land cleared or built-on with special precautions, and 1.5 (maximum) for land built-on without special precautions. Varnes (1984) refers to Vecchia’s (1978) use of criteria which reflect geomechanical rock mass classifications. As the terrain index is also intended to show a quantitative rating of stability, it is closer to the concept of susceptibility than hazard or risk.

Finally, Brabb et al. (1972) introduced a semi-quantitative method consisting of a bivariate analysis of landslide area percentages in slope angle intervals, expressed by relative susceptibility numbers, from which a susceptibility zonation was obtained. [...]

This pioneering paper offered a formal definition of landslide susceptibility as an indication of how prone to landsliding a land unit may be. It also offered a method to classify terrain units with a relative susceptibility number based on geological unit, slope angle and percentage of landslide in the unit, which was difficult to apply at that time. [...]

Another approach to mapping landslides involves landslide density or isopleth maps. Campbell (1973) presented a nominally objective method for a statistical assessment of regional landslide distribution based on Schmidt and Mac Cannel (1955), which was used and discussed in later works (Wright and Nilsen 1974; Wright et al. 1974; Pomeroy 1978; DeGraff and Canutti 1988; Collins 1987; DeGraff 1985; DeGraff 1987) and brought up to date for microcomputers and GIS for landslide spatial incidence by Bulut et al. (2000) and Valadaö et al. (2002) and for the assessment of landslide spatial–temporal incidence by Coe et al. (2000). The technique was based on a landslide inventory at a 1:24,000 scale (Campbell 1973) by estimating the surface covered by landslide deposits using a number of contiguous circles displayed on a grid, calculating the percentage of the surface area covered by each circle and contouring equal percentage intervals.

[...]

With the computer revolution, Lessing et al. (1976) in West Virginia (USA), Newman et al. (1978) in the San Francisco Bay region and Carrara et al. (1977, 1978) in the Ferro basin (Calabria, Italy), introduced computer techniques to analyse landsliding factors in order to obtain what they called slide-prone areas, landslide susceptibility or landslide hazard zonations, all of which lacked any temporal forecasting. The widespread

[Page 349]

availability of computing power allowed statistically supported landsliding zonations to be obtained, e.g. landslide susceptibility using discriminate factors (Simons et al. 1978) and landslide hazard using bivariate (Neulands 1976) or multivariate analysis (Carrara 1983). [...]

[...]

One of the really significant contributions to landslide research comes from the pioneering work of Carrara and Merenda (1976), Carrara (1983) and Carrara et al. (1977, 1978). Varnes (1984) described the contributions of Carrara et al. (1978), on the landslides in the basin of the Calabria–Lucania border, as ‘‘one of the more advanced and accessible state-of-the-art analyses of land attributes for production of landslide hazard maps, utilising computer processing’’. [...] The objectives were to statistically define slope instability by multivariate analysis and, using a computer, to create a slope instability hazard map and maps to relate the presence of man-made structures and landslide hazard. [...]



Brabb EE, Pampeyan EH (1972) Preliminary map of landslide deposits in San Mateo County, California. US Geological Survey Miscellaneous Field Studies, Map MF-344, scale 1:62,500

Campbell RH (1973) Isopleth map of landslide deposits. Point Duma Quadrangle, Los Angeles County, California: an experiment in generalizing and quantifying areal distribution of landslides. US Geological Survey Misc. Field Investigation Map MF-535. USGS, California

Carrara A (1983) Multivariate methods for landslide hazard evaluation. Math Geol 15:403–426

Carrara A, Merenda L (1976) Landslides inventory in northern Calabria, southern Italy. Geol Soc Am Bull 87:1229–1246

Carrara A, Pugliese E, Merenda L (1977) Computer-based data bank and statistical analysis of slope instability phenomena. Z Geomorph NF 21(2):187–222

Carrara A, Catalano E, Sorriso-Valvo M, Really C, Osso I (1978) Digital terrain analysis for land evaluation. Geologia Applicata e Idrogeologia 13:69–127

Lessing P, Kulander BR, Wilson BD, Dean SL, Woodring SM (1976) West Virginia landslides and slide-prone areas. West Virginia Geol Econ Surv, Environ Geol Bull 15:64

Méneroud JP (1978) Cartographie des risques dans les Alps- Maritimes (France). In: Proceedings of the IIIrd I.A.E.G. Congress, II, Chap. 46, pp 98–107

Méneroud JP, Olivier G (1978) Eboulement et chutes de pierres sur les routes. Methode de cartographie. Groupe d’Etudes des Falaises (GEF). Rapport de recherche LPC no. 80, L.C.P.Ch., Paris, France, 63 pp

Neulands H (1976) A prediction model of Landslip. Catena 5:215–30

Newman EB, Paradis AR, Brabb EE (1978) Feasibility and cost of using a computer to prepare landslide susceptibility maps of the San Francisco Bay region, California. US Geological Survey Bulletin 1443, USGS, USA, 23 pp

Schmid RH, MacCannel J (1955) Basic problems, techniques and theory of isopleth mapping. J Am Stat Assoc 50(269):220–239

Simons DB, Li RM, Ward TJ (1978) Mapping of potential landslide areas in terms of slope stability. Fort Collins, Colorado. Civil Engineering Dept., Colorado State University, 75 pp

Stevenson PC (1977) An empirical method for the evaluation of relative landslide risk.Int Ass Eng Geol Bull 16:69–72

Varnes DJ (1984) International Association of Engineering Geology Commission on Landslides and Other Mass Movements on Slopes. Landslide hazard zonation: a review of principles and practice. Int Assoc Eng Geol, UNESCO Natural Hazards Series no. 3, 63 pp

Vecchia O (1978)A simple terrain index for the stability of hillsides or scarps. In: Geddes JD(ed) Large ground movements and structures. Wiley, NewYork Toronto, pp 449-461

Anmerkungen

Although in most places nearly identical with exactly the same references, no hint is given that this text comes from another source.

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Their work initially used large square grid cells (200 * 200 m) as the basis for analysis. Whereas, later studies evolved towards the use of morphometric units, but the method itself has not undergone major changes.

Another example of multivariate analysis of landsliding using a GIS was presented by Bernknopf et al. (1988) who applied multiple regression analysis to a data set using presence or absence of landslides as the dependent variable and the factors used in the slope stability model (soil depth, soil strength, slope angle) as independent variables. Here, the resulting regression function allows the computation of landslide probability for each pixel.


Bernknopf, R. L., Champbell [sic], R. H., Brookshire, D. S., Saphiro [sic], C. D. (1988): A probabilistic approach landslide [sic] hazard mapping in Cincinnati, Ohio, with application [sic] of economic evaluation. Bulleting [sic] of the Association of Engineering Geologists, Vol. 25, No. 1, pp 39-56.

Their work initially used large rectangular grid cells as the basis for analysis (Carrara et al. 1978; Carrara 1983, 1988). Later studies evolved toward the use of morphometric units (Carrara et al. 1990, 1991, 1992). The method itself has not undergone major changes. [...]

Another example of multivariate analysis of landsliding using a GIS was presented by Bernknopf et al. (1988), who applied multiple regression analysis to a data set using presence or absence of landslides as the dependent variable and the factors used in a slope stability model (soil depth, soil strength, slope angle) as independent variables. Water table and cohesion data were not considered, however. The resulting regression function allows the computation of landslide probability for each pixel.


Bernknopf, R.L., R.H. Campbell, D.S. Brookshire, and C.D. Shapiro. 1988. A Probabilistic Approach to Landslide Hazard Mapping in Cincinnati, Ohio, with Applications for Economic Evaluation. Bulletin of the Association of Engineering Geologists, Vol. 25, No. l,pp. 39-56.

Anmerkungen

Nothing has been marked as a citation.

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[Also Baeza (1994) largely contributed to multivariate analysis and] mapping of the incidence of shallow landslides in the Pyrenees (Spain) using a statistical computer package.

The matrix-assessment approach (DeGraff and Romesburg, 1980) is an objective and quantitative method for establishing an index of instability over an area and evaluating landslide susceptibility. It is based on measured attributes of the bedrock, slope and aspect from aerial photo interpretation and field work and a landslide inventory. The total areas covered by landslides were placed in each appropriate cell and the amount of landslide terrain with the same particular combinations of bedrock, slope or aspect units was identified. A management unit matrix was constructed from all bedrock, slope and aspect combinations for landslide locations, giving rise to different management units within the matrix. Based on this method a quantitative landslide susceptibility zonation was obtained by grouping all the susceptibility values into classes. A non-hierarchical clustering method (Anderberg, 1973) using a W-function, by minimizing the sum of squared deviations about the three equally distributed groups, was adopted to obtain susceptibility classes for the final landslide susceptibility classification (DeGraff and Romesburg, 1980). Method has a little room left for personal judgment and was designed for large areas of wild lands. The use of the GIS matrix method has been made possible by the development of microcomputers and software over the last decades.


Anderberg, M. R. (1973): Cluster analysis for applications. Academic, New York, pp 359.

DeGraff, J. V., Romesburg, H. C. (1980): Regional landslide- susceptibility assessment for wildland management: a matrix approach. In: Coates DR, Vitek JD (eds) Chap. 19, pp 401–414.

[Page 349]

Also Baeza (1994) largely contributed to multivariate analysis and mapping of the incidence of shallow landslides in the Pyrenees (Spain) using a statistical computer package.

[...]

[Page 358]

Matrix approach

The matrix-assessment approach (DeGraff and Romesburg 1980) is an objective and quantitative method for establishing an index of instability over an area and evaluating landslide susceptibility. It is based on measured attributes of the bedrock, slope and aspect from aerial photo interpretation and field work and a landslide inventory. The total areas covered by landslides were placed in each appropriate cell and the amount of landslide terrain with the same particular combinations of bedrock, slope or aspect units was identified. A management unit matrix was constructed from all bedrock, slope and aspect combinations for landslide locations, giving rise to different management units within the matrix.

[Page 359]

Based on this method a quantitative landslide susceptibility zonation was obtained by grouping all the susceptibility values into classes. A non-hierarchical clustering method (Anderberg 1973) using a W function, by minimising the sum of squared deviations about the three equally distributed groups, was adopted to obtain susceptibility classes for the final landslide susceptibility classification (DeGraff and Romesburg 1980). As all of the attributes considered in the method may be easily measured from field research and aerial interpretation, little room is left for personal judgement, hence this is a quantitative assessment. The method was designed for large areas of wild lands. [...] The use of the GIS matrix method (Fig. 7) has been made possible by the development of microcomputers and software over the last two decades.


Anderberg MR (1973) Cluster analysis for applications. Academic, New York, p 359

Baeza C (1994). Evaluación de las condiciones de rotura y la movilidad de los deslizamientos superficiales mediante el uso de técnicas de análisis multivariante. PhD thesis, Department Ingeniería del Terreno y Cartográfica, UPC, Barcelona, Spain

DeGraff JV, Romesburg HC (1980) Regional landslide—susceptibility assessment for wildland management: a matrix approach. In: Coates DR, Vitek JD (eds) Chap. 19, pp 401–414

Anmerkungen

Although in most places nearly identical with exactly the same references, no hint is given that this text comes from another source.

