Use of GIS and Remote Sensing for Landslide Susceptibility Mapping

Use of GIS and Remote Sensing for Landslide Susceptibility Mapping

Arzu Erener (Kocaeli University, Turkey), Gulcan Sarp (Suleyman Demirel University, Turkey) and Sebnem H. Duzgun (Middle East Technical University, Turkey)
Copyright: © 2018 |Pages: 12
DOI: 10.4018/978-1-5225-2255-3.ch304


In recent years, geographical information systems (GISs) and Remote Sensing (RS) have proven to be common tools adopted for different studies in different scientific disciplines. GIS defined as a set of tools for the input, storage, retrieval, manipulation, management, modeling, analysis and output of spatial data. RS, on the other hand, can play a role in the production of a data and in the generation of thematic maps related to spatial studies. This study focuses on use of GIS and RS data for landslide susceptibility mapping. Five factors including Normalized Difference Vegetation Index (NDVI) and Topographic Wetness Index (TWI), slope; lineament density and distance to roads were used for the grid based approach for landslide susceptibility mappings. Results of this study suggest that geographic information systems can effectively be used to obtain susceptibility maps by compiling and overlaying several data layers relevant to landslide hazards.
Chapter Preview

Main Focus

The purpose of this study is to apply the grid based GIS techniques for landslide susceptibility mapping using five different factors including Normalized Difference Vegetation Index (NDVI), Topographic Wetness Index (TWI), slope, lineament density, and distance to roads. The scope includes the preparation of landslide susceptibility map to identify highly susceptible areas and, the accuracy assessment related to the obtained maps.

The susceptibility assessment methodology is demonstrated for More and Romsdal region in Norway (Erener & Duzgun, 2010). The study area occupies approximately 606.755 km2 in the west part of Norway. The upper left coordinates on 112707,770408 m - 6952112,603469 m and lower right coordinates 6929466,479194 m -144909,272731 m respectively (Figure 1).

Figure 1.

Study region (parts of figure adopted from NGI)

Key Terms in this Chapter

NDVI: Normalized Difference Vegetation Index is a most well-known index to detect vegetation and their condition in an area by using bands of remote sensing data.

Twi: Topographic Wetness Index describes the effect of topography on the location and size of saturated source areas of runoff generation.

GIS: Geographic Information Systems is a technology that is used to capture, store, manipulate, analyze, manage, and present spatial data.

Weighting Factors: Weighting Factors are estimated impact of values indicating relative importance.

RS: Remote sensing is the acquisition of information from earth without making physical contact with the object by using satellite- or aircraft-based sensor technologies.

Landslide Susceptibility: Landslide susceptibility is investigating the spatial likelihood of occurrence of landslides by correlating principal factors with landslide inventory data.

Complete Chapter List

Search this Book: