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Graf Isolan
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Untersuchte Arbeit:
Seite: 9, Zeilen: 1ff (complete page)
Quelle: Chacon et al 2006
Seite(n): 354, 355, 357, Zeilen: 354:left col. 14-23.26-29; 355:left col. 18-36.40-53 - right col. 1-18.32-35.38-51; 357:left col. 1-7
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.

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