The reference for Baeza (1994) is missing in Hja. Due to shortening the meaning of one of the sentences at the end has been changed into its opposite.

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Fall and Azzam (1998) used GIS to prepare a map indicating natural risk in the coastal area of Dakar, Senegal. GIS analysis was made in ArcInfo and ArcView (ESRI) to obtain a natural risk map based on three groups of instability factors: hydrogeology, coastal erosion and geotechnical parameters showing six zones of coastal slope dynamics. Also another risk assessment approaches were proposed by Kawakami and Saito (1984), Lee et al. (2001). The presence or absence of instability processes and hazard was proposed as a tool for better land-use planning of a coastal area affected by rapid urban development. Wachal and Hudak (2000) used GIS techniques to assess landsliding in a 1,500-2000 km2 area in Travis Country (USA), based on four factors: slope angle, geology, vegetation and distance to faults. Four classes of relative susceptibility were derived weighting these factors according to their contribution to instability processes. Moreiras (2004) proposed landslide incidence or susceptibility (Moreiras, 2005) zonation for a 1,600 km2 area west of Mendoza city, Argentina, based on air photo interpretation, digital analysis of satellite Spot and Landsat images and field control. The degree of relative susceptibility were assigned from GIS analysis taking into account both lithology and slope angle and a landslide inventory.

A very interesting new GIS methodology was proposed by Parise and Jibson (2000) to obtain a landslide seismic susceptibility rating. An inventory of landslides that occurred during the Northridge earthquake (1994, M: 6.7, California, USA) in the Santa Susana quadrangle was made. Distances to the epicenter fault zone and data about the dynamic intensity were expressed as Arias intensities (Arias, 1970). These were considered as a basis for a landslide susceptibility index (LSI is expressed as the ratio in percentage of the area covered by landslides in each geological unit to the total area of the outcrops of that unit) and landslide frequency index (number of landslide per km2). A zonation of four relative susceptibility classes was obtained with a resolution of 10 * 10 m at a scale of 1:24,000: very high (>2.5% landslide area or >30 LS/ km2), high (1.0–2.5% landslide area or 10–30 LS/km2), moderate (0.5–1.0% landslide area or 3–10 LS/km2) and low (<0.5% landslide area and <3 LS/km2).

GIS based rock fall hazard assessment and analysis was accomplished by many authors, for instance Ayala-Carcedo et al. (2003) analyzed a rock fall front in the Sierra de la Cabrera (Madrid, Spain) by a heuristic approach using ArcInfo (ESRI).

One of the first papers in the United States on a wholly GIS assessment of landslide susceptibility, hazard and risk (Mejıa-Navarro et al., 1994) used weighted factors in algorithms, relating debris flow susceptibility and determinant factors. The research was a pilot project done in ArcInfo (ESRI) and GRASS GIS to test the usefulness of GIS in an integrated planning decision support model evaluating different geological hazards. The base maps were at scales of between 1:4,000 and 1:25,000. Debris flow hazard susceptibility, at a scale 1:24,000, was derived from an algorithm which modeled the influence of several factors.


Arias, A. (1970): A measure of earthquake intensity. In: Hansen RJ (ed) Seismic design for nuclear power plants. Massachusetts Institute of Technology Press, Cambridge, pp 438–483.

Ayala-Carcedo, F. J., Cubillo-Nielsen, S., Alvarez, A., Dominguez, M. J., Lain, L., Lain, R., Ortiz, G. (2003) Large scales rock fall reach susceptibility maps in La Cabrera Sierra (Madrid) performed with GIS and dynamic analysis at 1:5.000. In: Chacon J, Corominas J (eds) Special issue on Landslides and GIS. Nat Hazards 30(3):341–360.

Fall, M., Azzam, R. (1998): Application de la ge´ologie [sic] de l’ingenieur et de SIG a` [sic] l’e´tude [sic] de la stabilite´ [sic] des versants coˆ tier [sic], Dakar, Senegal. In: Moore D, Hungr O (eds) Proceedings of the 8th IAEG Congress, Vancouver. A.A. Balkema, Rotterdam, pp 1011–1018.

Kawakami, H., Saito, Y. (1984): Landslide risk mapping by a quantification method. In: Proceedings of the IVth ISL Toronto, Canada, Vol. 2, pp 535–540.

Lee, S., Min, K. (2001): Statistical analysis of landslide susceptibility at Yongin, Korea. Environ. Geol., 40:1095–1113.

Lee, S., Chang, B., Choi, W., Shin, E. (2001): Regional susceptibility, possibility and risk analysis of landslide in Ulsan metropolitan city, Korea. In: Proceedings of the IGARSS 2001: scanning the present and resolving the future. IEEE, Australia, pp 1690–1692.

MejIa-Navarro, M., Wohl, E.W., Oaks, S. D. (1994): Geological hazards, vulnerability, and risk assessment using GIS: model for Glenwood Springs, Colorado. Geomorphology 10:331–354.

Moreiras, S. M. (2004): Landslide incidence zonation in the Rio Mendoza valley, Mendoza province, Argentina. Earth Surf Processes Landforms 29:255–266.

Moreiras, S. M. (2005): Landslide susceptibility zonation in the Rio Mendoza valley, Argentina. Geomorphology 66:345–357.

Parise, M., Jibson, R.W. (2000): A seismic landslide susceptibility rating of geologic units based on analysis of characteristics of landslides triggered by the 17 January, 1994 Northridge, California earthquake. Eng. Geol., 58:251–270.

Wachal, D. J., Hudak, P. F. (2000): Mapping landslide susceptibility in Travis County, Texas, USA. Geol. Journal 51:245–253.

[Page 352]

Fall and Azzam (1998) used GIS to prepare a map indicating natural risk in the coastal areas of Dakar, Senegal. Geological, hydrogeological and geotechnical surveys at a scale of 1:500, and topographical maps from 1953, 1961, 1977 and 1981 at scales of 1:1,000 to 1:5,000 were all digitised and a GIS analysis was made in Arc/Info and Arc View (ESRI). [...] After identifying the lithological units and areas more affected by landslides, GIS layers were superimposed and analysed to obtain a natural risk map based on three groups of instability factors: hydrogeology, coastal erosion and geotechnical parameters. This map revealed six zones of coastal slope dynamics, with a zone affected by instability processes and symbols showing different risks as active scarps and landslides, earth flows, rock fall, etc. [...] Also another risk assessment approaches were proposed by Kawakami and Saito (1984), Lee et al. (2001b). The presence or absence of instability processes and hazard was proposed as a tool for better land-use planning of a coastal area affected by rapid urban development. [...]

[...]

Moreiras (2004) proposed landslide incidence or susceptibility (Moreiras 2005) zonation for a 1,600 km2 area west of Mendoza city, in the Cordillera Frontal Ranges and Precordillera of Argentina, based on airphoto interpretation, digital analysis of satellite spot and Landsat images and field control. [...] The degrees of relative susceptibility were assigned from GIS analysis of every 200 · 200 m unit taking into account both lithology and slope angle and a landslide inventory. [...]

Wachal and Hudak (2000) used GIS techniques to assess landsliding in a 1,500–2,000 km2 area in Travis County (USA), based on four factors: slope angle, geology, vegetation and distance to faults. Four classes of relative susceptibility were derived weighting these factors (0–1) according to their contribution to instability processes. [...]

A very interesting new GIS methodology was proposed by Parise and Jibson (2000) to obtain a landslide seismic susceptibility rating. An inventory of landslides that occurred during the Northridge earthquake (1994, M: 6,7, California, USA) in the Santa Susana quadrangle was made. Distances to the epicentre fault zone and data about the dynamic intensity were expressed

[Page 353]

as Arias intensities (Arias 1970). These were considered as a basis for a LSI (the ratio in % of the area covered by landslides in each geological unit to the total area of the outcrops of that unit) and landslide frequency index (number of landslide per km2). A zonation of four relative susceptibility classes was obtained with a resolution of 10 x 10 m at a scale of 1:24,000: very high (>2.5% landslide area or >30 ls/ km2), high (1.0–2.5% landslide area or 10–30 ls/km2), moderate (0.5–1.0% landslide area or 3–10 ls/km2) and low (<0.5% landslide area and <3 ls/km2).

Ayala-Carcedo et al. (2003) analysed a rock fall front in the Sierra de la Cabrera (Madrid, Spain) by a heuristic approach using Arc Info (ESRI). [...] Also GIS rockfall hazard assessment and analysis was accomplished by Baillifard et al. (2004), Mene´ndez-Duarte and Marquı´- nez (2002) while GIS avalanche hazard was assessed by Barbolini et al. (2002), Brabec et al. (2001), Evans et al. (2001), McClung (2002a, b) and case studies on debris fall GIS mapping by Bathurst et al. (2003) , He et al. (2003), Hofmeister and Miller (2003), Nakagawa and Takahashi (1997).

[...]

One of the first papers in the United States on a wholly GIS assessment of landslide susceptibility, hazard and risk (Mejía-Navarro et al. 1994) used weighted factors in algorithms, relating debris flow susceptibility and determinant factors. Made in Arc/Info (ESRI) and GRASS GIS, the research was a pilot project to test the usefulness of GIS in an integrated planning decision support model evaluating different geological hazards (H—subsidence, rock fall, debris flows and flows) in an area of 6,500 ha around the city of Glenwood Springs, Garfield County, Colorado. The base maps were at scales of between 1:4,000 and 1:25,000. [...]

Debris flow hazard susceptibility, at a scale 1:24,000, was derived from an algorithm which modelled the influence of factors such as slope angle (slopedf); slope orientation (aspect); [...]


Arias A (1970) A measure of earthquake intensity. In: Hansen RJ (ed) Seismic design for nuclear power plants. Massachusetts Institute of Technology Press, Cambridge, pp 438–483

Ayala-Carcedo FJ, Cubillo-Nielsen S, Alvarez A, Domínguez MJ, Laín L, Laín R, Ortíz G (2003) Large scales rock fall reach susceptibility maps in La Cabrera Sierra (Madrid) performed with GIS and dynamic analysis at 1:5.000. In: Chacon J, Corominas J (eds) Special issue on Landslides and GIS. Nat Hazards 30(3):341–360

Fall M, Azzam R (1998) Application de la géologie de l’ingenieur et de SIG à l’étude de la stabilité des versants côtier, Dakar, Senegal. In: Moore D, Hungr O (eds) Proceedings of the 8th IAEG Congress, Vancouver. A.A. Balkema, Rotterdam, pp 1011–1018

Kawakami H, Saito Y (1984) Landslide risk mapping by a quantification method. In: Proceedings of the IVth ISL Toronto, Canada, vol 2, pp 535–540

Lee S, Chang B, Choi W, Shin E (2001b) Regional susceptibility, possibility and risk analysis of landslide in Ulsan metropolitan city, Korea. In: Proceedings of the IGARSS 2001: scanning the present and resolving the future. IEEE, Australia, pp 1690–1692

Mejía-Navarro M, Wohl EW, Oaks SD (1994) Geological hazards, vulnerability, and risk assessment using GIS: model for Glenwood Springs, Colorado. Geomorphology 10:331–354

Moreiras SM (2004) Landslide incidence zonation in the Rio Mendoza valley, Mendoza province, Argentina. Earth Surf Processes Landforms 29:255–266

Moreiras SM (2005) Landslide susceptibility zonation in the Rio Mendoza valley, Argentina. Geomorphology 66:345–357

Parise M, Jibson RW (2000) A seismic landslide susceptibility rating of geologic units based on analysis of characteristics of landslides triggered by the 17 January, 1994 Northridge, California earthquake. Eng Geol 58:251–270

Wachal DJ, Hudak PF (2000) Mapping landslide susceptibility in Travis County, Texas, USA. GeoJournal 51:245–253

Anmerkungen

Although in most places nearly identical with exactly the same references, no hint is given that this text comes from another source.

Obviously, the copying of the reference for Fall, M., Azzam, R. (1998) led to all the accents being in the wrong place in Hja.

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Another example of a weighting factor procedure was used by Temesgen et al. (2001) in a study of the Wondogenet area in the eastern margin of the Ethiopian rift in a raster GIS. Estimates were made of the frequencies of landslide occurrence considering lithology, drainage network, geology, slope angle, slope aspect and vegetation cover. Priority weightings were assigned on the basis of observed landslide densities for each class and the resultant maps were overlain to produce susceptibility maps. The final integration was made using pixel attributes, algebraic calculations and arithmetic means. The landslide hazard map was derived from the integration of all the susceptibility maps.

Van Westen et al. (2003) evaluated the importance of expert geomorphological knowledge in the production of landslide susceptibility maps using GIS supported indirect bivariate statistical analysis. A raster GIS software (ILWIS) and a cartographic package (ACE) were used to obtain an excellent 1:10,000 scale map. The test area was a mountain zone of 20.8 km2 in the Alpago basin, Italy. The data set was obtained at a 1:5,000 scale with a pixel resolution of 3 * 3 m. Detailed geomorphological mapping was undertaken and data on lithology, structural geology, superficial materials, slope classes, land use and distances from streams, roads and houses were collected. Direct and indirect landslide susceptibility mapping was undertaken. Direct mapping was performed after digitizing the geomorphological units assessed on the basis of susceptibility attributes determined directly from field observations. Indirect landslide susceptibility mapping was obtained from a statistical analysis of the result of overlaying the factor and inventory maps. The density of landslides in the area occupied by each factor, compared with the density of landslides in the entire area, was considered to be an expression of the importance of each factor in the instability process. Then, using the weights of evidence method (Bonham-Carter, 1994), indirect landslide susceptibility mapping was performed using the GIS. For this purpose, six different combinations of factors were tested against the results of the direct susceptibility mapping. The use of detailed geomorphological information in a bivariate analysis raised the overall accuracy of the final susceptibility map considerably. The authors concluded that the ‘‘actual generation of the susceptibility maps are best done by knowledge-driven methods, such as multiclass index overlaying or fuzzy logic methods’’.

Ayalew et al. (2004) developed a GIS based model which took account of both landslide frequencies and expert knowledge of the factors that influence slope instability in Tsugawa area of the Agano River, Japan, following layering and the assignment of six weighted factors using the linear combination method. IDRISI was used by Ayalew and Yamagishi (2005) to design a landslide susceptibility map of a 105 km2 area in the Kakuda-Yahiko Mountains of Japan by the logical regression method combined with bivariate statistical analyses. Also an interesting contribution to rank landslides weighted factors in a GIS application to an area in the Apennines (Italy) is presented by Donati and Turrini (2002).

Following the pioneering papers by Carrara and Merenda (1976), Carrara et al. (1977, 1978), Carrara (1983), it became clear that multivariate analysis and GIS were particularly suitable for landslide mapping, although external statistical packages were usually required for part of the data analysis (Chung, 1995; Baeza and Corominas, 1996; Luzi and Pergalani, 1996a, b; Chung and Fabri, 1999; Baeza and Corominas, 2001; Lee and Min, 2001; Marzorati, 2002; Park and Chi, 2003; Ercanoglu et al., 2004; Süzen and Doyuran, 2004b; Xie et al., 2004; Carrara and Guzzetti, 1995; Carrara et al., 1991a, b, 1992, 1995, 2003; Guzzetti et al., 1999, 2000, 2004, etc.). Some approaches adopted a probabilistic treatment of data for slope instability, such as the Monte Carlo method (Zhou et al., 2003). These methods have also been combined with uncertainty approaches. Many published papers used statistical techniques including [weighting factors, expert assessment techniques, fuzzy logic or neural networks in slope stability maps based on probabilistic reliability index methods.]


Ayalew, L., Yamagishi, H., Ugawa, N. (2004): Landslide susceptibility mapping using GIS-based weighted linear combination, the case in Tsugawa area of Agano river, Niigata Prefecture, Japan. Landslide 1:73–81.

Ayalew, L., Yamagishi, H. (2005): The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan. Geomorphology 65:15–31.

Baeza, C., Corominas, J. (1996): Assessment of shallow landslide susceptibility by means of statistical techniques. In: Kaare Senneset (ed) Proceedings of the VIth I.S.L., Trondheim, Norway, Vol. 1. A. A. Balkema, Rotterdam, pp 147–152.

Baeza, C., Corominas, J. (2001): Assessment of shallow landslide susceptibility by means of multivariate statistical techniques. Earth Surf Processes Landforms 26:1251–1263.

Bonham-Carter, G.F. (1994): Geographic information systems for geoscientists. Modeling with GIS. Pergamon Press, Oxford and Elsevier Science Inc, New York, pp 398.

Carrara, A., Merenda, L. (1976): Landslides inventory in northern Calabria, southern Italy. Geol. Soc. Am. Bull 87:1229–1246.

Carrara, A., Pugliese, E., Merenda, L. (1977): Computer based data bank and statistical analysis of slope instability phenomena. Z Geomorph NF 21(2):187–222.

Carrara, A., Catalano, E., Sorriso-Valvo, M., Really, C., Osso, I. (1978): Digital terrain analysis for land evaluation. Geologia Applicata e Idrogeologia 13:69–127.

Carrara, A. (1983): Multivariate methods for landslide hazard evaluation. Math Geol 15:403–426.

Carrara, A., Cardinali, M., Detti, R., Guzzetti, F., Pasqui, M., Reichenbach, P. (1991a): GIS techniques and statistical models in evaluation landslide hazard. Earth Surf Processes Landforms 16:427–445.

Carrara, A., Cardinali, M., Detti, R., Guzzeti, F., Pasqui, V., Reichenbach, P. (1991b): GIS techniques and statistical models in evaluating landslide hazard. Earth Surf Processes, Landforms 16:427–445. In: Carrara A, Guzzetti F (eds) Geographical information system in assessing natural hazards. Advances in Natural and Technological Hazards Research, vol 5. Kluwer, Dordrecht, pp 57–77.

Carrara, A., Cardinali, M., Guzzetti, F. (1992): Uncertainty in assessing landslide hazard and risk. ITC J 2:172–183.

Carrara, A., Cardinali, M., Guzzetti, F., Reichenbach, P. (1995): GIS technology in mapping landslide hazard. In: Carrara A, Guzzetti F (eds) Geographical information system in assessing natural hazards. Advances in Natural and Technological Hazards Research, vol 5. Kluwer, Dordrecht, pp 135-175.

Carrara, A., Crosta, G., Frattini, P. (2003): Geomorphological and historical data in assessing landslide hazard. Earth Surf Processes Landforms 28(10):1125–1142.

Chung, C.F., Fabbri, A.G., van Westen, C.J. (1995): Multivariate regression analysis for landslide hazard zonation. In: Carrara A, Guzzetti F (eds) Geographical information systems in assessing natural hazards. Kluwer, Dordrecht, pp 135–17.

Chung, C.F., Fabbri, A. G. (1999): Probabilistic prediction models for landslide hazard mapping. Photogrammetric Eng Remote Sen 65(12):1388–1399.

Donati, L., Turrini, M.C. (2002): An objective method to rank the importance of the factors predisposing to landslides with the GIS methodology: application to an area of the Appenines (Valnerina; Perugia, Italy). Eng Geol 63:277–289.

Ercanoglu, M., Gokceoglu, C., Van Asch, T. H. W. J. (2004): Landslide susceptibility Zoning North of Yenice (NW Turkey) by multivariate statistical techniques. Nat Hazards 32:1–23.

Guzzetti, F., Carrara, A., Cardinali, M., Reichenbach, P. (1999): Landslide hazard evaluation: a review of current techniques and their application in a multi-scale study, Central Italy. Geomorphology 31:181–216.

Guzzetti, F., Cardinali, M., Reinchenbach, P., Carrara, A. (2000): Comparing landslide maps: a case study in the Upper River Basin, Central Italy. Environ Manage 25(3):247–263.

Guzzetti, F., Reichenbach, P., Ghigi, S. (2004): Rockfall hazard and risk assessment along a transportation corridor in the Nera Valley, Central Italy. Environ Manage 34(2):191–208.

Lee, S., Min, K. (2001): Statistical analysis of landslide susceptibility at Yongin, Korea. Environ. Geol., 40:1095–1113.

Luzi, L., Pergalani, F. (1996a): Application of statistical and GIS techniques to slope instability zonation (1:50.000 Fabriano geological map sheet). Soil Dyn. Earthquake Eng., 15(2):83–94.

Luzi, L., Pergalani, F. (1996b): A methodology for slope vulnerability zonation using a probabilistic method. In: Chaco´n J, Irigaray C (eds) Proceedings of the Sexto Congreso Nacional y Conferencia Internacional sobre Riesgos Naturales, Ordenacio´ n del Territorio y Medio Ambiente, vol 1, S.E.G.A.O.T., Granada, Spain, pp 537–556.

Marzorati, S., Luzi, L., De Amicis, M. (2002): Rock falls induced by earthquakes: a statistical approach. Soil Dyn. Earthquake Eng., 22:565–577.

Park, N. W., Chi, K. H. (2003): A probabilistic approach to predictive spatial data fusion for geological hazard assessment. In: Proceedings of the IGARSS2003: IEEE International Geosciences and Remote Sensing Symposium. Learning from earth’s shapes and sizes, pp 2425–2427.

Süzen, M. L., Doyuran, V. (2004b): A comparison of the GIS based landslide susceptibility assessment methods: multivariate versus bivariate. Environ. Geol., 45:665–679.

Temesgen, B., Mohammed, M. U., Korme, T. (2001): Natural hazard assessment using GIS and remote sensing methods, with particular reference to the landslides in the Wondogenet Area, Ethiopia. Phys Chem Earth, Part C: Solar Terrest Planet Sci., 26/9:665–675.

