Earthquake‐induced landslide hazard monitoring and assessment using SOM and PROMETHEE techniques: A case study at the Chiufenershan area in Central Taiwan

Monitoring and assessment of landslide hazard is an important task for decision making and policy planning in the landslide area. Massive landslides, caused by the catastrophic Chi‐Chi earthquake in 1999, occurred in Central Taiwan, especially at Chiufenershan area in Nantou county. This study proposed two useful indicators coupled with the Self‐organizing map (SOM) neural network and the Preference Ranking Organization METHod for Enrichment Evaluations (PROMETHEE) technique to quickly extract accurate post‐quake landslides from multi‐temporal Système Probatoire de l'Observation de la Terre (SPOT) images. A GIS‐based system was developed to simplify and integrate the procedures such as image pre‐processing, the SOM training, the PROMETHEE calculation, landslide extraction and accuracy assessment. The evaluated result shows that the landslide area soon after the earthquake is 209.50 ha (Kappa coefficient 96.88%). Over seven years of vegetation recovery, the denudation area has declined to 112.64 ha (Kappa coefficient 90.64%). Most earthquake‐induced landslides could be restored by natural vegetation succession. The developed system is a useful decision‐making tool for landslide area planning.

[1]  M. Aniya,et al.  Landslide hazard mapping and its evaluation using GIS: an investigation of sampling schemes for a grid-cell based quantitative method. , 2000 .

[2]  Bertrand Mareschal,et al.  Prométhée: a new family of outranking methods in multicriteria analysis , 1984 .

[3]  Pi-Hui Huang,et al.  Vegetation recovery monitoring and assessment at landslides caused by earthquake in Central Taiwan , 2005 .

[4]  M. K. Arora,et al.  An artificial neural network approach for landslide hazard zonation in the Bhagirathi (Ganga) Valley, Himalayas , 2004 .

[5]  Sovan Lek,et al.  A comparison of self-organizing map algorithm and some conventional statistical methods for ecological community ordination , 2001 .

[6]  T. Fernández,et al.  Evaluation and validation of landslide-susceptibility maps obtained by a GIS matrix method: examples from the Betic Cordillera (southern Spain) , 2007 .

[7]  Pascal Matsakis,et al.  Evaluation of Fuzzy Partitions , 2000 .

[8]  Jorma Laaksonen,et al.  SOM_PAK: The Self-Organizing Map Program Package , 1996 .

[9]  M. Ridd,et al.  A Comparison of Four Algorithms for Change Detection in an Urban Environment , 1998 .

[10]  M. E. Andrews Deller.  Facies discrimination in laterites using Landsat Thematic Mapper, ASTER and ALI data—examples from Eritrea and Arabia , 2006 .

[11]  Oswald Marinoni,et al.  A stochastic spatial decision support system based on PROMETHEE , 2005, Int. J. Geogr. Inf. Sci..

[12]  Paul M. Mather,et al.  An assessment of the effectiveness of decision tree methods for land cover classification , 2003 .

[13]  Roberta Oliveira Parreiras,et al.  A multiplicative version of Promethee II applied to multiobjective optimization problems , 2007, Eur. J. Oper. Res..

[14]  Oswald Marinoni,et al.  A discussion on the computational limitations of outranking methods for land‐use suitability assessment , 2006, Int. J. Geogr. Inf. Sci..

[15]  Alexander Siegmund,et al.  Automatic land cover analysis for Tenerife by supervised classification using remotely sensed data , 2003 .

[16]  David J. Unwin,et al.  Modelling landslide distribution on loess soils in China: an investigation , 1992, Int. J. Geogr. Inf. Sci..

[17]  Paul L. Rosin,et al.  Monitoring landslides from optical remotely sensed imagery: the case history of Tessina landslide, Italy , 2003 .

[18]  T. Chou,et al.  Application of the PROMETHEE technique to determine depression outlet location and flow direction in DEM , 2004 .

[19]  L. Bastin.  Comparison of fuzzy c-means classification, linear mixture modelling and MLC probabilities as tools for unmixing coarse pixels , 1997 .

[20]  Andrea Baraldi,et al.  A neural network for unsupervised categorization of multivalued input patterns: an application to satellite imaee clustering , 1995 .

[21]  Paul M. Treitz,et al.  Road network detection from SPOT imagery for updating geographical information systems in the rural-urban fringe , 1992, Int. J. Geogr. Inf. Sci..

[22]  Saro Lee,et al.  Application of logistic regression model and its validation for landslide susceptibility mapping using GIS and remote sensing data , 2005 .

[23]  Russell G. Congalton,et al.  A review of assessing the accuracy of classifications of remotely sensed data , 1991 .

[24]  Alan M. MacEachren,et al.  Evaluating the usability of visualization methods in an exploratory geovisualization environment , 2006, Int. J. Geogr. Inf. Sci..

[25]  Saro Lee,et al.  Landslide susceptibility mapping using GIS and the weight-of-evidence model , 2004, Int. J. Geogr. Inf. Sci..

[26]  F. Mantovani,et al.  Remote sensing techniques for landslide studies and hazard zonation in Europe , 1996 .

[27]  Kyoji Sassa,et al.  Analysis of a spatial distribution of landslides triggered by the 2004 Chuetsu earthquakes of Niigata Prefecture, Japan , 2007 .

[28]  J. McKean,et al.  REMOTE SENSING AND LANDSLIDE HAZARD ASSESSMENT , 1991 .

[29]  K. Shou,et al.  Analysis of the Chiufengershan landslide triggered by the 1999 Chi-Chi earthquake in Taiwan , 2003 .

[30]  M. Komac.  A landslide susceptibility model using the Analytical Hierarchy Process method and multivariate statistics in perialpine Slovenia , 2006 .

[31]  Stephen J. Walsh,et al.  Morphometric and multispectral image analysis of debris flows for natural hazard assessment , 1997 .

[32]  Carlo Atzeni,et al.  Long term landslide monitoring by ground‐based synthetic aperture radar interferometer , 2006 .