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Untersuchte Arbeit: Seite: 10, Zeilen: 140  Quelle: Chacon et al 2006 Seite(n): 357, 358, 359, Zeilen: 357:left col. 317  right col. 1.78.12; 358:left col. 1023.3952; right col. 1333; 359:right col. 17.1517 

[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 km^{2} 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 km^{2} 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 km^{2} 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 km^{2} 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 (TydacIntera), 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 341411. 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 rainfallinduced 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 Postgraduate 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 GISbased 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 km^{2} 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 km^{2} 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 km^{2} 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 km^{2} 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 (TydacIntera), 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 rainfallinduced 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 Postgraduate 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 GISbased 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 
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. 