Van Westen, C. J., Rengers, N., Soeters, R. (2003): Use of geomorphological information in indirect landslide susceptibility assessment. In: Chacon J, Corominas J (eds) Special issue on Landslides and GIS. Nat. Hazards 30(3):399–419.

Xie, Q. M., Xia, Y. Y. (2004): Systems theory for risk evaluation of landslide hazard. Int. J Rock Mech. Min. Sci, vol. 41, no. 3, CD-ROM, Elsevier, Netherlands.

[Page 354]

Another example of a weighting factor procedure was used by Temesgen et al. (2001) in a study of the Wondogenet area in the eastern margin of the Ethiopian rift in a raster GIS. Estimates were made of the frequencies of landslide occurrence considering lithology, drainage network, geology, slope angle, slope aspect and vegetation cover. Priority weightings were assigned on the basis of observed landslide densities for each class and the resultant maps were overlain to produce susceptibility maps. [...] The final integration was made using pixel attributes, algebraic calculations and arithmetic means. The landslide hazard map was derived from the integration of all the susceptibility maps; [...]

[Page 355]

Van Westen et al. (2003) evaluated the importance of expert geomorphological knowledge in the production of landslide susceptibility maps using GIS supported indirect bivariate statistical analysis. Database processing software (ILWIS) and a cartographic package (ACE) were used to obtain an excellent 1:10,000 map. The test area was a mountain zone of 20.8 km2 with carbonate and flysch sediments in the Alpago basin (Italy). The data set was obtained at a 1:5,000 scale with a pixel resolution of 3 · 3 m. Detailed geomorphological mapping was undertaken and data on lithology, structural geology, superficial materials, slope classes, land use and distances from streams, roads and houses were collected. As in Barredo et al. (2000), direct and indirect landslide susceptibility mapping was undertaken. Direct mapping was performed after digitising the geomorphological units assessed on the basis of susceptibility attributes determined directly from field observations. [...]

Indirect landslide susceptibility mapping was obtained from a statistical analysis of the result of overlaying the factor and inventory maps. The density of landslides in the area occupied by each factor, compared with the density of landslides in the entire area, was considered to be an expression of the importance of each factor in the instability process. Then, using the weights of evidence method (Bonham-Carter 1994), indirect landslide susceptibility mapping was performed using the GIS. For this purpose, six different combinations of factors were tested against the results of the direct susceptibility mapping. The use of detailed geomorphological information in a bivariate analysis raised the overall accuracy of the final susceptibility map considerably. The authors concluded that the ‘‘actual generation of the susceptibility maps are best done by knowledge-driven methods, such as multiclass index overlaying or fuzzy logic methods’’.

Ayalew et al. (2004) mapped the Tsugawa area of the Agano River, Japan, plotting 791 landslide events in the 407 km2 area at a scale of 1:20,000 with a pixel resolution of 10 · 10 m. Following layering and the assignment of six weighted factors using the linear combination method, a GIS model was developed which took account of both landslide frequencies and expert knowledge of the factors that influence slope instability in the area.

IDRISI was used by Ayalew and Yamagishi (2005) to design a landslide susceptibility map of a 105 km2 area in the Kakuda-Yahiko Mountains of Japan by the logical regression method combined with bivariate statistical analyses. [...]

[...] Also an interesting contribution to rank landslides weighted factors in a GIS application to an area in the Apennines (Italy) is presented by Donati and Turrini (2002).

Statistical multivariate probabilistic analysis using GIS

Following the pioneering papers by Carrara and Merenda (1976), Carrara et al. (1977, 1978), Carrara (1983), it became clear that multivariate analysis and GIS were particularly suitable for landslide mapping, although external statistical packages were usually required for part of the data analysis (Chung 1995; Baeza and Corominas 1996; Luzi and Pergalani 1996a, b; Chung and Fabri 1999; Baeza and Corominas 2001; Lee and Min 2001; Marzorati 2002; Park and Chi 2003; Ercanoglu et al. 2004; SÜzen and Doyuran 2004b; Xie et al. 2004; Carrara and Guzzetti 1995; Carrara et al. 1991a, b, 1992, 1995, 2003; Guzzetti et al. 1999, 2000, 2004, etc.). Some approaches adopted a probabilistic treatment of data for slope instability, such as the

[Page 357]

Monte Carlo method (Zhou et al. 2003). These methods have also been combined with uncertainty approaches (e.g. Remondo et al. 2003). Many published papers use statistical techniques including weighting factors, expert assessment techniques, fuzzy logic or neural networks in slope stability maps based on probabilistic reliability index methods.



Ayalew L, Yamagishi H (2005) The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan. Geomorphology 65:15–31

Ayalew L, Yamagishi H, Ugawa N (2004) Landslide susceptibility mapping using GIS-based weighted linear combination, the case in Tsugawa area of Agano river, Niigata Prefecture, Japan. Landslide 1:73–81

Baeza C, Corominas J (1996) Assessment of shallow landslide susceptibility by means of statistical techniques. In: Kaare Senneset (ed) Proceedings of the VIth I.S.L., Trondheim, Norway, vol 1. A.A. Balkema, Rotterdam, pp 147–152

Baeza C, Corominas J (2001) Assessment of shallow landslide susceptibility by means of multivariate statistical techniques. Earth Surf Processes Landforms 26:1251–1263

Bonham-Carter GF (1994) Geographic information systems for geoscientists. Love Printing Service Ltd., Ontario

Carrara A (1983) Multivariate methods for landslide hazard evaluation. Math Geol 15:403–426

Carrara A, Merenda L (1976) Landslides inventory in northern Calabria, southern Italy. Geol Soc Am Bull 87:1229–1246

Carrara A, Pugliese E, Merenda L (1977) Computer-based data bank and statistical analysis of slope instability phenomena. Z Geomorph NF 21(2):187–222

Carrara A, Catalano E, Sorriso-Valvo M, Really C, Osso I (1978) Digital terrain analysis for land evaluation. Geologia Applicata e Idrogeologia 13:69–127

Carrara A, Cardinali M, Detti R, Guzzetti F, Pasqui M, Reichenbach P (1991a) GIS techniques and statistical models in evaluation landslide hazard. Earth Surf Processes Landforms 16:427–445

Carrara A, Cardinali M, Detti R, Guzzeti F, Pasqui V, Reichenbach P (1991b) GIS techniques and statistical models in evaluating landslide hazard. Earth Surf Processes Landforms 16:427–445. In: Carrara A, Guzzetti F (eds) Geographical information system in assessing natural hazards. Advances in Natural and Technological Hazards Research, vol 5. Kluwer, Dordrecht, pp 57–77

Carrara A, Cardinali M, Guzzetti F (1992) Uncertainty in assessing landslide hazard and risk. ITC J 2:172–183

Carrara A, Cardinali M, Guzzetti F, Reichenbach P (1995) GIS technology in mapping landslide hazard. In: Carrara A, Guzzetti F (eds) Geographical information system in assessing natural hazards. Advances in Natural and Technological Hazards Research, vol 5. Kluwer, Dordrecht, pp 135-175

Carrara A, Crosta G, Frattini P (2003) Geomorphological and historical data in assessing landslide hazard. Earth Surf Processes Landforms 28(10):1125–1142

Carrara A, Guzzetti F (eds) (1995) Geographical information systems in assessing natural hazards. Kluwer, Dordrecht

Chung CF, Fabbri AG, van Westen CJ (1995) Multivariate regression analysis for landslide hazard zonation. In: Carrara A, Guzzetti F (eds) Geographical information systems in assessing natural hazards. Kluwer, Dordrecht, pp 135–175

Chung CF, Fabbri AG (1999) Probabilistic prediction models for landslide hazard mapping. Photogrammetric Eng Remote Sen 65(12):1388–1399

Donati L, Turrini MC (2002) An objective method to rank the importance of the factors predisposing to landslides with the GIS methodology: application to an area of the Appenines (Valnerina; Perugia, Italy). Eng Geol 63:277–289

Ercanoglu M, Gokceoglu C, Van Asch THWJ (2004) Landslide susceptibility Zoning North of Yenice (NW Turkey) by multivariate statistical techniques. Nat Hazards 32:1–23

Guzzetti F, Carrara A, Cardinali M, Reichenbach P (1999) Landslide hazard evaluation: a review of current techniques and their application in a multi-scale study, Central Italy. Geomorphology 31:181–216

Guzzetti F, Cardinali M, Reinchenbach P, Carrara A (2000) Comparing landslide maps: a case study in the Upper River Basin, Central Italy. Environ Manage 25(3):247–263

Guzzetti F, Reichenbach P, Ghigi S (2004) Rockfall hazard and risk assessment along a transportation corridor in the Nera Valley, Central Italy. Environ Manage 34(2):191–208

Lee S, Min K (2001) Statistical analysis of landslide susceptibility at Yongin, Korea. Environ Geol 40:1095–1113

Luzi L, Pergalani F (1996a) Application of statistical and GIS techniques to slope instability zonation (1:50.000 Fabriano geological map sheet). Soil Dyn Earthquake Eng 15(2):83–94

Luzi L, Pergalani F (1996b) A methodology for slope vulnerability zonation using a probabilistic method. In: Chacón J, Irigaray C (eds) Proceedings of the Sexto Congreso Nacional y Conferencia Internacional sobre Riesgos Naturales, Ordenación del Territorio y Medio Ambiente, vol 1, S.E.G.A.O.T., Granada, Spain, pp 537–556

Marzorati S, Luzi L, De Amicis M (2002) Rock falls induced by earthquakes: a statistical approach. Soil Dyn Earthquake Eng 22:565–577

Park NW, Chi KH (2003) A probabilistic approach to predictive spatial data fusion for geological hazard assessment. In: Proceedings of the IGARSS2003: IEEE International Geoscience and Remote Sensing Symposium. Learning from earth’s shapes and sizes, pp 2425–2427

Süzen ML, Doyuran V (2004b) A comparison of the GIS based landslide susceptibility assessment methods: multivariate versus bivariate. Environ Geol 45:665–679

Temesgen B, Mohammed MU, Korme T (2001) Natural hazard assessment using GIS and remote sensing methods, with particular reference to the landslides in the Wondogenet Area, Ethiopia. Phys Chem Earth, Part C: Solar Terrest Planet Sci 26/9:665–675

Van Westen CJ, Rengers N, Soeters R (2003) Use of geomorphological information in indirect landslide susceptibility assessment. In: Chacon J, Corominas J (eds) Special issue on Landslides and GIS. Nat Hazards 30(3):399–419

Xie QM, Xia YY (2004) Systems theory for risk evaluation of landslide hazard. Int J Rock Mech Min Sci, vol 41, no. 3, CD-ROM, Elsevier, Netherlands

Anmerkungen

Although in most places nearly identical with exactly the same references, no hint is given that this text comes from another source.

Obviously, the copying of the reference for Luzi and Pergalani (1996b) led to all the accents being in the wrong place in Hja.

Sichter
(Graf Isolan)

[7.] Analyse:Hja/Fragment 010 01 - Diskussion
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Erstellt: 14. January 2015, 00:40 (Graf Isolan)
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Seite(n): 357, 358, 359, Zeilen: 357:left col. 3-17 - right col. 1.7-8.12; 358:left col. 10-23.39-52; right col. 13-33; 359:right col. 1-7.15-17
[Many published papers used statistical techniques including] weighting factors, expert assessment techniques, fuzzy logic or neural networks in slope stability maps based on probabilistic reliability index methods.

Hong Kong has been one of the most important sources of contributions to landslide forecasting maps and techniques. Good examples of GIS (ArcView, ESRI) applications to landslide susceptibility mapping are those authored by Dai et al. (2000), and Dai and Lee (2001, 2002a and b) for Lantau Island, which is frequently threatened by landslide events. Their methodology is based on ArcView (ESRI) and SPSS (statistical package) multivariate logistic regression of presence–absence of dependent variables relating landslides and various contributory factors. The scale used was 1:20,000 with a resolution of 20 * 20 m with an inventory of 800 landslides.

Yongin area in South korea [sic] was mapped by Lee and Min (2001) using bivariate and multivariate analysis and ArcInfo (ESRI) GIS. They used 14 different factors with the pixel resolution of 10 * 10 m. Internal validation was undertaken by checking the correlation between landslides and susceptibility classes in both statistical methods demonstrating good results and proving that the bivariate analysis is much easier to perform. Santacana et al. (2003) studied part of La Pobla de Lillet village (Pyrenees, Spain) by statistical multivariate and discriminant analysis using ArcInfo (ESRI). Seven different factors were integrated and spatial validation was undertaken.

Süzen and Doyuran (2004a) studied an area of 200 km2 in the Asarsuyu basin (Turkey) at a scale 1:25,000, using GIS and two methods of statistical analysis: bivariate and multivariate multiple regressions. The first method was quicker but less accurate while the second, more complex, method provided a better correspondence between the factor analysis and landslides. Thirteen factors were considered and analyzed for their relationship with an inventory of 49 landslides of different types, mostly earth flow and shallow translational slides. The zonation was validated by comparing the zonation with previous landslide activity.

Ercanoglu et al. (2004a) made a landslide susceptibility map of 64 km2 of the Yenice region of Turkey using GIS to overlay factors weighted by statistical multivariate and factorial analysis techniques. Spatial validation was undertaken by relating 57 recorded landslides to the susceptibility zones. Lee (2004) made a landslide susceptibility map of the Janghung area (South Korea) using bivariate and multivariate statistical methods and a pixel size of 10 *10 m. Most of the landslides in the 41 km2 study area were superficial movements. The bivariate method analyzed the probability relationship (landslide frequency) in each of 13 classes of contributory factors. Multivariate logistic regression, although a complex and time consuming process, resulted in a better correspondence of recorded landslides with defined susceptibility levels. Also Dias and Zuquette (2004) presented an interesting probabilistic landslide susceptibility mapping in Ouro Preto, Brazil and Ohlmacher and Davis (2003) a logistic regression method to landslide hazard mapping in Kansas, USA.

More powerful computing has become available allowing new GIS matrix methods (Irigaray, 1990, 1995- cited by Chacon et al., 2006) to deal with increasing numbers of attributes. For instance, in the Betic Cordillera (Southern Spain), a region of about 15,000 km2 has been covered by landslide susceptibility maps by many authors (cited by Chacon et al., 2006, pp. 359) using the GIS matrix method. Maps at scales from 1:2,000 to 1:50,000 have been prepared using Spans GIS (Tydac-Intera), ArcInfo and ArcGIS (ESRI), depending on the research objectives.


Chacon, J., Irigaray, C., Fernandez, T., El Hamdouni, R. (2006): Engineering geology maps: landslides and geographical information systems. Bulleting of Engineering Geology and the Environment, Vol. 65, Nr.04, Dec. 2006, pp 341-411.

Dai, F.C., Lee, C.F., Li, J., Xu, Z.W. (2000): Assessment of landslide susceptibility on the natural terrain of Lantau Island, Hong Kong. Environ Geol 40(3):381–391.

Dai, F.C., Lee, C.F. (2001): Frequency–volume relation and prediction of rainfall-induced landslides. Eng Geol 59(3/4):253– 266.

Dai, F.C., Lee, C.F. (2002a): Landslide characteristics and slope instability modeling using GIS, Lantau Island, Hong Kong. Geomorphology 42(3–4):213–228.

Dai, F.C., Lee, C.F. (2002b): Landslide on natural terrain—physical characteristics and susceptibility mapping in Hong Kong. Mt Res Dev 22(1):40–47.

Dias, E.C., Zuquette, L.V. (2004): Methodology adopted for probabilistic assessment of landslides in Ouro Preto, Brazil. In:Lacerda WA, Ehrlich M, Fontoura SAB, Sayao ASF (eds) Landslides: evaluation and stabilization. Balkema, Taylor & Francis Group, London, pp 287–292.

Ercanoglu, M., Gokceoglu, C., Van Asch, T. H. W. J. (2004): Landslide susceptibility Zoning North of Yenice (NW Turkey) by multivariate statistical techniques. Nat Hazards 32:1–23.

Irigaray, C. (1990): Cartografıa de riesgos geologicos asociados a movimientos de ladera en el sector de Colmenar (Ma´ laga) [sic]. Unpublished Post-graduate Thesis. University of Granada, pp 390.

Irigaray, C. (1995): Movimientos de ladera: inventario, analisisy cartografıa de susceptibilidad mediante un Sistema de Informacion Geografica: Aplicacio´n a las zonas de Colmenar(Ma´laga) [sic], Rute (Co´rdoba) [sic] y Montefrı´o [sic] (Granada). Unpublished PhD Thesis. University of Granada, Spain.

Lee, S., Min, K. (2001): Statistical analysis of landslide susceptibility at Yongin, Korea. Environ. Geol., 40:1095–1113.

Lee, S. (2004): Application of likelihood ratio and logistic regression models to landslide susceptibility mapping using GIS. Environ. Manage, 34(2): 223–232.

Santacana, N., Baeza, C., Corominas, J., de Paz, A., Marturia,´ [sic] J. (2003): A GIS-based multivariate statistical analysis for shallow landslide susceptibility mapping in La Pobla de Lillet Area (Eastern Pyrenees, Spain). In: Chacon J, Corominas J (eds) Special issue on Landslides and GIS. Nat. Hazards 30(3):281–295

Süzen, M. L., Doyuran, V. (2004a): Data driven bivariate landslide susceptibility assessment using geographical information system: a method and application to Asarsuyu catchment, Turkey. Eng. Geol., 71: 303–321.

[Page 357]

Many published papers use statistical techniques including weighting factors, expert assessment techniques, fuzzy logic or neural networks in slope stability maps based on probabilistic reliability index methods.

Hong Kong has been one of the most important sources of contributions to landslide forecasting maps and techniques. Good examples of GIS (Arc View, ESRI) applications to landslide susceptibility mapping are those authored by Dai et al. (2000), and Dai and Lee (2001, 2002a, b) for Lantau Island, which is frequently threatened by landslide events. Their methodology is based on an Arc View (ESRI) and SPSS (statistical package) multivariate logistic regression of presence–absence of dependent variables relating landslides and various contributory factors (lithology, slope angle, slope aspect, elevation, soil cover, distance to river channels). [...] The scale used was 1:20,000 with a resolution of 20 x 20 m. The degree of adjustment between inventory and factors was 82% (Dai et al. 2000), 77.1% with all the inventory events, 82.5% considering the most recent landslides only (Dai and Lee 2001) and 82.8% with an inventory of 800 landslides (Dai et al. 2001).

[Page 358]

In the South of Korea, the Yongin area was mapped by Lee and Min (2001) using bivariate and multivariate analysis and ArcInfo (ESRI) GIS. They used 14 different factors and the pixel resolution was 10 x 10 m. Internal validation was undertaken by checking the correlation between landslides and susceptibility classes in both statistical methods. This demonstrated good results but the bivariate analysis was much easier to perform (Fig. 6).

Santacana et al. (2003) studied part of La Pobla de Lillet village (Pyrenees, Spain) by statistical multivariate and discriminant analysis using Arc Info (ESRI). Seven different factors were integrated. Spatial validation was undertaken by comparing a data set of landslides from a part of the study region with the factors derived in other parts of the region and checking the result against the original susceptibility zonation.

[...]

Süzen and Doyuran (2004a) studied an area of 200 km2 in the Asarsuyu basin (Turkey) at a scale 1:25,000, using GIS and two methods of statistical analysis: bivariate (with the overlay of factor maps and use of weighting) and multivariate multiple regression. The first method was quicker but less accurate while the second, more complex, method provided a better correspondence between the factor analysis and landslides. Thirteen factors were considered and analysed for their relationship with an inventory of 49 landslides of different types, mostly earth flow and shallow translational slides. The zonation was validated by comparing the zonation with previous landslide activity. [...]

[...]

Ercanoglu et al. (2004) made a landslide susceptibility map of 64 km2 of the Yenice region of Turkey using GIS to overlay factors weighted by statistical multivariate and factorial analysis techniques. Spatial validation was undertaken by relating the 57 recorded landslides to the susceptibility zones.

Lee (2004) made a landslide susceptibility map of the Janghung area (South Korea) using bivariate and multivariate statistical methods and a pixel size of 10 x 10 m. Most of the landslides in the 41 km2 study were superficial movements. The bivariate method analysed the probability relationship (landslide frequency) in each of 13 classes of contributory factors. Multivariate logistic regression, although a complex and time consuming process, resulted in a better correspondence of recorded landslides with defined susceptibility levels. Also Dias and Zuquette (2004) presented an interesting probabilistic landslide susceptibility mapping in Ouro Preto, Brazil and Ohlmacher and Davis (2003) a logistic regression method to landslide hazard mapping in Kansas, USA.

[Page 359]

More powerful computing has become available at decreasing prices allowing new GIS matrix methods (Irigaray 1990, 1995) to deal with increasing numbers of attributes.

In the Betic Cordillera (Southern Spain), a region of about 15,000 km2 has been covered by landslide susceptibility maps using the GIS matrix method (Chacón 1994; Chacón and Irigaray 1992, 1999a, b; Chacón et al. 1992a, b, 1993a, b, 1994a, b, c, 1996a, b, c, 1998, 2003; El Hamdouni 2001; El Hamdouni et al. 1996a, b, 1997a, b, 2000, 2001, 2003; Fernández 2001; Fernández et al. 1994, 1996a, b, c, d, 1997a, b, c, 1998, 2000, 2003, 2004a, b; Irigaray 1990, 1995; Irigaray et al. 1994, 1996a, b, c, d, 1997a, b, 1998a, b, 1999, 2000, 2003). Using Spans Gis (Tydac-Intera), Arc/Info and ArcGIS (ESRI), maps at scales from 1:2,000 to 1:50,000 have been prepared depending on the research objectives.



