Landslide spatial susceptibility mapping by using GIS and remote sensing techniques: a case study in Zigui County, the Three Georges reservoir, China

Abstract Landslides are one of the most destructive phenomena in nature and damage both property and lives every year. In this paper, a logistic regression model with datasets developed via a geographic information system and remotely sensed data was used to create a landslide spatial susceptibility map for the Three Gorges Project reservoir region on the Yangtze River in Zigui County. The five causative factors used in the logistic regression model were evaluated in different ways: topographic slope and topographic aspect were derived from a topographical map at 1:50,000 scale; bed rock-slope relationship and lithology were obtained from a geological map at 1:50,000 scale; and fractional vegetation cover (FVC), which represents the reduced frequency of landslides due to the vegetation canopy and ground cover and is also one of the most difficult parameters to estimate over broad geographic areas, was generated using a back propagation neural network (BPNN) method based on CBERS (China–Brazil Earth Resources Satellite) data, the results of which were compared with values measured in the field. The obtained Pearson correlation coefficient (r) was 0.899. Then, the FVC factor and the other four factors were used as the input to a logistic regression model. By integrating the five factor maps in the geographical information system (GIS) via pixel-based computing, the landslide spatial susceptibility map was obtained. The study area was reclassified into four categories of landslide susceptibility: severe, moderate, low, and very low. Approximately 15.0 % of the study area was identified as severe susceptibility, and very low, low, and moderate susceptibility zones covered 21.8, 41.7, and 21.5 % of the area, respectively. These results have an accuracy of 78.90 %. Thus, by using a logistic regression model in a GIS environment, a spatial susceptibility map of landslides can be obtained, and the regions in Zigui County that are susceptible to landslides and need immediate protective and mitigation measures can be identified.

[1]  P. Aleotti,et al.  Landslide hazard assessment: summary review and new perspectives , 1999 .

[2]  R. Tateishi,et al.  Relationships between percent vegetation cover and vegetation indices , 1998 .

[3]  H. A. Nefeslioglu,et al.  Application of logistic regression for landslide susceptibility zoning of Cekmece Area, Istanbul, Turkey , 2006 .

[4]  C. F. Lee,et al.  On the spatial relationship between landslides and causative factors on Lantau Island, Hong Kong , 2002 .

[5]  H. A. Nefeslioglu,et al.  Landslide susceptibility mapping for a part of tectonic Kelkit Valley (Eastern Black Sea region of Turkey) , 2008 .

[6]  J. H. Schuenemeyer,et al.  Statistical Methods For Geographers , 1986 .

[7]  C. F. Lee,et al.  Landslide characteristics and, slope instability modeling using GIS, Lantau Island, Hong Kong , 2002 .

[8]  S. Menard.  Applied Logistic Regression Analysis , 1996 .

[9]  D. Hosmer,et al.  Applied Logistic Regression , 1991 .

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

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

[12]  Ling Shi,et al.  Investigations and assessment of the landslide hazards of Fengdu county in the reservoir region of the Three Gorges project on the Yangtze River , 2004 .

[13]  Xueling Wu,et al.  Landslide susceptibility mapping based on rough set theory and support vector machines: A case of the Three Gorges area, China , 2014 .

[14]  Pinggen Zhou,et al.  GIS-Based and Data-Driven Bivariate Landslide-Susceptibility Mapping in the Three Gorges Area, China , 2009 .

[15]  F. Ren,et al.  Landslide susceptibility mapping using rough sets and back-propagation neural networks in the Three Gorges, China , 2013, Environmental Earth Sciences.

[16]  Sophia Decker,et al.  Logistic Regression A Self Learning Text , 2016 .

[17]  H. Wang,et al.  Comparative evaluation of landslide susceptibility in Minamata area, Japan , 2005 .

[18]  Alan B. Anderson,et al.  Mapping and uncertainty of predictions based on multiple primary variables from joint co-simulation with Landsat TM image and polynomial regression , 2002 .

[19]  Thomas Glade,et al.  Linking debris-flow hazard assessments with geomorphology , 2005 .

[20]  K. T. Chau,et al.  Regional bias of landslide data in generating susceptibility maps using logistic regression: Case of Hong Kong Island , 2005 .

[21]  E. Yesilnacar,et al.  Landslide susceptibility mapping : A comparison of logistic regression and neural networks methods in a medium scale study, Hendek Region (Turkey) , 2005 .

[22]  Z. Cheng-hu.  Appropriate Grid Size for Terrain Based Landslide Risk Assessment in Lantau Island,Hong Kong , 2003 .

