Assessment of mapping accuracy of Landslides using image classification techniques
Scott L. Huang, Been K. Chen, Robert C. Speck
Department of Mining and Geological Engineering
University of Alaska Fairbanks, Fairbanks, Alaska 99775, U.S.A.
Abstract
Lnadsat-5 TM scene images of Healy, Alaska and terrain information (i.e. elevations, drainage system, bedrock formation, and geological structures) were processed using minimum distance, parallelepiped and Bayesian classifiers. Among the three methods, the Bayesian classification with a threshold value of 10
-1 revealed the best mapping accuracy of 16.60%.
Introduction
In the past two decades, remote sensing technologies including use of aerial photographs and satellite imageries have been applied widely to regional landslide investigations. Simonett et al. (1970), Scully (1973), Mc Donald and Grubbs (1975), Anderson et al. (1976), and Sauchyn and Trench (1978) relied on visual interpretation for classification of landslide phenomena on either aerial photographs or satellite images. Remotely sensed data along with image interpretation can provide terrain information pertaining to Landsliding (Gagon, 1975). Those important factors for assessment of landslide potentials regional physiography, geomorphology, and geological structures.
With an improvement of computer technology, research undertaken by Heath and Dowling (1980) and Stephens (1988) applied digital image processing to delineate landslide areas on satellite images. Certain terrain information such as elevations, drainage patterns, bedrock formations, and geological structures are valuable for predicting Landslides and this information, however, could not be obtained directly from image interpretation. Therefore, in the study the authors attempted to identify areas in Lignite Creek coal basin, Healy, Aaska (Figure 1) where Landslides are likely to occur by taking advantage of the terrain information while processing digital satellite images. The intention of this research was to assess the reliability of image classification techniques in the study of Landslides. Large number of Landslides that occurred in Lignite Creek basin, Healy, Alaska influence surface mining operation of a coal mine in the vicinity (Corser and Paker, 1987) . Prior knowledge of the potential Landslides can often permit more flexible and accurate design of a mining method to minimize financial risk and unnecessary engineering problems associated with slope movements.
Landsat TM-Images
A Thematic Mapper (TM) scene image (Figure 1) was acquired by Landsat -5 on September 22, 1984 (scene ID Y5020520430x0). The digital image was later loaded on the ADVAL VAX 11/750 computer at the University of Alaska Fairbanks. The scene's geographic center is at latitude N64°14'00" and longitude W147°58'00". The entire coverage includes about 4,300mi
2 (10,500 km
2) in the interior of Alaska.
The Land Analysis System (LAS) modules including Cooredt, Trancoord-II2utm, Tiemerage, Nullcorr, Tiefit, and Geom were performed to register the TM images to a common Universal Transverse Mercator- based (UTM) grid. In the registration process, twelve control points were chosen from a topographic map. The pixel size of the images was reformatted from 30 meters to 25 meters to increase geographic precision (Goodenough, 1988), and pixel values were resampled using the nearest neighbor (NN) method.
Terrain Information
A Digital Elevation Model (DEM) image of the Healy quadrangle was utilized as the original spatial data to generate elevation contours (DN
Con)
Percent slope (DN
SlP) and slope aspect (DN
asp) images through LAS's Topo modules.
The existing landslide deposits, bedrock lithologies, drainage system, and faults were digitized from a geologic map through AMS to generate spatial terrain images, designated as DN
eld , DN
grp, DN
flt, respectively . The images were then referred to the same UTM grid coordinates as that for TM's and the elevation derived images. Both drainage system (DN
drn) anf faults (DNflt) images were binary images composed of two digital values (i.e. 50 and 200 for better contrast) Binary images were created by running LAS's Filter module to dilate the linear boundary to a distance of 5 to 45 pixels on either side of the trace. The existing landslide deposits (DN
eld) image was a binary image as well, with digital value 50 representing non-landslide deposits and digital value 200 indicating existing landslide deposits. The bedrock lithology image (DN
grp) was comprised of eight rock units based on berrock formation in the area.
