Investigation on landslide susceptibility using remote sensing and GIS methods

  • Junyi Huang

Student thesis: Master's Thesis


Landslides are one of the most destructive disasters that cause damage to both property and life every year. Various methodologies have been reported for landslide susceptibility mapping. Statistical methods are widely used to fit the mathematical relationship between observed landslides and the factors considered to influence the slope failure, and have shown remarkable accuracy. Among these models, frequency ratio and logistic regression models are the most popular for its simplicity and high accuracy. However, virtually all previous studies randomly extracted and reserved a portion of historical landslide records to perform the model evaluation. The purpose of this study are: 1) To produce a landslide susceptibility map for Lantau Island by GIS and remote sensing methods as well as statistical modeling techniques 2) To add extra value to the literature of evaluating their “prediction rate” (rather than “success rate”) for landslide susceptibility mapping in a temporal context. The mountainous terrain, heavy and prolonged rainfall, as well as dense development near steep hillsides make Hong Kong as one of the most vulnerable metropolitans to the risk of landslides. As there is an increasingly high demand for land resource to support the growth of economic and population, regional specific landslide susceptibility assessment in Hong Kong is necessary for hazard management and effective land use planning. Firstly, the spatial relationship among landslide occurrence and nine causative factors (elevation, slope aspect, slope gradient, plan curvature, profile curvature, NDVI, distance to river, SPI and lithology) were explored. The distribution of landslides on Lantau Island is largely governed by a combination of geo-environmental conditions, such as elevation of 200m-300m, slope gradient of 25°-35°, slope aspect of west or northwest, high degree of positive or negative plan curvature and profile curvature, sparse vegetation in terms of NDVI in 0.3-0.5 (shrub/grassland), proximity (0.6-1.2km) to fault line, presence of volcanic bedrocks (especially rhyolite lava and tuff) and high stream power index. Second, landslide susceptibility maps were generated by frequency ratio and logistic regression model, respectively. Validations of the mapping results were performed by calculating relative operating characteristics (ROC). The models, trained by 1,864 (70%) landslides records in the Enhanced Natural Terrain Landslide Inventory (ENTLI) from 2000 to 2008, were validated by subsequent 799 (30%) landslide occurred from 2008 to 2009. The validation result shows that logistic regression model (88.70%) possesses a better prediction power than frequency ratio model (78.00%) for the study area. The findings suggested that logistic regression analysis is more reliable for landslide susceptibility mapping. The resultant maps are expected to provide a scientific assessment of the risk areas with respect to landslides on Lantau Island, and to serve as a basis for decisions or justification of the Lantau development planning. Keywords: landslide susceptibility; frequency ratio; logistic regression; temporal verification; GIS; Hong Kong
Date of Award18 Aug 2014
Original languageEnglish
SupervisorQiming ZHOU (Supervisor)

User-Defined Keywords

  • Environmental monitoring
  • Geographic information systems
  • Landslide hazard analysis
  • Landslides
  • Measurement
  • Remote sensing

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