Landslide Hazard Zonation using Remote Sensing and GIS: a case study of Dikrong river basin, Arunachal Pradesh, India

Landslides are among the most costly and damaging natural hazards in mountainous regions, triggered mainly under the influence of earthquakes and/or rainfall. In the present study, Landslide Hazard Zonation (LHZ) of Dikrong river basin of Arunachal Pradesh was carried out using Remote Sensing and Geographic Information System (GIS). Various thematic layers namely slope, photo-lineament buffer, thrust buffer, relative relief map, geology and land use / land cover map were generated using remote sensing data and GIS. The weighting-rating system based on the relative importance of various causative factors as derived from remotely sensed data and other thematic maps were used for the LHZ. The different classes of thematic layers were assigned the corresponding rating value as attribute information in the GIS and an “attribute map” was generated for each data layer. Each class within a thematic layer was assigned an ordinal rating from 0 to 9. Summation of these attribute maps were then multiplied by the corresponding weights to yield the Landslide Hazard Index (LHI) for each cell. Using trial and error method the weight-rating values have been re-adjusted. The LHI threshold values used were: 142, 165, 189 and 216. A LHZ map was prepared showing the five zones, namely “very low hazard”, “low hazard”, “moderate hazard”, “high hazard” and “very high hazard” by using the “slicing” operation.

[1]  D. Barrell,et al.  The geology of Dunedin, New Zealand, and the management of geological hazards , 2003 .

[2]  M. Arora,et al.  GIS-based Landslide Hazard Zonation in the Bhagirathi (Ganga) Valley, Himalayas , 2002 .

[3]  R. Nagarajan,et al.  Technical note Temporal remote sensing data and GIS application in landslide hazard zonation of part of Western ghat, India , 1998 .

[4]  Marion Michael-Leiba,et al.  Regional landslide risk to the Cairns community , 2003 .

[5]  N. Casagli,et al.  Landslide monitoring by ground-based radar interferometry: A field test in Valdarno (Italy) , 2003 .

[6]  Michael Sakellariou,et al.  GIS-BASED ESTIMATION OF SLOPE STABILITY , 2001 .

[7]  A. Fraser,et al.  A satellite remote sensing technique for geological structure horizon mapping , 1997 .

[8]  Jacob Cohen A Coefficient of Agreement for Nominal Scales , 1960 .

[9]  Olav Slaymaker,et al.  Landslide inventory in a rugged forested watershed: a comparison between air-photo and field survey data , 2003 .

[10]  C. J. van Westen,et al.  GIS in landslide hazard zonation : a review, with examples from the Andes of Colombia , 1994 .

[11]  R. Leemans,et al.  Comparing global vegetation maps with the Kappa statistic , 1992 .

[12]  T. Kamai,et al.  Detection of a landslide movement as geometric misregistration in image matching of SPOT HRV data of two different dates , 2003 .

[13]  W. KienzleS,et al.  Using DTMs and GIS to define input variables for hydrological and geomorphological analysis. , 1996 .

[14]  Paolo Salvaneschi,et al.  Embedding a Geographic Information System in a Decision Support System for Landslide Hazard Monitoring , 1999 .

[15]  Sankar Kumar Nath,et al.  Seismic Hazard Mapping and Microzonation in the Sikkim Himalaya through GIS Integration of Site Effects and Strong Ground Motion Attributes , 2004 .

[16]  Alessandro Pasuto,et al.  Major risk from rapid, large-volume landslides in Europe (EU Project RUNOUT) , 2003 .

[17]  C. J. Westen,et al.  Analyzing the evolution of the Tessina landslide using aerial photographs and digital elevation models , 2003 .

[18]  C. F. Lee,et al.  A dynamic model for rainfall-induced landslides on natural slopes , 2003 .

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

[20]  Saro Lee,et al.  Probabilistic landslide hazard mapping using GIS and remote sensing data at Boun, Korea , 2004 .

[21]  Saro Lee,et al.  Determination and application of the weights for landslide susceptibility mapping using an artificial neural network , 2004 .

[22]  Christophe Delacourt,et al.  Seventeen years of the “La Clapière” landslide evolution analysed from ortho-rectified aerial photographs , 2003 .

[23]  Martin F. Price,et al.  Mountain Environments and Geographic Information Systems , 1995 .

[24]  R. Soeters,et al.  Use of Geomorphological Information in Indirect Landslide Susceptibility Assessment , 2003 .

[25]  Giandomenico Spezzano,et al.  Simulation of a cellular landslide model with CAMELOT on high performance computers , 2003, Parallel Comput..

[26]  Thomas Glade,et al.  Landslide occurrence as a response to land use change: a review of evidence from New Zealand , 2003 .

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

[28]  L. V. Beek,et al.  Regional Assessment of the Effects of Land-Use Change on Landslide Hazard By Means of Physically Based Modelling , 2004 .

[29]  A. C. Seijmonsbergen,et al.  Comparing Landslide Hazard Maps , 1999 .

[30]  John C. Davis,et al.  Using multiple logistic regression and GIS technology to predict landslide hazard in northeast Kansas, USA , 2003 .

[31]  Fausto Guzzetti,et al.  Use of GIS Technology in the Prediction and Monitoring of Landslide Hazard , 1999 .

[32]  C. Gokceoglu,et al.  Landslide Susceptibility Zoning North of Yenice (NW Turkey) by Multivariate Statistical Techniques , 2004 .