Dai FC, Lee CF (2001) Frequency–volume relation and prediction of rainfall-induced landslides. Eng Geol 59(3/4):253– 266

Dai FC, Lee CF (2002a) Landslide characteristics and slope instability modelling using GIS, Lantau Island, Hong Kong. Geomorphology 42(3–4):213–228

Dai FC, Lee CF (2002b) Landslide on natural terrain—physical characteristics and susceptibility mapping in Hong Kong. Mt Res Dev 22(1):40–47

Dai FC, Lee CF, Li J, Xu ZW (2000) Assessment of landslide susceptibility on the natural terrain of Lantau Island, Hong Kong. Environ Geol 40(3):381–391

Dias EC, Zuquette LV (2004) Methodology adopted for probabilistic assessment of landslides in Ouro Preto, Brazil. In: Lacerda WA, Ehrlich M, Fontoura SAB, Sayao ASF (eds) Landslides: evaluation and stabilization. Balkema, Taylor & Francis Group, London, pp 287–292

Ercanoglu M, Gokceoglu C, Van Asch THWJ (2004) Landslide susceptibility Zoning North of Yenice (NW Turkey) by multivariate statistical techniques. Nat Hazards 32:1–23

Irigaray C (1990).Cartografía de riesgos geológicos asociados a movimientos de ladera en el sector de Colmenar (Málaga). Unpublished Post-graduate Thesis. University of Granada, 390 pp

Irigaray, C (1995) Movimientos de ladera: inventario, análisis y cartografía de susceptibilidad mediante un Sistema de Información Geograáfica: Aplicación a las zonas de Colmenar(Málaga), Rute (Córdoba) y Montefrío (Granada). Unpublished PhD Thesis. University of Granada, Spain

Lee S (2004) Application of likelihood ratio and logistic regression models to landslide susceptibility mapping using GIS. Environ Manage 34(2):223–232

Lee S, Min K (2001) Statistical analysis of landslide susceptibility at Yongin, Korea. Environ Geol 40:1095–1113

Ohlmacher GC, Davis JC (2003) Using multiple logistic regression and GIS technology to predict landslide hazard in northeast Kansas, USA. Eng Geol 69(3–4):331–343

Santacana N, Baeza C, Corominas J, de Paz A, Marturiá J (2003) A GIS-based multivariate statistical analysis for shallow landslide susceptibility mapping in La Pobla de Lillet Area (Eastern Pyrenees, Spain). In: Chacon J, Corominas J (eds) Special issue on Landslides and GIS. Nat Hazards 30(3):281–295

Süzen ML, Doyuran V (2004a) Data driven bivariate landslide susceptibility assessment using geographical information system: a method and application to Asarsuyu catchment, Turkey. Eng Geol 71: 303–321

Anmerkungen

Although in most places nearly identical with exactly the same references, only at the very end two small hints are given that Chacón et al. (2006) was known to Hja. The first time the mention could not be helped because "(Irigaray 1990, 1995)" refers to unpublished Spanish language sources, which makes it highly improbable that Hja would have access to them(or might have read them). With the second mention of Chacón et al. (2006) Hja avoids citing - here i.e. copying the references for - a score of original Spanish research literature (Hja: "by many authors").

Obviously, the copying of the reference for Santacana et al (2003) and Irigaray (1990, 1995) led to a number of accents being in the wrong place in Hja.

The reference for Ohlmacher and Davis (2003) is missing in Hja.

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[8.] Analyse:Hja/Fragment 011 11 - Diskussion
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Einstein (1988) suggested the temporal projection of susceptibility mapping based on higher probabilities of new landslides occurring within higher susceptibility zones. Recent validation of a susceptibility map (Irigaray et al., 1999, 2006) confirmed this view, when 125 new landslides occurred after heavy rainfalls in 1997 in the Iznajar river dam area (Granada, Spain) where a susceptibility map had been completed in 1994 using an inventory of 833 older landslides. Some 61.9% of the 1997 landslides plotted in the very high susceptibility zone and 23.1% in the high susceptibility zone. Similar practical validations were successfully obtained by many other authors too for instance like in southwestern Sierra Nevada (Spain) and in Torre Vedras (Portugal).

Einstein, H. H. (1988): Special lecture: landslide risk assessment procedure. In: Proceedings of the Vth ISL Lausanne, vol 2, pp 1075–1090.

Irigaray, C., Ferna´ndez [sic], T., El Hamdouni, R., Chaco´n [sic], J. (1999): Verification of landslide susceptibility mapping, a case study. Earth Surf Processes Landforms 24:537–544.

Irigaray, C., Ferna´ndez [sic], T., El Hamdouni, R., Chaco´n [sic], J. (2006): Evaluation and validation of landslidesusceptibility maps obtained by a GIS matrix method: examples from the Betic Cordillera (southern Spain). Natural Hazards, ISSN: 0921-030X (Paper) 1573-0840 (Online) DOI: 10.1007/s11069-006-9027-8.

Einstein (1988) suggested the temporal projection of

susceptibility mapping based on higher probabilities of new landslides occurring within higher susceptibility zones. Recent validation of a susceptibility map (Irigaray et al. 1999, 2006) confirmed this view, when 125 new landslides occurred after heavy rainfalls in 1997 in the Iznájar river dam area (Granada, Spain) where a susceptibility map had been completed in 1994 (Irigaray 1995) using an inventory of 833 older landslides. Some 61.9% of the 1997 landslides plotted in the very high susceptibility zone and 23.1% in the high susceptibility zone of the 1995 map (Figs. 2, 3). A similar practical validation was obtained for a susceptibility map of the southwestern Sierra Nevada (Spain) slopes by El Hamdouni (2001) (Fig. 4) or in Torre Vedras (Portugal) by Garcia and Zêzere 2004.


Einstein HH (1988) Special lecture: landslide risk assessment procedure. In: Proceedings of the Vth ISL Lausanne, vol 2, pp 1075–1090

El Hamdouni R (2001) Estudio de movimientos de ladera en la cuenca del río Ízbor mediante un SIG: contribución al conocimiento de la relación entre tectónic activa e inestabilidad de vertientes. 429 pp and 10 maps 1:25.000, Unpublished PhD thesis. Department of Civil Engineering. University of Granada, Spain

Garcia RAC, Zêzere JL (2004) Abadia Basin (Torres Vedras, Portugal) a case study of landslide susceptibility assessment and validation. In: Lacerda WA, Ehrlich M, Fontoura SAB, Sayao ASF (eds) Landslides: evaluation and stabilization. Balkema, Taylor & Francis Group, London, pp 137–146

Irigaray, C (1995) Movimientos de ladera: inventario, análisis y cartografía de susceptibilidad mediante un Sistema de Información Geográfica: Aplicación a las zonas de Colmenar(Málaga), Rute (Córdoba) y Montefrío (Granada). Unpublished PhD Thesis. University of Granada, Spain

Irigaray C, Fernández T, El Hamdouni R, Chacón J (1999) Verification of landslide susceptibility mapping. A case study. Earth Surf Processes Landforms 24:537–544

Anmerkungen

Although in most places nearly identical with exactly the same references, the original source is not given by Hja.

Obviously, the misplacements of accents in the references for Irigaray et al. (1999, 2006) was caused by blindly copying from the electronic version of the unnamed source.

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The stability index (SI) was defined as probability of slope stability [SI = Probability (Fs>1)] over the distribution of uncertain parameters (cohesion c, friction angle Φ effective rainfall Q, and soil transmissivity T). The stability index was employed to define six hazard classes from high stability (SI>1.5) to low stability (SI=0). Several different landslide stability maps were produced from SINMAP for different precipitation conditions showing increasing instability area as percentages. The model was used by authors to predict rainfall triggered landslides and the resulting maps were considered to be helpful for citizens, land use planners and engineers, to reduce losses by means of prevention, mitigation and avoidance of such events. The stability index (SI) was defined as probability of slope stability [SI = Prob(Fs>1)] over the distribution of uncertain parameters (cohesion C, friction angle Φ, effective rainfall q, and soil transmissivity T). The stability index was employed to define six hazard classes from high stability (SI>1.5) to low stability (SI=0). [...] Several different landslide stability maps were produced from SINMAP for different precipitation conditions showing increasing instability area as percentages: 35% for 10 mm/day, 50% for 35 mm/day, 79% for 105 mm/day and 86% for 145 mm/day. Since most of the landslides in the Xiaojiang watershed are rainfall triggered, the resulting maps were considered by the authors to be helpful for citizens, land use planners and engineers, to reduce losses by means of prevention, mitigation and avoidance.
Anmerkungen

Although in most places nearly identical, the original source is not given by Hja.

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Frattini et al. (2004) also used physically based models to simulate transient hydrological and geotechnical processes on the slopes of Sarno (Southern Italy) affected by a May 1998 earthquake. They used an infinite slope stability analysis coupled with two simple hydrological models: a quasi-dynamic model to compute the contribution of lateral inflow to slope instability by simulating the time-dependent evolution of the water table; and a diffusion model used to consider the influence of water pore pressure developed from vertical infiltration during heavy rainstorms. The latter model succeeded in predicting correctly the triggering time of more than 70% of the landslides in an unstable area representing only 7.3% of the total catchments. The quasi-dynamic model was able to predict correctly slope instability in zero-order basins where the failures developed into large debris flows. The results confirmed the author’s view of the influence of both vertical and lateral water fluxes in the triggering of landslides during the Sarno earthquake.

Xie et al. (2004b) developed an excellent GIS application for landslide time-hazard assessment based on coupled infiltration and slope stability models that took account of increasing rainfall-induced pore water pressure using ArcGIS (ESRI). The case study area was about 3.4 km2 around Harabun, in the northern part of the Sasebo district, Kyushu (south-western Japan) where a representative landslide occurred in July 1997. Slope stability calculations were based on limit equilibrium plane failure, taking account also of time-space changes in geotechnical conditions with depth of the wetting front over time. The evolution of slope safety factor with time and the triggered landslide areas were shown in different maps.


Frattini, P., Crosta, G. B., Fusi, N., Dal Negro, P. (2004): Shallow landslides in pyroclastic soils: a distributed modeling approach for hazard assessment. Eng Geol 73(3–4):277–295.

Xie, M., Esaki, T., Cai, M. (2004b): A time-space based approach for mapping rainfall-induced shallow landslide hazard. Environmental Geology (2004) 46:840–850.

[Page 374]

Frattini et al. (2004) also used physically based models to simulate transient hydrological and geotechnical processes on the slopes of Sarno (Southern Italy) affected by a May 1998 earthquake. They used an infinite slope stability analysis coupled with two simple hydrological models: a quasi-dynamic model to compute the contribution of lateral inflow to slope instability by simulating the time-dependent evolution of the water table; and a diffusion model used to consider the influence of water pore pressure devel-

[Page 375]

oped from vertical infiltration during heavy rainstorms. The latter model succeeded in predicting correctly the triggering time of more than 70% of the landslides in an unstable area representing only 7.3% of the total catchment. The quasi-dynamic model was able to predict correctly slope instability in zero-order basins where the failures developed into large debris flows. The results confirmed the author’s view of the influence of both vertical and lateral water fluxes in the triggering of landslides during the Sarno earthquake.

Xie et al. (2004b) developed an excellent GIS application for landslide time-hazard assessment based on coupled infiltration and slope stability models that took account of increasing rainfall-induced pore water pressure using ArcGIS (ESRI). The case study area was about 3.4 km2 around Harabun, in the northern part of the Sasebo district, Kyushu (southwestern Japan) where a representative landslide occurred in July 1997. Slope stability calculations were based on limit equilibrium plane failure, taking account also of time– space changes in geotechnical conditions with depth of the wetting front over time. Maps showing the evolution of slope safety factor with time and the triggered landslide areas are shown in Fig. 11.


Frattini P, Crosta GB, Fusi N, Dal Negro P (2004) Shallow landslides in pyroclastic soils: a distributed modelling approach for hazard assessment. Eng Geol 73(3–4):277–295

Xie M, Esaki T, Cai M (2004b) A time-space based approach for mapping rainfall-induced shallow landslide hazard. Environ Geol 46:840–850

Anmerkungen

Although in most places nearly identical, the original source is not given by Hja.

Blindly copied, so that "the author’s view" changes from Chacón's to Hja's.

Although there is a reference for Xie et al. (2004b) in Hja there is none for Xie et al. (2004a).

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Landslide in which the sliding surface is located within the soil mantle or weathered bed rock, typically to a depth from few decimeters to some meters are called shallow landslides. They usually include debris slides, debris flows, and failures of road cut-slopes.

Shallow landslides can often happen in areas that have slopes with high permeable soils on top of low permeable bottom soils. The low permeable, bottom soils trap the water in the shallower, high permeable soils creating high water pressure in the top soils. As the top soils are filled with water and become heavy, slopes can turn out to be very unstable and slide over the low permeable bottom soils.

Shallow landslide

Landslide in which the sliding surface is located within the soil mantle or weathered bedrock (typically to a depth from few decimetres to some metres). They usually include debris slides, debris flow, and failures of road cut-slopes. Landslides occurring as single large blocks of rock moving slowly down slope are sometimes called block glides.

Shallow landslides can often happen in areas that have slopes with high permeable soils on top of low permeable bottom soils. The low permeable, bottom soils trap the water in the shallower, high permeable soils creating high water pressure in the top soils. As the top soils are filled with water and become heavy, slopes can become very unstable and slide over the low permeable bottom soils.

Anmerkungen

Not marked as a citation.

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[For instance if] there is a slope with silt and sand as its top soil and bedrock as its bottom soil, during an intense rainstorm, the bedrock will keep the rain trapped in the top soils of silt and sand. As the topsoil becomes saturated and heavy, it can start to slide over the bedrock and become a shallow landslide. Studies on shallow landslides prove that if permeability decreases with depth, a perched water table may develop in soils at intense precipitation. When pore water pressures are sufficient to reduce effective normal stress to a critical level, failure occurs.

Landslides in which the sliding surface is mostly deeply located below the maximum rooting depth of trees, typically to depths greater than ten meters are called deep seated landslides. Deep-seated landslides usually involve deep regolith, weathered rock, and/or bedrock and include large slope failure associated with translational, rotational, or complex movement.

Say there is a slope with silt and sand as its top soil and bedrock as its bottom soil. During an intense rainstorm, the bedrock will keep the rain trapped in the top soils of silt and sand. As the topsoil becomes saturated and heavy, it can start to slide over the bedrock and become a shallow landslide. R. H. Campbell did a study on shallow landslides on Santa Cruz Island California. He notes that if permeability decreases with depth, a perched water table may develop in soils at intense precipitation. When pore water pressures are sufficient to reduce effective normal stress to a critical level, failure occurs.[5]

Deep-seated landslide

Landslides in which the sliding surface is mostly deeply located below the maximum rooting depth of trees (typically to depths greater than ten meters). Deep-seated landslides usually involve deep regolith, weathered rock, and/or bedrock and include large slope failure associated with translational, rotational, or complex movement.


5. Renwick,W., Brumbaugh,R. & Loeher,L. 1982. Landslide Morphology and Processes on Santa Cruz Island California. Geografiska Annaler. Series A, Physical Geography, Vol. 64, No. 3/4, pp. 149-159

Anmerkungen

Not marked as a citation.

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3 Slope instability hazard zonation and GIS

3.1 Overview and definitions

A great deal of research concerning slope instability hazard has been done over last four decades. Initially the investigations were oriented mainly towards solving problems at particular sites. Therefore, most researches emphasized site specific investigation techniques and the development of deterministic and probabilistic models. However, the heterogeneity of the natural environment and the large variability in geotechnical properties at the regional scale are in sharp contrast to the homogeneity required by deterministic models. This contrast, coupled with the costly and time consuming site investigation techniques makes such engineering approaches inappropriate for application over large areas. Furthermore, development projects in large areas must often be assessed during an early phase of planning and decision making process.

[Page 132]

2.4 General Trends

A great deal of research concerning slope instability hazard has been done over the last 30 years.

[Page 134]

Initially the investigations were oriented mainly toward solving instability problems at particular sites. [...] Therefore, research emphasized site investigation techniques and the development of deterministic and probabilistic models. However, the heterogeneity of the natural environment at the regional scale and the large variability in geotechnical properties such as cohesion and internal friction are in sharp contrast to the homogeneity required by deterministic models. This contrast, coupled with the costly and time- consuming site investigation techniques required to obtain property values, makes the engineering approach unsuitable for application over large areas.

In engineering projects, such large areas must often be assessed during early phases of planning and decision making.

Anmerkungen

Nothing has been marked as a citation.

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Hence, hazard analysis is seldom executed in accordance with the definition given above (Soeters et al., 1996). Therefore in most hazard maps the legend classes generally do not give more information than the susceptibility of certain areas to landsliding or relative indications of the degree of hazard, such as high, medium and low.

Soeters, R., Westen, C. J. van. (1996): Slope instability recognition, analysis, and zonation- Landslides investigation and mitigation. Edited by A. K. Turner and R. L. Schuster, pp 129-177, special report 247, Transportation Research Board, National Research Council, National Academic Press, Washington, DC.

Hazard analysis is seldom executed in accordance with the definition given above, since the probability of occurrence of potentially damaging phenomena is extremely difficult to determine for larger areas. [...] Therefore, in most hazard maps the legend classes used generally do not give more information than the susceptibility of certain areas to landsliding or relative indications of the degree of hazard, such as high, medium, and low.
Anmerkungen

Although the source is given nothing has been marked as a citation.

In other places Hja refers to the source more correctly as "(Soeters and van Westen, 1996)".

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The occurrence of slope failures depends generally on complex interactions among large number of partially interrelated factors. Hence, analysis of landslide susceptibility (or hazard) requires evaluation of relationships between a variety of spatially dependent terrain conditions and spatial representation of landslides. A geographic information system (GIS) allows for the storage and manipulation of information concerning the different terrain factors as distinct data layers and thus provides an excellent tool for slope instability hazard zonation (Soeters et al., 1996). A geographic information system is defined as a “powerful set of tools for collecting, storing, retrieving at will, transforming, and displaying spatial data from the real world for a particular set of purposes” (Burrough, 1986). Generally a GIS consists of the components of data input and verification, data storage and data-base manipulation, data transformation and analysis, and data output and presentation. An ideal GIS for landslide hazard zonation combines conventional GIS procedures with image-processing capabilities and a relational data base. The system should be able to perform spatial analysis on multiple-input maps and connected attribute data tables. Necessary GIS functions include map overlay, reclassification, interpolation and a variety of other spatial functions incorporating logical, arithmetic, conditional, and neighborhood operations. In many cases landslide modeling requires the iterative application of similar analyses using different parameters. Therefore, the GIS should allow for the use of batch files and macros to assist in performing these iterations (Soeters et al., 1996).

As compared with conventional techniques, by means of GIS, a much larger variety of analysis techniques became attainable. Because of its speed of calculations, complex techniques requiring a large number of map overlays and table calculations became feasible. It also provides the possibility to improve models by evaluating results and adjusting the input variables. Here, user can achieve the optimum results by a process of trial and error, running the models several times which was difficult to achieve even once in the conventional manner. Therefore, more accurate results can be expected. In the course of a landslide hazard assessment project, the input maps derived from field observations can be progressively updated when new data are collected. Prepared data can be used by many users in an effective manner although the data entry (digitizing) is time consuming work.


Burrough, P. A. (1986): Principles of Geographical Information Systems and Land Resources Assessment. Clarendon Press, Oxford, England, pp 194.

Soeters, R., Westen, C. J. van. (1996): Slope instability recognition, analysis, and zonation- Landslides investigation and mitigation. Edited by A. K. Turner and R. L. Schuster, pp 129-177, special report 247, Transportation Research Board, National Research Council, National Academic Press, Washington, DC.

[Page 163]

4. GEOGRAPHIC INFORMATION SYSTEMS IN HAZARD ZONATION

The occurrence of slope failures depends generally on complex interactions among a large number of partially interrelated factors. Analysis of landslide hazard requires evaluation of the relationships between various terrain conditions and landslide occurrences. [...] This procedure requires evaluation of the spatially varying terrain conditions as well as the spatial representation of the landslides. A geographic information system (GIS) allows for the storage and manipulation of information concerning the different terrain factors as distinct data layers and thus provides an excellent tool for slope instability hazard zonation.

4.1 Geographic Information Systems

A GIS is defined as a “powerful set of tools for collecting, storing, retrieving at will, transforming, and displaying spatial data from the real world for a particular set of purposes” (Burrough 1986). [...] Generally a GIS consists of the following components:

1. Data input and verification,

2. Data storage and data-base manipulation,

3. Data transformation and analysis, and

4. Data output and presentation.

[Page 164]

[...] An ideal GIS for landslide hazard zonation combines conventional GIS procedures with image-processing capabilities and a relational data base. [...] The system should be able to perform spatial analysis on multiple-input maps and connected attribute data tables. Necessary GIS functions include map overlay, reclassification, and a variety of other spatial functions incorporating logical, arithmetic, conditional, and neighborhood operations. In many cases landslide modeling requires the iterative application of similar analyses using different parameters. Therefore, the GIS should allow for the use of batch files and macros to assist in performing these iterations. [...]

The advantages of GIS for assessing landslide hazard include the following:

1. A much larger variety of hazard analysis techniques becomes attainable. Because of the speed of calculation, complex techniques requiring a large number of map overlays and table calculations become feasible.

2. It is possible to improve models by evaluating their results and adjusting the input variables. Users can achieve the optimum results by a process of trial and error, running the models several times, whereas it is difficult to use these models even once in the conventional manner. Therefore, more accurate results can be expected.

