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GIS in Geology

GIS in Geology. Milo š Marjanovi ć. Lesson 4 21.10.2010. GIS in Landslide assessment (basic). Introduction to landslides and landslide risk assessment Case studies: Landslide susceptibility analysis on Fru ška Gora Mountain, Serbia example of GIS application in Engineering Geology

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GIS in Geology

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  1. GIS in Geology Miloš Marjanović Lesson 4 21.10.2010.

  2. GIS in Landslide assessment (basic) • Introduction to landslides and landslide risk assessment • Case studies: Landslide susceptibility analysis on Fruška Gora Mountain, Serbia example of GIS application in Engineering Geology • Methods applied: • Raster modeling • Multi-criteria analysis • Entropy model • Statistical analisis • Data resources: • Topographic map 1: 50 000 (1: 5000 for the second case study) • Digital geological map 1: 50 000 (1: 5000 for the second case study) • Satellite imagery LANDSAT TM 7 • Hydrometeorological data • Accent on analytical potential of GIS environment in basic landslide assessment

  3. GIS in Landslide assessment (basic) Database Management Systems (DBMS) Image Processing (IP) Computer Aided Drawing (CAD) Desktop mapping Geographic Information System (GIS) Artificial Intelligence(AI) Desktop and Web publishing Geostatistics Spread- sheets Contouring and surface modeling General statistics

  4. GIS in Landslide assessment (basic) • Natural “hazards”

  5. GIS in Landslide assessment (basic) • Landslide phenomena – theoretical background • Definition • Typology • Slides • Falls • Topplings • Flows • Lateral spreads

  6. GIS in Landslide assessment (basic) • Landslide phenomena – theoretical background • Classification by velocity: • High • Medium • Low

  7. GIS in Landslide assessment (basic) • Landslide phenomena – theoretical background • Genesis and factors: • Predisposition factors: • Geological – Geo-mechanical weak & sensitive materials, sheared materials, weathered materials, fissured and jointed or inconvenient by other structural entity, contrast in permeability (heterogeneous materials), contrast in deformability… • Morphological • Triggering factors: • Increase of shear stress: erosion/excavation at the toe, loading at the crown, earthquake, rockfall • Decrease in strength: rainfall/meltdown/leakage, cyclic loading, Phys-Chemical changes • Combination: earthquake + liquefaction, vegetation removal…

  8. GIS in Landslide assessment (basic) • Landslide phenomena – theoretical background • Stage of activity

  9. GIS in Landslide assessment (basic) • Landslide Qualitative Risk Assessment terminology: • Susceptibility (S): intensity classification, volume/area and spatial distribution of existing or potential landslides • Hazard (H): spatial (Ps) and temporal (Pt) probability of landslide occurrence over an area in a given time sequence • Vulnerability (V): measure of exposure to adverse phenomena (0-100%) • Element at risk (ER): population and constructions (buildings, infrastructural objects) measured in units (#, $) • Risk (R): probability and severity for adverse phenomena to take effect ER H=WPs·WPt·S R=H·V(ER)

  10. GIS in Landslide assessment (basic)

  11. GIS in Landslide assessment (basic) • Landslide risk framework – from analysis to management

  12. GIS in Landslide assessment (basic) • Management has to deal with: • Scientific uncertainty • Acceptable and tolerable risk ($, # of casualties) • General risk (not only landslides) • Territory issue • Science vs. decision

  13. GIS in Landslide assessment (basic) scientist decision maker • models of the past • uncertainty • time-consuming • terminology • success • problems • models of the future • decision • immediate action • common or regulation language • limitations • solutions

  14. GIS in Landslide assessment (basic)

  15. GIS in Landslide assessment (basic) • Inventory (location, volume/area, travel distance) • Susceptibility zoning • Heuristic (basic) • Statistical: univariate, bivariate (weights of evidence, information value, frequency ratio), multivariate (discriminant, regression, Likelihood ratio cluster analysis, AI) • Deterministic • Hazard – frequency analysis • Probabilistic based on the historical data on landslides, data on landslide triggers, dendrochronology, lichenometry • Modeling the primary variable (the triggering variable) • Risk • Appending the individual, societal, and economic risk analysis

  16. GIS in Landslide assessment (basic) • Case study: Fruška Gora Mountain, Serbia study area acc. 40 km2 landslide occurrences

