Monografija - Geološki zavod Slovenije

Marko KomacLandslide occurrence prediction with analysis of satellite images and other spatial datamore useful for the landslide prediction than the classical RGB method. The score of theclassification of the landslide-prone and landslide-averse areas reached 79,2 %. It was shownthat high-resolution multi-spectral satellite images could be successfully used for the spatiallandslide prediction.Having combined all the spatial data available, developed numerous models were developed.Those that produced best results were then used to determine and locate the potentiallyhazardous areas and to draw the map of possible landslide occurrence. Using the landslidehazard-map, the risk to the inhabitants and infrastructure (roads) on the tested area wasassessed.Relatively high scores of reliability were achieved, but one must bear in mind that the resultsare based on the sample of 614 landslides. 135.000 inhabitants live in the test area, only asmall percentage of them (less than 3 %) inhabit the highly risky areas. 20 – 25 % of thestudied population live in areas that are considered to be landslide-prone. The creeping andsudden landslides represent the biggest threat to inhabitants. More than half of the roads lie inthe areas subjected to ground mass movement and 3 % of all roads lie in high-risk areas.The results of the research showed that taking all of the major spatial factors into accountallows us to construct a good and reliable landslide prediction model. This model successfullydefines the areas with greater risk of landslides, using the available spatial data of higherresolution and high-resolution multi-spectral satellite images. It was also demonstrated thatsome of the spatial factors do not have a significant influence on the landslide occurrence.The remaining factors, which proved to play an important role in the spatial distribution oflandslides, should be used for the hazard assessment in relation to landslides. This research isthe first attempt in Slovenia to approach the problem of the landslide prediction withstatistical tools and to draw a landslide-hazard map on the basis of statistical methods andmodelling.Key words: landslides, GIS, satellite images, landslide prediction map, modelling, CIEL*a*b* model, SloveniaAbstract - IIGeological Survey of Slovenia