3. In the course of a landslide hazard assessment project, the input maps derived from field observations can be updated rapidly when new data are collected. Also, after completion of the project, the data can be used by others in an effective manner.

The disadvantages of GIS for assessing landslide hazard include the following:

1. A large amount of time is needed for data entry. Digitizing is especially time-consuming.



Burrough, P.A. 1986. Principles of Geographical Information Systems and Land Resources Assessment. Clarendon Press, Oxford, England, 194 pp.

Anmerkungen

Although the source is given nothing has been marked as a citation.

In other places Hja refers to the source more correctly as "(Soeters and van Westen, 1996)".

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As compared with conventional techniques, by means of GIS, a much larger variety of analysis techniques became attainable. Because of its speed of calculations, complex techniques requiring a large number of map overlays and table calculations became feasible. It also provides the possibility to improve models by evaluating results and adjusting the input variables. Here, user [sic] can achieve the optimum results by a process of trial and error, running the models several times which was difficult to achieve even once in the conventional manner. Therefore, more accurate results can be expected. In the course of a landslide hazard assessment project, the input maps derived from field observations can be progressively updated when new data are collected. Prepared data can be used by many users in an effective manner although the data entry (digitizing) is time consuming work. Table 1.4 Advantages and disadvamtages of GIS

[left column]

Advantages

A much larger variety, analysis, techniques are available. Because of the speed of calculation, complex techniques requiring a large number of map overlays and table calculations become feasible.

It is possible to improve models by evaluating their results and adjusting the input variables. Users can achieve the optimum results by a process of trial and error, running the models several times, whereas it is difficult to use these models even once in the conventional manner. Therefore, more accurate results can be expected

In the course of a hazard assessment project, the input maps derived from field observations can be updated rapidly when new data are collected. Also, after completion of the project, the data can be used by others in an effective manner

[right column]

Disadvantages

A large amount of time is needed for data entry. Digitizing is especially time consuming

Anmerkungen

Not marked as a citation.

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The method utilizes the normalized landslide densities derived using the landslide occurrence in each factor class for calculating weight values. Information value method (Yin and Yan, 1988; Kobashi and Suzuki, 1988) and weights of evidence modeling (Spiegelhalter, 1986; Bonham-Carter et al., 1990) are two common bivariate methods applied in LHZ mapping.

Bonham-Carter, G. F., Agterberg, F. P. (1990): Application of a microcomputer based geographic information system to mineral potential mapping. In Microcomputer Based Applications in Geology (eds Hanley, T. and Merriam, D. F.), Pergamon Press, Oxford, Vol. 2.

Kobashi, S., Suzuki, M. (1988): Hazard index for the Judgment of Slope Stability in the Rokko Mountain Region. In. Proc., Interpraevent 1988, Graz, Austria, Vol. 1, pp 223-233.

Spiegelhalter, D. J. (1986): A statistical view of uncertainty in expert systems. In: Gale, W. (Ed.), Artificial Intelligence and Statistics. Addison-Wesley, Reading, MA, pp 17–55.

Ying [sic], K. L., Yan, T. Z. (1988): Statistical Prediction Model for Slope Instability of Metamorphosed Rocks. In Proc., Fifth International Symposium on Landslides, Lausanne (C. Bonnard, ed.), A. A. Balkema, Rotterdam, Netherlands, Vol. 2, pp 1269-1272.

The bivariate statistical methods utilize the normalized landslide densities derived using the landslide occurrence in each parameter class to arrive at the hazard map. Information value method and weights of evidence modelling are two common bivariate methods applied in LHZ mapping process.
Anmerkungen

A collage of the original text with material from Soeters and van Westen 1996 (with the references taken from there). Nothing has been marked as a citation. The sources are not given.

See also Hja/Fragment_040_11, where the other source of this collage is documented.

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Information value method (Yin and Yan, 1988; Kobashi and Suzuki, 1988) and weights of evidence modeling (Spiegelhalter, 1986; Bonham-Carter et al., 1990) are two common bivariate methods applied in LHZ mapping. Chung and Fabbri (1993) described several other methods, including Bayesian combination rules, Certainty factors, Dempster-Shafer belief function, and Fuzzy logic interpretation. Each method has its specific rules for data integration for producing total hazard map.

Bonham-Carter, G. F., Agterberg, F. P. (1990): Application of a microcomputer based geographic information system to mineral potential mapping. In Microcomputer Based Applications in Geology (eds Hanley, T. and Merriam, D. F.), Pergamon Press, Oxford, Vol. 2.

Chung, C. F., Fabbri, A. G. (1993): The Representation of Geosciences information for data integration. Nonrenewable Resource Vol. 2, No. 3, pp 122–139.

Kobashi, S., Suzuki, M. (1988): Hazard index for the Judgment of Slope Stability in the Rokko Mountain Region. In. Proc., Interpraevent 1988, Graz, Austria, Vol. 1, pp 223-233.

Spiegelhalter, D. J. (1986): A statistical view of uncertainty in expert systems. In: Gale, W. (Ed.), Artificial Intelligence and Statistics. Addison-Wesley, Reading, MA, pp 17–55.

Ying [sic], K. L., Yan, T. Z. (1988): Statistical Prediction Model for Slope Instability of Metamorphosed Rocks. In Proc., Fifth International Symposium on Landslides, Lausanne (C. Bonnard, ed.), A. A. Balkema, Rotterdam, Netherlands, Vol. 2, pp 1269-1272.

[Page 135]

these have been termed the landslide susceptibility method (Brabb 1984; van Westen 1992,1993), information value method (Yin and Yan 1988; Kobashi and Suzuki 1988), and weight-of-evidence modeling method (Spiegelhalter 1986). Chung and Fabbri (1993) described several methods, including Bayesian combination rules, certainty factors, Dempster-Shafer method, and fuzzy logic.

[Page 168]

Each method has its specific rules for data integration required to produce the total hazard map.



Chung, C.J., and A.G. Fabbri. 1993. The Representation of Geoscience Information for Data Integration. Nonrenewable Resources, Vol. 2, No. 3, pp. 122-139.

Kobashi, S., and M. Suzuki. 1988. Hazard Index for the Judgment of Slope Stability in the Rokko Mountain Region. In Proc., Interpraevent 1988, Graz, Austria, Vol. 1, pp. 223-233. Spiegelhalter, D.J. 1986. Uncertainty in Expert Systems. In Artificial Intelligence and Statistics (W.A. Gale, ed.), Addison-Wesley, Reading, Mass., pp. 17-55.

Yin, K.L., and T.Z. Yan. 1988. Statistical Prediction Model for Slope Instability of Metamorphosed Rocks. In Proc., Fifth International Symposium on Landslides, Lausanne (C. Bonnard, ed.), A.A. Balkema, Rotterdam, Netherlands, Vol. 2, pp. 1269-1272.

Anmerkungen

A collage of the original text (with the references also taken). Nothing has been marked as a citation. The source is not given.

See also Hja/Fragment_040_09, where the other source of this collage is documented.

Sichter
(Graf Isolan)

[19.] Analyse:Hja/Fragment 040 35 - Diskussion
Bearbeitet: 26. January 2015, 00:13 Graf Isolan
Erstellt: 25. January 2015, 23:57 (Graf Isolan)
BauernOpfer, Fragment, Hja, SMWFragment, Schutzlevel, Soeters and van Westen 1996, ZuSichten

Typus
BauernOpfer
Bearbeiter
Graf Isolan
Gesichtet
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Untersuchte Arbeit:
Seite: 40, Zeilen: 35-40
Quelle: Soeters and van Westen 1996
Seite(n): 136, 165, Zeilen: 136:left col. 3-7; 165:right col. 18-27
The main problem with deterministic models is their high degree of simplification. A deterministic method that is usually applied for translational slides is the infinite slope model (Ward et al., 1982) since they are simple to use for each pixel separately within the raster GIS environment. Hammond et al. (1992) presented methods in which the variability of the factor of safety is calculated from selected input variables utilizing Monte Carlo techniques. This implies a large number of repeated calculations, which are readily supported by use of a GIS (Soeters and van Westen, 1996).

Hammond, C. J., Prellwitz, R. W., Miller, S. M. (1992): Landslide Hazard Assessment Using Monte Carlo Simulation. In Proc., Sixth International Symposium on Landslides (D. H. Bell, ed.), Christchurch, New Zealand, A. A. Balkema, Rotterdam, Netherlands, Vol. 2, pp 959-964.

Soeters, R., Westen, C. J. van. (1996): Slope instability recognition, analysis, and zonation- Landslides investigation and mitigation. Edited by A. K. Turner and R. L. Schuster, pp 129-177, special report 247, Transportation Research Board, National Research Council, National Academic Press, Washington, DC.

Ward, T. J., Li, R. M., Simons, D. B. (1982): Mapping landslides in forested watersheds. Journal of the Geotechnical engineering Division 8, 319-324.

[Page 136]

The main problem with these methods is their high degree of oversimplification. A deterministic method that is usually applied for translational landslides is the infinite slope model (Ward et al. 1982).

[Page 165]

Most examples deal with infinite slope models, since they are simple to me for each pixel separately (Brass et al. 1989; Murphy and Vita-Finzi 1991; van Westen 1993). Hammond et al. (1992) presented methods in which the variability of the factor of safety is calculated from selected input variables utilizing Monte Carlo techniques. This implies a large number of repeated calculations, which are readily supported by use of a GIS.


Brass, A., G. Wadge, and A.J. Reading. 1989. Designing a Geographical Information System for the Prediction of Landsliding Potential in the West Indies. In Proc., Economic Geology and Geotechnics of Active Tectonic Regions, University College, University of London, April, 13 pp.

Hammond, C.]., R.W. Prellwitz, and S.M. Miller. 1992. Landslide Hazard Assessment Using Monte Carlo Simulation. In Proc., Sixth International Symposium on Landslides (D.H. Bell, ed.), Christchurch, New Zealand, A.A. Balkema, Rotterdam, Netherlands, Vol. 2, pp. 959-964.

Murphy, W., and C. Vita-Finzi. 1991. Landslides and Seismicity: An Application of Remote Sensing. In Proc., Eighth Thematic Conference on Geological Remote Sensing, Denver, Colo., Environmental Research Institute of Michigan, Ann Arbor, Vol. 2, pp. 771-784.

Ward, T.J., L. Ruh-Ming, and D.B. Simons. 1982. Mapping Landslide Hazard in Forest Watershed. Journal of the Geotechnical Engineering Division, ASCE, Vol. 108, No. GT2, pp. 319-324.

Anmerkungen

Although the source is given nothing has been marked as a citation.

In other places Hja refers to the source more correctly as "(Soeters and van Westen, 1996)".

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Quelle Autor Titel Verlag Jahr Lit.-V. FN
Hja/Chacon et al 2006 J. Chacón, C. Irigaray, T. Fernández, R. El Hamdouni Engineering geology maps: landslides and geographical information systems Springer 2006 ja ja
Hja/Wikipedia Landslide 2010 Landslide 2010 nein nein


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