  17. GIS in Landslide assessment (basic) • Case study: Fruška Gora Mountain, Serbia

  18. GIS in Landslide assessment (basic) • Scale and level selection: mid-scale of1:50 000 (in accordance to resources), preliminary zonation • Classification selection: for landslides according to Varnes et al. only slide movements, for susceptibility classes own system is developed • Method selection: heuristic – multi-criteria analysis • Input data type and region type selection: raster, pixel

  19. GIS in Landslide assessment (basic) Pros for raster data type Cons for raster data type • Overlaying and spatial analysis are easily feasible over referenced layers • Classification could be adjusted in bigger detail • The exporting to ASCII format provides excellent communication with GIS-coupled engines (for different modules generation, as well as for the machine learning algorithms) • Bulky and demanding in terms of memory capacity, and processing speed

  20. GIS in Landslide assessment (basic) • Multi-criteria analysis

  21. GIS in Landslide assessment (basic) • Geo-parameters modeling • Elevation (Pe) • suggesting the concept of Ep • derived from DEM • reclassified into 4 classes • normalized DNnorm=(DN – DNmin)/(DNmax – DNmin)

  22. GIS in Landslide assessment (basic) • Geo-parameters modeling • Slope angle (Ps) • suggesting the physical relation • derived from DEM • reclassified into 4 classes (5 degrees intervals) • normalized

  23. GIS in Landslide assessment (basic) • Geo-parameters modeling • Aspect (Pa) • suggesting the influence of moisture content, soil thickness • derived from DEM • reclassified into 4 classes (SE, SW, NE, NW) • weighted and normalized

  24. GIS in Landslide assessment (basic) • Geo-parameters modeling • Distance from streams (Pds) • suggesting the influence of the linear erosion pattern • buffered from drainage network vector • filtered for the erosional preference • reclassified into 4 intervals • normalized

  25. GIS in Landslide assessment (basic) • Geo-parameters modeling • Vegetation cover (Pv) • suggesting the influence of root system on the slope stability • mapped by NDVI by using 3,4 Landsat 7 TM chanel, due to chlorophyll spectra authenticy • reclassified into 2 classes • normalized

  26. GIS in Landslide assessment (basic) • Geo-parameters modeling • Lithological composition (Pl) • suggesting different stability conditions in different materials • digitized and simplified after geological map 1:50 000 • weighted • reclassified into 4 classis a-alluvions b-loess sediment c-calcareous sediments d-clayey soils • normalized

  27. GIS in Landslide assessment (basic) • Geo-parameters modeling • Rainfall (Pr) • suggesting the moist distribution governed by heavy rains • interpolated from sample point data set (tables from HMSS) by normal kriging with fitted parameters (sill/nugget) • reclassified into 4 classes • normalized

  28. GIS in Engineering Geology • Computing the weights of influence of geo-parameters • Analytical Hierarchy Process • Pair-wise matrix Comparing relative weights of influence of geo-parameters against each other and summing the columns • Eigenvector matrix Normalizing all members of the first matrix by the column sum and averaging the values by rows. The last column gives eigenvector – weights distribution function • GIS environment uses eigenvector to calculate the susceptibility raster map

  29. GIS in Landslide assessment (basic) • Computing the weights of influence of geo-parameters S = 0,29⋅Pl +0,27⋅Ps +0,15⋅Pr + 0,14⋅Pds +0,08⋅Pv + 0,05⋅Pe +0,02⋅Pa

  30. GIS in Landslide assessment (basic) • Calibration of classes • Entropy model optimal increase of information gain at 4 – 9 classes • Calibration using geomorphological reference map optimal error at 4 class intervals

  31. GIS in Landslide assessment (basic) • Final output map as an interpretation of landslide susceptibility • Susceptibility map • lowest zone • mild zone • moderate zone • highest zone

  32. GIS in Landslide assessment (basic) • Purpose: • Regional planning • Preliminary assessment for further detailed analysis • Base for hazard and risk mapping

  33. GIS in Geology Miloš Marjanović Exercise 4 4.11.2010.

  34. Exercise 4 – AHP-GIS extension

  35. Exercise 4 – AHP-GIS extension

  36. Exercise 4 – AHP-GIS extension

  37. Exercise 4 – AHP-GIS extension

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