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

[24]  Ling Shi,et al.  Zonation of the landslide hazards in the forereservoir region of the Three Gorges Project on the Yangtze River , 2001 .

[25]  P. Reichenbach,et al.  Landslide hazard evaluation: a review of current techniques and their application in a multi-scale study, Central Italy , 1999 .

[26]  L. Ayalew,et al.  Landslides in Sado Island of Japan: Part II. GIS-based susceptibility mapping with comparisons of results from two methods and verifications , 2005 .

[27]  Bo Du,et al.  Regional soil erosion risk mapping using RUSLE, GIS, and remote sensing: a case study in Miyun Watershed, North China , 2011 .

[28]  H. Lan,et al.  Landslide hazard spatial analysis and prediction using GIS in the Xiaojiang watershed, Yunnan, China , 2004 .

[29]  Philippa J. Mason,et al.  Landslide hazard assessment in the Three Gorges area of the Yangtze river using ASTER imagery: Zigui–Badong , 2004 .

[30]  R. Soeters,et al.  Slope instability recognition, analysis, and zonation , 1996 .

[31]  M. Hsu,et al.  Modeling typhoon- and earthquake-induced landslides in a mountainous watershed using logistic regression , 2007 .

[32]  Fuchu Dai,et al.  Landslide risk assessment and management: an overview , 2002 .

[33]  A. K. Turner,et al.  Landslides : investigation and mitigation , 1996 .

[34]  Per Kragh Andersen,et al.  3. Applied Logistic Regression. 2nd edn. David W. Hosmer and Stanley Lemeshow. Wiley, New York, 2000. No. of pages: xii+373. Price: £60.95. ISBN 0‐471‐35632‐8 , 2002 .

[35]  M. Sorriso-Valvo,et al.  Logistic Regression analysis in the evaluation of mass movements susceptibility : The Aspromonte case study, Calabria, Italy , 2007 .

[36]  Kurt Hornik,et al.  FEED FORWARD NETWORKS ARE UNIVERSAL APPROXIMATORS , 1989 .

[37]  Sijing Wang,et al.  Characteristics of rapid giant landslides in China , 2004 .

[38]  C. F. Lee,et al.  Assessment of landslide susceptibility on the natural terrain of Lantau Island, Hong Kong , 2001 .

[39]  R. Niu,et al.  Assessment of spatial distribution of soil loss over the upper basin of Miyun reservoir in China based on RS and GIS techniques , 2011, Environmental monitoring and assessment.

[40]  Hans C. Jessen,et al.  Applied Logistic Regression Analysis , 1996 .

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

[42]  John R. Dymond,et al.  Percentage vegetation cover of a degrading rangeland from SPOT , 1992 .

[43]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[44]  V. Doyuran,et al.  A comparison of the GIS based landslide susceptibility assessment methods: multivariate versus bivariate , 2004 .

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

[46]  S. L. Kuriakose,et al.  Spatial data for landslide susceptibility, hazard, and vulnerability assessment: An overview , 2008 .

[47]  T. Topal,et al.  GIS-based landslide susceptibility mapping for a problematic segment of the natural gas pipeline, Hendek (Turkey) , 2003 .

[48]  Saro Lee,et al.  Statistical analysis of landslide susceptibility at Yongin, Korea , 2001 .

[49]  Josef Cihlar,et al.  A METHODOLOGY FOR MAPPING AND MONITORING CROPLAND SOIL EROSION , 1987 .

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

[51]  Stephen J. McGregor,et al.  An integrated geographic information system approach for modeling the suitability of conifer habitat in an alpine environment , 1998 .

[52]  M. C. Bronsveld,et al.  A knowledge-based approach for C-factor mapping in Spain using Landsat TM and GIS , 1996 .

[53]  Jing-Wen Chen,et al.  The Application of Remote Sensing Technology to the Interpretation of Land Use for Rainfall-Induced Landslides Based on Genetic Algorithms and Artificial Neural Networks , 2009, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[54]  Guangxing Wang,et al.  Improvement in mapping vegetation cover factor for the universal soil loss equation by geostatistical methods with Landsat Thematic Mapper images , 2002 .

[55]  R. Pech,et al.  The assessment and monitoring of sparsely vegetated rangelands using calibrated Landsat data , 1988 .

[56]  P. Atkinson,et al.  Generalised linear modelling of susceptibility to landsliding in the Central Apennines, Italy , 1998 .

[57]  Ross K. Meentemeyer,et al.  Automated mapping of conformity between topographic and geological surfaces , 2000 .

[58]  L. Ayalew,et al.  The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan , 2005 .